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269 lines
10 KiB
C++
269 lines
10 KiB
C++
#include <fstream>
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#include <sstream>
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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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const char* keys =
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"{ help h | | Print help message. }"
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"{ device | 0 | camera device number. }"
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"{ input i | | Path to input image or video file. Skip this argument to capture frames from a camera. }"
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"{ model m | | Path to a binary file of model contains trained weights. "
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"It could be a file with extensions .caffemodel (Caffe), "
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".pb (TensorFlow), .t7 or .net (Torch), .weights (Darknet).}"
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"{ config c | | Path to a text file of model contains network configuration. "
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"It could be a file with extensions .prototxt (Caffe), .pbtxt (TensorFlow), .cfg (Darknet).}"
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"{ framework f | | Optional name of an origin framework of the model. Detect it automatically if it does not set. }"
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"{ classes | | Optional path to a text file with names of classes to label detected objects. }"
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"{ mean | | Preprocess input image by subtracting mean values. Mean values should be in BGR order and delimited by spaces. }"
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"{ scale | 1 | Preprocess input image by multiplying on a scale factor. }"
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"{ width | -1 | Preprocess input image by resizing to a specific width. }"
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"{ height | -1 | Preprocess input image by resizing to a specific height. }"
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"{ rgb | | Indicate that model works with RGB input images instead BGR ones. }"
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"{ thr | .5 | Confidence threshold. }"
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"{ backend | 0 | Choose one of computation backends: "
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"0: default C++ backend, "
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"1: Halide language (http://halide-lang.org/), "
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"2: Intel's Deep Learning Inference Engine (https://software.seek.intel.com/deep-learning-deployment)}"
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"{ target | 0 | Choose one of target computation devices: "
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"0: CPU target (by default),"
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"1: OpenCL }";
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using namespace cv;
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using namespace dnn;
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float confThreshold;
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std::vector<std::string> classes;
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void postprocess(Mat& frame, const std::vector<Mat>& out, Net& net);
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void drawPred(int classId, float conf, int left, int top, int right, int bottom, Mat& frame);
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void callback(int pos, void* userdata);
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std::vector<String> getOutputsNames(const Net& net);
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int main(int argc, char** argv)
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{
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CommandLineParser parser(argc, argv, keys);
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parser.about("Use this script to run object detection deep learning networks using OpenCV.");
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if (argc == 1 || parser.has("help"))
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{
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parser.printMessage();
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return 0;
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}
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confThreshold = parser.get<float>("thr");
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float scale = parser.get<float>("scale");
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Scalar mean = parser.get<Scalar>("mean");
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bool swapRB = parser.get<bool>("rgb");
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int inpWidth = parser.get<int>("width");
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int inpHeight = parser.get<int>("height");
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// Open file with classes names.
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if (parser.has("classes"))
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{
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std::string file = parser.get<String>("classes");
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std::ifstream ifs(file.c_str());
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if (!ifs.is_open())
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CV_Error(Error::StsError, "File " + file + " not found");
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std::string line;
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while (std::getline(ifs, line))
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{
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classes.push_back(line);
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}
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}
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// Load a model.
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CV_Assert(parser.has("model"));
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Net net = readNet(parser.get<String>("model"), parser.get<String>("config"), parser.get<String>("framework"));
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net.setPreferableBackend(parser.get<int>("backend"));
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net.setPreferableTarget(parser.get<int>("target"));
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// Create a window
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static const std::string kWinName = "Deep learning object detection in OpenCV";
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namedWindow(kWinName, WINDOW_NORMAL);
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int initialConf = (int)(confThreshold * 100);
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createTrackbar("Confidence threshold, %", kWinName, &initialConf, 99, callback);
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// Open a video file or an image file or a camera stream.
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VideoCapture cap;
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if (parser.has("input"))
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cap.open(parser.get<String>("input"));
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else
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cap.open(parser.get<int>("device"));
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// Process frames.
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Mat frame, blob;
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while (waitKey(1) < 0)
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{
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cap >> frame;
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if (frame.empty())
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{
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waitKey();
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break;
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}
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// Create a 4D blob from a frame.
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Size inpSize(inpWidth > 0 ? inpWidth : frame.cols,
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inpHeight > 0 ? inpHeight : frame.rows);
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blobFromImage(frame, blob, scale, inpSize, mean, swapRB, false);
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// Run a model.
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net.setInput(blob);
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if (net.getLayer(0)->outputNameToIndex("im_info") != -1) // Faster-RCNN or R-FCN
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{
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resize(frame, frame, inpSize);
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Mat imInfo = (Mat_<float>(1, 3) << inpSize.height, inpSize.width, 1.6f);
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net.setInput(imInfo, "im_info");
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}
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std::vector<Mat> outs;
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net.forward(outs, getOutputsNames(net));
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postprocess(frame, outs, net);
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// Put efficiency information.
