Python CV2 Color Space Conversion Fidelity Loss

问题

Observe the following image:

Observe the following Python code:

import cv2
img = cv2.imread("rainbow.png", cv2.IMREAD_COLOR)
img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) # convert it to hsv
img = cv2.cvtColor(img, cv2.COLOR_HSV2BGR) # convert back to BGR
cv2.imwrite("out.png", img)

Here's the output image:

If you can't see it, there's a clear loss of visual fidelity in the image here. For comparison's sake, here's the original next to the output image zoomed in around the yellows:

What's going on here? Is there any way to prevent these blocky artifacts from appearing? I need to convert to the HSL color space to rotate the hue, but I can't do that if I'm going to get these kinds of artifacts.

As a note, the output image does not have the artifacts when I don't do the two conversions; the conversions themselves are indeed the cause.

回答1:

Back at a computer now - try like this:

#!/usr/bin/env python3
import numpy as np
import cv2

img = cv2.imread("rainbow.png", cv2.IMREAD_COLOR)
img = img.astype(np.float32)/255           # go to 32-bit float on 0..1

img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) # convert it to hsv
img = cv2.cvtColor(img, cv2.COLOR_HSV2BGR) # convert back to BGR
cv2.imwrite("output.png", (img*255).astype(np.uint8))

I think the problem is that when you use unsigned 8-bit representation, the Hue gets "squished" from a range of 0..360 into a range of 0..180, in 2 degree increments in order to stay within 8-bit unsigned range of 0..255 causing steps between nearby values. A solution is to move to 32-bit floats and scale to the range 0..1.

来源：https://stackoverflow.com/questions/54065840/python-cv2-color-space-conversion-fidelity-loss

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