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707 lines
25 KiB
C++
707 lines
25 KiB
C++
//![includes]
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#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/imgcodecs.hpp>
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#include <opencv2/highgui.hpp>
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#include <mutex>
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#include <thread>
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#include <queue>
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#include "iostream"
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#include "common.hpp"
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//![includes]
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using namespace cv;
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using namespace dnn;
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using namespace std;
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const string about =
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"Firstly, download required models using `download_models.py` (if not already done). Set environment variable OPENCV_DOWNLOAD_CACHE_DIR to specify where models should be downloaded. Also, point OPENCV_SAMPLES_DATA_PATH to opencv/samples/data.\n"
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"To run:\n"
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"\t ./example_dnn_object_detection model_name --input=path/to/your/input/image/or/video (don't give --input flag if want to use device camera)\n"
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"Sample command:\n"
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"\t ./example_dnn_object_detection yolov8 --input=$OPENCV_SAMPLES_DATA_PATH/baboon.jpg\n"
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"Model path can also be specified using --model argument. ";
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const string param_keys =
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"{ help h | | Print help message. }"
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"{ @alias | | An alias name of model to extract preprocessing parameters from models.yml file. }"
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"{ zoo | ../dnn/models.yml | An optional path to file with preprocessing parameters }"
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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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"{ thr | .5 | Confidence threshold. }"
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"{ nms | .4 | Non-maximum suppression threshold. }"
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"{ async | 0 | Number of asynchronous forwards at the same time. "
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"Choose 0 for synchronous mode }"
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"{ padvalue | 114.0 | padding value. }"
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"{ paddingmode | 2 | Choose one of padding modes: "
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"0: resize to required input size without extra processing, "
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"1: Image will be cropped after resize, "
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"2: Resize image to the desired size while preserving the aspect ratio of original image }";
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const string backend_keys = format(
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"{ backend | default | Choose one of computation backends: "
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"default: automatically (by default), "
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"openvino: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), "
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"opencv: OpenCV implementation, "
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"vkcom: VKCOM, "
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"cuda: CUDA, "
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"webnn: WebNN }");
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const string target_keys = format(
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"{ target | cpu | Choose one of target computation devices: "
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"cpu: CPU target (by default), "
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"opencl: OpenCL, "
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"opencl_fp16: OpenCL fp16 (half-float precision), "
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"vpu: VPU, "
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"vulkan: Vulkan, "
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"cuda: CUDA, "
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"cuda_fp16: CUDA fp16 (half-float preprocess) }");
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string keys = param_keys + backend_keys + target_keys;
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float confThreshold, nmsThreshold, scale, paddingValue;
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vector<string> labels;
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Scalar meanv;
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bool swapRB;
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int inpWidth, inpHeight;
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size_t asyncNumReq = 0;
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ImagePaddingMode paddingMode;
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string modelName, framework;
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static void preprocess(const Mat& frame, Net& net, Size inpSize);
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static void postprocess(Mat& frame, const vector<Mat>& outs, Net& net, int backend, vector<int>& classIds, vector<float>& confidences, vector<Rect>& boxes, const string yolo_name);
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static void drawPred(vector<int>& classIds, vector<float>& confidences, vector<Rect>& boxes, Mat& frame, FontFace& sans, int stdSize, int stdWeight, int stdImgSize, int stdThickness);
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static void callback(int pos, void* userdata);
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static Scalar getColor(int classId);
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static void yoloPostProcessing(
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const vector<Mat>& outs,
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vector<int>& keep_classIds,
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vector<float>& keep_confidences,
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vector<Rect2d>& keep_boxes,
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float conf_threshold,
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float iou_threshold,
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const string& yolo_name);
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static void printAliases(string& zooFile){
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vector<string> aliases = findAliases(zooFile, "object_detection");
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cout<<"Alias choices: [ ";
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for (auto it: aliases){
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cout<<"'"<<it<<"' ";
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}
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cout<<"]"<<endl;
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}
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static Scalar getTextColor(Scalar bgColor) {
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double luminance = 0.299 * bgColor[2] + 0.587 * bgColor[1] + 0.114 * bgColor[0];
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return luminance > 128 ? Scalar(0, 0, 0) : Scalar(255, 255, 255);
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}
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template <typename T>
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class QueueFPS : public std::queue<T>
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{
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public:
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QueueFPS() : counter(0) {}
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void push(const T& entry)
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{
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std::lock_guard<std::mutex> lock(mutex);
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std::queue<T>::push(entry);
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counter += 1;
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if (counter == 1)
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{
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// Start counting from a second frame (warmup).
