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154 lines
4.5 KiB
154 lines
4.5 KiB
#include <opencv2/dnn.hpp> |
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#include <opencv2/imgproc.hpp> |
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#include <opencv2/highgui.hpp> |
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using namespace cv; |
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using namespace cv::dnn; |
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#include <fstream> |
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#include <iostream> |
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#include <cstdlib> |
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using namespace std; |
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//static void colorizeSegmentation(dnn::Blob& score, |
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// const vector<cv::Vec3b>& colors, |
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// cv::Mat& segm) |
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//{ |
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// const int rows = score.rows(); |
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// const int cols = score.cols(); |
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// const int chns = score.channels(); |
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// cv::Mat maxCl(rows, cols, CV_8UC1); |
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// cv::Mat maxVal(rows, cols, CV_32FC1); |
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// for (int ch = 0; ch < chns; ch++) |
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// { |
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// for (int row = 0; row < rows; row++) |
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// { |
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// const float* ptrScore = score.ptrf(0, ch, row); |
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// uchar* ptrMaxCl = maxCl.ptr<uchar>(row); |
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// float* ptrMaxVal = maxVal.ptr<float>(row); |
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// for (int col = 0; col < cols; col++) |
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// { |
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// if (ptrScore[col] > ptrMaxVal[col]) |
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// { |
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// ptrMaxVal[col] = ptrScore[col]; |
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// ptrMaxCl[col] = ch; |
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// } |
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// } |
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// } |
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// } |
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// segm.create(rows, cols, CV_8UC3); |
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// for (int row = 0; row < rows; row++) |
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// { |
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// const uchar* ptrMaxCl = maxCl.ptr<uchar>(row); |
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// cv::Vec3b* ptrSegm = segm.ptr<cv::Vec3b>(row); |
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// for (int col = 0; col < cols; col++) |
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// { |
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// ptrSegm[col] = colors[ptrMaxCl[col]]; |
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// } |
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// } |
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//} |
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const char* about = "This sample uses Single-Shot Detector to detect objects " |
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"from camera\n"; // TODO: link |
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const char* params |
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= "{ help | help | false | print usage }" |
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"{ proto | model prototxt file | | model configuration }" |
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"{ model | caffemodel file | | model weights }"; |
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int main(int argc, char** argv) |
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{ |
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cv::CommandLineParser parser(argc, argv, params); |
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if (parser.get<bool>("help")) |
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{ |
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std::cout << about << std::endl; |
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parser.printMessage(); |
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return 0; |
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} |
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String modelConfiguration = parser.get<string>("proto"); |
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String modelBinary = parser.get<string>("model"); |
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//! [Create the importer of Caffe model] |
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Ptr<dnn::Importer> importer; |
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// Import Caffe SSD model |
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try |
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{ |
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importer = dnn::createCaffeImporter(modelConfiguration, modelBinary); |
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} |
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catch (const cv::Exception &err) //Importer can throw errors, we will catch them |
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{ |
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cerr << err.msg << endl; |
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} |
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//! [Create the importer of Caffe model] |
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if (!importer) |
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{ |
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cerr << "Can't load network by using the following files: " << endl; |
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cerr << "prototxt: " << modelConfiguration << endl; |
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cerr << "caffemodel: " << modelBinary << endl; |
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cerr << "Models can be downloaded here:" << endl; |
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cerr << "https://github.com/weiliu89/caffe/tree/ssd#models" << endl; |
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exit(-1); |
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} |
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//! [Initialize network] |
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dnn::Net net; |
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importer->populateNet(net); |
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importer.release(); //We don't need importer anymore |
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//! [Initialize network] |
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VideoCapture camera; |
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if (!camera.open(0)) |
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{ |
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cout << "Unable to open camera stream" << endl; |
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return 0; |
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} |
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size_t i = 0; |
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for (;; ) |
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{ |
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Mat frame; |
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camera >> frame; |
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if (frame.empty()) |
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break; |
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//! [Prepare blob] |
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resize(frame, frame, Size(300, 300)); //SSD accepts 300x300 RGB-images |
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dnn::Blob inputBlob = dnn::Blob(frame); //Convert Mat to dnn::Blob image |
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//! [Prepare blob] |
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std::ostringstream stream; |
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stream << "folder/" << i << ".jpg"; |
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imwrite(stream.str(), frame); |
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//! [Set input blob] |
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net.setBlob(".data", inputBlob); //set the network input |
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//! [Set input blob] |
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//! [Make forward pass] |
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net.forward(); //compute output |
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//! [Make forward pass] |
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// //! [Gather output] |
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// dnn::Blob detection = net.getBlob("detection_out"); |
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// // cv::Mat colorize; |
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// // colorizeSegmentation(score, colors, colorize); |
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// // cv::Mat show; |
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// // cv::addWeighted(img, 0.4, colorize, 0.6, 0.0, show); |
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// // cv::imshow("show", show); |
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// // cv::waitKey(0); |
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// // return 0; |
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// imshow("frame", frame); |
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// if (waitKey(1) == 27) |
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// break; // stop capturing by pressing ESC |
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} |
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camera.release(); |
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} // main
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