lut[i] = int(255.0 *p[i]) #p[i]是是强度值小于或等于i的像素的数目。

def calcHist(images, channels, mask, histSize, ranges, hist=None, accumulate=None): 
    # real signature unknown; restored from __doc__
    """
    calcHist(images, channels, mask, histSize, ranges[, hist[, accumulate]]) -> hist
    .   @overload
    """
    pass

#_*_coding:utf-8_*_
import cv2  # opencv读取的格式是BGR
import numpy as np
import matplotlib.pyplot as plt  # Matplotlib读取的格式是RGB
 
img = cv2.imread('cat.jpg', 0)   #0 表示灰度图
hist = cv2.calcHist([img], [0], None, [256], [0, 256])
print(hist.shape)  #  (256, 1)

Histogram(a,bins=10,range=None,normed=False,weights=None)

hist, bins = np.histogram(img.ravel(), 255, [0,256])

matplotlib.pyplot.hist()

import numpy as np
import cv2 as cv
from matplotlib import pyplot as plt
 
img = cv.imread('cat.jpg', 0)
plt.hist(img.ravel(), 256, [0,256])
plt.show()

import numpy as np
import cv2 as cv
from matplotlib import pyplot as plt
 
img = cv.imread('cat.jpg')
color = ('b', 'g', 'r')
for i, col in enumerate(color):
    histr = cv.calcHist([img], [i], None, [256], [0,256])
    plt.plot(histr, color=col)
    plt.xlim([0,256])
plt.show()

import cv2
import numpy as np
 
 
def calcAndDrawHist(image, color):
    hist = cv2.calcHist([image], [0], None, [256], [0.0, 255.0])
    minVal, maxVal, minLoc, maxLoc = cv2.minMaxLoc(hist)
    histImg = np.zeros([256, 256, 3], np.uint8)
    hpt = int(0.9*256)
 
    for h in range(256):
        intensity = int(hist[h] * hpt / maxVal)
        cv2.line(histImg, (h, 256), (h, 256-intensity), color)
 
    return histImg
 
def show_histphoto(photo_path):
    image = cv2.imread(photo_path)
    b, g, r = cv2.split(image)
 
    histImgB = calcAndDrawHist(b, [255, 0, 0])
    histImgG = calcAndDrawHist(b, [0, 255, 0])
    histImgR = calcAndDrawHist(b, [0, 0, 255])
 
    cv2.imshow('histImgB', histImgB)
    cv2.imshow('histImgG', histImgG)
    cv2.imshow('histImgR', histImgR)
    cv2.imshow('Img', image)
    cv2.waitKey(0)
    cv2.destroyAllWindows()
 
 
if __name__ == '__main__':
    photo_path = 'cat.jpg'
    show_histphoto(photo_path)

def Line_chart(photo_path):
    image = cv2.imread(photo_path)
    # 创建用于绘制直方图的全0 图像
    h = np.zeros((256, 256, 3))
    # 直方图中各bin的顶点位置
    bins = np.arange(256).reshape(256, 1)
    # BGR 三种颜色
    color = [(255, 0, 0), (0, 255, 0), (0, 0, 255)]
    for ch, col in enumerate(color):
        originHist = cv2.calcHist([image], [ch], None, [256], [0, 256])
        cv2.normalize(originHist, originHist, 0, 255*0.9, cv2.NORM_MINMAX)
        hist = np.int32(np.around(originHist))
        pts = np.column_stack((bins, hist))
        cv2.polylines(h, [pts], False, col)
 
    h = np.flipud(h)
    cv2.imshow('colorhist', h)
    cv2.waitKey(0)
 
if __name__ == '__main__':
    photo_path = 'cat.jpg'
    # show_histphoto(photo_path)
    Line_chart(photo_path)

#_*_coding:utf-8_*_
import cv2  # opencv读取的格式是BGR
import numpy as np
import matplotlib.pyplot as plt  # Matplotlib读取的格式是RGB
 
img = cv2.imread('cat.jpg', 0)
 
# 创建 mask
mask = np.zeros(img.shape[:2], np.uint8)
print(mask.shape)  # (414, 500)
mask[100:300, 100:400] = 255
 
masked_img = cv2.bitwise_and(img, img, mask=mask)  # 与操作
 
hist_full = cv2.calcHist([img], [0], None, [256], [0, 256])
hist_mask = cv2.calcHist([img], [0], mask, [256], [0, 256])
 
plt.subplot(221), plt.imshow(img, 'gray'), plt.title('gray image')
plt.subplot(222), plt.imshow(mask, 'gray'), plt.title('mask image')
plt.subplot(223), plt.imshow(masked_img, 'gray'), plt.title('image bitwise and mask')
plt.subplot(224), plt.plot(hist_full), plt.plot(hist_mask), plt.title('hist image')
plt.xlim([0, 256])
plt.show()