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tm.reset();
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tm.start();
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}
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}
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T get()
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{
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std::lock_guard<std::mutex> lock(mutex);
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T entry = this->front();
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this->pop();
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return entry;
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}
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float getFPS()
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{
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tm.stop();
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double fps = counter / tm.getTimeSec();
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tm.start();
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return static_cast<float>(fps);
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}
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void clear()
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{
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std::lock_guard<std::mutex> lock(mutex);
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while (!this->empty())
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this->pop();
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}
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unsigned int counter;
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private:
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TickMeter tm;
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std::mutex mutex;
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};
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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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string zooFile = parser.get<String>("zoo");
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if (!parser.has("@alias") || parser.has("help"))
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{
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cout << about << endl;
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parser.printMessage();
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printAliases(zooFile);
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return -1;
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}
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zooFile = findFile(zooFile);
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modelName = parser.get<String>("@alias");
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keys += genPreprocArguments(modelName, zooFile);
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parser = CommandLineParser(argc, argv, keys);
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if (!parser.has("model"))
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{
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cout << "Path to model is not provided in command line or model alias is not correct" << endl;
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printAliases(zooFile);
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return -1;
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}
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confThreshold = parser.get<float>("thr");
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nmsThreshold = parser.get<float>("nms");
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//![preprocess_params]
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scale = parser.get<float>("scale");
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meanv = parser.get<Scalar>("mean");
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swapRB = parser.get<bool>("rgb");
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inpWidth = parser.get<int>("width");
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inpHeight = parser.get<int>("height");
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int async = parser.get<int>("async");
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paddingValue = parser.get<float>("padvalue");
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const string yolo_name = parser.get<String>("postprocessing");
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paddingMode = static_cast<ImagePaddingMode>(parser.get<int>("paddingmode"));
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//![preprocess_params]
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String sha1 = parser.get<String>("sha1");
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const string modelPath = findModel(parser.get<String>("model"), sha1);
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const string configPath = findFile(parser.get<String>("config"));
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framework = modelPath.substr(modelPath.rfind('.') + 1);
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if (parser.has("labels"))
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{
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const string file = findFile(parser.get<String>("labels"));
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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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string line;
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while (getline(ifs, line))
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{
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labels.push_back(line);
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}
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}
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//![read_net]
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Net net = readNet(modelPath, configPath);
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int backend = getBackendID(parser.get<String>("backend"));
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net.setPreferableBackend(backend);
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net.setPreferableTarget(getTargetID(parser.get<String>("target")));
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//![read_net]
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// Create a window
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static const string kWinName = "Deep learning object detection in OpenCV";
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namedWindow(kWinName, WINDOW_AUTOSIZE);
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int initialConf = (int)(confThreshold * 100);
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createTrackbar("Confidence threshold, %", kWinName, &initialConf, 99, callback, &net);
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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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bool openSuccess = parser.has("input") ? cap.open(parser.get<String>("input")) : cap.open(parser.get<int>("device"));
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if (!openSuccess){
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cout << "Could not open input file or camera device" << endl;
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return 0;
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}
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FontFace sans("sans");
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int stdSize = 15;
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int stdWeight = 150;
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int stdImgSize = 512;
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int stdThickness = 2;
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vector<int> classIds;
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vector<float> confidences;
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vector<Rect> boxes;
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if (async > 0 && backend == DNN_BACKEND_INFERENCE_ENGINE){
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asyncNumReq = async;
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}
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if (async != 0) {
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// Threading is enabled
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bool process = true;
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// Frames capturing thread
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QueueFPS<Mat> framesQueue;
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std::thread framesThread([&]() {
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Mat frame;
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while (process) {
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cap >> frame;
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if (!frame.empty())
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framesQueue.push(frame.clone());
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else
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break;
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}
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});
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// Frames processing thread
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QueueFPS<Mat> processedFramesQueue;
