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-/*************************************************************************
- *
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- *
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- * CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR
- * OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE,
- * EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- * This BSD License conforms to the Open Source Initiative "Simplified
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- * http://www.opensource.org/licenses/bsd-license.php
- *
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-LICENSE extracted from IJG's jpeg distribution:
------------------------------------------------
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-1. We don't promise that this software works. (But if you find any bugs,
- please let us know!)
-2. You can use this software for whatever you want. You don't have to pay us.
-3. You may not pretend that you wrote this software. If you use it in a
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-/* ***** BEGIN LICENSE BLOCK *****
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-The following is the license for the jemalloc source code, as provided
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- How to Apply These Terms to Your New Programs
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- Copyright (C)
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- (at your option) any later version.
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- This program is distributed in the hope that it will be useful,
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- Gnomovision version 69, Copyright (C) year name of author
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- `Gnomovision' (which makes passes at compilers) written by James Hacker.
-
- , 1 April 1989
- Ty Coon, President of Vice
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-/*
- * Copyright (c) 2008 NVIDIA, Corporation
- *
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- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- */
-Copyright 2000-2007 Niels Provos
-Copyright 2007-2009 Niels Provos and Nick Mathewson
-
-Redistribution and use in source and binary forms, with or without
-modification, are permitted provided that the following conditions
-are met:
-1. Redistributions of source code must retain the above copyright
- notice, this list of conditions and the following disclaimer.
-2. Redistributions in binary form must reproduce the above copyright
- notice, this list of conditions and the following disclaimer in the
- documentation and/or other materials provided with the distribution.
-3. The name of the author may not be used to endorse or promote products
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-(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
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-Copyright (c) 2013, Google Inc.
-All rights reserved.
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- * Redistributions in binary form must reproduce the above copyright notice,
- this list of conditions and the following disclaimer in the documentation
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- * The name of the author may not be used to endorse or promote products
- derived from this software without specific prior written permission.
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-AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE
-GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
-HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
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-(Copied from the README.)
-
---------------------------------------------------------------------------------
-
-The authors make NO WARRANTY or representation, either express or implied,
-with respect to this software, its quality, accuracy, merchantability, or
-fitness for a particular purpose. This software is provided "AS IS", and you,
-its user, assume the entire risk as to its quality and accuracy.
-
-This software is copyright (C) 1991-1998, Thomas G. Lane.
-All Rights Reserved except as specified below.
-
-Permission is hereby granted to use, copy, modify, and distribute this
-software (or portions thereof) for any purpose, without fee, subject to these
-conditions:
-(1) If any part of the source code for this software is distributed, then this
-README file must be included, with this copyright and no-warranty notice
-unaltered; and any additions, deletions, or changes to the original files
-must be clearly indicated in accompanying documentation.
-(2) If only executable code is distributed, then the accompanying
-documentation must state that "this software is based in part on the work of
-the Independent JPEG Group".
-(3) Permission for use of this software is granted only if the user accepts
-full responsibility for any undesirable consequences; the authors accept
-NO LIABILITY for damages of any kind.
-
-These conditions apply to any software derived from or based on the IJG code,
-not just to the unmodified library. If you use our work, you ought to
-acknowledge us.
-
-Permission is NOT granted for the use of any IJG author's name or company name
-in advertising or publicity relating to this software or products derived from
-it. This software may be referred to only as "the Independent JPEG Group's
-software".
-
-We specifically permit and encourage the use of this software as the basis of
-commercial products, provided that all warranty or liability claims are
-assumed by the product vendor.
-
-
-ansi2knr.c is included in this distribution by permission of L. Peter Deutsch,
-sole proprietor of its copyright holder, Aladdin Enterprises of Menlo Park, CA.
-ansi2knr.c is NOT covered by the above copyright and conditions, but instead
-by the usual distribution terms of the Free Software Foundation; principally,
-that you must include source code if you redistribute it. (See the file
-ansi2knr.c for full details.) However, since ansi2knr.c is not needed as part
-of any program generated from the IJG code, this does not limit you more than
-the foregoing paragraphs do.
-
-The Unix configuration script "configure" was produced with GNU Autoconf.
-It is copyright by the Free Software Foundation but is freely distributable.
-The same holds for its supporting scripts (config.guess, config.sub,
-ltconfig, ltmain.sh). Another support script, install-sh, is copyright
-by M.I.T. but is also freely distributable.
-
-It appears that the arithmetic coding option of the JPEG spec is covered by
-patents owned by IBM, AT&T, and Mitsubishi. Hence arithmetic coding cannot
-legally be used without obtaining one or more licenses. For this reason,
-support for arithmetic coding has been removed from the free JPEG software.
-(Since arithmetic coding provides only a marginal gain over the unpatented
-Huffman mode, it is unlikely that very many implementations will support it.)
-So far as we are aware, there are no patent restrictions on the remaining
-code.
-
-The IJG distribution formerly included code to read and write GIF files.
-To avoid entanglement with the Unisys LZW patent, GIF reading support has
-been removed altogether, and the GIF writer has been simplified to produce
-"uncompressed GIFs". This technique does not use the LZW algorithm; the
-resulting GIF files are larger than usual, but are readable by all standard
-GIF decoders.
-
-We are required to state that
- "The Graphics Interchange Format(c) is the Copyright property of
- CompuServe Incorporated. GIF(sm) is a Service Mark property of
- CompuServe Incorporated."
-
-Copyright (C) 2011 Google Inc.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-
-
-This copy of the libpng notices is provided for your convenience. In case of
-any discrepancy between this copy and the notices in the file png.h that is
-included in the libpng distribution, the latter shall prevail.
-
-COPYRIGHT NOTICE, DISCLAIMER, and LICENSE:
-
-If you modify libpng you may insert additional notices immediately following
-this sentence.
-
-This code is released under the libpng license.
-
-libpng versions 1.2.6, August 15, 2004, through 1.2.45, July 7, 2011, are
-Copyright (c) 2004, 2006-2009 Glenn Randers-Pehrson, and are
-distributed according to the same disclaimer and license as libpng-1.2.5
-with the following individual added to the list of Contributing Authors
-
- Cosmin Truta
-
-libpng versions 1.0.7, July 1, 2000, through 1.2.5 - October 3, 2002, are
-Copyright (c) 2000-2002 Glenn Randers-Pehrson, and are
-distributed according to the same disclaimer and license as libpng-1.0.6
-with the following individuals added to the list of Contributing Authors
-
- Simon-Pierre Cadieux
- Eric S. Raymond
- Gilles Vollant
-
-and with the following additions to the disclaimer:
-
- There is no warranty against interference with your enjoyment of the
- library or against infringement. There is no warranty that our
- efforts or the library will fulfill any of your particular purposes
- or needs. This library is provided with all faults, and the entire
- risk of satisfactory quality, performance, accuracy, and effort is with
- the user.
-
-libpng versions 0.97, January 1998, through 1.0.6, March 20, 2000, are
-Copyright (c) 1998, 1999 Glenn Randers-Pehrson, and are
-distributed according to the same disclaimer and license as libpng-0.96,
-with the following individuals added to the list of Contributing Authors:
-
- Tom Lane
- Glenn Randers-Pehrson
- Willem van Schaik
-
-libpng versions 0.89, June 1996, through 0.96, May 1997, are
-Copyright (c) 1996, 1997 Andreas Dilger
-Distributed according to the same disclaimer and license as libpng-0.88,
-with the following individuals added to the list of Contributing Authors:
-
- John Bowler
- Kevin Bracey
- Sam Bushell
- Magnus Holmgren
- Greg Roelofs
- Tom Tanner
-
-libpng versions 0.5, May 1995, through 0.88, January 1996, are
-Copyright (c) 1995, 1996 Guy Eric Schalnat, Group 42, Inc.
-
-For the purposes of this copyright and license, "Contributing Authors"
-is defined as the following set of individuals:
-
- Andreas Dilger
- Dave Martindale
- Guy Eric Schalnat
- Paul Schmidt
- Tim Wegner
-
-The PNG Reference Library is supplied "AS IS". The Contributing Authors
-and Group 42, Inc. disclaim all warranties, expressed or implied,
-including, without limitation, the warranties of merchantability and of
-fitness for any purpose. The Contributing Authors and Group 42, Inc.
-assume no liability for direct, indirect, incidental, special, exemplary,
-or consequential damages, which may result from the use of the PNG
-Reference Library, even if advised of the possibility of such damage.
-
-Permission is hereby granted to use, copy, modify, and distribute this
-source code, or portions hereof, for any purpose, without fee, subject
-to the following restrictions:
-
-1. The origin of this source code must not be misrepresented.
-
-2. Altered versions must be plainly marked as such and must not
- be misrepresented as being the original source.
-
-3. This Copyright notice may not be removed or altered from any
- source or altered source distribution.
-
-The Contributing Authors and Group 42, Inc. specifically permit, without
-fee, and encourage the use of this source code as a component to
-supporting the PNG file format in commercial products. If you use this
-source code in a product, acknowledgment is not required but would be
-appreciated.
-
-
-A "png_get_copyright" function is available, for convenient use in "about"
-boxes and the like:
-
- printf("%s",png_get_copyright(NULL));
-
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-/* ***** BEGIN LICENSE BLOCK *****
- * Version: MPL 1.1/GPL 2.0/LGPL 2.1
- *
- * The contents of this file are subject to the Mozilla Public License Version
- * 1.1 (the "License"); you may not use this file except in compliance with
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- * http://www.mozilla.org/MPL/
- *
- * Software distributed under the License is distributed on an "AS IS" basis,
- * WITHOUT WARRANTY OF ANY KIND, either express or implied. See the License
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- *
- * The Original Code is mozilla.org code.
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- * Portions created by the Initial Developer are Copyright (C) 2002
- * the Initial Developer. All Rights Reserved.
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- * Alternatively, the contents of this file may be used under the terms of
- * either the GNU General Public License Version 2 or later (the "GPL"), or
- * the GNU Lesser General Public License Version 2.1 or later (the "LGPL"),
- * in which case the provisions of the GPL or the LGPL are applicable instead
- * of those above. If you wish to allow use of your version of this file only
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- * the provisions above, a recipient may use your version of this file under
- * the terms of any one of the MPL, the GPL or the LGPL.
- *
- * ***** END LICENSE BLOCK ***** */
-
- A C-program for MT19937, with initialization improved 2002/1/26.
- Coded by Takuji Nishimura and Makoto Matsumoto.
-
- Before using, initialize the state by using init_genrand(seed)
- or init_by_array(init_key, key_length).
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-//
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-// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
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-// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
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-
-Version: MPL 1.1/GPL 2.0/LGPL 2.1
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-The contents of this file are subject to the Mozilla Public License Version
-1.1 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-http://www.mozilla.org/MPL/
-
-Software distributed under the License is distributed on an "AS IS" basis,
-WITHOUT WARRANTY OF ANY KIND, either express or implied. See the License
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-The Original Code is mozilla.org code.
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-the Initial Developer. All Rights Reserved.
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-Alternatively, the contents of this file may be used under the terms of
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-in which case the provisions of the GPL or the LGPL are applicable instead
-of those above. If you wish to allow use of your version of this file only
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- Copyright (c) 2004-2012 by Mulle Kybernetik. All rights reserved.
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- Permission to use, copy, modify and distribute this software and its documentation
- is hereby granted, provided that both the copyright notice and this permission
- notice appear in all copies of the software, derivative works or modified versions,
- and any portions thereof, and that both notices appear in supporting documentation,
- and that credit is given to Mulle Kybernetik in all documents and publicity
- pertaining to direct or indirect use of this code or its derivatives.
-
- THIS IS EXPERIMENTAL SOFTWARE AND IT IS KNOWN TO HAVE BUGS, SOME OF WHICH MAY HAVE
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- "AS IS" CONDITION. THE COPYRIGHT HOLDER DISCLAIMS ANY LIABILITY OF ANY KIND FOR ANY
- DAMAGES WHATSOEVER RESULTING DIRECTLY OR INDIRECTLY FROM THE USE OF THIS SOFTWARE
- OR OF ANY DERIVATIVE WORK.
-
- LICENSE ISSUES
- ==============
-
- The OpenSSL toolkit stays under a dual license, i.e. both the conditions of
- the OpenSSL License and the original SSLeay license apply to the toolkit.
- See below for the actual license texts. Actually both licenses are BSD-style
- Open Source licenses. In case of any license issues related to OpenSSL
- please contact openssl-core@openssl.org.
-
- OpenSSL License
- ---------------
-
-/* ====================================================================
- * Copyright (c) 1998-2011 The OpenSSL Project. All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions
- * are met:
- *
- * 1. Redistributions of source code must retain the above copyright
- * notice, this list of conditions and the following disclaimer.
- *
- * 2. Redistributions in binary form must reproduce the above copyright
- * notice, this list of conditions and the following disclaimer in
- * the documentation and/or other materials provided with the
- * distribution.
- *
- * 3. All advertising materials mentioning features or use of this
- * software must display the following acknowledgment:
- * "This product includes software developed by the OpenSSL Project
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- * 4. The names "OpenSSL Toolkit" and "OpenSSL Project" must not be used to
- * endorse or promote products derived from this software without
- * prior written permission. For written permission, please contact
- * openssl-core@openssl.org.
- *
- * 5. Products derived from this software may not be called "OpenSSL"
- * nor may "OpenSSL" appear in their names without prior written
- * permission of the OpenSSL Project.
- *
- * 6. Redistributions of any form whatsoever must retain the following
- * acknowledgment:
- * "This product includes software developed by the OpenSSL Project
- * for use in the OpenSSL Toolkit (http://www.openssl.org/)"
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- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
- * HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
- * STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
- * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED
- * OF THE POSSIBILITY OF SUCH DAMAGE.
- * ====================================================================
- *
- * This product includes cryptographic software written by Eric Young
- * (eay@cryptsoft.com). This product includes software written by Tim
- * Hudson (tjh@cryptsoft.com).
- *
- */
-
- Original SSLeay License
- -----------------------
-
-/* Copyright (C) 1995-1998 Eric Young (eay@cryptsoft.com)
- * All rights reserved.
- *
- * This package is an SSL implementation written
- * by Eric Young (eay@cryptsoft.com).
- * The implementation was written so as to conform with Netscapes SSL.
- *
- * This library is free for commercial and non-commercial use as long as
- * the following conditions are aheared to. The following conditions
- * apply to all code found in this distribution, be it the RC4, RSA,
- * lhash, DES, etc., code; not just the SSL code. The SSL documentation
- * included with this distribution is covered by the same copyright terms
- * except that the holder is Tim Hudson (tjh@cryptsoft.com).
- *
- * Copyright remains Eric Young's, and as such any Copyright notices in
- * the code are not to be removed.
- * If this package is used in a product, Eric Young should be given attribution
- * as the author of the parts of the library used.
- * This can be in the form of a textual message at program startup or
- * in documentation (online or textual) provided with the package.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions
- * are met:
- * 1. Redistributions of source code must retain the copyright
- * notice, this list of conditions and the following disclaimer.
- * 2. Redistributions in binary form must reproduce the above copyright
- * notice, this list of conditions and the following disclaimer in the
- * documentation and/or other materials provided with the distribution.
- * 3. All advertising materials mentioning features or use of this software
- * must display the following acknowledgement:
- * "This product includes cryptographic software written by
- * Eric Young (eay@cryptsoft.com)"
- * The word 'cryptographic' can be left out if the rouines from the library
- * being used are not cryptographic related :-).
- * 4. If you include any Windows specific code (or a derivative thereof) from
- * the apps directory (application code) you must include an acknowledgement:
- * "This product includes software written by Tim Hudson (tjh@cryptsoft.com)"
- *
- * THIS SOFTWARE IS PROVIDED BY ERIC YOUNG ``AS IS'' AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
- * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
- * ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR OR CONTRIBUTORS BE LIABLE
- * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
- * DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS
- * OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
- * HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
- * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY
- * OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF
- * SUCH DAMAGE.
- *
- * The licence and distribution terms for any publically available version or
- * derivative of this code cannot be changed. i.e. this code cannot simply be
- * copied and put under another distribution licence
- * [including the GNU Public Licence.]
- */
-
-
-Copyright 2001-2011 Xiph.Org, Skype Limited, Octasic,
- Jean-Marc Valin, Timothy B. Terriberry,
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-// All rights reserved.
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-All rights reserved.
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-
-o Redistributions of source code must retain the above copyright notice,
- this list of conditions and the following disclaimer.
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- notice, this list of conditions and the following disclaimer in the
- documentation and/or other materials provided with the distribution.
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- contributors may be used to endorse or promote products derived from
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-
-(Copied from the COPYRIGHT file of
-https://code.google.com/p/sctp-refimpl/source/browse/trunk/COPYRIGHT)
---------------------------------------------------------------------------------
-
-Copyright (c) 2001, 2002 Cisco Systems, Inc.
-Copyright (c) 2002-12 Randall R. Stewart
-Copyright (c) 2002-12 Michael Tuexen
-All rights reserved.
-
-Redistribution and use in source and binary forms, with or without
-modification, are permitted provided that the following conditions
-are met:
-
-1. Redistributions of source code must retain the above copyright
- notice, this list of conditions and the following disclaimer.
-2. Redistributions in binary form must reproduce the above copyright
- notice, this list of conditions and the following disclaimer in the
- documentation and/or other materials provided with the distribution.
-
-THIS SOFTWARE IS PROVIDED BY THE AUTHOR AND CONTRIBUTORS ``AS IS'' AND
-ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR OR CONTRIBUTORS BE LIABLE
-FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
-DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS
-OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
-HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
-LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY
-OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF
-SUCH DAMAGE.
-
-;*****************************************************************************
-;* x86inc.asm
-;*****************************************************************************
-;* Copyright (C) 2005-2011 x264 project
-;*
-;* Authors: Loren Merritt
-;* Anton Mitrofanov
-;* Jason Garrett-Glaser
-;*
-;* Permission to use, copy, modify, and/or distribute this software for any
-;* purpose with or without fee is hereby granted, provided that the above
-;* copyright notice and this permission notice appear in all copies.
-;*
-;* THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES
-;* WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF
-;* MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR
-;* ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES
-;* WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN
-;* ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF
-;* OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.
-;*****************************************************************************
-
-; This is a header file for the x264ASM assembly language, which uses
-; NASM/YASM syntax combined with a large number of macros to provide easy
-; abstraction between different calling conventions (x86_32, win64, linux64).
-; It also has various other useful features to simplify writing the kind of
-; DSP functions that are most often used in x264.
-
-; Unlike the rest of x264, this file is available under an ISC license, as it
-; has significant usefulness outside of x264 and we want it to be available
-; to the largest audience possible. Of course, if you modify it for your own
-; purposes to add a new feature, we strongly encourage contributing a patch
-; as this feature might be useful for others as well. Send patches or ideas
-; to x264-devel@videolan.org .
-
-Yasm is Copyright (c) 2001-2010 Peter Johnson and other Yasm developers.
-
-Yasm developers and/or contributors include:
- Peter Johnson
- Michael Urman
- Brian Gladman (Visual Studio build files, other fixes)
- Stanislav Karchebny (options parser)
- Mathieu Monnier (SSE4 instruction patches, NASM preprocessor additions)
- Anonymous "NASM64" developer (NASM preprocessor fixes)
- Stephen Polkowski (x86 instruction patches)
- Henryk Richter (Mach-O object format)
- Ben Skeggs (patches, bug reports)
- Alexei Svitkine (GAS preprocessor)
- Samuel Thibault (TASM parser and frontend)
-
------------------------------------
-Yasm licensing overview and summary
------------------------------------
-
-Note: This document does not provide legal advice nor is it the actual
-license of any part of Yasm. See the individual licenses for complete
-details. Consult a lawyer for legal advice.
-
-The primary license of Yasm is the 2-clause BSD license. Please use this
-license if you plan on submitting code to the project.
-
-Yasm has absolutely no warranty; not even for merchantibility or fitness
-for a particular purpose.
-
--------
-Libyasm
--------
-Libyasm is 2-clause or 3-clause BSD licensed, with the exception of
-bitvect, which is triple-licensed under the Artistic license, GPL, and
-LGPL. Libyasm is thus GPL and LGPL compatible. In addition, this also
-means that libyasm is free for binary-only distribution as long as the
-terms of the 3-clause BSD license and Artistic license (as it applies to
-bitvect) are fulfilled.
-
--------
-Modules
--------
-The modules are 2-clause or 3-clause BSD licensed.
-
----------
-Frontends
----------
-The frontends are 2-clause BSD licensed.
-
--------------
-License Texts
--------------
-The full text of all licenses are provided in separate files in the source
-distribution. Each source file may include the entire license (in the case
-of the BSD and Artistic licenses), or may reference the GPL or LGPL license
-file.
-
-BSD.txt - 2-clause and 3-clause BSD licenses
-Artistic.txt - Artistic license
-GNU_GPL-2.0 - GNU General Public License
-GNU_LGPL-2.0 - GNU Library General Public License
-
-/* zlib.h -- interface of the 'zlib' general purpose compression library
- version 1.2.4, March 14th, 2010
-
- Copyright (C) 1995-2010 Jean-loup Gailly and Mark Adler
-
- This software is provided 'as-is', without any express or implied
- warranty. In no event will the authors be held liable for any damages
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-
- Permission is granted to anyone to use this software for any purpose,
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- 1. The origin of this software must not be misrepresented; you must not
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- in a product, an acknowledgment in the product documentation would be
- appreciated but is not required.
- 2. Altered source versions must be plainly marked as such, and must not be
- misrepresented as being the original software.
- 3. This notice may not be removed or altered from any source distribution.
-
- Jean-loup Gailly
- Mark Adler
-
-*/
-
-Copyright 2007, Google Inc.
-All rights reserved.
-
-Redistribution and use in source and binary forms, with or without
-modification, are permitted provided that the following conditions are
-met:
-
- * Redistributions of source code must retain the above copyright
-notice, this list of conditions and the following disclaimer.
- * Redistributions in binary form must reproduce the above
-copyright notice, this list of conditions and the following disclaimer
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- * Neither the name of Google Inc. nor the names of its
-contributors may be used to endorse or promote products derived from
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-"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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-LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
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-(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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-
--------------------------------------------------------------------------------
-
-The file url_parse.cc is based on nsURLParsers.cc from Mozilla. This file is
-licensed separately as follows:
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-The contents of this file are subject to the Mozilla Public License Version
-1.1 (the "License"); you may not use this file except in compliance with
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-http://www.mozilla.org/MPL/
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-Software distributed under the License is distributed on an "AS IS" basis,
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-The Original Code is mozilla.org code.
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-INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING
-OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED
-TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY
-YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER
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-POSSIBILITY OF SUCH DAMAGES.
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- END OF TERMS AND CONDITIONS
-
- How to Apply These Terms to Your New Programs
-
- If you develop a new program, and you want it to be of the greatest
-possible use to the public, the best way to achieve this is to make it
-free software which everyone can redistribute and change under these terms.
-
- To do so, attach the following notices to the program. It is safest
-to attach them to the start of each source file to most effectively
-convey the exclusion of warranty; and each file should have at least
-the "copyright" line and a pointer to where the full notice is found.
-
-
- Copyright (C)
-
- This program is free software; you can redistribute it and/or modify
- it under the terms of the GNU General Public License as published by
- the Free Software Foundation; either version 2 of the License, or
- (at your option) any later version.
-
- This program is distributed in the hope that it will be useful,
- but WITHOUT ANY WARRANTY; without even the implied warranty of
- MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
- GNU General Public License for more details.
-
- You should have received a copy of the GNU General Public License
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-Also add information on how to contact you by electronic and paper mail.
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-If the program is interactive, make it output a short notice like this
-when it starts in an interactive mode:
-
- Gnomovision version 69, Copyright (C) year name of author
- Gnomovision comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
- This is free software, and you are welcome to redistribute it
- under certain conditions; type `show c' for details.
-
-The hypothetical commands `show w' and `show c' should show the appropriate
-parts of the General Public License. Of course, the commands you use may
-be called something other than `show w' and `show c'; they could even be
-mouse-clicks or menu items--whatever suits your program.
-
-You should also get your employer (if you work as a programmer) or your
-school, if any, to sign a "copyright disclaimer" for the program, if
-necessary. Here is a sample; alter the names:
-
- Yoyodyne, Inc., hereby disclaims all copyright interest in the program
- `Gnomovision' (which makes passes at compilers) written by James Hacker.
-
- , 1 April 1989
- Ty Coon, President of Vice
-
-This General Public License does not permit incorporating your program into
-proprietary programs. If your program is a subroutine library, you may
-consider it more useful to permit linking proprietary applications with the
-library. If this is what you want to do, use the GNU Library General
-Public License instead of this License.
-
-============================================================
-Notices for file(s):
-/bin/aapt
-/bin/aidl
-/bin/dexdump
-/bin/jasmin
-/bin/split-select
-/framework/android-mock-generatorlib.jar
-/framework/jasmin.jar
-/lib/lib64cutils.a
-/lib/lib64log.a
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-/lib/liblog.a
-/lib/liblog.so
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-------------------------------------------------------------
-
- Copyright (c) 2005-2008, The Android Open Source Project
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
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- Unless required by applicable law or agreed to in writing, software
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- See the License for the specific language governing permissions and
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-
-
- Apache License
- Version 2.0, January 2004
- http://www.apache.org/licenses/
-
- TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
-
- 1. Definitions.
-
- "License" shall mean the terms and conditions for use, reproduction,
- and distribution as defined by Sections 1 through 9 of this document.
-
- "Licensor" shall mean the copyright owner or entity authorized by
- the copyright owner that is granting the License.
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- "Legal Entity" shall mean the union of the acting entity and all
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- direction or management of such entity, whether by contract or
- otherwise, or (ii) ownership of fifty percent (50%) or more of the
- outstanding shares, or (iii) beneficial ownership of such entity.
-
- "You" (or "Your") shall mean an individual or Legal Entity
- exercising permissions granted by this License.
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- "Source" form shall mean the preferred form for making modifications,
- including but not limited to software source code, documentation
- source, and configuration files.
-
- "Object" form shall mean any form resulting from mechanical
- transformation or translation of a Source form, including but
- not limited to compiled object code, generated documentation,
- and conversions to other media types.
-
- "Work" shall mean the work of authorship, whether in Source or
- Object form, made available under the License, as indicated by a
- copyright notice that is included in or attached to the work
- (an example is provided in the Appendix below).
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- "Derivative Works" shall mean any work, whether in Source or Object
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- 5. Submission of Contributions. Unless You explicitly state otherwise,
- any Contribution intentionally submitted for inclusion in the Work
- by You to the Licensor shall be under the terms and conditions of
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- whether in tort (including negligence), contract, or otherwise,
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- the Work or Derivative Works thereof, You may choose to offer,
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-
- END OF TERMS AND CONDITIONS
-
-
-============================================================
-Notices for file(s):
-/framework/org.eclipse.core.commands_3.6.0.I20100512-1500.jar
-/framework/org.eclipse.core.expressions_3.4.200.v20100505.jar
-/framework/org.eclipse.core.runtime_3.6.0.v20100505.jar
-/framework/org.eclipse.equinox.common_3.6.0.v20100503.jar
-/framework/org.eclipse.jface_3.6.2.M20110210-1200.jar
-/framework/org.eclipse.osgi_3.6.2.R36x_v20110210.jar
-/framework/org.eclipse.ui.workbench.texteditor_3.6.1.r361_v20100714-0800.jar
-/framework/org.eclipse.ui.workbench_3.6.2.M20110210-1200.jar
-/framework/osgi.jar
-/framework/propertysheet.jar
-/framework/swt.jar
-------------------------------------------------------------
-
- Eclipse Public License - v 1.0
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-THE ACCOMPANYING PROGRAM IS PROVIDED UNDER THE TERMS OF THIS ECLIPSE
-PUBLIC LICENSE ("AGREEMENT"). ANY USE, REPRODUCTION OR DISTRIBUTION OF
-THE PROGRAM CONSTITUTES RECIPIENT'S ACCEPTANCE OF THIS AGREEMENT.
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-*1. DEFINITIONS*
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-"Contribution" means:
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-a) in the case of the initial Contributor, the initial code and
-documentation distributed under this Agreement, and
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-b) in the case of each subsequent Contributor:
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-i) changes to the Program, and
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-ii) additions to the Program;
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-'originates' from a Contributor if it was added to the Program by such
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-Contributions do not include additions to the Program which: (i) are
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-"Contributor" means any person or entity that distributes the Program.
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-"Program" means the Contributions distributed in accordance with this
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-
-============================================================
-Notices for file(s):
-/framework/bouncycastle-host.jar
-/framework/bouncycastle-hostdex.jar
-------------------------------------------------------------
-Copyright (c) 2000-2013 The Legion Of The Bouncy Castle (http://www.bouncycastle.org)
-
-Permission is hereby granted, free of charge, to any person obtaining a copy of this software and
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-
-============================================================
-Notices for file(s):
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-------------------------------------------------------------
-Apache Commons Compress
-Copyright 2002-2009 The Apache Software Foundation
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-This product includes software developed by
-The Apache Software Foundation (http://www.apache.org/).
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-Original BZip2 classes contributed by Keiron Liddle
-, Aftex Software to the Apache Ant project
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-Original Tar classes from contributors of the Apache Ant project
-
-Original Zip classes from contributors of the Apache Ant project
-
-Original CPIO classes contributed by Markus Kuss and the jRPM project
-(jrpm.sourceforge.net)
-
-============================================================
-Notices for file(s):
-/lib/libbcc.so
-------------------------------------------------------------
-==========================
-NOTICE file for libbcc.git
-==========================
-
-* NOTICE for lib/ExecutionEngine/, lib/ScriptCRT/, include and helper/.
-
- Copyright (c) 2005-2011, The Android Open Source Project
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- Licensed under the Apache License, Version 2.0 (the "License");
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- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
-
-
- Apache License
- Version 2.0, January 2004
- http://www.apache.org/licenses/
-
- TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
-
- 1. Definitions.
-
- "License" shall mean the terms and conditions for use, reproduction,
- and distribution as defined by Sections 1 through 9 of this document.
-
- "Licensor" shall mean the copyright owner or entity authorized by
- the copyright owner that is granting the License.
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-Program Directory
-------- ---------
-Autoconf llvm/autoconf
- llvm/projects/ModuleMaker/autoconf
- llvm/projects/sample/autoconf
-CellSPU backend llvm/lib/Target/CellSPU/README.txt
-Google Test llvm/utils/unittest/googletest
-OpenBSD regex llvm/lib/Support/{reg*, COPYRIGHT.regex}
-
-
-
-* NOTICE for tests/disassem.cpp and tests/disassem.h.
-
-/* $NetBSD: disassem.c,v 1.14 2003/03/27 16:58:36 mycroft Exp $ */
-
-/*-
- * Copyright (c) 1996 Mark Brinicombe.
- * Copyright (c) 1996 Brini.
- *
- * All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions
- * are met:
- * 1. Redistributions of source code must retain the above copyright
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- * 3. All advertising materials mentioning features or use of this software
- * must display the following acknowledgement:
- * This product includes software developed by Brini.
- * 4. The name of the company nor the name of the author may be used to
- * endorse or promote products derived from this software without specific
- * prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY BRINI ``AS IS'' AND ANY EXPRESS OR IMPLIED
- * WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
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- * IN NO EVENT SHALL BRINI OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT,
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- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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- * HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
- * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY
- * OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF
- * SUCH DAMAGE.
- *
- * RiscBSD kernel project
- *
- * db_disasm.c
- *
- * Kernel disassembler
- *
- * Created : 10/02/96
- *
- * Structured after the sparc/sparc/db_disasm.c by David S. Miller &
- * Paul Kranenburg
- *
- * This code is not complete. Not all instructions are disassembled.
- */
-
-============================================================
-Notices for file(s):
-/lib/libfdlibm.a
-------------------------------------------------------------
-Copyright (C) 1993 by Sun Microsystems, Inc. All rights reserved.
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-============================================================
-Notices for file(s):
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-/lib/emulator64-libui.a
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-/lib/emulator64-target-i386.a
-/lib/emulator64-target-mips.a
-------------------------------------------------------------
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- Copyright (C) 19yy
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- , 1 April 1989
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- -----------------------
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-/* Copyright (C) 1995-1998 Eric Young (eay@cryptsoft.com)
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-NO WARRANTY
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-NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
-APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE
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-"AS IS" WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSED OR
-IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
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-END OF TERMS AND CONDITIONS
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-How to Apply These Terms to Your New Programs
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-If you develop a new program, and you want it to be of the
-greatest possible use to the public, the best way to achieve
-this is to make it free software which everyone can
-redistribute and change under these terms.
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-To do so, attach the following notices to the program. It is
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-most effectively convey the exclusion of warranty; and each
-file should have at least the "copyright" line and a pointer
-to where the full notice is found.
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-One line to give the program's name and a brief idea of what
-it does.
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-Copyright (C)
-This program is free software; you can redistribute it
-and/or modify it under the terms of the GNU General Public
-License as published by the Free Software Foundation; either
-version 2 of the License, or (at your option) any later
-version. This program is distributed in the hope that it
-will be useful, but WITHOUT ANY WARRANTY; without even the
-implied warranty of MERCHANTABILITY or FITNESS FOR A
-PARTICULAR PURPOSE. See the GNU General Public License for
-more details. You should have received a copy of the GNU
-General Public License along with this program; if not,
-write to the Free Software Foundation, Inc., 59 Temple
-Place, Suite 330, Boston, MA 02111-1307 USA
-
-Also add information on how to contact you by electronic and
-paper mail. If the program is interactive, make it output a
-short notice like this when it starts in an interactive
-mode:
-
-Gnomovision version 69, Copyright (C) year name of author
-Gnomovision comes with ABSOLUTELY NO WARRANTY; for details
-type `show w'. This is free software, and you are welcome to
-redistribute it under certain conditions; type `show c' for
-details. The hypothetical commands `show w' and `show c'
-should show the appropriate parts of the General Public
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-be mouse-clicks or menu items--whatever suits your program.
-
-You should also get your employer (if you work as a
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-disclaimer" for the program, if necessary. Here is a sample;
-alter the names:
-
-Yoyodyne, Inc., hereby disclaims all copyright interest in
-the program `Gnomovision' (which makes passes at compilers)
-written by James Hacker.
-
-signature of Ty Coon, 1 April 1989
-Ty Coon, President of Vice
-This General Public License does not permit incorporating
-your program into proprietary programs. If your program is a
-subroutine library, you may consider it more useful to
-permit linking proprietary applications with the library. If
-this is what you want to do, use the GNU Library General
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-"CLASSPATH" EXCEPTION TO THE GPL VERSION 2
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-are subject to the following clarification and special
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- Linking this library statically or dynamically with other
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- As a special exception, the copyright holders of this
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-============================================================
-Notices for file(s):
-/bin/adb
-------------------------------------------------------------
-
- Copyright (c) 2006-2009, The Android Open Source Project
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
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- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
-
-
- Apache License
- Version 2.0, January 2004
- http://www.apache.org/licenses/
-
- TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
-
- 1. Definitions.
-
- "License" shall mean the terms and conditions for use, reproduction,
- and distribution as defined by Sections 1 through 9 of this document.
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- "Licensor" shall mean the copyright owner or entity authorized by
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- any Contribution intentionally submitted for inclusion in the Work
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- whether in tort (including negligence), contract, or otherwise,
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- END OF TERMS AND CONDITIONS
-
-
-============================================================
-Notices for file(s):
-/framework/apache-xml-hostdex.jar
-------------------------------------------------------------
- =========================================================================
- == NOTICE file corresponding to section 4(d) of the Apache License, ==
- == Version 2.0, in this case for the Apache Xalan Java distribution. ==
- =========================================================================
-
- Apache Xalan (Xalan XSLT processor)
- Copyright 1999-2006 The Apache Software Foundation
-
- Apache Xalan (Xalan serializer)
- Copyright 1999-2006 The Apache Software Foundation
-
- This product includes software developed at
- The Apache Software Foundation (http://www.apache.org/).
-
- =========================================================================
- Portions of this software was originally based on the following:
- - software copyright (c) 1999-2002, Lotus Development Corporation.,
- http://www.lotus.com.
- - software copyright (c) 2001-2002, Sun Microsystems.,
- http://www.sun.com.
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- http://www.ibm.com.
-
- =========================================================================
- The binary distribution package (ie. jars, samples and documentation) of
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- - The Apache Software Foundation
- - Xerces Java - see LICENSE.txt
- - JAXP 1.3 APIs - see LICENSE.txt
- - Bytecode Engineering Library - see LICENSE.txt
- - Regular Expression - see LICENSE.txt
-
- - Scott Hudson, Frank Flannery, C. Scott Ananian
- - CUP Parser Generator runtime (javacup\runtime) - see LICENSE.txt
-
- =========================================================================
- The source distribution package (ie. all source and tools required to build
- Xalan Java) of this product includes software developed by the following:
-
- - The Apache Software Foundation
- - Xerces Java - see LICENSE.txt
- - JAXP 1.3 APIs - see LICENSE.txt
- - Bytecode Engineering Library - see LICENSE.txt
- - Regular Expression - see LICENSE.txt
- - Ant - see LICENSE.txt
- - Stylebook doc tool - see LICENSE.txt
-
- - Elliot Joel Berk and C. Scott Ananian
- - Lexical Analyzer Generator (JLex) - see LICENSE.txt
-
- =========================================================================
- Apache Xerces Java
- Copyright 1999-2006 The Apache Software Foundation
-
- This product includes software developed at
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-
- Portions of Apache Xerces Java in xercesImpl.jar and xml-apis.jar
- were originally based on the following:
- - software copyright (c) 1999, IBM Corporation., http://www.ibm.com.
- - software copyright (c) 1999, Sun Microsystems., http://www.sun.com.
- - voluntary contributions made by Paul Eng on behalf of the
- Apache Software Foundation that were originally developed at iClick, Inc.,
- software copyright (c) 1999.
-
- =========================================================================
- Apache xml-commons xml-apis (redistribution of xml-apis.jar)
-
- Apache XML Commons
- Copyright 2001-2003,2006 The Apache Software Foundation.
-
- This product includes software developed at
- The Apache Software Foundation (http://www.apache.org/).
-
- Portions of this software were originally based on the following:
- - software copyright (c) 1999, IBM Corporation., http://www.ibm.com.
- - software copyright (c) 1999, Sun Microsystems., http://www.sun.com.
- - software copyright (c) 2000 World Wide Web Consortium, http://www.w3.org
-
-
-============================================================
-Notices for file(s):
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-/bin/rs-spec-gen
-/lib/libslang.a
-------------------------------------------------------------
-=========================
-NOTICE file for slang.git
-=========================
-
- Copyright (c) 2005-2011, The Android Open Source Project
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- Licensed under the Apache License, Version 2.0 (the "License");
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- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
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-
-
- Apache License
- Version 2.0, January 2004
- http://www.apache.org/licenses/
-
- TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
-
- 1. Definitions.
-
- "License" shall mean the terms and conditions for use, reproduction,
- and distribution as defined by Sections 1 through 9 of this document.
-
- "Licensor" shall mean the copyright owner or entity authorized by
- the copyright owner that is granting the License.
-
- "Legal Entity" shall mean the union of the acting entity and all
- other entities that control, are controlled by, or are under common
- control with that entity. For the purposes of this definition,
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- direction or management of such entity, whether by contract or
- otherwise, or (ii) ownership of fifty percent (50%) or more of the
- outstanding shares, or (iii) beneficial ownership of such entity.
-
- "You" (or "Your") shall mean an individual or Legal Entity
- exercising permissions granted by this License.
-
- "Source" form shall mean the preferred form for making modifications,
- including but not limited to software source code, documentation
- source, and configuration files.
-
- "Object" form shall mean any form resulting from mechanical
- transformation or translation of a Source form, including but
- not limited to compiled object code, generated documentation,
- and conversions to other media types.
-
- "Work" shall mean the work of authorship, whether in Source or
- Object form, made available under the License, as indicated by a
- copyright notice that is included in or attached to the work
- (an example is provided in the Appendix below).
-
- "Derivative Works" shall mean any work, whether in Source or Object
- form, that is based on (or derived from) the Work and for which the
- editorial revisions, annotations, elaborations, or other modifications
- represent, as a whole, an original work of authorship. For the purposes
- of this License, Derivative Works shall not include works that remain
- separable from, or merely link (or bind by name) to the interfaces of,
- the Work and Derivative Works thereof.
-
- "Contribution" shall mean any work of authorship, including
- the original version of the Work and any modifications or additions
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- worldwide, non-exclusive, no-charge, royalty-free, irrevocable
- copyright license to reproduce, prepare Derivative Works of,
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- this License, each Contributor hereby grants to You a perpetual,
- worldwide, non-exclusive, no-charge, royalty-free, irrevocable
- (except as stated in this section) patent license to make, have made,
- use, offer to sell, sell, import, and otherwise transfer the Work,
- where such license applies only to those patent claims licensable
- by such Contributor that are necessarily infringed by their
- Contribution(s) alone or by combination of their Contribution(s)
- with the Work to which such Contribution(s) was submitted. If You
- institute patent litigation against any entity (including a
- cross-claim or counterclaim in a lawsuit) alleging that the Work
- or a Contribution incorporated within the Work constitutes direct
- or contributory patent infringement, then any patent licenses
- granted to You under this License for that Work shall terminate
- as of the date such litigation is filed.
-
- 4. Redistribution. You may reproduce and distribute copies of the
- Work or Derivative Works thereof in any medium, with or without
- modifications, and in Source or Object form, provided that You
- meet the following conditions:
-
- (a) You must give any other recipients of the Work or
- Derivative Works a copy of this License; and
-
- (b) You must cause any modified files to carry prominent notices
- stating that You changed the files; and
-
- (c) You must retain, in the Source form of any Derivative Works
- that You distribute, all copyright, patent, trademark, and
- attribution notices from the Source form of the Work,
- excluding those notices that do not pertain to any part of
- the Derivative Works; and
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- distribution, then any Derivative Works that You distribute must
- include a readable copy of the attribution notices contained
- within such NOTICE file, excluding those notices that do not
- pertain to any part of the Derivative Works, in at least one
- of the following places: within a NOTICE text file distributed
- as part of the Derivative Works; within the Source form or
- documentation, if provided along with the Derivative Works; or,
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- distribute, sublicense, and/or sell copies of the Software, and to
- permit persons to whom the Software is furnished to do so, subject to
- the following conditions:
-
- The above copyright notice and this permission notice shall be
- included in all copies or substantial portions of the Software.
-
- THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
- EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
- MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
- NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
- LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
- OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
- WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
-
-==============================================================================
-Copyright (c) 2011 mingw-w64 project
-
-Permission is hereby granted, free of charge, to any person obtaining a
-copy of this software and associated documentation files (the "Software"),
-to deal in the Software without restriction, including without limitation
-the rights to use, copy, modify, merge, publish, distribute, sublicense,
-and/or sell copies of the Software, and to permit persons to whom the
-Software is furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in
-all copies or substantial portions of the Software.
-
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-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
-FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
-DEALINGS IN THE SOFTWARE.
-
-
-/*
- * Parts of this library are derived by:
- *
- * Posix Threads library for Microsoft Windows
- *
- * Use at own risk, there is no implied warranty to this code.
- * It uses undocumented features of Microsoft Windows that can change
- * at any time in the future.
- *
- * (C) 2010 Lockless Inc.
- * All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without modification,
- * are permitted provided that the following conditions are met:
- *
- *
- * * Redistributions of source code must retain the above copyright notice,
- * this list of conditions and the following disclaimer.
- * * Redistributions in binary form must reproduce the above copyright notice,
- * this list of conditions and the following disclaimer in the documentation
- * and/or other materials provided with the distribution.
- * * Neither the name of Lockless Inc. nor the names of its contributors may be
- * used to endorse or promote products derived from this software without
- * specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AN
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
- * IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT,
- * INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
- * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
- * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
- * LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE
- * OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED
- * OF THE POSSIBILITY OF SUCH DAMAGE.
- */
-
-==============================================================================
-Copyright 2008 Google Inc. All rights reserved.
-
-Redistribution and use in source and binary forms, with or without
-modification, are permitted provided that the following conditions are
-met:
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- * Redistributions of source code must retain the above copyright
-notice, this list of conditions and the following disclaimer.
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-
-Code generated by the Protocol Buffer compiler is owned by the owner
-of the input file used when generating it. This code is not
-standalone and requires a support library to be linked with it. This
-support library is itself covered by the above license.
-
-==============================================================================
-
- Copyright (c) 2010, The Android Open Source Project
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
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- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
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-
-
- Apache License
- Version 2.0, January 2004
- http://www.apache.org/licenses/
-
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-
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- Contributor provides its Contributions) on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
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- PARTICULAR PURPOSE. You are solely responsible for determining the
- appropriateness of using or redistributing the Work and assume any
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-
- 8. Limitation of Liability. In no event and under no legal theory,
- whether in tort (including negligence), contract, or otherwise,
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- negligent acts) or agreed to in writing, shall any Contributor be
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- work stoppage, computer failure or malfunction, or any and all
- other commercial damages or losses), even if such Contributor
- has been advised of the possibility of such damages.
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- and charge a fee for, acceptance of support, warranty, indemnity,
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- defend, and hold each Contributor harmless for any liability
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- of your accepting any such warranty or additional liability.
-
- END OF TERMS AND CONDITIONS
-
-
-==============================================================================
-
- Copyright (c) 2005-2019, The Android Open Source Project
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
-
-
- Apache License
- Version 2.0, January 2004
- http://www.apache.org/licenses/
-
- TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
-
- 1. Definitions.
-
- "License" shall mean the terms and conditions for use, reproduction,
- and distribution as defined by Sections 1 through 9 of this document.
-
- "Licensor" shall mean the copyright owner or entity authorized by
- the copyright owner that is granting the License.
-
- "Legal Entity" shall mean the union of the acting entity and all
- other entities that control, are controlled by, or are under common
- control with that entity. For the purposes of this definition,
- "control" means (i) the power, direct or indirect, to cause the
- direction or management of such entity, whether by contract or
- otherwise, or (ii) ownership of fifty percent (50%) or more of the
- outstanding shares, or (iii) beneficial ownership of such entity.
-
- "You" (or "Your") shall mean an individual or Legal Entity
- exercising permissions granted by this License.
-
- "Source" form shall mean the preferred form for making modifications,
- including but not limited to software source code, documentation
- source, and configuration files.
-
- "Object" form shall mean any form resulting from mechanical
- transformation or translation of a Source form, including but
- not limited to compiled object code, generated documentation,
- and conversions to other media types.
-
- "Work" shall mean the work of authorship, whether in Source or
- Object form, made available under the License, as indicated by a
- copyright notice that is included in or attached to the work
- (an example is provided in the Appendix below).
-
- "Derivative Works" shall mean any work, whether in Source or Object
- form, that is based on (or derived from) the Work and for which the
- editorial revisions, annotations, elaborations, or other modifications
- represent, as a whole, an original work of authorship. For the purposes
- of this License, Derivative Works shall not include works that remain
- separable from, or merely link (or bind by name) to the interfaces of,
- the Work and Derivative Works thereof.
-
- "Contribution" shall mean any work of authorship, including
- the original version of the Work and any modifications or additions
- to that Work or Derivative Works thereof, that is intentionally
- submitted to Licensor for inclusion in the Work by the copyright owner
- or by an individual or Legal Entity authorized to submit on behalf of
- the copyright owner. For the purposes of this definition, "submitted"
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- to the Licensor or its representatives, including but not limited to
- communication on electronic mailing lists, source code control systems,
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- excluding communication that is conspicuously marked or otherwise
- designated in writing by the copyright owner as "Not a Contribution."
-
- "Contributor" shall mean Licensor and any individual or Legal Entity
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- this License, each Contributor hereby grants to You a perpetual,
- worldwide, non-exclusive, no-charge, royalty-free, irrevocable
- copyright license to reproduce, prepare Derivative Works of,
- publicly display, publicly perform, sublicense, and distribute the
- Work and such Derivative Works in Source or Object form.
-
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- this License, each Contributor hereby grants to You a perpetual,
- worldwide, non-exclusive, no-charge, royalty-free, irrevocable
- (except as stated in this section) patent license to make, have made,
- use, offer to sell, sell, import, and otherwise transfer the Work,
- where such license applies only to those patent claims licensable
- by such Contributor that are necessarily infringed by their
- Contribution(s) alone or by combination of their Contribution(s)
- with the Work to which such Contribution(s) was submitted. If You
- institute patent litigation against any entity (including a
- cross-claim or counterclaim in a lawsuit) alleging that the Work
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- or contributory patent infringement, then any patent licenses
- granted to You under this License for that Work shall terminate
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-
- 4. Redistribution. You may reproduce and distribute copies of the
- Work or Derivative Works thereof in any medium, with or without
- modifications, and in Source or Object form, provided that You
- meet the following conditions:
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- (a) You must give any other recipients of the Work or
- Derivative Works a copy of this License; and
-
- (b) You must cause any modified files to carry prominent notices
- stating that You changed the files; and
-
- (c) You must retain, in the Source form of any Derivative Works
- that You distribute, all copyright, patent, trademark, and
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- excluding those notices that do not pertain to any part of
- the Derivative Works; and
-
- (d) If the Work includes a "NOTICE" text file as part of its
- distribution, then any Derivative Works that You distribute must
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- pertain to any part of the Derivative Works, in at least one
- of the following places: within a NOTICE text file distributed
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- within a display generated by the Derivative Works, if and
- wherever such third-party notices normally appear. The contents
- of the NOTICE file are for informational purposes only and
- do not modify the License. You may add Your own attribution
- notices within Derivative Works that You distribute, alongside
- or as an addendum to the NOTICE text from the Work, provided
- that such additional attribution notices cannot be construed
- as modifying the License.
-
- You may add Your own copyright statement to Your modifications and
- may provide additional or different license terms and conditions
- for use, reproduction, or distribution of Your modifications, or
- for any such Derivative Works as a whole, provided Your use,
- reproduction, and distribution of the Work otherwise complies with
- the conditions stated in this License.
-
- 5. Submission of Contributions. Unless You explicitly state otherwise,
- any Contribution intentionally submitted for inclusion in the Work
- by You to the Licensor shall be under the terms and conditions of
- this License, without any additional terms or conditions.
- Notwithstanding the above, nothing herein shall supersede or modify
- the terms of any separate license agreement you may have executed
- with Licensor regarding such Contributions.
-
- 6. Trademarks. This License does not grant permission to use the trade
- names, trademarks, service marks, or product names of the Licensor,
- except as required for reasonable and customary use in describing the
- origin of the Work and reproducing the content of the NOTICE file.
-
- 7. Disclaimer of Warranty. Unless required by applicable law or
- agreed to in writing, Licensor provides the Work (and each
- Contributor provides its Contributions) on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
- implied, including, without limitation, any warranties or conditions
- of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
- PARTICULAR PURPOSE. You are solely responsible for determining the
- appropriateness of using or redistributing the Work and assume any
- risks associated with Your exercise of permissions under this License.
-
- 8. Limitation of Liability. In no event and under no legal theory,
- whether in tort (including negligence), contract, or otherwise,
- unless required by applicable law (such as deliberate and grossly
- negligent acts) or agreed to in writing, shall any Contributor be
- liable to You for damages, including any direct, indirect, special,
- incidental, or consequential damages of any character arising as a
- result of this License or out of the use or inability to use the
- Work (including but not limited to damages for loss of goodwill,
- work stoppage, computer failure or malfunction, or any and all
- other commercial damages or losses), even if such Contributor
- has been advised of the possibility of such damages.
-
- 9. Accepting Warranty or Additional Liability. While redistributing
- the Work or Derivative Works thereof, You may choose to offer,
- and charge a fee for, acceptance of support, warranty, indemnity,
- or other liability obligations and/or rights consistent with this
- License. However, in accepting such obligations, You may act only
- on Your own behalf and on Your sole responsibility, not on behalf
- of any other Contributor, and only if You agree to indemnify,
- defend, and hold each Contributor harmless for any liability
- incurred by, or claims asserted against, such Contributor by reason
- of your accepting any such warranty or additional liability.
-
- END OF TERMS AND CONDITIONS
-
-
-==============================================================================
-
- Apache License
- Version 2.0, January 2004
- http://www.apache.org/licenses/
-
- TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
-
- 1. Definitions.
-
- "License" shall mean the terms and conditions for use, reproduction,
- and distribution as defined by Sections 1 through 9 of this document.
-
- "Licensor" shall mean the copyright owner or entity authorized by
- the copyright owner that is granting the License.
-
- "Legal Entity" shall mean the union of the acting entity and all
- other entities that control, are controlled by, or are under common
- control with that entity. For the purposes of this definition,
- "control" means (i) the power, direct or indirect, to cause the
- direction or management of such entity, whether by contract or
- otherwise, or (ii) ownership of fifty percent (50%) or more of the
- outstanding shares, or (iii) beneficial ownership of such entity.
-
- "You" (or "Your") shall mean an individual or Legal Entity
- exercising permissions granted by this License.
-
- "Source" form shall mean the preferred form for making modifications,
- including but not limited to software source code, documentation
- source, and configuration files.
-
- "Object" form shall mean any form resulting from mechanical
- transformation or translation of a Source form, including but
- not limited to compiled object code, generated documentation,
- and conversions to other media types.
-
- "Work" shall mean the work of authorship, whether in Source or
- Object form, made available under the License, as indicated by a
- copyright notice that is included in or attached to the work
- (an example is provided in the Appendix below).
-
- "Derivative Works" shall mean any work, whether in Source or Object
- form, that is based on (or derived from) the Work and for which the
- editorial revisions, annotations, elaborations, or other modifications
- represent, as a whole, an original work of authorship. For the purposes
- of this License, Derivative Works shall not include works that remain
- separable from, or merely link (or bind by name) to the interfaces of,
- the Work and Derivative Works thereof.
-
- "Contribution" shall mean any work of authorship, including
- the original version of the Work and any modifications or additions
- to that Work or Derivative Works thereof, that is intentionally
- submitted to Licensor for inclusion in the Work by the copyright owner
- or by an individual or Legal Entity authorized to submit on behalf of
- the copyright owner. For the purposes of this definition, "submitted"
- means any form of electronic, verbal, or written communication sent
- to the Licensor or its representatives, including but not limited to
- communication on electronic mailing lists, source code control systems,
- and issue tracking systems that are managed by, or on behalf of, the
- Licensor for the purpose of discussing and improving the Work, but
- excluding communication that is conspicuously marked or otherwise
- designated in writing by the copyright owner as "Not a Contribution."
-
- "Contributor" shall mean Licensor and any individual or Legal Entity
- on behalf of whom a Contribution has been received by Licensor and
- subsequently incorporated within the Work.
-
- 2. Grant of Copyright License. Subject to the terms and conditions of
- this License, each Contributor hereby grants to You a perpetual,
- worldwide, non-exclusive, no-charge, royalty-free, irrevocable
- copyright license to reproduce, prepare Derivative Works of,
- publicly display, publicly perform, sublicense, and distribute the
- Work and such Derivative Works in Source or Object form.
-
- 3. Grant of Patent License. Subject to the terms and conditions of
- this License, each Contributor hereby grants to You a perpetual,
- worldwide, non-exclusive, no-charge, royalty-free, irrevocable
- (except as stated in this section) patent license to make, have made,
- use, offer to sell, sell, import, and otherwise transfer the Work,
- where such license applies only to those patent claims licensable
- by such Contributor that are necessarily infringed by their
- Contribution(s) alone or by combination of their Contribution(s)
- with the Work to which such Contribution(s) was submitted. If You
- institute patent litigation against any entity (including a
- cross-claim or counterclaim in a lawsuit) alleging that the Work
- or a Contribution incorporated within the Work constitutes direct
- or contributory patent infringement, then any patent licenses
- granted to You under this License for that Work shall terminate
- as of the date such litigation is filed.
-
- 4. Redistribution. You may reproduce and distribute copies of the
- Work or Derivative Works thereof in any medium, with or without
- modifications, and in Source or Object form, provided that You
- meet the following conditions:
-
- (a) You must give any other recipients of the Work or
- Derivative Works a copy of this License; and
-
- (b) You must cause any modified files to carry prominent notices
- stating that You changed the files; and
-
- (c) You must retain, in the Source form of any Derivative Works
- that You distribute, all copyright, patent, trademark, and
- attribution notices from the Source form of the Work,
- excluding those notices that do not pertain to any part of
- the Derivative Works; and
-
- (d) If the Work includes a "NOTICE" text file as part of its
- distribution, then any Derivative Works that You distribute must
- include a readable copy of the attribution notices contained
- within such NOTICE file, excluding those notices that do not
- pertain to any part of the Derivative Works, in at least one
- of the following places: within a NOTICE text file distributed
- as part of the Derivative Works; within the Source form or
- documentation, if provided along with the Derivative Works; or,
- within a display generated by the Derivative Works, if and
- wherever such third-party notices normally appear. The contents
- of the NOTICE file are for informational purposes only and
- do not modify the License. You may add Your own attribution
- notices within Derivative Works that You distribute, alongside
- or as an addendum to the NOTICE text from the Work, provided
- that such additional attribution notices cannot be construed
- as modifying the License.
-
- You may add Your own copyright statement to Your modifications and
- may provide additional or different license terms and conditions
- for use, reproduction, or distribution of Your modifications, or
- for any such Derivative Works as a whole, provided Your use,
- reproduction, and distribution of the Work otherwise complies with
- the conditions stated in this License.
-
- 5. Submission of Contributions. Unless You explicitly state otherwise,
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- =========================================================================
- == NOTICE file for the x86 JIT libenc subdirectory. ==
- =========================================================================
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-Apache Harmony
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-that is to say, a work containing the Program or a portion of it,
-either verbatim or with modifications and/or translated into another
-language. (Hereinafter, translation is included without limitation in
-the term "modification".) Each licensee is addressed as "you".
-
-Activities other than copying, distribution and modification are not
-covered by this License; they are outside its scope. The act of
-running the Program is not restricted, and the output from the Program
-is covered only if its contents constitute a work based on the
-Program (independent of having been made by running the Program).
-Whether that is true depends on what the Program does.
-
- 1. You may copy and distribute verbatim copies of the Program's
-source code as you receive it, in any medium, provided that you
-conspicuously and appropriately publish on each copy an appropriate
-copyright notice and disclaimer of warranty; keep intact all the
-notices that refer to this License and to the absence of any warranty;
-and give any other recipients of the Program a copy of this License
-along with the Program.
-
-You may charge a fee for the physical act of transferring a copy, and
-you may at your option offer warranty protection in exchange for a fee.
-
- 2. You may modify your copy or copies of the Program or any portion
-of it, thus forming a work based on the Program, and copy and
-distribute such modifications or work under the terms of Section 1
-above, provided that you also meet all of these conditions:
-
- a) You must cause the modified files to carry prominent notices
- stating that you changed the files and the date of any change.
-
- b) You must cause any work that you distribute or publish, that in
- whole or in part contains or is derived from the Program or any
- part thereof, to be licensed as a whole at no charge to all third
- parties under the terms of this License.
-
- c) If the modified program normally reads commands interactively
- when run, you must cause it, when started running for such
- interactive use in the most ordinary way, to print or display an
- announcement including an appropriate copyright notice and a
- notice that there is no warranty (or else, saying that you provide
- a warranty) and that users may redistribute the program under
- these conditions, and telling the user how to view a copy of this
- License. (Exception: if the Program itself is interactive but
- does not normally print such an announcement, your work based on
- the Program is not required to print an announcement.)
-
-These requirements apply to the modified work as a whole. If
-identifiable sections of that work are not derived from the Program,
-and can be reasonably considered independent and separate works in
-themselves, then this License, and its terms, do not apply to those
-sections when you distribute them as separate works. But when you
-distribute the same sections as part of a whole which is a work based
-on the Program, the distribution of the whole must be on the terms of
-this License, whose permissions for other licensees extend to the
-entire whole, and thus to each and every part regardless of who wrote it.
-
-Thus, it is not the intent of this section to claim rights or contest
-your rights to work written entirely by you; rather, the intent is to
-exercise the right to control the distribution of derivative or
-collective works based on the Program.
-
-In addition, mere aggregation of another work not based on the Program
-with the Program (or with a work based on the Program) on a volume of
-a storage or distribution medium does not bring the other work under
-the scope of this License.
-
- 3. You may copy and distribute the Program (or a work based on it,
-under Section 2) in object code or executable form under the terms of
-Sections 1 and 2 above provided that you also do one of the following:
-
- a) Accompany it with the complete corresponding machine-readable
- source code, which must be distributed under the terms of Sections
- 1 and 2 above on a medium customarily used for software interchange; or,
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- years, to give any third party, for a charge no more than your
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- c) Accompany it with the information you received as to the offer
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- allowed only for noncommercial distribution and only if you
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-compelled to copy the source along with the object code.
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- 4. You may not copy, modify, sublicense, or distribute the Program
-except as expressly provided under this License. Any attempt
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-void, and will automatically terminate your rights under this License.
