如何使用 OpenCV 计算类似 skimage 的图像直方图？
How to calculate skimage-like histogram of image using OpenCV?
在之前创建的代码中，直方图是使用 skimage 模块计算的，计算方式如下：
h = skimage.exposure.histogram(img, nbins=256)
但是，现在，由于某些原因，我不能使用 skimage，只能使用 OpenCV。
所以，当我尝试
h = cv2.calcHist(img, [0], None, [256], [0,256])
skimage 的 h 和 OpenCV 的 h 的输出不同，此后的代码中断。
那么，我应该怎么做才能使 OpenCV 的直方图输出与以前相同？
示例图片：
(请点开图片看清楚，就是加了噪点的绿色)
然后我将其转换为 black-and-white 使用：
img = cv2.cvtColor(img, cv2.COLOR_BGR2YUV)
img = img/255 #to convert values from [0, 255] to [0, 1]
img = img[:, :, 0]
skimage 的直方图：
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0.15526195, 0.155553 , 0.15584406, 0.15613511, 0.15642616,
0.15671722]))
OpenCV 的直方图：
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恐怕这里有很多问题。为了重建您的结果，我使用了这段代码：
import cv2
import skimage.exposure
# Read image as-is
img = cv2.imread('TjFAo.png')
# Histogram OpenCV - incorrect usage
h_ocv = cv2.calcHist(img, [0], None, [256], [0, 256])
print(h_ocv.T)
# [[ 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 2. 0. 7. 8. 13. 12. 20. 20.
# 17. 5. 8. 4. 10. 3. 20. 2. 18. 11. 39. 12. 44. 13. 23. 8. 26. 8.
# ...
# Some image manipulation - why? whatfor?
img = cv2.cvtColor(img, cv2.COLOR_BGR2YUV)
img = img / 255
img = img[:, :, 0]
# Histogram skimage
h_ski = skimage.exposure.histogram(img, nbins=256)
print(h_ski[0])
# [ 4 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0
# 0 0 0 0 0 0 0 0 15 0 0 0 0 0 0 0 0 0
# ...
- 您在原始图像的蓝色通道上计算 OpenCV 直方图。或者，准确的说，你其实并没有那么做，因为这里的用法是不正确的。您也需要将
img 放在方括号中。
- 您将图像转换为 YUV 颜色 space，然后为
skimage 直方图取第一个通道。为什么？你有什么想法？
- 您不需要在此处为
skimage 强制执行 [0.0 ... 1.0] 范围内的值。
来看看正确的用法，先看看原图的蓝色通道：
import cv2
import skimage.exposure
# Read image as-is
img = cv2.imread('TjFAo.png')
# Histogram OpenCV - brackets around the image!
h_ocv = cv2.calcHist([img], [0], None, [256], [0, 256])
print(h_ocv.T)
# [[ 0. 0. 0. 0. 0. 0. 0. 0. 1. 2. 9. 10. 27. 62.
# 107. 154. 174. 208. 213. 193. 166. 134. 73. 52. 20. 4. 1. 1.
# ...
# Histogram skimage - set source_range!
h_ski = skimage.exposure.histogram(img[..., 0], nbins=256, source_range='dtype')
print(h_ski[0])
# [ 0 0 0 0 0 0 0 0 1 2 9 10 27 62 107 154 174 208
# 213 193 166 134 73 52 20 4 1 1 1 0 0 0 0 0 0 0
# ...
如果你真的想要灰度图像的直方图，首先转换图像：
import cv2
import skimage.exposure
# Read image as-is
img = cv2.imread('TjFAo.png')
# Convert to grayscale
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Histogram OpenCV - brackets around the image!
h_ocv = cv2.calcHist([img], [0], None, [256], [0, 256])
print(h_ocv.T)
# [[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
# 0. 0. 0. 0. 0. 0. 0. 4. 3. 15. 23. 49. 94. 152.
# ...
# Histogram skimage - set source_range!
h_ski = skimage.exposure.histogram(img, nbins=256, source_range='dtype')
print(h_ski[0])
# [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
# 0 0 0 4 3 15 23 49 94 152 183 207 200 187 165 141 78 58
# ...
----------------------------------------
System information
----------------------------------------
Platform: Windows-10-10.0.16299-SP0
Python: 3.9.1
PyCharm: 2021.1.1
OpenCV: 4.5.1
scikit-image: 0.18.1
----------------------------------------
注意skimage.exposure.histogram(img, nbins=256) returns一个有两个数组的元组，第一个是给定img中那个特定值的像素总数，第二个数组是标准化为 1.
