To process the colorful image in HSV color-space
is a good direction. And I split the channels and find the S
channel is great. Because S
is Saturation(饱和度)
of color.
Then threshold the S
with thresh of 100
, you will get this.
It will be easy to separate the colorful region in the threshed binary image.
As @Mark suggest, we can use adaptive thresh other than the fixed one. So, add THRESH_OTSU
in the flags.
Core python code is presented as follow:
##(1) read into bgr-space
img = cv2.imread("test.png")
##(2) convert to hsv-space, then split the channels
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
h,s,v = cv2.split(hsv)
##(3) threshold the S channel using adaptive method(`THRESH_OTSU`)
th, threshed = cv2.threshold(s, 100, 255, cv2.THRESH_OTSU|cv2.THRESH_BINARY)
##(4) print the thresh, and save the result
print("Thresh : {}".format(th))
cv2.imwrite("result.png", threshed)
## >>> Thresh : 85.0
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