metadata

title: Diff Highlighter
emoji: 🏢
colorFrom: indigo
colorTo: red
sdk: gradio
sdk_version: 5.22.0
app_file: app.py
pinned: false

Object Difference Highlighter

Overview

The Object Difference Highlighter is a computer vision application that identifies and isolates new objects in a scene. Given two images of the same scene—one without an object and another with an added object (taken seconds apart)—the app intelligently detects what has changed while filtering out environmental noise like moving foliage, slight shadows, or minor lighting changes.

Key Features

Intelligent Object Detection: Identifies real, meaningful changes between two similar images
Noise Filtering: Eliminates false positives from environmental variations (wind-moved objects, minor lighting changes)
Multiple Detection Algorithms:
- SSIM (Structural Similarity Index Measure): Detects structural differences
- Background Subtraction: Filters out background to focus on foreground changes
- Optical Flow: Detects motion-based changes between frames
Comprehensive Visualization: Six different views to analyze and understand the changes

Visual Outputs

The application provides six different views arranged in three rows:

Row 1: Base Comparison

Blended Image: A 50/50 blend of both input images
Raw Difference Overlay: Unprocessed pixel differences shown in magenta on the original image

Row 2: Algorithm Results

Highlighted Differences: Areas identified as changed by the selected algorithm
Black & White Mask: Binary mask showing exactly what areas the algorithm detected as changed

Row 3: Final Composition

Composite Image: Scene from the first image with only the detected object from the second image
Final Difference Overlay: Differences between the original scene and the composite image shown in magenta

Use Cases

Photography: Remove unwanted elements from otherwise perfect shots
Surveillance: Detect new objects in monitored areas
Research: Analyze scene changes in controlled environments
Wildlife Monitoring: Detect animals in natural settings
Retail/Inventory: Track object placement and removal

Installation & Setup

1. Clone the Repository

git clone https://github.com/your-username/object-difference-highlighter.git
cd object-difference-highlighter

2. Install Dependencies

Ensure you have Python installed, then run:

pip install -r requirements.txt

3. Run the Application

python app.py

The application will launch in your default web browser via Gradio.

Usage Guide

Step 1: Upload Images

Upload two images:
1. Image Without Object (Scene): The base scene without the object of interest
2. Image With Object: The same scene with the added object

Step 2: Configure Parameters

Comparison Method: Select the algorithm best suited for your images
- SSIM works well for most cases
- Background Subtraction is good for static cameras
- Optical Flow works well for slight camera movements
Gaussian Blur: Adjust to reduce noise (higher values for noisier images)
Thresholding Technique: When using SSIM, determines how differences are identified

Step 3: Process & Review Results

Click Process to generate all six visualizations
Compare the different views to understand how well the algorithm detected the new object
Use the mask and highlighted differences to evaluate detection quality
The composite image shows the final result with only the detected object added to the scene

Dependencies

Python 3.7+
OpenCV
NumPy
Scikit-Image
Gradio