There is a project that im working on which required the color white detection, after some research i decided to use covert RGB image to HSL image and thresh hold the lightness
You can do it with 4 easy steps:
Convert HLS
img = cv2.imread("HLS.png")
imgHLS = cv2.cvtColor(img, cv2.COLOR_BGR2HLS)
Get the L channel
Lchannel = imgHLS[:,:,1]
Create the mask
#change 250 to lower numbers to include more values as "white"
mask = cv2.inRange(Lchannel, 250, 255)
Apply Mask to original image
res = cv2.bitwise_and(img,img, mask= mask)
This also depends on what is white for you, and you may change the values :) I used inRange in the L channel but you can save one step and do
mask = cv2.inRange(imgHLS, np.array([0,250,0]), np.array([255,255,255]))
instead of the lines:
Lchannel = imgHLS[:,:,1]
mask = cv2.inRange(Lchannel, 250, 255)
It is shorter, but I did it the other way first to make it more explicit and to show what I was doing.
Image:
Result:
The result looks almost as the mask (almost binary), but depending on your lowerbound (I chose 250) you may get even some almost white colors.