所以，如果你只想找到给定值的像素总数，那么你可以使用这个：
def histogram(img):
img = (img*255).astype(int)
a = [0 for x in range(256)]
for x in range(256):
a[x] = np.count_nonzero(img == x)
return a
在之前创建的代码中，直方图是使用 skimage 模块计算的，计算方式如下：
h = skimage.exposure.histogram(img, nbins=256)
但是，现在，由于某些原因，我不能使用 skimage，只能使用 OpenCV。
所以，当我尝试
h = cv2.calcHist(img, [0], None, [256], [0,256])
skimage 的 h 和 OpenCV 的 h 的输出不同，此后的代码中断。
那么，我应该怎么做才能使 OpenCV 的直方图输出与以前相同？
示例图片：
(请点开图片看清楚，就是加了噪点的绿色)
然后我将其转换为 black-and-white 使用：
img = cv2.cvtColor(img, cv2.COLOR_BGR2YUV)
img = img/255 #to convert values from [0, 255] to [0, 1]
img = img[:, :, 0]
skimage 的直方图：
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0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
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0.12033548, 0.12062653, 0.12091759, 0.12120864, 0.12149969,
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0.15380668, 0.15409773, 0.15438879, 0.15467984, 0.15497089,
0.15526195, 0.155553 , 0.15584406, 0.15613511, 0.15642616,
0.15671722]))
OpenCV 的直方图：
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恐怕这里有很多问题。为了重建您的结果，我使用了这段代码：
import cv2
import skimage.exposure
# Read image as-is
img = cv2.imread('TjFAo.png')
# Histogram OpenCV - incorrect usage
h_ocv = cv2.calcHist(img, [0], None, [256], [0, 256])
print(h_ocv.T)
# [[ 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 2. 0. 7. 8. 13. 12. 20. 20.
# 17. 5. 8. 4. 10. 3. 20. 2. 18. 11. 39. 12. 44. 13. 23. 8. 26. 8.
# ...
# Some image manipulation - why? whatfor?
img = cv2.cvtColor(img, cv2.COLOR_BGR2YUV)
img = img / 255
img = img[:, :, 0]
# Histogram skimage
h_ski = skimage.exposure.histogram(img, nbins=256)
print(h_ski[0])
# [ 4 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0
# 0 0 0 0 0 0 0 0 15 0 0 0 0 0 0 0 0 0
# ...
- 您在原始图像的蓝色通道上计算 OpenCV 直方图。或者，准确的说，你其实并没有那么做，因为这里的用法是不正确的。您也需要将
img放在方括号中。 - 您将图像转换为 YUV 颜色 space，然后为
skimage直方图取第一个通道。为什么？你有什么想法？ - 您不需要在此处为
skimage强制执行[0.0 ... 1.0]范围内的值。
来看看正确的用法，先看看原图的蓝色通道：
import cv2
import skimage.exposure
# Read image as-is
img = cv2.imread('TjFAo.png')
# Histogram OpenCV - brackets around the image!
h_ocv = cv2.calcHist([img], [0], None, [256], [0, 256])
print(h_ocv.T)
# [[ 0. 0. 0. 0. 0. 0. 0. 0. 1. 2. 9. 10. 27. 62.
# 107. 154. 174. 208. 213. 193. 166. 134. 73. 52. 20. 4. 1. 1.
# ...
# Histogram skimage - set source_range!
h_ski = skimage.exposure.histogram(img[..., 0], nbins=256, source_range='dtype')
print(h_ski[0])
# [ 0 0 0 0 0 0 0 0 1 2 9 10 27 62 107 154 174 208
# 213 193 166 134 73 52 20 4 1 1 1 0 0 0 0 0 0 0
# ...
如果你真的想要灰度图像的直方图，首先转换图像：
import cv2
import skimage.exposure
# Read image as-is
img = cv2.imread('TjFAo.png')
# Convert to grayscale
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Histogram OpenCV - brackets around the image!
h_ocv = cv2.calcHist([img], [0], None, [256], [0, 256])
print(h_ocv.T)
# [[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
# 0. 0. 0. 0. 0. 0. 0. 4. 3. 15. 23. 49. 94. 152.
# ...
# Histogram skimage - set source_range!
h_ski = skimage.exposure.histogram(img, nbins=256, source_range='dtype')
print(h_ski[0])
# [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
# 0 0 0 4 3 15 23 49 94 152 183 207 200 187 165 141 78 58
# ...
----------------------------------------
System information
----------------------------------------
Platform: Windows-10-10.0.16299-SP0
Python: 3.9.1
PyCharm: 2021.1.1
OpenCV: 4.5.1
scikit-image: 0.18.1
----------------------------------------
注意skimage.exposure.histogram(img, nbins=256) returns一个有两个数组的元组，第一个是给定img中那个特定值的像素总数，第二个数组是标准化为 1.
所以，如果你只想找到给定值的像素总数，那么你可以使用这个：
def histogram(img):
img = (img*255).astype(int)
a = [0 for x in range(256)]
for x in range(256):
a[x] = np.count_nonzero(img == x)
return a