Template Matching
Find and locate template images within larger images using OpenCV's template matching algorithms with multiple methods
Main Image (Search Area)
Template Image (Pattern to Find)
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Upload images and click "Find Matches" to see results here
How Template Matching Works
Template matching is a technique in computer vision for finding small parts of an image which match a template image. The algorithm slides the template image across the main image and computes a similarity score at each position.
Privacy & Performance: All processing happens in your browser using OpenCV.js. Images are never uploaded to any server, ensuring your data remains private.
Limitations: Template matching doesn't handle rotation, scaling, or significant perspective changes. The template must be at the same scale and orientation as in the main image.
About the OpenCV Template Matching Tool
Find and locate template images within larger images using OpenCV's powerful template matching algorithms. Perfect for computer vision applications, automated testing, image analysis, and pattern recognition tasks.
Features:
- 6 different OpenCV template matching methods
- Single or multiple match detection
- Adjustable threshold for match sensitivity
- Visual results with bounding boxes
- Match confidence scores and coordinates
- Client-side processing (images never leave your browser)
- Download annotated result images
- Support for PNG, JPG, and other image formats
Template Matching Methods:
- TM_CCOEFF_NORMED: Correlation coefficient (normalized) - Best for most cases
- TM_CCORR_NORMED: Cross-correlation (normalized) - Good for similar templates
- TM_SQDIFF_NORMED: Squared difference (normalized) - Good for exact matches
- TM_CCOEFF: Correlation coefficient (unnormalized)
- TM_CCORR: Cross-correlation (unnormalized)
- TM_SQDIFF: Squared difference (unnormalized)
Use Cases:
- Automated UI testing - Find buttons and elements
- Game bot development - Detect game objects
- Quality control - Find defects in products
- Document processing - Locate logos or stamps
- Computer vision research and prototyping
Tips for Best Results:
- Template should be smaller than the main image
- Use clear, high-quality images for better accuracy
- Template and target should be at similar scales
- TM_CCOEFF_NORMED is usually the best starting point
- Normalized methods handle lighting variations better