I am doing a project in python for hand gesture recognition. So the usage of LAB color space will help to improve the accuracy of recognition because as we know that our skin color mainly comprises a ratio of red and yellow color and in case of Lαβ color space, the α component represents the pixel components position between red and green while the β component represents between yellow and blue making it less vulnerable to noise. But the problem is that, when i tried to convert the Lab image into binary using threshold function provided in opencv it returned some errors, because the input of threshold function should be a gray scale image. Anybody know how to solve this problem?
lab = cv2.cvtColor(img,cv2.COLOR_BGR2LAB)
blur = cv2.GaussianBlur(gray,(5,5),0)
ret,thresh1 = cv2.threshold(blur,70,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)
The error returned is Assertion Failed.
Anybody know how to threshold an LAB image?
The simplest thresholding methods replace each pixel in an image with a black pixel if the image intensity is less than some fixed constant T, or a white pixel if the image intensity is greater than that constant. Hence, for doing thresholding it is recommended to use gray scale images.
In opencv, cv2.threshold takes two arguments, First argument is the
source image, which should be a grayscale image. Second argument is the threshold value which is used to classify
the pixel values.
But in Wikipedia, there is a reference that we can threshold color images by designating a separate threshold for each of the RGB components of the image and then combine them with an AND operation.