samples/dnn/object_detection.cpp

Check the corresponding tutorial for more details

#include <fstream>
#include <sstream>
 
#include <opencv2/dnn.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>
 
#if defined(HAVE_THREADS)
#define USE_THREADS 1
#endif
 
#ifdef USE_THREADS
#include <mutex>
#include <thread>
#include <queue>
#endif
 
#include "common.hpp"
 
std::string param_keys =
 "{ help h | | Print help message. }"
 "{ @alias | | An alias name of model to extract preprocessing parameters from models.yml file. }"
 "{ zoo | models.yml | An optional path to file with preprocessing parameters }"
 "{ device | 0 | camera device number. }"
 "{ input i | | Path to input image or video file. Skip this argument to capture frames from a camera. }"
 "{ framework f | | Optional name of an origin framework of the model. Detect it automatically if it does not set. }"
 "{ classes | | Optional path to a text file with names of classes to label detected objects. }"
 "{ thr | .5 | Confidence threshold. }"
 "{ nms | .4 | Non-maximum suppression threshold. }"
 "{ async | 0 | Number of asynchronous forwards at the same time. "
 "Choose 0 for synchronous mode }";
std::string backend_keys = cv::format(
 "{ backend | 0 | Choose one of computation backends: "
 "%d: automatically (by default), "
 "%d: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), "
 "%d: OpenCV implementation, "
 "%d: VKCOM, "
 "%d: CUDA }", cv::dnn::DNN_BACKEND_DEFAULT, cv::dnn::DNN_BACKEND_INFERENCE_ENGINE, cv::dnn::DNN_BACKEND_OPENCV, cv::dnn::DNN_BACKEND_VKCOM, cv::dnn::DNN_BACKEND_CUDA);
std::string target_keys = cv::format(
 "{ target | 0 | Choose one of target computation devices: "
 "%d: CPU target (by default), "
 "%d: OpenCL, "
 "%d: OpenCL fp16 (half-float precision), "
 "%d: VPU, "
 "%d: Vulkan, "
 "%d: CUDA, "
 "%d: CUDA fp16 (half-float preprocess) }", cv::dnn::DNN_TARGET_CPU, cv::dnn::DNN_TARGET_OPENCL, cv::dnn::DNN_TARGET_OPENCL_FP16, cv::dnn::DNN_TARGET_MYRIAD, cv::dnn::DNN_TARGET_VULKAN, cv::dnn::DNN_TARGET_CUDA, cv::dnn::DNN_TARGET_CUDA_FP16);
std::string keys = param_keys + backend_keys + target_keys;
 
using namespace cv;
using namespace dnn;
 
float confThreshold, nmsThreshold;
std::vector<std::string> classes;
 
inline void preprocess(const Mat& frame, Net& net, Size inpSize, float scale,
 const Scalar& mean, bool swapRB);
 
void postprocess(Mat& frame, const std::vector<Mat>& out, Net& net, int backend);
 
void drawPred(int classId, float conf, int left, int top, int right, int bottom, Mat& frame);
 
void callback(int pos, void* userdata);
 
#ifdef USE_THREADS
template <typename T>
class QueueFPS : public std::queue<T>
{
public:
 QueueFPS() : counter(0) {}
 
 void push(const T& entry)
 {
 std::lock_guard<std::mutex> lock(mutex);
 
 std::queue<T>::push(entry);
 counter += 1;
 if (counter == 1)
 {
 // Start counting from a second frame (warmup).
 tm.reset();
 tm.start();
 }
 }
 
 T get()
 {
 std::lock_guard<std::mutex> lock(mutex);
 T entry = this->front();
 this->pop();
 return entry;
 }
 
 float getFPS()
 {
 tm.stop();
 double fps = counter / tm.getTimeSec();
 tm.start();
 return static_cast<float>(fps);
 }
 
 void clear()
 {
 std::lock_guard<std::mutex> lock(mutex);
 while (!this->empty())
 this->pop();
 }
 
 unsigned int counter;
 
private:
 TickMeter tm;
 std::mutex mutex;
};
#endif // USE_THREADS
 
int main(int argc, char** argv)
{
 CommandLineParser parser(argc, argv, keys);
 
 const std::string modelName = parser.get<String>("@alias");
 const std::string zooFile = parser.get<String>("zoo");
 
 keys += genPreprocArguments(modelName, zooFile);
 
 parser = CommandLineParser(argc, argv, keys);
 parser.about("Use this script to run object detection deep learning networks using OpenCV.");
 if (argc == 1 || parser.has("help"))
 {
 parser.printMessage();
 return 0;
 }
 
 confThreshold = parser.get<float>("thr");
 nmsThreshold = parser.get<float>("nms");
 float scale = parser.get<float>("scale");
 Scalar mean = parser.get<Scalar>("mean");
 bool swapRB = parser.get<bool>("rgb");
 int inpWidth = parser.get<int>("width");
 int inpHeight = parser.get<int>("height");
 size_t asyncNumReq = parser.get<int>("async");
 CV_Assert(parser.has("model"));
 std::string modelPath = findFile(parser.get<String>("model"));
 std::string configPath = findFile(parser.get<String>("config"));
 
