Copied to Clipboard
Func input_16("input_16");
input_16(x, y, c) = cast<int16_t>(clamped(x, y, c));
// 4. THE ALGORITHM: Apply the discrete convolution kernel
Func sharpen("sharpen");
sharpen(x, y, c) = 5 * input_16(x, y, c)
- input_16(x - 1, y, c)
- input_16(x + 1, y, c)
- input_16(x, y - 1, c)
- input_16(x, y + 1, c);
// 5. Finalize the output
// The result might be negative or greater than 255. We clamp the values
// to the valid 0-255 range, then safely cast back to unsigned 8-bit.
Func output("output");
output(x, y, c) = cast<uint8_t>(clamp(sharpen(x, y, c), 0, 255));
// 6. THE SCHEDULE
// This is where the magic happens. We tell the compiler to evaluate the
// 'y' coordinates in parallel (utilizing multithreading), and to process
// the 'x' coordinates in vectorized batches of 16 (utilizing SIMD).
output.parallel(y).vectorize(x, 16);
// 7. Realize the pipeline
// Until this point, no actual computation has happened. The 'realize' call
// triggers the Just-In-Time (JIT) compiler to generate optimized machine code
// and execute the pipeline over the specified dimensions.
Buffer<uint8_t> result = output.realize({input.width(), input.height(), input.channels()});
// 8. Save the processed image to disk
save_image(result, argv[2]);
printf("Success! Image sharpened.\n");
return 0;
}
Compiling and Running the Code
To compile this application, you must have the Halide release binaries available on your system, along with libpng and libjpeg to support the image I/O helper functions.
Because Halide utilizes modern C++ features, you must compile with at least C++17. A standard compilation command using GCC looks like this:
g++ main.cpp -g -I /path/to/halide/include -I /path/to/halide/tools \
-L /path/to/halide/lib -lHalide -lpng -ljpeg -lpthread -ldl -std=c++17 -o sharpen
Note: Ensure you replace /path/to/halide/ with the actual path where your Halide headers and libraries are located.
Once the code is compiled successfully, you can run the executable from your terminal, passing in the image you want to process and the desired name for the output file:
./sharpen my_blurry_photo.png crisp_sharpened_photo.png
Final Thoughts
By abstracting the memory layout and execution loops away from the mathematical logic, Halide drastically reduces the cognitive load required to build complex computer vision pipelines. Our sharpening filter is concise, mathematically readable, and incredibly fast.
More importantly, it is highly maintainable. If a new hardware architecture is released tomorrow with a completely different optimal memory access pattern, the algorithm itself remains untouched. The developer only needs to adjust the one-line schedule to accommodate the new hardware, ensuring that high-performance image processing code remains future-proof.