How to convert cv2 image to skimage?

Question

I am reading an image from a camera that comes in cv2.COLOR_RGB2BGR format. Below is a temporary work around for what I am trying to achieve:

import cv2
from skimage import transform, io

...
_, img = cam.read()
img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
cv2.imwrite("temp.png", img)
img = io.imread("temp.png", as_gray=True)
img = transform.resize(img, (320, 240), mode='symmetric', preserve_range=True)

I found one way to do this conversion from this post, however, it seems that the image data is not the same than if I read the same image from a path?

I’ve also found from this documentation that I can use img_as_float(cv2_img), but this conversion does not produce the same result as what is returned by io.imread("temp.png", as_gray=True)

What is the proper way to do this conversion efficiently? Should I first convert the image back to RGB then use img_as_float()?

Asked By: Ietpt123

||

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Answer 1

I guess, the basic problem you encounter, are the different luma calculations used by OpenCV and scikit-image:

OpenCV uses:
```
Y = 0.299 * R + 0.587 * G + 0.114 * B
```

scikit-image uses:

Y = 0.2125 * R + 0.7154 * G + 0.0721 * B

Let’s have some tests – using the following image for example:

import cv2
import numpy as np
from skimage import io

# Assuming we have some kind of "OpenCV image", i.e. BGR color ordering
cv2_bgr = cv2.imread('paddington.png')

# Convert to grayscale
cv2_gray = cv2.cvtColor(cv2_bgr, cv2.COLOR_BGR2GRAY)

# Save BGR image
cv2.imwrite('cv2_bgr.png', cv2_bgr)

# Save grayscale image
cv2.imwrite('cv2_gray.png', cv2_gray)

# Convert to grayscale with custom luma
cv2_custom_luma = np.uint8(0.2125 * cv2_bgr[..., 2] + 0.7154 * cv2_bgr[..., 1] + 0.0721 * cv2_bgr[..., 0])

# Load BGR saved image using scikit-image with as_gray; becomes np.float64
sc_bgr_w = io.imread('cv2_bgr.png', as_gray=True)

# Load grayscale saved image using scikit-image without as_gray; remains np.uint8
sc_gray_wo = io.imread('cv2_gray.png')

# Load grayscale saved image using scikit-image with as_gray; remains np.uint8
sc_gray_w = io.imread('cv2_gray.png', as_gray=True)

# OpenCV grayscale = scikit-image grayscale loaded image without as_gray? Yes.
print('Pixel mismatches:', cv2.countNonZero(cv2.absdiff(cv2_gray, sc_gray_wo)))
# Pixel mismatches: 0

# OpenCV grayscale = scikit-image grayscale loaded image with as_gray? Yes.
print('Pixel mismatches:', cv2.countNonZero(cv2.absdiff(cv2_gray, sc_gray_w)))
# Pixel mismatches: 0

# OpenCV grayscale = scikit-image BGR loaded (and scaled) image with as_gray? No.
print('Pixel mismatches:', cv2.countNonZero(cv2.absdiff(cv2_gray, np.uint8(sc_bgr_w * 255))))
# Pixel mismatches: 131244

# OpenCV grayscale with custom luma = scikit-image BGR loaded (and scaled) image with as_gray? Almost.
print('Pixel mismatches:', cv2.countNonZero(cv2.absdiff(cv2_custom_luma, np.uint8(sc_bgr_w * 255))))
# Pixel mismatches: 1

You see:

When opening the grayscale image, scikit-image simply uses the np.uint8 values, regardless of using as_gray=True or not.
When opening the color image with as_gray=True, scikit-image applies rgb2gray, scales all values to 0.0 ... 1.0, thus uses np.float64. Even scaling back to 0 ... 255 and np.uint8 yields a lot of pixel mismatches between this image and the OpenCV grayscale image – due to the different luma calculations.
When calculating the luma manually and accordingly to rgb2gray, the OpenCV grayscale image is almost identical. The one pixel mismatch might be due to floating point inaccuracies.

----------------------------------------
System information
----------------------------------------
Platform:      Windows-10-10.0.16299-SP0
Python:        3.9.1
NumPy:         1.20.1
OpenCV:        4.5.1
scikit-image:  0.18.1
----------------------------------------

Answered By: HansHirse

How to convert cv2 image to skimage?

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