From c7eb84afcf690010631f495af7dc5acfd44cb9f9 Mon Sep 17 00:00:00 2001 From: Antony Lee Date: Thu, 18 Apr 2024 10:49:47 +0200 Subject: [PATCH] Disable clipping in Agg resamplers. I chose to add macro guards directly in the agg source as that seemed easier than copy-pasting the whole code. I chose to bump the tolerance on test_rgba_antialias (as that's also getting bumped by the interpolation_stage antialias PR) and to test_pngsuite (which is not particularly relevant), and to update the other baselines. --- .../include/agg_span_image_filter_gray.h | 8 ++ lib/matplotlib/image.py | 101 ++-------------- .../imshow_masked_interpolation.png | Bin 31943 -> 31942 bytes .../imshow_masked_interpolation.svg | 112 +++++++++--------- .../test_image/rotate_image.png | Bin 24194 -> 24068 bytes lib/matplotlib/tests/test_image.py | 5 +- lib/matplotlib/tests/test_png.py | 2 +- src/_image_resample.h | 2 + 8 files changed, 78 insertions(+), 152 deletions(-) diff --git a/extern/agg24-svn/include/agg_span_image_filter_gray.h b/extern/agg24-svn/include/agg_span_image_filter_gray.h index e2c688e004cb..7ca583af724d 100644 --- a/extern/agg24-svn/include/agg_span_image_filter_gray.h +++ b/extern/agg24-svn/include/agg_span_image_filter_gray.h @@ -397,7 +397,9 @@ namespace agg fg += weight * *fg_ptr; fg >>= image_filter_shift; +#ifndef MPL_DISABLE_AGG_GRAY_CLIPPING if(fg > color_type::full_value()) fg = color_type::full_value(); +#endif span->v = (value_type)fg; span->a = color_type::full_value(); @@ -491,8 +493,10 @@ namespace agg } fg = color_type::downshift(fg, image_filter_shift); +#ifndef MPL_DISABLE_AGG_GRAY_CLIPPING if(fg < 0) fg = 0; if(fg > color_type::full_value()) fg = color_type::full_value(); +#endif span->v = (value_type)fg; span->a = color_type::full_value(); @@ -593,8 +597,10 @@ namespace agg } fg /= total_weight; +#ifndef MPL_DISABLE_AGG_GRAY_CLIPPING if(fg < 0) fg = 0; if(fg > color_type::full_value()) fg = color_type::full_value(); +#endif span->v = (value_type)fg; span->a = color_type::full_value(); @@ -701,8 +707,10 @@ namespace agg } fg /= total_weight; +#ifndef MPL_DISABLE_AGG_GRAY_CLIPPING if(fg < 0) fg = 0; if(fg > color_type::full_value()) fg = color_type::full_value(); +#endif span->v = (value_type)fg; span->a = color_type::full_value(); diff --git a/lib/matplotlib/image.py b/lib/matplotlib/image.py index 3b4dd4c75b5d..95994201b94e 100644 --- a/lib/matplotlib/image.py +++ b/lib/matplotlib/image.py @@ -457,93 +457,21 @@ def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0, # input data is not going to match the size on the screen so we # have to resample to the correct number of pixels - # TODO slice input array first - a_min = A.min() - a_max = A.max() - if a_min is np.ma.masked: # All masked; values don't matter. - a_min, a_max = np.int32(0), np.int32(1) if A.dtype.kind == 'f': # Float dtype: scale to same dtype. - scaled_dtype = np.dtype( - np.float64 if A.dtype.itemsize > 4 else np.float32) + scaled_dtype = np.dtype("f8" if A.dtype.itemsize > 4 else "f4") if scaled_dtype.itemsize < A.dtype.itemsize: _api.warn_external(f"Casting input data from {A.dtype}" f" to {scaled_dtype} for imshow.") else: # Int dtype, likely. + # TODO slice input array first # Scale to appropriately sized float: use float32 if the # dynamic range is small, to limit the memory footprint. - da = a_max.astype(np.float64) - a_min.astype(np.float64) - scaled_dtype = np.float64 if da > 1e8 else np.float32 - - # Scale the input data to [.1, .9]. The Agg interpolators clip - # to [0, 1] internally, and we use a smaller input scale to - # identify the interpolated points that need to be flagged as - # over/under. This may introduce numeric instabilities in very - # broadly scaled data. - - # Always copy, and don't allow array subtypes. - A_scaled = np.array(A, dtype=scaled_dtype) - # Clip scaled data around norm if necessary. This is necessary - # for big numbers at the edge of float64's ability to represent - # changes. Applying a norm first would be good, but ruins the - # interpolation of over numbers. - self.norm.autoscale_None(A) - dv = np.float64(self.norm.vmax) - np.float64(self.norm.vmin) - vmid = np.float64(self.norm.vmin) + dv / 2 - fact = 1e7 if scaled_dtype == np.float64 else 1e4 - newmin = vmid - dv * fact - if newmin < a_min: - newmin = None - else: - a_min = np.float64(newmin) - newmax = vmid + dv * fact - if newmax > a_max: - newmax = None - else: - a_max = np.float64(newmax) - if newmax is not None or newmin is not None: - np.clip(A_scaled, newmin, newmax, out=A_scaled) - - # Rescale the raw data to [offset, 1-offset] so that the - # resampling code will run cleanly. Using dyadic numbers here - # could reduce the error, but would not fully eliminate it and - # breaks a number of tests (due to the slightly different - # error bouncing some pixels across a boundary in the (very - # quantized) colormapping step). - offset = .1 - frac = .8 - # Run vmin/vmax through the same rescaling as the raw data; - # otherwise, data values close or equal to the boundaries can - # end up on the wrong side due to floating point error. - vmin, vmax = self.norm.vmin, self.norm.vmax - if vmin is np.ma.masked: - vmin, vmax = a_min, a_max - vrange = np.array([vmin, vmax], dtype=scaled_dtype) - - A_scaled -= a_min - vrange -= a_min - # .item() handles a_min/a_max being ndarray subclasses. - a_min = a_min.astype(scaled_dtype).item() - a_max = a_max.astype(scaled_dtype).item() - - if a_min != a_max: - A_scaled /= ((a_max - a_min) / frac) - vrange /= ((a_max - a_min) / frac) - A_scaled += offset - vrange += offset + da = A.max().astype("f8") - A.min().astype("f8") + scaled_dtype = "f8" if da > 1e8 else "f4" + # resample the input data to the correct resolution and shape - A_resampled = _resample(self, A_scaled, out_shape, t) - del A_scaled # Make sure we don't use A_scaled anymore! - # Un-scale the resampled data to approximately the original - # range. Things that interpolated to outside the original range - # will still be outside, but possibly clipped in the case of - # higher order interpolation + drastically changing data. - A_resampled -= offset - vrange -= offset - if a_min != a_max: - A_resampled *= ((a_max - a_min) / frac) - vrange *= ((a_max - a_min) / frac) - A_resampled += a_min - vrange += a_min + A_resampled = _resample(self, A.astype(scaled_dtype), out_shape, t) + # if using NoNorm, cast back to the original datatype if isinstance(self.norm, mcolors.NoNorm): A_resampled = A_resampled.astype(A.dtype) @@ -564,21 +492,10 @@ def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0, # Apply the pixel-by-pixel alpha values if present alpha = self.get_alpha() if alpha is not None and np.ndim(alpha) > 0: - out_alpha *= _resample(self, alpha, out_shape, - t, resample=True) + out_alpha *= _resample(self, alpha, out_shape, t, resample=True) # mask and run through the norm resampled_masked = np.ma.masked_array(A_resampled, out_mask) - # we have re-set the vmin/vmax to account for small errors - # that may have moved input values in/out of range - s_vmin, s_vmax = vrange - if isinstance(self.norm, mcolors.LogNorm) and s_vmin <= 0: - # Don't give 0 or negative values to LogNorm - s_vmin = np.finfo(scaled_dtype).eps - # Block the norm from sending an update signal during the - # temporary vmin/vmax change - with self.norm.callbacks.blocked(), \ - cbook._setattr_cm(self.norm, vmin=s_vmin, vmax=s_vmax): - output = self.norm(resampled_masked) + output = self.norm(resampled_masked) else: if A.ndim == 2: # interpolation_stage = 'rgba' self.norm.autoscale_None(A) diff --git a/lib/matplotlib/tests/baseline_images/test_image/imshow_masked_interpolation.png b/lib/matplotlib/tests/baseline_images/test_image/imshow_masked_interpolation.png index 72918a27fbc18ff25abd842c1b35e4696cbedeb1..9e68784cff4f2f8badc8917728de2b609e007f08 100644 GIT binary patch delta 7498 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zr|T_Gb9**#EDrclkfdIaUAzM3;gru#rFoR!WFG+&nnzwec>=w;J(FWze;)1Iv;n%$dsN9PYyr j!eIOVga79@;eb1S>7o3|ORVaL4?y#_&aIDXHu!%5g9q#q diff --git a/lib/matplotlib/tests/test_image.py b/lib/matplotlib/tests/test_image.py index 4340be96a38b..dfacfccb3e0e 100644 --- a/lib/matplotlib/tests/test_image.py +++ b/lib/matplotlib/tests/test_image.py @@ -696,7 +696,7 @@ def test_jpeg_alpha(): # If this fails, there will be only one color (all black). If this # is working, we should have all 256 shades of grey represented. num_colors = len(image.getcolors(256)) - assert 175 <= num_colors <= 210 + assert 175 <= num_colors <= 230 # The fully transparent part should be red. corner_pixel = image.getpixel((0, 0)) assert corner_pixel == (254, 0, 0) @@ -1404,8 +1404,7 @@ def test_nonuniform_logscale(): ax.add_image(im) -@image_comparison( - ['rgba_antialias.png'], style='mpl20', remove_text=True, tol=0.01) +@image_comparison(['rgba_antialias.png'], style='mpl20', remove_text=True, tol=0.02) def test_rgba_antialias(): fig, axs = plt.subplots(2, 2, figsize=(3.5, 3.5), sharex=False, sharey=False, constrained_layout=True) diff --git a/lib/matplotlib/tests/test_png.py b/lib/matplotlib/tests/test_png.py index aa7591508a67..9208c31df2bf 100644 --- a/lib/matplotlib/tests/test_png.py +++ b/lib/matplotlib/tests/test_png.py @@ -7,7 +7,7 @@ from matplotlib import cm, pyplot as plt -@image_comparison(['pngsuite.png'], tol=0.03) +@image_comparison(['pngsuite.png'], tol=0.04) def test_pngsuite(): files = sorted( (Path(__file__).parent / "baseline_images/pngsuite").glob("basn*.png")) diff --git a/src/_image_resample.h b/src/_image_resample.h index a6404092ea2d..ddf1a4050325 100644 --- a/src/_image_resample.h +++ b/src/_image_resample.h @@ -3,6 +3,8 @@ #ifndef MPL_RESAMPLE_H #define MPL_RESAMPLE_H +#define MPL_DISABLE_AGG_GRAY_CLIPPING + #include "agg_image_accessors.h" #include "agg_path_storage.h" #include "agg_pixfmt_gray.h"