This C program performs edge detection and gradient image generation using pre-computed horizontal and vertical differential images. It implements edge detection by identifying points where the gradient magnitude exceeds a specified threshold, marking them as edges in the output image. The gradient image is computed from the horizontal and vertical differential images using a 3x3 or 5x5 filter.
The program consists of two main functions:
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computeGradient: Computes the gradient image from the horizontal and vertical differential images. It calculates the magnitude of the gradient at each pixel using the Euclidean distance formula.
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detectEdges: Detects edge points in the gradient image based on a specified threshold. If the magnitude of the gradient at a pixel exceeds the threshold, it is marked as an edge point with a bright intensity (200). Otherwise, it is set to zero.
The program also includes utility functions to load images from files and perform memory allocation and deallocation.
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Compile the Program: Compile the program using a C compiler such as GCC.
Example:
gcc edge_detection.c -o edge_detection -lm -
Run the Program: Execute the compiled program.
Example:
./edge_detection -
Input Images: Ensure that the input horizontal and vertical differential images are provided in raw format. Adjust the file paths in the code accordingly.
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Output Images: The program generates gradient images and edge images for both 3x3 and 5x5 filters. The output images are saved as raw files.
- MTCNN for Face Detection: Uses pre-trained models from the Facenet PyTorch repository.
- OpenCV for Image Processing: Utilizes OpenCV library for image loading and manipulation.
- Scikit-learn for Cosine Similarity Calculation: Depends on Scikit-learn library for cosine similarity calculations.
- Overflow and underflow are handled by normalizing the filtered results into the range of a byte.
- Threshold values for edge detection can be adjusted manually through trial-and-error to achieve desired edge results.