How to fix the colors of the image to get a better thresholding result?-CodePudding

I'm trying to threshold the hand in the following image

As you can see it does show where the edge is. You may have to use large enough kernel to separate reliably (51 in this case). The only problem is, it is sensitive to noise in open spaces.

To remove this noise we can flood-fill from the edge. That is also why we blurred before adapt-thresholding, so that this noise is more or less connected.

flooded = blur_adaptive.copy()
for x in range(flooded.shape[1]):
    if flooded[0, x] == 0:
        break
cv2.floodFill(flooded, None, (x, 0), 255)
# invert flooded and make it bool
flooded = flooded < 255

There is still some noise, but we can deal with it later. Now let's merge your mask with what we got.

img_YCrCb = cv2.cvtColor(blur, cv2.COLOR_BGR2YCrCb)
#skin color range for hsv color space 
YCrCb_mask = cv2.inRange(img_YCrCb, (0, 135, 85), (255,180,135))
# make YCrCb_mask bool
YCrCb_mask = YCrCb_mask > 0

combined = np.bitwise_or(YCrCb_mask, flooded)

Not bad. Masks compensate each other's weaknesses. We can clear the residual noise by eliminating small contours. It will also fill enclosed lacunas.

contours, _ = cv2.findContours(combined.astype(np.uint8), 
    cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
area_threshold = 200
large_contours = [cnt for cnt in contours if cv2.contourArea(cnt) > area_threshold]
cleaned = np.zeros(combined.shape, dtype=np.uint8)
cv2.drawContours(cleaned,  large_contours, -1, 255, thickness=cv2.FILLED)

And morph-close to connect pieces, if there are any:

morph = cv2.morphologyEx(cleaned, cv2.MORPH_CLOSE, 
    cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (11, 31)))

There is still some work to be done here, but it looks better than before.

Warning! Adaptive-thresholding (with large kernel), morphological closing (with large kernel) and contour detection are expensive operations for large images. There are techniques to reduce the cost (like down-sampling before doing the operation), but they are beyond the scope of this post.

Edit bonus content: Here is another image that you posted before processed by this pipeline.