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https://github.com/opencv/opencv_contrib.git
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1a26f9956c
GSOC'16 - DNN modern (#720) * dnn_modern module sketelon add FindTinyCNN.cmake and setup CMakeLists.txt add find TBB and NNPACK to CMakeLists.txt add caffe converter skeleton add simple sample eval method return scores Update README.md Update README.md Update README.md * update for tiny-dnn package * add QUIET attribute to CMake to find tiny-dnn * update files due to library rename * disable warning in cmake * replace cnn_size_t -> size_t * disable serializer, remove Caffe dependency and remove tiny-dnn target * check CMake version to enable/disable the module * add -Wno-erro in module target * update README.md with tiny-dnn * remove redunadant CMake codes for TBB and OMP * update CMake to enable C++11 compiler * sync CMake compiler options with upstream project * remove unused FindTBB.cmake * minor fix to add const variables * tiny-cnn -> tiny-dnn
92 lines
2.8 KiB
C++
92 lines
2.8 KiB
C++
#include <opencv2/dnn_modern.hpp>
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#include <iostream>
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#include <fstream>
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using namespace std;
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using namespace cv;
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using namespace cv::dnn2;
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static void help() {
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cout
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<< "\n----------------------------------------------------------------------------\n"
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<< " This program shows how to import a Caffe model using the \n"
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<< " OpenCV Modern Deep Learning module (DNN2).\n"
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<< " Usage:\n"
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<< " example_dnn_modern_simple_test <model_file> <trained_file> <mean_file>\n"
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<< " <label_file> <image_file>\n"
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<< " where: model_file is the path to the *.prototxt\n"
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<< " trained_file is the path to the *.caffemodel\n"
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<< " mean_file is the path to the *.binaryproto\n"
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<< " label_file is the path to the labels file\n"
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<< " image_file is the path to the image to evaluate\n"
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<< "----------------------------------------------------------------------------\n\n"
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<< endl;
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}
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vector<string> get_label_list(const string& label_file);
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void print_n_labels(const vector<string>& labels,
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const vector<float_t>& result,
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const int top_n);
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vector<string> get_label_list(const string& label_file) {
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string line;
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ifstream ifs(label_file.c_str());
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if (ifs.fail() || ifs.bad()) {
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throw runtime_error("failed to open:" + label_file);
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}
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vector<string> lines;
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while (getline(ifs, line)) lines.push_back(line);
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return lines;
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}
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void print_n_labels(const vector<string>& labels,
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const vector<float_t>& result,
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const int top_n) {
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vector<float_t> sorted(result.begin(), result.end());
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partial_sort(sorted.begin(), sorted.begin()+top_n, sorted.end(), greater<float_t>());
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for (int i = 0; i < top_n; i++) {
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size_t idx = distance(result.begin(), find(result.begin(), result.end(), sorted[i]));
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cout << labels[idx] << "," << sorted[i] << endl;
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}
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}
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int main(int argc, char* argv[]) {
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if (argc < 6) {
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help();
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exit(0);
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}
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int arg_channel = 1;
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string model_file = argv[arg_channel++];
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string trained_file = argv[arg_channel++];
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string mean_file = argv[arg_channel++];
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string label_file = argv[arg_channel++];
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string img_file = argv[arg_channel++];
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// load Caffe model
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Ptr<CaffeConverter> caffe_ptr = CaffeConverter::create(
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model_file, trained_file, mean_file);
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// load input image
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cv::Mat img = cv::imread(img_file, -1);
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// inference !
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vector<float_t> scores;
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caffe_ptr->eval(img, &scores);
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// retrieve n labels
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const int n = 5;
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vector<string> labels = get_label_list(label_file);
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print_n_labels(labels, scores, n);
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return 0;
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}
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