Gagandeep Singh Gagandeep Singh - 1 year ago 312
Python Question

Opencv - Grayscale mode Vs gray color convertion

I am working in opencv(2.4.11) python(2.7) and was playing around with gray images. I found an unusual behavior when loading image in gray scale mode and converting image from BGR to GRAY. Following is my experimental code:

import cv2

path = 'some/path/to/color/image.jpg'

# Load color image (BGR) and convert to gray
img = cv2.imread(path)
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# Load in grayscale mode
img_gray_mode = cv2.imread(path, 0)

# diff = img_gray_mode - img_gray
diff = cv2.bitwise_xor(img_gray,img_gray_mode)

cv2.imshow('diff', diff)

When I viewed the difference image, I can see the left out pixels instead of jet black image. Can you suggest any reason? What is the correct way of working with gray images.

P.S. When I use both the images in SIFT, keypoints are different which may lead to different outcome specially when working with bad quality images.

Answer Source

Note: This is not a duplicate, because the OP is aware that the image from cv2.imread is in BGR format (unlike the suggested duplicate question that assumed it was RGB hence the provided answers only address that issue)

To illustrate, I've opened up this same color JPEG image:

enter image description here

once using the conversion

img = cv2.imread(path)
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

and another by loading it in gray scale mode

img_gray_mode = cv2.imread(path, vs2.IMREAD_GRAYSCALE)

Like you've documented, the diff between the two images is not perfectly 0, I can see diff pixels in towards the left and the bottom

enter image description here

I've summed up the diff too to see

import numpy as np
# I got 6143, on a 494 x 750 image

I tried all cv2.imread() modes

Among all the IMREAD_ modes for cv2.imread(), only IMREAD_COLOR and IMREAD_ANYCOLOR can be converted using COLOR_BGR2GRAY, and both of them gave me the same diff against the image opened in IMREAD_GRAYSCALE

The difference doesn't seem that big. My guess is comes from the differences in the numeric calculations in the two methods (loading grayscale vs conversion to grayscale)

Naturally what you want to avoid is fine tuning your code on a particular version of the image just to find out it was suboptimal for images coming from a different source.

In brief, let's not mix the versions and types in the processing pipeline.

So I'd keep the image sources homogenous, e.g. if you have capturing the image from a video camera in BGR, then I'd use BGR as the source, and do the BGR to grayscale conversion cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

Vice versa if my ultimate source is grayscale then I'd open the files and the video capture in gray scale cv2.imread(path, cv2.IMREAD_GRAYSCALE)

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