why does cv2.Sobel function returns a black and white image instead of grayscale image

Question

code -

    frame=cv2.imread('ball2.jpg',0)
    frame_blur=cv2.GaussianBlur(frame,(7,7),0)
    sobelx=cv2.Sobel(frame_blur,cv2.CV_64F,1,0,dst=None,ksize=5)
    sobely=cv2.Sobel(frame_blur,cv2.CV_64F,0,1,dst=None,ksize=5)
    lap=cv2.Laplacian(frame_blur,cv2.CV_64F)
    
   
    cv2.imshow('sobelx',sobelx)
    cv2.imshow('sobely',sobely)
    cv2.imshow('laplecian',lap)

why does cv2.solber returns a black and white image instead of grayscale and what is the threshold the cv2.solber function uses after finding the gradient in the image.

same thing happens with the cv2.laplecian function ^^^

one more question why do we normalise the image after applying solber kernel . i understand that sometimes we get a negative value after applying the solder kernel but doesn't opencv converts the negative no. to positive automatically for eg: -1 to 255 , -2 to 244

You need to scale the images to the [0,1] range for display. They probably look like black and white because they exceed that range by a large amount. — Cris Luengo
Where is the input image? Please post so that others can use to test your code. Post to some free hosting service and put the URL here. But @Cris Luengo probably has identified your issue. Or you could normalize the output to bias and scale to 0 to 255 and convert to uint8. — fmw42

fmw42 fmw42 · Accepted Answer · 2020-09-11T05:52:22

Here are two ways to create the sobel and normalize for saving in Python/OpenCV. Method 1: stretch min and max values to range -255 to 255, then clip negative values so range is 0 to 255. Method 2: stretch min and max values to range 0 to 255 (so zero in Sobel becomes mid gray).

Input:

import cv2
import numpy as np
import skimage.exposure as exposure

# read the image
img = cv2.imread('barn.jpg')

# convert to gray
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

# blur
blur = cv2.GaussianBlur(gray,(3,3),0)

# apply sobel x derivative
sobelx = cv2.Sobel(blur,cv2.CV_64F,1,0,ksize=5)
      
# normalize to range -1 to 1
sobelx_norm1a = exposure.rescale_intensity(sobelx, in_range='image', out_range=(-1,1))

# normalize to range -255 to 255 and clip negatives 
sobelx_norm1b = exposure.rescale_intensity(sobelx, in_range='image', out_range=(-255,255)).clip(0,255).astype(np.uint8)

# normalize to range 0 to 255
sobelx_norm8 = exposure.rescale_intensity(sobelx, in_range='image', out_range=(0,255)).astype(np.uint8)

# save results
cv2.imwrite('barn_sobel_norm1b.jpg', sobelx_norm1b)
cv2.imwrite('barn_sobel_norm8.jpg', sobelx_norm8)

# show results
cv2.imshow('sobelx_norm1a', sobelx_norm1a)  
cv2.imshow('sobelx_norm1b', sobelx_norm1b)  
cv2.imshow('sobelx_norm8', sobelx_norm8)  
cv2.waitKey(0)
cv2.destroyAllWindows()

Result 1:

Result 2:

I suspect you want the second method. But are getting the first method when you use float values that get negative values clipped as in my displayed result 1a.

why does cv2.Sobel function returns a black and white image instead of grayscale image

1 Answers