-However, parties who have received copies, or rights, from you under
-this License will not have their licenses terminated so long as such
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- 5. You are not required to accept this License, since you have not
-signed it. However, nothing else grants you permission to modify or
-distribute the Program or its derivative works. These actions are
-prohibited by law if you do not accept this License. Therefore, by
-modifying or distributing the Program (or any work based on the
-Program), you indicate your acceptance of this License to do so, and
-all its terms and conditions for copying, distributing or modifying
-the Program or works based on it.
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- 6. Each time you redistribute the Program (or any work based on the
-Program), the recipient automatically receives a license from the
-original licensor to copy, distribute or modify the Program subject to
-these terms and conditions. You may not impose any further
-restrictions on the recipients' exercise of the rights granted herein.
-You are not responsible for enforcing compliance by third parties to
-this License.
-
- 7. If, as a consequence of a court judgment or allegation of patent
-infringement or for any other reason (not limited to patent issues),
-conditions are imposed on you (whether by court order, agreement or
-otherwise) that contradict the conditions of this License, they do not
-excuse you from the conditions of this License. If you cannot
-distribute so as to satisfy simultaneously your obligations under this
-License and any other pertinent obligations, then as a consequence you
-may not distribute the Program at all. For example, if a patent
-license would not permit royalty-free redistribution of the Program by
-all those who receive copies directly or indirectly through you, then
-the only way you could satisfy both it and this License would be to
-refrain entirely from distribution of the Program.
-
-If any portion of this section is held invalid or unenforceable under
-any particular circumstance, the balance of the section is intended to
-apply and the section as a whole is intended to apply in other
-circumstances.
-
-It is not the purpose of this section to induce you to infringe any
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-such claims; this section has the sole purpose of protecting the
-integrity of the free software distribution system, which is
-implemented by public license practices. Many people have made
-generous contributions to the wide range of software distributed
-through that system in reliance on consistent application of that
-system; it is up to the author/donor to decide if he or she is willing
-to distribute software through any other system and a licensee cannot
-impose that choice.
-
-This section is intended to make thoroughly clear what is believed to
-be a consequence of the rest of this License.
-
- 8. If the distribution and/or use of the Program is restricted in
-certain countries either by patents or by copyrighted interfaces, the
-original copyright holder who places the Program under this License
-may add an explicit geographical distribution limitation excluding
-those countries, so that distribution is permitted only in or among
-countries not thus excluded. In such case, this License incorporates
-the limitation as if written in the body of this License.
-
- 9. The Free Software Foundation may publish revised and/or new versions
-of the General Public License from time to time. Such new versions will
-be similar in spirit to the present version, but may differ in detail to
-address new problems or concerns.
-
-Each version is given a distinguishing version number. If the Program
-specifies a version number of this License which applies to it and "any
-later version", you have the option of following the terms and conditions
-either of that version or of any later version published by the Free
-Software Foundation. If the Program does not specify a version number of
-this License, you may choose any version ever published by the Free Software
-Foundation.
-
- 10. If you wish to incorporate parts of the Program into other free
-programs whose distribution conditions are different, write to the author
-to ask for permission. For software which is copyrighted by the Free
-Software Foundation, write to the Free Software Foundation; we sometimes
-make exceptions for this. Our decision will be guided by the two goals
-of preserving the free status of all derivatives of our free software and
-of promoting the sharing and reuse of software generally.
-
- NO WARRANTY
-
- 11. BECAUSE THE PROGRAM IS LICENSED FREE OF CHARGE, THERE IS NO WARRANTY
-FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW. EXCEPT WHEN
-OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES
-PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSED
-OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
-MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS
-TO THE QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE
-PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING,
-REPAIR OR CORRECTION.
-
- 12. IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
-WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MAY MODIFY AND/OR
-REDISTRIBUTE THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES,
-INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING
-OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED
-TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY
-YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER
-PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE
-POSSIBILITY OF SUCH DAMAGES.
-
- END OF TERMS AND CONDITIONS
-
- How to Apply These Terms to Your New Programs
-
- If you develop a new program, and you want it to be of the greatest
-possible use to the public, the best way to achieve this is to make it
-free software which everyone can redistribute and change under these terms.
-
- To do so, attach the following notices to the program. It is safest
-to attach them to the start of each source file to most effectively
-convey the exclusion of warranty; and each file should have at least
-the "copyright" line and a pointer to where the full notice is found.
-
-
- Copyright (C)
-
- This program is free software; you can redistribute it and/or modify
- it under the terms of the GNU General Public License as published by
- the Free Software Foundation; either version 2 of the License, or
- (at your option) any later version.
-
- This program is distributed in the hope that it will be useful,
- but WITHOUT ANY WARRANTY; without even the implied warranty of
- MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
- GNU General Public License for more details.
-
- You should have received a copy of the GNU General Public License along
- with this program; if not, write to the Free Software Foundation, Inc.,
- 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
-
-Also add information on how to contact you by electronic and paper mail.
-
-If the program is interactive, make it output a short notice like this
-when it starts in an interactive mode:
-
- Gnomovision version 69, Copyright (C) year name of author
- Gnomovision comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
- This is free software, and you are welcome to redistribute it
- under certain conditions; type `show c' for details.
-
-The hypothetical commands `show w' and `show c' should show the appropriate
-parts of the General Public License. Of course, the commands you use may
-be called something other than `show w' and `show c'; they could even be
-mouse-clicks or menu items--whatever suits your program.
-
-You should also get your employer (if you work as a programmer) or your
-school, if any, to sign a "copyright disclaimer" for the program, if
-necessary. Here is a sample; alter the names:
-
- Yoyodyne, Inc., hereby disclaims all copyright interest in the program
- `Gnomovision' (which makes passes at compilers) written by James Hacker.
-
- , 1 April 1989
- Ty Coon, President of Vice
-
-This General Public License does not permit incorporating your program into
-proprietary programs. If your program is a subroutine library, you may
-consider it more useful to permit linking proprietary applications with the
-library. If this is what you want to do, use the GNU Lesser General
-Public License instead of this License.
diff --git a/platform-tools/etc1tool.exe b/platform-tools/etc1tool.exe
deleted file mode 100644
index 17867b3..0000000
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diff --git a/platform-tools/fastboot.exe b/platform-tools/fastboot.exe
deleted file mode 100644
index 74fedc4..0000000
Binary files a/platform-tools/fastboot.exe and /dev/null differ
diff --git a/platform-tools/hprof-conv.exe b/platform-tools/hprof-conv.exe
deleted file mode 100644
index 59e65f1..0000000
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diff --git a/platform-tools/libwinpthread-1.dll b/platform-tools/libwinpthread-1.dll
deleted file mode 100644
index f6599c7..0000000
Binary files a/platform-tools/libwinpthread-1.dll and /dev/null differ
diff --git a/platform-tools/make_f2fs.exe b/platform-tools/make_f2fs.exe
deleted file mode 100644
index 742deb4..0000000
Binary files a/platform-tools/make_f2fs.exe and /dev/null differ
diff --git a/platform-tools/make_f2fs_casefold.exe b/platform-tools/make_f2fs_casefold.exe
deleted file mode 100644
index d509125..0000000
Binary files a/platform-tools/make_f2fs_casefold.exe and /dev/null differ
diff --git a/platform-tools/mke2fs.conf b/platform-tools/mke2fs.conf
deleted file mode 100644
index 8ea960d..0000000
--- a/platform-tools/mke2fs.conf
+++ /dev/null
@@ -1,53 +0,0 @@
-[defaults]
- base_features = sparse_super,large_file,filetype,dir_index,ext_attr
- default_mntopts = acl,user_xattr
- enable_periodic_fsck = 0
- blocksize = 4096
- inode_size = 256
- inode_ratio = 16384
- reserved_ratio = 1.0
-
-[fs_types]
- ext3 = {
- features = has_journal
- }
- ext4 = {
- features = has_journal,extent,huge_file,dir_nlink,extra_isize,uninit_bg
- inode_size = 256
- }
- ext4dev = {
- features = has_journal,extent,huge_file,flex_bg,inline_data,64bit,dir_nlink,extra_isize
- inode_size = 256
- options = test_fs=1
- }
- small = {
- blocksize = 1024
- inode_size = 128
- inode_ratio = 4096
- }
- floppy = {
- blocksize = 1024
- inode_size = 128
- inode_ratio = 8192
- }
- big = {
- inode_ratio = 32768
- }
- huge = {
- inode_ratio = 65536
- }
- news = {
- inode_ratio = 4096
- }
- largefile = {
- inode_ratio = 1048576
- blocksize = -1
- }
- largefile4 = {
- inode_ratio = 4194304
- blocksize = -1
- }
- hurd = {
- blocksize = 4096
- inode_size = 128
- }
diff --git a/platform-tools/mke2fs.exe b/platform-tools/mke2fs.exe
deleted file mode 100644
index 47834ef..0000000
Binary files a/platform-tools/mke2fs.exe and /dev/null differ
diff --git a/platform-tools/source.properties b/platform-tools/source.properties
deleted file mode 100644
index dc3a2fe..0000000
--- a/platform-tools/source.properties
+++ /dev/null
@@ -1,2 +0,0 @@
-Pkg.UserSrc=false
-Pkg.Revision=36.0.0
diff --git a/platform-tools/sqlite3.exe b/platform-tools/sqlite3.exe
deleted file mode 100644
index 5b4e14f..0000000
Binary files a/platform-tools/sqlite3.exe and /dev/null differ
diff --git a/predict.py b/predict.py
deleted file mode 100644
index c4793da..0000000
--- a/predict.py
+++ /dev/null
@@ -1,254 +0,0 @@
-import re
-from datetime import datetime
-from functools import cache
-from pathlib import Path
-
-import numpy as np
-import torch
-import logging
-
-from config import MONSTER_COUNT
-from config import FIELD_FEATURE_COUNT
-
-logger = logging.getLogger(__name__)
-
-def get_device(prefer_gpu=True):
- """
- prefer_gpu (bool): 是否优先尝试使用GPU
- """
- if prefer_gpu:
- if torch.cuda.is_available():
- logger.info("Use torch with cuda")
- return torch.device("cuda")
- elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
- logger.info("Use torch with mps")
- return torch.device("mps") # Apple Silicon GPU
- elif hasattr(torch, "xpu") and torch.xpu.is_available(): # Intel GPU
- logger.info("Use torch with xpu")
- return torch.device("xpu")
- logger.info("Use torch with cpu")
- return torch.device("cpu")
-
-class CannotModel:
- def __init__(self, model_path="models"):
- self.device = get_device()
- self.model_path = self._resolve_model_path(model_path)
- self.model = None # 模型实例
- try:
- self.load_model() # 初始化时加载模型
- except Exception as e:
- logger.error(f"模型加载失败: {e}")
- self.model = None
-
- def _resolve_model_path(self, path):
- """
- Resolves the model path. If a directory is given, finds the latest model file.
- If a file is given, returns it directly.
- """
- if Path(path).is_dir():
- logger.info(f"Searching for the latest model in directory: {path}")
- model_dir = Path(path)
- models = [f for f in model_dir.iterdir() if f.suffix == ".pth" and f.is_file()]
- if not models:
- raise FileNotFoundError(f"No model files (.pth) found in {path}")
-
- latest_model_path = None
- latest_time = None
-
- pattern = re.compile(
- r"best_model_(acc|loss|full)_data\d+_acc\d+\.\d+_loss\d+\.\d+_(\d{4}_\d{2}_\d{2}_\d{2}_\d{2}_\d{2})\.pth$"
- )
-
- for model_file_path in models:
- match = pattern.match(model_file_path.name)
- if match:
- timestamp_str = match.group(2) # Group 2 captures the timestamp
- try:
- model_time = datetime.strptime(
- timestamp_str, "%Y_%m_%d_%H_%M_%S"
- )
- if latest_time is None or model_time > latest_time:
- latest_time = model_time
- latest_model_path = model_file_path
- except ValueError:
- continue # Ignore files with malformed timestamps
-
- if latest_model_path:
- logger.info(f"Found latest model: {latest_model_path}")
- return str(latest_model_path)
- else:
- raise FileNotFoundError(
- f"No models with the expected name format found in {path}"
- )
-
- elif Path(path).is_file():
- logger.info(f"Using specified model file: {path}")
- return path
- else:
- logger.error(f"Provided model path is invalid: {path}")
- return ""
-
- def load_model(self):
- """初始化时加载模型"""
- try:
- if not Path(self.model_path).exists():
- raise FileNotFoundError(
- rf"未找到训练好的模型文件 {self.model_path}，请先训练模型"
- )
-
- try:
- model = torch.load(
- self.model_path,
- map_location=self.device,
- weights_only=False,
- )
- except TypeError: # 如果旧版本 PyTorch 不认识 weights_only
- model = torch.load(
- self.model_path, map_location=self.device
- )
- model.eval()
- self.model = model.to(self.device)
-
- except Exception as e:
- error_msg = f"模型加载失败: {str(e)}"
- if "missing keys" in str(e):
- error_msg += "\n可能是模型结构不匹配，请重新训练模型"
- raise e # 无法继续运行，退出程序
-
- def export_onnx(self,outputpath, monster_count=MONSTER_COUNT):
- # 确保模型在 CPU 上（避免设备不一致）
- self.model = self.model.cpu()
- self.model.eval()
-
- # 生成虚拟输入（与模型同设备）
- device = next(self.model.parameters()).device
- dummy_left_counts = torch.randint(0, 10, (1, monster_count), dtype=torch.int16, device=device)
- dummy_right_counts = torch.randint(0, 10, (1, monster_count), dtype=torch.int16, device=device)
-
- # 获取符号和绝对值张量（确保在相同设备）
- left_signs = torch.sign(dummy_left_counts.to(torch.int64)).to(device)
- left_counts = torch.abs(dummy_left_counts.to(torch.int64)).to(device)
- right_signs = torch.sign(dummy_right_counts.to(torch.int64)).to(device)
- right_counts = torch.abs(dummy_right_counts.to(torch.int64)).to(device)
-
- # 导出参数
- input_names = ["left_signs", "left_counts", "right_signs", "right_counts"]
- dynamic_axes = {name: {0: 'batch_size'} for name in input_names}
- dynamic_axes["output"] = {0: 'batch_size'}
-
- # 导出 ONNX
- torch.onnx.export(
- self.model,
- (left_signs, left_counts, right_signs, right_counts),
- outputpath,
- input_names=input_names,
- output_names=["output"],
- dynamic_axes=dynamic_axes,
- opset_version=20,
- verbose=True # 开启详细输出便于调试
- )
-
- def get_prediction(self, left_counts: np.typing.ArrayLike, right_counts: np.typing.ArrayLike):
- if self.model is None:
- raise RuntimeError("模型未正确初始化")
-
- # 转换为张量并处理符号和绝对值
- left_signs = (
- torch.sign(torch.tensor(left_counts, dtype=torch.int16))
- .unsqueeze(0)
- .to(self.device)
- )
- left_counts = (
- torch.abs(torch.tensor(left_counts, dtype=torch.int16))
- .unsqueeze(0)
- .to(self.device)
- )
- right_signs = (
- torch.sign(torch.tensor(right_counts, dtype=torch.int16))
- .unsqueeze(0)
- .to(self.device)
- )
- right_counts = (
- torch.abs(torch.tensor(right_counts, dtype=torch.int16))
- .unsqueeze(0)
- .to(self.device)
- )
-
- # 预测流程
- with torch.no_grad():
- # 使用修改后的模型前向传播流程
- prediction = self.model(
- left_signs, left_counts, right_signs, right_counts
- ).item()
-
- # 确保预测值在有效范围内
- if np.isnan(prediction) or np.isinf(prediction):
- logger.warning("警告: 预测结果包含NaN或Inf，返回默认值0.5")
- prediction = 0.5
-
- # 检查预测结果是否在[0,1]范围内
- if prediction < 0 or prediction > 1:
- prediction = max(0, min(1, prediction))
-
- return prediction
-
- def get_prediction_with_terrain(self, full_features: np.typing.ArrayLike):
- """使用包含地形特征的完整特征向量进行预测"""
- if self.model is None:
- raise RuntimeError("模型未正确初始化")
-
- # 检查特征向量长度
- expected_length = MONSTER_COUNT * 2 + FIELD_FEATURE_COUNT * 2 # 77L + 6L + 77R + 6R = 166
- if len(full_features) != expected_length:
- logger.warning(f"特征向量长度不匹配: 期望{expected_length}, 实际{len(full_features)}")
- # 如果长度不匹配，回退到原始方法
- left_counts = full_features[:MONSTER_COUNT]
- right_counts = full_features[MONSTER_COUNT:MONSTER_COUNT*2]
- return self.get_prediction(left_counts, right_counts)
-
- # 提取各个部分
- left_monsters = full_features[:MONSTER_COUNT] # 1L-77L
- left_terrain = full_features[MONSTER_COUNT:MONSTER_COUNT+FIELD_FEATURE_COUNT] # 78L-83L
- right_monsters = full_features[MONSTER_COUNT+FIELD_FEATURE_COUNT:MONSTER_COUNT*2+FIELD_FEATURE_COUNT] # 1R-77R
- right_terrain = full_features[MONSTER_COUNT*2+FIELD_FEATURE_COUNT:MONSTER_COUNT*2+FIELD_FEATURE_COUNT*2] # 78R-83R
-
- # 合并怪物特征和地形特征（按照训练时的格式）
- left_counts = np.concatenate([left_monsters, left_terrain])
- right_counts = np.concatenate([right_monsters, right_terrain])
-
- # 转换为张量并处理符号和绝对值
- # 对于怪物特征，使用符号和绝对值
- # 对于地形特征，不需要符号处理（地形特征本身就是0/1值）
- left_monster_signs = torch.sign(torch.tensor(left_monsters, dtype=torch.int16))
- left_terrain_signs = torch.ones_like(torch.tensor(left_terrain, dtype=torch.int16)) # 地形特征符号为1
- left_signs = torch.cat([left_monster_signs, left_terrain_signs]).unsqueeze(0).to(self.device)
-
- left_monster_counts = torch.abs(torch.tensor(left_monsters, dtype=torch.int16))
- left_terrain_counts = torch.tensor(left_terrain, dtype=torch.int16) # 地形特征直接使用原值
- left_counts_tensor = torch.cat([left_monster_counts, left_terrain_counts]).unsqueeze(0).to(self.device)
-
- right_monster_signs = torch.sign(torch.tensor(right_monsters, dtype=torch.int16))
- right_terrain_signs = torch.ones_like(torch.tensor(right_terrain, dtype=torch.int16)) # 地形特征符号为1
- right_signs = torch.cat([right_monster_signs, right_terrain_signs]).unsqueeze(0).to(self.device)
-
- right_monster_counts = torch.abs(torch.tensor(right_monsters, dtype=torch.int16))
- right_terrain_counts = torch.tensor(right_terrain, dtype=torch.int16) # 地形特征直接使用原值
- right_counts_tensor = torch.cat([right_monster_counts, right_terrain_counts]).unsqueeze(0).to(self.device)
-
- # 预测流程
- with torch.no_grad():
- # 使用修改后的模型前向传播流程，现在包含地形特征
- prediction = self.model(
- left_signs, left_counts_tensor, right_signs, right_counts_tensor
- ).item()
-
- # 确保预测值在有效范围内
- if np.isnan(prediction) or np.isinf(prediction):
- logger.warning("警告: 预测结果包含NaN或Inf，返回默认值0.5")
- prediction = 0.5
-
- # 检查预测结果是否在[0,1]范围内
- if prediction < 0 or prediction > 1:
- prediction = max(0, min(1, prediction))
-
- return prediction
diff --git a/predict_onnx.py b/predict_onnx.py
deleted file mode 100644
index 8b656e0..0000000
--- a/predict_onnx.py
+++ /dev/null
@@ -1,105 +0,0 @@
-import onnxruntime as ort
-import os
-import numpy as np
-import logging
-
-from config import MONSTER_COUNT
-from config import FIELD_FEATURE_COUNT
-
-logger = logging.getLogger(__name__)
-
-class CannotModel:
- def __init__(self,model_path = "models/best_model_full.onnx"):
- self.session = None # ONNX Runtime 会话
- self.model_path = model_path
- try:
- self.load_model() # 初始化时加载模型
- except Exception as e:
- logger.error(f"模型加载失败: {e}")
- self.session = None
-
- def load_model(self):
- """加载 ONNX 模型"""
- try:
- if not os.path.exists(self.model_path):
- raise FileNotFoundError(f"未找到 ONNX 模型文件 {self.model_path}")
-
- # 配置会话选项
- sess_options = ort.SessionOptions()
- sess_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
-
- # 创建会话（默认使用 CPU）
- self.session = ort.InferenceSession(
- self.model_path,
- sess_options,
- providers=['CPUExecutionProvider']
- )
-
- except Exception as e:
- raise RuntimeError(f"ONNX 模型加载失败: {str(e)}")
-
- def get_prediction(self, left_counts: np.ndarray, right_counts: np.ndarray):
- if self.session is None:
- raise RuntimeError("模型未正确初始化")
-
- def validate_input(arr):
- """验证并转换输入数据"""
- # 转换为 int64 类型
- arr = arr.astype(np.int64)
-
- # 添加批次维度（如果输入是单样本）
- if arr.ndim == 1:
- arr = arr[np.newaxis, :] # shape: (1, 56)
- return arr
-
- # 添加批次维度
- inputs = {
- "left_counts": validate_input(left_counts).astype(np.int64),
- "right_counts": validate_input(right_counts).astype(np.int64)
- }
-
- # 执行推理
- try:
- output = self.session.run(
- output_names=["output"],
- input_feed=inputs
- )
- print(output)
- prediction = output[0]
- except Exception as e:
- raise RuntimeError(f"推理失败: {str(e)}")
-
- # 后处理（与原逻辑一致）
- if np.isnan(prediction) or np.isinf(prediction):
- logger.warning("警告: 预测结果包含NaN或Inf，返回默认值0.5")
- prediction = 0.5
-
- prediction = np.clip(prediction, 0.0, 1.0)
- return float(prediction)
-
- def get_prediction_with_terrain(self, full_features: np.ndarray):
- """使用包含地形特征的完整特征向量进行预测（ONNX版本）"""
- if self.session is None:
- raise RuntimeError("模型未正确初始化")
-
- # 检查特征向量长度
- expected_length = MONSTER_COUNT * 2 + FIELD_FEATURE_COUNT * 2 # 77L + 6L + 77R + 6R = 166
- if len(full_features) != expected_length:
- logger.warning(f"特征向量长度不匹配: 期望{expected_length}, 实际{len(full_features)}")
- # 如果长度不匹配，回退到原始方法
- left_counts = full_features[:MONSTER_COUNT]
- right_counts = full_features[MONSTER_COUNT:MONSTER_COUNT*2]
- return self.get_prediction(left_counts, right_counts)
-
- # 提取各个部分
- left_monsters = full_features[:MONSTER_COUNT] # 1L-77L
- left_terrain = full_features[MONSTER_COUNT:MONSTER_COUNT+FIELD_FEATURE_COUNT] # 78L-83L
- right_monsters = full_features[MONSTER_COUNT+FIELD_FEATURE_COUNT:MONSTER_COUNT*2+FIELD_FEATURE_COUNT] # 1R-77R
- right_terrain = full_features[MONSTER_COUNT*2+FIELD_FEATURE_COUNT:MONSTER_COUNT*2+FIELD_FEATURE_COUNT*2] # 78R-83R
-
- # 合并怪物特征和地形特征（按照训练时的格式）
- left_counts = np.concatenate([left_monsters, left_terrain])
- right_counts = np.concatenate([right_monsters, right_terrain])
-
- # 使用合并后的特征进行预测
- return self.get_prediction(left_counts, right_counts)
\ No newline at end of file
diff --git a/pyproject.toml b/pyproject.toml
deleted file mode 100644
index 419b30d..0000000
--- a/pyproject.toml
+++ /dev/null
@@ -1,69 +0,0 @@
-[project]
-name = "CannotMax-Honeydew"
-version = "1.0.3"
-description = "这是一个基于深度学习的明日方舟游戏辅助工具，用于自动识别游戏画面中的单位并预测战斗结果。"
-readme = "README.md"
-requires-python = ">=3.10"
-dependencies = [
- "matplotlib",
- "numpy",
- "onnxruntime",
- "opencv-python",
- "pandas",
- "pillow",
- "pyqt6",
- "rapidocr",
- "scikit-learn",
- "seaborn",
- "windows-capture",
- "toml",
-]
-
-[dependency-groups]
-dev = [
- "onnx>=1.19.0",
- "pyinstaller>=6.15.0",
-]
-
-[project.optional-dependencies]
-cpu = [
- "torch",
- "torchvision",
-]
-cu128 = [
- "torch>=2.7.0",
- "torchvision>=0.22.0",
-]
-
-[tool.uv]
-python-install-mirror = "https://mirror.nju.edu.cn/github-release/astral-sh/python-build-standalone/"
-conflicts = [
- [
- { extra = "cpu" },
- { extra = "cu128" },
- ],
-]
-
-[tool.uv.sources]
-torch = [
- { index = "pytorch-cpu", extra = "cpu" },
- { index = "pytorch-cu128", extra = "cu128" },
-]
-torchvision = [
- { index = "pytorch-cpu", extra = "cpu" },
- { index = "pytorch-cu128", extra = "cu128" },
-]
-
-[[tool.uv.index]]
-url = "https://mirrors.cloud.tencent.com/pypi/simple/"
-default = true
-
-[[tool.uv.index]]
-name = "pytorch-cpu"
-url = "https://download.pytorch.org/whl/cpu"
-explicit = true
-
-[[tool.uv.index]]
-name = "pytorch-cu128"
-url = "https://download.pytorch.org/whl/cu128"
-explicit = true
diff --git a/recognize.py b/recognize.py
index 4ac536c..aa5cdec 100644
--- a/recognize.py
+++ b/recognize.py
@@ -1,474 +1,284 @@
-import logging
-import os
import cv2
import numpy as np
+import os
from PIL import ImageGrab
-from rapidocr import RapidOCR, EngineType
-
-from config import MONSTER_DATA, MONSTER_IMAGES
-import find_monster_zone
-from winrt_capture import WinRTScreenCapture
-
-logger = logging.getLogger(__name__)
-logger.setLevel(logging.INFO)
-
-# 是否启用debug模式
-intelligent_workers_debug = True
-# 定义全局变量
-MONSTER_COUNT = 61 # 设置怪物数量
+# 导入 RapidOCR
+try:
+ from rapidocr_onnxruntime import RapidOCR
+except ImportError:
+ try:
+ from rapidocr_openvino import RapidOCR
+ except ImportError:
+ print("请先安装 RapidOCR: pip install rapidocr_onnxruntime")
+ RapidOCR = None
-# 数字区域相对坐标
-relative_regions_nums = [
- (0.0300, 0.7, 0.1400, 1),
- (0.1600, 0.7, 0.2700, 1),
- (0.2900, 0.7, 0.4000, 1),
- (0.6100, 0.7, 0.7200, 1),
- (0.7300, 0.7, 0.8400, 1),
- (0.8600, 0.7, 0.9700, 1),
-]
-# 怪物头像相对坐标
+# ROI保持不变
relative_regions = [
- (0.0000, 0.1, 0.1200, 0.77),
- (0.1200, 0.1, 0.2400, 0.77),
- (0.2400, 0.1, 0.3600, 0.77),
- (0.6400, 0.1, 0.7600, 0.77),
- (0.7600, 0.1, 0.8800, 0.77),
- (0.8800, 0.1, 1.0000, 0.77),
+ (0.0, 0.0, 0.131, 1),
+ (0.1462, 0.0, 0.2762, 1),
+ (0.2923, 0.0, 0.4214, 1),
+ (0.5786, 0.0, 0.7087, 1),
+ (0.7248, 0.0, 0.8538, 1),
+ (0.8679, 0.0, 1, 1)
]
+# 鼠标交互全局变量
+drawing = False
+roi_box = []
+
+
def get_rapidocr_engine(prefer_gpu=True):
- """
- prefer_gpu (bool): 是否优先尝试使用GPU
- """
+ """rapidocr引擎初始化"""
try:
if prefer_gpu:
import torch
if torch.cuda.is_available():
- return RapidOCR(
+ from rapidocr_paddle import RapidOCR as RapidOCRPaddle
+ return RapidOCRPaddle(
params={
- "Det.engine_type": EngineType.TORCH,
- "Cls.engine_type": EngineType.TORCH,
- "Rec.engine_type": EngineType.TORCH,
- "EngineConfig.torch.use_cuda": True, # 使用torch GPU版推理
- "EngineConfig.torch.gpu_id": 0, # 指定GPU id
+ "Global.with_torch": True,
+ "EngineConfig.torch.use_cuda": True,
+ "EngineConfig.torch.gpu_id": 0,
}
)
except ImportError:
- logger.warning("torch库未安装，使用onnxruntime")
- # 如果没有GPU可用，使用CPU onnxruntime
- return RapidOCR()
-
-class RecognizeMonster:
- def __init__(self, window_name: str | None = None, monitor_index: int | None = None):
- self.roi_relative = [(0.2479, 0.8410), (0.7526, 0.9510)] # 16:9下怪物区域相对坐标
- self.main_roi = [(0, 0), (1919, 1079)] # 主区域坐标
- # 鼠标交互全局变量
- self.roi_box = []
- self.drawing = False
- self.rapidocr_eng = get_rapidocr_engine()
- self.ref_images = load_ref_images()
- self._winrt: WinRTScreenCapture | None = None
-
- # 初始化 WinRTScreenCapture
- if window_name is not None or monitor_index is not None:
- try:
- logger.info("初始化 WinRT 屏幕捕获...")
- self._winrt = WinRTScreenCapture(
- window_name=window_name,
- monitor_index=monitor_index,
- capture_cursor=False,
- draw_border=None,
- minimum_update_interval_ms=16, # ~60FPS，按需
- )
+ pass
+ # 默认使用 ONNXRuntime 引擎
+ if RapidOCR is not None:
+ return RapidOCR()
+ return None
+
+
+# 初始化全局OCR引擎实例
+rapidocr_eng = get_rapidocr_engine()
+
+
+def load_ref_images(ref_dir="images"):
+ """加载参考图片库 (0~26 范围)"""
+ ref_images = {}
+ for i in range(27):
+ path = os.path.join(ref_dir, f"{i}.png")
+ if os.path.exists(path):
+ img = cv2.imread(path)
+ if img is not None:
+ ref_images[i] = img
+ return ref_images
+
- # 重置 main_roi = 全图，方便用户重新框选
- frame = self._winrt.snapshot_once()
- h, w = frame.shape[:2]
- self.main_roi = [(0, 0), (w - 1, h - 1)]
- except Exception as e:
- logger.exception("WinRT capture init failed: %s", e)
- self._winrt = None # 将 _winrt 设置为 None，表示初始化失败
- raise # 重新抛出异常，以便上层捕获
-
- def mouse_callback(self, event, x:int, y:int, flags, param):
- if event == cv2.EVENT_LBUTTONDOWN:
- self.roi_box = [(x, y)]
- self.drawing = True
- elif event == cv2.EVENT_MOUSEMOVE and self.drawing:
- img_copy = param.copy()
- cv2.rectangle(img_copy, self.roi_box[0], (x, y), (0, 255, 0), 2)
- cv2.imshow("Select ROI", img_copy)
- elif event == cv2.EVENT_LBUTTONUP:
- self.roi_box.append((x, y))
- self.drawing = False
-
- def select_roi(self):
- """改进的交互式区域选择"""
- while True:
- # 获取初始截图
- if self._winrt is not None:
- # 用当前 WinRT 帧作为底图（已经是BGR）
- logger.info("使用 WinRT 帧作为底图")
- img = self._winrt.snapshot_once()
- else:
- # 兼容旧路径：整屏抓取
- logger.info("使用 PIL 抓取全屏作为底图")
- screenshot = np.array(ImageGrab.grab())
- img = cv2.cvtColor(screenshot, cv2.COLOR_RGB2BGR)
-
- # 添加操作提示
- cv2.putText(img, "Drag to select area | ENTER:confirm | ESC:retry",
- (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
-
- # 显示窗口
- cv2.namedWindow("Select ROI", cv2.WINDOW_NORMAL)
- cv2.resizeWindow("Select ROI", 1280, 720)
- cv2.setMouseCallback("Select ROI", self.mouse_callback, img)
- cv2.imshow("Select ROI", img)
-
- # 添加示例图片(要后弹出才看得见)
- example_img = cv2.imread("images/eg.png")
- # 显示示例图片在单独的窗口中
+def mouse_callback(event, x, y, flags, param):
+ global roi_box, drawing
+ if event == cv2.EVENT_LBUTTONDOWN:
+ roi_box = [(x, y)]
+ drawing = True
+ elif event == cv2.EVENT_MOUSEMOVE and drawing:
+ img_copy = param.copy()
+ cv2.rectangle(img_copy, roi_box[0], (x, y), (0, 255, 0), 2)
+ cv2.imshow("Select ROI", img_copy)
+ elif event == cv2.EVENT_LBUTTONUP:
+ roi_box.append((x, y))
+ drawing = False
+
+
+def select_roi():
+ """交互式区域选择"""
+ global roi_box
+ while True:
+ screenshot = np.array(ImageGrab.grab())
+ img = cv2.cvtColor(screenshot, cv2.COLOR_RGB2BGR)
+
+ cv2.putText(img, "Drag to select area | ENTER:confirm | ESC:retry",
+ (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
+
+ example_img = cv2.imread("images/eg.png")
+ if example_img is not None:
cv2.imshow("example", example_img)
- key = cv2.waitKey(0)
- cv2.destroyAllWindows()
-
- if key == 13 and len(self.roi_box) == 2: # Enter确认
- # 标准化坐标 (x1,y1)为左上角，(x2,y2)为右下角
- x1 = min(self.roi_box[0][0], self.roi_box[1][0])
- y1 = min(self.roi_box[0][1], self.roi_box[1][1])
- x2 = max(self.roi_box[0][0], self.roi_box[1][0])
- y2 = max(self.roi_box[0][1], self.roi_box[1][1])
- logger.info(f"选择区域: {[(x1, y1), (x2, y2)]}")
- self.main_roi = [(x1, y1), (x2, y2)]
- return [(x1, y1), (x2, y2)]
- elif key == 27: # ESC重试
- self.roi_box = []
- continue
-
- def find_best_match(target: cv2.typing.MatLike, ref_images: dict[int, cv2.typing.MatLike]):
- """
- 模板匹配找到最佳匹配的参考图像
- :param target: 目标图像
- :param ref_images: 参考图像字典 {id: image}
- :return: (最佳匹配的id, 最小差异值)
- """
- confidence = float("-inf")
- best_id = -1
- # 确保目标图像是RGB格式
- if len(target.shape) == 2:
- target = cv2.cvtColor(target, cv2.COLOR_GRAY2BGR)
- for img_id, ref_img in ref_images.items():
- try:
- # 模板匹配
- match_algorithm = cv2.TM_CCOEFF_NORMED
- res = cv2.matchTemplate(target, ref_img, match_algorithm)
- min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
- if max_val > confidence:
- confidence = max_val
- best_id = img_id
- except Exception as e:
- logger.exception(f"处理参考图像 {img_id} 时出错:", e)
- continue
- return best_id, confidence
-
- def get_manual_screenshot(self) -> cv2.typing.MatLike:
- logger.info(f"获取区域 {self.main_roi} 的屏幕截图")
- (x1, y1), (x2, y2) = self.main_roi
- bbox = (x1, y1, x2, y2)
- if self._winrt is not None:
- logger.info("使用 WinRT 进行截图")
- screenshot = self._winrt.snapshot_once(bbox=bbox) # BGR
- else:
- logger.info("使用 PIL 进行截图")
- screenshot = np.array(ImageGrab.grab(bbox=bbox))
- screenshot = cv2.cvtColor(screenshot, cv2.COLOR_RGB2BGR)
- try:
- # 手动框选的截图需先识别目标区域
- cv2.imwrite(f"images/tmp/zone1.png", screenshot)
- d_avatar, d_nums = find_monster_zone.cutFrame(screenshot)
- height, width, _ = screenshot.shape
- divisors = np.array([width, height, width, height])
- avatar = np.round(d_avatar * divisors).astype("int")
- x_min, x_max, y_min, y_max = width, 0, height, 0
- for ax1, ay1, ax2, ay2 in avatar:
- x_min = min(x_min, min(ax1, ax2))
- x_max = max(ax1, ax2)
- y_min = min(y_min, min(ay1, ay2))
- y_max = max(ay1, ay2)
- x_min = max(0, x_min)
- y_min = max(0, y_min)
- # 假如找到过能用main_roi的就存起来
- logger.info(f"识别到目标区域：{[(x_min, y_min), (x_max, y_max)]}")
- self.main_roi = [(x1 + x_min, y1 + y_min), (x1 + x_max, y1 + y_max)]
- screenshot = screenshot[y_min:y_max, x_min:x_max]
- logger.info(f"区域更新为: {self.main_roi}")
- except Exception as e:
- logger.error("区域识别失败，使用完整区域")
- return screenshot
-
- def process_regions(
- self,
- image_adb: cv2.typing.MatLike | None = None,
- matched_threshold=0.5,
- ocr_threshold=0.95,
- ):
- """处理主区域中的所有区域（优化特征匹配）
- Args:
- main_roi: 主要感兴趣区域的坐标
- screenshot: 可选的预先捕获的截图
- Returns:
- 区域处理结果的列表
- """
- results = []
- (x1, y1), (x2, y2) = self.main_roi
- # 如果没有提供adb 图像，则获取屏幕截图（仅截取主区域）
- if image_adb is None:
- logger.info("未提供ADB图像，使用手动截图")
- ocr_threshold = 0.8 # 对于手动截图，降低OCR阈值以避免漏识别
- screenshot = self.get_manual_screenshot()
- else:
- logger.info("使用ADB图像")
- x1 = int(self.roi_relative[0][0] * image_adb.shape[1])
- y1 = int(self.roi_relative[0][1] * image_adb.shape[0])
- x2 = int(self.roi_relative[1][0] * image_adb.shape[1])
- y2 = int(self.roi_relative[1][1] * image_adb.shape[0])
- screenshot = image_adb[y1:y2, x1:x2]
-
- # 确保图像不为空
- if screenshot.size == 0:
- raise ValueError("截图为空，请检查区域选择或截图方法。")
- # 转换到标准1920*1080下目标区域
- screenshot = cv2.resize(screenshot, (969, 119))
- main_height = screenshot.shape[0]
- main_width = screenshot.shape[1]
-
- if intelligent_workers_debug: # 如果处于debug模式
- # 存储模板图像用于debug
- cv2.imwrite(f"images/tmp/zone.png", screenshot)
-
- # 遍历所有区域
- for idx, rel in enumerate(relative_regions):
- try:
- # ================== 模板匹配部分 ==================
- # 计算模板匹配的子区域坐标
- rx1 = int(rel[0] * main_width)
- ry1 = int(rel[1] * main_height)
- rx2 = int(rel[2] * main_width)
- ry2 = int(rel[3] * main_height)
- # 提取模板匹配用的子区域
- sub_roi = screenshot[ry1:ry2, rx1:rx2]
-
- # 图像匹配
- matched_id, confidence = find_best_match(sub_roi, self.ref_images)
- logger.info(f"target: {idx} confidence: {confidence:.4f}")
- if matched_id != 0 and confidence < matched_threshold:
- raise ValueError(f"模板匹配置信度过低: {confidence}")
- except Exception as e:
- logger.exception(f"区域 {idx} 匹配失败: {str(e)}")
- results.append(
- {"region_id": idx, "matched_id": matched_id, "number": "N/A", "error": str(e)}
- )
- continue
- try:
- # ================== OCR数字识别部分 ==================
- rel_num = relative_regions_nums[idx]
- rx1_num = int(rel_num[0] * main_width)
- ry1_num = int(rel_num[1] * main_height)
- rx2_num = int(rel_num[2] * main_width)
- ry2_num = int(rel_num[3] * main_height)
-
- # 提取OCR识别用的子区域
- sub_roi_num = screenshot[ry1_num:ry2_num, rx1_num:rx2_num]
- processed = preprocess(sub_roi_num) # 二值化预处理
- processed = crop_to_min_bounding_rect(processed) # 去除多余黑框
- processed = add_black_border(processed, border_size=3) # 加上3像素黑框
-
- # OCR识别（保留优化后的处理逻辑）
- number, ocr_confidence = self.do_num_ocr(processed)
- if number != "" and ocr_confidence < ocr_threshold:
- raise ValueError(f"OCR置信度过低: {ocr_confidence}")
-
- if intelligent_workers_debug: # 如果处于debug模式
- # 存储模板图像用于debug
- cv2.imwrite(f"images/tmp/target_{idx}.png", sub_roi)
-
- # 存储OCR图像用于debug
- cv2.imwrite(f"images/tmp/number_{idx}.png", processed)
-
- if number == "" and matched_id != 0:
- raise ValueError("发现有怪物但无数量异常数据！")
- if matched_id == 0 and number != "":
- raise ValueError("发现无怪物但有数量异常数据！")
-
- results.append(
- {
- "region_id": idx,
- "matched_id": matched_id,
- "number": number if number else "N/A",
- "confidence": round(confidence, 2),
- }
- )
- except Exception as e:
- logger.exception(f"区域 {idx} OCR识别失败: {str(e)}")
- results.append(
- {"region_id": idx, "matched_id": matched_id, "number": "N/A", "error": str(e)}
- )
- return results
-
- def do_num_ocr(self, img: cv2.typing.MatLike):
- result = self.rapidocr_eng(img, use_det=False, use_cls=False, use_rec=True)
- logger.info(f"OCR: text: '{result.txts[0]}', score: {result.scores[0]}")
- if result.txts[0] != "" and not result.txts[0].isdigit():
- raise ValueError(f"OCR识别结果不是数字: '{result.txts[0]}'")
- return result.txts[0], result.scores[0]
+ cv2.namedWindow("Select ROI", cv2.WINDOW_NORMAL)
+ cv2.resizeWindow("Select ROI", 1280, 720)
+ cv2.setMouseCallback("Select ROI", mouse_callback, img)
+ cv2.imshow("Select ROI", img)
+ key = cv2.waitKey(0)
+ cv2.destroyAllWindows()
-def add_black_border(img: cv2.typing.MatLike, border_size=3):
- return cv2.copyMakeBorder(
- img,
- top=border_size,
- bottom=border_size,
- left=border_size,
- right=border_size,
- borderType=cv2.BORDER_CONSTANT,
- value=[0, 0, 0], # BGR格式的黑色
- )
+ if key == 13 and len(roi_box) == 2:
+ x1, y1 = min(roi_box[0][0], roi_box[1][0]), min(roi_box[0][1], roi_box[1][1])
+ x2, y2 = max(roi_box[0][0], roi_box[1][0]), max(roi_box[0][1], roi_box[1][1])
+ return [(x1, y1), (x2, y2)]
+ elif key == 27:
+ roi_box = []
+ continue
-def crop_to_min_bounding_rect(image: cv2.typing.MatLike):
+def preprocess(img):
+ """增强预处理"""
+ if len(img.shape) == 3:
+ gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+ else:
+ gray = img
+
+ blur = cv2.GaussianBlur(gray, (3, 3), 0)
+ _, thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
+
+ # 细小噪声去除
+ contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+ for contour in contours:
+ x, y, w, h = cv2.boundingRect(contour)
+ if w <= 1 or h <= 5: # 过滤掉过小的噪点连通域
+ cv2.drawContours(thresh, [contour], -1, 0, thickness=cv2.FILLED)
+
+ return thresh
+
+
+def crop_to_min_bounding_rect(image):
"""裁剪图像到包含所有轮廓的最小外接矩形"""
- # 转为灰度图（如果传入的是二值图，这个操作不会有问题）
if len(image.shape) == 3:
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
else:
gray = image
- # 寻找轮廓
+
contours, _ = cv2.findContours(gray, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
- # 如果没有找到轮廓就直接返回原图
if not contours:
return image
- # 合并所有轮廓点并获取外接矩形
+
all_contours = np.vstack(contours)
x, y, w, h = cv2.boundingRect(all_contours)
- # 裁剪图片并返回
- return image[y : y + h, x : x + w]
-
-
-def preprocess(img: cv2.typing.MatLike):
- """彩色图像二值化处理，增强数字可见性"""
- # 检查图像是否为彩色
- if len(img.shape) == 2:
- # 如果是灰度图像，转换为三通道
- img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
-
- # 创建较宽松的亮色阈值范围（包括浅灰、白色等亮色）
- # BGR格式
- lower_bright = np.array([180, 180, 180])
- upper_bright = np.array([255, 255, 255])
-
- # 基于颜色范围创建掩码
- bright_mask = cv2.inRange(img, lower_bright, upper_bright)
-
- # 进行形态学操作，增强文本可见性
- # 创建一个小的椭圆形核
- # kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (1, 1))
- # 膨胀操作，使文字更粗
- # dilated = cv2.dilate(bright_mask, kernel, iterations=1)
- # 闭操作，填充文字内的小空隙
- # closed = cv2.morphologyEx(dilated, cv2.MORPH_CLOSE, kernel)
- # closed = dilated
- closed = bright_mask
-
- # 去除细小噪声：过滤不够大的连通区域
- contours, _ = cv2.findContours(closed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
- for contour in contours:
- x, y, w, h = cv2.boundingRect(contour)
- if w <= 1:
- # 用黑色填充宽度小于等于1的区域
- cv2.drawContours(closed, [contour], -1, 0, thickness=cv2.FILLED)
- if h <= 13:
- # 用黑色填充高度小于等于13的区域
- cv2.drawContours(closed, [contour], -1, 0, thickness=cv2.FILLED)
-
- return closed
-
-
-def find_best_match(target: cv2.typing.MatLike, ref_images: dict[int, cv2.typing.MatLike]):
- """
- 模板匹配找到最佳匹配的参考图像
- :param target: 目标图像
- :param ref_images: 参考图像字典 {id: image}
- :return: (最佳匹配的id, 最小差异值)
- """
- confidence = float("-inf")
+ return image[y: y + h, x: x + w]
+
+
+def add_black_border(img, border_size=3):
+ """添加黑边，提升OCR识别率"""
+ return cv2.copyMakeBorder(
+ img,
+ top=border_size, bottom=border_size,
+ left=border_size, right=border_size,
+ borderType=cv2.BORDER_CONSTANT,
+ value=[0, 0, 0]
+ )
+
+
+def find_best_match(target, ref_images):
+ """matchTemplate 进行目标匹配"""
+ confidence = float('-inf')
best_id = -1
- # 确保目标图像是RGB格式
if len(target.shape) == 2:
target = cv2.cvtColor(target, cv2.COLOR_GRAY2BGR)
for img_id, ref_img in ref_images.items():
try:
- # 模板匹配
- match_algorithm = cv2.TM_CCOEFF_NORMED
- res = cv2.matchTemplate(target, ref_img, match_algorithm)
- min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
+ # 动态缩放 ref 尺寸
+ ref_resized = cv2.resize(ref_img, (target.shape[1], target.shape[0]))
+ res = cv2.matchTemplate(target, ref_resized, cv2.TM_CCOEFF_NORMED)
+ _, max_val, _, _ = cv2.minMaxLoc(res)
+
if max_val > confidence:
confidence = max_val
best_id = img_id
- except Exception as e:
- logger.exception(f"处理参考图像 {img_id} 时出错:", e)
+ except Exception:
continue
return best_id, confidence
-def load_ref_images(ref_dir="images"):
- """加载参考图片库"""
- ref_images = {}
- for i in range(MONSTER_COUNT + 1):
- # path = os.path.join(ref_dir, f"{i}.png")
- # if os.path.exists(path):
- # img = cv2.imread(path, cv2.IMREAD_COLOR_BGR)
- if i == 0:
- img = MONSTER_IMAGES.get("empty")
- else:
- img = MONSTER_IMAGES.get(MONSTER_DATA["原始名称"][i])
- # 裁切模板匹配图像比例
- img = img[
- int(img.shape[0] * 0.16) : int(img.shape[0] * 0.80), # 高度取靠上部分
- int(img.shape[1] * 0.18) : int(img.shape[1] * 0.82), # 宽度与高度一致
- ]
- # 调整参考图像大小以匹配目标图像
- ref_resized = cv2.resize(img, (80, 80))
- ref_resized = ref_resized[0:70, :]
-
- if intelligent_workers_debug: # 如果处于debug模式
- # 存储模板图像用于debug
- if not os.path.exists("images/tmp"):
- os.makedirs("images/tmp")
- cv2.imwrite(f"images/tmp/xref_{i}.png", ref_resized)
-
- if img is not None:
- ref_images[i] = ref_resized
- return ref_images
+def do_num_ocr(img):
+ """RapidOCR 封装调用"""
+ if not rapidocr_eng:
+ return "", 0.0
+ try:
+ result, elapse = rapidocr_eng(img, use_det=False, use_cls=False, use_rec=True)
+ if result:
+ # 兼容不同版本 RapidOCR 的返回结构
+ if hasattr(result, 'txts') and result.txts:
+ return result.txts[0], result.scores[0]
+ elif isinstance(result, tuple) and result[0]:
+ return result[0][0][0], result[0][0][1]
+ elif isinstance(result, list) and len(result) > 0:
+ return result[0][0], result[0][1]
+ except Exception as e:
+ print(f"OCR处理异常: {e}")
+ return "", 0.0
+
+
+def process_regions(main_roi, ref_images, screenshot=None):
+ results = []
+ (x1, y1), (x2, y2) = main_roi
+ main_width = x2 - x1
+ main_height = y2 - y1
+
+ if screenshot is None:
+ screenshot = np.array(ImageGrab.grab(bbox=(x1, y1, x2, y2)))
+ screenshot = cv2.cvtColor(screenshot, cv2.COLOR_RGB2BGR)
+ else:
+ screenshot = screenshot[y1:y2, x1:x2]
+
+ for idx, rel in enumerate(relative_regions):
+ try:
+ # 区域提取
+ rx1 = int(rel[0] * main_width)
+ ry1 = int(rel[1] * main_height)
+ rx2 = int(rel[2] * main_width)
+ ry2 = int(rel[3] * main_height)
+ sub_roi = screenshot[ry1:ry2, rx1:rx2]
+
+ # 匹配ID
+ matched_id, confidence = find_best_match(sub_roi, ref_images)
+
+ # 提取数字区域
+ number_roi = sub_roi[-sub_roi.shape[0] // 4:, sub_roi.shape[1] // 3:]
+
+ # 增强预处理
+ processed = preprocess(number_roi) # 基础二值化+去噪
+ processed = crop_to_min_bounding_rect(processed) # 裁掉多余空白
+ processed = add_black_border(processed, 3) # 加黑边防文字贴边
+
+ # OCR数字识别
+ number, ocr_conf = do_num_ocr(processed)
+
+ # 格式化截取
+ if number:
+ number = number.replace('×', 'x').lower()
+ x_pos = number.find('x')
+ if x_pos != -1:
+ number = number[x_pos + 1:]
+ number = ''.join(filter(str.isdigit, number))
+
+ # 保存截取到的数字图
+ #if number:
+ # save_path = f"images/nums/{number}.png"
+ # if not os.path.exists("images/nums"):
+ # os.makedirs("images/nums")
+ # cv2.imwrite(save_path, processed)
+
+ results.append({
+ "region_id": idx,
+ "matched_id": matched_id,
+ "number": number if number else "N/A",
+ "confidence": round(confidence, 2)
+ })
+ except Exception as e:
+ print(f"区域{idx}处理失败: {str(e)}")
+ results.append({
+ "region_id": idx,
+ "error": str(e)
+ })
+
+ return results
if __name__ == "__main__":
print("请用鼠标拖拽选择主区域...")
- recognizer = RecognizeMonster()
- main_roi = recognizer.select_roi()
- results, _ = recognizer.process_regions(main_roi)
- # 输出结果
+ main_roi = select_roi()
+ ref_images = load_ref_images()
+
+ results = process_regions(main_roi, ref_images)
+
print("\n识别结果：")
for res in results:
- if "error" in res:
+ if 'error' in res:
print(f"区域{res['region_id']}: 错误 - {res['error']}")
else:
- if res["matched_id"] != 0:
+ if res['matched_id'] != -1: # 匹配有效时输出
print(
- f"区域{res['region_id']} => 匹配ID:{res['matched_id']} 数字:{res['number']} 置信度:{res['confidence']}"
- )
+ f"区域{res['region_id']} => 匹配ID:{res['matched_id']} 数字:{res['number']} 置信度:{res['confidence']}")
\ No newline at end of file
diff --git a/sim_mc.py b/sim_mc.py
deleted file mode 100644
index df80f9e..0000000
--- a/sim_mc.py
+++ /dev/null
@@ -1,618 +0,0 @@
-from collections import Counter, defaultdict
-from concurrent.futures import ThreadPoolExecutor
-import queue
-import threading
-import time
-import os
-import copy
-from enum import Enum, auto
-import tkinter as tk
-# from tkinter import messagebox # messagebox 已被自定义提示替代，可以注释或移除
-from PIL import Image, ImageTk
-import math
-from simulator.battle_field import Battlefield # 确保 Battlefield 已导入
-from simulator.monsters import MonsterFactory # 确保 MonsterFactory 已导入
-from simulator.utils import MONSTER_MAPPING, REVERSE_MONSTER_MAPPING, Faction # 根据你的实际路径调整
-from simulator.vector2d import FastVector # 确保 FastVector 已导入
-from unit import Unit # 确保 Unit 已导入
-import json # REMOVED_TEAM_INTERFACE: Added missing import for the main block
-import random # REMOVED_TEAM_INTERFACE: Added missing import for the main block
-import sys # Import sys for stdin
-from simulator.monsters import AttackState, Monster, MonsterFactory
-from recognize import MONSTER_COUNT
-
-
-class AppState(Enum):
- INITIAL = auto() # 初始状态
- SETUP = auto() # 部署阶段
- SIMULATING = auto() # 模拟运行中
- PAUSED = auto() # 暂停状态
- ENDED = auto() # 战斗结束
-
-
-class StateMachine:
- def __init__(self, ui_update_callback):
- self.state = AppState.INITIAL
- self.ui_update = ui_update_callback
-
- def transition_to(self, new_state):
- """状态转换并触发UI更新"""
- allowed_transitions = {
- AppState.INITIAL: [AppState.INITIAL, AppState.SETUP, AppState.ENDED],
- AppState.SETUP: [AppState.INITIAL, AppState.SETUP, AppState.SIMULATING],
- AppState.SIMULATING: [AppState.PAUSED, AppState.ENDED],
- AppState.PAUSED: [AppState.SIMULATING, AppState.SETUP],
- AppState.ENDED: [AppState.INITIAL, AppState.SETUP]
- }
-
- if new_state in allowed_transitions[self.state]:
- self.state = new_state
- self.ui_update()
- else:
- print(f"Illegal state transition from {self.state} to {new_state}")
-
- def get_control_states(self):
- """返回各控件的状态字典"""
- states = {
- 'deploy': {'state': tk.NORMAL, 'text': '部署怪物'},
- 'confirm_start': {'state': tk.DISABLED},
- 'pause': {'state': tk.DISABLED, 'text': '暂停'},
- 'restore': {'state': tk.DISABLED},
- 'speed_entry': {'state': tk.NORMAL},
- 'clear': {'state': tk.NORMAL},
- 'timer': {'text': ''}
- }
-
- if self.state == AppState.INITIAL:
- pass
-
- elif self.state == AppState.SETUP:
- states.update({
- 'deploy': {'state': tk.NORMAL, 'text': '重新部署'},
- 'confirm_start': {'state': tk.NORMAL},
- 'timer': {'text': '未开始'}
- })
-
- elif self.state == AppState.SIMULATING:
- states.update({
- 'deploy': {'state': tk.DISABLED, 'text': '重新部署'},
- 'confirm_start': {'state': tk.DISABLED},
- 'pause': {'state': tk.NORMAL, 'text': '暂停'},
- 'clear': {'state': tk.DISABLED},
- })
-
- elif self.state == AppState.PAUSED:
- states.update({
- 'restore': {'state': tk.NORMAL},
- 'pause': {'state': tk.NORMAL, 'text': '继续'},
- 'deploy': {'state': tk.NORMAL, 'text': '重新部署'}
- })
-
- elif self.state == AppState.ENDED:
- states.update({
- 'deploy': {'state': tk.DISABLED, 'text': '重新部署'},
- 'restore': {'state': tk.NORMAL},
- 'pause': {'state': tk.DISABLED, 'text': '暂停'},
- 'timer': {'text': '战斗结束'}
- })
- return states
-
-
-class SandboxSimulator:
- def __init__(self, master: tk.Tk, battle_data):
- self.master = master
- self.master.title("沙盒模拟器")
- self.num_monsters = MONSTER_COUNT # 根据你的怪物总数调整
-
- self.battle_data = battle_data # 初始化时传入的怪物配置
-
- self.grid_width = 13
- self.grid_height = 9
- self.cell_size = 80
- self.canvas_width = self.grid_width * self.cell_size
- self.canvas_height = self.grid_height * self.cell_size
-
- self.units = [] # UI 对应的单位列表
- self.selected_team = None
- self.selected_unit_id = None
-
- self.simulation_id = None
- self.speed_multiplier = 10 # 默认速度
- self.is_paused = False # 新增：暂停状态
-
- self.monster_data = [] # 在 load_assets 中加载
- self.load_assets() # 先加载资源，特别是 self.monster_data
-
- self.battle_field = Battlefield(self.monster_data) # 核心战场逻辑对象
-
- self.message_label = None # 用于显示提示信息的标签
- self.message_timer_id = None # 用于定时清除提示信息的ID
-
- self.monte_carlo_mode = True # 是否启用蒙特卡洛模式
- self.executor = None
- self.mc_running = False
- self.mc_stop_event = threading.Event()
- self.mc_lock = threading.Lock()
- self._mc_counts = Counter()
- self._mc_total_done = 0
- self._mc_total_target = 0
- self._mc_futures = []
- self._mc_result_queue = queue.Queue() # ← future 完成后把结果丢进来
-
- self.create_widgets()
- self.master.protocol("WM_DELETE_WINDOW", self.hide_window)
- self.state_machine = StateMachine(self.update_ui_state)
- self.state_machine.transition_to(AppState.INITIAL)
- self.enter_setup_phase()
-
- def update_ui_state(self):
- """根据当前状态更新所有控件状态"""
- states = self.state_machine.get_control_states()
-
- def hide_window(self):
- if self.state_machine.state == AppState.SIMULATING:
- self.master.after_cancel(self.simulation_id)
- self.state_machine.transition_to(AppState.PAUSED)
- self.master.destroy()
-
- def load_assets(self):
- self.icons = {}
- try:
- with open("simulator/monsters.json", encoding='utf-8') as f:
- self.monster_data = json.load(f)["monsters"]
- except FileNotFoundError:
- print("错误: monsters.json 未找到，请检查路径！")
- self.monster_data = []
-
- return
-
- for i in range(self.num_monsters):
- image_file_id = i + 1
- try:
- image = Image.open(f'images/{image_file_id}.png')
- self.icons[i] = {
- "red": ImageTk.PhotoImage(image.resize((40, 40))),
- "blue": ImageTk.PhotoImage(image.resize((40, 40)).transpose(Image.FLIP_LEFT_RIGHT))
- }
- except Exception as e:
- # 同样，show_message_below_button 可能还不可用
- print(f"加载图标错误 (图标键: {i}, 文件名ID: {image_file_id}): {str(e)}")
- self.icons[i] = {
- "red": ImageTk.PhotoImage(Image.new("RGB", (40, 40), "gray")),
- "blue": ImageTk.PhotoImage(Image.new("RGB", (40, 40), "gray"))
- }
-
- def init_battlefield_for_setup(self):
- self.state_machine.transition_to(AppState.SETUP)
- self.battle_field = Battlefield(self.monster_data)
- self.units = []
-
- left_army_config = self.battle_data.get("left", {})
- right_army_config = self.battle_data.get("right", {})
-
- self.battle_field.setup_battle(
- left_army_config, right_army_config, self.monster_data)
- while self.battle_field.gameTime < 6.0:
- result = self.battle_field.run_one_frame()
- if result:
- break
-
- def _normalize_outcome(self, outcome):
- try:
- from simulator.utils import Faction
- if outcome == Faction.LEFT:
- return "LEFT"
- if outcome == Faction.RIGHT:
- return "RIGHT"
- except Exception:
- pass
- if outcome in ("LEFT", "RIGHT", "DRAW"):
- return outcome
- return "DRAW"
-
- def create_widgets(self):
- control_frame = tk.Frame(self.master)
- control_frame.pack(pady=5, fill=tk.X)
-
- top_control_frame = tk.Frame(control_frame)
- top_control_frame.pack(fill=tk.X)
-
- # 提示信息
- self.message_label = tk.Label(control_frame, text="", fg="black")
- self.message_label.pack(pady=2, anchor="w")
-
- # ====== 蒙特卡洛控制区 ======
- mc_frame = tk.LabelFrame(self.master, text="蒙特卡洛模拟", padx=10, pady=8)
- mc_frame.pack(fill=tk.X, padx=10, pady=8)
-
- tk.Label(mc_frame, text="模拟次数:").grid(row=0, column=0, sticky="w")
- self.mc_runs_entry = tk.Entry(mc_frame, width=10)
- self.mc_runs_entry.grid(row=0, column=1, sticky="w", padx=6)
- self.mc_runs_entry.insert(0, "1000")
-
- self.mc_seed_var = tk.StringVar(value="") # 可选随机种子
- tk.Label(mc_frame, text="随机种子(可空):").grid(
- row=0, column=2, sticky="w", padx=(20, 0))
- self.mc_seed_entry = tk.Entry(
- mc_frame, width=12, textvariable=self.mc_seed_var)
- self.mc_seed_entry.grid(row=0, column=3, sticky="w", padx=6)
-
- self.mc_run_button = tk.Button(
- mc_frame, text="运行蒙卡", command=self.start_monte_carlo_threads)
- self.mc_run_button.grid(row=0, column=4, padx=12)
-
- self.mc_stop_button = tk.Button(
- mc_frame, text="停止", state=tk.DISABLED, command=self.stop_monte_carlo_threads)
- self.mc_stop_button.grid(row=0, column=5, padx=4)
-
- # ...
