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https://github.com/opencv/opencv_contrib.git
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41a5a5eaf5
GSoC'17 Learning compact models for object detection (#1253) * Final solver and model for SqueezeNet model * update README * update dependencies and CMakeLists * add global pooling * Add training scripts * fix typo * fix dependency of caffe * fix whitespace * Add squeezedet architecture * Pascal pre process script * Adding pre process scripts * Generate the graph of the model * more readable * fix some bugs in the graph * Post process class implementation * Complete minimal post processing and standalone running * Complete the base class * remove c++11 features and fix bugs * Complete example * fix bugs * Adding final scripts * Classification scripts * Update README.md * Add example code and results * Update README.md * Re-order and fix some bugs * fix build failure * Document classes and functions * Add instructions on how to use samples * update instructionos * fix docs failure * fix conversion types * fix type conversion warning * Change examples to sample directoryu * restructure directories * add more references * fix whitespace * retain aspect ratio * Add more examples * fix docs warnings * update with links to trained weights * threshold update * png -> jpg * fix tutorial * model files * precomp.hpp , fix readme links, module dependencies * copyrights - no copyright in samples - use new style OpenCV copyright header - precomp.hpp
68 lines
1.7 KiB
C++
68 lines
1.7 KiB
C++
#include <opencv2/imgproc.hpp>
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#include <opencv2/highgui.hpp>
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#include <opencv2/dnn.hpp>
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#include <iostream>
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#include <cstdlib>
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int main(int argc, char **argv)
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{
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if (argc < 4)
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{
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std::cerr << "Usage " << argv[0] << ": "
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<< "<model-definition-file> " << " "
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<< "<model-weights-file> " << " "
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<< "<test-image>\n";
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return -1;
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}
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cv::String model_prototxt = argv[1];
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cv::String model_binary = argv[2];
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cv::String test_image = argv[3];
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cv::dnn::Net net = cv::dnn::readNetFromCaffe(model_prototxt, model_binary);
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if (net.empty())
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{
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std::cerr << "Couldn't load the model !\n";
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return -2;
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}
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cv::Mat img = cv::imread(test_image);
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if (img.empty())
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{
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std::cerr << "Couldn't load image: " << test_image << "\n";
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return -3;
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}
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cv::Mat input_blob = cv::dnn::blobFromImage(
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img, 1.0, cv::Size(416, 416), cv::Scalar(104, 117, 123), false);
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cv::Mat prob;
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cv::TickMeter t;
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net.setInput(input_blob);
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t.start();
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prob = net.forward("predictions");
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t.stop();
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int prob_size[3] = {1000, 1, 1};
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cv::Mat prob_data(3, prob_size, CV_32F, prob.ptr<float>(0));
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double max_prob = -1.0;
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int class_idx = -1;
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for (int idx = 0; idx < prob.size[1]; ++idx)
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{
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double current_prob = prob_data.at<float>(idx, 0, 0);
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if (current_prob > max_prob)
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{
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max_prob = current_prob;
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class_idx = idx;
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}
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}
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std::cout << "Best class Index: " << class_idx << "\n";
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std::cout << "Time taken: " << t.getTimeSec() << "\n";
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std::cout << "Probability: " << max_prob * 100.0<< "\n";
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return 0;
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}
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