object_detection.cpp

#include <fstream>
#include <sstream>

#include <opencv2/dnn.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>

const char* keys =
    "{ help  h     | | Print help message. }"
    "{ input i     | | Path to input image or video file. Skip this argument to capture frames from a camera.}"
    "{ model m     | | 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) }"
    "{ config c    | | Path to a text file of model contains network configuration. "
                      "It could be a file with extensions .prototxt (Caffe), .pbtxt (TensorFlow), .cfg (Darknet) }"
    "{ 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. }"
    "{ mean        | | Preprocess input image by subtracting mean values. Mean values should be in BGR order and delimited by spaces. }"
    "{ scale       |  1 | Preprocess input image by multiplying on a scale factor. }"
    "{ width       | -1 | Preprocess input image by resizing to a specific width. }"
    "{ height      | -1 | Preprocess input image by resizing to a specific height. }"
    "{ rgb         |    | Indicate that model works with RGB input images instead BGR ones. }"
    "{ thr         | .5 | Confidence threshold. }"
    "{ backend     |  0 | Choose one of computation backends: "
                         "0: default C++ backend, "
                         "1: Halide language (http://halide-lang.org/), "
                         "2: Intel's Deep Learning Inference Engine (https://software.seek.intel.com/deep-learning-deployment)}"
    "{ target      |  0 | Choose one of target computation devices: "
                         "0: CPU target (by default),"
                         "1: OpenCL }";

using namespace cv;
using namespace dnn;

float confThreshold;
std::vector<std::string> classes;

void postprocess(Mat& frame, const Mat& 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);

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

    // 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.
    CV_Assert(parser.has("model"));
    Net net = readNet(parser.get<String>("model"), parser.get<String>("config"), parser.get<String>("framework"));
    net.setPreferableBackend(parser.get<int>("backend"));
    net.setPreferableTarget(parser.get<int>("target"));

    // 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(0);

    // Process frames.
    Mat frame, blob;
    while (waitKey(1) < 0)
    {
        cap >> frame;
        if (frame.empty())
        {
            waitKey();
            break;
        }

        // Create a 4D blob from a frame.
        Size inpSize(inpWidth > 0 ? inpWidth : frame.cols,
                     inpHeight > 0 ? inpHeight : frame.rows);
        blobFromImage(frame, blob, scale, inpSize, mean, swapRB, false);

        // Run a model.
        net.setInput(blob);
        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");
        }
        Mat out = net.forward();

        postprocess(frame, out, net);

        // 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);
    }
    return 0;
}

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

    float* data = (float*)out.data;
    if (net.getLayer(0)->outputNameToIndex("im_info") != -1)  // Faster-RCNN or R-FCN
    {
        // 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]
        for (size_t i = 0; i < out.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 classId = (int)(data[i + 1]) - 1;  // Skip 0th background class id.
                drawPred(classId, confidence, left, top, right, bottom, frame);
            }
        }
    }
    else 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]
        for (size_t i = 0; i < out.total(); i += 7)
        {
            float confidence = data[i + 2];
            if (confidence > confThreshold)
            {
                int left = (int)(data[i + 3] * frame.cols);
                int top = (int)(data[i + 4] * frame.rows);
                int right = (int)(data[i + 5] * frame.cols);
                int bottom = (int)(data[i + 6] * frame.rows);
                int classId = (int)(data[i + 1]) - 1;  // Skip 0th background class id.
                drawPred(classId, confidence, left, top, right, bottom, frame);
            }
        }
    }
    else if (outLayerType == "Region")
    {
        // 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]
        for (int i = 0; i < out.rows; ++i, data += out.cols)
        {
            Mat confidences = out.row(i).colRange(5, out.cols);
            Point classIdPoint;
            double confidence;
            minMaxLoc(confidences, 0, &confidence, 0, &classIdPoint);
            if (confidence > confThreshold)
            {
                int classId = classIdPoint.x;
                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;
                drawPred(classId, (float)confidence, left, top, left + width, top + height, frame);
            }
        }
    }
    else
        CV_Error(Error::StsNotImplemented, "Unknown output layer type: " + outLayerType);
}

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;
}