IMDER - Image Blender

IMDER is a professional-grade interactive image and video processor that creates smooth, mesmerizing animations through pixel-level transformations. Built for creatives, developers, and visual artists, IMDER delivers superior quality, blazing-fast performance, and unmatched flexibility for generating unique visual content.

📦 Latest Release: v1.2.5 (February 2026) — Featuring custom resolutions up to 16384×16384, configurable FPS (30-240), and smart scaling support.

Important Notes:

• 🔧 Run from source for the latest version. Pre-built binaries are available for Windows/Linux(C++ Build) v1.2.5 and macOS/Linux(Python Build) v1.0.0, but running from source ensures you always have the newest features and fixes.
• 🤖 For AI agents and automated tools: See Bots.md

📦 IMDER is available as a Python library on PyPI: Install with pip install imder for CLI automation and integration into your projects. See Python Library Docs

📋 For detailed version history, see CHANGELOG.md

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Quick Start

Option 1: Python Library (Recommended for Automation)

# Install from PyPI
pip install imder

# Launch interactive CLI
imder

# Or use in your Python code
python -c "import imder; imder.process('base.jpg', 'target.jpg', './out', ['gif'], 'shuffle', 512, 'mute')"

Option 2: Run from Source (Full GUI Experience)

# Clone the repository
git clone https://github.com/HAKORADev/IMDER.git
cd IMDER

# Install dependencies
pip install -r requirements.txt

# Run GUI
python src/imder.py

Installation Requirements

# Install FFmpeg (required for video/audio synthesis)
# Windows: winget install FFmpeg
# macOS: brew install ffmpeg
# Linux: sudo apt install ffmpeg

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Two Ways to Use IMDER

IMDER operates as both a standalone GUI application (source) and a Python package (PyPI):

Feature	GUI (Source)	Library (PyPI)
Interface	PyQt5 GUI	CLI + Python API
Best for	Interactive editing	Automation, batch processing
Algorithms	10 modes + Shape tools + Drawer	4 core modes
Usage	Point-and-click	Code integration
Dependencies	PyQt5, OpenCV, NumPy	OpenCV, NumPy, Pillow

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Core Capabilities

🎨 10 Processing Algorithms

IMDER offers 10 distinct pixel manipulation algorithms, each designed for specific visual effects:

Mode	Description	Mask Required	Video Support
Shuffle	Random pixel swapping with brightness balance	No	✅ Yes
Merge	Grayscale sorting for smooth transitions	No	✅ Yes
Missform	Binary mask morphing for shape transformations	No	✅ Yes
Fusion	Selective transformation with color blending	Optional	❌ No
Pattern	Texture transfer via color quantization	Yes	❌ No
Disguise	Brightness-matched pixel rearrangement	Yes	❌ No
Navigate	Morton curve-guided pixel movement	Yes	❌ No
Swap	Bidirectional pixel exchange	Yes	❌ No
Blend	Physics-inspired fluid dynamics	Yes	❌ No
Drawer	Canvas-based sketch to image transformation	N/A	❌ No

🎬 Video Processing

Full video-to-video and video-to-image processing capabilities:

• Frame-accurate pixel manipulation
• Support for MP4, AVI, MOV, MKV formats
• Audio generation and extraction (pixel synthesis or target track)
• Configurable FPS output: 30, 60, 90, 120, or 240 FPS
• Crossfade transitions between frames

🖼️ Advanced Image Features

• Resolution Flexibility: Standard presets (128×128 to 2048×2048) plus custom resolutions up to 16384×16384
• Smart Scaling: Automatic upscaling using nearest-neighbor interpolation before processing, ensuring no quality loss from downscaling-only workflows
• Shape Selection: Automatic k-means segmentation or manual pen-tool masking
• Transform Operations: 90° rotation increments and horizontal flip
• Multi-segment Support: Combine multiple selections for complex transformations

🎵 Audio Integration

Option	Description
Mute	No audio (default)
Pixel Sound	Synthesize audio from frame pixel data
Target Audio	Extract and preserve audio from source video
Quality Levels	10%-100% bitrate preservation for target audio

💾 Export Formats

• PNG: Static final frame
• MP4: H.264 encoded video with configurable FPS
• GIF: Animated with optimized duration
• Synchronized Audio: MP4 with embedded audio tracks

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Showcase

Smooth Transformations

IMDER algorithms create fluid transitions between images at any resolution:

Book ↔ Girl transformation using Merge Algorithm

Drawer Mode: Sketch to Reality

Transform hand-drawn sketches into photorealistic images:

Draw on canvas → Transform into target image

Features:

• 1024×1024 drawing canvas with adjustable brush sizes (1-50px)
• Undo/redo history (50 states)
• Color picker with full RGB support
• Base image overlay support (trace existing images)
• Intelligent pixel distribution algorithm

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Installation

Prerequisites

• Python 3.8+
• pip package manager
• FFmpeg (REQUIRED for video/audio processing)

Method 1: PyPI Installation (CLI/Library)

pip install imder

Provides the imder command globally and enables import imder in Python scripts.