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QueueFPS<std::vector<Mat>> predictionsQueue;
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std::thread processingThread([&]() {
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std::queue<AsyncArray> futureOutputs;
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Mat blob;
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while (process) {
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// Get the next frame
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Mat frame;
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{
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if (!framesQueue.empty()) {
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frame = framesQueue.get();
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if (asyncNumReq) {
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if (futureOutputs.size() == asyncNumReq)
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frame = Mat();
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}
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}
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}
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// Process the frame
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if (!frame.empty()) {
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preprocess(frame, net, Size(inpWidth, inpHeight));
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processedFramesQueue.push(frame);
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if (asyncNumReq) {
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futureOutputs.push(net.forwardAsync());
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} else {
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//![forward]
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vector<Mat> outs;
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net.forward(outs, net.getUnconnectedOutLayersNames());
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predictionsQueue.push(outs);
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//![forward]
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}
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}
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while (!futureOutputs.empty() &&
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futureOutputs.front().wait_for(std::chrono::seconds(0))) {
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AsyncArray async_out = futureOutputs.front();
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futureOutputs.pop();
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Mat out;
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async_out.get(out);
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predictionsQueue.push({out});
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}
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}
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});
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// Postprocessing and rendering loop
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while (waitKey(100) < 0) {
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if (predictionsQueue.empty())
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continue;
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vector<Mat> outs = predictionsQueue.get();
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Mat frame = processedFramesQueue.get();
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classIds.clear();
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confidences.clear();
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boxes.clear();
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postprocess(frame, outs, net, backend, classIds, confidences, boxes, yolo_name);
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drawPred(classIds, confidences, boxes, frame, sans, stdSize, stdWeight, stdImgSize, stdThickness);
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int imgWidth = max(frame.rows, frame.cols);
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int size = static_cast<int>((stdSize * imgWidth) / (stdImgSize * 1.5));
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int weight = static_cast<int>((stdWeight * imgWidth) / (stdImgSize * 1.5));
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if (predictionsQueue.counter > 1) {
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string label = format("Camera: %.2f FPS", framesQueue.getFPS());
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rectangle(frame, Point(0, 0), Point(10 * size, 3 * size + size / 4), Scalar::all(255), FILLED);
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putText(frame, label, Point(0, size), Scalar::all(0), sans, size, weight);
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label = format("Network: %.2f FPS", predictionsQueue.getFPS());
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putText(frame, label, Point(0, 2 * size), Scalar::all(0), sans, size, weight);
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label = format("Skipped frames: %d", framesQueue.counter - predictionsQueue.counter);
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putText(frame, label, Point(0, 3 * size), Scalar::all(0), sans, size, weight);
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}
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imshow(kWinName, frame);
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}
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process = false;
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framesThread.join();
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processingThread.join();
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} else {
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if (asyncNumReq)
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CV_Error(Error::StsNotImplemented, "Asynchronous forward is supported only with Inference Engine backend.");
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// Threading is disabled, run synchronously
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Mat frame, blob;
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while (waitKey(100) < 0) {
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cap >> frame;
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if (frame.empty()) {
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waitKey();
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break;
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}
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preprocess(frame, net, Size(inpWidth, inpHeight));
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vector<Mat> outs;
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net.forward(outs, net.getUnconnectedOutLayersNames());
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classIds.clear();
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confidences.clear();
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boxes.clear();
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postprocess(frame, outs, net, backend, classIds, confidences, boxes, yolo_name);
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drawPred(classIds, confidences, boxes, frame, sans, stdSize, stdWeight, stdImgSize, stdThickness);
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vector<double> layersTimes;
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int imgWidth = max(frame.rows, frame.cols);
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int size = static_cast<int>((stdSize * imgWidth) / (stdImgSize * 1.5));
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int weight = static_cast<int>((stdWeight * imgWidth) / (stdImgSize * 1.5));
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double freq = getTickFrequency() / 1000;
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double t = net.getPerfProfile(layersTimes) / freq;
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string label = format("Inference time: %.2f ms", t);
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putText(frame, label, Point(0, size), Scalar(0, 255, 0), sans, size, weight);
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imshow(kWinName, frame);
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}
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}
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return 0;
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}
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void preprocess(const Mat& frame, Net& net, Size inpSize)
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{
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Size size(inpSize.width <= 0 ? frame.cols : inpSize.width, inpSize.height <= 0 ? frame.rows : inpSize.height);
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// Prepare the blob from the image
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Mat inp;
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if(framework == "weights"){ // checks whether model is darknet
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blobFromImage(frame, inp, scale, size, meanv, swapRB, false, CV_32F);
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}
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else{
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//![preprocess_call]
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Image2BlobParams imgParams(
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scale,
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size,