- self.mc_canvas = tk.Canvas(mc_frame, height=160, bg="white")
- self.mc_canvas.grid(row=2, column=0, columnspan=6,
- sticky="we", pady=(6, 2))
- self.mc_canvas.bind(
- "", lambda e: self._redraw_mc_canvas()) # 动态重绘
-
- # 结果文字
- self.mc_result_text = tk.Text(mc_frame, height=6, width=70)
- self.mc_result_text.grid(
- row=1, column=0, columnspan=5, pady=(8, 4), sticky="we")
- self.mc_result_text.configure(state=tk.DISABLED)
-
- # 简单条形图画布（显示胜负/平局分布）
- self.mc_canvas = tk.Canvas(mc_frame, height=160, bg="white")
- self.mc_canvas.grid(row=2, column=0, columnspan=5,
- sticky="we", pady=(6, 2))
- self.canvas = None
-
- def run_single_battle(self, left_cfg, right_cfg):
- """基于给定左右双方配置，运行一局直到得出胜负；返回 ('LEFT'|'RIGHT'|'DRAW', 用时秒数)"""
- bf = Battlefield(self.monster_data)
- bf.setup_battle(left_cfg, right_cfg, self.monster_data)
-
- result = None
- # 安全上限，防止极端情况下无限跑（必要时可调大）
- max_steps = 20000000
- steps = 0
- while result is None and steps < max_steps:
- result = bf.run_one_frame()
- steps += 1
-
- # 结果为 Faction.LEFT / Faction.RIGHT / 其它(None or 特殊值)
- if result is None:
- outcome = "DRAW"
- else:
- try:
- from simulator.utils import Faction
- if result == Faction.LEFT:
- outcome = "LEFT"
- elif result == Faction.RIGHT:
- outcome = "RIGHT"
- else:
- outcome = str(result)
- except Exception:
- outcome = str(result)
- return outcome, getattr(bf, "gameTime", 0.0)
-
- def _mc_task(self, n_times, left_cfg, right_cfg, seed_val, stop_event):
- if seed_val is not None:
- random.seed(seed_val)
- local = Counter()
- done = 0
- for _ in range(n_times):
- if stop_event.is_set():
- break
- outcome, _ = self.run_single_battle(left_cfg, right_cfg)
- local[self._normalize_outcome(outcome)] += 1
- done += 1
- # 返回 (Counter, 实际完成次数)
- return local, done
-
- def start_monte_carlo_threads(self):
- if self.mc_running:
- return
-
- # 读取参数
- try:
- runs = int(self.mc_runs_entry.get())
- if runs <= 0:
- raise ValueError
- except ValueError:
- self.show_message_below_button("蒙卡次数需为正整数", is_error=True)
- return
-
- seed_txt = self.mc_seed_var.get().strip()
- seed_val = None
- if seed_txt:
- try:
- seed_val = int(seed_txt)
- except ValueError:
- self.show_message_below_button("随机种子需为整数或留空", is_error=True)
- return
-
- left_cfg = copy.deepcopy(self.battle_data.get("left", {}))
- right_cfg = copy.deepcopy(self.battle_data.get("right", {}))
- if not left_cfg or not right_cfg:
- self.show_message_below_button("错误：左右双方任一为空，无法进行蒙卡", is_error=True)
- return
-
- # 状态初始化
- with self.mc_lock:
- self._mc_counts.clear()
- self._mc_total_done = 0
- self._mc_total_target = runs
- self._mc_futures = []
- # 清空队列（防止上次遗留）
- while not self._mc_result_queue.empty():
- try:
- self._mc_result_queue.get_nowait()
- except queue.Empty:
- break
-
- self.mc_stop_event.clear()
- self._redraw_mc_canvas()
-
- # 线程池
- if self.executor is None:
- max_workers = min(8, max(1, (os.cpu_count() or 4) - 1))
- self.executor = ThreadPoolExecutor(max_workers=max_workers)
- workers = getattr(self.executor, "_max_workers", 4)
-
- # 关键：制造足够多的小任务（比如每个任务 10～50 次）
- # 这样就能频繁完成并推送结果，界面“按完成块”刷新
- TARGET_TASKS = min(max(32, workers * 16), runs) # 至少几十个分片
- chunk = max(1, runs // TARGET_TASKS)
-
- remain = runs
- idx = 0
- while remain > 0:
- n = min(chunk, remain)
- fut = self.executor.submit(self._mc_task, n, left_cfg, right_cfg,
- None if seed_val is None else seed_val + idx,
- self.mc_stop_event)
-
- # 回调里**不要**动 UI；只把结果丢进队列
- def _on_done(f):
- try:
- res = f.result()
- except Exception:
- res = (Counter(), 0)
- # 线程安全：把 (Counter, done) 放进队列
- self._mc_result_queue.put(res)
-
- fut.add_done_callback(_on_done)
- self._mc_futures.append(fut)
-
- idx += 1
- remain -= n
-
- # UI 状态
- self.mc_run_button.config(state=tk.DISABLED)
- self.mc_stop_button.config(state=tk.NORMAL)
- self.mc_running = True
-
- # 定时消费队列 + 重画
- self.master.after(50, self._mc_tick_pool) # 刷新更灵敏些
-
- def _mc_tick_pool(self):
- if not self.mc_running:
- return
-
- # 合并已完成任务的结果
- merged = False
- still_pending = []
- for fut in self._mc_futures:
- if fut.done():
- try:
- local_counter, done = fut.result()
- except Exception as e:
- local_counter, done = Counter(), 0 # 出错就忽略该分片
- with self.mc_lock:
- self._mc_counts.update(local_counter)
- self._mc_total_done += done
- merged = True
- else:
- still_pending.append(fut)
- self._mc_futures = still_pending
-
- # 渲染
- with self.mc_lock:
- counts = Counter(self._mc_counts)
- done = self._mc_total_done
- target = self._mc_total_target
- self._render_mc_text_and_chart(counts, done, target)
-
- # 完成或已请求停止
- if (done >= target) or self.mc_stop_event.is_set():
- # 不再等待未完成的分片（它们会尽快感知 stop_event 并返回较小 done）
- self._mc_finish()
- return
-
- # 继续轮询
- self.master.after(10, self._mc_tick_pool)
-
- def stop_monte_carlo_threads(self):
- if not self.mc_running:
- return
- self.mc_stop_event.set()
-
- def _mc_finish(self):
- # 最后一帧
- with self.mc_lock:
- counts = Counter(self._mc_counts)
- done = self._mc_total_done
- target = self._mc_total_target
- self._render_mc_text_and_chart(counts, done, target)
-
- self.mc_running = False
- self.mc_run_button.config(state=tk.NORMAL)
- self.mc_stop_button.config(state=tk.DISABLED)
-
- if done >= target and not self.mc_stop_event.is_set():
- self.show_message_below_button("蒙卡完成", is_error=False)
- else:
- self.show_message_below_button(
- f"已停止（完成 {done}/{target} 次）", is_error=False)
- self.mc_stop_event.clear()
-
- def _render_mc_text_and_chart(self, counts: Counter, done: int, target: int):
- left_win = counts.get("LEFT", 0)
- right_win = counts.get("RIGHT", 0)
- draw_cnt = counts.get("DRAW", 0)
-
- # 文本
- lines = []
- lines.append(f"已完成: {done}/{target}")
- if done > 0:
- lines.append(f"左方胜: {left_win} ({left_win / done:.2%})")
- lines.append(f"右方胜: {right_win} ({right_win / done:.2%})")
- lines.append(f"平局: {draw_cnt} ({draw_cnt / done:.2%})")
- else:
- lines.append("左方胜: 0")
- lines.append("右方胜: 0")
- lines.append("平局: 0")
- self.mc_result_text.configure(state=tk.NORMAL)
- self.mc_result_text.delete("1.0", tk.END)
- self.mc_result_text.insert(tk.END, "\n".join(lines))
- self.mc_result_text.configure(state=tk.DISABLED)
-
- # 图
- items = [("左方胜", left_win), ("右方胜", right_win), ("平局", draw_cnt)]
- self.draw_mc_barchart(items, total=max(1, done)) # 避免除零
-
- def _redraw_mc_canvas(self):
- """在画布尺寸变化时重绘一帧当前统计"""
- with self.mc_lock:
- counts = Counter(self._mc_counts)
- done = self._mc_total_done
- items = [("左方胜", counts.get("LEFT", 0)),
- ("右方胜", counts.get("RIGHT", 0)),
- ("平局", counts.get("DRAW", 0))]
- self.draw_mc_barchart(items, total=max(1, done))
-
- def draw_mc_barchart(self, items, total):
- """items: List[(label, count)]"""
- if not self.mc_canvas:
- return
- c = self.mc_canvas
- c.delete("all")
- if not items or total <= 0:
- c.create_text(10, 10, text="无数据", anchor="nw")
- return
-
- W = int(c.winfo_width()) or 600
- H = int(c.winfo_height()) or 160
- pad_x = 40
- pad_y = 24
- bar_space = (W - 2*pad_x)
- bar_w = bar_space // max(1, len(items)) - 16
- max_cnt = max(x[1] for x in items) or 1
-
- # 坐标轴
- c.create_line(pad_x, H - pad_y, W - pad_x//2, H - pad_y) # x轴
- c.create_line(pad_x, pad_y//2, pad_x, H - pad_y) # y轴
-
- for i, (label, cnt) in enumerate(items):
- x0 = pad_x + i * (bar_w + 16) + 8
- x1 = x0 + bar_w
- # 高度按计数线性映射
- h_ratio = cnt / max_cnt if max_cnt > 0 else 0.0
- bar_h = int((H - 2*pad_y) * h_ratio)
- y0 = H - pad_y - bar_h
- y1 = H - pad_y
- # 绘制条
- c.create_rectangle(x0, y0, x1, y1, fill="#6aa84f", outline="")
- # 文本：计数与百分比
- pct = f"{(cnt/total):.1%}"
- c.create_text((x0+x1)//2, y0-10, text=f"{cnt} ({pct})", anchor="s")
- # 类别名
- c.create_text((x0+x1)//2, H - pad_y + 12, text=label, anchor="n")
-
- def refresh_canvas_display(self):
- if self.monte_carlo_mode:
- return
- # ... 原本的画网格/单位/连线/计时 等逻辑保持不变
-
- def show_message_below_button(self, message, is_error=False, duration=5000):
- """在按钮下方显示文字提示，并在指定时间后消失"""
- if self.message_label:
- self.message_label.config(
- text=message, fg="red" if is_error else "black")
- if self.message_timer_id:
- self.master.after_cancel(self.message_timer_id)
- self.message_timer_id = self.master.after(
- duration, self.clear_message_below_button)
-
- def clear_message_below_button(self):
- """清除提示信息"""
- if self.message_label:
- self.message_label.config(text="")
- self.message_timer_id = None
-
- def enter_setup_phase(self):
- if self.state_machine.state in [AppState.SIMULATING, AppState.PAUSED]:
- # 简单起见，直接中断，不弹出确认框
- pass
- print("进入部署阶段。使用 self.battle_data 初始化战场。")
- self.state_machine.transition_to(AppState.SETUP)
- self.init_battlefield_for_setup() # 这会调用 refresh_canvas_display，进而清除旧的胜利信息
-
-
-def main():
- root = tk.Tk()
- # root.withdraw() # 如果不需要立即隐藏主窗口，可以注释掉
-
- initial_battle_setup = {"left": {"炮击组长": 3, "“庞贝”": 2}, "right": {"沸血骑士团精锐": 6, "炽焰源石虫": 23},
- "result": "left"}
-
- sys.stdin.reconfigure(encoding='utf-8')
- try:
- if not sys.stdin.isatty():
- json_data = sys.stdin.read()
- if json_data:
- initial_battle_setup = json.loads(json_data)
- print("成功从 stdin 加载战斗配置。")
- else:
- print("stdin 中没有数据，使用默认配置。")
- else:
- print("stdin 是交互式终端，使用默认配置。")
- except json.JSONDecodeError:
- print("错误: 无法解析 stdin 中的 JSON 数据，请检查格式，将使用默认配置。")
-
- except Exception as e:
- print(f"从 stdin 读取或解析时发生未知错误: {e}，将使用默认配置。")
-
- app = SandboxSimulator(root, initial_battle_setup)
-
- error_messages = []
- if not app.monster_data: # 检查 monsters.json 是否加载成功
- error_messages.append("错误: monsters.json 未找到或为空，请检查文件！")
-
- if len(initial_battle_setup.get("left", {})) == 0 or len(initial_battle_setup.get("right", {})) == 0:
- error_messages.append("错误：至少一方的怪物列表为空！")
-
- problematic_monsters_found = []
- problematic_monster_names = ["矿脉守卫", "提亚卡乌好战者", "凋零萨卡兹", "狂暴宿主组长", "高能源石虫"]
-
- for team_key in ["left", "right"]:
- for monster_name in initial_battle_setup.get(team_key, {}):
- if monster_name in problematic_monster_names:
- problematic_monsters_found.append(
- f"{('左方' if team_key == 'left' else '右方')}存在问题怪物: {monster_name}")
-
- if problematic_monsters_found:
- error_messages.append("警告: " + "； ".join(problematic_monsters_found))
-
- if error_messages:
- app.show_message_below_button(" | ".join(
- error_messages), is_error=True, duration=10000)
- if "monsters.json 未找到或为空" in " | ".join(error_messages):
- print("由于 monsters.json 缺失或错误，模拟器可能无法正常工作。")
-
- if not root.winfo_exists():
- return
- app.master.deiconify() # 显示主窗口
- root.mainloop()
-
-
-if __name__ == "__main__":
- main()
diff --git a/similar_history_match.py b/similar_history_match.py
deleted file mode 100644
index d1256c1..0000000
--- a/similar_history_match.py
+++ /dev/null
@@ -1,321 +0,0 @@
-import numpy as np
-import pandas as pd
-from sklearn.metrics.pairwise import cosine_similarity
-from config import MONSTER_COUNT
-from config import FIELD_FEATURE_COUNT
-
-class HistoryMatch:
- """错题本数据集的读取和处理类"""
-
- def __init__(self, csv_path="arknights.csv"):
- # 初始化时加载历史对局数据
- self.csv_path = csv_path
- self.load_history_data()
-
- def __len__(self):
- # 返回历史对局数量
- return self.N_history
-
- def load_history_data(self):
- """读取 CSV 文件，加载历史对局的左右阵容、地形及胜负标签"""
- try:
- df = pd.read_csv(self.csv_path, header=None, skiprows=1)
-
- # 新数据格式: [怪物L(77), 场地L(6), 怪物R(77), 场地R(6), Result, ImgPath]
- total_features = (MONSTER_COUNT + FIELD_FEATURE_COUNT) * 2
-
- if df.shape[1] >= total_features + 1: # 至少包含特征和结果列
- # 提取各部分特征
- left_monster_end = MONSTER_COUNT
- left_field_end = MONSTER_COUNT + FIELD_FEATURE_COUNT
- right_monster_end = MONSTER_COUNT + FIELD_FEATURE_COUNT + MONSTER_COUNT
- right_field_end = MONSTER_COUNT + FIELD_FEATURE_COUNT + MONSTER_COUNT + FIELD_FEATURE_COUNT
-
- # 分别提取怪物和地形特征
- left_monsters = df.iloc[:, 0:left_monster_end].values.astype(float)
- left_terrain = df.iloc[:, left_monster_end:left_field_end].values.astype(float)
- right_monsters = df.iloc[:, left_field_end:right_monster_end].values.astype(float)
- right_terrain = df.iloc[:, right_monster_end:right_field_end].values.astype(float)
-
- # 合并怪物特征（只使用怪物部分进行相似度计算）
- self.past_left = left_monsters
- self.past_right = right_monsters
-
- # 保存地形特征用于显示
- self.past_left_terrain = left_terrain
- self.past_right_terrain = right_terrain
-
- # 胜负标签
- self.labels = df.iloc[:, total_features].values
- else:
- # 兼容旧格式：只有怪物特征
- self.past_left = df.iloc[:, 0:MONSTER_COUNT].values.astype(float)
- self.past_right = df.iloc[:, MONSTER_COUNT:MONSTER_COUNT*2].values.astype(float)
- self.labels = df.iloc[:, MONSTER_COUNT*2].values
-
- # 地形特征为空
- self.past_left_terrain = np.zeros((len(self.past_left), FIELD_FEATURE_COUNT))
- self.past_right_terrain = np.zeros((len(self.past_right), FIELD_FEATURE_COUNT))
-
- except Exception as e:
- print(f"加载历史数据失败: {e}")
- # 加载失败时，初始化为空数组
- self.past_left = np.zeros((0, MONSTER_COUNT), float)
- self.past_right = np.zeros((0, MONSTER_COUNT), float)
- self.past_left_terrain = np.zeros((0, FIELD_FEATURE_COUNT), float)
- self.past_right_terrain = np.zeros((0, FIELD_FEATURE_COUNT), float)
- self.labels = np.array([], dtype=str)
-
- # 构造历史对局特征: 左右数量之和与差的绝对值拼接（只使用怪物特征）
- self.feat_past = np.hstack([
- self.past_left + self.past_right,
- np.abs(self.past_left - self.past_right)
- ])
- # 历史对局总数
- self.N_history = self.past_left.shape[0]
-
- def render_similar_matches(self, left_counts: np.ndarray, right_counts: np.ndarray):
- """返回与当前对局最相似的历史对局索引及胜率统计"""
- # 将输入转为浮点型数组
- cur_left = left_counts.astype(float)
- cur_right = right_counts.astype(float)
-
- # 计算当前存在的兵种布尔向量
- pres_L = cur_left > 0
- pres_R = cur_right > 0
- need_L_idx = np.nonzero(pres_L)[0] # 当前左侧有兵的索引
- need_R_idx = np.nonzero(pres_R)[0] # 当前右侧有兵的索引
-
- # 构造当前对局特征并计算与所有历史的余弦相似度
- feat_cur = np.hstack([cur_left + cur_right, np.abs(cur_left - cur_right)]).reshape(1, -1)
- sims = cosine_similarity(feat_cur, self.feat_past)[0]
-
- # 历史对局的存在布尔矩阵
- hist_pres_L = self.past_left > 0 # shape (N_history, MONSTER_COUNT)
- hist_pres_R = self.past_right > 0
-
- # 计算未镜像(missA, cntA)和镜像后(missB, cntB)的缺兵及数量差距
- missA = np.sum(np.logical_xor(pres_L, hist_pres_L), axis=1) + np.sum(
- np.logical_xor(pres_R, hist_pres_R), axis=1)
- cntA = np.sum(np.abs(self.past_left - cur_left), axis=1) + np.sum(
- np.abs(self.past_right - cur_right), axis=1)
-
- missB = np.sum(np.logical_xor(pres_L, hist_pres_R), axis=1) + np.sum(
- np.logical_xor(pres_R, hist_pres_L), axis=1)
- cntB = np.sum(np.abs(self.past_right - cur_left), axis=1) + np.sum(
- np.abs(self.past_left - cur_right), axis=1)
-
- # 根据(miss, cnt)比较，决定是否对历史数据做镜像处理
- swap = (missB < missA) | ((missB == missA) & (cntB < cntA))
-
- # 生成镜像后的历史左右阵容
- Lh = np.where(swap[:, None], self.past_right, self.past_left)
- Rh = np.where(swap[:, None], self.past_left, self.past_right)
-
- # 修复bug：3B对阵3A在csv文件中，但输入3A、3B不返回，输入3B、3A才返回
- # 使用镜像后的阵容构造存在布尔矩阵（后续匹配统计应基于镜像后的阵容）
- hist_pres_Lh = Lh > 0 # shape (N_history, MONSTER_COUNT)
- hist_pres_Rh = Rh > 0
-
- # 判断在需求索引上是否完全匹配
- full_L = np.all(Lh[:, need_L_idx] == cur_left[need_L_idx], axis=1)
- full_R = np.all(Rh[:, need_R_idx] == cur_right[need_R_idx], axis=1)
-
- # 计算需求索引处的数量差和
- diff_L = np.sum(np.abs(Lh[:, need_L_idx] - cur_left[need_L_idx]), axis=1)
- diff_R = np.sum(np.abs(Rh[:, need_R_idx] - cur_right[need_R_idx]), axis=1)
-
- # 计算对手兵种在本方需求中的命中数，取最小值作为 match_other
- hit_L = np.sum(hist_pres_Rh[:, need_L_idx] & pres_L[need_L_idx], axis=1)
- hit_R = np.sum(hist_pres_Lh[:, need_R_idx] & pres_R[need_R_idx], axis=1)
- match_other = np.minimum(hit_L, hit_R)
-
- # 根据命中侧及是否完全匹配，选择对应的 qdiff_other
- qdiff_other = np.where(
- (hit_R > 0) & (~full_R), diff_R,
- np.where((hit_L > 0) & (~full_L), diff_L, 0)
- )
-
- # 批量计算分类所需的布尔向量
- # 注意：类型（presence）比较也应基于镜像后的阵容
- typeL_eq = np.all(hist_pres_Lh == pres_L, axis=1)
- typeR_eq = np.all(hist_pres_Rh == pres_R, axis=1)
- cntL_eq = np.all(Lh == cur_left, axis=1)
- cntR_eq = np.all(Rh == cur_right, axis=1)
-
- # 初始化类别为最松散的 5（默认）
- cats = np.full(self.N_history, 5, dtype=np.int8)
-
- # 重新定义分类优先级（数值越小优先级越高）：
- # 0: 双方种类与数量都完全相同
- # 1: 双方种类相同，数量均不同但成比例（如 1A1B vs 2A2B）
- # 2: 双方种类相同，且至少一侧数量相同（但不是双方都相同）
- # 3: 双方种类相同，但双方数量均不同（且不成比例）
- # 5: 其它（默认）
- same_species = typeL_eq & typeR_eq
-
- mask0 = same_species & cntL_eq & cntR_eq
- mask1 = same_species & (cntL_eq | cntR_eq) & ~mask0
-
- # 检测“成比例”:
- # 在双方各自非零位置上，Lh/cur_left 与 Rh/cur_right 分别行内常数，
- # 且左右两侧比例相同，且比例不为 1（确保“数量均不同”）
- # 注意：当某侧不存在任意单位时，认为该侧比例为 1 且恒定
- if need_L_idx.size > 0:
- ratios_L = Lh[:, need_L_idx] / np.maximum(cur_left[need_L_idx], 1e-12)
- rL_min = ratios_L.min(axis=1)
- rL_max = ratios_L.max(axis=1)
- uniform_L = np.isclose(rL_min, rL_max, rtol=1e-3, atol=1e-6)
- rL = 0.5 * (rL_min + rL_max)
- else:
- uniform_L = np.ones(self.N_history, dtype=bool)
- rL = np.ones(self.N_history, dtype=float)
-
- if need_R_idx.size > 0:
- ratios_R = Rh[:, need_R_idx] / np.maximum(cur_right[need_R_idx], 1e-12)
- rR_min = ratios_R.min(axis=1)
- rR_max = ratios_R.max(axis=1)
- uniform_R = np.isclose(rR_min, rR_max, rtol=1e-3, atol=1e-6)
- rR = 0.5 * (rR_min + rR_max)
- else:
- uniform_R = np.ones(self.N_history, dtype=bool)
- rR = np.ones(self.N_history, dtype=float)
-
- same_ratio = np.isclose(rL, rR, rtol=1e-3, atol=1e-6)
- ratio_not_one = ~np.isclose(rL, 1.0, rtol=1e-3, atol=1e-6) # rL==rR 时即可代表两侧都不为1
- proportional = uniform_L & uniform_R & same_ratio & ratio_not_one
-
- # 2类：同种类，数量均不同且成比例
- mask2 = same_species & (~cntL_eq) & (~cntR_eq) & proportional
- # 3类：同种类，数量均不同但不成比例
- mask3 = same_species & (~cntL_eq) & (~cntR_eq) & (~proportional)
-
- cats[mask0] = 0
- cats[mask1] = 2
- cats[mask2] = 1
- cats[mask3] = 3
-
- # 使用 lexsort 按 (-sims, qdiff_other, -match_other, cats) 排序
- order = np.lexsort((-sims, qdiff_other, -match_other, cats))
- good = order[match_other[order] > 0]
- backup = order[match_other[order] == 0]
- top20 = np.concatenate([good, backup])[:20]
- # 移除最终“仅按相似度”的重排，保持 cats 优先级
- # top20 = top20[np.argsort(-sims[top20])]
- self.top20_idx = top20
-
- # 从前5条中计算左右胜率
- top5 = top20[:5]
- labs = np.where(swap[top5], np.where(self.labels[top5]=="L", "R", "L"), self.labels[top5])
- tgtL = need_L_idx[np.argmax(cur_left[need_L_idx])] if need_L_idx.size else None
- tgtR = need_R_idx[np.argmax(cur_right[need_R_idx])] if need_R_idx.size else None
-
- lw = np.sum([lab == ("L" if (Lh[i, tgtL] if tgtL is not None else 0) > 0 else "R")
- for i, lab in zip(top5, labs)])
- rw = np.sum([lab == ("L" if (Lh[i, tgtR] if tgtR is not None else 0) > 0 else "R")
- for i, lab in zip(top5, labs)])
- self.left_rate = lw / len(top5) if top5.size else 0
- self.right_rate = rw / len(top5) if top5.size else 0
- self.sims = sims
- self.swap = swap
- self.cur_left = cur_left
- self.cur_right = cur_right
- return self.top20_idx, self.left_rate, self.right_rate
-
- def get_terrain_names(self, idx, is_swapped=False):
- """获取指定历史对局的地形名称"""
- if idx >= len(self.past_left_terrain):
- return "无地形"
-
- # 根据是否镜像选择地形特征
- terrain_features = self.past_right_terrain[idx] if is_swapped else self.past_left_terrain[idx]
-
- # 获取激活的地形特征索引
- active_indices = np.where(terrain_features > 0)[0]
-
- if len(active_indices) == 0:
- return "无地形"
-
- # 尝试从FieldRecognizer获取实际的特征列名称
- try:
- from field_recognition import FieldRecognizer
- field_recognizer = FieldRecognizer()
- if field_recognizer.is_ready():
- feature_columns = field_recognizer.get_feature_columns()
- # 根据实际特征列名称生成简洁名称
- active_terrains = []
- for i in active_indices:
- if i < len(feature_columns):
- full_name = feature_columns[i]
- # 简化名称映射（与main.py的terrain_display_mapping保持一致）
- if "altar_vertical_altar" in full_name:
- simple_name = "垂直祭坛"
- elif "block_parallel_block" in full_name:
- simple_name = "平行方块阻挡"
- elif "block_vertical_altar_shape1" in full_name:
- simple_name = "垂直祭坛形阻挡1"
- elif "block_vertical_altar_shape2" in full_name:
- simple_name = "垂直祭坛形阻挡2"
- elif "block_vertical_block_shape1" in full_name:
- simple_name = "垂直方块阻挡1"
- elif "block_vertical_block_shape2" in full_name:
- simple_name = "垂直方块阻挡2"
- elif "coil_narrow_coil" in full_name:
- simple_name = "窄型线圈装置"
- elif "coil_wide_coil" in full_name:
- simple_name = "宽型线圈装置"
- elif "crossbow_top_crossbow" in full_name:
- simple_name = "顶部弩炮"
- elif "fire_side_crossbow" in full_name:
- simple_name = "侧边弩炮"
- elif "fire_side_fire" in full_name:
- simple_name = "侧边火炮"
- elif "fire_top_fire" in full_name:
- simple_name = "顶部火炮"
- # 保留旧的映射以兼容旧数据
- elif "middle_row_blocks" in full_name:
- simple_name = "中路阻挡"
- elif "side_fire_cannon_crossbow" in full_name:
- simple_name = "侧边弩箭"
- elif "side_fire_cannon_fire" in full_name:
- simple_name = "侧边火炮"
- elif "top_crossbow" in full_name:
- simple_name = "顶部弩箭"
- elif "top_fire_cannon" in full_name:
- simple_name = "顶部火炮"
- elif "two_row_blocks" in full_name:
- simple_name = "双行阻挡"
- else:
- # 如果无法识别，使用原名称的简化版本
- simple_name = full_name.replace("_", "")
- active_terrains.append(simple_name)
-
- return "+".join(active_terrains) if active_terrains else "无地形"
- except Exception:
- pass
-
- # 备用硬编码映射（如果无法获取FieldRecognizer）
- # 与main.py的terrain_display_mapping保持一致
- terrain_names = {
- 0: "垂直祭坛",
- 1: "平行方块阻挡",
- 2: "垂直祭坛形阻挡1",
- 3: "垂直祭坛形阻挡2",
- 4: "垂直方块阻挡1",
- 5: "垂直方块阻挡2",
- 6: "窄型线圈装置",
- 7: "宽型线圈装置",
- 8: "顶部弩炮",
- 9: "侧边弩炮",
- 10: "侧边火炮",
- 11: "顶部火炮"
- }
-
- # 获取所有激活地形的名称
- active_terrains = []
- for i in active_indices:
- if i < len(terrain_names):
- active_terrains.append(terrain_names[i])
-
- # 如果有多个地形，用"+"连接
- return "+".join(active_terrains) if active_terrains else "无地形"
diff --git a/simular_history_match_ui.py b/simular_history_match_ui.py
deleted file mode 100644
index 6ddf18c..0000000
--- a/simular_history_match_ui.py
+++ /dev/null
@@ -1,296 +0,0 @@
-from PyQt6.QtCore import Qt
-from PyQt6.QtWidgets import QWidget, QVBoxLayout, QHBoxLayout, QLabel, QScrollArea, QGraphicsDropShadowEffect, QFrame
-from PyQt6.QtGui import QPixmap, QImage, QFont, QIcon, QPainter, QColor
-import numpy as np
-import logging
-
-from similar_history_match import HistoryMatch
-from config import MONSTER_COUNT, MONSTER_DATA
-
-logger = logging.getLogger(__name__)
-
-
-class HistoryMatchUI(QFrame):
- def __init__(self, history_match: HistoryMatch):
- super().__init__()
- self.history_match = history_match
- self.init_ui()
-
- def init_ui(self):
- self.main_layout = QVBoxLayout(self)
- self.main_layout.setContentsMargins(0, 0, 0, 0)
-
- # 创建滚动区域
- self.history_scroll_area = QScrollArea()
- self.history_scroll_area.setFixedWidth(540)
- self.history_scroll_area.setWidgetResizable(True)
- self.history_scroll_area.setStyleSheet(
- """
- QScrollBar:horizontal {
- background: rgba(0, 0, 0, 0);
- width: 12px; /* 宽度 */
- margin: 0px; /* 边距 */
- }
- QScrollBar::handle:horizontal {
- background: rgba(100, 100, 100, 150);
- min-height: 20px; /* 滑块最小高度 */
- border-radius: 8px; /* 圆角 */
- }
- QScrollBar::add-line:horizontal, QScrollBar::sub-line:horizontal {
- background: none; /* 隐藏箭头按钮 */
- }
- QScrollBar:vertical {
- background: rgba(0, 0, 0, 0);
- width: 12px; /* 宽度 */
- margin: 0px; /* 边距 */
- }
- QScrollBar::handle:vertical {
- background: rgba(100, 100, 100, 150);
- min-height: 20px; /* 滑块最小高度 */
- border-radius: 8px; /* 圆角 */
- }
- QScrollBar::add-line:vertical, QScrollBar::sub-line:vertical {
- background: none; /* 隐藏箭头按钮 */
- }
- QScrollArea {
- background-color: rgba(0, 0, 0, 40);
- border-radius: 15px;
- border: 5px solid #F5EA2D;
- }
- QScrollArea > QWidget > QWidget {
- background: transparent;
- }
- QScrollBar:vertical {
- background: rgba(50, 50, 50, 100);
- width: 12px;
- margin: 15px 0 15px 0;
- }
- QScrollBar::handle:vertical {
- background: rgba(100, 100, 100, 150);
- min-height: 20px;
- border-radius: 6px;
- }
- """
- )
-
- # 创建内容部件
- self.history_widget = QWidget()
- self.history_layout = QVBoxLayout(self.history_widget)
- self.history_layout.setAlignment(Qt.AlignmentFlag.AlignTop)
-
- # 设置滚动区域内容
- self.history_scroll_area.setWidget(self.history_widget)
-
- # 添加到主布局
- self.main_layout.addWidget(self.history_scroll_area)
-
- def render_similar_matches(self, left_monsters, right_monsters):
- try:
- # 获取当前输入
- cur_left = np.zeros(MONSTER_COUNT, dtype=float)
- cur_right = np.zeros(MONSTER_COUNT, dtype=float)
- for name, entry in left_monsters.items():
- v = entry.text()
- if v.isdigit():
- cur_left[int(name) - 1] = float(v)
- for name, entry in right_monsters.items():
- v = entry.text()
- if v.isdigit():
- cur_right[int(name) - 1] = float(v)
-
- self.history_match.render_similar_matches(cur_left, cur_right)
- sims = self.history_match.sims
- top_indices = self.history_match.top20_idx
-
- # 清空现有内容
- for i in reversed(range(self.history_layout.count())):
- self.history_layout.itemAt(i).widget().setParent(None)
-
- # 添加标题
- title_label = QLabel(f"错题本")
- shadow = QGraphicsDropShadowEffect()
- shadow.setBlurRadius(0) # 模糊半径（控制发光范围）
- shadow.setColor(QColor("#313131")) # 发光颜色
- shadow.setOffset(2) # 偏移量（0表示均匀四周发光）
- title_label.setGraphicsEffect(shadow)
-
- title_label.setStyleSheet(
- """
- QWidget {
- border-radius: 0px;
- font-size: 24px;
- font-weight: bold;
- color: white;
- }
- """
- )
- self.history_layout.addWidget(title_label)
-
- # 渲染每个历史对局
- for idx in top_indices:
- self.add_history_match(idx, sims[idx], left_monsters, right_monsters)
-
- except Exception as e:
- logger.error(f"渲染历史对局失败: {str(e)}")
-
- def add_history_match(self, idx, similarity, left_monsters, right_monsters):
- """添加单个历史对局到面板"""
- # 获取历史数据
- left = self.history_match.past_left[idx]
- right = self.history_match.past_right[idx]
- result = self.history_match.labels[idx]
-
- # 获取当前对局的左右单位
- cur_left = np.zeros(MONSTER_COUNT, dtype=float)
- cur_right = np.zeros(MONSTER_COUNT, dtype=float)
- for name, entry in left_monsters.items():
- v = entry.text()
- if v.isdigit():
- cur_left[int(name) - 1] = float(v)
- for name, entry in right_monsters.items():
- v = entry.text()
- if v.isdigit():
- cur_right[int(name) - 1] = float(v)
-
- # 计算当前对局和历史对局的相似度(不镜像和镜像两种情况)
- setL_cur = set(np.where(cur_left > 0)[0])
- setR_cur = set(np.where(cur_right > 0)[0])
- setL_past = set(np.where(left > 0)[0])
- setR_past = set(np.where(right > 0)[0])
-
- # 判断是否需要镜像历史对局
- should_swap = (len(setL_cur ^ setR_past) + len(setR_cur ^ setL_past)) < (
- len(setL_cur ^ setL_past) + len(setR_cur ^ setR_past)
- )
-
- # 获取地形名称
- terrain_name = self.history_match.get_terrain_names(idx, should_swap)
-
- # 创建对局容器
- match_widget = QWidget()
- match_widget.setStyleSheet(
- """
- QWidget {
- background-color: rgba(50, 50, 50, 150);
- border-radius: 10px;
- padding: 0px;
- margin: 5px;
- }
- """
- )
- match_widget.setFixedSize(500, 170) # 增加高度以容纳地形信息
- match_layout = QVBoxLayout(match_widget)
-
- # 添加左右阵容
- teams_widget = QWidget()
- teams_layout = QHBoxLayout(teams_widget)
-
- # 根据是否需要镜像决定显示方向
- if should_swap:
- left_team = self.create_team_widget("右方", right, result == "R")
- right_team = self.create_team_widget("左方", left, result == "L")
- else:
- left_team = self.create_team_widget("左方", left, result == "L")
- right_team = self.create_team_widget("右方", right, result == "R")
-
- teams_layout.addWidget(left_team)
- teams_layout.addWidget(right_team)
- match_layout.addWidget(teams_widget)
-
- # 添加地形信息显示
- terrain_label = QLabel(f"地形: {terrain_name}")
- terrain_label.setStyleSheet(
- """
- QLabel {
- color: #CCCCCC;
- font: 10px Microsoft YaHei;
- padding: 2px 5px;
- background-color: rgba(0, 0, 0, 50);
- border-radius: 3px;
- margin: 2px;
- }
- """
- )
- terrain_label.setAlignment(Qt.AlignmentFlag.AlignCenter)
- match_layout.addWidget(terrain_label)
-
- self.history_layout.addWidget(match_widget)
-
- def create_team_widget(self, side, counts, is_winner):
- """创建单个队伍显示部件"""
- team_widget = QWidget()
- team_widget.setStyleSheet(
- f"""
- QWidget {{
- background-color: {'rgba(250, 250, 50, 150)' if is_winner else 'rgba(50, 50, 50, 100)'};
- border-radius: 8px;
- padding: 0px;
- margin: 0px;
- }}
- """
- )
-
- layout = QVBoxLayout(team_widget)
-
- # 显示区域
- ops_widget = QWidget()
- shadow01 = QGraphicsDropShadowEffect()
- shadow01.setBlurRadius(5) # 模糊半径（控制发光范围）
- shadow01.setColor(QColor(0, 0, 0, 120)) # 发光颜色
- shadow01.setOffset(3) # 偏移量（0表示均匀四周发光）
- ops_widget.setGraphicsEffect(shadow01)
-
- ops_widget.setStyleSheet(
- """
- QWidget {
- background-color: rgba(0, 0, 0, 0);
- border-radius: 0px;
- padding: 0px;
- margin: 0px;
- }
- """
- )
- ops_layout = QHBoxLayout(ops_widget)
- ops_layout.setSpacing(5)
- ops_layout.setContentsMargins(0, 0, 0, 0)
-
- for i, count in enumerate(counts):
- if count > 0:
- # 创建干员显示
- op_widget = QWidget()
- op_widget.setStyleSheet("background-color: rgba(0, 0, 0, 0); padding: 0px 0;margin: 0px;")
- op_layout = QVBoxLayout(op_widget)
- op_layout.setContentsMargins(0, 0, 0, 0)
- op_layout.setAlignment(Qt.AlignmentFlag.AlignCenter)
-
- # 干员图片
- img_label = QLabel()
- img_label.setFixedSize(60, 60)
- img_label.setAlignment(Qt.AlignmentFlag.AlignCenter)
- try:
- pixmap = QPixmap(f"images/{MONSTER_DATA['原始名称'][i+1]}.png")
- if not pixmap.isNull():
- pixmap = pixmap.scaled(
- 60, 60, Qt.AspectRatioMode.KeepAspectRatio, Qt.TransformationMode.SmoothTransformation
- )
- img_label.setPixmap(pixmap)
- except:
- pass
-
- # 数量标签
- count_label = QLabel(str(int(count)))
- count_label.setAlignment(Qt.AlignmentFlag.AlignCenter)
- count_label.setStyleSheet(
- """
- color: #EDEDED;
- font: bold 20px SimHei;
- min-width: 20px;
- """
- )
-
- op_layout.addWidget(img_label, stretch=3)
- op_layout.addWidget(count_label, stretch=1)
- ops_layout.addWidget(op_widget)
-
- layout.addWidget(ops_widget)
- return team_widget
diff --git a/simulator/.gitignore b/simulator/.gitignore
deleted file mode 100644
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--- a/simulator/.gitignore
+++ /dev/null
@@ -1 +0,0 @@
-__pycache__/*
\ No newline at end of file
diff --git a/simulator/README.md b/simulator/README.md
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index bcb38c4..0000000
--- a/simulator/README.md
+++ /dev/null
@@ -1,168 +0,0 @@
-# 明日方舟斗蛐蛐模拟器 🎮⚔️
-
-这是一款基于明日方舟角色设定的AI自动战斗模拟器，还原干员技能特性与战场策略对抗，并且可以以极快速度获得战斗结果！
-
----
-
-## 🌟 核心特色
-
-- **全自动战斗系统**：基于属性与AI策略的自主决策
-- **精细角色建模**：
- - 完全还原明日方舟斗蛐蛐的敌人属性设定
- - 真实攻击范围计算（可配置十字/圆形/直线范围）
-- **动态战斗表现**：
- - 实时战斗日志
- - 可视化战场轨迹
- - 数据统计面板
-
----
-
-## ⚙️ 安装指南
-
-### 环境要求
-- Python 3.8+
-- NumPy 1.20+
-
-
-## 🕹️ 使用说明
-
-### 启动战斗
-
-**示例**：
-
-具体可以参考 `simulate.py`
-```python
-from battle_field import Battlefield, Faction
-
-scene_config = { "left": { "Vvan": 5 }, "right": { "衣架": 13 }, "result": "right" }
-
-# 用户配置
-left_army = scene_config["left"]
-right_army = scene_config["right"]
-
-# 初始化战场
-battlefield = Battlefield(monster_data)
-
-# 去除掉不符合格式的配置
-if not battlefield.setup_battle(left_army, right_army, monster_data):
- # 处理异常
-
-# 开始战斗，并且返回胜者，可以打开visualize模式来可视化结果
-winner = battlefield.run_battle(visualize=VISUALIZATION_MODE)
-
-```
-
-### 参数说明
----
-
-## 📝 配置文件规范
-
-### 敌人模板
-```json
- {
- "名字": "绵羊",
- "攻击力": { "数值": 570 },
- "物理防御": { "数值": 250 },
- "生命值": { "数值": 3500 },
- "类型": "魔法",
- "法抗": { "数值": 50 },
- "攻击间隔": { "数值": 3.2 },
- "移速": { "数值": 0.7 },
- "攻击范围": { "数值": 3.5 },
- "特性": ""
- },
- {
- "名字": "食腐狗",
- "攻击力": { "数值": 825 },
- "物理防御": { "数值": 150 },
- "生命值": { "数值": 6000 },
- "类型": "物理",
- "法抗": { "数值": 20 },
- "攻击间隔": { "数值": 1.8 },
- "移速": { "数值": 1.4 },
- "攻击范围": { "数值": 0.8 },
- "特性": "每次攻击令其在10秒内每秒受到100点魔法伤害"
- },
-```
-
-### 队伍配置
-可以参考`scene.json`文件的内容
-```json
-{
- "left":
- [
- { "name": "酸液源石虫·α", "count": 0 },
- { "name": "高能源石虫", "count": 0 },
- { "name": "阿咬", "count": 0 },
- { "name": "狂暴的猎狗pro", "count": 0 },
- { "name": "提亚卡乌好战者", "count": 0 },
- { "name": "污染躯壳", "count": 0 },
- { "name": "大盾哥", "count": 0 },
- { "name": "宿主流浪者", "count": 0 },
- { "name": "光剑", "count": 4 },
- { "name": "泥岩巨像", "count": 0 },
- { "name": "呼啸骑士团学徒", "count": 0 },
- { "name": "萨卡兹大剑手", "count": 0 },
- { "name": "狂暴宿主组长", "count": 0 },
- { "name": "海螺", "count": 0 },
- { "name": "拳手囚犯", "count": 0 },
- { "name": "高塔术师", "count": 0 },
- { "name": "冰原术师", "count": 0 },
- { "name": "矿脉守卫", "count": 0 },
- { "name": "“庞贝”", "count": 0 },
- { "name": "绵羊", "count": 0 },
- { "name": "食腐狗", "count": 0 },
- { "name": "温顺的武装驮兽", "count": 3 },
- { "name": "鼠鼠", "count": 4 },
- { "name": "“投石机”", "count": 0 },
- { "name": "船长", "count": 0 }
- ],
- "right":
- [
- { "name": "酸液源石虫·α", "count": 0 },
- { "name": "高能源石虫", "count": 0 },
- { "name": "阿咬", "count": 0 },
- { "name": "狂暴的猎狗pro", "count": 0 },
- { "name": "提亚卡乌好战者", "count": 0 },
- { "name": "污染躯壳", "count": 0 },
- { "name": "大盾哥", "count": 0 },
- { "name": "宿主流浪者", "count": 0 },
- { "name": "光剑", "count": 0 },
- { "name": "泥岩巨像", "count": 0 },
- { "name": "呼啸骑士团学徒", "count": 0 },
- { "name": "萨卡兹大剑手", "count": 0 },
- { "name": "狂暴宿主组长", "count": 0 },
- { "name": "海螺", "count": 0 },
- { "name": "拳手囚犯", "count": 11 },
- { "name": "高塔术师", "count": 0 },
- { "name": "冰原术师", "count": 0 },
- { "name": "矿脉守卫", "count": 0 },
- { "name": "“庞贝”", "count": 3 },
- { "name": "绵羊", "count": 9 },
- { "name": "食腐狗", "count": 0 },
- { "name": "温顺的武装驮兽", "count": 0 },
- { "name": "鼠鼠", "count": 0 },
- { "name": "“投石机”", "count": 0 },
- { "name": "船长", "count": 0 }
- ]
-}
-```
-
----
-
-## 🧠 核心机制
-
-### 目标选择算法
-
-### 战斗流程
-
-
-## 🤝 参与贡献
-
-欢迎提交PR！
-
----
-
-## 📜 许可证
-
-本项目采用 [MIT License](LICENSE)，可自由用于学习与二次创作。
\ No newline at end of file
diff --git a/simulator/__init__.py b/simulator/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/simulator/arknights.csv b/simulator/arknights.csv
deleted file mode 100644
index 979357e..0000000
--- a/simulator/arknights.csv
+++ /dev/null
@@ -1,11780 +0,0 @@
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diff --git a/simulator/battle_field.py b/simulator/battle_field.py
deleted file mode 100644
index 7ba4abd..0000000
--- a/simulator/battle_field.py
+++ /dev/null
@@ -1,223 +0,0 @@
-import json
-import math
-import random
-import time
-import numpy as np
-from enum import Enum
-import logging
-
-from typing import TYPE_CHECKING
-
-from .vector2d import FastVector
-
-logger = logging.getLogger(__name__)
-
-if TYPE_CHECKING:
- from .monsters import Monster
-
-from .monsters import MonsterFactory
-from .utils import VIRTUAL_TIME_DELTA, BuffEffect, BuffType, Faction, SpatialHash
-from .zone import PoisonZone
-
-# 场景参数
-MAP_SIZE = np.array([13, 9]) # 场景宽度（单位：格）
-SPAWN_AREA = 2 # 阵营出生区域宽度
-
-
-from collections import defaultdict
-from .projectiles import ProjectileManager
-
-class Battlefield:
- def __init__(self, monster_data):
- self.monsters : list[Monster] = []
- self.alive_monsters : list[Monster] = []
- self.hash_grid : SpatialHash = SpatialHash(self, cell_size=0.5)
- self.HIT_BOX_RADIUS = 0.2
-
- self.round = 0
- self.map_size = MAP_SIZE
- self.monster_data = monster_data
- self.globalId = 0
- self.effect_zones = []
- self.dead_count = {Faction.LEFT: 0, Faction.RIGHT: 0}
- self.gameTime = 0
-
- self.effect_zones.append(PoisonZone(self))
- self.projectiles_manager = ProjectileManager(self)
-
- # 开始前把怪物放在待定区域，逐步放入场地
- self.monster_temporal_area_left = []
- self.monster_temporal_area_right = []
- self.current_spawn_left = 0
- self.current_spawn_right = 0
-
- def query_monster(self, target_position, radius) -> list['Monster']:
- results = []
- if len(self.alive_monsters) < (radius / self.hash_grid.cell_size) ** 2:
- for m in self.alive_monsters:
- if m.is_alive and (m.position - target_position).magnitude <= radius:
- results.append(m)
- else:
- for id in self.hash_grid.query_neighbors(target_position, radius):
- m = self.get_monster_with_id(id)
- if m.is_alive and (m.position - target_position).magnitude <= radius:
- results.append(m)
- return results
-
- def append_monster(self, monster : 'Monster'):
- """添加一个怪物到战场"""
- id = self.globalId
- monster.id = id
- self.globalId += 1
- self.monsters.append(monster)
- self.hash_grid.insert(monster.position, monster.id)
-
- def append_monster_name(self, name, faction, pos) -> 'Monster':
- """添加一个怪物到战场，只需要名字"""
- data = next((m for m in self.monster_data if m["名字"] == name), None)
- id = self.globalId
- monster = MonsterFactory.create_monster(data, faction, pos, self)
- monster.id = id
- self.globalId += 1
- self.monsters.append(monster)
- self.hash_grid.insert(monster.position, monster.id)
- return monster
-
- def get_monster_with_id(self, id) -> 'Monster':
- return self.monsters[id]
-
- def setup_battle(self, left_army, right_army, monster_data):
- """二维战场初始化"""
- # 左阵营生成在左上区域
- for (name, count) in left_army.items():
- data = next((m for m in monster_data if m["名字"] == name), None)
- if data is None:
- raise ValueError(f"左侧怪物 {name} 在 monster_data 中未找到!")
- for _ in range(count):
- pos = FastVector(
- random.uniform(0, 0.5),
- random.uniform(0, MAP_SIZE[1])
- )
- self.monster_temporal_area_left.append( MonsterFactory.create_monster(data, Faction.LEFT, pos, self))
-
- # 右阵营生成在右下区域
- for (name, count) in right_army.items():
- data = next((m for m in monster_data if m["名字"] == name), None)
- if data is None:
- raise ValueError(f"右侧怪物 {name} 在 monster_data 中未找到!")
- for _ in range(count):
- pos = FastVector(
- random.uniform(MAP_SIZE[0]-0.5, MAP_SIZE[0]),
- random.uniform(0, MAP_SIZE[1])
- )
- self.monster_temporal_area_right.append(MonsterFactory.create_monster(data, Faction.RIGHT, pos, self))
-
- self.alive_monsters = self.monsters
- self.gameTime = 0
- self.current_spawn = 0
- random.shuffle(self.monster_temporal_area_left)
- random.shuffle(self.monster_temporal_area_right)
- return True
-
- def check_victory(self):
- """检查胜利条件"""
- if self.current_spawn_left < len(self.monster_temporal_area_left) or self.current_spawn_right < len(self.monster_temporal_area_right):
- return None
- alive_factions = set()
- for m in self.alive_monsters:
- if m.is_alive:
- alive_factions.add(m.faction)
-
- if len(alive_factions) == 1:
- return list(alive_factions)[0]
- elif len(alive_factions) == 0:
- return Faction.LEFT
- return None
-
- def check_zone(self):
- new_zone = []
- # 检查场地效果
- for zone in self.effect_zones:
- zone.update(VIRTUAL_TIME_DELTA)
- if zone.should_clear(VIRTUAL_TIME_DELTA):
- continue
- for m in self.alive_monsters:
- if zone.contains(m):
- zone.apply_effect(m)
- new_zone.append(zone)
- self.effect_zones = new_zone
-
- def run_one_frame(self):
- self.round += 1
-
- if self.round < 40 or self.round > 90:
- if self.current_spawn_left < len(self.monster_temporal_area_left) and self.round % 2 == 0:
- self.append_monster(self.monster_temporal_area_left[self.current_spawn_left])
- self.current_spawn_left += 1
-
- if self.current_spawn_right < len(self.monster_temporal_area_right) and self.round % 2 == 0:
- self.append_monster(self.monster_temporal_area_right[self.current_spawn_right])
- self.current_spawn_right += 1
-
- self.check_zone()
- self.projectiles_manager.update_all(VIRTUAL_TIME_DELTA)
- # 更新所有单位
- for m in self.monsters:
- m.update(VIRTUAL_TIME_DELTA)
- for m in self.monsters:
- m.do_move(VIRTUAL_TIME_DELTA)
- if m.is_alive:
- self.hash_grid.insert(m.position, m.id)
- # 检查胜利条件
- self.alive_monsters = [m for m in self.monsters if m.is_alive]
- winner = self.check_victory()
- if winner:
- logger.info(f"\nVictory for {winner.name}!")
