OpenCV Better Detection Of Red Color? - Stack Overflow

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Learn more about Labs OpenCV better detection of red color? Ask Question Asked 9 years, 2 months ago Modified 2 years, 6 months ago Viewed 68k times 31

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?

• c++
• opencv
• image-processing
• colors

Share Improve this question Follow edited May 7, 2022 at 19:43 Jeru Luke 21.1k1313 gold badges8383 silver badges9191 bronze badges asked Sep 11, 2015 at 11:58 Kristian VitozevKristian Vitozev 5,94166 gold badges3737 silver badges5656 bronze badges Add a comment |

2 Answers 2

Sorted by: Reset to default Highest score (default) Trending (recent votes count more) Date modified (newest first) Date created (oldest first) 66

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; }

Share Improve this answer Follow edited May 23, 2017 at 12:26 CommunityBot 111 silver badge answered Sep 11, 2015 at 12:27 MikiMiki 41.7k1313 gold badges128128 silver badges208208 bronze badges 3

• 4 Just a cool little trick from @fmw42 that I wanted to share... you can invert the image and look for cyans which don't wrap :-) stackoverflow.com/a/43926013/2836621 – Mark Setchell Commented May 12, 2017 at 8:12
• 1 @MarkSetchell that's clever! Added to the answer with C++ code for completeness. Thank you very much! – Miki Commented May 12, 2017 at 8:30
• Can you help me please? my imshow("Mask", mask) gives an error. error: (-215) size.width>0 && size.height>0 in function imshow. As far as I understood, the error is because mask.data == null . What am I doing wrong? Thanks in advance – gbossa Commented Jul 23, 2020 at 19:20

Add a comment | 3

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.

Share Improve this answer Follow edited May 27, 2022 at 19:35 answered May 7, 2022 at 19:59 Jeru LukeJeru Luke 21.1k1313 gold badges8383 silver badges9191 bronze badges Add a comment |

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