OpenCV object_detection.cpp

 

#include 
#include 

#include 
#include 
#include 

#ifdef CV_CXX11
#include 
#include 
#include 
#endif

#include "common.hpp"

std::string 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. }"
    "{ backend     |  0 | Choose one of computation backends: "
                         "0: automatically (by default), "
                         "1: Halide language (http://halide-lang.org/), "
                         "2: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), "
                         "3: OpenCV implementation }"
    "{ target      | 0 | Choose one of target computation devices: "
                         "0: CPU target (by default), "
                         "1: OpenCL, "
                         "2: OpenCL fp16 (half-float precision), "
                         "3: VPU }"
    "{ async       | 0 | Number of asynchronous forwards at the same time. "
                        "Choose 0 for synchronous mode }";

using namespace cv;
using namespace dnn;

float confThreshold, nmsThreshold;
std::vector 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& out, Net& net);

void drawPred(int classId, float conf, int left, int top, int right, int bottom, Mat& frame);

void callback(int pos, void* userdata);

#ifdef CV_CXX11
template 
class QueueFPS : public std::queue
{
public:
    QueueFPS() : counter(0) {}

    void push(const T& entry)
    {
        std::lock_guard lock(mutex);

        std::queue::push(entry);
        counter += 1;
        if (counter == 1)
        {
            // Start counting from a second frame (warmup).
            tm.reset();
            tm.start();
        }
    }

    T get()
    {
        std::lock_guard lock(mutex);
        T entry = this->front();
        this->pop();
        return entry;
    }

    float getFPS()
    {
        tm.stop();
        double fps = counter / tm.getTimeSec();
        tm.start();
        return static_cast(fps);
    }

    void clear()
    {
        std::lock_guard lock(mutex);
        while (!this->empty())
            this->pop();
    }

    unsigned int counter;

private:
    TickMeter tm;
    std::mutex mutex;
};
#endif  // CV_CXX11

int main(int argc, char** argv)
{
    CommandLineParser parser(argc, argv, keys);

    const std::string modelName = parser.get("@alias");
    const std::string zooFile = parser.get("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("thr");
    nmsThreshold = parser.get("nms");
    float scale = parser.get("scale");
    Scalar mean = parser.get("mean");
    bool swapRB = parser.get("rgb");
    int inpWidth = parser.get("width");
    int inpHeight = parser.get("height");
    size_t asyncNumReq = parser.get("async");
    CV_Assert(parser.has("model"));
    std::string modelPath = findFile(parser.get("model"));
    std::string configPath = findFile(parser.get("config"));

    // Open file with classes names.
    if (parser.has("classes"))
    {
        std::string file = parser.get("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("framework"));
    net.setPreferableBackend(parser.get("backend"));
    net.setPreferableTarget(parser.get("target"));
    std::vector 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("input"));
    else
        cap.open(parser.get("device"));

#ifdef CV_CXX11
    bool process = true;

    // Frames capturing thread
    QueueFPS framesQueue;
    std::thread framesThread([&](){
        Mat frame;
        while (process)
        {
            cap >> frame;
            if (!frame.empty())
                framesQueue.push(frame.clone());
            else
                break;
        }
    });

    // Frames processing thread
    QueueFPS processedFramesQueue;
    QueueFPS > predictionsQueue;
    std::thread processingThread([&](){
        std::queue 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 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 outs = predictionsQueue.get();
        Mat frame = processedFramesQueue.get();

        postprocess(frame, outs, net);

        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  // CV_CXX11
    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 outs;
        net.forward(outs, outNames);

        postprocess(frame, outs, net);

        // Put efficiency information.
        std::vector 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  // CV_CXX11
    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_(1, 3) << inpSize.height, inpSize.width, 1.6f);
        net.setInput(imInfo, "im_info");
    }
}

void postprocess(Mat& frame, const std::vector& outs, Net& net)
{
    static std::vector outLayers = net.getUnconnectedOutLayers();
    static std::string outLayerType = net.getLayer(outLayers[0])->type;

    std::vector classIds;
    std::vector confidences;
    std::vector 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);

    std::vector indices;
    NMSBoxes(boxes, confidences, confThreshold, nmsThreshold, indices);
    for (size_t i = 0; i < indices.size(); ++i)
    {
        int idx = indices[i];
        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;
}

Use this script to run object detection deep learning networks using OpenCV.
Usage: test1.exe [params] alias

        --async (value:0)
                Number of asynchronous forwards at the same time. Choose 0 for synchronous mode
        --backend (value:0)
                Choose one of computation backends: 0: automatically (by default), 1: Halide language (http://halide-lang.org/), 2: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), 3: OpenCV implementation
        -c, --config
                Path to a text file of model contains network configuration. It could be a file with extensions .prototxt (Caffe), .pbtxt (TensorFlow), .cfg (Darknet), .xml (OpenVINO).
        --classes
                Optional path to a text file with names of classes to label detected objects.
        --device (value:0)
                camera device number.
        -f, --framework
                Optional name of an origin framework of the model. Detect it automatically if it does not set.
        -h, --help
                Print help message.
        --height (value:-1)
                Preprocess input image by resizing to a specific height.
        -i, --input
                Path to input image or video file. Skip this argument to capture frames from a camera.
        -m, --model
                Path to a binary file of model contains trained weights. It could be a file with extensions .caffemodel (Caffe), .pb (TensorFlow), .t7 or .net (Torch), .weights (Darknet), .bin (OpenVINO).
        --mean
                Preprocess input image by subtracting mean values. Mean values should be in BGR order and delimited by spaces.
        --nms (value:.4)
                Non-maximum suppression threshold.
        --rgb
                Indicate that model works with RGB input images instead BGR ones.
        --scale (value:1.0)
                Preprocess input image by multiplying on a scale factor.
        --target (value:0)
                Choose one of target computation devices: 0: CPU target (by default), 1: OpenCL, 2: OpenCL fp16 (half-float precision), 3: VPU
        --thr (value:.5)
                Confidence threshold.
        --width (value:-1)
                Preprocess input image by resizing to a specific width.
        --zoo (value:models.yml)
                An optional path to file with preprocessing parameters

        alias
                An alias name of model to extract preprocessing parameters from models.yml file.