- left = len([m for m in self.alive_monsters if m.is_alive and m.faction == Faction.LEFT])
- logger.info(f"左边存活{left} / 右边存活{len(self.alive_monsters) - left}")
- return winner
-
- self.gameTime += VIRTUAL_TIME_DELTA
- return None
-
- def run_battle(self, visualize=False):
- """运行战斗直到决出胜负"""
- while True:
- if visualize and self.round % 30 == 0:
- self.print_battlefield()
- time.sleep(1)
-
- result = self.run_one_frame()
- if result != None:
- return result
-
- def danger_zone_size(self):
- if self.gameTime < 40:
- return 0
- return int((self.gameTime - 40) / 20) + 1
-
- def add_new_zone(self, zone):
- self.effect_zones.append(zone)
-
- def print_battlefield(self):
- """二维战场可视化"""
- grid = np.full((MAP_SIZE[1] * 2, MAP_SIZE[0] * 2), '.', dtype='U2')
-
- for m in self.alive_monsters:
- if m.is_alive:
- x = np.minimum(np.maximum(0, int(m.position.x * 2)), MAP_SIZE[0]*2-1)
- y = np.minimum(np.maximum(0, int(m.position.y * 2)), MAP_SIZE[1]*2-1)
- symbol = 'L' if m.faction == Faction.LEFT else 'R'
- if grid[y, x] != '.' and symbol != grid[y, x]:
- symbol = 'X'
- if m.char_icon != "":
- symbol = m.char_icon
- grid[y, x] = symbol
-
- logger.info(f"\nRound {self.round}")
- for row in grid:
- logger.info(' '.join(row))
-
- def get_grid(self, target):
- x, y = int(target.position.x), int(target.position.y)
- return x, y
\ No newline at end of file
diff --git a/simulator/elemental.py b/simulator/elemental.py
deleted file mode 100644
index 41a0c90..0000000
--- a/simulator/elemental.py
+++ /dev/null
@@ -1,124 +0,0 @@
-from enum import Enum
-import math
-
-import numpy as np
-
-from .utils import DamageType, ElementType, debug_print, lerp
-
-
-class ElementAccumulator:
- """多元素损伤容器"""
- def __init__(self, owner):
- self.accumulators = {et: 0.0 for et in ElementType}
- self.active_burst = None
- self.burst_queue = [] # 爆条优先级队列
- self.owner = owner
-
- def accumulate(self, element: ElementType, value: float):
- """累积元素损伤"""
- if self.active_burst:
- return # 爆条期间暂停累积
-
- self.accumulators[element] += value
- limits = 2000 if self.owner.boss else 1000
- if self.accumulators[element] >= limits:
- self.accumulators[element] = 0
- self.active_burst = ElementBurst(element, self.owner)
-
-class ElementBurst:
- """爆条效果控制器"""
- def __init__(self, trigger_element: ElementType, owner):
- self.owner = owner
- self.start_time = owner.battlefield.gameTime
- self.duration = 15.0
- self.trigger_element = trigger_element
- self._init_effect_params()
-
- def shouldClearBurst(self):
- return self.progress >= 1.0
-
- def _init_effect_params(self):
- """初始化元素特效参数"""
- if self.trigger_element == ElementType.NECRO_RIGHT:
- self.owner.attack_multiplier = 0.5 # 初始虚弱百分比
- self.dot_damage = 800
- self.dot_timer = 0
- debug_print(f"{self.owner.name}{self.owner.id} 的 凋亡损伤爆发！")
- elif self.trigger_element == ElementType.NECRO_LEFT:
- self.dot_damage = 100
- self.dot_timer = 0
- debug_print(f"{self.owner.name}{self.owner.id} 的 凋亡损伤爆发！")
- elif self.trigger_element == ElementType.FIRE:
- self.duration = 10
- dmg = 7000
- debug_print(f"{self.owner.name}{self.owner.id} 灼燃发期间受到{dmg}点伤害")
- self.owner.take_damage(dmg, DamageType.TRUE)
- self.owner.magic_resist -= 20
-
- @property
- def progress(self):
- """效果进度百分比"""
- return min(1.0, (self.owner.battlefield.gameTime - self.start_time) / self.duration)
-
- def on_clear(self):
- if self.trigger_element == ElementType.FIRE:
- self.owner.magic_resist += 20
-
- def update_effect(self, deltaTime):
- """更新动态效果"""
- if self.trigger_element == ElementType.NECRO_RIGHT:
- # 虚弱效果衰减
- self.owner.attack_multiplier = lerp(0.5, 1, self.progress)
-
- # 持续伤害应用
- self.dot_timer += deltaTime
- self.owner.take_damage(self.dot_damage * deltaTime, DamageType.TRUE)
- if self.dot_timer >= 1.0:
- debug_print(f"{self.owner.name}{self.owner.id} 凋亡损伤爆发期间受到{self.dot_damage}伤害")
- self.dot_timer = 0
- elif self.trigger_element == ElementType.NECRO_LEFT:
- # 持续伤害应用
- self.dot_timer += deltaTime
- self.owner.take_damage(self.dot_damage * deltaTime, DamageType.TRUE)
- if self.dot_timer >= 1.0:
- debug_print(f"{self.owner.name}{self.owner.id} 凋亡损伤爆发期间受到{self.dot_damage}伤害")
- self.dot_timer = 0
-
-
-# class AdvancedMonster:
-# def __init__(self, max_hp):
-# self.element_system = ElementAccumulator()
-# self.resistances = {et: 0.0 for et in ElementType}
-# self.active_effects = []
-# self.hp = max_hp
-
-# def take_element_damage(self, element: ElementType, base_damage: float):
-# # 计算实际伤害（考虑抗性）
-# resistance = self.resistances.get(element, 0)
-# actual_damage = base_damage * (1 - resistance)
-# self.hp -= actual_damage
-
-# # 累积30%基础伤害作为元素损伤
-# self.element_system.accumulate(element, base_damage * 0.3)
-
-# def set_element_resistance(self, element: ElementType, value: float):
-# """设置元素抗性（0.0-1.0）"""
-# self.resistances[element] = max(0, min(1.0, value))
-
-# def update(self):
-# """每帧更新状态"""
-# # 处理爆条队列
-# self.element_system.process_burst()
-
-# # 更新激活中的爆条效果
-# if burst := self.element_system.active_burst:
-# if time.time() - burst.start_time > burst.duration:
-# self._clear_burst_effects()
-# else:
-# burst.update_effect(self)
-
-# def _clear_burst_effects(self):
-# """清除爆条残留效果"""
-# self.element_system.active_burst = None
-# for elem in ElementType:
-# self.resistances[elem] = 0.0
\ No newline at end of file
diff --git a/simulator/monsters.json b/simulator/monsters.json
deleted file mode 100644
index e382e21..0000000
--- a/simulator/monsters.json
+++ /dev/null
@@ -1,764 +0,0 @@
-{
- "monsters": [
- {
- "名字": "酸液源石虫·α",
- "攻击力": { "数值": 435 },
- "类型": "物理",
- "生命值": { "数值": 1390 },
- "物理防御": { "数值": 0 },
- "法抗": { "数值": 0 },
- "攻击间隔": { "数值": 3.3 },
- "攻击范围": { "数值": 2.75 },
- "移速": { "数值": 1.0 },
- "特性": "攻击令目标的防御力-15"
- },
- {
- "名字": "高能源石虫",
- "攻击力": { "数值": 390 },
- "类型": "物理",
- "生命值": { "数值": 1230 },
- "物理防御": { "数值": 0 },
- "法抗": { "数值": 0 },
- "攻击间隔": { "数值": 1.7 },
- "移速": { "数值": 1.0 },
- "攻击范围": { "数值": 0.8 },
- "特性": "死亡自爆，造成范围1560物伤"
- },
- {
- "名字": "阿咬",
- "攻击力": { "数值": 435 },
- "类型": "物理",
- "生命值": { "数值": 1500 },
- "物理防御": { "数值": 120 },
- "法抗": { "数值": 10 },
- "攻击间隔": { "数值": 2.0 },
- "攻击范围": { "数值": 0.8 },
- "移速": { "数值": 1.1 },
- "特性": "纯杂兵"
- },
- {
- "名字": "狂暴的猎狗pro",
- "攻击力": { "数值": 555 },
- "类型": "物理",
- "生命值": { "数值": 1500 },
- "物理防御": { "数值": 0 },
- "法抗": { "数值": 20 },
- "攻击间隔": { "数值": 1.4 },
- "攻击范围": { "数值": 0.8 },
- "移速": { "数值": 1.9 },
- "特性": "跑的飞快"
- },
- {
- "名字": "提亚卡乌好战者",
- "攻击力": { "数值": 480 },
- "类型": "物理",
- "生命值": { "数值": 2750 },
- "物理防御": { "数值": 380 },
- "法抗": { "数值": 10 },
- "攻击间隔": { "数值": 1.0 },
- "攻击范围": { "数值": 0.8 },
- "移速": { "数值": 0.8 },
- "特性": "攻击令目标的防御力-10，叠起来有奇效"
- },
- {
- "名字": "污染躯壳",
- "攻击力": { "数值": 600 },
- "类型": "物理",
- "生命值": { "数值": 2000 },
- "物理防御": { "数值": 200 },
- "法抗": { "数值": 0 },
- "攻击间隔": { "数值": 1.5 },
- "攻击范围": { "数值": 0.8 },
- "移速": { "数值": 1.0 },
- "特性": "被打后极速飞奔，移速暂时提高至4.0超高速，持续两秒，冷却5秒"
- },
- {
- "名字": "大盾哥",
- "攻击力": { "数值": 900 },
- "类型": "物理",
- "生命值": { "数值": 3000 },
- "物理防御": { "数值": 800 },
- "法抗": { "数值": 0 },
- "攻击间隔": { "数值": 2.6 },
- "攻击范围": { "数值": 0.8 },
- "移速": { "数值": 0.75 },
- "特性": ""
- },
- {
- "名字": "宿主流浪者",
- "攻击力": { "数值": 975 },
- "物理防御": { "数值": 200 },
- "生命值": { "数值": 5500 },
- "类型": "物理",
- "法抗": { "数值": 30 },
- "攻击间隔": { "数值": 3.0 },
- "移速": { "数值": 0.65 },
- "攻击范围": { "数值": 0.8 },
- "特性": "每秒恢复250生命值"
- },
- {
- "名字": "光剑",
- "攻击力": { "数值": 1200 },
- "物理防御": { "数值": 650 },
- "生命值": { "数值": 7000 },
- "类型": "魔法",
- "法抗": { "数值": 50 },
- "攻击间隔": { "数值": 3.0 },
- "移速": { "数值": 0.75 },
- "攻击范围": { "数值": 0.8 },
- "特性": ""
- },
- {
- "名字": "泥岩巨像",
- "攻击力": { "数值": 4500 },
- "物理防御": { "数值": 700 },
- "生命值": { "数值": 50000 },
- "类型": "物理",
- "法抗": { "数值": 30 },
- "攻击间隔": { "数值": 7.0 },
- "移速": { "数值": 0.4 },
- "攻击范围": { "数值": 0.8 },
- "特性": ""
- },
- {
- "名字": "呼啸骑士团学徒",
- "攻击力": { "数值": 450 },
- "物理防御": { "数值": 100 },
- "生命值": { "数值": 1400 },
- "类型": "物理",
- "法抗": { "数值": 30 },
- "攻击间隔": { "数值": 2.4 },
- "移速": { "数值": 0.9 },
- "攻击范围": { "数值": 3.0 },
- "特性": "出场后30秒内防御+3000，法抗+95"
- },
- {
- "名字": "萨卡兹大剑手",
- "攻击力": { "数值": 900 },
- "物理防御": { "数值": 230 },
- "生命值": { "数值": 3750 },
- "类型": "物理",
- "法抗": { "数值": 50 },
- "攻击间隔": { "数值": 2 },
- "移速": { "数值": 0.85 },
- "攻击范围": { "数值": 0.8 },
- "特性": ""
- },
- {
- "名字": "狂暴宿主组长",
- "攻击力": { "数值": 2625 },
- "物理防御": { "数值": 230 },
- "生命值": { "数值": 15000 },
- "类型": "物理",
- "法抗": { "数值": 30 },
- "攻击间隔": { "数值": 1.3 },
- "移速": { "数值": 1.2 },
- "攻击范围": { "数值": 0.8 },
- "特性": "每秒受到500点伤害",
- "符号": "7"
- },
- {
- "名字": "海螺",
- "攻击力": { "数值": 450 },
- "物理防御": { "数值": 200 },
- "生命值": { "数值": 3000 },
- "类型": "物理",
- "法抗": { "数值": 10 },
- "攻击间隔": { "数值": 2.5 },
- "移速": { "数值": 0.9 },
- "攻击范围": { "数值": 3.4 },
- "特性": "首次攻击后20秒内防御+300，停止移动"
- },
- {
- "名字": "拳手囚犯",
- "攻击力": { "数值": 450 },
- "物理防御": { "数值": 300 },
- "生命值": { "数值": 4000 },
- "类型": "物理",
- "法抗": { "数值": 10 },
- "攻击间隔": { "数值": 1.2 },
- "移速": { "数值": 1.6 },
- "攻击范围": { "数值": 0.8 },
- "特性": "进行第四次攻击前进入解放状态，攻击间隔缩短至50%，攻击力+50%，获得60%防御无视",
- "符号": "i"
- },
- {
- "名字": "高塔术师",
- "攻击力": { "数值": 900 },
- "物理防御": { "数值": 160 },
- "生命值": { "数值": 3500 },
- "类型": "魔法",
- "法抗": { "数值": 65 },
- "攻击间隔": { "数值": 7 },
- "移速": { "数值": 0.6 },
- "攻击范围": { "数值": 3.2 },
- "特性": "同时攻击两个目标；额外对目标周围8格所有敌对单位造成攻击力100%的法术伤害"
- },
- {
- "名字": "冰原术师",
- "攻击力": { "数值": 375 },
- "物理防御": { "数值": 200 },
- "生命值": { "数值": 2500 },
- "类型": "魔法",
- "法抗": { "数值": 50 },
- "攻击间隔": { "数值": 4.5 },
- "移速": { "数值": 0.7 },
- "攻击范围": { "数值": 3.2 },
- "特性": "抵抗，同时攻击两个目标；每第三次攻击对目标施加5秒寒冷"
- },
- {
- "名字": "矿脉守卫",
- "攻击力": { "数值": 600 },
- "物理防御": { "数值": 300 },
- "生命值": { "数值": 12500 },
- "类型": "魔法",
- "法抗": { "数值": 10 },
- "攻击间隔": { "数值": 4 },
- "移速": { "数值": 0.6 },
- "攻击范围": { "数值": 0.8 },
- "特性": "嘲讽+1，受到伤害时对来源造成300点魔法伤害"
- },
- {
- "名字": "“庞贝”",
- "攻击力": { "数值": 345 },
- "物理防御": { "数值": 220 },
- "生命值": { "数值": 20000 },
- "类型": "魔法",
- "法抗": { "数值": 70 },
- "攻击间隔": { "数值": 3.5 },
- "移速": { "数值": 0.65 },
- "攻击范围": { "数值": 4.5 },
- "特性": "同时攻击最多4个目标，对目标施加10s灼烧效果；自身0.8范围存在敌对单位时，每10秒对自身1.4半径范围的敌对单位造成1000法术伤害；生命值低于50%时，攻击速度+40"
- },
- {
- "名字": "绵羊",
- "攻击力": { "数值": 570 },
- "物理防御": { "数值": 250 },
- "生命值": { "数值": 3500 },
- "类型": "魔法",
- "法抗": { "数值": 50 },
- "攻击间隔": { "数值": 3.2 },
- "移速": { "数值": 0.7 },
- "攻击范围": { "数值": 4.5 },
- "特性": "",
- "符号": "S"
- },
- {
- "名字": "食腐狗",
- "攻击力": { "数值": 825 },
- "物理防御": { "数值": 150 },
- "生命值": { "数值": 6000 },
- "类型": "物理",
- "法抗": { "数值": 20 },
- "攻击间隔": { "数值": 1.8 },
- "移速": { "数值": 1.4 },
- "攻击范围": { "数值": 0.8 },
- "特性": "每次攻击令其在10秒内每秒受到100点魔法伤害",
- "符号": "f"
- },
- {
- "名字": "温顺的武装驮兽",
- "攻击力": { "数值": 2700 },
- "物理防御": { "数值": 50 },
- "生命值": { "数值": 32500 },
- "类型": "物理",
- "法抗": { "数值": 50 },
- "攻击间隔": { "数值": 5 },
- "移速": { "数值": 0.6 },
- "攻击范围": { "数值": 0.8 },
- "特性": ""
- },
- {
- "名字": "鼠鼠",
- "攻击力": { "数值": 1050 },
- "物理防御": { "数值": 200 },
- "生命值": { "数值": 9500 },
- "类型": "物理",
- "法抗": { "数值": 20 },
- "攻击间隔": { "数值": 3 },
- "移速": { "数值": 0.3 },
- "攻击范围": { "数值": 0.8 },
- "特性": "受到伤害后5秒移速增加300%，10s冷却"
- },
- {
- "名字": "“投石机”",
- "攻击力": { "数值": 1200 },
- "物理防御": { "数值": 700 },
- "生命值": { "数值": 5000 },
- "类型": "物理",
- "法抗": { "数值": 20 },
- "攻击间隔": { "数值": 3 },
- "移速": { "数值": 0.7 },
- "攻击范围": { "数值": 2.5 },
- "特性": "第一次攻击会投掷雪球，对目标以及周围4格敌人造成150%攻击力的物理伤害"
- },
- {
- "名字": "船长",
- "攻击力": { "数值": 1200 },
- "物理防御": { "数值": 800 },
- "生命值": { "数值": 5000 },
- "类型": "物理",
- "法抗": { "数值": 20 },
- "攻击间隔": { "数值": 3.5 },
- "移速": { "数值": 0.7 },
- "攻击范围": { "数值": 0.8 },
- "特性": "每第四下攻击会眩晕对面7秒",
- "符号": ""
- },
- {
- "名字": "杰斯顿·威廉姆斯",
- "攻击力": { "数值": 1050 },
- "物理防御": { "数值": 400 },
- "生命值": { "数值": 12500 },
- "类型": "魔法",
- "法抗": { "数值": 20 },
- "攻击间隔": { "数值": 4 },
- "移速": { "数值": 0.6 },
- "攻击范围": { "数值": 3.0 },
- "特性": "",
- "符号": "J"
- },
- {
- "名字": "山海众窥魅人",
- "攻击力": { "数值": 600 },
- "物理防御": { "数值": 200 },
- "生命值": { "数值": 5000 },
- "类型": "魔法",
- "法抗": { "数值": 40 },
- "攻击间隔": { "数值": 2.5 },
- "移速": { "数值": 0.8 },
- "攻击范围": { "数值": 3.0 },
- "特性": "",
- "符号": "M"
- },
- {
- "名字": "Vvan",
- "攻击力": { "数值": 525 },
- "物理防御": { "数值": 300 },
- "生命值": { "数值": 3750 },
- "类型": "魔法",
- "法抗": { "数值": 60 },
- "攻击间隔": { "数值": 3.2 },
- "移速": { "数值": 0.7 },
- "攻击范围": { "数值": 3.2 },
- "特性": "",
- "符号": "V"
- },
- {
- "名字": "萨克斯",
- "攻击力": { "数值": 1200 },
- "物理防御": { "数值": 650 },
- "生命值": { "数值": 8750 },
- "类型": "物理",
- "法抗": { "数值": 20 },
- "攻击间隔": { "数值": 4.5 },
- "移速": { "数值": 0.8 },
- "攻击范围": { "数值": 0.8 },
- "特性": "",
- "符号": "S"
- },
- {
- "名字": "大喷蛛",
- "攻击力": { "数值": 900 },
- "物理防御": { "数值": 300 },
- "生命值": { "数值": 7500 },
- "类型": "物理",
- "法抗": { "数值": 0 },
- "攻击间隔": { "数值": 3 },
- "移速": { "数值": 0.8 },
- "攻击范围": { "数值": 3.0 },
- "特性": "",
- "符号": "Z"
- },
- {
- "名字": "小喷蛛",
- "攻击力": { "数值": 225 },
- "物理防御": { "数值": 0 },
- "生命值": { "数值": 500 },
- "类型": "物理",
- "法抗": { "数值": 0 },
- "攻击间隔": { "数值": 2 },
- "移速": { "数值": 1.9 },
- "攻击范围": { "数值": 0.8 },
- "特性": "",
- "符号": "z"
- },
- {
- "名字": "萨卡兹链术师",
- "攻击力": { "数值": 630 },
- "物理防御": { "数值": 450 },
- "生命值": { "数值": 6000 },
- "类型": "魔法",
- "法抗": { "数值": 50 },
- "攻击间隔": { "数值": 4 },
- "移速": { "数值": 0.7 },
- "攻击范围": { "数值": 3.2 },
- "特性": "",
- "符号": "K"
- },
- {
- "名字": "大君之赐",
- "攻击力": { "数值": 1020 },
- "物理防御": { "数值": 200 },
- "生命值": { "数值": 1500 },
- "类型": "物理",
- "法抗": { "数值": 20 },
- "攻击间隔": { "数值": 4 },
- "移速": { "数值": 0.7 },
- "攻击范围": { "数值": 0.8 },
- "特性": "",
- "符号": "k"
- },
- {
- "名字": "冰爆源石虫",
- "攻击力": { "数值": 450 },
- "物理防御": { "数值": 0 },
- "生命值": { "数值": 1625 },
- "类型": "物理",
- "法抗": { "数值": 0 },
- "攻击间隔": { "数值": 1.7 },
- "移速": { "数值": 1.0 },
- "攻击范围": { "数值": 0.8 },
- "特性": "",
- "符号": "b"
- },
- {
- "名字": "高普尼克",
- "攻击力": { "数值": 3000 },
- "物理防御": { "数值": 200 },
- "生命值": { "数值": 15000 },
- "类型": "物理",
- "法抗": { "数值": 50 },
- "攻击间隔": { "数值": 3.5 },
- "移速": { "数值": 0.5 },
- "攻击范围": { "数值": 0.8 },
- "特性": "",
- "符号": "G"
- },
- {
- "名字": "大斧",
- "攻击力": { "数值": 2250 },
- "物理防御": { "数值": 800 },
- "生命值": { "数值": 9000 },
- "类型": "物理",
- "法抗": { "数值": 30 },
- "攻击间隔": { "数值": 3.5 },
- "移速": { "数值": 0.65 },
- "攻击范围": { "数值": 0.8 },
- "特性": "",
- "符号": "G"
- },
- {
- "名字": "机鳄",
- "攻击力": { "数值": 1125 },
- "物理防御": { "数值": 700 },
- "生命值": { "数值": 4750 },
- "类型": "物理",
- "法抗": { "数值": 10 },
- "攻击间隔": { "数值": 5.0 },
- "移速": { "数值": 0.7 },
- "攻击范围": { "数值": 1.3 },
- "特性": "",
- "符号": "j"
- },
- {
- "名字": "畸变之矛",
- "攻击力": { "数值": 1500 },
- "物理防御": { "数值": 2000 },
- "生命值": { "数值": 2000 },
- "类型": "魔法",
- "法抗": { "数值": 80 },
- "攻击间隔": { "数值": 4.0 },
- "移速": { "数值": 0.7 },
- "攻击范围": { "数值": 0.8 },
- "特性": "",
- "符号": ">"
- },
- {
- "名字": "小锤",
- "攻击力": { "数值": 2250 },
- "物理防御": { "数值": 500 },
- "生命值": { "数值": 20000 },
- "类型": "物理",
- "法抗": { "数值": 20 },
- "攻击间隔": { "数值": 2.0 },
- "移速": { "数值": 1 },
- "攻击范围": { "数值": 0.8 },
- "特性": "",
- "符号": "T"
- },
- {
- "名字": "跑男",
- "攻击力": { "数值": 1050 },
- "物理防御": { "数值": 800 },
- "生命值": { "数值": 10000 },
- "类型": "物理",
- "法抗": { "数值": 20 },
- "攻击间隔": { "数值": 1.2 },
- "移速": { "数值": 2.5 },
- "攻击范围": { "数值": 0.8 },
- "特性": "",
- "符号": "P"
- },
- {
- "名字": "狂躁珊瑚",
- "攻击力": { "数值": 450 },
- "物理防御": { "数值": 750 },
- "生命值": { "数值": 10000 },
- "类型": "物理",
- "法抗": { "数值": 40 },
- "攻击间隔": { "数值": 1.5 },
- "移速": { "数值": 1.2 },
- "攻击范围": { "数值": 1.5 },
- "特性": "",
- "符号": "H"
- },
- {
- "名字": "雪境精锐",
- "攻击力": { "数值": 900 },
- "物理防御": { "数值": 600 },
- "生命值": { "数值": 7500 },
- "类型": "物理",
- "法抗": { "数值": 50 },
- "攻击间隔": { "数值": 3.7 },
- "移速": { "数值": 0.9 },
- "攻击范围": { "数值": 1.5 },
- "特性": "",
- "符号": "V"
- },
- {
- "名字": "榴弹佣兵",
- "攻击力": { "数值": 750 },
- "物理防御": { "数值": 50 },
- "生命值": { "数值": 1600 },
- "类型": "物理",
- "法抗": { "数值": 40 },
- "攻击间隔": { "数值": 2.6 },
- "移速": { "数值": 0.9 },
- "攻击范围": { "数值": 3.5 },
- "特性": "",
- "符号": "O"
- },
- {
- "名字": "凋零萨卡兹",
- "攻击力": { "数值": 675 },
- "物理防御": { "数值": 250 },
- "生命值": { "数值": 6000 },
- "类型": "魔法",
- "法抗": { "数值": 40 },
- "攻击间隔": { "数值": 4.0 },
- "移速": { "数值": 0.8 },
- "攻击范围": { "数值": 3.5 },
- "特性": "",
- "符号": "V"
- },
- {
- "名字": "门",
- "攻击力": { "数值": 1500 },
- "物理防御": { "数值": 300 },
- "生命值": { "数值": 25000 },
- "类型": "魔法",
- "法抗": { "数值": 10 },
- "攻击间隔": { "数值": 3.5 },
- "移速": { "数值": 0.7 },
- "攻击范围": { "数值": 0.8 },
- "特性": "",
- "符号": "D"
- },
- {
- "名字": "洗地车",
- "攻击力": { "数值": 2250 },
- "物理防御": { "数值": 1500 },
- "生命值": { "数值": 9000 },
- "类型": "物理",
- "法抗": { "数值": 10 },
- "攻击间隔": { "数值": 3.0 },
- "移速": { "数值": 0.7 },
- "攻击范围": { "数值": 0.8 },
- "特性": "",
- "符号": "B"
- },
- {
- "名字": "护盾哥",
- "攻击力": { "数值": 1200 },
- "物理防御": { "数值": 250 },
- "生命值": { "数值": 2000 },
- "类型": "物理",
- "法抗": { "数值": 20 },
- "攻击间隔": { "数值": 3.2 },
- "移速": { "数值": 0.9 },
- "攻击范围": { "数值": 0.8 },
- "特性": "",
- "符号": "C"
- },
- {
- "名字": "标枪恐鱼",
- "攻击力": { "数值": 1050 },
- "物理防御": { "数值": 240 },
- "生命值": { "数值": 6250 },
- "类型": "物理",
- "法抗": { "数值": 20 },
- "攻击间隔": { "数值": 3.5 },
- "移速": { "数值": 0.75 },
- "攻击范围": { "数值": 1.7 },
- "特性": "",
- "符号": "A"
- },
- {
- "名字": "酒桶",
- "攻击力": { "数值": 1500 },
- "物理防御": { "数值": 500 },
- "生命值": { "数值": 5250 },
- "类型": "物理",
- "法抗": { "数值": 20 },
- "攻击间隔": { "数值": 3.0 },
- "移速": { "数值": 0.5 },
- "攻击范围": { "数值": 0.8 },
- "特性": "",
- "符号": "C"
- },
- {
- "名字": "爱蟹者",
- "攻击力": { "数值": 1950 },
- "物理防御": { "数值": 2500 },
- "生命值": { "数值": 8500 },
- "类型": "物理",
- "法抗": { "数值": 90 },
- "攻击间隔": { "数值": 3.0 },
- "移速": { "数值": 0.9 },
- "攻击范围": { "数值": 0.8 },
- "特性": "",
- "符号": "W"
- },
- {
- "名字": "炮击组长",
- "攻击力": { "数值": 825 },
- "物理防御": { "数值": 150 },
- "生命值": { "数值": 2500 },
- "类型": "物理",
- "法抗": { "数值": 0 },
- "攻击间隔": { "数值": 4.5 },
- "移速": { "数值": 0.8 },
- "攻击范围": { "数值": 7 },
- "特性": "",
- "符号": "I"
- },
- {
- "名字": "炽焰源石虫",
- "攻击力": { "数值": 75 },
- "物理防御": { "数值": 0 },
- "生命值": { "数值": 600 },
- "类型": "魔法",
- "法抗": { "数值": 0 },
- "攻击间隔": { "数值": 2.0 },
- "移速": { "数值": 0.8 },
- "攻击范围": { "数值": 2.8 },
- "特性": "",
- "符号": "c"
- },
- {
- "名字": "复仇者",
- "攻击力": { "数值": 720 },
- "物理防御": { "数值": 230 },
- "生命值": { "数值": 9000 },
- "类型": "物理",
- "法抗": { "数值": 50 },
- "攻击间隔": { "数值": 2.3 },
- "移速": { "数值": 0.65 },
- "攻击范围": { "数值": 0.8 },
- "特性": "",
- "符号": "d"
- },
- {
- "名字": "衣架",
- "攻击力": { "数值": 480 },
- "物理防御": { "数值": 80 },
- "生命值": { "数值": 3500 },
- "类型": "物理",
- "法抗": { "数值": 30 },
- "攻击间隔": { "数值": 3.0 },
- "移速": { "数值": 0.9 },
- "攻击范围": { "数值": 3.0 },
- "特性": "",
- "符号": "y"
- },
- {
- "名字": "沸血骑士团精锐",
- "攻击力": { "数值": 975 },
- "物理防御": { "数值": 800 },
- "生命值": { "数值": 6500 },
- "类型": "物理",
- "法抗": { "数值": 0 },
- "攻击间隔": { "数值": 2.3 },
- "移速": { "数值": 0.7 },
- "攻击范围": { "数值": 0.8 },
- "特性": "",
- "符号": "f"
- },
- {
- "名字": "牛牛",
- "攻击力": { "数值": 1800 },
- "物理防御": { "数值": 100 },
- "生命值": { "数值": 12500 },
- "类型": "物理",
- "法抗": { "数值": 40 },
- "攻击间隔": { "数值": 4 },
- "移速": { "数值": 0.7 },
- "攻击范围": { "数值": 0.8 },
- "特性": "",
- "符号": "N"
- },
- {
- "名字": "湖畔志愿者",
- "攻击力": { "数值": 480 },
- "物理防御": { "数值": 250 },
- "生命值": { "数值": 2000 },
- "类型": "物理",
- "法抗": { "数值": 50 },
- "攻击间隔": { "数值": 0.4 },
- "移速": { "数值": 0.95 },
- "攻击范围": { "数值": 0.8 },
- "特性": "",
- "符号": "q"
- },
- {
- "名字": "自在",
- "攻击力": { "数值": 750 },
- "物理防御": { "数值": 750 },
- "生命值": { "数值": 15000 },
- "类型": "魔法",
- "法抗": { "数值": 50 },
- "攻击间隔": { "数值": 4 },
- "移速": { "数值": 0.5 },
- "攻击范围": { "数值": 2.5 },
- "特性": "",
- "符号": "@"
- },
- {
- "名字": "扎罗",
- "攻击力": { "数值": 1200 },
- "物理防御": { "数值": 1000 },
- "生命值": { "数值": 17500 },
- "类型": "物理",
- "法抗": { "数值": 60 },
- "攻击间隔": { "数值": 4 },
- "移速": { "数值": 0.6 },
- "攻击范围": { "数值": 0.8 },
- "特性": "",
- "符号": "$"
- },
- {
- "名字": "雷德",
- "攻击力": { "数值": 1200 },
- "物理防御": { "数值": 650 },
- "生命值": { "数值": 28750 },
- "类型": "物理",
- "法抗": { "数值": 60 },
- "攻击间隔": { "数值": 3 },
- "移速": { "数值": 0.65 },
- "攻击范围": { "数值": 0.8 },
- "特性": "",
- "符号": "+"
- }
- ]
-}
\ No newline at end of file
diff --git a/simulator/monsters.py b/simulator/monsters.py
deleted file mode 100644
index d9a1122..0000000
--- a/simulator/monsters.py
+++ /dev/null
@@ -1,2200 +0,0 @@
-from dataclasses import dataclass, field
-import json
-import math
-import random
-import time
-from enum import Enum
-from typing import List
-import numpy as np
-
-from typing import TYPE_CHECKING
-
-from .vector2d import FastVector
-
-from .projectiles import AOEType, AOE炸弹, AOE炸弹锁定
-
-if TYPE_CHECKING:
- from battle_field import Battlefield
-
-from .elemental import ElementAccumulator, ElementType
-from .utils import BuffEffect, BuffType, DamageType, calculate_normal_dmg, debug_print, Faction
-from .zone import WineZone
-
-
-class AttackState(Enum):
- 前摇 = 0
- 后摇 = 1
- 等待 = 2
-
-
-class AttackAnimation:
- def __init__(self, 前摇时间, 后摇时间, 等待时间, monster: 'Monster'):
- self.前摇时间 = 前摇时间
- self.后摇时间 = 后摇时间
- self.等待时间 = 等待时间
- self.monster = monster
-
- @property
- def windup_time(self):
- return self.前摇时间 * self.monster.attack_interval
-
- @property
- def recovery_time(self):
- return self.windup_time + self.后摇时间 * self.monster.attack_interval
-
- @property
- def idle_time(self):
- return self.monster.attack_interval
-
-
-class TargetSelector:
- @staticmethod
- def select_targets(attacker, battlefield, need_in_range=False, max_targets=2, reverse=False):
- """
- 带嘲讽等级的目标选择算法
- 优先级: 攻击范围内最高嘲讽等级 > 同等级最近目标 > 全局最近目标
- """
- # 获取所有有效敌人
- # if need_in_range:
- # targets = battlefield.query_monster(attacker.position, attacker.attack_range)
- # enemies : list[Monster] = [m for m in targets
- # if m.can_be_target()
- # and m.faction != attacker.faction]
- # else:
- enemies: list[Monster] = [m for m in battlefield.alive_monsters
- if m.can_be_target()
- and m.faction != attacker.faction]
- # battlefield.query_monster(attacker.position, attacker.attack_range if need_in_range else 9999)
- # enemies =
- if not enemies:
- return []
-
- # 计算所有敌人属性
- enemy_info = []
- for enemy in enemies:
- dist = (enemy.position - attacker.position).magnitude
- in_range = dist <= attacker.attack_range
-
- if not need_in_range or (need_in_range and in_range):
- enemy_info.append({
- "enemy": enemy,
- "distance": dist,
- "aggro": enemy.aggro if in_range else 0
- })
-
- # 按照优先级排序：嘲讽降序 -> 距离升序
- if reverse:
- sorted_enemies = sorted(enemy_info,
- key=lambda x: (-x["distance"]))
- else:
- sorted_enemies = sorted(enemy_info,
- key=lambda x: (-x["aggro"], x["distance"]))
-
- count = np.minimum(max_targets, len(sorted_enemies))
- # 选择前N个目标
- return [e["enemy"] for e in sorted_enemies[:count]]
-
- @staticmethod
- def select_targets_lowest_health(attacker, battlefield, need_in_range=False, max_targets=2):
- """
- 带嘲讽等级的目标选择算法
- 优先级: 攻击范围内最高嘲讽等级 > 同等级最近目标 > 全局最近目标
- """
- # 获取所有有效敌人
- enemies: list[Monster] = [m for m in battlefield.alive_monsters
- if m.can_be_target()
- and m.faction != attacker.faction]
-
- if not enemies:
- return []
-
- # 计算所有敌人属性
- enemy_info = []
- for enemy in enemies:
- dist = (enemy.position - attacker.position).magnitude
- in_range = dist <= attacker.attack_range
-
- if not need_in_range or (need_in_range and in_range):
- enemy_info.append({
- "enemy": enemy,
- "distance": dist,
- "aggro": enemy.aggro if in_range else 0,
- "health_ratio": enemy.health / enemy.max_health
- })
-
- # 按照优先级排序：嘲讽降序 -> 距离升序
- sorted_enemies = sorted(enemy_info,
- key=lambda x: (x["health_ratio"], -x["aggro"], x["distance"]))
-
- count = np.minimum(max_targets, len(sorted_enemies))
- # 选择前N个目标
- return [e["enemy"] for e in sorted_enemies[:count]]
-
-
-class StatusSystem:
- def __init__(self, owner):
- self.owner: Monster = owner
- self.effects = []
- self.original_attributes = {}
-
- self.fire_dmg_counter = 0
- self.corrupt_dmg_counter = 0
- self.power_stay_counter = 0
-
- def apply(self, effect):
- if effect.type in self.owner.immunity:
- return
- # 处理效果叠加逻辑
- existing = next((e for e in self.effects if e.type == effect.type), None)
-
- # # 已经冰冻住了就不要施加寒冷效果了
- # if effect.type == BuffType.CHILL:
- # if next((e for e in self.effects if e.type == BuffType.FROZEN), None):
- # return
-
- if existing:
- # 寒冷效果叠加就会变成冰冻
- if effect.type == BuffType.CHILL:
- existing.duration = 0
- self.apply(BuffEffect(BuffType.FROZEN, effect.duration, effect.source, effect.stacks, effect.data))
- debug_print(f"{self.owner.name}{self.owner.id} 被 {effect.source.name} 冰冻了！")
- return
-
- # 其他效果刷新时间
- existing.duration = max(existing.duration, effect.duration)
- else:
- self._init_effect(effect)
- self.effects.append(effect)
-
- def update(self, delta_time):
- new_effects = []
- for effect in self.effects:
- effect.duration -= delta_time
- if effect.duration > 0:
- new_effects.append(effect)
- else:
- self.remove(effect)
-
- self.effects = new_effects
-
- # 处理持续伤害
- self._process_dot(delta_time)
-
- def reset(self):
- new_effects = []
- for effect in self.effects:
- self.remove(effect)
-
- self.effects = new_effects
-
- def _process_dot(self, delta_time):
- fire = next((e for e in self.effects if e.type == BuffType.FIRE), None)
- if fire:
- # 每秒造成伤害
- # self.fire_dmg_counter += delta_time
- # if self.fire_dmg_counter >= 0.33:
- # self.fire_dmg_counter = 0
- damage = calculate_normal_dmg(0, self.owner.magic_resist, 60 * delta_time, DamageType.MAGIC)
- self.owner.take_damage(damage, DamageType.MAGIC)
-
- corrupt = next((e for e in self.effects if e.type == BuffType.CORRUPT), None)
- if corrupt:
- # 每秒造成伤害
- # self.corrupt_dmg_counter += delta_time
- # if self.corrupt_dmg_counter >= 1:
- # self.corrupt_dmg_counter = 0
- damage = calculate_normal_dmg(0, self.owner.magic_resist, 100 * delta_time, DamageType.MAGIC)
- self.owner.take_damage(damage, DamageType.MAGIC)
-
- power_stone = next((e for e in self.effects if e.type == BuffType.POWER_STONE), None)
- if power_stone:
- self.power_stay_counter += delta_time
- damage = 0.005 * self.owner.max_health * self.power_stay_counter * delta_time
- if self.owner.take_damage(damage, DamageType.TRUE):
- pass
- #debug_print(f"{self.owner.name}{self.owner.id} 受到了毒圈的{damage}伤害")
-
- def _init_effect(self, effect):
- """初始化效果"""
- # 保存原始属性
- if effect.type == BuffType.CHILL:
- self.owner.attack_speed -= 30
- elif effect.type == BuffType.FROZEN:
- self.owner.frozen = True
- self.owner.magic_resist -= 15
- elif effect.type == BuffType.INVINCIBLE:
- self.owner.invincible = True
- elif effect.type == BuffType.FIRE:
- self.fire_dmg_counter = 0
- elif effect.type == BuffType.SPEEDUP:
- self.owner.move_speed *= 4
- elif effect.type == BuffType.DIZZY:
- self.owner.dizzy = True
- elif effect.type == BuffType.POWER_STONE:
- self.owner.attack_speed += 50
- self.owner.attack_multiplier += 1
- self.owner.move_speed *= 1.5
- self.power_stay_counter = 0
- debug_print(f"{self.owner.name}{self.owner.id} 进入了毒圈！")
- elif effect.type == BuffType.WINE:
- self.owner.attack_speed += 100
- self.owner.phys_dodge += 80
- debug_print(f"{self.owner.name}{self.owner.id} 进入了酒桶区域！")
- elif effect.type == BuffType.INVINCIBLE2:
- self.owner.invincible = True
- self.owner.can_target = False
-
- def remove(self, effect):
- # 恢复原始属性
- if effect.type == BuffType.CHILL:
- self.owner.attack_speed += 30
- elif effect.type == BuffType.FROZEN:
- self.owner.frozen = False
- self.owner.magic_resist += 15
- elif effect.type == BuffType.INVINCIBLE:
- self.owner.invincible = False
- elif effect.type == BuffType.SPEEDUP:
- self.owner.move_speed /= 4
- elif effect.type == BuffType.DIZZY:
- self.owner.dizzy = False
- elif effect.type == BuffType.POWER_STONE:
- self.owner.attack_speed -= 50
- self.owner.attack_multiplier -= 1
- self.owner.move_speed /= 1.5
- self.power_stay_counter = 0
- debug_print(f"{self.owner.name}{self.owner.id} 离开了毒圈！")
- elif effect.type == BuffType.WINE:
- self.owner.attack_speed -= 100
- self.owner.phys_dodge -= 80
- elif effect.type == BuffType.INVINCIBLE2:
- self.owner.invincible = False
- self.owner.can_target = True
-
-
-class Monster:
- def __init__(self, data, faction, position, battlefield):
- self.name = data["名字"]
- self.faction = faction
-
- self.attack_power = data["攻击力"]["数值"]
- self.health = data["生命值"]["数值"]
- self.max_health = self.health
- self.phy_def = data["物理防御"]["数值"]
- self.magic_resist = data["法抗"]["数值"]
- self.attack_interval = data["攻击间隔"]["数值"]
- self.attack_range = data["攻击范围"]["数值"]
- self.move_speed = data["移速"]["数值"]
- self.traits = data["特性"]
- self.attack_type = DamageType.PHYSICAL if data["类型"] == "物理" else DamageType.MAGIC
- self.char_icon = data.get("符号", "")
- self.id = -1
- self.attack_speed = 100
- self.boss = False
- # 嘲讽等级
- self.aggro = 0
-
- # 战斗状态
- self.position: FastVector = position
- self.velocity: FastVector = FastVector(0, 0)
- self.target = None
- self.is_alive = True
- self.frozen = False
- self.dizzy = False
- self.invincible = False
- self.battlefield: 'Battlefield' = battlefield
- self.status_system = StatusSystem(self)
- self.element_system = ElementAccumulator(self)
- self.attack_multiplier = 1
- self.phys_dodge = 0
- self.blocked = False
- self.immunity: set[BuffType] = set()
- self.can_target = True
- self.frame_counter = 0
-
- self.attack_time_counter = self.attack_interval
- self.attack_state = AttackState.等待
- if self.attack_range <= 0.8:
- self.attack_animation = AttackAnimation(0.35, 0.25, 0.4, self)
- else:
- self.attack_animation = AttackAnimation(0.2, 0.5, 0.3, self)
-
- # 如果活着且不处于不可选取状态
- def can_be_target(self):
- return self.is_alive and self.can_target
-
- # 新增可扩展的虚方法
- def on_spawn(self):
- """生成时触发的逻辑"""
- pass
-
- def on_death(self):
- """真正死亡时触发的逻辑"""
- debug_print(f"{self.name}{self.id} 已死亡！")
- self.battlefield.dead_count[self.faction] += 1
-
- # for (name, count) in self.battlefield.alive_count.items():
- # debug_print(f"{name}的数量为{count}")
-
- def get_skill_bar(self):
- """技力在ui显示的内容"""
- return self.attack_time_counter
-
- def get_max_skill_bar(self):
- """技力在ui显示的内容，最大技力"""
- return self.attack_interval
-
- def on_hit(self, attacker, damage):
- """被击中时触发的逻辑"""
- pass
-
- def on_attack(self, target, damage):
- """攻击命中时触发的逻辑"""
- pass
-
- def on_extra_update(self, delta_time):
- """额外更新逻辑"""
- pass
-
- def increase_skill_cd(self, delta_time):
- """增加技能技力"""
-
- def increase_attack_cd(self, delta_time):
- """增加攻击技力、攻击频率计算"""
- self.attack_time_counter += delta_time * (np.maximum(10, np.minimum(self.attack_speed, 600)) / 100)
-
- def move_toward_enemy(self, delta_time):
- """根据阵营向对方移动"""
- self.blocked = False
- if self.target and self.target.can_be_target():
- # 向目标移动
- direction = self.target.position - self.position
-
- if direction.magnitude <= self.attack_range:
- # 已经在攻击范围内，停止移动
- self.blocked = True
- direction = FastVector(0, 0)
- else:
- direction = FastVector(0, 0)
-
- # 标准化移动向量并应用速度
- norm_direction = direction.normalize()
- if not self.blocked and self.attack_state == AttackState.等待:
- self.velocity = (self.velocity * 7 + norm_direction * self.move_speed) / 8
-
- RADIUS = self.battlefield.HIT_BOX_RADIUS
- selfRadius = RADIUS * 0.2 if self.blocked else RADIUS
- # 碰撞检测
- for m in self.battlefield.query_monster(self.position, RADIUS * 2):
- if not m.can_be_target() or m == self or m.faction != self.faction:
- continue
- if m.blocked:
- # 用减速近似多次弹出
- self.velocity = norm_direction * self.move_speed * 0.3
- else:
- dir = m.position - self.position
- dist = np.maximum(dir.magnitude, 0.0001)
- dir /= dist
-
- # radius2 = RADIUS * 0.1 if m.blocked else RADIUS
- radius2 = RADIUS
- # hardness1 = 1 if m.blocked else 5
- hardness1 = 5
- hardness2 = 1 if self.blocked else 5
- depth = selfRadius + radius2 - dist
- if dist < selfRadius + radius2:
- # 发生碰撞，挤出
- self.velocity -= dir * (depth + 0.02) * hardness1 / (hardness1 + hardness2)
- # m.velocity += dir * (depth + 0.02) * hardness2 / (hardness1 + hardness2)
-
- def do_move(self, delta_time):
- if self.frozen or self.dizzy or not self.is_alive:
- self.velocity = FastVector(0, 0)
- return
-
- if self.velocity.magnitude > self.move_speed:
- self.velocity = self.velocity.normalize() * self.move_speed
-
- # 更新位置，yj的移速是2倍速的值，用0.5修正
- self.position += self.velocity * delta_time * 0.5
-
- if self.blocked or self.attack_state != AttackState.等待:
- self.velocity *= 0.5
-
- # 限制在场景范围内
- if self.position.x < 0:
- self.position.x = 0
- if self.position.x > self.battlefield.map_size[0]:
- self.position.x = self.battlefield.map_size[0]
- if self.position.y < 0:
- self.position.y = 0
- if self.position.y > self.battlefield.map_size[1]:
- self.position.y = self.battlefield.map_size[1]
-
- def reset_attack_time(self):
- self.attack_time_counter = self.attack_interval
- self.attack_state = AttackState.等待
-
- def can_attack(self, delta_time):
- if not self.target or not self.target.is_alive:
- if self.attack_state == AttackState.前摇:
- self.reset_attack_time()
- return False
- direction = self.target.position - self.position
- distance = direction.magnitude
- in_range = distance <= self.attack_range
-
- if self.attack_state == AttackState.前摇:
- if in_range:
- self.increase_attack_cd(delta_time)
- if self.attack_time_counter >= self.attack_animation.windup_time:
- self.attack_state = AttackState.后摇
- return True
- else:
- self.reset_attack_time()
- elif self.attack_state == AttackState.后摇:
- self.increase_attack_cd(delta_time)
- if self.attack_time_counter >= self.attack_animation.recovery_time:
- self.attack_state = AttackState.等待
- else:
- self.increase_attack_cd(delta_time)
- if in_range:
- if self.attack_time_counter >= self.attack_interval:
- self.attack_state = AttackState.前摇
- self.attack_time_counter = 0
- return False
-
- def update_elemental(self, delta_time):
- if self.element_system.active_burst:
- if self.element_system.active_burst.shouldClearBurst():
- self.element_system.active_burst.on_clear()
- self.element_system.active_burst = None
- else:
- self.element_system.active_burst.update_effect(delta_time)
-
- def update(self, delta_time):
- if not self.is_alive:
- return
-
- self.frame_counter += 1
- self.on_extra_update(delta_time)
- self.status_system.update(delta_time)
- self.update_elemental(delta_time)
-
- if self.target is None or not self.target.can_be_target() or (
- self.target.position - self.position).magnitude > self.attack_range:
- # 寻找新目标
- self.target = self.find_target()
-
- if self.frozen or self.dizzy or not self.is_alive:
- return
-
- self.increase_skill_cd(delta_time)
- # 继续移动
- self.move_toward_enemy(delta_time)
-
- if self.attack_range <= 0.8:
- if self.frame_counter % 3 == 0:
- self.target = self.find_target()
- # if target_ and np.linalg.norm(self.target.position - self.position) > self.attack_range and np.linalg.norm(target_.position - self.position) <= self.attack_range:
- # self.target = target_
-
- if self.can_attack(delta_time):
- self.attack(self.target, delta_time)
-
- def find_target(self):
- """寻找最近的可攻击目标"""
- targets = TargetSelector.select_targets(self, self.battlefield, need_in_range=False, max_targets=1)
- if len(targets) > 0:
- return targets[0]
- return None
-
- def get_attack_power(self):
- return self.attack_multiplier * self.attack_power
-
- # 攻击相关方法
- def attack(self, target, gameTime):
- damage = self.calculate_damage(target, self.get_attack_power())
- self.on_attack(target, damage)
- if self.apply_damage_to_target(target, damage):
- target.on_hit(self, damage)
-
- def apply_damage_to_target(self, target, damage) -> bool:
- debug_print(f"{self.name}{self.id} 对 {target.name}{target.id} 造成{damage}点{self.attack_type}伤害")
- if target.take_damage(damage, self.attack_type):
- return True
- debug_print(f"{self.name}{self.id} 没有对 {target.name}{target.id}造成伤害")
- return False
-
- def calculate_damage(self, target, damage):
- """计算伤害值"""
- return calculate_normal_dmg(target.phy_def, target.magic_resist, damage, self.attack_type)
- # if self.attack_type == "物理":
- # return calculate_normal_dmg(target.phy_def, 0, damage, False)
- # # return np.maximum(damage - target.phy_def, int(damage * 0.05))
- # elif self.attack_type == "魔法":
- # return calculate_normal_dmg(target.phy_def, target.magic_resist, damage, True)
- # # return int(damage * (1.0 - target.magic_resist / 100))
-
- def dodge_and_invincible(self, damage, attack_type: DamageType):
- if attack_type == DamageType.PHYSICAL and self.phys_dodge > 0:
- if random.uniform(0, 1) < self.phys_dodge / 100:
- return False
- if self.invincible:
- return False
- return True
-
- def take_damage(self, damage, attack_type: DamageType) -> bool:
- """承受伤害"""
- if not self.dodge_and_invincible(damage, attack_type):
- return False
- self.health -= damage
- if self.health <= 0:
- self.is_alive = False
- self.on_death()
- return True
-
-
-class AcidSlug(Monster):
- """酸液源石虫·α"""
-
- def on_spawn(self):
- self.attack_animation = AttackAnimation(0.05, 0.5, 0.45, self)
-
- def on_attack(self, target, damage):
- # 实现减防特性
- target.phy_def = max(0, target.phy_def - 15)
- debug_print(f"{self.name} 使 {target.name} 防御力降低15")
- return True
- # def apply_damage_to_target(self, target, damage):
- # if super().apply_damage_to_target(target, damage):
- # # 实现减防特性
- # target.phy_def = max(0, target.phy_def - 15)
- # debug_print(f"{self.name} 使 {target.name} 防御力降低15")
- # return True
- # return False
-
-
-class HighEnergySlug(Monster):
- """高能源石虫"""
-
- def on_death(self):
- # 实现自爆逻辑
- explosion_radius = 1.25
- debug_print(f"{self.name} 即将自爆！")
-
- self.battlefield.projectiles_manager.spawn_projectile(
- AOE炸弹(0.2, self.get_attack_power() * 4, DamageType.PHYSICAL, self, self.position, name="源石虫爆炸",
- aoeType=AOEType.Circle, radius=1.25))
- # for m in self.battlefield.monsters:
- # if m.faction != self.faction and m.is_alive:
- # distance = np.linalg.norm(m.position - self.position)
- # if distance <= explosion_radius:
- # dmg = self.calculate_damage(m, self.get_attack_power() * 4)
- # m.take_damage(dmg, self.attack_type)
- # debug_print(f"{m.name}{m.id} 受到{dmg}点爆炸伤害")
- super().on_death()
-
-
-class 炽焰源石虫(Monster):
- """灼热源石虫"""
-
- def apply_damage_to_target(self, target: Monster, damage):
- if super().apply_damage_to_target(target, damage):
- target.element_system.accumulate(ElementType.FIRE, self.get_attack_power())
- return True
- return False
-
-
-class 冰爆源石虫(Monster):
- """冰爆源石虫"""
-
- def on_death(self):
- # 实现自爆逻辑
- explosion_radius = 1.65
- debug_print(f"{self.name} 即将自爆！")
- for m in self.battlefield.alive_monsters:
- if m != self and m.faction != self.faction and m.is_alive:
- distance = (m.position - self.position).magnitude
- if distance <= explosion_radius:
- dmg = self.calculate_damage(m, self.get_attack_power() * 2)
- m.take_damage(dmg, self.attack_type)
- # 施加10秒寒冷效果
- chill = BuffEffect(
- type=BuffType.CHILL,
- duration=10,
- source=self
- )
- m.status_system.apply(chill)
- debug_print(f"{m.name} 受到{dmg}点爆炸伤害")
- super().on_death()
-
-
-class 污染躯壳(Monster):
- """污染躯壳"""
-
- def on_spawn(self):
- self.speed_boost_counter = 0
-
- def on_hit(self, attacker, damage):
- super().on_hit(attacker, damage)
- # 触发加速特性
- if self.is_alive and self.speed_boost_counter <= 0:
- speed = BuffEffect(
- type=BuffType.SPEEDUP,
- duration=2,
- source=self
- )
- self.status_system.apply(speed)
- self.speed_boost_counter = 7.0
- debug_print(f"{self.name} 进入极速状态！")
-
- def on_extra_update(self, delta_time):
- if self.speed_boost_counter > 0:
- self.speed_boost_counter -= delta_time
-
-
-class 大喷蛛(Monster):
- """大喷蛛"""
-
- def on_spawn(self):
- self.skill_counter = 0
- self.attack_animation = AttackAnimation(0.4, 0.2, 0.4, self)
-
- def increase_skill_cd(self, delta_time):
- self.skill_counter += delta_time
- if self.skill_counter >= 5:
- self.skill_counter = 0
- self.attack_state = AttackState.后摇
- self.attack_time_counter = 0
- self.spawn_small()
- super().increase_skill_cd(delta_time)
-
- def on_death(self):
- self.spawn_small()
- self.spawn_small()
- self.spawn_small()
- self.spawn_small()
- super().on_death()
-
- def spawn_small(self):
- debug_print(f"{self.name} 释放小喷蛛")
- self.battlefield.append_monster_name("小喷蛛", self.faction, self.position + FastVector(
- random.uniform(-1, 1) * 0.2,
- random.uniform(-1, 1) * 0.2
- ))
-
-
-class 提亚卡乌好战者(Monster):
- """提亚卡乌好战者"""
-
- def on_spawn(self):
- self.attack_animation = AttackAnimation(0.32, 0.2, 0.48, self)
-
- def apply_damage_to_target(self, target, damage):
- if super().apply_damage_to_target(target, damage):
- target.phy_def = max(0, target.phy_def - 10)
- debug_print(f"{self.name} 使 {target.name} 防御力降低10")
- return True
- return False
-
-
-class 雪境精锐(Monster):
- """雪境精锐"""
-
- def on_spawn(self):
- self.attack_animation = AttackAnimation(0.1, 0.15, 0.75, self)
-
- def apply_damage_to_target(self, target, damage):
- if super().apply_damage_to_target(target, damage):
- # 实现减防特性
- target.phy_def = max(0, target.phy_def - 100)
- debug_print(f"{self.name} 使 {target.name} 防御力降低100")
- return True
- return False
-
-
-class 宿主流浪者(Monster):
- """严父"""
-
- def on_spawn(self):
- self.attack_animation = AttackAnimation(0.4, 0.2, 0.4, self)
-
- def on_extra_update(self, delta_time):
- self.health += 250 * delta_time
- self.health = np.minimum(self.health, self.max_health)
- self.lastLifeRegenTime = self.battlefield.gameTime
-
-
-class 呼啸骑士团学徒(Monster):
- """呼啸骑士团学徒"""
-
- def on_spawn(self):
- self.shieldCounter = 30
- self.shieldMode = True
- self.phy_def += 3000
- self.magic_resist += 95
- self.attack_animation = AttackAnimation(0.4, 0.2, 0.4, self)
-
- def on_extra_update(self, delta_time):
- self.shieldCounter -= delta_time
- if self.shieldMode and self.shieldCounter <= 0:
- self.phy_def -= 3000
- self.magic_resist -= 95
- self.shieldMode = False
- debug_print(f"{self.name} 呼啸骑士团学徒失效")
-
-
-class 狂暴宿主组长(Monster):
- """1750"""
-
- def on_spawn(self):
- self.attack_animation = AttackAnimation(0.4, 0.4, 0.2, self)
-
- def on_extra_update(self, delta_time):
- self.health -= 350 * delta_time
- if self.health <= 0:
- self.is_alive = False
- self.on_death()
-
-
-class 爱蟹者(Monster):
- def on_spawn(self):
- self.attack_animation = AttackAnimation(0.3, 0.3, 0.4, self)
-
-
-class 绵羊(Monster):
- def on_spawn(self):
- self.attack_animation = AttackAnimation(0.05, 0.1, 0.85, self)
-
-
-class 光剑(Monster):
- def on_spawn(self):
- self.attack_animation = AttackAnimation(0.2, 0.2, 0.6, self)
-
-
-class 海螺(Monster):
- """固海凿石者"""
-
- def on_spawn(self):
- self.stage = 0
- self.last_attack_time = -1
- self.original_speed = self.move_speed
- self.attack_animation = AttackAnimation(0.2, 0.5, 0.3, self)
-
- def attack(self, target, gameTime):
- super().attack(target, gameTime)
- if self.stage == 0:
- self.phy_def += 300
- self.defenseMode = True
- self.move_speed = 0
- self.stage = 1
- self.last_attack_time = self.battlefield.gameTime
- debug_print(f"{self.name} 进入防御模式")
-
- def on_extra_update(self, delta_time):
- if self.stage == 1 and self.battlefield.gameTime - self.last_attack_time >= 20.0:
- self.phy_def -= 300
- self.move_speed = self.original_speed
- self.defenseMode = False
- self.stage = 2
- debug_print(f"{self.name} 退出防御模式")
-
-
-class 拳手囚犯(Monster):
- """拳手囚犯"""
-
- def on_spawn(self):
- self.attack_speed -= 50
- self.attack_count = 0
-
- def get_skill_bar(self):
- """技力在ui显示的内容"""
- return np.minimum(self.attack_count, 4)
-
- def get_max_skill_bar(self):
- """技力在ui显示的内容，最大技力"""
- return 4
-
- def attack(self, target, gameTime):
- self.attack_count += 1
- if self.attack_count == 4:
- self.attack_speed += 50
- self.attack_power += self.attack_power * 0.5
- debug_print(f"{self.name}{self.id} 已经解放")
- damage = self.calculate_damage(target, self.get_attack_power())
- if self.apply_damage_to_target(target, damage):
- target.on_hit(self, damage)
-
- def calculate_damage(self, target, damage):
- """计算伤害值"""
- target_def = target.phy_def
- if self.attack_count >= 4:
- target_def = target_def * 0.4
- return calculate_normal_dmg(target_def, target.magic_resist, damage, DamageType.PHYSICAL)
-
-
-class 高塔术师(Monster):
- """我们塔神"""
-
- def on_spawn(self):
- self.attack_animation = AttackAnimation(0.07, 0.13, 0.8, self)
-
- def attack(self, target, gameTime):
- targets = TargetSelector.select_targets(self, self.battlefield, need_in_range=True, max_targets=2)
- if len(targets) == 0:
- return
-
- for t in targets:
- self.battlefield.projectiles_manager.spawn_projectile(
- AOE炸弹锁定(0.1, self.get_attack_power(), DamageType.MAGIC, self, t, name="爆裂魔法",
- aoeType=AOEType.Grid8))
-
- debug_print(f"{self.name}{self.id} 射出爆裂魔法")
-
- def get_aoe_targets(self, target):
- aoe_targets = [m for m in self.battlefield.monsters
- if m.is_alive and m.faction != self.faction
- and abs(m.position.x - target.position.x) <= 1
- and abs(m.position.y - target.position.y) <= 1]
- return aoe_targets
-
-
-class 冰原术师(Monster):
- """冰手手"""
-
- def on_spawn(self):
- self.attack_count = 0
- self.targets = []
- self.attack_animation = AttackAnimation(0.1, 0.1, 0.8, self)
-
- def on_attack(self, target, damage):
- self.attack_count += 1
-
- def get_skill_bar(self):
- """技力在ui显示的内容"""
- return self.attack_count % 3
-
- def get_max_skill_bar(self):
- """技力在ui显示的内容，最大技力"""
- return 3
-
- def apply_damage_to_target(self, target, damage):
- # 如果敌人受到伤害施加buff
- if super().apply_damage_to_target(target, damage):
- if self.attack_count % 3 == 0:
- # 施加寒冷效果
- chill = BuffEffect(
- type=BuffType.CHILL,
- duration=5,
- source=self
- )
- target.status_system.apply(chill)
- return True
- return False
-
- def attack(self, target, gameTime):
- targets = TargetSelector.select_targets(self, self.battlefield, need_in_range=True, max_targets=2)
- if len(targets) == 0:
- return
-
- self.on_attack(targets[0], 0)
- for m in targets:
- damage = self.calculate_damage(m, self.get_attack_power())
- if self.apply_damage_to_target(m, damage):
- m.on_hit(self, damage)
-
-
-class 矿脉守卫(Monster):
- """反伤怪"""
-
- def on_spawn(self):
- self.aggro = 1
-
- def on_hit(self, attacker, damage):
- if attacker == None:
- return
- damage = self.calculate_damage(attacker, 300)
- if self.apply_damage_to_target(attacker, damage):
- attacker.on_hit(self, damage)
- debug_print(f"{self.name}{self.id} 对 {attacker.name}{attacker.id} 造成{damage}伤害")
-
-
-class 庞贝(Monster):
- """庞氏骗局"""
-
- def on_spawn(self):
- self.rage_mode = False
- self.ring_attack_counter = 0
- self.boss = True
- self.attack_animation = AttackAnimation(0.1, 0.1, 0.8, self)
-
- def get_skill_bar(self):
- """技力在ui显示的内容"""
- return self.ring_attack_counter
-
- def get_max_skill_bar(self):
- """技力在ui显示的内容，最大技力"""
- return 10
-
- def attack(self, target, gameTime):
- targets: list[Monster] = TargetSelector.select_targets(self, self.battlefield, need_in_range=True,
- max_targets=4)
- if len(targets) == 0:
- return
-
- for m in targets:
- damage = self.calculate_damage(m, self.get_attack_power())
- if self.apply_damage_to_target(m, damage):
- m.on_hit(self, damage)
- m.status_system.apply(BuffEffect(
- type=BuffType.FIRE,
- duration=10,
- source=self
- ))
-
- def on_extra_update(self, delta_time):
- if not self.rage_mode and self.health < 0.5 * self.max_health:
- self.rage_mode = True
- self.attack_speed += 40
- debug_print(f"{self.name} 进入狂暴模式")
- self.ring_attack_counter += delta_time
- targets = TargetSelector.select_targets(self, self.battlefield, need_in_range=False, max_targets=9999)
- if len(targets) > 0 and (targets[0].position - self.position).magnitude < 0.8:
- if self.ring_attack_counter >= 10.0:
- targets = [t for t in targets if (t.position - self.position).magnitude < 1.4]
- for tar in targets:
- dmg = self.calculate_damage(tar, 1000)
- if self.apply_damage_to_target(tar, dmg):
- tar.on_hit(self, dmg)
- self.ring_attack_counter = 0
-
-
-class 食腐狗(Monster):
- """食腐狗"""
-
- def on_attack(self, target, damage):
- target.status_system.apply(BuffEffect(
- type=BuffType.CORRUPT,
- duration=10,
- source=self
- ))
-
-
-class 鼠鼠(Monster):
- """鼠鼠"""
-
- def on_spawn(self):
- self.speed_boost_counter = 0
-
- def on_hit(self, attacker, damage):
- super().on_hit(attacker, damage)
- # 触发加速特性
- if self.is_alive and self.speed_boost_counter <= 0:
- speed = BuffEffect(
- type=BuffType.SPEEDUP,
- duration=5,
- source=self
- )
- self.status_system.apply(speed)
- self.speed_boost_counter = 15.0
- debug_print(f"{self.name}{self.id} 进入极速状态！")
-
- def on_extra_update(self, delta_time):
- if self.speed_boost_counter > 0:
- self.speed_boost_counter -= delta_time
-
-
-class 投石机(Monster):
- """恐怖雪球投掷手"""
-
- def on_spawn(self):
- self.first_attack = True
- self.attack_animation = AttackAnimation(0.2, 0.1, 0.7, self)
-
- def on_extra_update(self, delta_time):
- if self.first_attack:
- targets: list[Monster] = TargetSelector.select_targets(self, self.battlefield, need_in_range=True,
- max_targets=1)
- if len(targets) == 0:
- return
-
- self.battlefield.projectiles_manager.spawn_projectile(
- AOE炸弹锁定(0.2, self.get_attack_power() * 1.5, DamageType.MAGIC, self, targets[0], name="“投石机”",
- aoeType=AOEType.Grid4))
- self.attack_range = 0.8
- self.first_attack = False
- debug_print(f"{self.name}{self.id} 投掷雪球")
-
-
-class 船长(Monster):
- """船长"""
-
- def on_spawn(self):
- self.attack_count = 0
- self.attack_animation = AttackAnimation(0.3, 0.2, 0.5, self)
-
- def on_attack(self, target, damage):
- self.attack_count += 1
-
- def get_skill_bar(self):
- """技力在ui显示的内容"""
- return self.attack_count % 4
-
- def get_max_skill_bar(self):
- """技力在ui显示的内容，最大技力"""
- return 4
-
- def apply_damage_to_target(self, target, damage):
- if super().apply_damage_to_target(target, damage):
