OpenCV better detection of red color? - c++

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.