Webcam capture and imgshow low FPS

asked 2016-10-28 15:53:35 -0500

CLeBeR gravatar image

updated 2016-11-02 14:22:29 -0500

System information (version) - OpenCV => 2.4.9.1 - Operating System / Platform => Linux Ubuntu 64 Bit - Compiler => CMake 3.5.1

Detailed description

I use imgshow to prompt the webcam capture and add a rectangle and the subject found with FisherFace algorithm AT&T orl_face photo base. The problem is the program use 80% of CPU (i3) and the FPS of camera are very slow.

EDIT : I tweak the detectMultiScale parameters (flag,minSize,maxSize). It's the maxSize which change the FPS of camera. But, if I change it, there is no prediction (even if I change the minNeighbors parameter).

Steps to reproduce

#include "opencv2/core/core.hpp"
#include "opencv2/contrib/contrib.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/objdetect/objdetect.hpp"

#include <iostream>
#include <fstream>
#include <sstream>

using namespace cv;
using namespace std;

static void read_csv(const string& filename, vector<Mat>& images, vector<int>& labels, char separator = ';') {
    std::ifstream file(filename.c_str(), ifstream::in);
    if (!file) {
        string error_message = "No valid input file was given, please check the given filename.";
        CV_Error(CV_StsBadArg, error_message);
    }
    string line, path, classlabel;
    while (getline(file, line)) {
        stringstream liness(line);
        getline(liness, path, separator);
        getline(liness, classlabel);
        if(!path.empty() && !classlabel.empty()) {
            Mat m = imread(path, 1);
            if (m.empty())
            {
                cerr << path << " could not be read." << endl;
                continue;
            }
            Mat m2;
            cvtColor(m,m2,CV_BGR2GRAY);
            images.push_back(m2);
            labels.push_back(atoi(classlabel.c_str()));
        }
    }
    cout << endl << "Read finish";
}

int main(int argc, const char *argv[]) {
    if (argc != 4) {
        cout << "usage: " << argv[0] << " </path/to/haar_cascade> </path/to/csv.ext> </path/to/device id>" << endl;
        cout << "\t </path/to/haar_cascade> -- Path to the Haar Cascade for face detection." << endl;
        cout << "\t </path/to/csv.ext> -- Path to the CSV file with the face database." << endl;
        cout << "\t <device id> -- The webcam device id to grab frames from." << endl;
        exit(1);
    }
    string fn_haar = string(argv[1]);
    string fn_csv = string(argv[2]);
    int deviceId = atoi(argv[3]);
    vector<Mat> images;
    vector<int> labels;
    try {
        read_csv(fn_csv, images, labels);
    } catch (cv::Exception& e) {
        cerr << "Error opening file \"" << fn_csv << "\". Reason: " << e.msg << endl;
        // nothing more we can do
        exit(1);
    }
    int im_width = images[0].cols;
    int im_height = images[0].rows;
    Ptr<FaceRecognizer> model = createFisherFaceRecognizer();
    model->train(images, labels);
    CascadeClassifier haar_cascade;
    haar_cascade.load(fn_haar);
    VideoCapture cap(deviceId);
    if(!cap.isOpened()) {
        cerr << "Capture Device ID " << deviceId << "cannot be opened." << endl;
        return -1;
    }
    Mat frame;
    for(;;) {
        cap >> frame;
        Mat original = frame.clone();
        Mat gray;
        if(original.empty()){
            cout << "An empty matrice has been detected" << endl;
            break;
        }
        else if(original.channels()>1){
            cout << "Matrice has been converted";
            cvtColor(original, gray, CV_BGR2GRAY);
        }
        else gray = original;
        vector< Rect_<int> > faces;
        haar_cascade.detectMultiScale(gray, faces, 1.1, 3);
        for(int i = 0; i < faces.size(); i++) {
            Rect face_i = faces[i];
            Mat face = gray(face_i);
            Mat face_resized;
            cv::resize(face, face_resized, Size(im_width, im_height), 1.0, 1.0, INTER_CUBIC);
            int prediction = model->predict(face_resized);
            rectangle(original, face_i, CV_RGB(0, 255,0), 1);
            string box_text = format("Prediction = %d", prediction);
            int pos_x = std::max(face_i.tl().x - 10 ...
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Comments

if you do some profiling, you'll likely find, that most time is spend in the cascade detection so try:

  • lower image resolution from the camera
  • tweak the detection params, increase scale factor, set min/max size values
berak gravatar imageberak ( 2016-10-31 04:19:42 -0500 )edit