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https://github.com/lordmathis/CUDANet.git
synced 2025-11-06 01:34:22 +00:00
Move preprocessing out of main
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@@ -3,34 +3,8 @@
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#include <cudanet.cuh>
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#include <iostream>
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#include <opencv2/opencv.hpp>
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#include <vector>
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std::vector<float>
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readAndNormalizeImage(const std::string &imagePath, int width, int height) {
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// Read the image using OpenCV
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cv::Mat image = cv::imread(imagePath, cv::IMREAD_COLOR);
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// Resize and normalize the image
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cv::resize(image, image, cv::Size(width, height));
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image.convertTo(image, CV_32FC3, 1.0 / 255.0);
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// Normalize the image https://pytorch.org/hub/pytorch_vision_alexnet/
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cv::Mat mean(image.size(), CV_32FC3, cv::Scalar(0.485, 0.456, 0.406));
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cv::Mat std(image.size(), CV_32FC3, cv::Scalar(0.229, 0.224, 0.225));
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cv::subtract(image, mean, image);
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cv::divide(image, std, image);
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// Convert the 3D image matrix to a 1D array of floats
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std::vector<float> imageData;
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for (int c = 0; c < image.channels(); ++c) {
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for (int i = 0; i < image.rows; ++i) {
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for (int j = 0; j < image.cols; ++j) {
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imageData.push_back(image.at<cv::Vec3f>(i, j)[c]);
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}
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}
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}
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return imageData;
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}
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int main(int argc, const char *const argv[]) {
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if (argc != 3) {
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@@ -55,12 +29,12 @@ int main(int argc, const char *const argv[]) {
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inception_v3->loadWeights(modelWeightsPath);
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std::vector<float> imageData =
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readAndNormalizeImage(imagePath, inputSize.first, inputSize.second);
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readAndNormalizeImage(imagePath, inputSize.first, inputSize.first);
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// Print the size of the image data
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const float *output = inception_v3->predict(imageData.data());
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// Get max index
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// Get max index
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int maxIndex = 0;
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for (int i = 0; i < outputSize; i++) {
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if (output[i] > output[maxIndex]) {
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