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meanv,
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swapRB,
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CV_32F,
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DNN_LAYOUT_NCHW,
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paddingMode,
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paddingValue);
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inp = blobFromImageWithParams(frame, imgParams);
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//![preprocess_call]
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}
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// Set the blob as the network input
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net.setInput(inp);
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// Check if the model is Faster-RCNN or R-FCN
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if (net.getLayer(0)->outputNameToIndex("im_info") != -1)
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{
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// Resize the frame and prepare imInfo
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resize(frame, frame, size);
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Mat imInfo = (Mat_<float>(1, 3) << size.height, size.width, 1.6f);
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net.setInput(imInfo, "im_info");
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}
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}
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void yoloPostProcessing(
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const vector<Mat>& outs,
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vector<int>& keep_classIds,
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vector<float>& keep_confidences,
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vector<Rect2d>& keep_boxes,
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float conf_threshold,
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float iou_threshold,
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const string& yolo_name)
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{
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// Retrieve
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vector<int> classIds;
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vector<float> confidences;
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vector<Rect2d> boxes;
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vector<Mat> outs_copy = outs;
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if (yolo_name == "yolov8")
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{
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transposeND(outs_copy[0], {0, 2, 1}, outs_copy[0]);
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}
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if (yolo_name == "yolonas")
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{
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// outs contains 2 elements of shape [1, 8400, 80] and [1, 8400, 4]. Concat them to get [1, 8400, 84]
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Mat concat_out;
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// squeeze the first dimension
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outs_copy[0] = outs_copy[0].reshape(1, outs_copy[0].size[1]);
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outs_copy[1] = outs_copy[1].reshape(1, outs_copy[1].size[1]);
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hconcat(outs_copy[1], outs_copy[0], concat_out);
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outs_copy[0] = concat_out;
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// remove the second element
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outs_copy.pop_back();
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// unsqueeze the first dimension
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outs_copy[0] = outs_copy[0].reshape(0, vector<int>{1, 8400, 84});
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}
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for (auto preds : outs_copy)
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{
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preds = preds.reshape(1, preds.size[1]); // [1, 8400, 85] -> [8400, 85]
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for (int i = 0; i < preds.rows; ++i)
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{
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// filter out non-object
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float obj_conf = (yolo_name == "yolov8" || yolo_name == "yolonas") ? 1.0f : preds.at<float>(i, 4);
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if (obj_conf < conf_threshold)
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continue;
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Mat scores = preds.row(i).colRange((yolo_name == "yolov8" || yolo_name == "yolonas") ? 4 : 5, preds.cols);
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double conf;
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Point maxLoc;
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minMaxLoc(scores, 0, &conf, 0, &maxLoc);
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conf = (yolo_name == "yolov8" || yolo_name == "yolonas") ? conf : conf * obj_conf;
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if (conf < conf_threshold)
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continue;
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// get bbox coords
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float* det = preds.ptr<float>(i);
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double cx = det[0];
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double cy = det[1];
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double w = det[2];
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double h = det[3];
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// [x1, y1, x2, y2]
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if (yolo_name == "yolonas") {
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boxes.push_back(Rect2d(cx, cy, w, h));
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} else {
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boxes.push_back(Rect2d(cx - 0.5 * w, cy - 0.5 * h,
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cx + 0.5 * w, cy + 0.5 * h));
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}
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classIds.push_back(maxLoc.x);
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confidences.push_back(static_cast<float>(conf));
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}
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}
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// NMS
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vector<int> keep_idx;
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NMSBoxes(boxes, confidences, conf_threshold, iou_threshold, keep_idx);
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for (auto i : keep_idx)
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{
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keep_classIds.push_back(classIds[i]);
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keep_confidences.push_back(confidences[i]);
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keep_boxes.push_back(boxes[i]);
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}
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}
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void postprocess(Mat& frame, const vector<Mat>& outs, Net& net, int backend, vector<int>& classIds, vector<float>& confidences, vector<Rect>& boxes, const string yolo_name)
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{
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static vector<int> outLayers = net.getUnconnectedOutLayers();
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static string outLayerType = net.getLayer(outLayers[0])->type;
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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() > 0);
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for (size_t k = 0; k < outs.size(); k++)
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{
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float* data = (float*)outs[k].data;
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for (size_t i = 0; i < outs[k].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 width = right - left + 1;
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int height = bottom - top + 1;
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if (width <= 2 || height <= 2)
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{
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left = (int)(data[i + 3] * frame.cols);
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top = (int)(data[i + 4] * frame.rows);
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right = (int)(data[i + 5] * frame.cols);
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bottom = (int)(data[i + 6] * frame.rows);
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width = right - left + 1;
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height = bottom - top + 1;
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}
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classIds.push_back((int)(data[i + 1]) - 1); // Skip 0th background class id.
