Color histogram grayscale image & color image

1. Read pictures cv2.imread（ Picture path ,cv2.IMREAD_COLOR）

2. Definition color Variable =（'b','g','r'） Represents three channels

3. Color image as an example

    for i,col in enumerate（color）： Traverse three channels

        --- cv2.calcHist() Function assigned to variable histr

        ---   Make a picture plt.plot（histr,color=col）,col There are three channels

plt.show（） Exhibition

enumerate The Chinese translation means " enumeration ".

stay Python Generally, it is for traversable objects , Such as the list 、 Tuple or string traversal .

enumerate(sequence, [start=0]).

such as ：

s=[1,2,6,9]

for index,item in enumerate(s):

print(index,item)

And what you get is ：

0 1

1 2

2 6

3 9

The default is from the subscript to 0 Start , Of course, you can specify start For other numbers , But the number here represents the starting number , Not the real subscript of a list or other data type ！

 cv2.calcHist() Function function ： Through histogram, we can have a good understanding of the gray distribution of the whole image , Histogram of x Axis is gray value （0~255）,y The axis is the number of points with the same gray value in the picture .

cv2.calcHist(images,channels,mask,histSize,ranges)

images: The original image format is uint8 or float32. When you pass in a function, you should Use brackets [] For example [img]
channels: In the same way, it will tell us the function in brackets Like histogram . If the input image is a grayscale image, its value is [0] If it's a color image The parameters passed in to can be [0][1][2] They correspond to BGR.
mask: mask image . The histogram of the whole image is taken as None. But if you want to control the histogram of a certain part of the image, you make a mask image and use it .
histSize:BIN Number of . It should also be enclosed in brackets
BINS ： The histogram above shows the number of pixels per pixel value , From 0 To 255. That is, you need 256 Only values can display the above histogram . But consider , If you don't need to find the number of pixels of all pixel values separately , Instead, look for the number of pixels in the pixel value interval , What should I do ？ for example , You need to find somewhere between 0 To 15 The number of pixels between , And then there was 16 To 31、...、240 To 255. You just need 16 To represent the histogram .
therefore , Just split the whole histogram into 16 Sub part , The value of each sub part is the sum of all the pixel counts in it . Each of these sub parts is called "BIN". In the first case , The number of columns is 256（ One per pixel ）, And in the second case , It's just 16.BINS stay OpenCV There are terms in the document histSize Express .
ranges: The range of pixel values is usually [0 256]

Example ：
import cv2
import numpy as np
import matplotlib.pyplot as plt
 
img = cv2.imread('cat.jpg', 0)  # 0 Represents a grayscale image
hist = cv2.calcHist([img], [0], None, [256], [0, 256])
# print(hist)
# print(hist[175])
# print(type(hist))
plt.hist(img.ravel(),256);
#print(img.ravel())
plt.show()
Output results ：

Example 2： Of multichannel graphs “ Histogram ”
import cv2
import numpy as np
import matplotlib.pyplot as plt
 
 
img = cv2.imread('cat.jpg') 
color = ('b','g','r')
for i,col in enumerate(color): 
    histr = cv2.calcHist([img],[i],None,[256],[0,256]) 
    plt.plot(histr,color = col) 
    plt.xlim([0,256]) 
    print(i,col)
Output results ：

Here, the complete histogram is drawn like this , But you may be right calcHist The return value of , What exactly is returned to draw such an image ？ Next we will discuss the return value of this function
cv2.calcHist() The return value of the function

about hist = cv2.calcHist([img],[0],None,[256],[0,256]) in ,hist It's a 256*1 Matrix , Each value represents the number of pixels corresponding to each gray value
print(type(hist))
<class 'numpy.ndarray'>
The output indicates that this is a multidimensional array
print(hist)
[[1.000e+00]
 [0.000e+00]
 [2.000e+00]
 [0.000e+00]
       .......
 [2.010e+02]
 [2.180e+02]
 [2.160e+02]
 [2.660e+02]
 [3.350e+02]
 [3.110e+02]
 [4.670e+02]]
Calculated , share 256 It's worth , That is, it can be output hist[0] To hist[255]. Whole hist[i] Represents the gray value

print(hist[175],hist[255])
[3181.] [467.]
It can be seen from the histogram that the output result is the number of pixels corresponding to each gray value