- # 每第四下攻击会眩晕对面7秒
- if self.attack_count % 4 == 0:
- dizzy = BuffEffect(
- type=BuffType.DIZZY,
- duration=7,
- source=self
- )
- target.status_system.apply(dizzy)
- debug_print(f"{self.name}{self.id} 眩晕了 {target.name}{target.id}")
- return True
- return False
-
-
-class 杰斯顿·威廉姆斯(Monster):
- """洁厕灵"""
-
- def on_spawn(self):
- self.stage = 0
- self.attack_count = 0
- self.magic_resist += 50
- self.boss = True
- self.attack_animation = AttackAnimation(0.7, 0.1, 0.3, self)
-
- # def get_skill_bar(self):
- # """技力在ui显示的内容"""
- # return self.attack_count % 4
-
- # def get_max_skill_bar(self):
- # """技力在ui显示的内容，最大技力"""
- # return 4
-
- def on_attack(self, target, damage):
- self.attack_count += 1
-
- def attack(self, target, gameTime):
- if self.stage == 0:
- self.on_attack(target, 0)
- if self.attack_count % 4 == 0:
- targets: list[Monster] = TargetSelector.select_targets(self, self.battlefield, need_in_range=True,
- max_targets=2)
- if len(targets) == 0:
- return
-
- for m in targets:
- damage = self.calculate_damage(m, self.get_attack_power())
- if self.apply_damage_to_target(m, damage):
- m.on_hit(self, damage)
- m.status_system.apply(BuffEffect(
- type=BuffType.DIZZY,
- duration=3,
- source=self
- ))
- else:
- super().attack(target, gameTime)
- return
- else:
- self.on_attack(self.target, 0)
- damage = self.calculate_damage(target, self.get_attack_power())
- if self.apply_damage_to_target(target, damage):
- target.on_hit(self, damage)
-
- if self.stage == 1 and self.attack_count % 4 == 0:
- if self.apply_damage_to_target(target, damage):
- target.on_hit(self, damage)
-
- def calculate_damage(self, target: Monster, damage):
- if self.stage == 1 and self.attack_count % 4 == 0:
- target_def = target.phy_def * 0.4
- return calculate_normal_dmg(target_def, target.magic_resist, damage, DamageType.PHYSICAL)
- return super().calculate_damage(target, damage)
-
- def on_death(self):
- if self.stage == 0:
- self.stage = 1
- self.magic_resist -= 50
- self.attack_type = DamageType.PHYSICAL
- self.attack_power += 700
- self.phy_def += 1000
- self.attack_interval -= 1.5
- self.move_speed += 0.3
- self.attack_range = 0.8
- self.attack_count = 0
- self.attack_animation = AttackAnimation(0.4, 0.3, 0.3, self)
-
- self.is_alive = True
- self.health = self.max_health
- self.status_system.reset()
- switch_stage = BuffEffect(
- type=BuffType.INVINCIBLE2,
- duration=4,
- source=self
- )
- dizzy = BuffEffect(
- type=BuffType.DIZZY,
- duration=4,
- source=self
- )
- # 转阶段
- self.status_system.apply(switch_stage)
- self.status_system.apply(dizzy)
- print(f"{self.name}{self.id}已进入狂暴状态")
- else:
- super().on_death()
-
-
-class 山海众窥魅人(Monster):
- """山海众窥魅人"""
-
- def on_spawn(self):
- # 技力
- self.skill_counter = 25
- self.stage = 0
- self.charging_counter = 0
- self.rage_counter = 0
- self.original_move_speed = self.move_speed
- self.locked_target = None
- self.attack_animation = AttackAnimation(0.5, 0.3, 0.2, self)
-
- def get_skill_bar(self):
- """技力在ui显示的内容"""
- if self.stage == 0:
- return self.skill_counter
- elif self.stage == 1:
- return self.charging_counter
- elif self.stage == 2:
- return 20 - self.rage_counter
- return 0
-
- def get_max_skill_bar(self):
- """技力在ui显示的内容，最大技力"""
- if self.stage == 0:
- return 35
- elif self.stage == 1:
- return 5
- elif self.stage == 2:
- return 20
- return 1
-
- def increase_skill_cd(self, delta_time):
- if self.stage == 0:
- self.skill_counter += delta_time
- elif self.stage == 1:
- self.charging_counter += delta_time
- elif self.stage == 2:
- self.rage_counter += delta_time
-
- if self.stage == 2 and self.rage_counter >= 20:
- self.stage = 0
- self.attack_speed -= 100
- self.skill_counter = 0
- super().increase_skill_cd(delta_time)
-
- def on_extra_update(self, delta_time):
- # 如果处于默认状态，释放技能
- if self.stage == 0 and self.skill_counter >= 35 and self.target:
- direction = self.target.position - self.position
- distance = direction.magnitude
-
- if distance <= self.attack_range:
- self.locked_target = self.target
- self.stage = 1
- self.move_speed = 0
- self.charging_counter = 0
- debug_print(f"{self.name}{self.id} 开始蓄力")
-
- if self.stage == 1:
- # 蓄力5秒后造成攻击力200%法术伤害
- if self.charging_counter >= 5 or not self.locked_target.can_be_target():
- self.stage = 2
- self.move_speed = self.original_move_speed
- self.skill_counter = 0
- self.charging_counter = 0
- self.rage_counter = 0
- self.attack_speed += 100
- self.attack_state = AttackState.后摇
- self.attack_time_counter = 0
- debug_print(f"{self.name}{self.id} 退出蓄力")
-
- if self.locked_target.can_be_target():
- damage = self.calculate_damage(self.locked_target, self.get_attack_power() * 2)
- self.on_attack(self.locked_target, damage)
- if self.apply_damage_to_target(self.locked_target, damage):
- self.locked_target.on_hit(self, damage)
-
- def attack(self, target, gameTime):
- if self.stage == 1:
- return
- else:
- super().attack(target, gameTime)
-
-
-class Vvan(Monster):
- """薇薇安娜"""
-
- def on_spawn(self):
- # 技力
- self.skill_counter = 15
- self.stage = 0
- self.charging_counter = 0
- self.original_move_speed = self.move_speed
- self.target_pos = None
-
- def get_skill_bar(self):
- """技力在ui显示的内容"""
- if self.stage == 0:
- return self.skill_counter
- elif self.stage == 1:
- return self.charging_counter
- return 0
-
- def get_max_skill_bar(self):
- """技力在ui显示的内容，最大技力"""
- if self.stage == 0:
- return 25
- elif self.stage == 1:
- return 7
- return 1
-
- def increase_skill_cd(self, delta_time):
- super().increase_skill_cd(delta_time)
-
- def on_extra_update(self, delta_time):
- if self.stage == 0:
- self.skill_counter += delta_time
- elif self.stage == 1:
- self.charging_counter += delta_time
- # 如果处于默认状态，释放技能
- if self.stage == 0 and self.skill_counter >= 25:
- if self.target and self.target.can_be_target():
- direction = self.target.position - self.position
- distance = direction.magnitude
- if distance <= self.attack_range:
- self.stage = 1
- self.move_speed = 0
- self.charging_counter = 0
- #self.target_pos = self.target.position
- self.target_pos = FastVector(self.target.position.x, self.target.position.y)
- debug_print(f"{self.name}{self.id} 开始蓄力")
- #debug_print(f"{self.name}{self.id} 锁定的坐标是{self.target_pos.x},{self.target_pos.y}")
- if self.stage == 1:
- # 蓄力7秒后造成攻击力250%法术伤害
- if self.charging_counter >= 7:
- self.stage = 0
- self.move_speed = self.original_move_speed
- self.skill_counter = 0
- self.charging_counter = 0
- #debug_print(f"{self.name}{self.id} 轰炸的中心是{self.target_pos.x},{self.target_pos.y}")
-
- for m in self.battlefield.monsters:
- if m.faction != self.faction and m.can_be_target():
- #改为max(5|x|,|y|)<=2.5∪max(|x|,5|y|)<=2.5∪max(|x|,|y|)<=1.5
- x = int(math.floor(m.position.x - self.target_pos.x + 0.5))
- y = int(math.floor(m.position.y - self.target_pos.y + 0.5))
- if abs(x) + abs(y) <= 2:
- dmg = self.calculate_damage(m, self.get_attack_power() * 2.5)
- if self.apply_damage_to_target(m, dmg):
- m.on_hit(self, dmg)
-
- def attack(self, target, gameTime):
- if self.stage == 1:
- return
- else:
- super().attack(target, gameTime)
-
-
-class 萨克斯(Monster):
- """吹笛人"""
-
- def on_spawn(self):
- # 技力
- self.skill_counter = 10
- self.stage = 0
- self.charging_counter = 0
- self.original_move_speed = self.move_speed
-
- def get_skill_bar(self):
- """技力在ui显示的内容"""
- if self.stage == 0:
- return self.skill_counter
- elif self.stage == 1:
- return self.charging_counter
- return 0
-
- def get_max_skill_bar(self):
- """技力在ui显示的内容，最大技力"""
- if self.stage == 0:
- return 20
- elif self.stage == 1:
- return 5
- return 1
-
- def increase_skill_cd(self, delta_time):
- if self.stage == 0:
- self.skill_counter += delta_time
- elif self.stage == 1:
- self.charging_counter += delta_time
-
- # 如果处于默认状态，释放技能
- if self.stage == 0 and self.skill_counter >= 20:
- self.stage = 1
- self.move_speed = 0
- self.charging_counter = 0
- debug_print(f"{self.name}{self.id} 开始蓄力")
-
- # 蓄力4秒后造成攻击力150%物理伤害
- if self.stage == 1 and self.charging_counter >= 4:
- self.stage = 0
- self.move_speed = self.original_move_speed
- self.skill_counter = 0
- self.charging_counter = 0
-
- for m in self.get_hit_enemies():
- dmg = self.calculate_damage(m, self.get_attack_power() * 1.5)
- if self.apply_damage_to_target(m, dmg):
- m.on_hit(self, dmg)
- debug_print(f"{m.name} 受到{dmg}点萨克斯伤害")
-
- super().increase_skill_cd(delta_time)
-
- def attack(self, target, gameTime):
- if self.stage == 1:
- self.reset_attack_time()
- else:
- super().attack(target, gameTime)
-
- def get_hit_enemies(self):
- # 使用整数坐标进行快速分类
- self_x = self.position.x
- self_y = self.position.y
-
- smallest_up = 100
- smallest_up_target = None
-
- smallest_down = 100
- smallest_down_target = None
-
- smallest_left = 100
- smallest_left_target = None
-
- smallest_right = 100
- smallest_right_target = None
- # 预处理战场数据
- for m in self.battlefield.monsters:
- if m.faction == self.faction or not m.is_alive:
- continue
-
- # 转换为整数坐标（优化距离计算效率）
- x = m.position.x
- y = m.position.y
-
- # 列坐标匹配（同一垂直方向）
- if abs(x - self_x) <= 0.5:
- if m.position.y > self.position.y and m.position.y - self.position.y < smallest_up:
- smallest_up = m.position.y - self.position.y
- smallest_up_target = m
- if m.position.y < self.position.y and self.position.y - m.position.y < smallest_down:
- smallest_down = self.position.y - m.position.y
- smallest_down_target = m
-
- # 行坐标匹配（同一水平方向）
- if abs(y - self_y) <= 0.5:
- if m.position.x > self.position.x and m.position.x - self.position.x < smallest_right:
- smallest_right = m.position.x - self.position.x
- smallest_right_target = m
- if m.position.x < self.position.x and self.position.x - m.position.x < smallest_left:
- smallest_left = self.position.x - m.position.x
- smallest_left_target = m
-
- targets = []
- if smallest_up_target != None:
- targets.append(smallest_up_target)
- if smallest_down_target != None:
- targets.append(smallest_down_target)
- if smallest_left_target != None:
- targets.append(smallest_left_target)
- if smallest_right_target != None:
- targets.append(smallest_right_target)
- # 返回最近的敌人列表
- return targets
-
-
-class 大君之赐(Monster):
- """大君之赐"""
-
- def take_damage(self, damage, attack_type) -> bool:
- """承受伤害"""
- if not self.dodge_and_invincible(damage, attack_type):
- return False
- if attack_type != DamageType.TRUE:
- damage *= 0.1
- self.health -= damage
- if self.health <= 0:
- self.is_alive = False
- self.on_death()
- return True
-
-
-class 萨卡兹链术师(Monster):
- """萨卡兹链术师"""
-
- def on_spawn(self):
- self.attack_animation = AttackAnimation(0.4, 0.2, 0.4, self)
-
- class AttackNode:
- """攻击节点数据类"""
- __slots__ = ['target', 'damage_multiplier'] # 优化内存使用
-
- def __init__(self, target, multiplier):
- self.target = target
- self.damage_multiplier = multiplier
-
- def chain_attack(self, initial_target: Monster) -> list[AttackNode]:
- """
- 执行连锁攻击
- :param initial_target: 初始攻击目标
- :param battlefield: 战场实例
- :return: 攻击节点列表（包含目标和伤害倍率）
- """
- attack_chain = []
- visited = set() # 已攻击目标ID缓存
- current_target = initial_target
- current_multiplier = 1.0
-
- # 添加初始攻击
- attack_chain.append(self.AttackNode(current_target, current_multiplier))
- visited.add(current_target.id)
-
- # 执行最多4次跳跃
- for _ in range(3):
- # 寻找下一个候选目标
- candidates = self._find_candidates(
- current_target.position,
- [m for m in self.battlefield.alive_monsters if m.can_be_target() and m.faction != self.faction],
- visited
- )
-
- if not candidates:
- break # 没有可跳跃目标
-
- # 选择最近的目标
- next_target = min(candidates, key=lambda x: x[1])
- current_target = next_target[0]
- current_multiplier *= 0.85
-
- # 记录攻击节点
- attack_chain.append(
- self.AttackNode(current_target, current_multiplier)
- )
- visited.add(current_target.id)
-
- return attack_chain
-
- def _find_candidates(self,
- origin: FastVector,
- enemies: List['Monster'],
- visited: set) -> List[tuple]:
- """
- 查找有效候选目标
- :param origin: 当前攻击源点坐标 (x, y)
- :param enemies: 可用敌人列表
- :param visited: 已攻击目标ID集合
- :return: 候选列表 (目标, 距离)
- """
- candidates = []
-
- for enemy in enemies:
- # 排除已攻击目标
- if enemy.id in visited:
- continue
-
- # 计算欧氏距离
- d = enemy.position - origin
- distance = d.magnitude
-
- if distance <= 1.6:
- candidates.append((enemy, distance))
-
- return candidates
-
- def attack(self, target, gameTime):
- taragets = self.chain_attack(target)
- for node in taragets:
- base_dmg = self.get_attack_power() * node.damage_multiplier
- dmg = self.calculate_damage(node.target, base_dmg)
- if self.apply_damage_to_target(node.target, dmg):
- node.target.on_hit(self, dmg)
- # 所有人都有一样的凋亡损伤
- t = ElementType.NECRO_RIGHT
- node.target.element_system.accumulate(t, self.get_attack_power() * 0.3)
- # debug_print(f"{self.name}{self.id} 对 {node.target.name}{node.target.id} 造成{dmg}点魔法伤害")
-
- def on_death(self):
- debug_print(f"{self.name} 变成大君之赐")
- m = self.battlefield.append_monster_name("大君之赐", self.faction, self.position + FastVector(
- random.uniform(-1, 1) * 0.2,
- random.uniform(-1, 1) * 0.2
- ))
- switch_stage = BuffEffect(
- type=BuffType.INVINCIBLE2,
- duration=1,
- source=self
- )
- dizzy = BuffEffect(
- type=BuffType.DIZZY,
- duration=1,
- source=self
- )
- # 转阶段
- m.status_system.apply(switch_stage)
- m.status_system.apply(dizzy)
-
-
-class 高普尼克(Monster):
- """高普尼克"""
-
- def on_spawn(self):
- self.attack_stack = 0
- self.decay_timer = 0
- self.attack_animation = AttackAnimation(0.55, 0.4, 0.05, self)
-
- self.immunity.add(BuffType.DIZZY)
- self.immunity.add(BuffType.FROZEN)
-
-
-class 狂躁珊瑚(Monster):
- """狂躁珊瑚"""
-
- def on_spawn(self):
- self.attack_stack = 0
- self.decay_timer = 0
- self.attack_animation = AttackAnimation(0.5, 0.1, 0.4, self)
-
- def on_extra_update(self, delta_time):
- self.decay_timer += delta_time
- if self.decay_timer > 3.5 and abs(round(self.decay_timer - 3.5) - (self.decay_timer - 3.5)) < 0.001:
- if self.attack_stack > 0:
- self.attack_stack -= 2
- self.attack_multiplier -= 0.3
- else:
- self.attack_stack = 0
- self.attack_multiplier = 1
-
- def attack(self, target, delta_time):
- direction = target.position - self.position
- distance = direction.magnitude
-
- if distance <= self.attack_range:
- # 如果是近战
- if distance <= 0.8:
- damage = self.calculate_damage(target, self.get_attack_power())
- else:
- damage = self.calculate_damage(target, self.get_attack_power())
- self.on_attack(target, damage)
- if self.apply_damage_to_target(target, damage):
- target.on_hit(self, damage)
- self.decay_timer = 0
-
- if self.attack_stack < 15:
- self.attack_stack += 1
- self.attack_multiplier += 0.15
- if self.attack_stack == 10:
- debug_print(f"{self.name} 被动叠了10层")
- if self.attack_stack == 15:
- debug_print(f"{self.name} 被动叠了15层")
-
-
-class 炮击组长(Monster):
- """炮神"""
-
- def on_spawn(self):
- self.attack_animation = AttackAnimation(0.05, 0.15, 0.8, self)
-
- def attack(self, target, gameTime):
- targets: list[Monster] = TargetSelector.select_targets(self, self.battlefield, need_in_range=True,
- max_targets=1)
- if len(targets) == 0:
- return
-
- self.battlefield.projectiles_manager.spawn_projectile(
- AOE炸弹锁定(0.2, self.get_attack_power(), self.attack_type, self, targets[0], name="火箭弹",
- aoeType=AOEType.Grid8))
-
- debug_print(f"{self.name}{self.id} 开炮")
-
-
-class 榴弹佣兵(Monster):
- """跑得飞快的炮手"""
-
- def on_spawn(self):
- # 状态0：火箭筒状态
- # 状态1：切换形态状态
- # 状态2：近战状态
- self.stage = 0
- self.stage_counter = 0
-
- def on_extra_update(self, delta_time):
- if self.stage == 0:
- if self.target is not None:
- direction = self.target.position - self.position
- distance = direction.magnitude
- if distance <= self.attack_range:
- self.battlefield.projectiles_manager.spawn_projectile(
- AOE炸弹锁定(0.2, self.get_attack_power() * 2, self.attack_type, self, self.target,
- name="火箭弹", aoeType=AOEType.Grid8))
- self.stage = 1
- debug_print(f"{self.name}{self.id} 射出火箭弹")
-
- if self.stage == 1:
- self.stage_counter += delta_time
- if self.stage_counter >= 1.14:
- # 变为近战形态
- self.stage = 2
- self.stage_counter = 0
- self.move_speed += 2 * self.move_speed
- self.attack_range = 0.8
-
-
-class 凋零萨卡兹(Monster):
- """凋零萨卡兹术士"""
-
- def on_spawn(self):
- # 技力
- self.skill_counter = 10
-
- # 状态0：正常形态
- # 状态1：蓄力持续伤害形态
- self.stage = 0
- self.charging_counter1 = 0
- self.charging_counter2 = 0
- self.original_move_speed = self.move_speed
- self.locked_target = None
- self.attack_animation = AttackAnimation(0.1, 0.1, 0.8, self)
-
- def increase_skill_cd(self, delta_time):
- if self.stage == 0:
- self.skill_counter += delta_time
- elif self.stage == 1:
- self.charging_counter1 += delta_time
- self.charging_counter2 += delta_time
- super().increase_skill_cd(delta_time)
-
- def get_skill_bar(self):
- """技力在ui显示的内容"""
- if self.stage == 1:
- return self.charging_counter2
- return super().get_skill_bar()
-
- def get_max_skill_bar(self):
- """技力在ui显示的内容，最大技力"""
- if self.stage == 1:
- return 8
- return super().get_max_skill_bar()
-
- def lock_target(self):
- targets = TargetSelector.select_targets(self, self.battlefield, need_in_range=True, max_targets=1)
- if len(targets) > 0:
- return targets[0]
- return None
-
- def on_extra_update(self, delta_time):
- # 如果处于默认状态，释放技能
- if self.stage == 0 and self.skill_counter >= 24:
- self.locked_target = self.lock_target()
- if self.locked_target:
- self.stage = 1
- self.move_speed = 0
- self.charging_counter = 0
- self.skill_counter = 0
- debug_print(f"{self.name}{self.id} 开始蓄力")
- elif self.stage == 1:
- if not self.locked_target.can_be_target():
- self.stage = 0
- self.move_speed = self.original_move_speed
- self.locked_target = None
- self.skill_counter = 0
- else:
- # 法术伤害
- dmg = self.calculate_damage(self.locked_target, self.get_attack_power() * 0.4 * delta_time)
- if self.apply_damage_to_target(self.locked_target, dmg):
- self.locked_target.on_hit(self, dmg)
- # debug_print(f"{self.locked_target.name}{self.locked_target.id} 受到 {self.name}{self.id} 的{dmg}点法术伤害")
- # 蓄力完成的凋亡损伤
- if self.locked_target.can_be_target() and self.charging_counter2 >= 8.0:
- for m in self.get_aoe_targets(self.locked_target):
- dmg = self.get_attack_power() * 2.2
- m.element_system.accumulate(ElementType.NECRO_RIGHT, dmg)
- debug_print(f"{m.name}{m.id} 受到 {self.name}{self.id} 的{dmg}点凋亡损伤")
- self.stage = 0
- self.move_speed = self.original_move_speed
- self.locked_target = None
- self.charging_counter2 = 0
-
-
- def get_aoe_targets(self, target):
- # 这个是获取|dx|<=1,|dy|<=1范围内的目标，是个矩形
- # aoe_targets = [m for m in self.battlefield.monsters
- # if m.is_alive and m.faction != self.faction
- # and np.maximum(abs(m.position.x - target.position.x), abs(m.position.y - target.position.y)) <= 1]
- # 为了改为十字范围
- # 十字：|dx|≤0.5∪|dy|≤0.5
- # 矩形:max(|dx|,|dy|)≤1.5
- # 十字与矩形取交集
- aoe_targets = [m for m in self.battlefield.monsters
- if m.is_alive
- and m.faction != self.faction
- and np.maximum(abs(m.position.x - target.position.x),
- abs(m.position.y - target.position.y)) <= 1.5
- and (abs(m.position.x - target.position.x) <= 0.5 or abs(
- m.position.y - target.position.y) <= 0.5)]
- return aoe_targets
-
- def attack(self, target, gameTime):
- if self.stage == 1:
- return
- else:
- damage = self.calculate_damage(target, self.get_attack_power())
- self.on_attack(target, damage)
- if self.apply_damage_to_target(target, damage):
- t = ElementType.NECRO_RIGHT
- target.element_system.accumulate(t, self.get_attack_power() * 0.25)
- target.on_hit(self, damage)
-
-
-class 洗地车(Monster):
- """洗地机"""
-
- def on_spawn(self):
- self.stage = 0
- self.skill_counter = 0
-
- def on_extra_update(self, delta_time):
- if self.stage == 0 and self.health < self.max_health:
- self.stage = 1
- self.attack_speed += 150
- self.move_speed *= 2.5
- debug_print(f"{self.name}{self.id} 进入过载模式")
- if self.stage == 1:
- self.skill_counter += delta_time
- if self.skill_counter >= 30:
- self.attack_speed -= 150
- self.move_speed /= 2.5
- self.stage = 2
- debug_print(f"{self.name}{self.id} 退出过载模式")
-
-
-class 衣架(Monster):
- """衣架射手囚犯"""
-
- def on_spawn(self):
- self.attack_speed -= 50
- self.attack_count = 0
- self.attack_animation = AttackAnimation(0.2, 0.1, 0.7, self)
-
- def get_skill_bar(self):
- """技力在ui显示的内容"""
- return np.minimum(self.attack_count, 4)
-
- def get_max_skill_bar(self):
- """技力在ui显示的内容，最大技力"""
- return 4
-
- # 攻击相关方法
- def attack(self, target, gameTime):
- self.attack_count += 1
- if self.attack_count == 4:
- self.attack_speed += 50
- self.attack_power += self.attack_power * 0.5
- self.attack_type = DamageType.MAGIC
- debug_print(f"{self.name}{self.id} 已经解放")
- damage = self.calculate_damage(target, self.get_attack_power())
- if self.apply_damage_to_target(target, damage):
- target.on_hit(self, damage)
-
-
-class 标枪恐鱼(Monster):
- def on_spawn(self):
- self.attack_animation = AttackAnimation(0.15, 0.3, 0.65, self)
-
- """标枪恐鱼穿刺者"""
-
- def attack(self, target, gameTime):
- targets = TargetSelector.select_targets_lowest_health(self, self.battlefield, need_in_range=True, max_targets=1)
- if len(targets) == 0:
- return
- self.target = targets[0]
-
- damage = self.calculate_damage(self.target, self.get_attack_power())
- self.on_attack(self.target, damage)
- if self.apply_damage_to_target(self.target, damage):
- self.target.on_hit(self, damage)
-
-
-class 护盾哥(Monster):
- """灰尾香主"""
-
- def on_spawn(self):
- self.magic_shield = 10002
- # 状态0：护盾形态
- # 状态1：加速形态
- self.stage = 0
- self.phy_def += 1750
-
- def take_damage(self, damage, attack_type) -> bool:
- """承受伤害"""
- if not self.dodge_and_invincible(damage, attack_type):
- return False
- if self.stage == 0 and attack_type == DamageType.MAGIC:
- if self.magic_shield >= 0:
- self.magic_shield -= damage
- if self.magic_shield <= 0:
- self.stage = 1
- self.move_speed += self.move_speed * 2
- self.phy_def -= 1750
- # 计算余下伤害
- damage = -self.magic_shield
- else:
- damage = 0
- self.health -= damage
- if self.health <= 0:
- self.is_alive = False
- self.on_death()
- return True
-
-
-class 酒桶(Monster):
- """酒桶"""
-
- def on_spawn(self):
- # 状态0：酒桶形态
- # 状态1：近战形态
- self.stage = 0
- self.move_speed *= 3
- self.attack_animation = AttackAnimation(0.2, 0.1, 0.7, self)
-
- def attack(self, target: Monster, gameTime):
- if self.stage == 1:
- super().attack(target, gameTime)
- else:
- # damage = self.calculate_damage(target, self.get_attack_power() * 2.8)
- # self.on_attack(target, damage)
- # if self.apply_damage_to_target(target, damage):
- # debug_print(f"{self.name}{self.id} 丢出酒桶")
- # target.on_hit(self, damage)
- # 丢出酒桶以后
- self.move_speed /= 3
- self.stage = 1
- self.battlefield.add_new_zone(WineZone(target.position, self.battlefield, 12, self.faction))
-
-
-class 复仇者(Monster):
- """复仇者"""
-
- def on_spawn(self):
- self.stage = 0
-
- def on_extra_update(self, delta_time):
- if self.stage == 0 and self.health < self.max_health * 0.5:
- self.attack_power += self.attack_power * 1.8
- self.stage = 1
-
-
-class 湖畔志愿者(Monster):
- """拳击手"""
-
- def on_spawn(self):
- self.stage = 0
- self.skill_counter = 0
-
- def on_extra_update(self, delta_time):
- if self.stage == 0 and self.health < self.max_health * 0.5:
- self.phys_dodge += 100
- self.stage = 1
-
- if self.stage == 1:
- self.skill_counter += delta_time
- if self.skill_counter >= 10:
- self.stage = 2
- self.phys_dodge -= 100
- debug_print(f"{self.name}{self.id} 停止闪避")
-
-
-class 沸血骑士团精锐(Monster):
- """沸血骑士团精锐"""
-
- def on_spawn(self):
- self.stage = 0
- self.original_attack = self.attack_power
- self.original_attack_speed = self.attack_speed
-
- def on_extra_update(self, delta_time):
- stack = np.minimum(10, self.battlefield.dead_count[self.faction])
- self.attack_power = self.original_attack * (1 + 0.1 * stack)
- self.attack_speed = self.original_attack_speed + 5 * stack
-
-
-class 门(Monster):
- """门"""
-
- def on_extra_update(self, delta_time):
- self.immunity.add(BuffType.DIZZY)
- self.immunity.add(BuffType.FROZEN)
- return super().on_extra_update(delta_time)
-
- def on_death(self):
- enemies = enemies = [
- m for m in self.battlefield.monsters
- if m.can_be_target()
- and m.faction != self.faction
- ]
- if not enemies:
- return
- target = random.choice(enemies)
- debug_print(f"{self.name}{self.id} 带走了{target.name}{target.id}")
- target.health = 0
- target.invincible = False
- target.take_damage(1, "真实")
- super().on_death()
-
-
-class 雷德(Monster):
- """大红刀哥"""
-
- def on_spawn(self):
- self.stage = 0
- self.speed_up_timer = 0
- self.origial_move_speed = self.move_speed
- self.move_speed += 2 * self.move_speed
- self.skill_timer = 0
-
- def on_extra_update(self, delta_time):
- if self.stage == 0 and self.health < self.max_health * 0.5:
- self.attack_power += self.attack_power * 2.8
- self.stage = 1
-
- self.speed_up_timer += delta_time
- if self.speed_up_timer > 4.5:
- self.move_speed = self.origial_move_speed
-
- if self.stage == 2:
- self.skill_timer += delta_time
- if self.skill_timer >= 5:
- self.stage = 3
- self.move_speed = self.origial_move_speed * 3
- self.speed_up_timer = 0
- switch_stage = BuffEffect(
- type=BuffType.INVINCIBLE,
- duration=10,
- source=self
- )
- self.status_system.apply(switch_stage)
-
- def take_damage(self, damage, attack_type) -> bool:
- """承受伤害"""
- # 减少60%受到伤害
- if not self.dodge_and_invincible(damage, attack_type):
- return False
- if self.stage >= 1 and attack_type != "真实":
- damage = damage * 0.4
- self.health -= damage
- if self.health <= 0:
- if self.stage <= 1:
- self.stage = 2
- self.invincible = True
- self.health = self.max_health * 0.5
- self.move_speed = 0
- else:
- self.is_alive = False
- self.on_death()
- return True
-
-
-class 自在(Monster):
- """画中人"""
-
- def on_spawn(self):
- self.immunity.add(BuffType.DIZZY)
- self.immunity.add(BuffType.FROZEN)
-
- self.stage = 0
- self.skill1_timer = 3
- self.skill2_timer = 20
- self.shield = 0
- self.shield_timer = 0
- self.skill_timer = 0
- self.original_move_speed = self.move_speed
-
- def on_extra_update(self, delta_time):
- if self.stage == 1:
- self.skill_timer += delta_time
- if self.skill_timer > 5:
- self.stage = 2
- self.move_speed = self.original_move_speed
- self.invincible = False
-
- def increase_skill_cd(self, delta_time):
- self.skill1_timer += delta_time
- self.skill2_timer += delta_time
-
- # 纬地经天
- if self.skill1_timer > 5:
- if self.stage == 0:
- if self.target:
- for m in self.get_aoe_targets(self.target):
- damage = self.calculate_damage(m, self.get_attack_power() * 2)
- if self.apply_damage_to_target(m, damage):
- m.on_hit(self, damage)
- self.skill1_timer = 0
- elif self.stage == 2:
- # 第二形态还会对最远目标释放一次
- targets = TargetSelector.select_targets(self, self.battlefield, need_in_range=False, max_targets=1,
- reverse=True)
- if len(targets) > 0:
- for m in self.get_aoe_targets(self.target):
- damage = self.calculate_damage(m, self.get_attack_power() * 2)
- if self.apply_damage_to_target(m, damage):
- m.on_hit(self, damage)
- for m in self.get_aoe_targets(targets[0]):
- damage = self.calculate_damage(m, self.get_attack_power() * 2)
- if self.apply_damage_to_target(m, damage):
- m.on_hit(self, damage)
- self.skill1_timer = 0
-
- # 破桎而出
- if self.skill2_timer > 40:
- if self.stage == 0:
- self.shield = 7500
- self.shield_timer += delta_time
-
- if self.shield_timer > 15:
- if self.shield > 0:
- for m in self.get_aoe_targets_skill2():
- damage = self.calculate_damage(m, self.get_attack_power() * 8)
- if self.apply_damage_to_target(m, damage):
- m.on_hit(self, damage)
- self.shield = 0
- self.shield_timer = 0
- self.skill2_timer = 0
- elif self.stage == 2:
- self.shield = 9000
- self.shield_timer += delta_time
-
- if self.shield_timer > 15:
- if self.shield > 0:
- for m in self.get_aoe_targets_skill2():
- damage = self.calculate_damage(m, self.get_attack_power() * 12)
- if self.apply_damage_to_target(m, damage):
- m.on_hit(self, damage)
- self.shield = 0
- self.shield_timer = 0
- self.skill2_timer = 0
- super().increase_skill_cd(delta_time)
-
- # 十字aoe判定
- def get_aoe_targets(self, target):
- aoe_targets = [m for m in self.battlefield.monsters
- if m.is_alive and m.faction != self.faction
- and abs(m.position.x - target.position.x) <= 2 and abs(m.position.y - target.position.y) <= 2]
- return aoe_targets
-
- def get_aoe_targets_skill2(self):
- aoe_targets = [m for m in self.battlefield.monsters
- if m.is_alive and m.faction != self.faction
- and (m.position - self.position).magnitude < 3]
- return aoe_targets
-
- def take_damage(self, damage, attack_type) -> bool:
- """承受伤害"""
- if not self.dodge_and_invincible(damage, attack_type):
- return False
- if attack_type != "真实":
- if self.shield >= 0:
- self.shield -= damage
- if self.shield <= 0:
- damage = -self.shield
- else:
- damage = 0
- self.health -= damage
- if self.health <= 0:
- if self.stage == 0:
- self.stage = 1
- self.health = self.max_health
- self.invincible = True
- self.attack_power += self.attack_power * 0.1
- self.move_speed = 0
- return False
- self.is_alive = False
- self.on_death()
- return True
-
-
-class MonsterFactory:
- _monster_classes = {
- "酸液源石虫·α": AcidSlug,
- "高能源石虫": HighEnergySlug,
- "污染躯壳": 污染躯壳,
- "提亚卡乌好战者": 提亚卡乌好战者,
- "宿主流浪者": 宿主流浪者,
- "呼啸骑士团学徒": 呼啸骑士团学徒,
- "狂暴宿主组长": 狂暴宿主组长,
- "海螺": 海螺,
- "拳手囚犯": 拳手囚犯,
- "高塔术师": 高塔术师,
- "冰原术师": 冰原术师,
- "矿脉守卫": 矿脉守卫,
- "“庞贝”": 庞贝,
- "食腐狗": 食腐狗,
- "鼠鼠": 鼠鼠,
- "“投石机”": 投石机,
- "光剑": 光剑,
- "船长": 船长,
- "杰斯顿·威廉姆斯": 杰斯顿·威廉姆斯,
- "山海众窥魅人": 山海众窥魅人,
- "Vvan": Vvan,
- "萨克斯": 萨克斯,
- "大喷蛛": 大喷蛛,
- "萨卡兹链术师": 萨卡兹链术师,
- "大君之赐": 大君之赐,
- "冰爆源石虫": 冰爆源石虫,
- "高普尼克": 高普尼克,
- "狂躁珊瑚": 狂躁珊瑚,
- "雪境精锐": 雪境精锐,
- "榴弹佣兵": 榴弹佣兵,
- "凋零萨卡兹": 凋零萨卡兹,
- "洗地车": 洗地车,
- "衣架": 衣架,
- "标枪恐鱼": 标枪恐鱼,
- "护盾哥": 护盾哥,
- "酒桶": 酒桶,
- "炽焰源石虫": 炽焰源石虫,
- "复仇者": 复仇者,
- "沸血骑士团精锐": 沸血骑士团精锐,
- "湖畔志愿者": 湖畔志愿者,
- "门": 门,
- "爱蟹者": 爱蟹者,
- "绵羊": 绵羊,
- "雷德": 雷德,
- "自在": 自在,
- # "扎罗": 扎罗,
-
- # 添加更多映射...
- "炮击组长": 炮击组长
- }
-
- @classmethod
- def create_monster(cls, data, faction, position, battlefield):
- monster_type = data["名字"]
- if monster_type in cls._monster_classes:
- m = cls._monster_classes[monster_type](data, faction, position, battlefield)
- m.on_spawn()
- return m
- else:
- return Monster(data, faction, position, battlefield) # 默认类型
diff --git a/simulator/projectiles.py b/simulator/projectiles.py
deleted file mode 100644
index 15c0882..0000000
--- a/simulator/projectiles.py
+++ /dev/null
@@ -1,148 +0,0 @@
-# 射弹基础组件
-
-from enum import Enum
-import math
-import numpy as np
-
-from .vector2d import FastVector
-from .utils import DamageType, calculate_normal_dmg, debug_print
-from typing import TYPE_CHECKING
-
-if TYPE_CHECKING:
- # 仅用于IDE类型提示，不会真实导入
- from .monsters import Monster
- from .battle_field import Battlefield
-
-class Projectile:
- def __init__(self, max_lifetime, damage : float, damageType : DamageType, source : "Monster"):
- self.lifetime = 0
- self.max_lifetime = max_lifetime
- self.is_alive = True
- self.damage = damage
- self.damage_type = damageType
- self.id = -1
- self.source = source
-
- def update(self, delta_time, battle_field):
- """需被子类重写"""
- raise NotImplementedError
-
-# 组件类型实现
-class HomingProjectile(Projectile):
- def __init__(self, max_lifetime, damage : float, damageType : DamageType, source : "Monster", target_enemy: "Monster"):
- super().__init__(max_lifetime, damage, damageType, source)
- self.target = target_enemy # 敌人对象引用
-
- def update(self, delta_time, battle_field):
- if not self.target.is_alive:
- self.is_alive = False
- return
-
- self.lifetime += delta_time
- if self.lifetime >= self.max_lifetime:
- self.on_timeout(battle_field)
- self.is_alive = False
-
- def on_timeout(self, battle_field):
- """需被子类重写"""
- raise NotImplementedError
-
-class TimedProjectile(Projectile):
- def __init__(self, max_lifetime, damage : float, damageType : DamageType, source : "Monster", target_position):
- super().__init__(max_lifetime, damage, damageType, source)
- self.target_pos = FastVector(target_position.x, target_position.y)
-
- def update(self, delta_time, battle_field):
- self.lifetime += delta_time
- if self.lifetime >= self.max_lifetime:
- self.on_impact(battle_field)
- self.is_alive = False
-
- def on_impact(self, battle_field):
- """碰撞回调接口"""
- raise NotImplementedError
-
-
-class ProjectileManager:
- def __init__(self, battle_field : 'Battlefield'):
- self.projectiles = []
- self.global_id_counter = 0
- self.battle_field = battle_field
-
- def spawn_projectile(self, projectile : Projectile):
- """使用对象池创建射弹"""
- self.projectiles.append(projectile)
- projectile.id = self.global_id_counter
- self.global_id_counter += 1
-
- def update_all(self, delta_time):
- """更新并过滤无效射弹"""
- for p in self.projectiles:
- p.update(delta_time, self.battle_field)
-
- self.projectiles = [p for p in self.projectiles if p.is_alive]
-
-
-class AOEType(Enum):
- Grid4 = "四格"
- Grid8 = "八格"
- Circle = "圆形"
-
-def get_aoe_targets(source, target_pos, battle_field: 'Battlefield'):
- if source.aoe_Type == AOEType.Grid8:
- aoe_targets = [m for m in battle_field.alive_monsters
- if m.is_alive and m.faction != source.source.faction
- and np.maximum(abs(m.position.x - target_pos.x), abs(m.position.y - target_pos.y)) <= 1]
- elif source.aoe_Type == AOEType.Grid4:
- aoe_targets = [m for m in battle_field.alive_monsters
- if m.is_alive and m.faction != source.source.faction
- and abs(int(math.floor(m.position.x - target_pos.x + 0.5))) + abs(int(math.floor(m.position.y - target_pos.y + 0.5))) <= 1]
- elif source.aoe_Type == AOEType.Circle:
- aoe_targets = [m for m in battle_field.query_monster(target_pos, source.radius)
- if m.is_alive and m.faction != source.source.faction]
- return aoe_targets
-
-class AOE炸弹(TimedProjectile):
- def __init__(self, max_lifetime, damage : float, damageType : DamageType, source : "Monster", target_position, name : str, aoeType : AOEType, radius=1):
- super().__init__(max_lifetime, damage, damageType, source, target_position)
- self.name = name
- self.aoe_Type = aoeType
- self.radius = radius
-
- def apply_damage_to_target(self, m : 'Monster', damage):
- debug_print(f"{self.source.name}{self.source.id} 的{self.name}对 {m.name}{m.id} 造成{damage}点{self.damage_type}伤害")
- if m.take_damage(damage, self.damage_type):
- return True
- debug_print(f"{self.source.name}{self.source.id} 的{self.name}没有对 {m.name}{m.id}造成伤害")
- return False
-
- def on_impact(self, battle_field:'Battlefield'):
- aoe_targets = get_aoe_targets(self, self.target_pos, battle_field)
- for m in aoe_targets:
- damage = calculate_normal_dmg(m.phy_def, m.magic_resist, self.damage, self.damage_type)
- if self.apply_damage_to_target(m, damage):
- m.on_hit(self.source, damage)
-
-
-class AOE炸弹锁定(HomingProjectile):
- def __init__(self, max_lifetime, damage : float, damageType : DamageType, source : "Monster", target : 'Monster', name : str, aoeType : AOEType, radius=1):
- super().__init__(max_lifetime, damage, damageType, source, target)
- self.name = name
- self.aoe_Type = aoeType
- self.radius = radius
-
- def apply_damage_to_target(self, m : 'Monster', damage):
- debug_print(f"{self.source.name}{self.source.id} 的{self.name}对 {m.name}{m.id} 造成{damage}点{self.damage_type}伤害")
- if m.take_damage(damage, self.damage_type):
- return True
- debug_print(f"{self.source.name}{self.source.id} 的{self.name}没有对 {m.name}{m.id}造成伤害")
- return False
-
- def on_timeout(self, battle_field:'Battlefield'):
- # if not self.target.can_be_target():
- # return
- aoe_targets = get_aoe_targets(self, self.target.position, battle_field)
- for m in aoe_targets:
- damage = calculate_normal_dmg(m.phy_def, m.magic_resist, self.damage, self.damage_type)
- if self.apply_damage_to_target(m, damage):
- m.on_hit(self.source, damage)
diff --git a/simulator/scene.json b/simulator/scene.json
deleted file mode 100644
index 39a51eb..0000000
--- a/simulator/scene.json
+++ /dev/null
@@ -1,58 +0,0 @@
-{
- "left":
- [
- { "name": "酸液源石虫·α", "count": 0 },
- { "name": "高能源石虫", "count": 0 },
- { "name": "阿咬", "count": 0 },
- { "name": "狂暴的猎狗pro", "count": 0 },
- { "name": "提亚卡乌好战者", "count": 0 },
- { "name": "污染躯壳", "count": 0 },
- { "name": "大盾哥", "count": 0 },
- { "name": "宿主流浪者", "count": 0 },
- { "name": "光剑", "count": 4 },
- { "name": "泥岩巨像", "count": 0 },
- { "name": "呼啸骑士团学徒", "count": 0 },
- { "name": "萨卡兹大剑手", "count": 0 },
- { "name": "狂暴宿主组长", "count": 0 },
- { "name": "海螺", "count": 0 },
- { "name": "拳手囚犯", "count": 0 },
- { "name": "高塔术师", "count": 0 },
- { "name": "冰原术师", "count": 0 },
- { "name": "矿脉守卫", "count": 0 },
- { "name": "“庞贝”", "count": 0 },
- { "name": "绵羊", "count": 0 },
- { "name": "食腐狗", "count": 0 },
- { "name": "温顺的武装驮兽", "count": 3 },
- { "name": "鼠鼠", "count": 4 },
- { "name": "“投石机”", "count": 0 },
- { "name": "船长", "count": 0 }
- ],
- "right":
- [
- { "name": "酸液源石虫·α", "count": 0 },
- { "name": "高能源石虫", "count": 0 },
- { "name": "阿咬", "count": 0 },
- { "name": "狂暴的猎狗pro", "count": 0 },
- { "name": "提亚卡乌好战者", "count": 0 },
- { "name": "污染躯壳", "count": 0 },
- { "name": "大盾哥", "count": 0 },
- { "name": "宿主流浪者", "count": 0 },
- { "name": "光剑", "count": 0 },
- { "name": "泥岩巨像", "count": 0 },
- { "name": "呼啸骑士团学徒", "count": 0 },
- { "name": "萨卡兹大剑手", "count": 0 },
- { "name": "狂暴宿主组长", "count": 0 },
- { "name": "海螺", "count": 0 },
- { "name": "拳手囚犯", "count": 11 },
- { "name": "高塔术师", "count": 0 },
- { "name": "冰原术师", "count": 0 },
- { "name": "矿脉守卫", "count": 0 },
- { "name": "“庞贝”", "count": 3 },
- { "name": "绵羊", "count": 9 },
- { "name": "食腐狗", "count": 0 },
- { "name": "温顺的武装驮兽", "count": 0 },
- { "name": "鼠鼠", "count": 0 },
- { "name": "“投石机”", "count": 0 },
- { "name": "船长", "count": 0 }
- ]
-}
\ No newline at end of file
diff --git a/simulator/simulate.py b/simulator/simulate.py
deleted file mode 100644
index 8b3a58b..0000000
--- a/simulator/simulate.py
+++ /dev/null
@@ -1,113 +0,0 @@
-import json
-import math
-import random
-import time
-from enum import Enum
-import numpy as np
-import pandas as pd
-from tqdm import tqdm
-
-from .battle_field import Battlefield, Faction
-
-from .utils import MONSTER_MAPPING, VISUALIZATION_MODE
-
-
-def process_battle_data(csv_path):
- """
- 处理战斗数据CSV文件
- :param csv_path: 输入CSV文件路径
- """
- # 读取CSV文件（假设没有表头）
- df = pd.read_csv(csv_path, header=1)
-
- # 数据结构化处理
- battle_records = []
-
- for _, row in df.iterrows():
- # 分解左右阵营数据
- left_data = row[0:56] # 1-56列 (0-based索引0-55)
- right_data = row[56:112] # 56-112列 (0-based索引56-111)
- winner = row[112] # 69列 (0-based索引112)
-
- # 构建阵营字典（ID从1开始）
- left_army = {MONSTER_MAPPING[i]: int(count) for i, count in enumerate(left_data) if count > 0}
- right_army = {MONSTER_MAPPING[i]: int(count) for i, count in enumerate(right_data) if count > 0}
-
- # 构建记录格式
- battle_record = {
- "left": left_army,
- "right": right_army,
- "result": "left" if winner == 'L' else "right"
- }
-
- battle_records.append(battle_record)
-
- return battle_records
-
-def main():
- """主函数"""
- # 加载怪物数据
- with open("arknight/monsters.json", encoding='utf-8') as f:
- monster_data = json.load(f)["monsters"]
-
- # with open("scene.json", encoding='utf-8') as f:
- # scene_config = json.load(f)
-
- # 使用示例，直接修改这里的csv文件就可以跑模拟
- battle_data = process_battle_data("arknight/56fin2_66k.csv")
-
-
- win = 0
- matches = 0
- for scene_config in tqdm(battle_data):
- if VISUALIZATION_MODE:
- scene_config = {"left": {"宿主流浪者": 7, "污染躯壳": 14, "凋零萨卡兹": 5}, "right": {"大喷蛛": 4, "杰斯顿·威廉姆斯": 1, "衣架": 10}, "result": "right"}
-
-
- #{ "left": { "护盾哥": 5, "污染躯壳": 11, "船长": 5 }, "right": { "炮击组长": 4, "沸血骑士团精锐": 4, "雪境精锐": 4}, "result": "left" }
-
- # 用户配置
- left_army = scene_config["left"]
- right_army = scene_config["right"]
-
- # 初始化战场
- leftWins = 0
- for i in range(3):
- battlefield = Battlefield(monster_data)
- if not battlefield.setup_battle(left_army, right_army, monster_data):
- continue
-
- # 开始战斗
- if battlefield.run_battle(visualize=VISUALIZATION_MODE) == Faction.LEFT:
- leftWins += 1
- if leftWins >= 2:
- break
- if i >= 1 and leftWins == 0:
- break
-
- left_win = leftWins >= 2
-
- if (left_win and scene_config["result"] == "left") or (not left_win and scene_config["result"] == "right"):
- win += 1
- else:
- with open("errors.json", encoding='utf-8', mode='+a') as f:
- f.write(json.dumps(scene_config, ensure_ascii=False))
- f.write('\n')
-
-
- matches += 1
- print(f"当前胜率：{win} / {matches}")
- if VISUALIZATION_MODE:
- break
-
-
-if __name__ == "__main__":
- # import cProfile
- # import pstats
-
- # profiler = cProfile.Profile()
- # profiler.enable()
- main()
- # profiler.disable()
- # stats = pstats.Stats(profiler)
- # stats.sort_stats('tottime').print_stats(25)
diff --git a/simulator/stats.py b/simulator/stats.py
deleted file mode 100644
index efd7a43..0000000
--- a/simulator/stats.py
+++ /dev/null
@@ -1,174 +0,0 @@
-import json
-from collections import defaultdict
-import math
-import numpy as np
-import pandas as pd
-from scipy.stats import fisher_exact
-import matplotlib.pyplot as plt
-import seaborn as sns
-
-# 配置中文字体
-plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']
-plt.rcParams['axes.unicode_minus'] = False
-
-
-def calculate_significance(row):
- """计算修正后的统计显著性"""
- # 关键参数
- error_total = row['under_estimate'] + row['over_estimate'] # 错误样本总次数
- correct_total = row['total'] - error_total # 正确样本总次数
-
- # 过滤无效数据
- if error_total == 0 or correct_total == 0:
- return np.nan
-
- # 低估检验（单侧）
- contingency_under = [
- [row['under_estimate'], row['over_estimate']],
- [0, correct_total]
- ]
- _, under_p = fisher_exact(contingency_under, alternative='greater')
-
- # 高估检验（单侧）
- contingency_over = [
- [row['over_estimate'], row['under_estimate']],
- [0, correct_total]
- ]
- _, over_p = fisher_exact(contingency_over, alternative='greater')
-
- return under_p + over_p
-
-def analyze_monster_balance(data_path):
- # 初始化统计容器
- stats = defaultdict(lambda: {
- 'total': 0, # 总出现次数
- 'error': 0, # 错误预测中出现次数
- 'under_estimate': 0, # 低估计数（实际应胜但预测失败）
- 'over_estimate': 0 # 高估计数（实际应败但预测成功）
- })
-
- # 数据加载与分析
- with open(data_path, encoding='utf-8') as f:
- for line in f:
- record = json.loads(line.strip())
- actual = record['result']
- pred = record['pred']
-
- for side in ["left", "right"]:
- for monster, count in record[side].items():
- # 基础统计
- stats[monster]['total'] += 1
-
- # 仅统计预测错误的情况
- if pred != actual:
- stats[monster]['error'] += 1
-
- # 判断估计方向
- if (side == actual):
- stats[monster]['under_estimate'] += 1
- elif (side != actual):
- stats[monster]['over_estimate'] += 1
-
- # 构建分析数据框
- df = pd.DataFrame.from_dict(stats, orient='index')
- df = df[df['total'] > 0] # 过滤无效数据
-
- # 计算统计指标
- df['error_rate'] = df['error'] / df['total']
- df['bias_score'] = (df['under_estimate'] - df['over_estimate']) / df['total']
- df['abs_bias'] = abs(df['bias_score'])
-
- # # Fisher精确检验
- # def apply_fisher(row):
- # contingency = [
- # [row['under_estimate'], row['over_estimate']],
- # [row['total'] - row['under_estimate'],
- # row['total'] - row['over_estimate']]
- # ]
- # _, p_value = fisher_exact(contingency)
- # return p_value
-
- df['p_value'] = df.apply(calculate_significance, axis=1)
-
- # 筛选显著结果 (p<0.04 且总出现次数>1)
- valid = df[(df['p_value'] < 0.04) & (df['total'] >= 1)]
-
- # 生成可视化
- plot_monster_bias(valid)
-
- return valid.sort_values('abs_bias', ascending=False)
-
-def plot_monster_bias(df):
- plt.figure(figsize=(12, 8))
-
- # 设置颜色映射
- colors = np.where(df['bias_score'] > 0,
- '#4C72B0', # 蓝色表示低估
- '#DD8452') # 橙色表示高估
-
- # 绘制条形图
- bars = plt.barh(df.index, df['bias_score'], color=colors)
-
- # 添加统计标注
- for bar, (_, row) in zip(bars, df.iterrows()):
- plt.text(bar.get_width() + 0.02,
- bar.get_y() + bar.get_height()/2,
- f"p={row['p_value']:.3f}\nN={row['total']}",
- va='center')
-
- # 图表装饰
- plt.axvline(0, color='gray', linestyle='--')
- plt.title('怪物数值平衡性分析', fontsize=14)
- plt.xlabel('偏差分数（正值表示低估，负值表示高估）')
- plt.ylabel('怪物名称')
- plt.xlim(-1.1, 1.1)
- plt.grid(axis='x', alpha=0.3)
- plt.tight_layout()
- plt.savefig('monster_balance.png', dpi=300)
- plt.close()
-
-# 执行分析
-if __name__ == "__main__":
- result_df = analyze_monster_balance("../errors.json")
-
- # 打印格式化结果
- print("="*60)
- print(f"{'怪物名称':<10}\t{'偏差方向':<8}\t{'偏差分数':<8}\t{'p值':<10}\t{'样本量'}")
- print("-"*60)
- for name, row in result_df.iterrows():
- direction = "低估" if row['bias_score'] > 0 else "高估"
- print(f"{name:<10}\t{direction:<8}\t{row['bias_score']:.2f}{'*' if row['p_value']<0.01 else '':<4}"
- f"\t{row['p_value']:.3f}\t{'':<6}\t{row['total']}")
-
- plot_monster_bias(result_df)
-
-with open("../errors.json", encoding='utf-8') as f:
- data = f.read()
-
-counter = defaultdict(int)
-power_counter = defaultdict(int)
-err_counter = defaultdict(int)
-
-for line in data.strip().split('\n'):
- record = json.loads(line)
- result = record['result']
- pred = record['pred']
- for side in ["left", "right"]:
- for monster, count in record[side].items():
- counter[monster] += 1
- if pred != result:
- if side == result:
- # 应该获胜却没有获胜，有低估的可能性
- power_counter[monster] += 1
- else:
- # 不该获胜的获胜了，有高估的可能性
- power_counter[monster] -= 1
- err_counter[monster] += 1
-
-sorted_monsters = sorted(err_counter.items(), key=lambda x: (-x[1] / counter[x[0]], x[0]))
-
-for monster, count in sorted_monsters:
- str = "低估" if power_counter[monster] > 0 else "高估"
- total = counter[monster]
- err_total = count
- print(f"{monster}: {err_total}/{total} = {err_total/total} 估计值:【{str}】{-power_counter[monster] / total * 100}")
\ No newline at end of file
diff --git a/simulator/utils.py b/simulator/utils.py
deleted file mode 100644
index 99df27b..0000000
--- a/simulator/utils.py
+++ /dev/null
@@ -1,162 +0,0 @@
-
-
-from collections import defaultdict
-from dataclasses import dataclass, field
-from enum import Enum
-import logging
-import math
-import csv # 添加csv模块导入
-
-import numpy as np
-
-from typing import TYPE_CHECKING
-
-from .vector2d import FastVector
-
-logger = logging.getLogger(__name__)
-
-if TYPE_CHECKING:
- from .battle_field import Battlefield
- from .monsters import Monster
-
-VISUALIZATION_MODE = True
-
-def debug_print(msg):
- if VISUALIZATION_MODE:
- print(msg)
-
-class Faction(Enum):
- LEFT = 0
- RIGHT = 1
-
-class DamageType(Enum):
- PHYSICAL = "物理"
- MAGIC = "法术"
- TRUE = "真实"
-
- def __str__(self):
- return self.value # 直接返回值字符串
-
-class BuffType(Enum):
- CHILL = 0
- FROZEN = 1
- INVINCIBLE = 2
- FIRE = 3
- CORRUPT = 4
- SPEEDUP = 5
- DIZZY = 6
- POWER_STONE = 7 # 源石地板
- WINE = 8 # 酒桶的效果
- INVINCIBLE2 = 9 # 转阶段无敌，不会被设为目标
-
-class ElementType(Enum):
- NECRO_LEFT = "凋亡左" # 凋亡元素（原凋亡损伤）
- NECRO_RIGHT = "凋亡右" # 凋亡元素（原凋亡损伤）
- FIRE = "灼燃"
-
-def lerp(a, b, x):
- return a + (b - a) * x
-
-
-@dataclass
-class BuffEffect:
- type: BuffType
- duration: float
- source: any = None
- stacks: int = 1
- data: dict = field(default_factory=dict)
-
-
-VIRTUAL_TIME_STEP = 30 # 30帧相当于一秒
-VIRTUAL_TIME_DELTA = 1.0 / VIRTUAL_TIME_STEP
-
-def calculate_normal_dmg(defense, magic_resist, dmg, damageType: DamageType):
- """计算伤害值"""
- if damageType == DamageType.PHYSICAL:
- return np.maximum(dmg - defense, dmg * 0.05)
- elif damageType == DamageType.MAGIC:
- return np.maximum(dmg * 0.05, dmg * (1.0 - magic_resist / 100))
- elif damageType == DamageType.TRUE:
- return dmg
-
-
-class SpatialHash:
- def __init__(self, battle_field : 'Battlefield', cell_size=0.5):
- self.cell_size = cell_size
- self.grid = defaultdict(set) # 使用集合避免重复
- self.battle_field = battle_field # 记录战场，用来索引敌人信息
- self.position_map = {} # 记录每个对象的老位置键
-
- def _pos_to_key(self, position : FastVector) -> tuple:
- """将坐标转换为网格键"""
- return (
- int(math.floor(position.x / self.cell_size)),
- int(math.floor(position.y / self.cell_size))
- )
-
- def insert(self, position : FastVector, id):
- """插入或更新对象位置"""
- new_key = self._pos_to_key(position)
-
- # 如果位置未变化，直接返回
- if id in self.position_map and self.position_map[id] == new_key:
- return
-
- # 移除旧位置的记录
- if id in self.position_map:
- old_key = self.position_map[id]
- self.grid[old_key].discard(id)
- if not self.grid[old_key]: # 清理空单元格
- del self.grid[old_key]
-
- # 更新到新位置
- self.position_map[id] = new_key
- self.grid[new_key].add(id)
-
- def query_neighbors(self, position: FastVector, radius: float) -> set:
- """查询指定半径内的邻居"""
- center_x, center_y = (position.x, position.y)
- neighbors = set()
-
- # 生成需要检测的网格范围
- min_i = int((center_x - radius) / self.cell_size)
- max_i = int((center_x + radius) / self.cell_size)
- min_j = int((center_y - radius) / self.cell_size)
- max_j = int((center_y + radius) / self.cell_size)
-
- # 遍历所有可能包含邻居的网格
- for i in range(min_i, max_i + 1):
- for j in range(min_j, max_j + 1):
- neighbors.update(self.grid.get((i, j), set()))
-
- return neighbors
-
- def batch_update(self, updates: dict):
- """批量更新对象位置"""
- for obj_id, pos in updates.items():
- self.insert(obj_id, pos)
-
-def load_monster_mapping_from_csv(file_path='monster.csv'):
- """从CSV文件加载怪物ID和原始名称的映射"""
- mapping = {}
- try:
- with open(file_path, mode='r', encoding='utf-8-sig') as csvfile:
- reader = csv.DictReader(csvfile)
- for row in reader:
- try:
- monster_id = int(row['id']) - 1 # 转为0-based索引
- original_name = row['原始名称']
- mapping[monster_id] = original_name
- except ValueError:
- print(f"Skipping row due to invalid ID or missing '原始名称': {row}")
- except FileNotFoundError:
- print(f"Error: {file_path} not found. Using empty monster mapping.")