img = cv.imread('cat,jpg', 0)
equ = cv.equalizeHist(img)
res = np.hstack((img, equ)) # 并排叠加图片
cv.imwrite('res.png', res)

plt.hist(img.ravel(), 256)
plt.hist(equ.ravel(), 256)
plt.show()

def createCLAHE(clipLimit=None, tileGridSize=None): # real signature unknown; restored from __doc__
    """
    createCLAHE([, clipLimit[, tileGridSize]]) -> retval
    .   @brief Creates a smart pointer to a cv::CLAHE class and initializes it.
    .   
    .   @param clipLimit Threshold for contrast limiting.
    .   @param tileGridSize Size of grid for histogram equalization. Input image will be divided into
    .   equally sized rectangular tiles. tileGridSize defines the number of tiles in row and column.
    """
    pass

#_*_coding:utf-8_*_
import cv2  # opencv读取的格式是BGR
import numpy as np
import matplotlib.pyplot as plt  # Matplotlib读取的格式是RGB
 
img = cv2.imread('cat.jpg', 0)
 
equ = cv2.equalizeHist(img)
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
 
res_clahe = clahe.apply(img)
 
 
res = np.hstack((img, equ, res_clahe)) 
cv2.imshow('res', res)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2 as cv
import numpy as np 
from matplotlib import pyplot as plt
 
 
im = cv.imread("test.png", 0)
cv.imshow("before", im)
 
# Histogram Equalization
im2 = cv.equalizeHist(im)
print(im2)
 
cv.imshow("after", im2)
plt.show()
cv.waitKey(0)

import cv2 as cv
import numpy as np 
from matplotlib import pyplot as plt
 
 
im = cv.imread("test.jpg")
cv.imshow("before", im)
 
# split g,b,r
g = im[:,:,0]
b = im[:,:,1]
r = im[:,:,2]
 
 
# Histogram Equalization
r2 = cv.equalizeHist(r)
g2 = cv.equalizeHist(g)
b2 = cv.equalizeHist(b)
 
im2 = im.copy()
im2[:,:,0] = g2
im2[:,:,1] = b2
im2[:,:,2] = r2
 
print(im2)
 
cv.imshow("after", im2)
plt.show()
cv.waitKey(0)

import cv2 as cv
import numpy as np 
from matplotlib import pyplot as plt
 
im = cv.imread("test.png", 0)
cv.imshow("before", im)
mask = cv.imread("test_mask2.png", 0)
cv.imshow("mask", mask)
 
# calculate histogram with mask
hist_mask = cv.calcHist([im], [0], mask, [256], [0,256])
 
# calculate cdf with mask
cdf = hist_mask.cumsum()
 
# Histogram Equalization
cdf = (cdf-cdf[0])*255/(cdf[-1]-1)
cdf = cdf.astype(np.uint8)# Transform from float64 back to unit8
 
# generate img after Histogram Equalization
im2 = np.zeros((384, 495, 1), dtype =np.uint8)
im2 = cdf[im]
 
# im2 = cv.equalizeHist(im)
print(im2)
 
cv.imshow("after", im2)
plt.show()
cv.waitKey(0)

numpy.cumsum(a, axis=None, dtype=None, out=None)

>>>import numpy as np  
>>> b=[1,2,3,4,5,6,7]  
>>> np.cumsum(a)  
array([  1,   3,   6,  10,  15,  21,  28,  36,  45,  55,  75, 105])

import numpy as np
import cv2 as cv
from matplotlib import pyplot as plt
 
img = cv.imread('wiki.jpg', 0)
 
hist, bins = np.histogram(img.flatten(), 256, [0,256])
 
cdf = hist.cumsum()
cdf_normalized = cdf*float(hist.max())/cdf.max()
 
plt.plot(cdf_normalized, color = 'b')
plt.hist(img.flatten(),256,[0,256], color = 'r')
plt.xlim([0,256])
plt.legend(('cdf','histogram'), loc = 'upper left')
plt.show()

cdf = (cdf-cdf[0]) *255/ (cdf[-1]-1)
cdf = cdf.astype(np.uint8)

img2 = cdf[img]

import cv2 as cv
import numpy as np 
from matplotlib import pyplot as plt
 
img = cv.imread("test.png", 0)
cv.imshow("before", img)
 
# calculate hist
hist, bins = np.histogram(img, 256)
# calculate cdf
cdf = hist.cumsum()
# plot hist
plt.plot(hist,'r')
 
# remap cdf to [0,255]
cdf = (cdf-cdf[0])*255/(cdf[-1]-1)
cdf = cdf.astype(np.uint8)# Transform from float64 back to unit8
 
# generate img after Histogram Equalization
img2 = np.zeros((384, 495, 1), dtype =np.uint8)
img2 = cdf[img]
 
hist2, bins2 = np.histogram(img2, 256)
cdf2 = hist2.cumsum()
plt.plot(hist2, 'g')
 
cv.imshow("after", img2)
plt.show()
cv.waitKey(0)

import cv2 as cv
import numpy as np 
from matplotlib import pyplot as plt
 
 
img = cv.imread("test.png")
cv.imshow("before", img)
 
