samples/dnn/object_detection.cpp

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#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 keys =
    "{ help h | | 打印帮助信息。 }"
    "{ @alias | | 模型的别名，用于从 models.yml 文件中提取预处理参数。 }"
    "{ zoo | models.yml | 预处理参数文件的可选路径 }"
    "{ device | 0 | 摄像头设备编号。 }"
    "{ input i | | 输入图像或视频文件的路径。 跳过此参数以从摄像头捕获帧。 }"
    "{ framework f | | 模型来源框架的可选名称。 如果未设置，则自动检测。 }"
    "{ classes | | 类别名称的文本文件的可选路径，用于标记检测到的对象。 }"
    "{ thr | .5 | 置信度阈值。 }"
    "{ nms | .4 | 非极大值抑制阈值。 }"
    "{ backend | 0 | 选择一个计算后端： "
                         "0: 自动（默认）， "
                         "1: Halide 语言 (http://halide-lang.org/), "
                         "2: 英特尔的深度学习推理引擎 (https://software.intel.com/openvino-toolkit), "
                         "3: OpenCV 实现， "
                         "4: VKCOM, "
                         "5: CUDA }"
    "{ target | 0 | 选择一个目标计算设备： "
                         "0: CPU 目标（默认）， "
                         "1: OpenCL, "
                         "2: OpenCL fp16 (半精度浮点数)， "
                         "3: VPU, "
                         "4: Vulkan, "
                         "6: CUDA, "
                         "7: CUDA fp16 (半精度浮点数预处理) }"
    "{ async | 0 | 同时进行的异步前向传递的数量。 "
                        "选择 0 表示同步模式 }";
 
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)
        {
            // 从第二帧开始计数（预热）。
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("使用此脚本使用 OpenCV 运行对象检测深度学习网络。");
    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"));
 
    // 打开包含类别名称的文件。
    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, "文件 " + file + " 未找到");
std::string line;
        while (std::getline(ifs, line))
        {
classes.push_back(line);
        }
    }
 
    // 加载模型。
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();
 
    // 创建窗口
    static const std::string kWinName = "OpenCV 中的深度学习对象检测";
    namedWindow(kWinName, WINDOW_NORMAL);
    int initialConf = (int)(confThreshold * 100);
    createTrackbar("置信度阈值，%", kWinName, &initialConf, 99, callback);
 
    // 打开视频文件或图像文件或摄像头流。
    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;
 
    // 帧捕获线程
QueueFPS<Mat> framesQueue;
std::thread framesThread([&](){
        Mat frame;
        while (process)
        {
cap >> frame;
            if (!frame.empty())
framesQueue.push(frame.clone());
            else
                break;
        }
    });
 
    // 帧处理线程
QueueFPS<Mat> processedFramesQueue;
QueueFPS<std::vector<Mat> > predictionsQueue;
std::thread processingThread([&](){
std::queue<AsyncArray> futureOutputs;
        Mat blob;
        while (process)
        {
            // 获取下一帧
            Mat frame;
            {
                if (!framesQueue.empty())
                {
frame = framesQueue.get();
if (asyncNumReq)
                    {
if (futureOutputs.size() == asyncNumReq)
frame = Mat();
                    }
                    else
framesQueue.clear(); // 跳过剩余的帧
                }
            }
 
            // 处理帧
            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});
            }
        }
    });
 
    // 后处理和渲染循环
    while (waitKey(1) < 0)
    {
        if (predictionsQueue.empty())
            continue;
 
std::vector<Mat> outs = predictionsQueue.get();
        Mat frame = processedFramesQueue.get();
 
        后处理(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());
            
 
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);
 
        后处理(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;
}