feat: 切换后端至PaddleOCR-NCNN，切换工程为CMake
1.项目后端整体迁移至PaddleOCR-NCNN算法，已通过基本的兼容性测试 2.工程改为使用CMake组织，后续为了更好地兼容第三方库，不再提供QMake工程 3.重整权利声明文件，重整代码工程，确保最小化侵权风险 Log: 切换后端至PaddleOCR-NCNN，切换工程为CMake Change-Id: I4d5d2c5d37505a4a24b389b1a4c5d12f17bfa38c
This commit is contained in:
wangzhengyang
476
3rdparty/opencv-4.5.4/samples/dnn/object_detection.cpp
vendored
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476
3rdparty/opencv-4.5.4/samples/dnn/object_detection.cpp
vendored
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@ -0,0 +1,476 @@
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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/highgui.hpp>
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#ifdef CV_CXX11
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#include <mutex>
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#include <thread>
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#include <queue>
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#endif
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#include "common.hpp"
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std::string 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 | 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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"{ 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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"{ thr | .5 | Confidence threshold. }"
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"{ nms | .4 | Non-maximum suppression threshold. }"
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"{ backend | 0 | Choose one of computation backends: "
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"0: automatically (by default), "
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"1: Halide language (http://halide-lang.org/), "
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"2: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), "
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"3: OpenCV implementation, "
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"4: VKCOM, "
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"5: CUDA }"
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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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"2: OpenCL fp16 (half-float precision), "
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"3: VPU, "
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"4: Vulkan, "
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"6: CUDA, "
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"7: CUDA fp16 (half-float preprocess) }"
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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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using namespace cv;
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using namespace dnn;
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float confThreshold, nmsThreshold;
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std::vector<std::string> classes;
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inline void preprocess(const Mat& frame, Net& net, Size inpSize, float scale,
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const Scalar& mean, bool swapRB);
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void postprocess(Mat& frame, const std::vector<Mat>& out, Net& net, int backend);
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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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#ifdef CV_CXX11
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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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#endif // CV_CXX11
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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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const std::string modelName = parser.get<String>("@alias");
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const std::string zooFile = parser.get<String>("zoo");
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keys += genPreprocArguments(modelName, zooFile);
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parser = CommandLineParser(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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nmsThreshold = parser.get<float>("nms");
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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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size_t asyncNumReq = parser.get<int>("async");
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CV_Assert(parser.has("model"));
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std::string modelPath = findFile(parser.get<String>("model"));
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std::string configPath = findFile(parser.get<String>("config"));
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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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Net net = readNet(modelPath, configPath, parser.get<String>("framework"));
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int backend = parser.get<int>("backend");
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net.setPreferableBackend(backend);
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net.setPreferableTarget(parser.get<int>("target"));
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std::vector<String> outNames = net.getUnconnectedOutLayersNames();
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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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#ifdef CV_CXX11
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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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{
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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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{
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// Get a next frame
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Mat frame;
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{
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if (!framesQueue.empty())
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{
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frame = framesQueue.get();
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if (asyncNumReq)
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{
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if (futureOutputs.size() == asyncNumReq)
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frame = Mat();
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}
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else
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framesQueue.clear(); // Skip the rest of frames
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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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{
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preprocess(frame, net, Size(inpWidth, inpHeight), scale, mean, swapRB);
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processedFramesQueue.push(frame);
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if (asyncNumReq)
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{
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futureOutputs.push(net.forwardAsync());
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}
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else
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{
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std::vector<Mat> outs;
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net.forward(outs, outNames);
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predictionsQueue.push(outs);
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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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{
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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(1) < 0)
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{
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if (predictionsQueue.empty())
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continue;
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std::vector<Mat> outs = predictionsQueue.get();
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Mat frame = processedFramesQueue.get();
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postprocess(frame, outs, net, backend);
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if (predictionsQueue.counter > 1)
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{
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std::string label = format("Camera: %.2f FPS", framesQueue.getFPS());
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putText(frame, label, Point(0, 15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0));
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label = format("Network: %.2f FPS", predictionsQueue.getFPS());
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putText(frame, label, Point(0, 30), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0));
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label = format("Skipped frames: %d", framesQueue.counter - predictionsQueue.counter);
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putText(frame, label, Point(0, 45), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0));
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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 // CV_CXX11
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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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// 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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preprocess(frame, net, Size(inpWidth, inpHeight), scale, mean, swapRB);
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std::vector<Mat> outs;
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net.forward(outs, outNames);
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postprocess(frame, outs, net, backend);
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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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#endif // CV_CXX11
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return 0;
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}
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inline void preprocess(const Mat& frame, Net& net, Size inpSize, float scale,
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const Scalar& mean, bool swapRB)
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{
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static Mat blob;
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// Create a 4D blob from a frame.
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if (inpSize.width <= 0) inpSize.width = frame.cols;
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if (inpSize.height <= 0) inpSize.height = frame.rows;
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blobFromImage(frame, blob, 1.0, inpSize, Scalar(), swapRB, false, CV_8U);
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// Run a model.
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net.setInput(blob, "", scale, mean);
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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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}
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void postprocess(Mat& frame, const std::vector<Mat>& outs, Net& net, int backend)
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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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std::vector<int> classIds;
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std::vector<float> confidences;
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std::vector<Rect> boxes;
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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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{
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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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}
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else
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CV_Error(Error::StsNotImplemented, "Unknown output layer type: " + outLayerType);
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// NMS is used inside Region layer only on DNN_BACKEND_OPENCV for another backends we need NMS in sample
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// or NMS is required if number of outputs > 1
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if (outLayers.size() > 1 || (outLayerType == "Region" && backend != DNN_BACKEND_OPENCV))
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{
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std::map<int, std::vector<size_t> > class2indices;
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for (size_t i = 0; i < classIds.size(); i++)
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{
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if (confidences[i] >= confThreshold)
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{
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class2indices[classIds[i]].push_back(i);
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}
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}
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std::vector<Rect> nmsBoxes;
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std::vector<float> nmsConfidences;
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std::vector<int> nmsClassIds;
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for (std::map<int, std::vector<size_t> >::iterator it = class2indices.begin(); it != class2indices.end(); ++it)
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{
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std::vector<Rect> localBoxes;
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std::vector<float> localConfidences;
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std::vector<size_t> classIndices = it->second;
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for (size_t i = 0; i < classIndices.size(); i++)
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{
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localBoxes.push_back(boxes[classIndices[i]]);
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localConfidences.push_back(confidences[classIndices[i]]);
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}
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std::vector<int> nmsIndices;
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NMSBoxes(localBoxes, localConfidences, confThreshold, nmsThreshold, nmsIndices);
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for (size_t i = 0; i < nmsIndices.size(); i++)
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{
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size_t idx = nmsIndices[i];
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nmsBoxes.push_back(localBoxes[idx]);
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nmsConfidences.push_back(localConfidences[idx]);
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nmsClassIds.push_back(it->first);
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}
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}
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boxes = nmsBoxes;
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classIds = nmsClassIds;
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confidences = nmsConfidences;
|
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}
|
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for (size_t idx = 0; idx < boxes.size(); ++idx)
|
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{
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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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||||
|
||||
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);
|
||||
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;
|
||||
}
|
||||
Reference in New Issue
Block a user