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Merge pull request #1253 from kvmanohar22:GSoC17_dnn_objdetect
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
This commit is contained in:
Kv Manohar
committed by
Alexander Alekhin
Alexander Alekhin
parent
c0b298c5f3
commit
41a5a5eaf5
169
modules/dnn_objdetect/samples/obj_detect.cpp
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169
modules/dnn_objdetect/samples/obj_detect.cpp
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#include <opencv2/dnn.hpp>
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#include <opencv2/imgproc.hpp>
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#include <opencv2/highgui.hpp>
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#include <fstream>
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#include <iostream>
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#include <cstdlib>
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#include <opencv2/core_detect.hpp>
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using namespace cv;
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using namespace std;
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using namespace cv::dnn;
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using namespace cv::dnn_objdetect;
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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> "
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<< "<threshold>(optional)\n";
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return -1;
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}
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std::string model_prototxt = argv[1];
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std::string model_binary = argv[2];
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std::string test_input_image = argv[3];
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double threshold = 0.7;
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if (argc == 5)
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{
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threshold = atof(argv[4]);
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if (threshold > 1.0 || threshold < 0.0)
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{
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std::cerr << "Threshold should belong to [0, 1]\n";
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return -1;
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}
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}
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// Load the network
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std::cout << "Loading the network...\n";
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Net net = 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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else
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{
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std::cout << "Done loading the network !\n\n";
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}
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// Load the test image
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Mat img = cv::imread(test_input_image);
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Mat original_img(img);
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if (img.empty())
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{
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std::cerr << "Couldn't load image: " << test_input_image << "\n";
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return -3;
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}
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cv::namedWindow("Initial Image", WINDOW_AUTOSIZE);
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cv::imshow("Initial Image", img);
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cv::resize(img, img, cv::Size(416, 416));
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Mat img_copy(img);
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img.convertTo(img, CV_32FC3);
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Mat input_blob = blobFromImage(img, 1.0, Size(), cv::Scalar(104, 117, 123), false);
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// Set the input blob
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// Set the output layers
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std::cout << "Getting the output of all the three blobs...\n";
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std::vector<Mat> outblobs(3);
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std::vector<cv::String> out_layers;
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out_layers.push_back("slice");
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out_layers.push_back("softmax");
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out_layers.push_back("sigmoid");
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// Bbox delta blob
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std::vector<Mat> temp_blob;
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net.setInput(input_blob);
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cv::TickMeter t;
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t.start();
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net.forward(temp_blob, out_layers[0]);
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t.stop();
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outblobs[0] = temp_blob[2];
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// class_scores blob
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net.setInput(input_blob);
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t.start();
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outblobs[1] = net.forward(out_layers[1]);
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t.stop();
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// conf_scores blob
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net.setInput(input_blob);
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t.start();
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outblobs[2] = net.forward(out_layers[2]);
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t.stop();
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// Check that the blobs are valid
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for (size_t i = 0; i < outblobs.size(); ++i)
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{
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if (outblobs[i].empty())
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{
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std::cerr << "Blob: " << i << " is empty !\n";
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}
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}
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int delta_bbox_size[3] = {23, 23, 36};
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Mat delta_bbox(3, delta_bbox_size, CV_32F, outblobs[0].ptr<float>());
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int class_scores_size[2] = {4761, 20};
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Mat class_scores(2, class_scores_size, CV_32F, outblobs[1].ptr<float>());
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int conf_scores_size[3] = {23, 23, 9};
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Mat conf_scores(3, conf_scores_size, CV_32F, outblobs[2].ptr<float>());
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InferBbox inf(delta_bbox, class_scores, conf_scores);
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inf.filter(threshold);
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double average_time = t.getTimeSec() / t.getCounter();
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std::cout << "\nTotal objects detected: " << inf.detections.size()
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<< " in " << average_time << " seconds\n";
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std::cout << "------\n";
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float x_ratio = (float)original_img.cols / img_copy.cols;
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float y_ratio = (float)original_img.rows / img_copy.rows;
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for (size_t i = 0; i < inf.detections.size(); ++i)
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{
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int xmin = inf.detections[i].xmin;
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int ymin = inf.detections[i].ymin;
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int xmax = inf.detections[i].xmax;
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int ymax = inf.detections[i].ymax;
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cv::String class_name = inf.detections[i].label_name;
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std::cout << "Class: " << class_name << "\n"
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<< "Probability: " << inf.detections[i].class_prob << "\n"
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<< "Co-ordinates: " << inf.detections[i].xmin << " "
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<< inf.detections[i].ymin << " "
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<< inf.detections[i].xmax << " "
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<< inf.detections[i].ymax << "\n";
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std::cout << "------\n";
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// Draw the corresponding bounding box(s)
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cv::rectangle(original_img, cv::Point((int)(xmin * x_ratio), (int)(ymin * y_ratio)),
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cv::Point((int)(xmax * x_ratio), (int)(ymax * y_ratio)), cv::Scalar(255, 0, 0), 2);
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cv::putText(original_img, class_name, cv::Point((int)(xmin * x_ratio), (int)(ymin * y_ratio)),
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cv::FONT_HERSHEY_SIMPLEX, 0.7, cv::Scalar(255, 0, 0), 1);
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}
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try
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{
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cv::namedWindow("Final Detections", WINDOW_AUTOSIZE);
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cv::imshow("Final Detections", original_img);
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cv::imwrite("image.png", original_img);
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cv::waitKey(0);
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}
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catch (const char* msg)
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{
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std::cerr << msg << "\n";
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return -4;
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}
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return 0;
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}
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