how to threshold LAB image

Question

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?

Answer 1

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.

Answer 2

Opencv threshold Documentation:

input array (single-channel, 8-bit or 32-bit floating point).

You can't threshold a color image. And where did grey come from? You never use the lab converted image.

Answer 3

Input image should be a single channel 8-bit or 32-bit float like M4rtini said. However, an RGB, Lab, HSV are all images build up from 3 8-bit channels. If you split the channels

L, a, b = cv2.split(lab)

the result will be 3 single channel images. These you can input into the function

 ret,thresh_L = cv2.threshold(L,70,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)
 ret,thresh_a = cv2.threshold(a,70,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)
 ret,thresh_b = cv2.threshold(b,70,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)

basically, you can input any 2d numpy array into the threshold function as long as its 8-bit or 32-bit float. OpenCv scales Lab colorspace to 0-256.