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boxes.push_back(Rect(left, top, width, height));
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confidences.push_back(confidence);
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}
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|
}
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|
}
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}
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else if (outLayerType == "Region")
|
|
{
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|
for (size_t i = 0; i < outs.size(); ++i)
|
|
{
|
|
// Network produces output blob with a shape NxC where N is a number of
|
|
// detected objects and C is a number of classes + 4 where the first 4
|
|
// numbers are [center_x, center_y, width, height]
|
|
float* data = (float*)outs[i].data;
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for (int j = 0; j < outs[i].rows; ++j, data += outs[i].cols)
|
|
{
|
|
Mat scores = outs[i].row(j).colRange(5, outs[i].cols);
|
|
Point classIdPoint;
|
|
double confidence;
|
|
minMaxLoc(scores, 0, &confidence, 0, &classIdPoint);
|
|
if (confidence > confThreshold)
|
|
{
|
|
int centerX = (int)(data[0] * frame.cols);
|
|
int centerY = (int)(data[1] * frame.rows);
|
|
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;
|
|
|
|
classIds.push_back(classIdPoint.x);
|
|
confidences.push_back((float)confidence);
|
|
boxes.push_back(Rect(left, top, width, height));
|
|
}
|
|
}
|
|
}
|
|
}
|
|
else if (outLayerType == "Identity")
|
|
{
|
|
//![forward_buffers]
|
|
vector<int> keep_classIds;
|
|
vector<float> keep_confidences;
|
|
vector<Rect2d> keep_boxes;
|
|
//![forward_buffers]
|
|
|
|
//![postprocess]
|
|
yoloPostProcessing(outs, keep_classIds, keep_confidences, keep_boxes, confThreshold, nmsThreshold, yolo_name);
|
|
//![postprocess]
|
|
|
|
for (size_t i = 0; i < keep_classIds.size(); ++i)
|
|
{
|
|
classIds.push_back(keep_classIds[i]);
|
|
confidences.push_back(keep_confidences[i]);
|
|
Rect2d box = keep_boxes[i];
|
|
boxes.push_back(Rect(cvFloor(box.x), cvFloor(box.y), cvFloor(box.width-box.x), cvFloor(box.height-box.y)));
|
|
}
|
|
if (framework == "onnx"){
|
|
Image2BlobParams paramNet;
|
|
paramNet.scalefactor = scale;
|
|
paramNet.size = Size(inpWidth, inpHeight);
|
|
paramNet.mean = meanv;
|
|
paramNet.swapRB = swapRB;
|
|
paramNet.paddingmode = paddingMode;
|
|
|
|
paramNet.blobRectsToImageRects(boxes, boxes, frame.size());
|
|
}
|
|
}
|
|
else
|
|
{
|
|
CV_Error(Error::StsNotImplemented, "Unknown output layer type: " + outLayerType);
|
|
}
|
|
|
|
// NMS is used inside Region layer only on DNN_BACKEND_OPENCV for other backends we need NMS in sample
|
|
// or NMS is required if the number of outputs > 1
|
|
if (outLayers.size() > 1 || (outLayerType == "Region" && backend != DNN_BACKEND_OPENCV))
|
|
{
|
|
map<int, vector<size_t> > class2indices;
|
|
for (size_t i = 0; i < classIds.size(); i++)
|