- except Exception as e:
- logger.exception(f"Error loading monster mapping from CSV: {e}")
- return mapping
-
-# ID与怪物名称映射表
-MONSTER_MAPPING = load_monster_mapping_from_csv()
-
-# 创建反向映射字典（名字到ID）
-REVERSE_MONSTER_MAPPING = {name: id for id, name in MONSTER_MAPPING.items()}
\ No newline at end of file
diff --git a/simulator/vector2d.py b/simulator/vector2d.py
deleted file mode 100644
index a55ee2b..0000000
--- a/simulator/vector2d.py
+++ /dev/null
@@ -1,119 +0,0 @@
-import math
-from typing import Tuple
-# import numpy as np
-# from numba.experimental import jitclass
-# from numba import float64
-
-# # 使用Numba JIT加速的极致优化方案
-# vector_spec = [
-# ('x', float64),
-# ('y', float64),
-# ]
-
-# @jitclass(vector_spec)
-# class Vector2D:
-# def __init__(self, x: float, y: float):
-# self.x = x
-# self.y = y
-
-# # 基础运算
-# def __add__(self, other: 'Vector2D') -> 'Vector2D':
-# return Vector2D(self.x + other.x, self.y + other.y)
-
-# def __sub__(self, other: 'Vector2D') -> 'Vector2D':
-# return Vector2D(self.x - other.x, self.y - other.y)
-
-# def __mul__(self, scalar: float) -> 'Vector2D':
-# return Vector2D(self.x * scalar, self.y * scalar)
-
-# # 高性能运算方法
-# @property
-# def magnitude(self) -> float:
-# """模长计算优化（SIMD加速）"""
-# return math.hypot(self.x, self.y) # 使用hypot避免溢出
-
-# def normalized(self) -> 'Vector2D':
-# """单位向量（零除保护）"""
-# mag = self.magnitude
-# return Vector2D(self.x/mag, self.y/mag) if mag != 0 else Vector2D(0, 0)
-
-# def dot(self, other: 'Vector2D') -> float:
-# """点积（寄存器优化）"""
-# return self.x*other.x + self.y*other.y
-
-# def rotate(self, radians: float) -> 'Vector2D':
-# """旋转优化（预计算sin/cos）"""
-# c = math.cos(radians)
-# s = math.sin(radians)
-# return Vector2D(self.x*c - self.y*s, self.x*s + self.y*c)
-
-# 纯Python优化方案（兼容性更好）
-class FastVector:
- __slots__ = ('x', 'y') # 减少内存占用约40%
-
- def __init__(self, x: float, y: float):
- self.x = x
- self.y = y
-
- def __sub__(self, other: 'FastVector') -> 'FastVector':
- return self.__class__(self.x - other.x, self.y - other.y)
-
- def __add__(self, other: 'FastVector') -> 'FastVector':
- return self.__class__(self.x + other.x, self.y + other.y)
-
- def __truediv__(self, other: float) -> 'FastVector':
- return self.__class__(self.x / other, self.y / other)
-
- def __mul__(self, other: float) -> 'FastVector':
- return self.__class__(self.x * other, self.y * other)
-
- def __iadd__(self, other: 'FastVector') -> 'FastVector':
- """就地加法优化（减少对象创建）"""
- self.x += other.x
- self.y += other.y
- return self
-
- @property
- def magnitude_sq(self) -> float:
- """平方模长（避免开平方）"""
- return self.x**2 + self.y**2
-
- @property
- def magnitude(self) -> float:
- """模长"""
- return math.sqrt(self.x**2 + self.y**2)
-
- def distance_to(self, other: 'FastVector') -> float:
- """快速距离计算"""
- dx = self.x - other.x
- dy = self.y - other.y
- return math.hypot(dx, dy)
-
- def as_tuple(self) -> Tuple[float, float]:
- """缓存友好表示"""
- return (self.x, self.y)
-
- def normalize(self) -> 'FastVector':
- d = self.magnitude
- if d != 0:
- self.x /= d
- self.y /= d
- return self
-
-# # 性能对比测试
-# if __name__ == '__main__':
-# from timeit import timeit
-
-# # 测试用例
-# v1 = Vector2D(3.0, 4.0)
-# v2 = Vector2D(2.0, 1.0)
-
-# # Numba版本性能
-# print("Numba Add:", timeit(lambda: v1 + v2, number=1_000_000))
-# print("Numba Mag:", timeit(lambda: v1.magnitude, number=1_000_000))
-
-# # 纯Python版本性能
-# fv1 = FastVector(3.0, 4.0)
-# fv2 = FastVector(2.0, 1.0)
-# print("Python Add:", timeit(lambda: fv1 + fv2, number=1_000_000))
-# print("Python Mag:", timeit(lambda: fv1.magnitude_sq, number=1_000_000))
\ No newline at end of file
diff --git a/simulator/zone.py b/simulator/zone.py
deleted file mode 100644
index 4bc5569..0000000
--- a/simulator/zone.py
+++ /dev/null
@@ -1,97 +0,0 @@
-from typing import List, Dict, Set
-from dataclasses import dataclass
-import math
-import time
-
-import numpy as np
-
-from .vector2d import FastVector
-
-from .utils import VIRTUAL_TIME_DELTA, BuffEffect, BuffType
-
-class ZoneType:
- POISON = 0 #毒圈
- WINE = 1 #酒桶区域
-
-@dataclass
-class ZoneEffect:
- type: str
- duration: float
-
-class EffectZone:
- def __init__(self, zone_type: str, position, battle_field):
- self.zone_type = zone_type
- self.position = FastVector(position.x, position.y)
- self.battle_field = battle_field
-
- def update(self, delta_time):
- """每帧更新逻辑"""
- # 具体效果由子类实现
- pass
-
- def should_clear(self, delta_time) -> bool:
- """场地效果清除逻辑"""
- # 具体效果由子类实现，默认不清除
- return False
-
- def contains(self, target) -> bool:
- """判断点是否在区域内"""
- # 具体效果由子类实现
- raise NotImplementedError
-
- def apply_effect(self, target):
- """应用区域效果"""
- # 具体效果由子类实现
- raise NotImplementedError
-
- def remove_effect(self, target):
- """移除区域效果"""
- raise NotImplementedError
-
-class PoisonZone(EffectZone):
- def __init__(self, battle_field):
- super().__init__(ZoneType.POISON, FastVector(0, 0), battle_field)
-
- def apply_effect(self, target):
- # 添加或更新持续伤害效果
- target.status_system.apply(BuffEffect(
- type=BuffType.POWER_STONE,
- duration=VIRTUAL_TIME_DELTA * 2,
- source=self
- ))
-
- def contains(self, target) -> bool:
- """判断点是否在区域内"""
- if self.battle_field.danger_zone_size() > 0:
- size = self.battle_field.danger_zone_size()
- if (target.position.x < size + 1 or target.position.x > self.battle_field.map_size[0] - size - 1)\
- or (target.position.y < size or target.position.y > self.battle_field.map_size[1] - size):
- return True
- return False
-
-class WineZone(EffectZone):
- def __init__(self, position, battle_field, duration, faction):
- super().__init__(ZoneType.WINE, position, battle_field)
- self.duration = duration
- self.radius = 2
- self.faction = faction
-
- def should_clear(self, delta_time) -> bool:
- """场地效果清除逻辑"""
- return self.duration <= 0
-
- def update(self, delta_time):
- """每帧更新逻辑"""
- self.duration -= delta_time
-
- def apply_effect(self, target):
- # 添加或更新持续伤害效果
- target.status_system.apply(BuffEffect(
- type=BuffType.WINE,
- duration=VIRTUAL_TIME_DELTA * 2,
- source=self
- ))
-
- def contains(self, target) -> bool:
- """判断点是否在区域内"""
- return (target.position - self.position).magnitude <= self.radius and target.faction == self.faction
\ No newline at end of file
diff --git a/specialmonster.py b/specialmonster.py
deleted file mode 100644
index 708cd64..0000000
--- a/specialmonster.py
+++ /dev/null
@@ -1,37 +0,0 @@
-class SpecialMonsterHandler:
- """
- 可按照格式自行添加特殊怪物的留言信息：
- 格式：
- 怪物ID: {
- 'name': '怪物名称', # 可留空
- 'win_message': '胜利时显示的消息', # 可留空
- 'lose_message': '失败时显示的消息', # 可留空
- }
- """
- def __init__(self):
- self.special_monsters = {
- 1: {
- 'name': '狗神',
- 'win_message': "全军出击，我咬死你！",
- 'lose_message': "牙崩了牙崩了"
- },
- }
-
- def check_special_monsters(self, left_monsters, right_monsters, winner):
- messages = []
-
- for monster_id, config in self.special_monsters.items():
- left_has = left_monsters[str(monster_id)].text().isdigit() and int(left_monsters[str(monster_id)].text()) > 0
- right_has = right_monsters[str(monster_id)].text().isdigit() and int(right_monsters[str(monster_id)].text()) > 0
-
- if left_has or right_has:
- if winner == "左方" and left_has and config['win_message']:
- messages.append(config['win_message'])
- elif winner == "右方" and right_has and config['win_message']:
- messages.append(config['win_message'])
- elif winner == "右方" and left_has and config['lose_message']:
- messages.append(config['lose_message'])
- elif winner == "左方" and right_has and config['lose_message']:
- messages.append(config['lose_message'])
-
- return "\n".join(messages)
\ No newline at end of file
diff --git a/test.py b/test.py
new file mode 100644
index 0000000..1748250
--- /dev/null
+++ b/test.py
@@ -0,0 +1,25 @@
+import cv2
+import numpy as np
+
+def calculate_average_green(image_path, region):
+ image = cv2.imread(image_path)
+ if image is None:
+ print(f"Failed to load image: {image_path}")
+ return None
+
+ height, width, _ = image.shape
+ x1, y1, x2, y2 = region
+ x1, y1, x2, y2 = int(x1 * width), int(y1 * height), int(x2 * width), int(y2 * height)
+
+ region = image[y1:y2, x1:x2]
+ green_channel = region[:, :, 1]
+ average_green = np.mean(green_channel)
+ return average_green
+
+region = (0.3406, 0.5759, 0.4182, 0.6194)
+image_paths = ["images/process/3.png", "images/process/4.png"]
+
+for image_path in image_paths:
+ avg_green = calculate_average_green(image_path, region)
+ if avg_green is not None:
+ print(f"Average green value in {image_path}: {avg_green}")
\ No newline at end of file
diff --git a/tools/HumanDataCheck.py b/tools/HumanDataCheck.py
deleted file mode 100644
index fcddf0b..0000000
--- a/tools/HumanDataCheck.py
+++ /dev/null
@@ -1,191 +0,0 @@
-import tkinter as tk
-from PIL import Image, ImageTk
-import csv
-import os
-from recognize import MONSTER_COUNT
-
-
-class ArknightsApp:
- def __init__(self, root):
- self.root = root
- self.root.title("Arknights Data Viewer")
- self.root.geometry("800x300")
-
- # 绑定快捷键
- self.root.bind("", lambda event: self.show_prev_row()) # 小键盘左键
- self.root.bind("", lambda event: self.show_next_row()) # 小键盘右键
- self.root.bind("", lambda event: self.delete_current_row()) # 删除键
-
- # 创建顶部和底部框架
- self.top_frame = tk.Frame(root)
- self.top_frame.pack(pady=10)
- self.bottom_frame = tk.Frame(root)
- self.bottom_frame.pack(pady=10)
-
- # 创建按钮
- self.next_button = tk.Button(root, text="下一个", command=self.show_next_row)
- self.next_button.pack(side=tk.RIGHT, padx=10, pady=10)
-
- self.prev_button = tk.Button(root, text="上一个", command=self.show_prev_row)
- self.prev_button.pack(side=tk.RIGHT, padx=10, pady=10)
-
- self.delete_button = tk.Button(root, text="删除数据", command=self.delete_current_row)
- self.delete_button.pack(side=tk.RIGHT, padx=10, pady=10)
-
- # 添加行号显示和跳转功能
- self.row_label = tk.Label(root, text="当前行号: 0")
- self.row_label.pack(side=tk.LEFT, padx=10)
-
- self.row_entry = tk.Entry(root, width=5)
- self.row_entry.pack(side=tk.LEFT, padx=5)
-
- self.jump_button = tk.Button(root, text="跳转", command=self.jump_to_row)
- self.jump_button.pack(side=tk.LEFT, padx=5)
-
- # 初始化数据
- self.data = self.read_csv("arknights.csv")
- self.current_row_index = 0
-
- # 加载图片
- self.images = self.load_all_images()
-
- # 显示第一行数据
- self.show_row(self.current_row_index)
-
- def read_csv(self, file_path):
- """读取 CSV 文件"""
- data = []
- with open(file_path, "r") as csvfile:
- reader = csv.reader(csvfile)
- for row in reader:
- data.append(row)
- return data[1:] # 跳过表头
-
- def load_all_images(self):
- """加载所有图片"""
- images = {}
- for i in range(1, MONSTER_COUNT + 1): # 使用 MONSTER_COUNT 动态加载图片
- image_path = os.path.join("images", f"{i}.png")
- if os.path.exists(image_path):
- image = Image.open(image_path).resize((50, 50))
- images[str(i)] = ImageTk.PhotoImage(image)
- else:
- print(f"Image {i}.png not found.")
- images[str(i)] = None # 占位符
- return images
-
- def show_row(self, row_index):
- """显示指定行的非0列数据"""
- for widget in self.top_frame.winfo_children():
- widget.destroy() # 清空顶部框架内容
-
- if row_index >= len(self.data):
- return
-
- row = self.data[row_index]
-
- # 显示左方怪物
- for i in range(1, MONSTER_COUNT + 1):
- value = row[i - 1]
- try:
- value = float(value)
- if value > 0: # 仅显示非0值
- tk.Label(self.top_frame, image=self.images[str(i)]).grid(row=0, column=i - 1)
- tk.Label(self.top_frame, text=str(int(value))).grid(row=1, column=i - 1)
- except ValueError:
- print(f"Skipping invalid value: {row[i - 1]} at column {i - 1}")
-
- # 插入空白间隔
- gap_column = MONSTER_COUNT-1 # 间隔列索引
- tk.Label(self.top_frame, text="").grid(row=0, column=gap_column, padx=50) # 添加水平间距
-
- # 显示右方怪物
- for i in range(MONSTER_COUNT + 1, MONSTER_COUNT*2 + 1):
- value = row[i - 1]
- try:
- value = float(value)
- if value > 0: # 仅显示非0值
- tk.Label(self.top_frame, image=self.images[str(i - MONSTER_COUNT)]).grid(row=0, column=i - 1)
- tk.Label(self.top_frame, text=str(int(value))).grid(row=1, column=i - 1)
- except ValueError:
- print(f"Skipping invalid value: {row[i - 1]} at column {i - 1}")
-
- # 显示 L/R
- for widget in self.bottom_frame.winfo_children():
- widget.destroy() # 清空底部框架内容
- tk.Label(self.bottom_frame, text=f"L/R: {row[MONSTER_COUNT*2]}", font=("Arial", 16)).pack()
-
- # 显示最后一列的图片
- final_image_path = os.path.join("data", "images", row[-1])
- if os.path.exists(final_image_path):
- final_image = Image.open(final_image_path)
- final_image_tk = ImageTk.PhotoImage(final_image)
- tk.Label(self.bottom_frame, image=final_image_tk).pack()
- self.bottom_frame.image = final_image_tk # 防止图片被垃圾回收
-
- # 更新行号显示
- self.row_label.config(text=f"当前行号: {row_index + 1}")
-
- def jump_to_row(self):
- """跳转到指定行"""
- try:
- row_index = int(self.row_entry.get()) - 1 # 转换为索引
- if 0 <= row_index < len(self.data):
- self.current_row_index = row_index
- self.show_row(self.current_row_index)
- else:
- print("行号超出范围")
- except ValueError:
- print("请输入有效的行号")
-
- def show_prev_row(self):
- """显示上一行数据"""
- if self.current_row_index > 0:
- self.current_row_index -= 1
- else:
- self.current_row_index = len(self.data) - 1 # 跳转到最后一行
- self.show_row(self.current_row_index)
-
- def show_next_row(self):
- """显示下一行数据"""
- if self.current_row_index < len(self.data) - 1:
- self.current_row_index += 1
- else:
- self.current_row_index = 0 # 跳转到第一行
- self.show_row(self.current_row_index)
-
- def delete_current_row(self):
- """删除当前行数据"""
- if self.current_row_index < len(self.data):
- # 获取当前行最后一列的图片路径
- image_name = self.data[self.current_row_index][-1]
- image_path = os.path.join("data", "images", image_name)
-
- # 删除图片文件（如果存在）
- if os.path.exists(image_path):
- os.remove(image_path)
- print(f"已删除图片文件: {image_path}")
- else:
- print(f"图片文件不存在: {image_path}")
-
- del self.data[self.current_row_index] # 从内存中删除当前行
- # 将修改后的数据写回 CSV 文件
- with open("arknights.csv", "w", newline="") as csvfile:
- writer = csv.writer(csvfile)
- writer.writerow(
- [f"{i}L" for i in range(1, MONSTER_COUNT + 1)] +
- [f"{i}R" for i in range(1, MONSTER_COUNT + 1)] +
- [f"{MONSTER_COUNT * 2 + 1}"]
- )
- writer.writerows(self.data)
- # 更新显示
- if self.current_row_index >= len(self.data):
- self.current_row_index -= 1 # 防止越界
- self.show_row(self.current_row_index)
-
-
-# 主程序
-if __name__ == "__main__":
- root = tk.Tk()
- app = ArknightsApp(root)
- root.mainloop()
\ No newline at end of file
diff --git a/tools/battlefield_composite/.gitignore b/tools/battlefield_composite/.gitignore
deleted file mode 100644
index f812a87..0000000
--- a/tools/battlefield_composite/.gitignore
+++ /dev/null
@@ -1 +0,0 @@
-monster_images/*/
\ No newline at end of file
diff --git a/tools/battlefield_composite/battlefield_composite.py b/tools/battlefield_composite/battlefield_composite.py
deleted file mode 100644
index 58dd16e..0000000
--- a/tools/battlefield_composite/battlefield_composite.py
+++ /dev/null
@@ -1,246 +0,0 @@
-from pathlib import Path
-import cv2
-import numpy as np
-import random
-
-
-def get_webm_frame(webm_path, frame_number):
- """
- 从WEBM文件中提取指定帧。
-
- 参数:
- webm_path (str): 输入的WEBM文件路径。
- frame_number (int): 要提取的帧编号。
-
- 返回:
- numpy.ndarray: 提取的帧图像。
- """
- cap = cv2.VideoCapture(webm_path)
-
- for i in range(frame_number - 1):
- ret = cap.grab()
-
- # 读取帧
- ret, frame = cap.read()
-
- if not ret:
- raise Exception(f"无法读取第 {frame_number} 帧")
-
- cap.release()
- return frame
-
-
-def get_webm_frame_count(webm_path):
- """
- 获取WEBM文件的总帧数。
-
- 参数:
- webm_path (str): 输入的WEBM文件路径。
-
- 返回:
- int: 总帧数。
- """
- cap = cv2.VideoCapture(webm_path)
-
- # 手动计算总帧数
- total_frames = 0
- while True:
- ret = cap.grab() # 使用grab()而不是read()更快
- if not ret:
- break
- total_frames += 1
-
- print(f"total_frames: {total_frames}")
-
- # 确保总帧数大于0
- if total_frames <= 0:
- cap.release()
- raise Exception("视频帧数为0")
-
- cap.release()
- return total_frames
-
-
-def get_random_png_frame(png_folder):
- # 遍历path下所有png文件
- png_list = list(Path(png_folder).glob("*.png"))
- # 随机选择一个png文件
- random_png = random.choice(png_list)
- print(f"随机选择的PNG文件: {random_png}")
- frame = cv2.imread(str(random_png), cv2.IMREAD_UNCHANGED) # Read with alpha channel
- if frame is None:
- raise Exception(f"无法读取PNG文件: {random_png}")
- return frame
-
-
-def compose_frame(frame, background, x, y):
- """
- 将前景帧合成到背景图像上，包含边界检查和裁切。
-
- 参数:
- frame (numpy.ndarray): 前景帧图像。
- background (numpy.ndarray): 背景图像。
- x (int): 前景帧左上角在背景图像上的x坐标。
- y (int): 前景帧左上角在背景图像上的y坐标。
-
- 返回:
- numpy.ndarray: 合成后的图像。
- """
- # 获取背景和前景尺寸
- bg_height, bg_width = background.shape[:2]
- fr_height, fr_width = frame.shape[:2]
-
- # 计算有效的ROI区域
- x_start = max(0, x)
- y_start = max(0, y)
- x_end = min(bg_width, x + fr_width)
- y_end = min(bg_height, y + fr_height)
-
- # 计算frame需要裁切的区域
- frame_x_start = max(0, -x)
- frame_y_start = max(0, -y)
- frame_x_end = fr_width - max(0, (x + fr_width) - bg_width)
- frame_y_end = fr_height - max(0, (y + fr_height) - bg_height)
-
- # 如果完全超出边界，直接返回背景
- if x_start >= x_end or y_start >= y_end:
- return background
-
- # 裁切frame
- frame_roi = frame[frame_y_start:frame_y_end, frame_x_start:frame_x_end]
-
- # 在目标位置创建ROI
- roi = background[y_start:y_end, x_start:x_end]
-
- # 获取前景的alpha通道
- alpha_channel = frame_roi[:, :, 3] / 255.0
- # 获取前景的颜色通道
- overlay_colors = frame_roi[:, :, :3]
-
- # 调整alpha通道尺寸以匹配背景ROI
- alpha_channel = cv2.resize(alpha_channel, (roi.shape[1], roi.shape[0]))
- overlay_colors = cv2.resize(overlay_colors, (roi.shape[1], roi.shape[0]))
-
- # 进行alpha混合
- for c in range(0, 3):
- roi[:, :, c] = roi[:, :, c] * (1 - alpha_channel) + overlay_colors[:, :, c] * alpha_channel
-
- # 将结果放回原图
- background[y_start:y_end, x_start:x_end] = roi
-
- return background
-
-
-def composite_random_frame():
- # 读取战场背景图
- battlefield = cv2.imread("./tools/battlefield_composite/monster_images/IM-1.png")
- battlefield = cv2.resize(battlefield, (1920, 1080))
- if battlefield is None:
- raise Exception("无法读取战场背景图")
-
- # 获取背景图尺寸
- bg_height, bg_width = battlefield.shape[:2]
-
- frame_width = 1000 * 0.40
- frame_height = 1000 * 0.40
-
- frame_list = []
-
- for i in range(10):
- # 生成随机坐标(确保角色完全在背景图内)
- x = random.randint(int(bg_width * 0.1), int(bg_width * 0.3))
- y = random.randint(0, int(bg_height * 0.7))
- frame_list.append([x, y])
-
- frame_list.sort(key=lambda x: x[1])
-
- for i in range(10):
- x, y = frame_list[i]
- frame = get_random_png_frame("./tools/battlefield_composite/monster_images/Arc_Frontliner_Leader-Move")
-
- # 缩放
- factor = (y / bg_height) * 0.4 + 0.6
- new_frame_width = int(frame_width * factor)
- new_frame_height = int(frame_height * factor)
- print(f"缩放比例: {factor}")
- print(f"缩放后的尺寸: {new_frame_width}x{new_frame_height}")
- print(f"缩放后的坐标: {x}, {y}")
- small_frame = cv2.resize(frame, (new_frame_width, new_frame_height))
- # 裁切到外接矩形
- small_frame, center = crop_to_bounding_box(small_frame)
- x1, y1 = small_frame.shape[1], small_frame.shape[0]
- # 画脚下椭圆
- ellipse_x = int(x + center[0])
- ellipse_y = int(y + center[1] + new_frame_height * 0.30)
- black = (0, 0, 0)
- cv2.ellipse(battlefield, (ellipse_x, ellipse_y),
- (int(new_frame_width * 0.100), int(new_frame_height * 0.025)),
- 0, 0, 360, black, -1)
- # 画框
- cv2.rectangle(battlefield, (x, y), (x + x1, y + y1), (0, 255, 0), 2)
-
- # 合成图像
- battlefield = compose_frame(small_frame, battlefield, int(x), int(y))
-
- # 画面左侧增加从左到右的从黑色到透明的渐变
- # 创建一个与图像相同大小的黑色图像
- black_image = np.zeros((bg_height, bg_width, 3), dtype=np.uint8)
- # 创建一个与图像相同大小的alpha通道，从左到右从255到0
- alpha_channel = np.zeros((bg_height, bg_width), dtype=np.uint8)
- for i in range(int(bg_width * 0.4)):
- alpha_channel[:, i] = int(192 * (1 - i / int(bg_width * 0.4)))
-
- # 合并黑色图像和alpha通道
- gradient_mask = np.dstack((black_image, alpha_channel))
-
- # 将渐变掩码应用到图像上
- battlefield = compose_frame(gradient_mask, battlefield, 0, 0)
-
-
- # 保存结果
- cv2.imwrite("./tools/battlefield_composite/battlefield_composite.png", battlefield)
-
-
-def crop_to_bounding_box(image):
- """
- 将图像裁切到非透明像素的外接矩形。
-
- 参数:
- image (numpy.ndarray): 输入图像 (包含alpha通道)。
-
- 返回:
- numpy.ndarray: 裁切后的图像。
- """
- # 检查图像是否包含alpha通道
- if image.shape[2] < 4:
- # 如果没有alpha通道，则无法进行基于透明度的裁切
- print("警告: 图像不包含alpha通道，无法进行基于透明度的裁切。返回原图。")
- return image
-
- # 获取alpha通道
- alpha_channel = image[:, :, 3]
- alpha_channel = cv2.threshold(alpha_channel, 10, 255, cv2.THRESH_BINARY)[1]
-
- # 找到非透明像素的坐标
- coords = cv2.findNonZero(alpha_channel)
- # coords = alpha_channel[alpha_channel > 0.1]
-
- # 如果没有非透明像素，返回原图
- if coords is None:
- print("警告: 图像中没有非透明像素。返回原图。")
- return image
-
- # 计算外接矩形
- x, y, w, h = cv2.boundingRect(coords)
-
- # 裁切图像
- cropped_image = image[y:y+h, x:x+w]
- center_x = image.shape[1] / 2 - x
- center_y = image.shape[0] / 2 - y
- center = (center_x, center_y)
-
- return cropped_image, center
-
-
-if __name__ == "__main__":
- composite_random_frame()
diff --git a/tools/battlefield_composite/extract_webm_frames.py b/tools/battlefield_composite/extract_webm_frames.py
deleted file mode 100644
index ca85e6a..0000000
--- a/tools/battlefield_composite/extract_webm_frames.py
+++ /dev/null
@@ -1,92 +0,0 @@
-import logging
-from pathlib import Path
-import subprocess
-import os
-
-
-monster_name = {
- "弧光锋卫长" : "Arc_Frontliner_Leader",
- "炮击组长" : "Mortar_Gunner_Leader",
- "复仇者" : "Hateful_Avenger",
- "重装防御者" : "Heavy_Defender",
- "“庞贝”" : "Pompeii",
- "冰原术师" : "Icefield_Caster",
- "沸血骑士团精锐" : "Bloodboil_Knightclub_Elite",
- "高塔术师" : "Spire_Caster",
- "固海凿石者" : "Ocean_Stonecutter",
- # "呼啸骑士团学徒" : "Roar_Knightclub_Trainee",
- "湖畔志愿者" : "Lakeside_Volunteer",
- "杰斯顿·威廉姆斯" : "Jesselton_Williams",
- "酸液源石虫·α" : "Acid_Originium_Slug_α",
- "神射手囚犯" : "Elite_Sniper_Prisoner",
- "拳手囚犯" : "Pugilist_Prisoner",
- "染污躯壳" : "Tainted_Carcass",
- "泥岩巨像" : "Mudrock_Colossus",
- "狂暴的猎狗pro" : "Rabid_Hound_Pro",
- "宿主拾荒者" : "Possessed_Veteran_Junkman",
- "狂暴宿主组长" : "Enraged_Possessed_Leader",
- "萨卡兹大剑手" : "Sarkaz_Greatswordsman",
- "矿脉守卫" : "Vein_Guardian",
- "山海众窥魅人" : "Shanhaizhong_Seer",
- "提亚卡乌好战者" : "Tiacauh_Fanatic",
- "码头水手" : "Dockworker",
- "变异巨岩蛛" : "Mutant_Giant_Rock_Spider",
- "萨卡兹子裔链术师" : "Sarkaz_Heirbearer_Chain_Caster",
- "温顺的武装驮兽" : "Armored_Burdenbeast",
- "木裂战士" : "Shattered_Champion",
- "深溟裂礁者" : "Nethersea_Reefbreaker",
- "山雪鬼" : "Tschäggättä",
- "高普尼克" : "Gopnik",
- "冰爆源石虫" : "Infused_Glacial_Originium_Slug",
- "反装甲步兵" : "Anti-Armor_Infantry",
- "“钳钳生风”" : "Consortium_of_Pincers",
- "富营养的穿刺者" : "Nourished_Piercer",
- "高级武装人员" : "Senior_Armed_Militant",
- "朗姆酒推荐者" : "Rum_Connoisseur",
- "烈酒级醒酒助手" : "Whiskey-Grade_Waker-Upper",
- "萨卡兹王庭军术师" : "Sarkaz_Royal_Court_Caster",
- # "灰尾香主" : "Greytail_Leader",
- "阵地击人手" : "Field_Bludgeoner",
- "源石畸变体" : "Originiutant",
- "提亚卡乌破坏王" : "Tiacauh_Annihilator",
- "逐腐兽" : "Rotchaser",
- "高能源石虫" : "Infused_Originium_Slug",
- "风情街“星术师”" : "Fashion_Street_Stellar_Caster",
- "田鼷力士" : "Fieldmus_Bruiser",
- "残党萨克斯手" : "Remnant_Saxophonist",
- "散华骑士团学徒" : "Nova_Knightclub_Trainee",
- "“阿咬”" : "Bitey",
- # "“门”" : "",
- "“投石机”" : "Catapult",
- # "“复仇者”" : '"Hateful Avenger"',
- # "扎罗，“狼之主”" : "Zaaro",
- # "“自在”" : "Free",
- # "灼热源石虫" : "Blazing_Originium_Slug",
- # "萨卡兹子裔责罚者" : "Sarkaz_Heirbearer_Punisher",
- # "" : "",
-}
-
-def extract_webm(webm_path: Path, output_folder: Path):
- extract_webm_cmd = f"ffmpeg -c:v libvpx -i \"{webm_path}\" \"{output_folder}/frame%d.png\""
- subprocess.run(extract_webm_cmd, shell=True, check=True)
-
-
-def main():
- webm_folder = "./tools/battlefield_composite/monster_images"
- for webm_path in Path(webm_folder).glob("*.webm"):
- name_split = webm_path.parts[-1].split("-")
- print(name_split)
- if (name := monster_name.get(name_split[0])) is not None:
- output_directory = webm_path.parent / (name + "-" + name_split[3])
- print(output_directory)
- if not output_directory.exists():
- output_directory.mkdir(exist_ok=True)
- extract_webm(webm_path, output_directory)
- else:
- logging.warning(f"{output_directory} already exists")
- else:
- logging.error(f"Name: {name_split[0]} not found!")
-
-
-if __name__ == "__main__":
- main()
diff --git a/tools/battlefield_composite/monster_images/IM-1.png b/tools/battlefield_composite/monster_images/IM-1.png
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index 5a4c968..0000000
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diff --git a/tools/battlefield_recognize/class_to_idx.json b/tools/battlefield_recognize/class_to_idx.json
deleted file mode 100644
index c47a82c..0000000
--- a/tools/battlefield_recognize/class_to_idx.json
+++ /dev/null
@@ -1,25 +0,0 @@
-{
- "altar_vertical_altar": 0,
- "altar_vertical_none": 1,
- "block_parallel_block": 2,
- "block_parallel_none": 3,
- "block_vertical_altar_shape1": 4,
- "block_vertical_altar_shape2": 5,
- "block_vertical_block_shape1": 6,
- "block_vertical_block_shape2": 7,
- "block_vertical_none": 8,
- "coil_narrow_coil": 9,
- "coil_narrow_none": 10,
- "coil_wide_coil": 11,
- "coil_wide_none": 12,
- "crossbow_top_crossbow": 13,
- "crossbow_top_none": 14,
- "fire_side_left_crossbow": 15,
- "fire_side_left_fire": 16,
- "fire_side_left_none": 17,
- "fire_side_right_crossbow": 18,
- "fire_side_right_fire": 19,
- "fire_side_right_none": 20,
- "fire_top_fire": 21,
- "fire_top_none": 22
-}
\ No newline at end of file
diff --git "a/tools/battlefield_recognize/\345\234\272\345\234\260\350\257\206\345\210\253\345\255\227\346\256\265\350\257\264\346\230\216.txt" "b/tools/battlefield_recognize/\345\234\272\345\234\260\350\257\206\345\210\253\345\255\227\346\256\265\350\257\264\346\230\216.txt"
deleted file mode 100644
index 41457b5..0000000
--- "a/tools/battlefield_recognize/\345\234\272\345\234\260\350\257\206\345\210\253\345\255\227\346\256\265\350\257\264\346\230\216.txt"
+++ /dev/null
@@ -1,20 +0,0 @@
-使用data_cleaning_with_field_recognize.py清洗后的csv文件应该包括：
-
-列A~列BY：1L~77L，值为77种敌人在左侧的识别数量。
-列BZ：78L，值为“单行障碍物”的识别结果，1表示存在，0表示不存在，-1表示识别错误或图像不存在。
-列CA：79L，值为“侧边十字弩”的识别结果，1表示存在，0表示不存在，-1表示识别错误或图像不存在。
-列CB：80L，值为“侧边火焰炮”的识别结果，1表示存在，0表示不存在，-1表示识别错误或图像不存在。
-列CC：81L，值为“顶部十字弩”的识别结果，1表示存在，0表示不存在，-1表示识别错误或图像不存在。
-列CD：82L，值为“顶部火焰炮”的识别结果，1表示存在，0表示不存在，-1表示识别错误或图像不存在。
-列CE：83L，值为“双行障碍物”的识别结果，1表示存在，0表示不存在，-1表示识别错误或图像不存在。
-
-列CF~列FD：1R~77R，值为77种敌人在右侧的识别数量。
-列FE：78R，值为“单行障碍物”的识别结果，1表示存在，0表示不存在，-1表示识别错误或图像不存在。(应当与78L相同）
-列FF：79R，值为“侧边十字弩”的识别结果，1表示存在，0表示不存在，-1表示识别错误或图像不存在。(应当与79L相同）
-列FG：80R，值为“侧边火焰炮”的识别结果，1表示存在，0表示不存在，-1表示识别错误或图像不存在。(应当与80L相同）
-列FH：81R，值为“顶部十字弩”的识别结果，1表示存在，0表示不存在，-1表示识别错误或图像不存在。(应当与81L相同）
-列FI：82R，值为“顶部火焰炮”的识别结果，1表示存在，0表示不存在，-1表示识别错误或图像不存在。(应当与82L相同）
-列FJ：83R，值为“双行障碍物”的识别结果，1表示存在，0表示不存在，-1表示识别错误或图像不存在。(应当与83L相同）
-
-FK：Result，值为胜负结果。
-FL：ImgPath，值为截图文件名。
\ No newline at end of file
diff --git a/tools/convert_model.py b/tools/convert_model.py
deleted file mode 100644
index f55852b..0000000
--- a/tools/convert_model.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import sys
-sys.path.append(".")
-
-import predict
-from train import UnitAwareTransformer
-import predict_onnx
-import numpy as np
-from recognize import MONSTER_COUNT
-
-model_path = "models/best_model_full.pth"
-
-def replace_suffix(s):
- idx = s.rfind('.')
- if idx == -1:
- return s + '.onnx'
- else:
- return s[:idx] + '.onnx'
-
-output_path = replace_suffix(model_path)
-
-model = predict.CannotModel(model_path)
-model.load_model() # 加载原始 PyTorch 模型
-model.export_onnx(output_path) # 导出 ONNX 模型
-print(f"模型已成功导出为 ONNX 格式，保存路径: {output_path}")
-
-#验证导出结果
-data_array = np.zeros(MONSTER_COUNT * 2, dtype=np.int64)
-data_array[28] = 16
-data_array[MONSTER_COUNT + 30] = 22
-left = data_array[:MONSTER_COUNT]
-right = data_array[MONSTER_COUNT:]
-
-Onnxmodel = predict_onnx.CannotModel(model_path = output_path)
-prediction = Onnxmodel.get_prediction(left, right)
-print("预测结果:", prediction)
diff --git a/tools/data_cleaning.py b/tools/data_cleaning.py
deleted file mode 100644
index 98f86c5..0000000
--- a/tools/data_cleaning.py
+++ /dev/null
@@ -1,223 +0,0 @@
-import pandas as pd
-import numpy as np
-
-
-def clean_data(file_path, output_path):
- print(f"开始清洗数据文件: {file_path}")
-
- # 读取CSV文件，不设置表头，并添加原始行号列（从1开始）
- data = pd.read_csv(file_path, header=None)
- data['original_index'] = data.index + 1 # 保存原始行号（从1开始）
- original_rows = len(data)
- print(f"原始数据行数: {original_rows}")
-
- # 获取数据中特征的列数（不包括标签列和原始行号列）
- feature_count = data.shape[1] - 2 # 减去标签列和原始行号列
- print(f"特征总数: {feature_count}")
-
- # 分离特征、标签和原始行号
- features = data.iloc[:, :-2] # 不包含标签列和原始行号列
- labels = data.iloc[:, -2] # 标签列
- original_indices = data.iloc[:, -1] # 原始行号列
-
- # 检查最后一行是否满足条件
- last_row_features = features.iloc[-1].values
- last_row_valid = True
-
- # 检查最后一行28列和62列是否大于6
- if abs(last_row_features[27]) > 6 or abs(last_row_features[61]) > 6:
- last_row_valid = False
- print("警告: 最后一行的28列或62列数据大于6")
-
- # 检查最后一行是否有任何3位数
- if np.any(np.abs(last_row_features) >= 100):
- last_row_valid = False
- print("警告: 最后一行包含3位数")
-
- if not last_row_valid:
- print("错误: 最后一行不满足清洗条件，无法用于替换")
- return
-
- # 保存最后一行用于替换（包括原始行号）
- last_row = data.iloc[-1].copy()
-
- # 创建过滤条件
- rows_to_remove = []
-
- # 检查每一行
- for i in range(len(features)):
- row = features.iloc[i].values
-
- # 检查是否有任何3位数
- if np.any(np.abs(row) >= 100):
- rows_to_remove.append(i)
-
- print(f"发现需要删除的行数: {len(rows_to_remove)}")
-
- # 创建新的数据框（保留原始行号）
- cleaned_data = data.drop(rows_to_remove).reset_index(drop=True)
-
- # 如果删除了最后一行，则不需要保留副本
- if len(data) - 1 in rows_to_remove:
- print("最后一行被删除，不需要特别处理")
- else:
- # 删除最后一行（因为我们有副本）
- cleaned_data = cleaned_data.iloc[:-1]
-
- # 添加替换行
- replacement_count = len(rows_to_remove)
- for _ in range(replacement_count):
- cleaned_data = pd.concat([cleaned_data, pd.DataFrame([last_row])], ignore_index=True)
-
- print(f"清洗后的数据行数: {len(cleaned_data)}")
- print(f"替换了 {replacement_count} 行数据")
-
- # 去重操作
- duplicated_count_before = cleaned_data.duplicated(subset=cleaned_data.columns[:-1]).sum() # 不包含原始行号列
- print(f"去重前的重复行数: {duplicated_count_before}")
-
- # 对特征列进行去重，保留标签和原始行号
- duplicate_indices = cleaned_data.iloc[:, :-2].duplicated(keep='first') # 只比较特征列
- duplicate_count = duplicate_indices.sum()
-
- # 如果有重复行，去除重复行
- if duplicate_count > 0:
- print(f"发现 {duplicate_count} 行特征重复")
- cleaned_data = cleaned_data[~duplicate_indices].reset_index(drop=True)
- print(f"去重后的数据行数: {len(cleaned_data)}")
- else:
- print("没有发现重复的特征")
-
- # 筛选异常波动数据
- print("\n开始筛选异常波动数据...")
-
- # 分离特征、标签和原始行号
- features_cleaned = cleaned_data.iloc[:, :-2] # 特征列
- labels_cleaned = cleaned_data.iloc[:, -2] # 标签列
- original_indices_cleaned = cleaned_data.iloc[:, -1] # 原始行号列
-
- def get_threshold(a):
- """动态阈值阶梯表（基于较小值）"""
- if a == 1:
- return 0.65
- elif a == 2:
- return 0.51
- elif 3 <= a <= 9:
- return 0.49
- elif 10 <= a <= 19:
- return 0.33
- else:
- return 0.25
-
- def enhanced_clean(column_data, original_indices, col_idx):
- """带完整前后对比的智能清洗"""
- # 获取实际数值列
- column_values = column_data
-
- original = sorted([float(x) for x in column_values[column_values != 0].unique()])
- if not original:
- return set(), []
-
- current_values = original.copy()
- anomalies = set()
-
- while True:
- # 寻找当前最优断点
- best_gap = 0
- best_idx = -1
- for i in range(len(current_values) - 1):
- a, b = current_values[i], current_values[i + 1]
- gap = (b - a) / b
- threshold = get_threshold(a)
-
- if gap > threshold and gap > best_gap:
- best_gap = gap
- best_idx = i
-
- if best_idx == -1:
- break
-
- # 执行切割
- a, b = current_values[best_idx], current_values[best_idx + 1]
- left = current_values[:best_idx + 1]
- right = current_values[best_idx + 1:]
-
- # 智能选择保留区间
- if len(left) < len(right) or (len(left) == len(right) and sum(left) < sum(right)):
- removed = left
- current_values = right
- else:
- removed = right
- current_values = left
-
- # 记录异常值
- anomalies.update(removed)
-
- # 获取被删除行的原始行号
- removed_indices = original_indices[column_values.isin(anomalies)].tolist()
- return anomalies, removed_indices
-
- # 记录处理前后的数值分布
- anomaly_report = {}
- has_anomaly = False # 标记是否有异常列
-
- # 为每列生成异常值集合
- for col in features_cleaned.columns:
- # 获取当前列数据和对应的原始行号
- col_data = features_cleaned[col].astype(float)
- anomaly_vals, removed_indices = enhanced_clean(col_data, original_indices_cleaned, col)
-
- if anomaly_vals: # 仅处理有异常的列
- has_anomaly = True
- # 记录处理前分布
- pre_counts = {float(k): v for k, v in col_data.value_counts().to_dict().items() if v > 0}
-
- # 筛选异常行
- mask = ~col_data.isin(anomaly_vals)
- features_cleaned = features_cleaned[mask].copy()
- labels_cleaned = labels_cleaned[mask].copy()
- original_indices_cleaned = original_indices_cleaned[mask].copy()
-
- # 记录处理后分布
- post_counts = {float(k): v for k, v in features_cleaned[col].value_counts().to_dict().items() if v > 0}
-
- # 生成报告
- anomaly_report[col] = {
- 'pre': sorted(pre_counts.keys()),
- 'post': sorted(post_counts.keys()),
- 'anomalies': sorted(anomaly_vals),
- 'removed_rows': removed_indices
- }
-
- # 仅当有异常时才输出总结报告
- if has_anomaly:
- print("\n异常波动处理报告:")
- for col, report in anomaly_report.items():
- print(f"\n列 {col + 1}:")
- print(f"删除前数值: {report['pre']}")
- print(f"识别异常值: {report['anomalies']}")
- print(f"删除后数值: {report['post']}")
- print(f"删除的行号: {report['removed_rows']}")
- else:
- print("\n所有列均未发现需要处理的异常波动")
-
- # 合并处理后的数据（不包含原始行号列）
- cleaned_data = pd.concat([features_cleaned, labels_cleaned], axis=1)
-
- # 添加表头
- headers = [f"{i}" for i in range(1, 114)] # 生成表头1到69
- # 保存清洗后的数据
- cleaned_data.to_csv(output_path, index=False, header=headers)
- print(f"\n清洗后的数据已保存到: {output_path}")
-
- # 输出标签分布
- label_counts = labels_cleaned.value_counts()
- print("\n标签分布:")
- for label, count in label_counts.items():
- print(f" {label}: {count} 行")
-
-
-if __name__ == "__main__":
- input_file = "arknights.csv"
- output_file = "arknights_cleaned.csv"
- clean_data(input_file, output_file)
diff --git a/tools/data_cleaning_with_field_recognize.py b/tools/data_cleaning_with_field_recognize.py
deleted file mode 100644
index 7e1aa72..0000000
--- a/tools/data_cleaning_with_field_recognize.py
+++ /dev/null
@@ -1,249 +0,0 @@
-import pandas as pd
-import numpy as np
-import os
-import json
-import re
-from collections import defaultdict
-from PIL import Image
-import onnxruntime as ort
-
-# ==============================================================================
-# SECTION 1: 游戏画面元素识别模块 (无变更)
-# ==============================================================================
-
-ROI_COORDINATES = {
- "altar_vertical": [
- {"x": 910, "y": 174, "width": 95, "height": 104},
- {"x": 910, "y": 429, "width": 102, "height": 108},
- {"x": 900, "y": 755, "width": 120, "height": 108}
- ],
- "block_parallel": [
- {"x": 694, "y": 240, "width": 530, "height": 122},
- {"x": 651, "y": 614, "width": 620, "height": 143}
- ],
- "block_vertical": [
- {"x": 647, "y": 233, "width": 153, "height": 523},
- {"x": 1112, "y": 239, "width": 159, "height": 514}
- ],
- "coil_narrow": [
- {"x": 915, "y": 110, "width": 85, "height": 89},
- {"x": 815, "y": 257, "width": 86, "height": 98},
- {"x": 1024, "y": 258, "width": 79, "height": 98},
- {"x": 790, "y": 643, "width": 97, "height": 102},
- {"x": 1031, "y": 639, "width": 102, "height": 108}
- ],
- "coil_wide": [
- {"x": 719, "y": 181, "width": 81, "height": 89},
- {"x": 602, "y": 346, "width": 81, "height": 94},
- {"x": 578, "y": 535, "width": 81, "height": 95},
- {"x": 669, "y": 759, "width": 91, "height": 95},
- {"x": 1159, "y": 757, "width": 93, "height": 92},
- {"x": 1257, "y": 533, "width": 94, "height": 102},
- {"x": 1236, "y": 344, "width": 85, "height": 97},
- {"x": 1120, "y": 180, "width": 75, "height": 91}
- ],
- "crossbow_top": [
- {"x": 718, "y": 13, "width": 484, "height": 106}
- ],
- "fire_side_left": [
- {"x": 98, "y": 246, "width": 184, "height": 281}
- ],
- "fire_side_right": [
- {"x": 1656, "y": 430, "width": 235, "height": 315}
- ],
- "fire_top": [
- {"x": 532, "y": 17, "width": 188, "height": 97},
- {"x": 1325, "y": 14, "width": 60, "height": 100}
- ]
-}
-
-
-def preprocess_pil_image(img: Image.Image) -> np.ndarray:
- img = img.resize((224, 224), Image.Resampling.LANCZOS)
- img_array = np.array(img, dtype=np.float32) / 255.0
- img_array = img_array.transpose(2, 0, 1)
- mean = np.array([0.485, 0.456, 0.406], dtype=np.float32).reshape(3, 1, 1)
- std = np.array([0.229, 0.224, 0.225], dtype=np.float32).reshape(3, 1, 1)
- normalized_array = (img_array - mean) / std
- return np.expand_dims(normalized_array, axis=0)
-
-
-def softmax(x: np.ndarray) -> np.ndarray:
- e_x = np.exp(x - np.max(x))
- return e_x / e_x.sum(axis=-1, keepdims=True)
-
-
-def predict_scene(session: ort.InferenceSession, idx_to_class: dict, image_path: str, threshold: float = 0.5) -> list[
- str]:
- try:
- full_image = Image.open(image_path).convert('RGB')
- except Exception:
- return []
- if full_image.size != (1920, 1080):
- return []
- input_name, output_name = session.get_inputs()[0].name, session.get_outputs()[0].name
- detected_classes = []
- for location, boxes in ROI_COORDINATES.items():
- for i, box in enumerate(boxes):
- x, y, w, h = box['x'], box['y'], box['width'], box['height']
- roi_pil = full_image.crop((x, y, x + w, y + h))
- input_tensor = preprocess_pil_image(roi_pil)
- outputs = session.run([output_name], {input_name: input_tensor})
- logits = outputs[0][0]
- probabilities = softmax(logits)
- predicted_index = np.argmax(probabilities)
- if probabilities[predicted_index] >= threshold:
- predicted_class = idx_to_class[predicted_index]
- if not predicted_class.endswith('_none'):
- detected_classes.append(predicted_class)
- return detected_classes
-
-
-# ==============================================================================
-# SECTION 2: 数据清洗主模块 (修改了最后的合并与保存部分)
-# ==============================================================================
-
-def clean_data(file_path, output_path, screenshots_base_path, onnx_model_path, class_map_path):
- print(f"开始清洗数据文件: {file_path}")
- try:
- data = pd.read_csv(file_path, header=0)
- except FileNotFoundError:
- print(f"错误: 找不到数据文件 '{file_path}'")
- return
- data['original_index'] = data.index + 1
- # --- 原始清洗逻辑部分 (无变更) ---
- features = data.iloc[:, :-3]
- labels = data.iloc[:, -3]
- pic_names = data.iloc[:, -2]
- print(f"原始特征总数: {features.shape[1]}")
- # ... (其余清洗逻辑与原脚本完全相同)
- last_row_features = features.iloc[-1].values
- last_row_valid = True
- if abs(last_row_features[27]) > 6 or abs(last_row_features[61]) > 6: last_row_valid = False
- if np.any(np.abs(last_row_features) >= 100): last_row_valid = False
- if not last_row_valid: print("错误: 最后一行不满足清洗条件"); return
- last_row = data.iloc[-1].copy()
- rows_to_remove = [i for i, row in enumerate(features.values) if np.any(np.abs(row) >= 100)]
- cleaned_data = data.drop(rows_to_remove).reset_index(drop=True)
- if not (len(data) - 1 in rows_to_remove): cleaned_data = cleaned_data.iloc[:-1]
- if rows_to_remove: cleaned_data = pd.concat([cleaned_data, pd.DataFrame([last_row] * len(rows_to_remove))],
- ignore_index=True)
- cleaned_data = cleaned_data.drop_duplicates(subset=cleaned_data.columns[:-3], keep='first').reset_index(drop=True)
- features_cleaned, labels_cleaned, pic_names_cleaned = cleaned_data.iloc[:, :-3], cleaned_data.iloc[:,
- -3], cleaned_data.iloc[:, -2]
- # ... (异常波动筛选逻辑完全相同)
-
- # --- 画面元素识别集成部分 (无变更) ---
- print("\n开始识别截图中的游戏元素...")
- try:
- session = ort.InferenceSession(onnx_model_path)
- with open(class_map_path, 'r', encoding='utf-8') as f:
- class_to_idx = json.load(f)
- idx_to_class = {v: k for k, v in class_to_idx.items()}
- except Exception as e:
- print(f"错误：加载模型或class_map文件失败: {e}");
- return
- grouped_elements = defaultdict(list)
- for class_name in class_to_idx.keys():
- if class_name.endswith('_none'):
- continue
- condensed_name = re.sub(r'_left_', '_', class_name)
- condensed_name = re.sub(r'_right_', '_', condensed_name)
- grouped_elements[condensed_name].append(class_name)
- image_feature_columns = sorted(grouped_elements.keys())
- print(f"将聚合生成 {len(image_feature_columns)} 个新特征列。")
- all_rows_image_data = []
- total_pics = len(pic_names_cleaned)
- for idx, pic_name in enumerate(pic_names_cleaned):
- print(f"\r处理图片: {idx + 1}/{total_pics} ({pic_name})", end="")
- image_path = os.path.join(screenshots_base_path, str(pic_name))
- if not os.path.exists(image_path):
- row_image_data = {col: -1 for col in image_feature_columns}
- all_rows_image_data.append(row_image_data)
- continue
- try:
- detected_full_names = set(predict_scene(session, idx_to_class, image_path, threshold=0.5))
- row_image_data = {}
- for condensed_name, full_names in grouped_elements.items():
- num_positions = len(full_names)
- if num_positions == 1:
- row_image_data[condensed_name] = 1 if full_names[0] in detected_full_names else 0
- else:
- detections_in_group = [fn in detected_full_names for fn in full_names]
- num_detected = sum(detections_in_group)
- if num_detected == num_positions:
- row_image_data[condensed_name] = 1
- elif num_detected == 0:
- row_image_data[condensed_name] = 0
- else:
- row_image_data[condensed_name] = -1
- except Exception as e:
- row_image_data = {col: -20 for col in image_feature_columns}
- all_rows_image_data.append(row_image_data)
- print("\n截图元素识别完成。")
- image_data_df = pd.DataFrame(all_rows_image_data)
-
- # --- MODIFIED: 合并与保存 ---
- # 重置所有数据部分的索引，确保能够正确拼接
- features_cleaned.reset_index(drop=True, inplace=True)
- image_data_df.reset_index(drop=True, inplace=True)
- labels_cleaned.reset_index(drop=True, inplace=True)
- pic_names_cleaned.reset_index(drop=True, inplace=True)
-
- # 1. 检查并拆分155个原始特征为L(77)和R(78)两组
- if features_cleaned.shape[1] != 122:
- print(f"警告: 期望122个原始特征，但检测到{features_cleaned.shape[1]}个。将按前61列和剩余列进行分割。")
-
- features_L = features_cleaned.iloc[:, :61]
- features_R = features_cleaned.iloc[:, 61:]
-
- # 2. 按照您的要求生成新的表头列表
- num_element_features = len(image_feature_columns)
- num_r_features = features_R.shape[1]
-
- headers_L = [f"{i}L" for i in range(1, 62)] # 1L to 77L
- headers_elements_L = [f"{i}L" for i in range(62, 62 + num_element_features)]
-
- headers_R = [f"{i}R" for i in range(1, num_r_features + 1)] # 1R to 78R
- headers_elements_R = [f"{i}R" for i in range(num_r_features + 1, num_r_features + 1 + num_element_features)]
-
- # 最终表头顺序
- final_headers = (headers_L + headers_elements_L +
- headers_R + headers_elements_R +
- ['Result', 'ImgPath'])
-
- # 3. 为Series命名，以便在拼接时作为列名
- labels_cleaned.name = 'Result'
- pic_names_cleaned.name = 'ImgPath'
-
- # 4. 按照新的顺序拼接所有数据部分
- final_cleaned_data = pd.concat([
- features_L, # 1L-61L
- image_data_df, # 12个元素特征
- features_R, # 1R-61R
- image_data_df.copy(), # 12个元素特征 (副本)
- labels_cleaned, # label
- pic_names_cleaned # screenshot_filename
- ], axis=1)
-
- # 5. 将新生成的表头赋予DataFrame
- final_cleaned_data.columns = final_headers
-
- # 6. 保存到CSV，使用新的表头
- final_cleaned_data.to_csv(output_path, index=False, header=True)
-
- print(f"\n清洗和识别后的数据已保存到: {output_path}")
- print(f"最终数据维度: {final_cleaned_data.shape[0]} 行, {final_cleaned_data.shape[1]} 列")
- print(f"已按要求生成自定义表头。")
-
-
-if __name__ == "__main__":
- # 路径配置与之前保持一致
- input_file = r"arknights.csv"
- output_file = r"arknights_with_field_recognize_v2.csv"
- screenshots_base_path = r"images"
-
- model_dir = r"battlefield_recognize"
- onnx_model_path = os.path.join(model_dir, 'field_recognize.onnx')
- class_map_path = os.path.join(model_dir, 'class_to_idx.json')
- clean_data(input_file, output_file, screenshots_base_path, onnx_model_path, class_map_path)
\ No newline at end of file
diff --git a/tools/data_cleaning_with_field_recognize_gpu.py b/tools/data_cleaning_with_field_recognize_gpu.py
deleted file mode 100644
index 13ae6af..0000000
--- a/tools/data_cleaning_with_field_recognize_gpu.py
+++ /dev/null
@@ -1,285 +0,0 @@
-import pandas as pd
-import numpy as np
-import os
-import json
-import re
-from collections import defaultdict
-from PIL import Image
-import torch
-import torch.nn as nn
-from torchvision import models, transforms
-
-# ==============================================================================
-# SECTION 1: 游戏画面元素识别模块 (已修改为PyTorch+GPU)
-# ==============================================================================
-
-ROI_COORDINATES = {
- "altar_vertical": [
- {"x": 910, "y": 174, "width": 95, "height": 104},
- {"x": 910, "y": 429, "width": 102, "height": 108},
- {"x": 900, "y": 755, "width": 120, "height": 108}
- ],
- "block_parallel": [
- {"x": 694, "y": 240, "width": 530, "height": 122},
- {"x": 651, "y": 614, "width": 620, "height": 143}
- ],
- "block_vertical": [
- {"x": 647, "y": 233, "width": 153, "height": 523},
- {"x": 1112, "y": 239, "width": 159, "height": 514}
- ],
- "coil_narrow": [
- {"x": 915, "y": 110, "width": 85, "height": 89},
- {"x": 815, "y": 257, "width": 86, "height": 98},
- {"x": 1024, "y": 258, "width": 79, "height": 98},
- {"x": 790, "y": 643, "width": 97, "height": 102},
- {"x": 1031, "y": 639, "width": 102, "height": 108}
- ],
- "coil_wide": [
- {"x": 719, "y": 181, "width": 81, "height": 89},
- {"x": 602, "y": 346, "width": 81, "height": 94},
- {"x": 578, "y": 535, "width": 81, "height": 95},
- {"x": 669, "y": 759, "width": 91, "height": 95},
- {"x": 1159, "y": 757, "width": 93, "height": 92},
- {"x": 1257, "y": 533, "width": 94, "height": 102},
- {"x": 1236, "y": 344, "width": 85, "height": 97},
- {"x": 1120, "y": 180, "width": 75, "height": 91}
- ],
- "crossbow_top": [
- {"x": 718, "y": 13, "width": 484, "height": 106}
- ],
- "fire_side_left": [
- {"x": 98, "y": 246, "width": 184, "height": 281}
- ],
- "fire_side_right": [
- {"x": 1656, "y": 430, "width": 235, "height": 315}
- ],
- "fire_top": [
- {"x": 532, "y": 17, "width": 188, "height": 97},
- {"x": 1325, "y": 14, "width": 60, "height": 100}
- ]
-}
-
-
-def predict_scene_pytorch(
- model: nn.Module,
- idx_to_class: dict,
- image_path: str,
- transform: transforms.Compose,
- device: torch.device,
- threshold: float = 0.5
-) -> list[str]:
- """使用PyTorch模型对图片进行场景识别（GPU加速版）"""
- try:
- full_image = Image.open(image_path).convert('RGB')
- except Exception:
- return []
-
- if full_image.size != (1920, 1080):
- return []
-
- detected_classes = []
- with torch.no_grad():
- for location, boxes in ROI_COORDINATES.items():
- for i, box in enumerate(boxes):
- x, y, w, h = box['x'], box['y'], box['width'], box['height']
- roi_pil = full_image.crop((x, y, x + w, y + h))
- input_tensor = transform(roi_pil).unsqueeze(0).to(device)
- outputs = model(input_tensor)
- probabilities = torch.nn.functional.softmax(outputs, dim=1)[0]
- max_prob, predicted_index_tensor = torch.max(probabilities, 0)
- predicted_index = predicted_index_tensor.item()
-
- if max_prob.item() >= threshold:
- predicted_class = idx_to_class[predicted_index]
- if not predicted_class.endswith('_none'):
- detected_classes.append(predicted_class)
- return detected_classes
-
-
-# ==============================================================================
-# SECTION 2: 数据清洗主模块 (修改了最后的合并与保存部分)
-# ==============================================================================
-
-def clean_data(file_path, output_path, screenshots_base_path, onnx_model_path, class_map_path):
- print(f"开始清洗数据文件: {file_path}")
- try:
- data = pd.read_csv(file_path, header=0)
- except FileNotFoundError:
- print(f"错误: 找不到数据文件 '{file_path}'")
- return
- data['original_index'] = data.index + 1
-
- # --- 原始清洗逻辑部分 (无变更) ---
- features = data.iloc[:, :-3]
- labels = data.iloc[:, -3]
- pic_names = data.iloc[:, -2]
- print(f"原始特征总数: {features.shape[1]}")
- # ... (其余清洗逻辑与原脚本完全相同)
- last_row_features = features.iloc[-1].values
- last_row_valid = True
- if abs(last_row_features[27]) > 6 or abs(last_row_features[61]) > 6: last_row_valid = False
- if np.any(np.abs(last_row_features) >= 100): last_row_valid = False
- if not last_row_valid: print("错误: 最后一行不满足清洗条件"); return
- last_row = data.iloc[-1].copy()
- rows_to_remove = [i for i, row in enumerate(features.values) if np.any(np.abs(row) >= 100)]
- cleaned_data = data.drop(rows_to_remove).reset_index(drop=True)
- if not (len(data) - 1 in rows_to_remove): cleaned_data = cleaned_data.iloc[:-1]
- if rows_to_remove: cleaned_data = pd.concat([cleaned_data, pd.DataFrame([last_row] * len(rows_to_remove))],
- ignore_index=True)
- cleaned_data = cleaned_data.drop_duplicates(subset=cleaned_data.columns[:-3], keep='first').reset_index(drop=True)
- features_cleaned, labels_cleaned, pic_names_cleaned = cleaned_data.iloc[:, :-3], cleaned_data.iloc[:,
- -3], cleaned_data.iloc[:, -2]
- # ... (异常波动筛选逻辑完全相同)
-
- # --- 画面元素识别集成部分 (修改为PyTorch+GPU) ---
- print("\n开始使用PyTorch和GPU识别截图中的游戏元素...")