 // Open file with classes names.
 if (parser.has("classes"))
 {
 std::string file = parser.get<String>("classes");
 std::ifstream ifs(file.c_str());
 if (!ifs.is_open())
 CV_Error(Error::StsError, "File " + file + " not found");
 std::string line;
 while (std::getline(ifs, line))
 {
 classes.push_back(line);
 }
 }
 
 // Load a model.
 Net net = readNet(modelPath, configPath, parser.get<String>("framework"));
 int backend = parser.get<int>("backend");
 net.setPreferableBackend(backend);
 net.setPreferableTarget(parser.get<int>("target"));
 std::vector<String> outNames = net.getUnconnectedOutLayersNames();
 
 // Create a window
 static const std::string kWinName = "Deep learning object detection in OpenCV";
 namedWindow(kWinName, WINDOW_NORMAL);
 int initialConf = (int)(confThreshold * 100);
 createTrackbar("Confidence threshold, %", kWinName, &initialConf, 99, callback);
 
 // Open a video file or an image file or a camera stream.
 VideoCapture cap;
 if (parser.has("input"))
 cap.open(parser.get<String>("input"));
 else
 cap.open(parser.get<int>("device"));
 
#ifdef USE_THREADS
 bool process = true;
 
 // Frames capturing thread
 QueueFPS<Mat> framesQueue;
 std::thread framesThread([&](){
 Mat frame;
 while (process)
 {
 cap >> frame;
 if (!frame.empty())
 framesQueue.push(frame.clone());
 else
 break;
 }
 });
 
 // Frames processing thread
 QueueFPS<Mat> processedFramesQueue;
 QueueFPS<std::vector<Mat> > predictionsQueue;
 std::thread processingThread([&](){
 std::queue<AsyncArray> futureOutputs;
 Mat blob;
 while (process)
 {
 // Get a next frame
 Mat frame;
 {
 if (!framesQueue.empty())
 {
 frame = framesQueue.get();
 if (asyncNumReq)
 {
 if (futureOutputs.size() == asyncNumReq)
 frame = Mat();
 }
 else
 framesQueue.clear(); // Skip the rest of frames
 }
 }
 
 // Process the frame
 if (!frame.empty())
 {
 preprocess(frame, net, Size(inpWidth, inpHeight), scale, mean, swapRB);
 processedFramesQueue.push(frame);
 
 if (asyncNumReq)
 {
 futureOutputs.push(net.forwardAsync());
 }
 else
 {
 std::vector<Mat> outs;
 net.forward(outs, outNames);
 predictionsQueue.push(outs);
 }
 }
 
 while (!futureOutputs.empty() &&
 futureOutputs.front().wait_for(std::chrono::seconds(0)))
 {
 AsyncArray async_out = futureOutputs.front();
 futureOutputs.pop();
 Mat out;
 async_out.get(out);
 predictionsQueue.push({out});
 }
 }
 });
 
 // Postprocessing and rendering loop
 while (waitKey(1) < 0)
 {
 if (predictionsQueue.empty())
 continue;
 
 std::vector<Mat> outs = predictionsQueue.get();
 Mat frame = processedFramesQueue.get();
 
 postprocess(frame, outs, net, backend);
 
 if (predictionsQueue.counter > 1)
 {
 std::string label = format("Camera: %.2f FPS", framesQueue.getFPS());
 putText(frame, label, Point(0, 15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0));
 
 label = format("Network: %.2f FPS", predictionsQueue.getFPS());
 putText(frame, label, Point(0, 30), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0));
 
 label = format("Skipped frames: %d", framesQueue.counter - predictionsQueue.counter);
 putText(frame, label, Point(0, 45), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0));
 }
 imshow(kWinName, frame);
 }
 
 process = false;
 framesThread.join();
 processingThread.join();
 
#else // USE_THREADS
 if (asyncNumReq)
 CV_Error(Error::StsNotImplemented, "Asynchronous forward is supported only with Inference Engine backend.");
 
 // Process frames.
 Mat frame, blob;
 while (waitKey(1) < 0)
 {
 cap >> frame;
 if (frame.empty())
 {
 waitKey();
 break;
 }
 
 preprocess(frame, net, Size(inpWidth, inpHeight), scale, mean, swapRB);
 
 std::vector<Mat> outs;
 net.forward(outs, outNames);
 
 postprocess(frame, outs, net, backend);
 
 // Put efficiency information.
 std::vector<double> layersTimes;
 double freq = getTickFrequency() / 1000;
 double t = net.getPerfProfile(layersTimes) / freq;
 std::string label = format("Inference time: %.2f ms", t);
 putText(frame, label, Point(0, 15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0));
 
 imshow(kWinName, frame);
 }
#endif // USE_THREADS
 return 0;
}
 
inline void preprocess(const Mat& frame, Net& net, Size inpSize, float scale,
 const Scalar& mean, bool swapRB)
{
 static Mat blob;
 // Create a 4D blob from a frame.
 if (inpSize.width <= 0) inpSize.width = frame.cols;
 if (inpSize.height <= 0) inpSize.height = frame.rows;
 blobFromImage(frame, blob, 1.0, inpSize, Scalar(), swapRB, false, CV_8U);
 