|
{
|
|
if (confidences[i] >= confThreshold)
|
|
{
|
|
class2indices[classIds[i]].push_back(i);
|
|
}
|
|
}
|
|
vector<Rect> nmsBoxes;
|
|
vector<float> nmsConfidences;
|
|
vector<int> nmsClassIds;
|
|
for (map<int, vector<size_t> >::iterator it = class2indices.begin(); it != class2indices.end(); ++it)
|
|
{
|
|
vector<Rect> localBoxes;
|
|
vector<float> localConfidences;
|
|
vector<size_t> classIndices = it->second;
|
|
for (size_t i = 0; i < classIndices.size(); i++)
|
|
{
|
|
localBoxes.push_back(boxes[classIndices[i]]);
|
|
localConfidences.push_back(confidences[classIndices[i]]);
|
|
}
|
|
vector<int> nmsIndices;
|
|
NMSBoxes(localBoxes, localConfidences, confThreshold, nmsThreshold, nmsIndices);
|
|
for (size_t i = 0; i < nmsIndices.size(); i++)
|
|
{
|
|
size_t idx = nmsIndices[i];
|
|
nmsBoxes.push_back(localBoxes[idx]);
|
|
nmsConfidences.push_back(localConfidences[idx]);
|
|
nmsClassIds.push_back(it->first);
|
|
}
|
|
}
|
|
boxes = nmsBoxes;
|
|
classIds = nmsClassIds;
|
|
confidences = nmsConfidences;
|
|
}
|
|
}
|
|
|
|
void drawPred(vector<int>& classIds, vector<float>& confidences, vector<Rect>& boxes, Mat& frame, FontFace& sans, int stdSize, int stdWeight, int stdImgSize, int stdThickness)
|
|
{
|
|
//![draw_boxes]
|
|
int imgWidth = max(frame.rows, frame.cols);
|
|
int size = (stdSize*imgWidth)/stdImgSize;
|
|
int weight = (stdWeight*imgWidth)/stdImgSize;
|
|
int thickness = (stdThickness*imgWidth)/stdImgSize;
|
|
|
|
for (size_t idx = 0; idx < boxes.size(); ++idx){
|
|
Scalar boxColor = getColor(classIds[idx]);
|
|
int left = boxes[idx].x;
|
|
int top = boxes[idx].y;
|
|
int right = boxes[idx].x + boxes[idx].width;
|
|
int bottom = boxes[idx].y + boxes[idx].height;
|
|
rectangle(frame, Point(left, top), Point(right, bottom), boxColor, thickness);
|
|
|
|
string label = format("%.2f", confidences[idx]);
|
|
if (!labels.empty())
|
|
{
|
|
CV_Assert(classIds[idx] < (int)labels.size());
|
|
label = labels[classIds[idx]] + ": " + label;
|
|
}
|
|
|
|
Rect r = getTextSize(Size(), label, Point(), sans, size, weight);
|
|
int baseline = r.y + r.height;
|
|
Size labelSize = Size(r.width, r.height + size/4 - baseline);
|
|
|
|
top = max(top-thickness/2, labelSize.height);
|
|
rectangle(frame, Point(left-thickness/2, top-(labelSize.height)),
|
|
Point(left + labelSize.width, top), boxColor, FILLED);
|
|
putText(frame, label, Point(left, top-size/4), getTextColor(boxColor), sans, size, weight);
|
|
}
|
|
//![draw_boxes]
|
|
}
|
|
|
|
void callback(int pos, void*)
|
|
{
|
|
confThreshold = pos * 0.01f;
|
|
}
|
|
|
|
Scalar getColor(int classId) {
|
|
int r = min((classId >> 0 & 1) * 128 + (classId >> 3 & 1) * 64 + (classId >> 6 & 1) * 32 + 80, 255);
|
|
int g = min((classId >> 1 & 1) * 128 + (classId >> 4 & 1) * 64 + (classId >> 7 & 1) * 32 + 40, 255);
|
|
int b = min((classId >> 2 & 1) * 128 + (classId >> 5 & 1) * 64 + (classId >> 8 & 1) * 32 + 40, 255);
|
|
return Scalar(b, g, r);
|
|
}
|