- device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
- print(f"将使用的推理设备: {device}")
-
- try:
- # 从ONNX路径生成对应的PTH路径
- pth_model_path = onnx_model_path.replace('.onnx', '.pth')
- print(f"加载PyTorch模型: {pth_model_path}")
-
- # 加载类别映射
- with open(class_map_path, 'r', encoding='utf-8') as f:
- class_to_idx = json.load(f)
- idx_to_class = {v: k for k, v in class_to_idx.items()}
- num_classes = len(class_to_idx)
-
- # 加载PyTorch模型
- model = models.mobilenet_v3_small(weights=None)
- num_ftrs = model.classifier[-1].in_features
- model.classifier[-1] = nn.Linear(num_ftrs, num_classes)
- model.load_state_dict(torch.load(pth_model_path, map_location=device))
- model.to(device)
- model.eval()
-
- # 定义图像转换
- transform = transforms.Compose([
- transforms.Resize((224, 224)),
- transforms.ToTensor(),
- transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
- ])
-
- # 重新定义predict_scene函数使用PyTorch模型
- def predict_scene(session, idx_to_class, image_path, threshold=0.5):
- return predict_scene_pytorch(
- model, idx_to_class, image_path, transform, device, threshold
- )
-
- except Exception as e:
- print(f"错误：加载模型或class_map文件失败: {e}")
- return
-
- grouped_elements = defaultdict(list)
- for class_name in class_to_idx.keys():
- if class_name.endswith('_none'):
- continue
- condensed_name = re.sub(r'_left_', '_', class_name)
- condensed_name = re.sub(r'_right_', '_', condensed_name)
- grouped_elements[condensed_name].append(class_name)
- image_feature_columns = sorted(grouped_elements.keys())
- print(f"将聚合生成 {len(image_feature_columns)} 个新特征列。")
- all_rows_image_data = []
- total_pics = len(pic_names_cleaned)
- for idx, pic_name in enumerate(pic_names_cleaned):
- print(f"\r处理图片: {idx + 1}/{total_pics} ({pic_name})", end="")
- image_path = os.path.join(screenshots_base_path, str(pic_name))
- if not os.path.exists(image_path):
- row_image_data = {col: -1 for col in image_feature_columns}
- all_rows_image_data.append(row_image_data)
- continue
- try:
- detected_full_names = set(predict_scene(None, idx_to_class, image_path, threshold=0.5))
- row_image_data = {}
- for condensed_name, full_names in grouped_elements.items():
- num_positions = len(full_names)
- if num_positions == 1:
- row_image_data[condensed_name] = 1 if full_names[0] in detected_full_names else 0
- else:
- detections_in_group = [fn in detected_full_names for fn in full_names]
- num_detected = sum(detections_in_group)
- if num_detected == num_positions:
- row_image_data[condensed_name] = 1
- elif num_detected == 0:
- row_image_data[condensed_name] = 0
- else:
- row_image_data[condensed_name] = -1
- except Exception as e:
- row_image_data = {col: -20 for col in image_feature_columns}
- all_rows_image_data.append(row_image_data)
- print("\n截图元素识别完成。")
- image_data_df = pd.DataFrame(all_rows_image_data)
-
- # --- MODIFIED: 合并与保存 ---
- # 重置所有数据部分的索引，确保能够正确拼接
- features_cleaned.reset_index(drop=True, inplace=True)
- image_data_df.reset_index(drop=True, inplace=True)
- labels_cleaned.reset_index(drop=True, inplace=True)
- pic_names_cleaned.reset_index(drop=True, inplace=True)
-
- # 1. 检查并拆分155个原始特征为L(77)和R(78)两组
- if features_cleaned.shape[1] != 122:
- print(f"警告: 期望122个原始特征，但检测到{features_cleaned.shape[1]}个。将按前61列和剩余列进行分割。")
-
- features_L = features_cleaned.iloc[:, :61]
- features_R = features_cleaned.iloc[:, 61:]
-
- # 2. 按照您的要求生成新的表头列表
- num_element_features = len(image_feature_columns)
- num_r_features = features_R.shape[1]
-
- headers_L = [f"{i}L" for i in range(1, 62)] # 1L to 61L
- headers_elements_L = [f"{i}L" for i in range(62, 62 + num_element_features)]
-
- headers_R = [f"{i}R" for i in range(1, num_r_features + 1)] # 1R to 61R
- headers_elements_R = [f"{i}R" for i in range(num_r_features + 1, num_r_features + 1 + num_element_features)]
-
- # 最终表头顺序
- final_headers = (headers_L + headers_elements_L +
- headers_R + headers_elements_R +
- ['Result', 'ImgPath'])
-
- # 3. 为Series命名，以便在拼接时作为列名
- labels_cleaned.name = 'Result'
- pic_names_cleaned.name = 'ImgPath'
-
- # 4. 按照新的顺序拼接所有数据部分
- final_cleaned_data = pd.concat([
- features_L, # 1L-61L
- image_data_df, # 元素特征
- features_R, # 1R-61R
- image_data_df.copy(), # 元素特征 (副本)
- labels_cleaned, # label
- pic_names_cleaned # screenshot_filename
- ], axis=1)
-
- # 5. 将新生成的表头赋予DataFrame
- final_cleaned_data.columns = final_headers
-
- # 6. 保存到CSV，使用新的表头
- final_cleaned_data.to_csv(output_path, index=False, header=True)
-
- print(f"\n清洗和识别后的数据已保存到: {output_path}")
- print(f"最终数据维度: {final_cleaned_data.shape[0]} 行, {final_cleaned_data.shape[1]} 列")
- print(f"已按要求生成自定义表头。")
-
-
-if __name__ == "__main__":
- # 路径配置与之前保持一致
- input_file = r"arknights.csv"
- output_file = r"arknights_with_field_recognize_v2.csv"
- screenshots_base_path = r"images"
-
- model_dir = r"battlefield_recognize"
- onnx_model_path = os.path.join(model_dir, 'field_recognize.onnx')
- class_map_path = os.path.join(model_dir, 'class_to_idx.json')
-
- # 自动检查PTH文件是否存在
- pth_model_path = onnx_model_path.replace('.onnx', '.pth')
- if not os.path.exists(pth_model_path):
- print(f"错误: 找不到对应的PTH模型文件 '{pth_model_path}'")
- print(f"请确保存在与ONNX同名的PTH文件: {os.path.basename(pth_model_path)}")
- else:
- clean_data(input_file, output_file, screenshots_base_path, onnx_model_path, class_map_path)
\ No newline at end of file
diff --git a/tools/data_nanWriter.py b/tools/data_nanWriter.py
deleted file mode 100644
index ddd6ad4..0000000
--- a/tools/data_nanWriter.py
+++ /dev/null
@@ -1,106 +0,0 @@
-import pandas as pd
-import torch
-import numpy as np
-from tqdm import tqdm
-from train import UnitAwareTransformer
-
-# 配置参数（需要与训练时一致）
-CONFIG = {
- "csv_path": "arknights.csv",
- "model_path": "models/best_model_full.pth",
- "max_feature_value": 300,
- "embed_dim": 128,
- "num_heads": 8,
- "num_layers": 4,
- "device": "cuda" if torch.cuda.is_available() else "cpu"
-}
-
-class SafetyChecker:
- def __init__(self):
- self.model = self._load_model()
- self.model.eval()
- # 创建输出路径
- self.clean_csv_path = CONFIG["csv_path"].replace(".csv", "_clean.csv")
-
- def _load_model(self):
- """加载训练好的模型"""
- model = UnitAwareTransformer(
- num_units=35,
- embed_dim=CONFIG["embed_dim"],
- num_heads=CONFIG["num_heads"],
- num_layers=CONFIG["num_layers"]
- )
- #model.load_state_dict(torch.load(CONFIG["model_path"], map_location=CONFIG["device"]))
- #model = torch.load(CONFIG["model_path"], map_location=CONFIG["device"])
- try:
- model = torch.load(CONFIG["model_path"], map_location=CONFIG["device"], weights_only=False)
- except TypeError: # 如果旧版本 PyTorch 不认识 weights_only
- model = torch.load(CONFIG["model_path"], map_location=CONFIG["device"])
-
- return model.to(CONFIG["device"])
-
- def _process_row(self, row):
- """处理单行数据（与ArknightsDataset保持一致）"""
- features = row[:-1].astype(np.float32)
- midpoint = len(features) // 2
-
- # 处理左右特征
- left_sign = np.sign(features[:midpoint])
- left_count = np.clip(np.abs(features[:midpoint]), 0, CONFIG["max_feature_value"])
- right_sign = np.sign(features[midpoint:])
- right_count = np.clip(np.abs(features[midpoint:]), 0, CONFIG["max_feature_value"])
-
- # 转换为张量并添加batch维度
- return (
- torch.tensor(left_sign, dtype=torch.float32).unsqueeze(0).to(CONFIG["device"]),
- torch.tensor(left_count, dtype=torch.float32).unsqueeze(0).to(CONFIG["device"]),
- torch.tensor(right_sign, dtype=torch.float32).unsqueeze(0).to(CONFIG["device"]),
- torch.tensor(right_count, dtype=torch.float32).unsqueeze(0).to(CONFIG["device"])
- )
-
- def check_csv(self):
- """检查整个CSV文件并保存干净版本"""
- problematic_rows = []
-
- # 初始化干净文件（清空已存在内容）
- pd.DataFrame().to_csv(self.clean_csv_path, mode='w', header=False, index=False)
-
- chunk_size = 1000
- total_rows = sum(1 for _ in open(CONFIG["csv_path"]))
-
- with tqdm(total=total_rows, desc="Checking rows") as pbar:
- for chunk in pd.read_csv(CONFIG["csv_path"], header=None, chunksize=chunk_size):
- normal_chunk = []
- for idx, row in chunk.iterrows():
- global_idx = pbar.n % total_rows + 1
- try:
- tensors = self._process_row(row.values)
- with torch.no_grad():
- output = self.model(*tensors)
- if torch.isnan(output).any() or torch.isinf(output).any():
- problematic_rows.append(global_idx)
- else:
- # 保留正常行数据
- normal_chunk.append(row.values.tolist())
- except Exception as e:
- problematic_rows.append((global_idx, str(e)))
- pbar.update(1)
-
- # 将本chunk的正常行写入文件
- if normal_chunk:
- pd.DataFrame(normal_chunk).to_csv(
- self.clean_csv_path,
- mode='a',
- header=False,
- index=False
- )
-
- print(f"\n已保存干净数据至: {self.clean_csv_path}")
- print("\n检查完成！发现异常的行：")
- for row in problematic_rows:
- print(f"行号 {row[0] if isinstance(row, tuple) else row}: ",
- row[1] if isinstance(row, tuple) else "输出包含NaN/Inf")
-
-if __name__ == "__main__":
- checker = SafetyChecker()
- checker.check_csv()
\ No newline at end of file
diff --git a/tools/data_washer_new.py b/tools/data_washer_new.py
deleted file mode 100644
index 3f10486..0000000
--- a/tools/data_washer_new.py
+++ /dev/null
@@ -1,904 +0,0 @@
-import logging
-from pathlib import Path
-import cv2
-import numpy as np
-import time
-import csv
-import os
-import sys
-import recognize
-import tqdm
-
-MONSTER_NUM=56
-black_list_rows = []
-
-def merge(nums):
- if not nums:
- return ""
- intervals = []
- start = end = nums[0]
- for num in nums[1:]:
- if num == end + 1:
- end = num
- else:
- intervals.append((start, end))
- start = end = num
- intervals.append((start, end)) # 添加最后一个区间
-
- parts = []
- for s, e in intervals:
- if s == e:
- parts.append(str(s))
- else:
- parts.append(f"{s}-{e}")
- return ','.join(parts)
-
-def is_continuous_sublist(sub, main):
- return any(sub == main[i:i+len(sub)] for i in range(len(main) - len(sub) + 1))
-
-def remove_duplicate_subsequences_easy(listdata, threshold=3):
- record = []
- for i in range(len(listdata)-threshold-1):
- if is_continuous_sublist(listdata[i+1:i+threshold+2], listdata[:i+threshold+1]):
- record.extend(list(range(i+1,i+threshold+2)))
- reco_last = list(set(record))
- reco_last.sort()
- processed_data = [listdata[j] for j in range(len(listdata)) if j not in reco_last]
- return processed_data, reco_last
-
-def remove_duplicate_subsequences(arr, threshold=3):
- """
- 处理二维数组版本，将每行视为独立元素，避免内存溢出
- :param arr: 二维np数组，形状为(N, D)
- :param threshold: 需要删除的连续重复子序列最小长度
- :return: 去重后的数组，被删除的索引列表
- """
- if arr.ndim != 2:
- raise ValueError("输入必须是二维数组")
-
- n = arr.shape[0]
- print(arr[9])
- # 哈希化每行以便快速比较
- dtype = np.dtype((np.void, arr.dtype.itemsize * arr.shape[1]))
- hashed_arr = np.ascontiguousarray(arr).view(dtype).flatten()
-
- # 初始化滚动数组和列最大值记录
- prev_row = np.zeros(n, dtype=int)
- max_per_col = np.zeros(n, dtype=int)
- targ = 10
- for i in range(n):
- # 生成当前行比较掩码
- equal_mask = (hashed_arr[i] == hashed_arr)
-
- # 计算当前行DP值
- curr_row = np.zeros(n, dtype=int)
- curr_row[0] = equal_mask[0] # 处理j=0
-
- if i > 0:
- # 向量化计算j>=1的情况
- curr_row[1:] = np.where(equal_mask[1:], prev_row[:-1] + 1, 0)
-
- # 更新列最大值（只考虑i<=j的情况）
- col_mask = np.arange(n) > i
- max_per_col = np.maximum(max_per_col, curr_row * col_mask)
-
- # 滚动更新
- prev_row = curr_row
- if i*100/n >= targ:
- print("检查重复数据，处理进度: {:.1f}%: ".format(i*100/n))
- targ += 10
-
- # 确定有效列并生成删除索引
- valid_cols = np.where(max_per_col >= threshold)[0]
- to_remove = set()
-
- for j in valid_cols:
- length = max_per_col[j]
- start = max(0, j - length)
- to_remove.update(range(start, j+1))
-
- final_indices = sorted(to_remove)
- return np.delete(arr, final_indices, axis=0), final_indices
-
-def isfloat(value):
- try:
- float(value)
- return True
- except ValueError:
- return False
-
-
-def read_and_remove_zeros(filename,MONSTER_NUM=56):
- '''
- 输入数据文件名
- 输出去0和空之后的数组，和删去的行号列表
- '''
- data = []
- datafull = []
- row_id = 0
- kong = []
- short = []
- lines_num = MONSTER_NUM*2
- with open(filename, 'r') as file:
- csv_reader = csv.reader(file)
- for row in csv_reader:
- if len(row) < lines_num + 1:
- short.append(row_id)
- data.append([0]*lines_num)
- datafull.append([0]*lines_num)
- # 分离数字部分和末尾的字母
- elif isfloat(row[0]) and '' not in row[:lines_num]:
- numbers = list(map(int,map(float, row[:lines_num]))) # 转换为整数列表
- vals = row[lines_num:]
- datafull.append(numbers+vals)
- data.append(numbers)
- else:
- kong.append(row_id)
- data.append([0]*lines_num)
- datafull.append([0]*lines_num)
- #把这一行转化为全0行暂时录入
- row_id += 1
- print('原数据总长度：',len(datafull))
- print('数据长度过短的行：',merge(short))
- print('含有不合法数据的行：',merge(kong))
-
- np_array = np.array(data)
- # 去除全零行
- all_zeros = []
- for i in range(np_array.shape[0]):
- if np.all(np_array[i][MONSTER_NUM:] == 0) or np.all(np_array[i][:MONSTER_NUM] == 0):
- all_zeros.append(i)
- #np.delete(np_array, all_zeros, axis=0)
-
- data_new = [datafull[j] for j in range(len(datafull)) if j not in all_zeros]
-
- all_zeros_idx = [i for i in all_zeros if i not in kong+short]
- print('一侧数据全为0的行：',merge(all_zeros_idx))
- print('筛选后数据总长度：',len(data_new))
- return data_new,all_zeros,len(datafull)
-
-def do_duplicate(listdata):
- if listdata == []:
- return [],[]
- num_data = [list(map(int,map(float, i[:MONSTER_NUM*2]))) for i in listdata]
- np_num_data = np.array(num_data)
- _,remove_list = remove_duplicate_subsequences(np_num_data, threshold=3)
- #print(remove_list)
- result = [listdata[j] for j in range(len(listdata)) if j not in remove_list]
- return result,remove_list
-
-def ori_pos(n,del1,del2):
- remaining_after_first = [i for i in range(n) if i not in del1]
- second_deleted_original = [remaining_after_first[t] for t in del2]
- all_deleted = del1 + second_deleted_original
- all_deleted.sort()
- return all_deleted,second_deleted_original
-
-def view_monster_counts(listdata):
- if listdata == []:
- return True,[],[]
- wrong_counts = []
- num_left = [list(map(int,map(float, i[:MONSTER_NUM]))) for i in listdata]
- num_right = [list(map(int,map(float, i[MONSTER_NUM:MONSTER_NUM*2]))) for i in listdata]
- print(len(num_left[0]),len(num_right[0]))
- black_listed = False
- MONSTER_MIN = 0
- MONSTER_MAX = 100
- MONSTER_LIMIT = {
- 0:[range(0,100),'狗',False],
- 1:[range(0,50),'红虫',False],
- 2:[range(0,30),'大盾',False],
- 3:[range(0,30),'大剑',False],
- 6:[range(0,6),'庞贝',False],
- 8:[range(0,4),'石头人',False],
- 27:[range(0,4),'杰斯顿',False],
- 28:[[0],'自在',True],
- 29:[[0],'狼主',True],
- 30:[[0],'雷德',True] #三大boss全设为0
- }
- ind = 0
- for i1,i2 in zip(num_left,num_right):
- for x1 in i1:
- if x1 < MONSTER_MIN:
- print(f'{ind}行左侧发现小于0的数据!')
- if ind not in wrong_counts:
- wrong_counts.append(ind)
- if x1 > MONSTER_MAX:
- print(f'{ind}行左侧发现大于100的数据!')
- if ind not in wrong_counts:
- wrong_counts.append(ind)
- for x2 in i2:
- if x2 < MONSTER_MIN:
- print(f'{ind}行右侧发现小于0的数据!')
- if ind not in wrong_counts:
- wrong_counts.append(ind)
- if x2 > MONSTER_MAX:
- print(f'{ind}行右侧发现大于100的数据!')
- if ind not in wrong_counts:
- wrong_counts.append(ind)
- for j in MONSTER_LIMIT:
- if i1[j] not in MONSTER_LIMIT[j][0]:
- print(f'{ind}行左侧发现{MONSTER_LIMIT[j][1]}，数量：{i1[j]}')
- if ind not in wrong_counts:
- wrong_counts.append(ind)
- if MONSTER_LIMIT[j][2] and not black_listed:
- black_listed = True
- print(f'确认为30人数据，文档加入黑名单。')
- if i2[j] not in MONSTER_LIMIT[j][0]:
- print(f'{ind}行右侧发现{MONSTER_LIMIT[j][1]}，数量：{i2[j]}')
- if ind not in wrong_counts:
- wrong_counts.append(ind)
- if MONSTER_LIMIT[j][2] and not black_listed:
- black_listed = True
- print(f'确认为30人数据，文档加入黑名单。')
- ind += 1
- processed_data = [listdata[j] for j in range(len(listdata)) if j not in wrong_counts]
- mwdata = is_list_true_np(processed_data)
- processed_data2 = [processed_data[j] for j in range(len(processed_data)) if j not in mwdata]
- print(f'怪物信息不符合权重分配的数据行：{mwdata}')
- return black_listed,wrong_counts,mwdata,processed_data2
-
-def del_duplicate_by_time(listdata,delete_no_time = True):
- ind = 0
- no_time = []
- timedata = []
- wrong_time = []
- for i in listdata:
- if len(i) < MONSTER_NUM*2+2 or i[-1] == 'N/A':
- no_time.append(ind)
- ind += 1
- print(merge(no_time),'行：未发现时间戳！')
- data_with_time = [listdata[j] for j in range(len(listdata)) if j not in no_time]
- ind = 0
- for i in data_with_time:
- if i[-1] not in timedata:
- timedata.append(i[-1])
- else:
- wrong_time.append(ind)
- print(f'{timedata.index(i[-1])}行与{ind}行发现同名截图，文件名：{i[-1]}')
- ind += 1
- if not delete_no_time:
- #先找到wrongtime元素的原始位置，再从原列表删除
- remaining_after_first = [i for i in range(len(listdata)) if i not in no_time]
- second_deleted_original = [remaining_after_first[t] for t in wrong_time]
- data_with_time_ok = [listdata[j] for j in range(len(listdata)) if j not in second_deleted_original]
- wrong_time = second_deleted_original
- else:
- data_with_time_ok = [data_with_time[j] for j in range(len(data_with_time)) if j not in wrong_time]
- return data_with_time_ok, no_time, wrong_time
-
-def savecsv(listdata,outputfile):
- # 处理数字转换
- processed = []
- for row in listdata:
- new_row = []
- for item in row:
- if isinstance(item, (int, float)):
- new_row.append(int(item))
- else:
- new_row.append(item)
- processed.append(new_row)
-
- # 写入CSV文件
- with open(outputfile, 'w', newline='') as f:
- csv.writer(f).writerows(processed)
- print(f'已保存至{outputfile}')
-
-
-def find_csv_files(root_dir):
- csv_files = []
- for root, dirs, files in os.walk(root_dir):
- for file in files:
- if file.lower().endswith('.csv'):
- csv_path = os.path.join(root, file)
- csv_files.append(csv_path)
- return csv_files
-
-def easydata2data(easydata):
- # 测试用函数
- # easydata格式：[[[序号,数量][序号,数量][序号,数量]],[[序号,数量][序号,数量][序号,数量]],结果]，没有的序号和数量留-1，序号是真实序号
- datalist = [0]*MONSTER_NUM*2
- for i in easydata[0]:
- if i[0] > 0:
- datalist[i[0]-1] = i[1]
- for i in easydata[1]:
- if i[0] > 0:
- datalist[i[0]-1+MONSTER_NUM] = i[1]
- datalist.extend(easydata[2])
- return datalist
-
-def find_where_from(easydata,floder_path):
- # easydata格式：[[[序号,数量][序号,数量][序号,数量]],[[序号,数量][序号,数量][序号,数量]],结果]，没有的序号和数量留-1，序号是真实序号
- datalist = [0]*MONSTER_NUM*2
- from_list = []
- for i in easydata[0]:
- if i[0] > 0:
- datalist[i[0]-1] = i[1]
- for i in easydata[1]:
- if i[0] > 0:
- datalist[i[0]-1+MONSTER_NUM] = i[1]
- datalist.extend(easydata[2])
- print(datalist)
- csvlist = find_csv_files(floder_path)
- for c in csvlist:
- print(c)
- for c in csvlist:
- print(f'正在检查：{c}…………………………')
- lines_num = MONSTER_NUM*2
- datafull = []
- row_id = 0
- with open(c, 'r') as file:
- csv_reader = csv.reader(file)
- for row in csv_reader:
- if isfloat(row[0]) and '' not in row[:lines_num] and len(row) > lines_num:
- numbers = list(map(int,map(float, row[:lines_num]))) # 转换为整数列表
- vals = [row[lines_num]]
- if datalist == numbers+vals:
- from_list.append([c,row_id+1])
- print(f'数据来源于：{c}，第{row_id+1}行！')
- row_id += 1
- str_from = ''
- if from_list != []:
- str_from = "\n".join([i[0] + '第：' + str(i[1]) + '行' for i in from_list])
- print(f'可能的数据来源：{str_from}')
-
-def is_distance_not_over_60(a,b,c,d):
- #a, b = interval1
- #c, d = interval2
- # 判断区间是否有交集
- if max(a, c) <= min(b, d):
- return True # 有交集时距离为0，未超过60
- # 计算不重叠时的间隔
- if b < c:
- distance = c - b # interval1在左，interval2在右
- else:
- distance = a - d # interval2在左，interval1在右
- return distance <= 60
-
-def is_list_true_np(fulllist):
- cost_list = [
- [2,0], [2,0.1], [7,0], [7,0], [3,0.1], [10,0], [25,15], [22,0], [25,100], [7,0],
- [5,0.2], [7,0], [2,0], [15,2], [13,0], [12,1.5],
- [6,0.2], [18,3], [15,3], [18,1], [11,0], [10,1], [16,0], [5,0.5], [15,2], [14,0],
- [30,0], [35,100], [-1,-1], [-1,-1], [-1,-1], [6,0.5], [6,0],
- [16,0], [15,5], [11,0], [26,0], [15,0], [4,0.1], [10,0], [21,0], [5,0.2],
- [18,0], [9,1.5], [8,0.5], [16,0], [21,0], [7,0], [36,10], [10,2], [30,15],
- [25,0], [27,0], [32,6], [25,50], [15,5]
- ]
- round_cost_list = [[50,70],[70,90],[90,110],[110,130],[120,160],[140,180],[160,200],[170,230],[190,250],[210,270]]
-
- # Convert to numpy arrays
- cost_arr = np.array(cost_list)
- round_cost_arr = np.array(round_cost_list)
- round_low = round_cost_arr[:, 0]
- round_high = round_cost_arr[:, 1]
-
- # Validity mask for cost entries not equal to [-1, -1]
- valid_mask = np.all(cost_arr != [-1, -1], axis=1)
-
- # Split the input into left and right parts
- fulllist_np = np.array([i[:112] for i in fulllist], dtype=np.float64)
- N = fulllist_np.shape[0]
- left_part = fulllist_np[:, :56]
- right_part = fulllist_np[:, 56:112]
-
- # Calculate valid entries (cost not [-1,-1] and count >0)
- valid_left = valid_mask[np.newaxis, :] & (left_part > 0)
- valid_right = valid_mask[np.newaxis, :] & (right_part > 0)
-
- # Compute mincostL and maxcostL for left
- left_min_terms = (
- (left_part - 1) * cost_arr[np.newaxis, :, 0] +
- ((left_part - 1) * (left_part - 2) * cost_arr[np.newaxis, :, 1]) / 2 +
- 0.01
- ) * valid_left
- mincostL = left_min_terms.sum(axis=1)
-
- left_max_terms = (
- (left_part + 1) * cost_arr[np.newaxis, :, 0] +
- (left_part * (left_part + 1) * cost_arr[np.newaxis, :, 1]) / 2 -
- 0.01
- ) * valid_left
- maxcostL = left_max_terms.sum(axis=1)
-
- # Compute mincostR and maxcostR for right
- right_min_terms = (
- (right_part - 1) * cost_arr[np.newaxis, :, 0] +
- ((right_part - 1) * (right_part - 2) * cost_arr[np.newaxis, :, 1]) / 2 +
- 0.01
- ) * valid_right
- mincostR = right_min_terms.sum(axis=1)
-
- right_max_terms = (
- (right_part + 1) * cost_arr[np.newaxis, :, 0] +
- (right_part * (right_part + 1) * cost_arr[np.newaxis, :, 1]) / 2 -
- 0.01
- ) * valid_right
- maxcostR = right_max_terms.sum(axis=1)
-
- # Check overlap with round costs
- left_low = np.maximum(mincostL[:, np.newaxis], round_low)
- left_high = np.minimum(maxcostL[:, np.newaxis], round_high)
- left_cond = left_low <= left_high
-
- right_low = np.maximum(mincostR[:, np.newaxis], round_low)
- right_high = np.minimum(maxcostR[:, np.newaxis], round_high)
- right_cond = right_low <= right_high
-
- both_cond = left_cond & right_cond
- any_round = np.any(both_cond, axis=1)
-
- # Get indices where no round condition is satisfied
- false_indices = np.where(~any_round)[0].tolist()
- return false_indices
-
-def is_list_true(onelist):
- roundlist = []
- #费用和附加费用，写死在代码里吧，不想读文件了。
- cost_list = [[2,0],[2,0.1],[7,0],[7,0],[3,0.1],[10,0],[25,15],[22,0],[25,100],[7,0],[5,0.2],[7,0],[2,0],[15,2],[13,0],[12,1.5],
- [6,0.2],[18,3],[15,3],[18,1],[11,0],[10,1],[16,0],[5,0.5],[15,2],[14,0],[30,0],[35,100],[-1,-1],[-1,-1],[-1,-1],[6,0.5],[6,0],
- [16,0],[15,5],[11,0],[26,0],[15,0],[4,0.1],[10,0],[21,0],[5,0.2],[18,0],[9,1.5],[8,0.5],[16,0],[21,0],[7,0],[36,10],[10,2],[30,15],
- [25,0],[27,0],[32,6],[25,50],[15,5]]
- #
- round_cost_list = [[50,70],[70,90],[90,110],[110,130],[120,160],[140,180],[160,200],[170,230],[190,250],[210,270]]
- left = onelist[:MONSTER_NUM]
- right = onelist[MONSTER_NUM:MONSTER_NUM*2]
- #result = onelist[MONSTER_NUM*2]
- #print(left,right,result)
- mincostL = sum([(left[i]-1)*cost_list[i][0]+(left[i]-1)*(left[i]-2)*cost_list[i][1]/2+0.01 for i in range(len(left)) if (cost_list[i] != [-1,-1]) and (left[i] > 0)])
- maxcostL = sum([(left[i]+1)*cost_list[i][0]+left[i]*(left[i]+1)*cost_list[i][1]/2-0.01 for i in range(len(left)) if (cost_list[i] != [-1,-1]) and (left[i] > 0)])
- mincostR = sum([(right[i]-1)*cost_list[i][0]+(right[i]-1)*(right[i]-2)*cost_list[i][1]/2+0.01 for i in range(len(right)) if (cost_list[i] != [-1,-1]) and (right[i] > 0)])
- maxcostR = sum([(right[i]+1)*cost_list[i][0]+right[i]*(right[i]+1)*cost_list[i][1]/2-0.01 for i in range(len(right)) if (cost_list[i] != [-1,-1]) and (right[i] > 0)])
- print(mincostL,maxcostL,mincostR,maxcostR)
- for i in range(len(round_cost_list)):
- if max(mincostL, round_cost_list[i][0]) <= min(maxcostL, round_cost_list[i][1]) and max(mincostR, round_cost_list[i][0]) <= min(maxcostR, round_cost_list[i][1]):
- roundlist.append(i)
- if roundlist != []:
- return True
- else:
- print(f'{onelist}is not true！！！')
- return False
- #return is_distance_not_over_60(mincostL,maxcostL,mincostR,maxcostR)
-
-def recognize_review(data,img_floder,matched_threshold = 0.1,ocr_threshold = 0.5):
- print("正在进行识别数据检查")
- print("data行数：",len(data))
- ref_row = [0] * (recognize.MONSTER_COUNT * 2)
- need_delete = [False] * len(data)
- for idx, row in tqdm.tqdm(enumerate(data), total=len(data), desc="Processing rows"):
- ref_row = [0] * (recognize.MONSTER_COUNT * 2)
- try:
- img_name = row[recognize.MONSTER_COUNT * 2 + 1]
- img_path = img_floder / Path(img_name)
- if not img_path.exists():
- print(f"未找到对应的图像： {img_name} ")
- continue
- img = cv2.imread(img_path)
- main_roi = ((0, 0), (img.shape[1], img.shape[0]))
- results = recognize.process_regions(main_roi, img,matched_threshold,ocr_threshold)
- # 处理结果
- for res in results:
- if "error" in res:
- print(f"识别失败 行号: {idx}, 图片: {img_name}, 错误类型: {res['error']}", file=sys.stderr)
- break
- if res["matched_id"]:
- if res["region_id"] < 3:
- ref_row[res["matched_id"] - 1] = int(res["number"])
- else:
- ref_row[res["matched_id"] - 1 + MONSTER_NUM] = int(res["number"])
- else:
- # 检查数据行是否与参考行匹配
- data_row = row[0 : recognize.MONSTER_COUNT * 2]
- if data_row != ref_row:
- print(f"找到不匹配的数据行： {idx} 行，对应图片文件: {img_name}", file=sys.stderr)
- print(f"识别结果 : {ref_row}", file=sys.stderr)
- print(f"文件数据 : {data_row}", file=sys.stderr)
- need_delete[idx] = True
- else:
- need_delete[idx] = False
- except Exception as e:
- logging.exception(f"Error processing line {idx}", e)
- need_delete[idx] = True
- newdata = [row for idx, row in enumerate(data) if not need_delete[idx]]
- deleted = [idx for idx, del_flag in enumerate(need_delete) if del_flag]
- return newdata, deleted
-
-#newdata,deleted,ori_len = read_and_remove_zeros('0502.csv',MONSTER_NUM=56)
-#_,inc = remove_duplicate_subsequences()
-#print('数据例：',newdata[:3])
-#result,deleted2 = do_duplicate(newdata)
-#print(deleted,deleted2)
-#dt = ori_pos(ori_len,deleted,deleted2)
-#print(dt)
-#print('筛选后数据总长度：',len(result))
-#view_monster_counts(newdata)
-#del_duplicate_by_time(newdata)
-
-def process_full(filename,do_remove_duplicate_subsequences = False,delete_no_time = True,open_black_list = True,re_recognize_imgs = False,img_floder = '',matched_threshold=0.1, ocr_threshold=0.5):
- wrong_type_list = []
- newdata,deleted0,ori_len = read_and_remove_zeros(filename,MONSTER_NUM=56)
- deleted1 = []
- if do_remove_duplicate_subsequences:
- newdata,deleted1 = do_duplicate(newdata)
- newdata, deleted2, deleted3 = del_duplicate_by_time(newdata,delete_no_time)
- if not delete_no_time:
- deleted2 = []
- black_listed,deleted4,deleted5,newdata = view_monster_counts(newdata)
- deleted6 = []
- if re_recognize_imgs:
- newdata, deleted6 = recognize_review(newdata,img_floder,matched_threshold, ocr_threshold)
- deleted7 = []
- if open_black_list:
- newdata,deleted7 = process_black_list(newdata)
-
- dl = deleted0
- flag = 0
- for i in [deleted1,deleted2,deleted3,deleted4,deleted5,deleted6,deleted7]:
- if i != []:
- dl,secori = ori_pos(ori_len,dl,i)
- if flag == 0:
- wrong_type_list.append(['不合法的数据：',merge([i + 1 for i in deleted0])])
- wrong_type_list.append(['重复出现的连续数据*：',merge([i + 1 for i in secori])])
- elif flag == 1:
- wrong_type_list.append(['未包含时间轴的数据：',merge([i + 1 for i in secori])])
- elif flag == 2:
- wrong_type_list.append(['时间轴信息重复的数据：',merge([i + 1 for i in secori])])
- elif flag == 3:
- wrong_type_list.append(['怪物信息错误的数据：',merge([i + 1 for i in secori])])
- elif flag == 4:
- wrong_type_list.append(['不符合出怪权重规则的数据：',merge([i + 1 for i in secori])])
- elif flag == 5:
- wrong_type_list.append(['经图片识别错误的数据*：',merge([i + 1 for i in secori])])
- elif flag == 6:
- wrong_type_list.append(['黑名单内数据：',merge([i + 1 for i in secori])])
- flag += 1
- return black_listed,newdata,dl,wrong_type_list
-
-
-def test1():
- black_listed,newdata,dl,wrong_type_list = process_full('0502processed.csv')
- dllist = [i + 1 for i in dl]
- print(f'删除了{dllist}行的数据')
- for i in wrong_type_list:
- print(i)
- savecsv(newdata,'0502processed2.csv')
-
-def process_floder(flodername,savefilename,lastsavefilename,do_remove_duplicate_subsequences = True,delete_no_time = True,open_black_list = True,re_recognize_imgs = False,img_floder = '',matched_threshold=0.1, ocr_threshold=0.5):
- '''
- 输入：
- flodername：需要处理的文件夹名
- savefilename：全部整合保存到的文件名（不进行总去重）
- lastsavefilename：全部整合并去重保存到的最终文件名
- do_remove_duplicate_subsequences：是否清理连续3个以上重复元素的重复序列
- delete_no_time：是否删除没有时间戳的数据行
- '''
- global black_list_rows
- full_data_list = []
- csvlist = find_csv_files(flodername)
- for csv in csvlist:
- print(csv)
- for csv in csvlist:
- print(f'正在处理：{csv}…………………………')
- black_listed,newdata,dl,wrong_type_list = process_full(csv,do_remove_duplicate_subsequences,delete_no_time,open_black_list,re_recognize_imgs,img_floder,matched_threshold, ocr_threshold)
- dllist = [i + 1 for i in dl]
- print(f'删除了{merge(dllist)}行的数据')
- for i in wrong_type_list:
- print(i)
- if not black_listed:
- #未进黑名单则合并至全部数据
- full_data_list += newdata
- else:
- print(f'该数据为30人局数据，自动进入黑名单，不计入总数据！')
- if len(newdata) < 5000:#不是整合数据
- black_list_rows += newdata
- savecsv(full_data_list,savefilename)
- black_listed,newdata,dl,wrong_type_list = process_full(savefilename,do_remove_duplicate_subsequences,delete_no_time,open_black_list,re_recognize_imgs,img_floder,matched_threshold, ocr_threshold)
- #保存后再总处理去重
- dllist = [i + 1 for i in dl]
- print(f'删除了{merge(dllist)}行的数据')
- for i in wrong_type_list:
- print(i)
- savecsv(newdata,lastsavefilename)
-
-def process_black_list(full_data):
- #黑名单里所有的数据检测到重复的就删
- global black_list_rows
- delete_rows = []
- ok_data = []
- idx = 0
- for i in full_data:
- if i in black_list_rows:
- delete_rows.append(idx)
- else:
- ok_data.append(i)
- idx += 1
- print(f'黑名单内数据：{merge(delete_rows)}')
- return ok_data,delete_rows
-
-
-def process_file(filename,savefilename,do_remove_duplicate_subsequences = True,delete_no_time = True,open_black_list = True,re_recognize_imgs = False,img_floder = '',matched_threshold=0.1, ocr_threshold=0.5):
- '''
- 输入：
- filename：需要处理的文件名
- savefilename：处理后保存到的文件名
- do_remove_duplicate_subsequences：是否清理连续3个以上重复元素的重复序列
- delete_no_time：是否删除没有时间戳的数据行
- '''
- black_listed,newdata,dl,wrong_type_list = process_full(filename,do_remove_duplicate_subsequences,delete_no_time,open_black_list,re_recognize_imgs,img_floder,matched_threshold, ocr_threshold)
- #保存后再总处理去重
- dllist = [i + 1 for i in dl]
- print(f'删除了{merge(dllist)}行的数据')
- for i in wrong_type_list:
- print(i)
- savecsv(newdata,savefilename)
-
-
-#process_floder(r'D:\Backup\Downloads\arcdata','arcdata_fullaa.csv','arcdata_full_washed_plus.csv')
-
-import tkinter as tk
-from tkinter import ttk, filedialog, messagebox
-import sys
-import threading
-import queue
-
-class RedirectText(object):
- def __init__(self, text_widget, log_file="processing.log"):
- self.text_widget = text_widget
- self.log_file = log_file
- self.queue = queue.Queue()
- self.root = text_widget.master
- self.lock = threading.Lock()
-
- # 初始化日志文件
- self.setup_logfile()
-
- def setup_logfile(self):
- try:
- # 使用追加模式打开日志文件
- self.log_fd = open(self.log_file, "a", encoding="utf-8")
- except Exception as e:
- self.log_fd = None
- self.write(f"无法打开日志文件: {str(e)}\n")
-
- def write(self, message):
- # 写入日志文件（带线程锁）
- with self.lock:
- if self.log_fd:
- try:
- self.log_fd.write(message)
- self.log_fd.flush() # 确保立即写入磁盘
- except Exception as e:
- self.log_fd = None
- self.queue.put(f"日志写入失败: {str(e)}\n")
-
- # 写入队列供界面显示
- self.queue.put(message)
- self.root.after(100, self.update_text)
-
- def update_text(self):
- while not self.queue.empty():
- msg = self.queue.get_nowait()
- self.text_widget.insert(tk.END, msg)
- self.text_widget.see(tk.END)
-
- def flush(self):
- pass
-
- def close_logfile(self):
- with self.lock:
- if self.log_fd:
- self.log_fd.close()
- self.log_fd = None
-
-class ProcessingThread(threading.Thread):
- def __init__(self, func, args=(), kwargs={}, callback=None):
- super().__init__()
- self.func = func
- self.args = args
- self.kwargs = kwargs
- self.callback = callback
- self.daemon = True
- self.exception = None
-
- def run(self):
- try:
- self.func(*self.args, **self.kwargs)
- except Exception as e:
- self.exception = e
- finally:
- if self.callback:
- self.callback(self.exception)
-
-
-def create_gui():
- root = tk.Tk()
- root.title("数据搅拌机")
- root.geometry("800x600")
-
- # 在此处定义关闭事件处理函数（推荐位置）
- def on_close():
- sys.stdout.close_logfile() # 关闭日志文件
- if messagebox.askokcancel("退出", "确定要退出程序吗？"): # 添加确认对话框
- root.destroy() # 销毁窗口
-
- # 绑定关闭事件处理
- root.protocol("WM_DELETE_WINDOW", on_close)
-
- # 创建文本输出区域
- output_text = tk.Text(root, wrap=tk.WORD)
- output_text.grid(row=3, column=0, columnspan=2, padx=10, pady=10, sticky="nsew")
- sys.stdout = RedirectText(output_text, "data_processing.log")
-
- # 处理文件夹的Frame
- folder_frame = ttk.LabelFrame(root, text="处理文件夹(处理文件夹及其所有子文件夹下的CSV文件，并合并为一个)")
- folder_frame.grid(row=0, column=0, padx=10, pady=5, sticky="ew")
-
- # 处理文件夹的组件
- ttk.Label(folder_frame, text="选择文件夹:").grid(row=0, column=0, padx=5, sticky="w")
- folder_path = tk.StringVar()
- folder_entry = ttk.Entry(folder_frame, textvariable=folder_path, width=40)
- folder_entry.grid(row=0, column=1, padx=5)
- ttk.Button(folder_frame, text="浏览", command=lambda: folder_path.set(filedialog.askdirectory())).grid(row=0, column=2, padx=5)
-
- ttk.Label(folder_frame, text="中间保存文件（不进行最终去重）:").grid(row=1, column=0, padx=5, sticky="w")
- interim_save = tk.StringVar()
- ttk.Entry(folder_frame, textvariable=interim_save, width=40).grid(row=1, column=1, padx=5)
- ttk.Button(folder_frame, text="浏览", command=lambda: interim_save.set(filedialog.asksaveasfilename(defaultextension=".csv",filetypes=[("CSV文件", "*.csv"), ("所有文件", "*.*")]))).grid(row=1, column=2, padx=5)
-
- ttk.Label(folder_frame, text="最终保存文件:").grid(row=2, column=0, padx=5, sticky="w")
- final_save = tk.StringVar()
- ttk.Entry(folder_frame, textvariable=final_save, width=40).grid(row=2, column=1, padx=5)
- ttk.Button(folder_frame, text="浏览", command=lambda: final_save.set(filedialog.asksaveasfilename(defaultextension=".csv",filetypes=[("CSV文件", "*.csv"), ("所有文件", "*.*")]))).grid(row=2, column=2, padx=5)
-
- # 复选框
- remove_dup = tk.BooleanVar(value=False)
- ttk.Checkbutton(folder_frame, text="不依赖时间戳清理重复子序列（在大数据集会非常慢，通常关闭）", variable=remove_dup).grid(row=3, column=0, columnspan=3, sticky="w")
-
- del_time = tk.BooleanVar(value=True)
- ttk.Checkbutton(folder_frame, text="删除无时间戳数据", variable=del_time).grid(row=4, column=0, columnspan=3, sticky="w")
-
- open_black = tk.BooleanVar(value=True)
- ttk.Checkbutton(folder_frame, text="将黑名单文件内的所有数据行同时加入黑名单", variable=open_black).grid(row=5, column=0, columnspan=3, sticky="w")
-
- # 修改后的图片识别行（将复选框和阈值输入放在同一行）
- re_recognize_var = tk.BooleanVar(value=False)
- ttk.Checkbutton(folder_frame, text="启用图片二次识别（必须指定图片路径）", variable=re_recognize_var).grid(row=6, column=0, padx=5, sticky="w")
-
- # 添加匹配阈值设置
- ttk.Label(folder_frame, text="匹配阈值:").grid(row=6, column=1, padx=(20,5), sticky="e")
- matched_threshold_var = tk.DoubleVar(value=0.1)
- ttk.Entry(folder_frame, textvariable=matched_threshold_var, width=6).grid(row=6, column=2, sticky="w")
-
- # 添加OCR阈值设置
- ttk.Label(folder_frame, text="OCR阈值:").grid(row=6, column=3, padx=(20,5), sticky="e")
- ocr_threshold_var = tk.DoubleVar(value=0.5)
- ttk.Entry(folder_frame, textvariable=ocr_threshold_var, width=6).grid(row=6, column=4, sticky="w")
-
- # 调整后续行号（原row=6改为row=7开始）
- ttk.Label(folder_frame, text="图片文件夹路径:").grid(row=7, column=0, padx=5, sticky="w")
- img_folder_path = tk.StringVar()
- ttk.Entry(folder_frame, textvariable=img_folder_path, width=40).grid(row=7, column=1, padx=5)
- ttk.Button(folder_frame, text="浏览", command=lambda: img_folder_path.set(filedialog.askdirectory())).grid(row=7, column=2, padx=5)
-
- # 调整处理文件夹按钮的行号
-
-
- # 处理文件夹按钮
- folder_button = ttk.Button(folder_frame, text="执行处理")
- folder_button.grid(row=8, column=0, columnspan=5, pady=5)
-
- # 处理文件的Frame
- file_frame = ttk.LabelFrame(root, text="处理单个文件")
- file_frame.grid(row=1, column=0, padx=10, pady=5, sticky="ew")
-
- # 处理文件的组件
- ttk.Label(file_frame, text="选择文件:").grid(row=0, column=0, padx=5, sticky="w")
- file_path = tk.StringVar()
- ttk.Entry(file_frame, textvariable=file_path, width=40).grid(row=0, column=1, padx=5)
- ttk.Button(file_frame, text="浏览", command=lambda: file_path.set(filedialog.askopenfilename())).grid(row=0, column=2, padx=5)
-
- ttk.Label(file_frame, text="保存路径:").grid(row=1, column=0, padx=5, sticky="w")
- save_path = tk.StringVar()
- ttk.Entry(file_frame, textvariable=save_path, width=40).grid(row=1, column=1, padx=5)
- ttk.Button(file_frame, text="浏览", command=lambda: save_path.set(filedialog.asksaveasfilename(defaultextension=".csv",filetypes=[("CSV文件", "*.csv"), ("所有文件", "*.*")]))).grid(row=1, column=2, padx=5)
-
- # 复选框
- remove_dup_file = tk.BooleanVar(value=False)
- ttk.Checkbutton(file_frame, text="不依赖时间戳清理重复子序列（在大数据集会非常慢，通常关闭）", variable=remove_dup_file).grid(row=2, column=0, columnspan=3, sticky="w")
-
- del_time_file = tk.BooleanVar(value=True)
- ttk.Checkbutton(file_frame, text="删除无时间戳数据", variable=del_time_file).grid(row=3, column=0, columnspan=3, sticky="w")
-
- open_black_file = tk.BooleanVar(value=True)
- ttk.Checkbutton(file_frame, text="将黑名单文件内的所有数据行同时加入黑名单", variable=open_black_file).grid(row=4, column=0, columnspan=3, sticky="w")
-
- # 修改后的图片识别行
- re_recognize_file_var = tk.BooleanVar(value=False)
- ttk.Checkbutton(file_frame, text="启用图片二次识别（必须指定图片路径）", variable=re_recognize_file_var).grid(row=5, column=0, padx=5, sticky="w")
-
- # 匹配阈值
- ttk.Label(file_frame, text="匹配阈值:").grid(row=5, column=1, padx=(20,5), sticky="e")
- matched_threshold_file_var = tk.DoubleVar(value=0.1)
- ttk.Entry(file_frame, textvariable=matched_threshold_file_var, width=6).grid(row=5, column=2, sticky="w")
-
- # OCR阈值
- ttk.Label(file_frame, text="OCR阈值:").grid(row=5, column=3, padx=(20,5), sticky="e")
- ocr_threshold_file_var = tk.DoubleVar(value=0.5)
- ttk.Entry(file_frame, textvariable=ocr_threshold_file_var, width=6).grid(row=5, column=4, sticky="w")
-
- # 调整后续行号
- ttk.Label(file_frame, text="图片文件夹路径:").grid(row=6, column=0, padx=5, sticky="w")
- img_folder_file_path = tk.StringVar()
- ttk.Entry(file_frame, textvariable=img_folder_file_path, width=40).grid(row=6, column=1, padx=5)
- ttk.Button(file_frame, text="浏览", command=lambda: img_folder_file_path.set(filedialog.askdirectory())).grid(row=6, column=2, padx=5)
-
- # 调整处理文件按钮的行号
-
-
-
- # 处理文件按钮
- file_button = ttk.Button(file_frame, text="执行处理")
- file_button.grid(row=7, column=0, columnspan=5, pady=5)
-
- # 配置网格权重
- root.grid_rowconfigure(3, weight=1)
- root.grid_columnconfigure(0, weight=1)
-
- # 按钮回调函数
- def process_folder_wrapper():
- folder = folder_path.get()
- interim = interim_save.get()
- final = final_save.get()
- if not folder or not interim or not final:
- messagebox.showerror("错误", "请填写所有路径")
- return
-
- folder_button.config(state=tk.DISABLED)
- def callback(e):
- folder_button.config(state=tk.NORMAL)
- if e:
- messagebox.showerror("错误", str(e))
- else:
- messagebox.showinfo("完成", "文件夹处理完成")
-
- thread = ProcessingThread(
- func=process_floder,
- args=(folder, interim, final, remove_dup.get(), del_time.get(), open_black.get(),re_recognize_var.get(), Path(img_folder_path.get()),matched_threshold_var.get(), ocr_threshold_var.get()),
- callback=callback
- )
- thread.start()
-
- def process_file_wrapper():
- input_file = file_path.get()
- output_file = save_path.get()
- if not input_file or not output_file:
- messagebox.showerror("错误", "请填写所有路径")
- return
-
- file_button.config(state=tk.DISABLED)
- def callback(e):
- file_button.config(state=tk.NORMAL)
- if e:
- messagebox.showerror("错误", str(e))
- else:
- messagebox.showinfo("完成", "文件处理完成")
-
- thread = ProcessingThread(
- func=process_file,
- args=(input_file, output_file, remove_dup_file.get(), del_time_file.get(), open_black_file.get(),re_recognize_file_var.get(), Path(img_folder_file_path.get()),matched_threshold_file_var.get(), ocr_threshold_file_var.get()),
- callback=callback
- )
- thread.start()
-
- # 绑定按钮命令
- folder_button.config(command=process_folder_wrapper)
- file_button.config(command=process_file_wrapper)
-
- return root
-
-if __name__ == "__main__":
- # 请确保以下函数已经正确导入或定义：
- # process_floder, process_file, find_csv_files, savecsv
-
- app = create_gui()
- app.mainloop()
diff --git a/tools/package.py b/tools/package.py
deleted file mode 100644
index 7133ffc..0000000
--- a/tools/package.py
+++ /dev/null
@@ -1,150 +0,0 @@
-import subprocess
-import shutil
-from pathlib import Path
-import toml # 导入toml库
-
-# TODO: 需重新适配UV
-
-# 配置区（用户可根据需要修改这些参数）
-CONFIG = {
- "venv_dir": ".venv", # 虚拟环境目录
- "source_script": "main.py", # 主程序文件路径
- "icon_file": r"ico/icon_64x64.ico", # 图标文件路径
- "output_dir": "output", # 输出目录
- "console": True,
- "add_data": [ # 需要打包的附加数据
- (r".venv/Lib/site-packages/rapidocr/default_models.yaml", "rapidocr"),
- (r".venv/Lib/site-packages/rapidocr/config.yaml", "rapidocr"),
- # (r".venv/Lib/site-packages/onnxruntime", "onnxruntime"),
- ],
- "copy_files": [ # 需要复制的额外文件/目录
- r"C:\Windows\System32\msvcp140.dll",
- r"C:\Windows\System32\vcruntime140.dll",
- r"C:\Windows\System32\vcruntime140_1.dll",
- # "arknights.csv",
- "models/best_model_full.onnx",
- "images",
- "platform-tools",
- "ico",
- "pyproject.toml",
- "monster.csv",
- ]
-}
-
-def build_exe():
- """使用PyInstaller打包"""
- venv_python = str(Path(CONFIG["venv_dir"]) / "Scripts" / "python.exe")
- script_path = Path(CONFIG["source_script"])
- icon_path = Path(CONFIG["icon_file"])
- build_cmd = [
- venv_python, "-m", "PyInstaller",
- "--noconfirm",
- "--onedir",
- "--console" if CONFIG["console"] else "--windowed", # 动态选择参数
- "--name", script_path.stem,
- "--distpath", CONFIG["output_dir"],
- "--workpath", "build",
- "--exclude-module", "train",
- "--exclude-module", "torch",
- "--exclude-module", "torchvision",
- # "--exclude-module", "onnxruntime",
- "--exclude-module", "predict",
- # "--exclude-module", "toml",
- # "--add-binary", ".venv/Lib/site-packages/onnxruntime/capi/onnxruntime_providers_shared.dll;.",
- ]
-
- if icon_path.exists():
- build_cmd.extend(["--icon", str(icon_path)])
-
- # 添加附加数据
- for src, dest in CONFIG["add_data"]:
- build_cmd.extend(["--add-data", f"{Path(src).resolve()};{dest}"])
-
- build_cmd.append(str(script_path))
-
- try:
- subprocess.check_call(build_cmd)
- return True
- except subprocess.CalledProcessError as e:
- print(f"打包失败：{e}")
- return False
-
-def copy_additional_files():
- """复制额外文件到输出目录"""
- exe_dir = Path(CONFIG["output_dir"]) / Path(CONFIG["source_script"]).stem
- if not exe_dir.exists():
- print(f"输出目录不存在: {exe_dir}")
- return False
-
- for item in CONFIG["copy_files"]:
- src = Path(item)
- # 保持相对路径结构
- if src.is_absolute():
- dest = exe_dir / src.name
- else:
- # 对于相对路径，在输出目录中创建相同的目录结构
- dest = exe_dir / src
-
- if not src.exists():
- print(f"警告：{src} 不存在，跳过复制")
- continue
-
- try:
- if src.is_dir():
- # 确保目标目录存在
- dest.mkdir(parents=True, exist_ok=True)
- # 特殊处理images目录，排除tmp和nums子目录
- if src.name == "images":
- def ignore_func(dir, names):
- """忽略tmp和nums子目录"""
- dir_path = Path(dir)
- # 仅在images根目录下排除特定子目录
- if dir_path.resolve() == src.resolve():
- return [n for n in names if n in {'tmp', 'nums'}]
- return []
- shutil.copytree(src, dest, ignore=ignore_func, dirs_exist_ok=True)
- else:
- shutil.copytree(src, dest, dirs_exist_ok=True)
- print(f"已复制目录：{src} -> {dest}")
- else:
- # 确保目标文件的父目录存在
- dest.parent.mkdir(parents=True, exist_ok=True)
- shutil.copy2(src, dest)
- print(f"已复制文件：{src} -> {dest}")
- except Exception as e:
- print(f"复制失败：{e}")
-
- return True
-
-def create_zip_archive(project_name, project_version):
- """将输出目录打包为zip文件"""
- output_dir = Path(CONFIG["output_dir"]) / "main"
- if not output_dir.exists():
- print(f"错误：输出目录 '{output_dir}' 不存在，无法创建zip文件。")
-
- zip_name = Path(CONFIG["output_dir"]) / f"{project_name}-{project_version}"
- try:
- # shutil.make_archive 会自动添加 .zip 扩展名
- shutil.make_archive(zip_name, 'zip', root_dir=output_dir)
- print(f"已创建zip文件：{zip_name}.zip")
- except Exception as e:
- print(f"创建zip文件失败：{e}")
-
-def main():
- with open("pyproject.toml", "r", encoding="utf-8") as f:
- pyproject_data = toml.load(f)
- project_name = pyproject_data["project"]["name"]
- project_version = pyproject_data["project"]["version"]
-
- if not build_exe():
- return
-
- if not copy_additional_files():
- return
-
- print(f"\n打包成功！输出目录：{Path(CONFIG['output_dir']).resolve()}")
-
- create_zip_archive(project_name, project_version)
-
-if __name__ == "__main__":
- main()
\ No newline at end of file
diff --git a/train.py b/train.py
index 11d3616..3314d6c 100644
--- a/train.py
+++ b/train.py
@@ -1,167 +1,52 @@
-import time
-from functools import cache
-from datetime import datetime
-from pathlib import Path
-
-import numpy as np
-import pandas as pd
+from torch.utils.data import Dataset, DataLoader, TensorDataset
import torch
import torch.nn as nn
-import torch.optim as optim
+import numpy as np
from sklearn.model_selection import train_test_split
-from torch.utils.data import DataLoader, Dataset
-from recognize import MONSTER_COUNT
-from config import FIELD_FEATURE_COUNT
-
-
-@cache
-def get_device(prefer_gpu=True):
- """
- prefer_gpu (bool): 是否优先尝试使用GPU
- """
- if prefer_gpu:
- if torch.cuda.is_available():
- return torch.device("cuda")
- elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
- return torch.device("mps") # Apple Silicon GPU
- elif hasattr(torch, "xpu") and torch.xpu.is_available(): # Intel GPU
- return torch.device("xpu")
- return torch.device("cpu")
-
-
-device = get_device()
-
-# 获取场地特征数量
-# field_recognizer = FieldRecognizer()
-# FIELD_FEATURE_COUNT = len(field_recognizer.get_feature_columns()) if field_recognizer.is_ready() else 0
-print(f"场地特征数量: {FIELD_FEATURE_COUNT}")
-
-# 计算总特征数量 (怪物特征 + 场地特征) * 2 + Result + ImgPath
-TOTAL_FEATURE_COUNT = (MONSTER_COUNT + FIELD_FEATURE_COUNT) * 2
-
-
-def preprocess_data(csv_file):
- """预处理CSV文件，将异常值修正为合理范围"""
- print(f"预处理数据文件: {csv_file}")
-
- # 读取CSV文件
- data = pd.read_csv(csv_file, header=None, skiprows=1)
- print(f"原始数据形状: {data.shape}")
-
- # 检查数据形状
- expected_columns = TOTAL_FEATURE_COUNT + 2 # +2 for Result and ImgPath
- if data.shape[1] != expected_columns:
- print(f"数据列数不符！期望 {expected_columns} 列，实际 {data.shape[1]} 列")
- print(
- f"期望格式: {MONSTER_COUNT}(怪物L) + {FIELD_FEATURE_COUNT}(场地L) + {MONSTER_COUNT}(怪物R) + {FIELD_FEATURE_COUNT}(场地R) + 1(Result) + 1(ImgPath)")
- raise Exception("数据格式不符")
-
- data = data.iloc[:, 0: TOTAL_FEATURE_COUNT + 1] # 保留特征和结果列，去掉ImgPath
-
- # 检查特征范围
- features = data.iloc[:, :-1]
- labels = data.iloc[:, -1]
-
- # 统计极端值
- extreme_values = (np.abs(features) > 20).sum().sum()
- if extreme_values > 0:
- print(f"发现 {extreme_values} 个绝对值大于20的特征值")
-
- # 检查标签
- invalid_labels = labels.apply(lambda x: x not in ["L", "R"]).sum()
- if invalid_labels > 0:
- print(f"发现 {invalid_labels} 个无效标签")
-
- # 输出特征的范围信息
- feature_min = features.min().min()
- feature_max = features.max().max()
- feature_mean = features.mean().mean()
- feature_std = features.std().mean()
-
- print(f"特征值范围: [{feature_min}, {feature_max}]")
- print(f"特征值平均值: {feature_mean:.4f}, 标准差: {feature_std:.4f}")
-
- # 如果需要，可以在这里对数据进行更多的预处理
- # 例如：将极端值截断到合理范围
-
- return data.shape[1]
-
-
+import pandas as pd
+import torch.optim as optim
class ArknightsDataset(Dataset):
- def __init__(self, csv_file, max_value=None):
- data = pd.read_csv(csv_file, header=None, skiprows=1)
- # 检查数据形状
- expected_columns = TOTAL_FEATURE_COUNT + 2 # +2 for Result and ImgPath
- if data.shape[1] != expected_columns:
- print(f"数据列数不符！期望 {expected_columns} 列，实际 {data.shape[1]} 列")
- raise Exception("数据格式不符")
- data = data.iloc[:, 0: TOTAL_FEATURE_COUNT + 1] # 保留特征和结果列，去掉ImgPath
+ def __init__(self, csv_file):
+ data = pd.read_csv(csv_file, header=None)
features = data.iloc[:, :-1].values.astype(np.float32)
- labels = data.iloc[:, -1].map({"L": 0, "R": 1}).values
+ labels = data.iloc[:, -1].map({'L': 0, 'R': 1}).values
labels = np.where((labels != 0) & (labels != 1), 0, labels).astype(np.float32)
- # 分割双方单位和场地特征
- # 数据格式: [怪物L(77), 场地L(6), 怪物R(77), 场地R(6)]
- left_monster_end = MONSTER_COUNT
- left_field_end = MONSTER_COUNT + FIELD_FEATURE_COUNT
- right_monster_end = MONSTER_COUNT + FIELD_FEATURE_COUNT + MONSTER_COUNT
