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

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.