OpenCV4开发入门教程159：基于颜色的对象跟踪

索引地址：系列索引

本文实现跟踪指定颜色，其实就是将指定颜色的目标明显的表示出来。既然是跟踪那么肯定是动态的目标。

我的移动硬盘是蓝色，本文就来跟踪它。

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/highgui/highgui.hpp"

#include <iostream>

using namespace cv;
using namespace std;

int main(int argc, char* argv[])
{
    // Create the capture object
    // 0 -> input arg that specifies it should take the input from the webcam
    VideoCapture cap(0);

    if(!cap.isOpened())
    {
        cerr << "Unable to open the webcam. Exiting!" << endl;
        return -1;
    }

    Mat frame, hsvImage, mask, outputImage;
    char ch;

    // Image size scaling factor for the input frames from the webcam
    float scalingFactor = 0.75;

    // Iterate until the user presses the Esc key
    while(true)
    {
        // Initialize the output image before each iteration
        outputImage = Scalar(0,0,0);

        // Capture the current frame
        cap >> frame;

        // Check if 'frame' is empty
        if(frame.empty())
            break;

        // Resize the frame
        resize(frame, frame, Size(), scalingFactor, scalingFactor, INTER_AREA);

        // Convert to HSV colorspace
        cvtColor(frame, hsvImage, COLOR_BGR2HSV);

        // Define the range of "blue" color in HSV colorspace
        Scalar lowerLimit = Scalar(60,100,100);
        Scalar upperLimit = Scalar(180,255,255);

        // Threshold the HSV image to get only blue color
        inRange(hsvImage, lowerLimit, upperLimit, mask);

        // Compute bitwise-AND of input image and mask
        bitwise_and(frame, frame, outputImage, mask=mask);

        // Run median filter on the output to smoothen it
        medianBlur(outputImage, outputImage, 5);

        // Display the input and output image
        imshow("Input", frame);
        imshow("Output", outputImage);

        // Get the keyboard input and check if it's 'Esc'
        // 30 -> wait for 30 ms
        // 27 -> ASCII value of 'ESC' key
        ch = waitKey(30);
        if (ch == 27) {
            break;
        }
    }

    return 0;
}

效果为：

colortracker

动态调整颜色范围

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
// Program to display a video from attached default camera device and detect colored blobs using
// simple R G and B thresholding Author: Samarth Manoj Brahmbhatt, University of Pennsylvania

//---------------------------------【头文件、命名空间包含部分】----------------------------
//      描述：包含程序所使用的头文件和命名空间
//------------------------------------------------------------------------------------------------
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/opencv.hpp>
using namespace cv;
using namespace std;

//-----------------------------------【全局变量声明部分】---------------------------------------
//      描述：声明全局变量
//---------------------------------------------------------------------------------------------------
Mat frame, frame_thresholded;
int rgb_slider = 0, low_slider = 30, high_slider = 100;
int low_r = 30, low_g = 30, low_b = 30, high_r = 100, high_g = 100, high_b = 100;

//-----------------------------------【on_rgb_trackbar( )函数】------------------------------
//      描述：滑动条回调函数
//-------------------------------------------------------------------------------------------------
void on_rgb_trackbar(int, void *) {
    switch (rgb_slider) {
        case 0:
            setTrackbarPos("Low threshold", "Segmentation", low_r);
            setTrackbarPos("High threshold", "Segmentation", high_r);
            break;
        case 1:
            setTrackbarPos("Low threshold", "Segmentation", low_g);
            setTrackbarPos("High threshold", "Segmentation", high_g);
            break;
        case 2:
            setTrackbarPos("Low threshold", "Segmentation", low_b);
            setTrackbarPos("High threshold", "Segmentation", high_b);
            break;
    }
}

//------------------------------【on_low_thresh_trackbar( )函数】--------------------------
//      描述：滑动条回调函数
//-------------------------------------------------------------------------------------------------
void on_low_thresh_trackbar(int, void *) {
    switch (rgb_slider) {
        case 0:
            low_r = min(high_slider - 1, low_slider);
            setTrackbarPos("Low threshold", "Segmentation", low_r);
            break;
        case 1:
            low_g = min(high_slider - 1, low_slider);
            setTrackbarPos("Low threshold", "Segmentation", low_g);
            break;
        case 2:
            low_b = min(high_slider - 1, low_slider);
            setTrackbarPos("Low threshold", "Segmentation", low_b);
            break;
    }
}

//------------------------------【on_high_thresh_trackbar( )函数】-------------------------
//      描述：滑动条回调函数
//-------------------------------------------------------------------------------------------------
void on_high_thresh_trackbar(int, void *) {
    switch (rgb_slider) {
        case 0:
            high_r = max(low_slider + 1, high_slider);
            setTrackbarPos("High threshold", "Segmentation", high_r);
            break;
        case 1:
            high_g = max(low_slider + 1, high_slider);
            setTrackbarPos("High threshold", "Segmentation", high_g);
            break;
        case 2:
            high_b = max(low_slider + 1, high_slider);
            setTrackbarPos("High threshold", "Segmentation", high_b);
            break;
    }
}

//-----------------------------------【main( )函数】--------------------------------------------
//      描述：控制台应用程序的入口函数，我们的程序从这里开始
//-------------------------------------------------------------------------------------------------
int main() {
    //从摄像头载入视频
    VideoCapture cap(0);

    if (!cap.isOpened()) {
        cout << "Capture could not be opened succesfully" << endl;
        return -1;
    }

    namedWindow("Video");
    namedWindow("Segmentation");

    createTrackbar("0. R\n1. G\n2.B", "Segmentation", &rgb_slider, 2, on_rgb_trackbar);
    createTrackbar("Low threshold", "Segmentation", &low_slider, 255, on_low_thresh_trackbar);
    createTrackbar("High threshold", "Segmentation", &high_slider, 255, on_high_thresh_trackbar);

    double       time   = 0;
    unsigned int frames = 0;
    while (char(waitKey(1)) != 'q' && cap.isOpened()) {
        frames++;
        double t0 = getTickCount();
        cap >> frame;
        // 视频是否为空
        if (frame.empty()) {
            cout << "Video over" << endl;
            break;
        }

        inRange(frame, Scalar(low_b, low_g, low_r), Scalar(high_b, high_g, high_r),
                frame_thresholded);

        imshow("Video", frame);
        imshow("Segmentation", frame_thresholded);
        time += (getTickCount() - t0) / getTickFrequency();
        cout << frames / time << " fps" << endl;
    }

    return 0;
}

效果为

dyna

OpenCV
#OpenCV #目标识别
OpenCV4开发入门教程159：基于颜色的对象跟踪
https://feater.top/opencv/opencv-track-colored-object
作者
JackeyLea
发布于
2020年11月10日
许可协议