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std::vector<double> layersTimes;
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double freq = getTickFrequency() / 1000;
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double t = net.getPerfProfile(layersTimes) / freq;
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std::string label = format("Inference time: %.2f ms", t);
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putText(frame, label, Point(0, 15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0));
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imshow(kWinName, frame);
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}
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return 0;
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}
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void postprocess(Mat& frame, const std::vector<Mat>& outs, Net& net)
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{
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static std::vector<int> outLayers = net.getUnconnectedOutLayers();
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static std::string outLayerType = net.getLayer(outLayers[0])->type;
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if (net.getLayer(0)->outputNameToIndex("im_info") != -1) // Faster-RCNN or R-FCN
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{
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// Network produces output blob with a shape 1x1xNx7 where N is a number of
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// detections and an every detection is a vector of values
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// [batchId, classId, confidence, left, top, right, bottom]
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CV_Assert(outs.size() == 1);
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float* data = (float*)outs[0].data;
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for (size_t i = 0; i < outs[0].total(); i += 7)
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{
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float confidence = data[i + 2];
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if (confidence > confThreshold)
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{
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int left = (int)data[i + 3];
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int top = (int)data[i + 4];
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int right = (int)data[i + 5];
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int bottom = (int)data[i + 6];
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int classId = (int)(data[i + 1]) - 1; // Skip 0th background class id.
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drawPred(classId, confidence, left, top, right, bottom, frame);
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}
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}
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}
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else if (outLayerType == "DetectionOutput")
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{
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// Network produces output blob with a shape 1x1xNx7 where N is a number of
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// detections and an every detection is a vector of values
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// [batchId, classId, confidence, left, top, right, bottom]
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CV_Assert(outs.size() == 1);
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float* data = (float*)outs[0].data;
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for (size_t i = 0; i < outs[0].total(); i += 7)
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{
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float confidence = data[i + 2];
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if (confidence > confThreshold)
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{
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int left = (int)(data[i + 3] * frame.cols);
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int top = (int)(data[i + 4] * frame.rows);
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int right = (int)(data[i + 5] * frame.cols);
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int bottom = (int)(data[i + 6] * frame.rows);
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int classId = (int)(data[i + 1]) - 1; // Skip 0th background class id.
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drawPred(classId, confidence, left, top, right, bottom, frame);
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}
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}
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}
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else if (outLayerType == "Region")
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{
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std::vector<int> classIds;
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std::vector<float> confidences;
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std::vector<Rect> boxes;
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for (size_t i = 0; i < outs.size(); ++i)
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{
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// Network produces output blob with a shape NxC where N is a number of
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// detected objects and C is a number of classes + 4 where the first 4
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// numbers are [center_x, center_y, width, height]
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float* data = (float*)outs[i].data;
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for (int j = 0; j < outs[i].rows; ++j, data += outs[i].cols)
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{
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Mat scores = outs[i].row(j).colRange(5, outs[i].cols);
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Point classIdPoint;
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double confidence;
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minMaxLoc(scores, 0, &confidence, 0, &classIdPoint);
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if (confidence > confThreshold)
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{
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int centerX = (int)(data[0] * frame.cols);
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int centerY = (int)(data[1] * frame.rows);
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int width = (int)(data[2] * frame.cols);
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int height = (int)(data[3] * frame.rows);
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int left = centerX - width / 2;
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int top = centerY - height / 2;
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classIds.push_back(classIdPoint.x);
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confidences.push_back((float)confidence);
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boxes.push_back(Rect(left, top, width, height));
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}
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}
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}
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std::vector<int> indices;
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NMSBoxes(boxes, confidences, confThreshold, 0.4f, indices);
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for (size_t i = 0; i < indices.size(); ++i)
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{
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int idx = indices[i];
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Rect box = boxes[idx];
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drawPred(classIds[idx], confidences[idx], box.x, box.y,
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box.x + box.width, box.y + box.height, frame);
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}
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}
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else
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CV_Error(Error::StsNotImplemented, "Unknown output layer type: " + outLayerType);
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}
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void drawPred(int classId, float conf, int left, int top, int right, int bottom, Mat& frame)
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{
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rectangle(frame, Point(left, top), Point(right, bottom), Scalar(0, 255, 0));
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std::string label = format("%.2f", conf);
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if (!classes.empty())
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{
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CV_Assert(classId < (int)classes.size());
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label = classes[classId] + ": " + label;
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}
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int baseLine;
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Size labelSize = getTextSize(label, FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
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top = max(top, labelSize.height);
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rectangle(frame, Point(left, top - labelSize.height),
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Point(left + labelSize.width, top + baseLine), Scalar::all(255), FILLED);
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putText(frame, label, Point(left, top), FONT_HERSHEY_SIMPLEX, 0.5, Scalar());
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}
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void callback(int pos, void*)
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{
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confThreshold = pos * 0.01f;
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}
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std::vector<String> getOutputsNames(const Net& net)
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{
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static std::vector<String> names;
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if (names.empty())
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{
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std::vector<int> outLayers = net.getUnconnectedOutLayers();
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std::vector<String> layersNames = net.getLayerNames();
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names.resize(outLayers.size());
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for (size_t i = 0; i < outLayers.size(); ++i)
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names[i] = layersNames[outLayers[i] - 1];
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}
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return names;
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}
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