Method 2: Source Installation (Full GUI)

git clone https://github.com/HAKORADev/IMDER.git
cd IMDER
pip install -r requirements.txt
python src/imder.py

Dependencies:

• PyQt5 — GUI framework
• opencv-python — Image/video processing
• numpy — Numerical operations
• Pillow — Image format handling

FFmpeg Setup

# Windows
winget install FFmpeg

# macOS
brew install ffmpeg

# Linux (Debian/Ubuntu)
sudo apt install ffmpeg

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Usage Guide

Python Library

Interactive Mode:

imder

Direct Processing:

# Image processing with specific algorithm
imder base.jpg target.jpg ./output --results gif mp4 --algo merge --res 1024

# Video processing with audio extraction
imder video1.mp4 video2.mp4 ./output --results mp4 --sound target --sq 8

Python API:

import imder

# Single transformation
imder.process(
    base="input.jpg",
    target="output.jpg",
    result="./renders",
    results=["png", "mp4", "gif"],
    algo="missform",
    res=2048,
    sound="mute"
)

# Batch processing
for i in range(100):
    imder.process(
        base=f"frame_{i:03d}.png",
        target="target.jpg",
        result="./batch_output",
        results=["png"],
        algo="shuffle",
        res=512,
        sound="mute"
    )

See pip-imder.md for complete API documentation.

GUI Mode

1. Launch: python src/imder.py
2. Select algorithm from dropdown (10 available modes)
3. Choose resolution:
   • Standard presets: 128×128 to 2048×2048
   • Custom: Click "Custom" to enter any value up to 16384×16384
4. Set FPS for video exports (30/60/90/120/240)
5. Load base and target media (images or videos)
6. Apply transforms (rotate/flip) if needed
7. For mask-dependent algorithms, use "Analyze Shapes" or Pen tool
8. Configure audio options for video exports
9. Click "Start Processing" for real-time preview
10. Export final results (PNG/MP4/GIF)

Drawer Mode Workflow:

1. Select "Drawer" from mode dropdown
2. Draw on canvas using mouse/tablet
3. Load target image in right panel
4. Process to see drawing transform into target

CLI Mode (Source)

Interactive:

python src/imder.py cli

Direct Arguments:

python src/imder.py base.jpg target.jpg missform 1024
python src/imder.py video1.mp4 video2.mp4 merge 512 target-sound 7

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Technical Highlights

• Square Resolution Processing: Algorithmically optimized 1:1 aspect ratio processing for consistent pixel mapping and Morton code operations
• Bidirectional Scaling: Nearest-neighbor upscaling ensures pixel integrity when source images are smaller than target resolution
• Morton Code Ordering: Z-order curve spatial indexing for organic pixel movement paths (Navigate algorithm)
• K-Means Segmentation: Intelligent automatic shape detection for mask generation
• QThread Architecture: Non-blocking GUI during heavy processing operations
• FFmpeg Integration: Professional-grade video encoding and audio handling
• Frame-Accurate Processing: Frame-by-frame video manipulation with temporal consistency

Performance Characteristics

Resolution	Approximate Time	Use Case
128×128	~2 seconds	Preview, testing
512×512	~8 seconds	Web content, drafts
1024×1024	~20 seconds	Standard output
2048×2048	~45 seconds	High quality
16384×16384	Minutes	Maximum quality

Timings vary based on hardware (CPU-bound processing) and selected algorithm.

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Documentation

• Algorithms.md — Detailed algorithm explanations, technical implementation, and creative techniques
• pip-imder.md — Python library API reference
• CHANGELOG.md — Version history and release notes
• Bots.md — Guidelines for AI agents and automated systems

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Contributing

IMDER is open-source (MIT License) and welcomes contributions:

• New algorithms and processing modes
• Video processing enhancements
• UI/UX improvements
• Performance optimizations
• Documentation and translations
• Bug reports and feature requests

Please submit pull requests or issues via GitHub.

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License

MIT License — See LICENSE for full details.

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Acknowledgments

Built with appreciation for the open-source computer vision community and pixel manipulation techniques developed by digital artists worldwide.

Resources: GitHub Releases | PyPI Package | Issue Tracker

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