 // Run a model.
 net.setInput(blob, "", scale, mean);
 if (net.getLayer(0)->outputNameToIndex("im_info") != -1) // Faster-RCNN or R-FCN
 {
 resize(frame, frame, inpSize);
 Mat imInfo = (Mat_<float>(1, 3) << inpSize.height, inpSize.width, 1.6f);
 net.setInput(imInfo, "im_info");
 }
}
 
void postprocess(Mat& frame, const std::vector<Mat>& outs, Net& net, int backend)
{
 static std::vector<int> outLayers = net.getUnconnectedOutLayers();
 static std::string outLayerType = net.getLayer(outLayers[0])->type;
 
 std::vector<int> classIds;
 std::vector<float> confidences;
 std::vector<Rect> boxes;
 if (outLayerType == "DetectionOutput")
 {
 // Network produces output blob with a shape 1x1xNx7 where N is a number of
 // detections and an every detection is a vector of values
 // [batchId, classId, confidence, left, top, right, bottom]
 CV_Assert(outs.size() > 0);
 for (size_t k = 0; k < outs.size(); k++)
 {
 float* data = (float*)outs[k].data;
 for (size_t i = 0; i < outs[k].total(); i += 7)
 {
 float confidence = data[i + 2];
 if (confidence > confThreshold)
 {
 int left = (int)data[i + 3];
 int top = (int)data[i + 4];
 int right = (int)data[i + 5];
 int bottom = (int)data[i + 6];
 int width = right - left + 1;
 int height = bottom - top + 1;
 if (width <= 2 || height <= 2)
 {
 left = (int)(data[i + 3] * frame.cols);
 top = (int)(data[i + 4] * frame.rows);
 right = (int)(data[i + 5] * frame.cols);
 bottom = (int)(data[i + 6] * frame.rows);
 width = right - left + 1;
 height = bottom - top + 1;
 }
 classIds.push_back((int)(data[i + 1]) - 1); // Skip 0th background class id.
 boxes.push_back(Rect(left, top, width, height));
 confidences.push_back(confidence);
 }
 }
 }
 }
 else if (outLayerType == "Region")
 {
 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;
 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);
 int height = (int)(data[3] * frame.rows);
 int left = centerX - width / 2;
 int top = centerY - height / 2;
 
 classIds.push_back(classIdPoint.x);
 confidences.push_back((float)confidence);
 boxes.push_back(Rect(left, top, width, height));
 }
 }
 }
 }
 else
 CV_Error(Error::StsNotImplemented, "Unknown output layer type: " + outLayerType);
 
 // NMS is used inside Region layer only on DNN_BACKEND_OPENCV for another backends we need NMS in sample
 // or NMS is required if number of outputs > 1
 if (outLayers.size() > 1 || (outLayerType == "Region" && backend != DNN_BACKEND_OPENCV))
 {
 std::map<int, std::vector<size_t> > class2indices;
 for (size_t i = 0; i < classIds.size(); i++)
 {
 if (confidences[i] >= confThreshold)
 {
 class2indices[classIds[i]].push_back(i);
 }
 }
 std::vector<Rect> nmsBoxes;
 std::vector<float> nmsConfidences;
 std::vector<int> nmsClassIds;
 for (std::map<int, std::vector<size_t> >::iterator it = class2indices.begin(); it != class2indices.end(); ++it)
 {
 std::vector<Rect> localBoxes;
 std::vector<float> localConfidences;
 std::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]]);
 }
 std::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;
 }
 
 for (size_t idx = 0; idx < boxes.size(); ++idx)
 {
 Rect box = boxes[idx];
 drawPred(classIds[idx], confidences[idx], box.x, box.y,
 box.x + box.width, box.y + box.height, frame);
 }
}
 
void drawPred(int classId, float conf, int left, int top, int right, int bottom, Mat& frame)
{
 rectangle(frame, Point(left, top), Point(right, bottom), Scalar(0, 255, 0));
 
 std::string label = format("%.2f", conf);
 if (!classes.empty())
 {
 CV_Assert(classId < (int)classes.size());
 label = classes[classId] + ": " + label;
 }
 
 int baseLine;
 Size labelSize = getTextSize(label, FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
 
 top = max(top, labelSize.height);
 rectangle(frame, Point(left, top - labelSize.height),
 Point(left + labelSize.width, top + baseLine), Scalar::all(255), FILLED);
 putText(frame, label, Point(left, top), FONT_HERSHEY_SIMPLEX, 0.5, Scalar());
}
 
void callback(int pos, void*)
{
 confThreshold = pos * 0.01f;
}