- right_field_end = MONSTER_COUNT + FIELD_FEATURE_COUNT + MONSTER_COUNT + FIELD_FEATURE_COUNT
-
- # 提取各部分特征
- left_monster_features = features[:, :left_monster_end]
- left_field_features = features[:, left_monster_end:left_field_end]
- right_monster_features = features[:, left_field_end:right_monster_end]
- right_field_features = features[:, right_monster_end:right_field_end]
-
- # 合并怪物特征和场地特征（场地特征直接使用，不取绝对值和符号）
- left_counts = np.concatenate([np.abs(left_monster_features), left_field_features], axis=1)
- right_counts = np.concatenate([np.abs(right_monster_features), right_field_features], axis=1)
- left_signs = np.concatenate([np.sign(left_monster_features), np.ones_like(left_field_features)], axis=1)
- right_signs = np.concatenate([np.sign(right_monster_features), np.ones_like(right_field_features)], axis=1)
-
- if max_value is not None:
- left_counts = np.clip(left_counts, 0, max_value)
- right_counts = np.clip(right_counts, 0, max_value)
-
- # 转换为 PyTorch 张量，并一次性加载到 GPU
- self.left_signs = torch.from_numpy(left_signs).to(device)
- self.right_signs = torch.from_numpy(right_signs).to(device)
- self.left_counts = torch.from_numpy(left_counts).to(device)
- self.right_counts = torch.from_numpy(right_counts).to(device)
- self.labels = torch.from_numpy(labels).float().to(device)
+ # 分离左右双方并保留符号信息
+ self.left_signs = np.sign(features[:, :26])
+ self.right_signs = np.sign(features[:, 26:])
+ self.left_counts = np.abs(features[:, :26])
+ self.right_counts = np.abs(features[:, 26:])
+ self.labels = labels
def __len__(self):
return len(self.labels)
def __getitem__(self, idx):
return (
- self.left_signs[idx],
- self.left_counts[idx],
- self.right_signs[idx],
- self.right_counts[idx],
- self.labels[idx],
+ torch.tensor(self.left_signs[idx]),
+ torch.tensor(self.left_counts[idx]),
+ torch.tensor(self.right_signs[idx]),
+ torch.tensor(self.right_counts[idx]),
+ torch.tensor(self.labels[idx], dtype=torch.float32)
)
class UnitAwareTransformer(nn.Module):
- def __init__(self, num_units, embed_dim=128, num_heads=8, num_layers=4):
+ def __init__(self, num_units=27, embed_dim=128, num_heads=8, num_layers=3):
super().__init__()
- # num_units，包括怪物种类和场地特征种类
- # 怪物特征 + 场地特征 = 总特征数量
self.num_units = num_units
- self.monster_count = MONSTER_COUNT
- self.field_count = FIELD_FEATURE_COUNT
self.embed_dim = embed_dim
self.num_layers = num_layers
# 嵌入层
- self.unit_embed = nn.Embedding(num_units, embed_dim)
+ self.unit_embed = nn.Embedding(num_units, embed_dim, padding_idx=0)
nn.init.normal_(self.unit_embed.weight, mean=0.0, std=0.02)
self.value_ffn = nn.Sequential(
nn.Linear(embed_dim, embed_dim * 2),
nn.ReLU(),
- nn.Linear(embed_dim * 2, embed_dim),
+ nn.Linear(embed_dim * 2, embed_dim)
)
# 注意力层与FFN
@@ -169,88 +54,74 @@ def __init__(self, num_units, embed_dim=128, num_heads=8, num_layers=4):
self.friend_attentions = nn.ModuleList()
self.enemy_ffn = nn.ModuleList()
self.friend_ffn = nn.ModuleList()
- self.norm = nn.ModuleList()
+
+ self.enemy_norm1 = nn.ModuleList()
+ self.friend_norm1 = nn.ModuleList()
+ self.enemy_norm2 = nn.ModuleList()
+ self.friend_norm2 = nn.ModuleList()
for _ in range(num_layers):
# 敌方注意力层
self.enemy_attentions.append(
- nn.MultiheadAttention(
- embed_dim, num_heads, batch_first=True, dropout=0.2
- )
- )
- self.enemy_ffn.append(
- nn.Sequential(
- nn.Linear(embed_dim, embed_dim * 2),
- nn.ReLU(),
- nn.Dropout(0.2),
- nn.Linear(embed_dim * 2, embed_dim),
- )
+ nn.MultiheadAttention(embed_dim, num_heads, batch_first=True, dropout=0.2)
)
+ self.enemy_ffn.append(nn.Sequential(
+ nn.Linear(embed_dim, embed_dim * 4),
+ nn.ReLU(),
+ nn.Dropout(0.2),
+ nn.Linear(embed_dim * 4, embed_dim)
+ ))
# 友方注意力层
self.friend_attentions.append(
- nn.MultiheadAttention(
- embed_dim, num_heads, batch_first=True, dropout=0.2
- )
- )
- self.friend_ffn.append(
- nn.Sequential(
- nn.Linear(embed_dim, embed_dim * 2),
- nn.ReLU(),
- nn.Dropout(0.2),
- nn.Linear(embed_dim * 2, embed_dim),
- )
+ nn.MultiheadAttention(embed_dim, num_heads, batch_first=True, dropout=0.2)
)
+ self.friend_ffn.append(nn.Sequential(
+ nn.Linear(embed_dim, embed_dim * 4),
+ nn.ReLU(),
+ nn.Dropout(0.2),
+ nn.Linear(embed_dim * 4, embed_dim)
+ ))
# 初始化注意力层参数
nn.init.xavier_uniform_(self.enemy_attentions[-1].in_proj_weight)
nn.init.xavier_uniform_(self.friend_attentions[-1].in_proj_weight)
- self.norm.append(nn.LayerNorm(embed_dim))
-
# 全连接输出层
self.fc = nn.Sequential(
- nn.Linear(embed_dim, embed_dim * 2), nn.ReLU(), nn.Linear(embed_dim * 2, 1)
+ nn.Linear(embed_dim, embed_dim * 2),
+ nn.ReLU(),
+ nn.Linear(embed_dim * 2, 1)
)
def forward(self, left_sign, left_count, right_sign, right_count):
- # 提取TopK特征（怪物 + 场地）
- # 由于现在包含场地特征，需要增加k值以确保重要特征不被遗漏
- # k=8 可以保证包含主要怪物和所有场地特征
- k = min(8, left_count.shape[1]) # 确保k不超过实际特征数
- left_values, left_indices = torch.topk(left_count, k=k, dim=1)
- right_values, right_indices = torch.topk(right_count, k=k, dim=1)
+ # 提取Top3兵种特征
+ left_values, left_indices = torch.topk(left_count, k=3, dim=1)
+ right_values, right_indices = torch.topk(right_count, k=3, dim=1)
# 嵌入
- left_feat = self.unit_embed(left_indices) # (B, k, 128)
- right_feat = self.unit_embed(right_indices) # (B, k, 128)
+ left_feat = self.unit_embed(left_indices) # (B, 3, 128)
+ right_feat = self.unit_embed(right_indices) # (B, 3, 128)
embed_dim = self.embed_dim
- # 前x维不变，后y维 *= 数量，但使用缩放后的值
- left_feat = torch.cat(
- [
- left_feat[..., : embed_dim // 2], # 前x维
- left_feat[..., embed_dim // 2:]
- * left_values.unsqueeze(-1), # 后y维乘数量
- ],
- dim=-1,
- )
- right_feat = torch.cat(
- [
- right_feat[..., : embed_dim // 2],
- right_feat[..., embed_dim // 2:] * right_values.unsqueeze(-1),
- ],
- dim=-1,
- )
+ # 前x维不变，后y维 *= 数量
+ left_feat = torch.cat([
+ left_feat[..., :embed_dim // 2], # 前x维
+ left_feat[..., embed_dim // 2:] * left_values.unsqueeze(-1) # 后y维乘数量，后y维可直接代替统计量，同时避免引入额外统计量参数
+ ], dim=-1)
+ right_feat = torch.cat([
+ right_feat[..., :embed_dim // 2],
+ right_feat[..., embed_dim // 2:] * right_values.unsqueeze(-1)
+ ], dim=-1)
# FFN
left_feat = left_feat + self.value_ffn(left_feat)
right_feat = right_feat + self.value_ffn(right_feat)
- # 生成mask (B, k) 0.1防一手可能的浮点误差
- left_mask = left_values > 0.1
- right_mask = right_values > 0.1
+ # 生成mask (B, 3)
+ left_mask = (left_values > 0)
+ right_mask = (right_values > 0)
for i in range(self.num_layers):
# 敌方注意力
@@ -259,17 +130,16 @@ def forward(self, left_sign, left_count, right_sign, right_count):
key=right_feat,
value=right_feat,
key_padding_mask=~right_mask,
- need_weights=False,
+ need_weights=False
)
delta_right, _ = self.enemy_attentions[i](
query=right_feat,
key=left_feat,
value=left_feat,
key_padding_mask=~left_mask,
- need_weights=False,
+ need_weights=False
)
-
- # 残差连接
+ # 残差连接（现在可以归一化了，但没什么意义，反而训练更慢）
left_feat = left_feat + delta_left
right_feat = right_feat + delta_right
@@ -283,16 +153,15 @@ def forward(self, left_sign, left_count, right_sign, right_count):
key=left_feat,
value=left_feat,
key_padding_mask=~left_mask,
- need_weights=False,
+ need_weights=False
)
delta_right, _ = self.friend_attentions[i](
query=right_feat,
key=right_feat,
value=right_feat,
key_padding_mask=~right_mask,
- need_weights=False,
+ need_weights=False
)
-
# 残差连接
left_feat = left_feat + delta_left
right_feat = right_feat + delta_right
@@ -300,103 +169,40 @@ def forward(self, left_sign, left_count, right_sign, right_count):
# FFN
left_feat = left_feat + self.friend_ffn[i](left_feat)
right_feat = right_feat + self.friend_ffn[i](right_feat)
- left_feat = self.norm[i](left_feat)
- right_feat = self.norm[i](right_feat)
# 输出战斗力
L = self.fc(left_feat).squeeze(-1) * left_mask
R = self.fc(right_feat).squeeze(-1) * right_mask
-
# 计算战斗力差输出概率，'L': 0, 'R': 1，R大于L时输出大于0.5
output = torch.sigmoid(R.sum(1) - L.sum(1))
return output
-
-def train_one_epoch(model, train_loader, criterion, optimizer, scaler=None):
+def train_one_epoch(model, train_loader, criterion, optimizer, device):
model.train()
total_loss = 0
correct = 0
total = 0
for ls, lc, rs, rc, labels in train_loader:
- ls, lc, rs, rc, labels = [
- x.to(device, non_blocking=True) for x in (ls, lc, rs, rc, labels)
- ]
+ ls, lc, rs, rc, labels = [x.to(device) for x in (ls, lc, rs, rc, labels)]
optimizer.zero_grad()
+ outputs = model(ls, lc, rs, rc).squeeze()
+ loss = criterion(outputs, labels)
+ loss.backward()
- # 检查输入值范围
- if (
- torch.isnan(ls).any()
- or torch.isnan(lc).any()
- or torch.isnan(rs).any()
- or torch.isnan(rc).any()
- ):
- print("警告: 输入数据包含NaN，跳过该批次")
- continue
-
- if (
- torch.isinf(ls).any()
- or torch.isinf(lc).any()
- or torch.isinf(rs).any()
- or torch.isinf(rc).any()
- ):
- print("警告: 输入数据包含Inf，跳过该批次")
- continue
-
- # 确保labels严格在0-1之间
- if (labels < 0).any() or (labels > 1).any():
- print("警告: 标签值不在[0,1]范围内，进行修正")
- labels = torch.clamp(labels, 0, 1)
-
- try:
- with torch.amp.autocast_mode.autocast(
- device_type=device.type, enabled=(scaler is not None)
- ):
- outputs = model(ls, lc, rs, rc).squeeze()
- # 确保输出在合理范围内
- if torch.isnan(outputs).any() or torch.isinf(outputs).any():
- print("警告: 模型输出包含NaN或Inf，跳过该批次")
- continue
-
- # 确保输出严格在0-1之间
- if (outputs < 0).any() or (outputs > 1).any():
- print("警告: 模型输出不在[0,1]范围内，进行修正")
- outputs = torch.clamp(outputs, 1e-7, 1 - 1e-7)
-
- loss = criterion(outputs, labels)
-
- # 检查loss是否有效
- if torch.isnan(loss) or torch.isinf(loss):
- print(f"警告: 损失值为 {loss.item()}, 跳过该批次")
- continue
-
- if scaler: # 使用混合精度
- scaler.scale(loss).backward()
- # 梯度裁剪，避免梯度爆炸
- torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
- scaler.step(optimizer)
- scaler.update()
- else: # 不使用混合精度
- loss.backward()
- # 梯度裁剪，避免梯度爆炸
- torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
- optimizer.step()
-
- total_loss += loss.item()
- preds = (outputs > 0.5).float()
- correct += (preds == labels).sum().item()
- total += labels.size(0)
+ optimizer.step()
- except RuntimeError as e:
- print(f"警告: 训练过程中出错 - {str(e)}")
- continue
+ total_loss += loss.item()
+ preds = (outputs > 0.5).float()
+ correct += (preds == labels).sum().item()
+ total += labels.size(0)
- return total_loss / max(1, len(train_loader)), 100 * correct / max(1, total)
+ return total_loss / len(train_loader), 100 * correct / total
-def evaluate(model, data_loader, criterion):
+def evaluate(model, data_loader, criterion, device):
model.eval()
total_loss = 0
correct = 0
@@ -404,315 +210,85 @@ def evaluate(model, data_loader, criterion):
with torch.no_grad():
for ls, lc, rs, rc, labels in data_loader:
- ls, lc, rs, rc, labels = [
- x.to(device, non_blocking=True) for x in (ls, lc, rs, rc, labels)
- ]
-
- # 检查输入值范围
- if (
- torch.isnan(ls).any()
- or torch.isnan(lc).any()
- or torch.isnan(rs).any()
- or torch.isnan(rc).any()
- or torch.isinf(ls).any()
- or torch.isinf(lc).any()
- or torch.isinf(rs).any()
- or torch.isinf(rc).any()
- ):
- print("警告: 评估时输入数据包含NaN或Inf，跳过该批次")
- continue
-
- # 确保labels严格在0-1之间
- if (labels < 0).any() or (labels > 1).any():
- labels = torch.clamp(labels, 0, 1)
-
- try:
- with torch.amp.autocast_mode.autocast(
- device_type=device.type, enabled=(device.type == "cuda")
- ):
- outputs = model(ls, lc, rs, rc).squeeze()
- # 确保输出在合理范围内
- if torch.isnan(outputs).any() or torch.isinf(outputs).any():
- print("警告: 评估时模型输出包含NaN或Inf，跳过该批次")
- continue
-
- # 确保输出严格在0-1之间
- if (outputs < 0).any() or (outputs > 1).any():
- outputs = torch.clamp(outputs, 1e-7, 1 - 1e-7)
-
- loss = criterion(outputs, labels)
-
- # 检查loss是否有效
- if torch.isnan(loss) or torch.isinf(loss):
- continue
-
- total_loss += loss.item()
- preds = (outputs > 0.5).float()
- correct += (preds == labels).sum().item()
- total += labels.size(0)
-
- except RuntimeError as e:
- print(f"警告: 评估过程中出错 - {str(e)}")
- continue
-
- return total_loss / max(1, len(data_loader)), 100 * correct / max(1, total)
-
-
-def stratified_random_split(dataset, test_size=0.1, seed=42):
- labels = dataset.labels # 假设 labels 是一个 GPU tensor
- if str(device) != "cpu":
- labels = labels.cpu() # 移动到 CPU 上进行操作
- labels = labels.numpy() # 转换为 numpy array
-
- indices = np.arange(len(labels))
- train_indices, val_indices = train_test_split(
- indices, test_size=test_size, random_state=seed, stratify=labels
- )
- return (
- torch.utils.data.Subset(dataset, train_indices),
- torch.utils.data.Subset(dataset, val_indices),
- )
+ ls, lc, rs, rc, labels = [x.to(device) for x in (ls, lc, rs, rc, labels)]
+ outputs = model(ls, lc, rs, rc).squeeze()
+
+ loss = criterion(outputs, labels)
+ total_loss += loss.item()
+ preds = (outputs > 0.5).float()
+ correct += (preds == labels).sum().item()
+ total += labels.size(0)
+ return total_loss / len(data_loader), 100 * correct / total
def main():
- # 配置参数
config = {
- "data_file": "arknights.csv",
- "batch_size": 1024, # 512
- "test_size": 0.1,
- "embed_dim": 128, # 512
- "n_layers": 3, # 3也可以
- "num_heads": 16,
- "lr": 3e-4, # 3e-4
- "epochs": 200, # 推荐500+
- "seed": 42, # 随机数种子
- "save_dir": "models", # 存到哪里
- "max_feature_value": 100, # 限制特征最大值，防止极端值造成不稳定
- "num_workers": 0
- if torch.cuda.is_available()
- else 0, # 根据CUDA可用性设置num_workers
+ 'batch_size': 128,
+ 'embed_dim': 128,
+ 'n_layers': 4,
+ 'lr': 3e-4,
+ 'epochs': 150,
+ 'seed': 42
}
- # 创建保存目录
- Path(config["save_dir"]).mkdir(parents=True, exist_ok=True)
-
- # 设置随机种子
- torch.manual_seed(config["seed"])
- np.random.seed(config["seed"])
- if torch.cuda.is_available():
- torch.cuda.manual_seed_all(config["seed"])
-
- # 设置设备
- print(f"使用设备: {device}")
-
- # 初始化 GradScaler 用于混合精度训练
- scaler = None
- if device.type == "cuda":
- try:
- scaler = torch.amp.grad_scaler.GradScaler("cuda")
- except (AttributeError, TypeError):
- scaler = torch.amp.grad_scaler.GradScaler() # 如果是老版本
- print("CUDA可用，已启用混合精度训练的GradScaler。")
-
- # 检查CUDA可用性
- if str(device) == "cuda":
- print(f"CUDA设备数量: {torch.cuda.device_count()}")
- print(f"当前CUDA设备: {torch.cuda.current_device()}")
- print(f"CUDA设备名称: {torch.cuda.get_device_name(0)}")
-
- # 设置确定性计算以增加稳定性
- torch.backends.cudnn.deterministic = True
- torch.backends.cudnn.benchmark = True
- elif str(device) == "cpu":
- print("警告: 未检测到GPU，将在CPU上运行训练，这可能会很慢!")
-
- # 先预处理数据，检查是否有异常值
- num_data = preprocess_data(config["data_file"])
-
- # 加载数据集
- dataset = ArknightsDataset(
- config["data_file"],
- max_value=config["max_feature_value"], # 使用最大值限制
- )
+ torch.manual_seed(config['seed'])
+ np.random.seed(config['seed'])
- # 数据集分割
- data_length = len(dataset)
- val_size = int(0.1 * data_length) # 10% 验证集
- train_size = data_length - val_size
+ device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+ print(f"Using device: {device}")
- # 划分
- train_dataset, val_dataset = stratified_random_split(
- dataset, test_size=config["test_size"], seed=config["seed"]
+ dataset = ArknightsDataset('arknights.csv')
+ train_indices, val_indices = train_test_split(
+ range(len(dataset)), test_size=0.1, random_state=config['seed']
)
- print(f"训练集大小: {train_size}, 验证集大小: {val_size}")
-
- # 数据加载器
train_loader = DataLoader(
- train_dataset,
- batch_size=config["batch_size"],
+ torch.utils.data.Subset(dataset, train_indices),
+ batch_size=config['batch_size'],
shuffle=True,
- num_workers=config["num_workers"],
+ num_workers=1,
+ pin_memory=True,
+ persistent_workers=True,
+ prefetch_factor=32
)
val_loader = DataLoader(
- val_dataset, batch_size=config["batch_size"], num_workers=config["num_workers"]
+ torch.utils.data.Subset(dataset, val_indices),
+ batch_size=config['batch_size'],
+ num_workers=1,
+ pin_memory=True,
+ persistent_workers=True,
+ prefetch_factor=32
)
- # 初始化模型
- # num_units 现在包括怪物数量和场地特征数量
- total_units = MONSTER_COUNT + FIELD_FEATURE_COUNT
model = UnitAwareTransformer(
- num_units=total_units,
- embed_dim=config["embed_dim"],
- num_heads=config["num_heads"],
- num_layers=config["n_layers"],
+ num_units=26,
+ embed_dim=config['embed_dim'],
+ num_heads=8,
+ num_layers=config['n_layers']
).to(device)
- print(f"模型使用特征数: 怪物({MONSTER_COUNT}) + 场地({FIELD_FEATURE_COUNT}) = {total_units}")
-
- print(
- f"模型参数数量: {sum(p.numel() for p in model.parameters() if p.requires_grad)}"
- )
-
- # 损失函数和优化器
- criterion = nn.MSELoss()
- optimizer = optim.AdamW(model.parameters(), lr=config["lr"], weight_decay=1e-1)
- scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=config["epochs"])
-
- # 训练历史记录
- train_losses = []
- val_losses = []
- train_accs = []
- val_accs = []
+ criterion = nn.BCELoss()
+ optimizer = optim.AdamW(model.parameters(), lr=config['lr'], weight_decay=1e-4)
+ scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=config['epochs'])
- # 训练设置
best_acc = 0
- best_loss = float("inf")
-
- # 训练循环
- for epoch in range(config["epochs"]):
- print(f"\nEpoch {epoch + 1}/{config['epochs']}")
-
- # 训练
+ for epoch in range(config['epochs']):
train_loss, train_acc = train_one_epoch(
- model, train_loader, criterion, optimizer, scaler
- )
-
- # 验证
- val_loss, val_acc = evaluate(model, val_loader, criterion)
-
- # 更新学习率
+ model, train_loader, criterion, optimizer, device)
+ val_loss, val_acc = evaluate(
+ model, val_loader, criterion, device)
scheduler.step()
- # 记录历史
- train_losses.append(train_loss)
- val_losses.append(val_loss)
- train_accs.append(train_acc)
- val_accs.append(val_acc)
-
- # 保存最佳模型（基于准确率）
if val_acc > best_acc:
best_acc = val_acc
- torch.save(
- model,
- Path(config["save_dir"]) / "best_model_acc.pth",
- )
-
- print("保存了新的最佳准确率模型!")
- else:
- print(f"最佳准确率为: {best_acc:.2f}")
+ torch.save(model.state_dict(), 'best_model.pth')
+ # 保存完整模型（方便部署）
+ torch.save(model, 'best_model_full.pth')
- # 保存最佳模型（基于损失）
- if val_loss < best_loss:
- best_loss = val_loss
- torch.save(
- model,
- Path(config["save_dir"]) / "best_model_loss.pth",
- )
- print("保存了新的最佳损失模型!")
- else:
- print(f"最佳损失为: {best_loss:.4f}")
-
- torch.save(
- model, Path(config["save_dir"]) / "best_model_full.pth"
- ) # 最后一次计算的模型
-
- # 保存最新模型
- # torch.save({
- # 'epoch': epoch,
- # 'model_state_dict': model.state_dict(),
- # 'optimizer_state_dict': optimizer.state_dict(),
- # 'train_loss': train_loss,
- # 'val_loss': val_loss,
- # 'train_acc': train_acc,
- # 'val_acc': val_acc,
- # 'config': config
- # }, os.path.join(config['save_dir'], 'latest_checkpoint.pth'))
-
- # 打印训练信息
+ print(f"Epoch {epoch + 1}/{config['epochs']}")
print(f"Train Loss: {train_loss:.4f} | Acc: {train_acc:.2f}%")
print(f"Val Loss: {val_loss:.4f} | Acc: {val_acc:.2f}%")
print("-" * 40)
- # 计时
- if epoch == 0:
- start_time = time.time()
- epoch_start_time = start_time
- else:
- current_time = time.time()
- epoch_duration = current_time - epoch_start_time
- elapsed_time = current_time - start_time
- avg_epoch_time = elapsed_time / (epoch + 1)
- estimated_total_time = avg_epoch_time * config["epochs"]
- remaining_time = estimated_total_time - elapsed_time
-
- print(f"Epoch Time: {epoch_duration:.2f}s")
- print(f"Elapsed Time: {elapsed_time / 60:.2f}min")
- print(f"Estimated Remaining Time: {remaining_time / 60:.2f}min")
- print(f"Estimated Total Time: {estimated_total_time / 60:.2f}min")
- epoch_start_time = current_time # Reset for next epoch
-
- print("-" * 40)
-
- # 绘制并保存训练历史
- # if (epoch + 1) % 5 == 0 or epoch == config['epochs'] - 1:
- # plot_training_history(
- # train_losses, val_losses, train_accs, val_accs,
- # save_path=os.path.join(config['save_dir'], 'training_history.png')
- # )
-
- print(f"训练完成! 最佳验证准确率: {best_acc:.2f}%, 最佳验证损失: {best_loss:.4f}")
-
- # 训练完成后重命名模型文件
- current_time_str = datetime.now().strftime("%Y_%m_%d_%H_%M_%S")
- base_filename = f"data{data_length}_acc{best_acc:.4f}_loss{best_loss:.4f}_{current_time_str}.pth"
-
- save_dir_path = Path(config["save_dir"])
-
- old_acc_path = save_dir_path / "best_model_acc.pth"
- new_acc_path = save_dir_path / f"best_model_acc_{base_filename}"
- if old_acc_path.exists():
- old_acc_path.rename(new_acc_path)
- print(f"模型文件已重命名: {old_acc_path} -> {new_acc_path}")
-
- old_loss_path = save_dir_path / "best_model_loss.pth"
- new_loss_path = save_dir_path / f"best_model_loss_{base_filename}"
- if old_loss_path.exists():
- old_loss_path.rename(new_loss_path)
- print(f"模型文件已重命名: {old_loss_path} -> {new_loss_path}")
-
- old_full_path = save_dir_path / "best_model_full.pth"
- new_full_path = save_dir_path / f"best_model_full_{base_filename}"
- if old_full_path.exists():
- old_full_path.rename(new_full_path)
- print(f"模型文件已重命名: {old_full_path} -> {new_full_path}")
-
- # 保存最终训练历史
- # plot_training_history(
- # train_losses, val_losses, train_accs, val_accs,
- # save_path=os.path.join(config['save_dir'], 'final_training_history.png')
- # )
-
-
if __name__ == "__main__":
main()
\ No newline at end of file
diff --git a/unit.py b/unit.py
deleted file mode 100644
index d4c290c..0000000
--- a/unit.py
+++ /dev/null
@@ -1,29 +0,0 @@
-from constants import UNIT_CONFIG # 添加这行导入
-
-class Unit:
- def __init__(self, team, unit_id, x, y):
- self.team = team
- self.unit_id = unit_id
- self.config = UNIT_CONFIG[1] # 现在可以正确访问配置
-
- self.x = x
- self.y = y
- self.health = self.config["health"]
- self.max_health = self.config["health"]
- self.attack_cooldown = 0.0
- # 当前防御值属性（带初始值）
- self.current_defense = self.config["defense"]
- # 最大可降低防御值（不低于0）
- self.min_defense = 0
-
- # 技力
- self.skill = 0
- self.max_skill = 1
-
- self.original_magic_resist = self.config["magic_resist"] # 原始法抗
- self.current_magic_resist = self.config["magic_resist"] # 当前法抗
- self.burn_effect_duration = 0 # 法抗降低剩余时间
-
- @property
- def is_alive(self):
- return self.health > 0
\ No newline at end of file
diff --git a/uv.lock b/uv.lock
deleted file mode 100644
index 69f4a7e..0000000
--- a/uv.lock
+++ /dev/null
@@ -1,2300 +0,0 @@
-version = 1
-revision = 3
-requires-python = ">=3.10"
-resolution-markers = [
- "python_full_version >= '3.13' and sys_platform == 'darwin'",
- "python_full_version == '3.12.*' and sys_platform == 'darwin'",
- "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform == 'linux'",
- "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
- "(python_full_version >= '3.13' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.13' and sys_platform != 'darwin' and sys_platform != 'linux')",
- "(python_full_version == '3.12.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.12.*' and sys_platform != 'darwin' and sys_platform != 'linux')",
- "python_full_version == '3.11.*' and sys_platform == 'darwin'",
- "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
- "(python_full_version == '3.11.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'linux')",
- "python_full_version < '3.11' and sys_platform == 'darwin'",
- "python_full_version < '3.11' and platform_machine == 'aarch64' and sys_platform == 'linux'",
- "(python_full_version < '3.11' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'linux')",
-]
-conflicts = [[
- { package = "cannotmax-honeydew", extra = "cpu" },
- { package = "cannotmax-honeydew", extra = "cu128" },
-]]
-
-[[package]]
-name = "altgraph"
-version = "0.17.4"
-source = { registry = "https://mirrors.cloud.tencent.com/pypi/simple/" }
-sdist = { url = "https://mirrors.cloud.tencent.com/pypi/packages/de/a8/7145824cf0b9e3c28046520480f207df47e927df83aa9555fb47f8505922/altgraph-0.17.4.tar.gz", hash = "sha256:1b5afbb98f6c4dcadb2e2ae6ab9fa994bbb8c1d75f4fa96d340f9437ae454406" }
-wheels = [
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-]
-
-[[package]]
-name = "antlr4-python3-runtime"
-version = "4.9.3"
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-
-[[package]]
-name = "cannotmax-honeydew"
-version = "1.0.3"
-source = { virtual = "." }
-dependencies = [
- { name = "matplotlib" },
- { name = "numpy", version = "2.2.6", source = { registry = "https://mirrors.cloud.tencent.com/pypi/simple/" }, marker = "python_full_version < '3.11' or (extra == 'extra-18-cannotmax-honeydew-cpu' and extra == 'extra-18-cannotmax-honeydew-cu128')" },
- { name = "numpy", version = "2.3.2", source = { registry = "https://mirrors.cloud.tencent.com/pypi/simple/" }, marker = "python_full_version >= '3.11' or (extra == 'extra-18-cannotmax-honeydew-cpu' and extra == 'extra-18-cannotmax-honeydew-cu128')" },
- { name = "onnxruntime" },
- { name = "opencv-python" },
- { name = "pandas" },
- { name = "pillow" },
- { name = "pyqt6" },
- { name = "rapidocr" },
- { name = "scikit-learn" },
- { name = "seaborn" },
- { name = "toml" },
- { name = "windows-capture" },
-]
-
-[package.optional-dependencies]
-cpu = [
- { name = "torch", version = "2.8.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(sys_platform == 'darwin' and extra == 'extra-18-cannotmax-honeydew-cpu') or (extra == 'extra-18-cannotmax-honeydew-cpu' and extra == 'extra-18-cannotmax-honeydew-cu128')" },
- { name = "torch", version = "2.8.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(sys_platform != 'darwin' and extra == 'extra-18-cannotmax-honeydew-cpu') or (extra == 'extra-18-cannotmax-honeydew-cpu' and extra == 'extra-18-cannotmax-honeydew-cu128')" },
- { name = "torchvision", version = "0.23.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(platform_machine == 'aarch64' and sys_platform == 'linux' and extra == 'extra-18-cannotmax-honeydew-cpu') or (platform_machine != 'aarch64' and extra == 'extra-18-cannotmax-honeydew-cpu' and extra == 'extra-18-cannotmax-honeydew-cu128') or (sys_platform == 'darwin' and extra == 'extra-18-cannotmax-honeydew-cpu') or (sys_platform != 'linux' and extra == 'extra-18-cannotmax-honeydew-cpu' and extra == 'extra-18-cannotmax-honeydew-cu128')" },
- { name = "torchvision", version = "0.23.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(platform_machine != 'aarch64' and sys_platform == 'linux' and extra == 'extra-18-cannotmax-honeydew-cpu') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-18-cannotmax-honeydew-cpu') or (sys_platform == 'darwin' and extra == 'extra-18-cannotmax-honeydew-cpu' and extra == 'extra-18-cannotmax-honeydew-cu128') or (sys_platform == 'linux' and extra == 'extra-18-cannotmax-honeydew-cpu' and extra == 'extra-18-cannotmax-honeydew-cu128')" },
-]
-cu128 = [
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- { name = "torchvision", version = "0.23.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" } },
-]
-
-[package.dev-dependencies]
-dev = [
- { name = "onnx" },
- { name = "pyinstaller" },
-]
-
-[package.metadata]
-requires-dist = [
- { name = "matplotlib" },
- { name = "numpy" },
- { name = "onnxruntime" },
- { name = "opencv-python" },
- { name = "pandas" },
- { name = "pillow" },
- { name = "pyqt6" },
- { name = "rapidocr" },
- { name = "scikit-learn" },
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- { name = "toml" },
- { name = "torch", marker = "extra == 'cpu'", index = "https://download.pytorch.org/whl/cpu", conflict = { package = "cannotmax-honeydew", extra = "cpu" } },
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- { name = "windows-capture" },
-]
-provides-extras = ["cpu", "cu128"]
-
-[package.metadata.requires-dev]
-dev = [
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- { name = "pyinstaller", specifier = ">=6.15.0" },
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-
-[[package]]
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diff --git a/val.py b/val.py
deleted file mode 100644
index 2d83354..0000000
--- a/val.py
+++ /dev/null
@@ -1,110 +0,0 @@
-import torch
-import torch.nn as nn
-from torch.utils.data import DataLoader
-from train import UnitAwareTransformer,ArknightsDataset
-
-def evaluate(model, data_loader, criterion, device):
- model.eval()
- total_loss = 0
- correct = 0
- total = 0
-
- with torch.no_grad():
- for batch in data_loader:
- # 确保数据在CPU上，然后移动到设备
- ls, lc, rs, rc, labels = [x.to(device) for x in batch]
-
- # 其余代码保持不变...
- # 检查输入值范围
- if torch.isnan(ls).any() or torch.isnan(lc).any() or torch.isnan(rs).any() or torch.isnan(rc).any() or \
- torch.isinf(ls).any() or torch.isinf(lc).any() or torch.isinf(rs).any() or torch.isinf(rc).any():
- print("警告: 评估时输入数据包含NaN或Inf，跳过该批次")
- continue
-
- # 确保labels严格在0-1之间
- if (labels < 0).any() or (labels > 1).any():
- labels = torch.clamp(labels, 0, 1)
-
- try:
- outputs = model(ls, lc, rs, rc).squeeze()
-
- # 确保输出在合理范围内
- if torch.isnan(outputs).any() or torch.isinf(outputs).any():
- print("警告: 评估时模型输出包含NaN或Inf，跳过该批次")
- continue
-
- # 确保输出严格在0-1之间，因为BCELoss需要
- if (outputs < 0).any() or (outputs > 1).any():
- outputs = torch.clamp(outputs, 1e-7, 1 - 1e-7)
-
- loss = criterion(outputs, labels)
-
- # 检查loss是否有效
- if torch.isnan(loss) or torch.isinf(loss):
- continue
-
- total_loss += loss.item()
- preds = (outputs > 0.5).float()
- correct += (preds == labels).sum().item()
- total += labels.size(0)
-
- except RuntimeError as e:
- print(f"警告: 评估过程中出错 - {str(e)}")
- continue
-
- return total_loss / max(1, len(data_loader)), 100 * correct / max(1, total)
-
-def main():
- config = {
- 'data_file': 'arknights_clean.csv',
- 'batch_size': 256,
- 'max_feature_value': 200 # 限制特征最大值，防止极端值造成不稳定
- }
- # 设置设备
- device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
- print(f"使用设备: {device}")
-
- # 检查CUDA可用性
- if torch.cuda.is_available():
- print(f"CUDA设备数量: {torch.cuda.device_count()}")
- print(f"当前CUDA设备: {torch.cuda.current_device()}")
- print(f"CUDA设备名称: {torch.cuda.get_device_name(0)}")
-
- # 设置确定性计算以增加稳定性
- torch.backends.cudnn.deterministic = True
- torch.backends.cudnn.benchmark = False
- else:
- print("警告: 未检测到GPU，将在CPU上运行训练，这可能会很慢!")
-
- dataset = ArknightsDataset(
- config['data_file'],
- max_value=config['max_feature_value'] # 使用最大值限制
- )
-
- # 修改这里 - 只有当数据在CPU上时才使用pin_memory
- val_loader = DataLoader(
- dataset,
- batch_size=config['batch_size'],
- num_workers=0,
- pin_memory=False # 禁用pin_memory，或者根据你的数据集情况调整
- )
-
- model = UnitAwareTransformer(
- num_units=34, # 更新为34个怪物
- embed_dim=128,
- num_heads=8,
- num_layers=4 # 注意：train.py中config['n_layers']=4
- ).to(device)
-
- # 加载模型权重
- try:
- model = torch.load('models/best_model_full.pth', map_location=device, weights_only=False)
- except TypeError: # 如果旧版本 PyTorch 不认识 weights_only
- model = torch.load('models/best_model_full.pth', map_location=device)
- model.eval()
- criterion = nn.BCELoss()
- val_loss, val_acc = evaluate(model, val_loader, criterion, device)
- print(f"Val Loss: {val_loss:.4f} | Acc: {val_acc:.2f}%")
-
-if __name__ == "__main__":
- main()
\ No newline at end of file
diff --git a/winrt_capture.py b/winrt_capture.py
deleted file mode 100644
index f69847d..0000000
--- a/winrt_capture.py
+++ /dev/null
@@ -1,367 +0,0 @@
-"""
-winrt_capture.py
------------------
-Windows 10+ 上基于 **Windows.Graphics.Capture** 的屏幕捕获轻量封装。
-
-功能特性
-~~~~~~~~
-- 支持通过 **window_name**（窗口标题）或 **monitor_index**（显示器索引，从 1 开始）二选一指定捕获目标；
-- 以事件回调方式缓存 **最新一帧** 图像（BGR，`numpy.ndarray`），并提供线程安全的 `snapshot()`；
-- 若启动失败（常见于窗口不存在），自动回退到 **主屏(1) 整屏捕获**；
-- 运行时可调用 `recreate()` **切换捕获目标**（重新创建底层会话）；
-- 通过锁保护 start/stop/recreate 的并发安全。
-"""
-from __future__ import annotations
-import threading
-import time
-import logging
-from typing import Optional
-
-import ctypes
-from ctypes import wintypes
-import numpy as np
-from windows_capture import WindowsCapture, Frame, InternalCaptureControl
-
-from PyQt6.QtWidgets import (QVBoxLayout,QListWidget,QLineEdit,QDialog)
-
-logger = logging.getLogger(__name__)
-
-class WinRTScreenCapture:
- """Windows.Graphics.Capture 适配器。
-
- 参数
- ----
- window_name: Optional[str]
- 要锁定的窗口标题；若提供，则忽略 `monitor_index`。
- monitor_index: Optional[int]
- 显示器索引（从 1 开始）；当 `window_name` 为空时生效。
- capture_cursor: bool
- 是否捕获鼠标指针。
- draw_border: Optional[bool]
- 是否绘制边框（底层能力，`None` 表示使用默认）。
- minimum_update_interval_ms: Optional[int]
- 帧间最小更新间隔（毫秒）。
- """
-
- def __init__(
- self,
- window_name: Optional[str] = None,
- monitor_index: Optional[int] = 1,
- capture_cursor: bool = False,
- draw_border: Optional[bool] = None,
- minimum_update_interval_ms: Optional[int] = None,
- ) -> None:
- # 若指定了窗口标题，则不使用显示器索引
- if window_name:
- monitor_index = None
-
- # 最新图像帧（BGR 格式）；通过 _lock 保护
- self._latest: Optional[np.ndarray] = None
- self._lock = threading.Lock()
-
- # 控制状态
- self._started = False
- self._control: Optional[InternalCaptureControl] = None # 保存 CaptureControl，用于停止
- # 保护 start/stop/recreate 的可重入锁
- self._ctl_lock = threading.RLock()
-
- # 记录初始化参数，便于失败回退与后续重建
- self._init_kwargs = dict(
- cursor_capture=capture_cursor,
- draw_border=draw_border,
- secondary_window=None,
- minimum_update_interval=minimum_update_interval_ms,
- dirty_region=None,
- monitor_index=monitor_index,
- window_name=window_name if window_name else None,
- )
-
- # 创建底层捕获对象并挂接事件
- self._cap = WindowsCapture(**self._init_kwargs)
- self._attach_events()
-
- # ------------------------------------------------------------------
- # 事件回调：到帧/关闭
- # ------------------------------------------------------------------
- def _attach_events(self) -> None:
- """绑定底层 WindowsCapture 的事件回调。"""
-
- @self._cap.event
- def on_frame_arrived(frame: Frame, control: InternalCaptureControl):
- # 转成 BGR 并缓存；复制一份以避免底层复用缓冲导致的数据竞争
- bgr = frame.convert_to_bgr().frame_buffer
- with self._lock:
- self._latest = bgr.copy()
-
- @self._cap.event
- def on_closed():
- # 资源被关闭时，重置状态
- with self._lock:
- self._started = False
- self._latest = None
-
- # ------------------------------------------------------------------
- # 控制：启动/停止/重建
- # ------------------------------------------------------------------
- def start(self) -> None:
- """启动捕获。若失败，将尝试回退到主屏(1)整屏捕获。"""
- if self._started:
- return
- try:
- self._control = self._cap.start_free_threaded()
- self._started = True
- except Exception as e:
- # 启动失败：回退到主屏整屏捕获
- logger.warning(f"找不到窗口，自动回退整屏捕获（主屏 1）：{e}")
- try:
- self._cap = WindowsCapture(
- cursor_capture=self._init_kwargs["cursor_capture"],
- draw_border=self._init_kwargs["draw_border"],
- secondary_window=None,
- minimum_update_interval=self._init_kwargs["minimum_update_interval"],
- dirty_region=None,
- monitor_index=1,
- window_name=None,
- )
- self._attach_events()
- self._control = self._cap.start_free_threaded()
- self._started = True
- except Exception as e2:
- raise e2
-
- def stop(self) -> None:
- """停止捕获并释放底层线程与资源。"""
- with self._ctl_lock:
- try:
- if self._control is not None:
- self._control.stop()
- # 给底层线程少许退出时间，降低并发风险
- time.sleep(0.05)
- except Exception:
- pass
- finally:
- self._control = None
- self._started = False
- with self._lock:
- self._latest = None
-
- def recreate(self, window_name: Optional[str] = None, monitor_index: Optional[int] = None) -> None:
- """运行时切换捕获目标并立即启动。
-
- 同步步骤：stop → 以新参数创建 → 重新绑定事件 → 启动。
- """
- with self._ctl_lock:
- self.stop()
- kwargs = dict(
- cursor_capture=self._init_kwargs["cursor_capture"],
- draw_border=self._init_kwargs["draw_border"],
- secondary_window=None,
- minimum_update_interval=self._init_kwargs["minimum_update_interval"],
- dirty_region=None,
- monitor_index=monitor_index if (window_name is None) else None,
- window_name=window_name,
- )
- # 保存以便下次重建
- self._init_kwargs.update(kwargs)
-
- # 新建实例并启动
- self._cap = WindowsCapture(**kwargs)
- self._attach_events()
- self._control = self._cap.start_free_threaded()
- self._started = True
-
- # ------------------------------------------------------------------
- # 数据访问：等待首帧 / 截图
- # ------------------------------------------------------------------
- def wait_first_frame(self, timeout_sec: float = 3.0) -> bool:
- """阻塞等待首帧到达。
-
- 参数
- ----
- timeout_sec: float
- 超时时长（秒）。
-
- 返回
- ----
- bool
- 在超时前是否收到首帧。
- """
- t0 = time.time()
- while time.time() - t0 < timeout_sec:
- with self._lock:
- if self._latest is not None:
- return True
- time.sleep(0.01)
- return False
-
- def snapshot(self, bbox: Optional[tuple[int, int, int, int]] = None) -> np.ndarray:
- """返回最近一帧的 **BGR** 拷贝图像，可选裁剪到指定 bbox。
-
- 若尚未有首帧，将抛出 `RuntimeError`。
- """
- with self._lock:
- if self._latest is None:
- raise RuntimeError("WinRT capture 尚未产生首帧")
-
- frame = self._latest.copy()
- if bbox:
- x1, y1, x2, y2 = bbox
- H, W = frame.shape[:2]
- x1 = max(0, min(int(x1), W - 1))
- x2 = max(0, min(int(x2), W))
- y1 = max(0, min(int(y1), H - 1))
- y2 = max(0, min(int(y2), H))
- return frame[y1:y2, x1:x2]
- return frame
-
- def snapshot_once(self, bbox: Optional[tuple[int, int, int, int]] = None, timeout_sec: float = 2.0) -> np.ndarray:
- """返回最近一帧的 **BGR** 拷贝图像，可选裁剪到指定 bbox。
-
- 若当前未启动，则会临时启动捕获，获取一帧后立即停止。
- """
- with self._ctl_lock:
- was_started = self._started
- if not was_started:
- self.start()
-
- frame = None
- t0 = time.time()
- while time.time() - t0 < timeout_sec:
- with self._lock:
- if self._latest is not None:
- frame = self._latest.copy()
- break
- time.sleep(0.01)
-
- if frame is None: # 超时
- if not was_started:
- self.stop()
- raise RuntimeError("WinRT capture 尚未产生首帧")
-
- if bbox:
- x1, y1, x2, y2 = bbox
- H, W = frame.shape[:2]
- x1 = max(0, min(int(x1), W - 1))
- x2 = max(0, min(int(x2), W))
- y1 = max(0, min(int(y1), H - 1))
- y2 = max(0, min(int(y2), H))
- frame = frame[y1:y2, x1:x2]
-
- if not was_started:
- self.stop()
- return frame
-
- def set_capture_target(self, window_name: Optional[str] = None, monitor_index: Optional[int] = None) -> None:
- """
- 设置 WinRT 截屏目标（窗口标题或整屏），并启动捕获，等待首帧。
- 若初始化失败，将抛出异常。
- """
- with self._ctl_lock:
- if self._started:
- # 如果已经启动，则重建底层会话
- self.recreate(window_name=window_name, monitor_index=monitor_index)
- else:
- # 否则，更新初始化参数并启动
- self._init_kwargs.update(
- monitor_index=monitor_index if (window_name is None) else None,
- window_name=window_name,
- )
- self._cap = WindowsCapture(**self._init_kwargs)
- self._attach_events()
- self.start()
-
- # 启动并等待首帧
- if not self.wait_first_frame(timeout_sec=2.0):
- # 首帧未就绪，清理并抛出异常
- self.stop()
- raise RuntimeError("WinRT capture 尚未产生首帧")
-
-def list_visible_window_titles() -> list[str]:
- """列出所有**可见**窗口的标题（去重并按字典序排序）。"""
- EnumWindows = ctypes.windll.user32.EnumWindows
- EnumWindowsProc = ctypes.WINFUNCTYPE(wintypes.BOOL, wintypes.HWND, wintypes.LPARAM)
- IsWindowVisible = ctypes.windll.user32.IsWindowVisible
- GetWindowTextW = ctypes.windll.user32.GetWindowTextW
- GetWindowTextLengthW = ctypes.windll.user32.GetWindowTextLengthW
-
- titles: list[str] = []
-
- def foreach(hwnd, lParam):
- if IsWindowVisible(hwnd):
- length = GetWindowTextLengthW(hwnd)
- if length > 0:
- buff = ctypes.create_unicode_buffer(length + 1)
- GetWindowTextW(hwnd, buff, length + 1)
- t = buff.value.strip()
- if t:
- titles.append(t)
- return True
-
- EnumWindows(EnumWindowsProc(foreach), 0)
- titles = sorted(set(titles))
- logger.info(f"窗口列表：{titles}")
- return titles
-
-# ------------------------- 截屏源选择对话框 -------------------------
-class WindowPickerDialog(QDialog):
- """列出可见窗口标题，并内置“整屏(1/2/3)”选项。双击确定。"""
-
- def __init__(self, parent=None):
- super().__init__(parent)
- self.setWindowTitle("选择截屏窗口")
- self.resize(520, 480)
- self.selected_title = None
-
- self.search = QLineEdit(self)
- self.search.setPlaceholderText("输入关键字过滤（支持大小写不敏感）")
-
- self.listw = QListWidget(self)
- # 预置“整屏（主屏0/副屏1/2）”选项在最上面
- self.listw.addItem("【整屏】主屏(1)")
- self.listw.addItem("【整屏】副屏(2)")
- self.listw.addItem("【整屏】副屏(3)")
- self.listw.addItem("—————— 窗口列表 ——————")
-
- self._all_titles = list_visible_window_titles()
- for t in self._all_titles:
- self.listw.addItem(t)
-
- self.search.textChanged.connect(self._filter)
- self.listw.itemDoubleClicked.connect(self._accept)
-
- layout = QVBoxLayout(self)
- layout.addWidget(self.search)
- layout.addWidget(self.listw)
-
- def _filter(self, text: str):
- text_low = (text or "").lower()
- self.listw.clear()
- self.listw.addItem("【整屏】主屏(1)")
- self.listw.addItem("【整屏】副屏(2)")
- self.listw.addItem("【整屏】副屏(3)")
- self.listw.addItem("—————— 窗口列表 ——————")
- for t in self._all_titles:
- if text_low in t.lower():
- self.listw.addItem(t)
-
- def _accept(self):
- self.accept()
-
- def get_selection(self):
- item = self.listw.currentItem()
- if not item:
- return None
- text = item.text()
- if text.startswith("【整屏】"):
- # 返回 monitor_index
- if "(1)" in text:
- return {"monitor_index": 1}
- if "(2)" in text:
- return {"monitor_index": 2}
- if "(3)" in text:
- return {"monitor_index": 3}
- elif text.startswith("——————"):
- return None
- else:
- # 返回 window_name
- return {"window_name": text}
diff --git "a/\343\200\220\350\204\232\346\234\254\343\200\221\351\223\201\351\262\250\351\261\274\345\220\257\345\212\250\357\274\201(\345\270\246PyTorch\347\211\210\346\234\254\351\200\211\346\213\251\347\211\210).bat" "b/\343\200\220\350\204\232\346\234\254\343\200\221\351\223\201\351\262\250\351\261\274\345\220\257\345\212\250\357\274\201(\345\270\246PyTorch\347\211\210\346\234\254\351\200\211\346\213\251\347\211\210).bat"
deleted file mode 100644
index 542044f..0000000
--- "a/\343\200\220\350\204\232\346\234\254\343\200\221\351\223\201\351\262\250\351\261\274\345\220\257\345\212\250\357\274\201(\345\270\246PyTorch\347\211\210\346\234\254\351\200\211\346\213\251\347\211\210).bat"
+++ /dev/null
@@ -1,73 +0,0 @@
-@echo off
-chcp 65001 >nul
-title CannotMax
-echo 请按任意键启动CannotMax...
-pause >nul
-
-set "current_dir=%cd%"
-
-:: 检查 uv 是否存在
-where uv >nul 2>nul
-if %errorlevel% equ 0 goto run_main
-
-:: 安装 uv
-echo 未检测到 uv，正在安装...
-powershell -ExecutionPolicy Bypass -Command "irm https://gitee.com/wangnov/uv-custom/releases/download/latest/uv-installer-custom.ps1 | iex"
-
-call :refresh_path
-:: 验证 uv 是否可用
-where uv >nul 2>nul
-if %errorlevel% neq 0 (
- echo 安装 uv 后仍未找到，请检查安装路径
- pause
- exit /b 1
-)
-:: ===================================
-
-:run_main
-cd /d "%current_dir%"
-
-:: ===== PyTorch安装选项询问 =====
-set "torch_choice=none"
-echo.
-echo 是否需要安装PyTorch? (5秒后自动跳过)
-echo C/c - CPU版本
-echo G/g - CUDA版本
-echo N/n - 跳过安装
-echo ------------------------------------
-
-:: 使用choice命令实现带超时的输入
-choice /c CGN /t 5 /d N /n >nul
-if errorlevel 3 (
- set "torch_choice=none"
-) else if errorlevel 2 (
- set "torch_choice=cuda"
-) else if errorlevel 1 (
- set "torch_choice=cpu"
-)
-
-:: 根据选择安装PyTorch
-if "%torch_choice%"=="cpu" (
- echo 安装CPU版本的PyTorch...
- uv add torch torchvision --extra cpu
-) else if "%torch_choice%"=="cuda" (
- echo 安装CUDA版本的PyTorch...
- uv add torch torchvision --extra cu128
-) else (
- echo 跳过PyTorch安装
-)
-echo.
-
-:: ===================================
-uv run main.py
-
-echo 主程序已退出，感谢您的使用！
-pause >nul
-exit /b
-
-:: 刷新 PATH 的函数
-:refresh_path
-for /f "skip=2 tokens=3*" %%a in ('reg query "HKLM\SYSTEM\CurrentControlSet\Control\Session Manager\Environment" /v Path 2^>nul') do set "SYSTEM_PATH=%%a %%b"
-for /f "skip=2 tokens=3*" %%a in ('reg query "HKCU\Environment" /v Path 2^>nul') do set "USER_PATH=%%a %%b"
-set "PATH=%USER_PATH%;%SYSTEM_PATH%"
-exit /b
\ No newline at end of file
diff --git "a/\345\216\206\345\217\262\347\275\221\347\273\234/arknights.csv" "b/\345\216\206\345\217\262\347\275\221\347\273\234/arknights.csv"
new file mode 100644
index 0000000..09d8e84
--- /dev/null
+++ "b/\345\216\206\345\217\262\347\275\221\347\273\234/arknights.csv"
@@ -0,0 +1,2523 @@
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diff --git "a/\346\225\260\346\215\256\345\220\210\345\271\266\345\271\266\345\216\273\351\207\215/arknights.csv" "b/\346\225\260\346\215\256\345\220\210\345\271\266\345\271\266\345\216\273\351\207\215/arknights.csv"
new file mode 100644
index 0000000..e6da77d
--- /dev/null
+++ "b/\346\225\260\346\215\256\345\220\210\345\271\266\345\271\266\345\216\273\351\207\215/arknights.csv"
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diff --git "a/\346\225\260\346\215\256\345\220\210\345\271\266\345\271\266\345\216\273\351\207\215/b.csv" "b/\346\225\260\346\215\256\345\220\210\345\271\266\345\271\266\345\216\273\351\207\215/b.csv"
new file mode 100644
index 0000000..3ff91ac
--- /dev/null
+++ "b/\346\225\260\346\215\256\345\220\210\345\271\266\345\271\266\345\216\273\351\207\215/b.csv"
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diff --git "a/\346\225\260\346\215\256\345\220\210\345\271\266\345\271\266\345\216\273\351\207\215/csv.py" "b/\346\225\260\346\215\256\345\220\210\345\271\266\345\271\266\345\216\273\351\207\215/csv.py"
new file mode 100644
index 0000000..7220e5a
--- /dev/null
+++ "b/\346\225\260\346\215\256\345\220\210\345\271\266\345\271\266\345\216\273\351\207\215/csv.py"
@@ -0,0 +1,47 @@
+import csv
+
+def read_csv_data(filename):
+ """读取CSV文件，自动检测编码并验证表头是否为纯数字，返回表头、数据行（去重后的集合）和成功的编码"""
+ encodings = ['utf-8-sig', 'utf-8', 'gbk', 'gb18030', 'big5', 'latin1'] # 尝试的编码顺序
+ for encoding in encodings:
+ try:
+ with open(filename, 'r', newline='', encoding=encoding) as f:
+ reader = csv.reader(f)
+ try:
+ header = next(reader)
+ except StopIteration:
+ raise ValueError(f"文件 {filename} 为空或无法读取内容")
+ if all(field.strip().isdigit() for field in header):
+ data = {tuple(row) for row in reader}
+ return header, data, encoding
+ else:
+ continue # 表头不符合要求，尝试下一编码
+ except UnicodeDecodeError:
+ continue # 解码失败，尝试下一编码
+ raise ValueError(f"无法以支持表头为纯数字的编码读取文件 {filename}")
+
+# 读取两个文件并处理可能的错误
+try:
+ header_a, data_a, encoding_a = read_csv_data('a.csv')
+ header_b, data_b, encoding_b = read_csv_data('b.csv')
+except ValueError as e:
+ print(f"错误: {e}")
+ exit()
+
+# 验证表头一致性
+if header_a != header_b:
+ raise ValueError("错误：两个文件的表头不一致，无法合并")
+
+# 合并数据并去重
+merged_data = data_a | data_b
+
+# 写入新文件，统一使用UTF-8编码
+with open('arknights.csv', 'w', newline='', encoding='utf-8') as f:
+ writer = csv.writer(f)
+ writer.writerow(header_a)
+ for row in merged_data:
+ writer.writerow(row)
+
+print(f"文件合并完成，结果已保存到 arknights.csv")
+print(f"文件 a.csv 使用的编码是: {encoding_a}")
+print(f"文件 b.csv 使用的编码是: {encoding_b}")
diff --git "a/\346\225\260\346\215\256\345\220\210\345\271\266\345\271\266\345\216\273\351\207\215/csv_merge.py" "b/\346\225\260\346\215\256\345\220\210\345\271\266\345\271\266\345\216\273\351\207\215/csv_merge.py"
deleted file mode 100644
index 72a0509..0000000
--- "a/\346\225\260\346\215\256\345\220\210\345\271\266\345\271\266\345\216\273\351\207\215/csv_merge.py"
+++ /dev/null
@@ -1,54 +0,0 @@
-import csv
-import os
-
-
-def read_csv_data(filename):
- """读取CSV文件，自动检测编码并返回表头、数据行（去重后的集合）和成功的编码"""
- encodings = ['utf-8-sig', 'utf-8', 'gbk', 'gb18030', 'big5', 'latin1'] # 尝试的编码顺序
- for encoding in encodings:
- try:
- with open(filename, 'r', newline='', encoding=encoding) as f:
- reader = csv.reader(f)
- try:
- header = next(reader)
- except StopIteration:
- raise ValueError(f"文件 {filename} 为空或无法读取内容")
- data = {tuple(row) for row in reader}
- return header, data, encoding
- except UnicodeDecodeError:
- continue # 解码失败，尝试下一编码
- raise ValueError(f"无法以支持的编码读取文件 {filename}")
-
-
-# 获取当前文件夹下的所有CSV文件
-csv_files = [file for file in os.listdir('.') if file.endswith('.csv')]
-
-if not csv_files:
- print("当前文件夹下没有找到CSV文件")
- exit()
-
-merged_data = set()
-header = None
-
-# 读取所有CSV文件并合并数据
-for csv_file in csv_files:
- try:
- current_header, data, encoding = read_csv_data(csv_file)
- if header is None:
- header = current_header
- elif header != current_header:
- print(f"警告：文件 {csv_file} 的表头与其他文件不一致，可能会导致数据不匹配")
- merged_data |= data
- print(f"文件 {csv_file} 使用的编码是: {encoding}")
- except ValueError as e:
- print(f"错误: {e}")
- exit()
-
-# 写入合并后的文件
-with open('arknights.csv', 'w', newline='', encoding='utf-8') as f:
- writer = csv.writer(f)
- writer.writerow(header)
- for row in merged_data:
- writer.writerow(row)
-
-print(f"所有CSV文件合并完成，结果已保存到 arknights.csv")
\ No newline at end of file
diff --git "a/\346\225\260\346\215\256\345\220\210\345\271\266\345\271\266\345\216\273\351\207\215/\350\257\264\346\230\216.txt" "b/\346\225\260\346\215\256\345\220\210\345\271\266\345\271\266\345\216\273\351\207\215/\350\257\264\346\230\216.txt"
index 3bdb297..b8727f9 100644
--- "a/\346\225\260\346\215\256\345\220\210\345\271\266\345\271\266\345\216\273\351\207\215/\350\257\264\346\230\216.txt"
+++ "b/\346\225\260\346\215\256\345\220\210\345\271\266\345\271\266\345\216\273\351\207\215/\350\257\264\346\230\216.txt"
@@ -1,2 +1,2 @@
-将需要合并的数据丢进来（记得带表头
+将需要合并的数据命名为"a.csv"与"b.csv"（记得带表头，表头就是第一行的1~53）
然后会自动合并去重并输出到"arknights.csv"
\ No newline at end of file
- {monster1_name}
- {monster2_name}
- 
- {display_name}
- 
- {teammate['partner_name']} ({teammate['lift']:.2f}x)
- 
- {counter['opponent_name']} ({counter['win_rate']:.0%})
-