OpenCV better detection of red color?

Question

I have the following image:

I would like to detect the red rectangle using cv::inRange method and HSV color space.

int H_MIN = 0;
int H_MAX = 10;
int S_MIN = 70; 
int S_MAX = 255;
int V_MIN = 50;
int V_MAX = 255;

cv::cvtColor( input, imageHSV, cv::COLOR_BGR2HSV );

cv::inRange( imageHSV, cv::Scalar( H_MIN, S_MIN, V_MIN ), cv::Scalar( H_MAX, S_MAX, V_MAX ), imgThreshold0 );

I already created dynamic trackbars in order to change the values for HSV, but I can't get the desired result.

Any suggestion for best values (and maybe filters) to use?

Answer 1

In HSV space, the red color wraps around 180. So you need the H values to be both in [0,10] and [170, 180].

Try this:

#include <opencv2\opencv.hpp>
using namespace cv;

int main()
{
    Mat3b bgr = imread("path_to_image");

    Mat3b hsv;
    cvtColor(bgr, hsv, COLOR_BGR2HSV);

    Mat1b mask1, mask2;
    inRange(hsv, Scalar(0, 70, 50), Scalar(10, 255, 255), mask1);
    inRange(hsv, Scalar(170, 70, 50), Scalar(180, 255, 255), mask2);

    Mat1b mask = mask1 | mask2;

    imshow("Mask", mask);
    waitKey();

    return 0;
}

Your previous result:

Result adding range [170, 180]:

---

Another interesting approach which needs to check a single range only is:

• invert the BGR image
• convert to HSV
• look for cyan color

This idea has been proposed by fmw42 and kindly pointed out by Mark Setchell. Thank you very much for that.

#include <opencv2\opencv.hpp>
using namespace cv;

int main()
{
    Mat3b bgr = imread("path_to_image");

    Mat3b bgr_inv = ~bgr;
    Mat3b hsv_inv;
    cvtColor(bgr_inv, hsv_inv, COLOR_BGR2HSV);

    Mat1b mask; 
    inRange(hsv_inv, Scalar(90 - 10, 70, 50), Scalar(90 + 10, 255, 255), mask); // Cyan is 90

    imshow("Mask", mask);
    waitKey();

    return 0;
}

Answer 2

While working with dominant colors such as red, blue, green and yellow; analyzing the two color channels of the LAB color space keeps things simple. All you need to do is apply a suitable threshold on either of the two color channels.

1. Detecting Red color

Background :

The LAB color space represents:

• the brightness value in the image in the primary channel (L-channel)

while colors are expressed in the two remaining channels:

• the color variations between red and green are expressed in the secondary channel (A-channel)
• the color variations between yellow and blue are expressed in the third channel (B-channel)

Code :

import cv2
img = cv2.imread('red.png')

# convert to LAB color space
lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)

# Perform Otsu threshold on the A-channel 
th = cv2.threshold(lab[:,:,1], 127, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]

Result:

I have placed the LAB converted image and the threshold image besides each other.

2. Detecting Blue color

Now lets see how to detect blue color

Sample image:

Since I am working with blue color:

• Analyze the B-channel (since it expresses blue color better)
• Perform inverse threshold to make the blue region appear white

(Note: the code changes below compared to the one above)

Code :

import cv2
img = cv2.imread('blue.jpg')

# convert to LAB color space
lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)

# Perform Otsu threshold on the A-channel 
th = cv2.threshold(lab[:,:,2], 127, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]

Result:

Again, stacking the LAB and final image:

Conclusion :

• Similar processing can be performed on green and yellow colors
• Moreover segmenting a range of one of these dominant colors is also much simpler.