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python - OpenCv反转:将BGR转换为HSV,然后再返回BGR-我是否应该获得相同的图像?

转载 作者:行者123 更新时间:2023-12-02 17:34:56 36 4
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例如,当我运行此命令时:

import cv2
import numpy as np
from collections import Counter

# load image, calculate some basic stats
path = 'path/to/my/image.png'
bgr_img = cv2.imread(path)
h, w, c = bgr_img.shape
print("There are %d entries in this %d channel image" % (h*w*c, c))

# convert and then invert, we should get the same image
bgr_img2 = cv2.cvtColor(cv2.cvtColor(bgr_img, cv2.COLOR_BGR2HSV), cv2.COLOR_HSV2BGR)
print("all entries match: %s" % np.all(bgr_img == bgr_img2))

# hmm, we don't. let's see where the differences are
mismatches = np.where(bgr_img != bgr_img2)
print("there are %d mismatched entries" % len(mismatches[0]))
print("mismatches are along the following channels: %s" % Counter(mismatches[2]))

# ok, clearly lots of them. maybe they're all just off by 1 or 2?
mm = zip(mismatches[0], mismatches[1], mismatches[2])
differences = []
for x, y, c in mm:
diff = bgr_img[x, y, c] - bgr_img2[x, y, c]
differences.append(diff)

print(differences)

我得到以下输出:
There are 1228800 entries in this 3 channel image
all entries match: False
there are 524511 mismatched entries
mismatches are along the following channels: Counter({1: 270572, 0: 253939})
[1, 1, 1, 254, 1, 254, 1, 3, 1, 3, 1, 3, 254, 254, 254, 254, 254, 254, 254, 254, 254, 254, 254, 254, 254, 254, 1, 3, 1, 3, 1, 3, 1, 254, 1, 254, 254, 1, 1, 1, 1, 1, 2, 2, 2, 2, 3, ...]

我看到了几种可能性。首先是BGR-> HSV转换不是严格可逆的。其他是我使用的OpenCV不正确。哪有

最佳答案

您应该获得几乎相同的图像,但除了边缘情况外,不会获得相同的位。从BGR到HSV(以及向后)涉及到空间之间的转换,这些空间对于其他空间中的所有值没有相同的表示形式。小数舍入误差会逐渐增加。

请参阅https://docs.opencv.org/3.4/df/d9d/tutorial_py_colorspaces.html中有关色调范围为[0,179]的注释。如果您考虑一下,这意味着从BGR(或RGB)转换时会丢失信息。您无法可靠地收回这些位。

关于python - OpenCv反转:将BGR转换为HSV,然后再返回BGR-我是否应该获得相同的图像?,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/52141240/

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