Image Toolkit Pro

Image Toolkit Pro is a polished standalone desktop computer vision application built with Python, OpenCV, NumPy, Pillow, and Tkinter.

It combines a lightweight image editor, OpenCV laboratory, webcam capture tool, detection analytics workspace, and batch-processing engine into one local desktop product.

Current release: v1.0.0

Project Overview

Image Toolkit Pro was designed as a portfolio-grade desktop application, not a tutorial script or web demo. The product demonstrates practical Python engineering, GUI architecture, image-processing workflows, computer vision fundamentals, and release-oriented product thinking.

Everything runs locally. There is no backend server, database, cloud storage, authentication system, or SaaS infrastructure.

Feature Highlights

• Open, save, save as, and export images
• Metadata panel for filename, dimensions, channels, format, and file size
• Mouse-driven drawing tools: line, rectangle, circle, freehand pen, and text
• Undo and redo with bounded history
• Crop, resize, rotate, flip, brightness, and contrast workflows
• Filters: grayscale, blur, sharpen, edge detection, thresholds, histogram equalization, HSV, and LAB views
• Haar cascade detection: face, eye, smile, and detect all
• Detection analytics with counts, runtime, density, timestamp, and exportable reports
• Webcam live feed with FPS metrics
• Real-time webcam detection modes
• Screenshot capture and MP4 recording
• Batch image processing with progress, ETA, cancellation, logs, and JSON reports
• Batch detection with annotated outputs, CSV reports, and aggregate analytics
• Recent files, preferences, keyboard shortcuts, unsaved-change protection, and session restore
• Splash screen, app icon, About window, version metadata, changelog, and license

Technology Stack

Layer	Technologies
Language	Python 3.11+
Computer Vision	OpenCV, Haar Cascades
Image Processing	OpenCV, NumPy, Pillow
Desktop GUI	Tkinter, ttk
Packaging	PyInstaller
Reports	JSON, CSV, TXT

Architecture Overview

The application is intentionally modular:

• gui/ coordinates user interaction and Tkinter UI state.
• core/ contains reusable image-processing, detection, webcam, drawing, transform, and batch engines.
• utils/ owns file IO, image conversion, constants, and persistent settings.
• assets/, screenshots/, and outputs/ separate branding, portfolio media, and runtime artifacts.

See docs/architecture.md for the full architecture notes. For recruiter-facing explanations, see docs/architecture_explanation.md.

Screenshots

Main Workspace

Drawing and Annotation

Detection Analytics

Webcam Recording

Batch Processing

Product UX

Demo

A 90-second recruiter-focused demo script is available at docs/recruiter_demo_script_90s.md.

Suggested demo flow:

1. Launch the app and show the splash screen.
2. Open an image and annotate it.
3. Apply a transform and a filter.
4. Run Detect All and export the detection report.
5. Start webcam detection and record a short clip.
6. Run a batch detection job and show the generated reports.
7. Close with the About window and GitHub Release download.

After recording the demo, add the video link here:

Demo Video: https://github.com/hammad986/Image-Toolkit-Pro/releases

Installation

Clone the repository:

git clone https://github.com/hammad986/Image-Toolkit-Pro.git
cd image-toolkit-pro

Create and activate a virtual environment:

python -m venv .venv
.venv\Scripts\activate

Install dependencies:

pip install -r requirements.txt

Run the application:

python main.py

Download

Download the latest Windows executable from GitHub Releases:

https://github.com/hammad986/Image-Toolkit-Pro/releases/latest/download/ImageToolkitPro.exe

https://github.com/hammad986/Image-Toolkit-Pro/releases/latest/download/ImageToolkitPro.exe

Before publishing, verify this URL matches the final GitHub repository path.

Portfolio Launch Kit

• Project descriptions: docs/portfolio_project_description.md
• Resume entry: docs/resume_project_entry.md
• LinkedIn showcase post: docs/linkedin_project_showcase.md
• Interview preparation: docs/interview_preparation.md

Build From Source

Install runtime and build dependencies:

pip install -r requirements.txt
pip install -r requirements-dev.txt

Build the Windows executable:

.\scripts\build_exe.ps1

Expected output:

dist\ImageToolkitPro.exe

See docs/release_checklist.md for the full release workflow.

Roadmap

• Publish GitHub repository
• Build and attach Windows EXE to GitHub Release
• Record two-minute demo video
• Add real application screenshots from production test runs
• Optional: create Windows installer
• Optional: create portable ZIP package

No additional computer vision features are planned for v1.0.0. Future work should focus on release quality, packaging, documentation, and demo assets.

License

This project is released under the MIT License.