# split g,b,r
g = img[:,:,0]
b = img[:,:,1]
r = img[:,:,2]
 
# calculate hist
hist_r, bins_r = np.histogram(r, 256)
hist_g, bins_g = np.histogram(g, 256)
hist_b, bins_b = np.histogram(b, 256)
 
# calculate cdf
cdf_r = hist_r.cumsum()
cdf_g = hist_g.cumsum()
cdf_b = hist_b.cumsum()
 
# remap cdf to [0,255]
cdf_r = (cdf_r-cdf_r[0])*255/(cdf_r[-1]-1)
cdf_r = cdf_r.astype(np.uint8)# Transform from float64 back to unit8
cdf_g = (cdf_g-cdf_g[0])*255/(cdf_g[-1]-1)
cdf_g = cdf_g.astype(np.uint8)# Transform from float64 back to unit8
cdf_b = (cdf_b-cdf_b[0])*255/(cdf_b[-1]-1)
cdf_b = cdf_b.astype(np.uint8)# Transform from float64 back to unit8
 
# get pixel by cdf table
r2 = cdf_r[r]
g2 = cdf_g[g]
b2 = cdf_b[b]
 
# merge g,b,r channel
img2 = img.copy()
img2[:,:,0] = g2
img2[:,:,1] = b2
img2[:,:,2] = r2
 
# show img after histogram equalization
cv.imshow("img2", img2)
 
cv.waitKey(0)

import cv2 as cv
import numpy as np 
from matplotlib import pyplot as plt
 
 
img = cv.imread("test.png", 0)
cv.imshow("src", img)
 
# load mask img
mask = cv.imread("test_mask2.png", 0)
cv.imshow("mask", mask)
 
# apply mask to src
masked_img = np.ma.masked_array(img, mask = mask)
masked_img = np.ma.filled(masked_img,0).astype('uint8')
# print(masked_img)
masked_img = np.ma.masked_equal(masked_img,0)
# print(masked_img)
cv.imshow("masked_img", masked_img)
 
 
# calculate hist
hist, bins = np.histogram(masked_img.compressed(), 256) # img have to be compressed() to let mask work
# calculate cdf
cdf = hist.cumsum()
 
print(cdf)
# plot hist
plt.plot(hist,'r')
 
# remap cdf to [0,255]
cdf = (cdf-cdf[0])*255/(cdf[-1]-1)
cdf = cdf.astype(np.uint8)# Transform from float64 back to unit8
 
# generate img after Histogram Equalization
img2 = np.zeros((384, 495, 1), dtype =np.uint8)
img2 = cdf[img]
 
hist2, bins2 = np.histogram(img2, 256)
cdf2 = hist2.cumsum()
plt.plot(hist2, 'g')
 
cv.imshow("dst", img2)
plt.show()
cv.waitKey(0)

import cv2
import numpy as np
import matplotlib.pyplot as plt
 
 
def show_histphoto(photo_path):
    image = cv2.imread(photo_path, 0)
    print(image.shape)
    hist = cv2.calcHist([image], [0], None, [256], [0.0, 256.0])
    # print(hist.shape)
    plt.plot(hist)
 
def Line_chart(photo_path):
    image = cv2.imread(photo_path)
    # 创建用于绘制直方图的全0 图像
    h = np.zeros((256, 256, 3))
    # 直方图中各bin的顶点位置
    bins = np.arange(256).reshape(256, 1)
    # BGR 三种颜色
    color = [(255, 0, 0), (0, 255, 0), (0, 0, 255)]
    for ch, col in enumerate(color):
        originHist = cv2.calcHist([image], [ch], None, [256], [0, 256])
        cv2.normalize(originHist, originHist, 0, 255*0.9, cv2.NORM_MINMAX)
        hist = np.int32(np.around(originHist))
        pts = np.column_stack((bins, hist))
        cv2.polylines(h, [pts], False, col)
 
    h = np.flipud(h)
    cv2.imshow('colorhist', h)
    cv2.waitKey(0)
 
if __name__ == '__main__':
    photo_path = 'test.jpg'
    # show_histphoto(photo_path)
    Line_chart(photo_path)

minBinNo, maxBinNo = 0, 255
 
# 计算从左起第一个不为0的直方图位置
for binNo, binValue in enumerate(hist):
    if binValue != 0:
        minBinNo = binNo
        break
 
# 计算从右起第一个不为0的直方图位置
for binNo, binValue in enumerate(reversed(hist)):
    if binValue != 0:
        maxBinNo = 255 - binNo
        break
 
# 生成查找表
for i, v in enumerate(lut):
    if i < minBinNo:
        lut[i] = 0
    elif i > maxBinNo:
        lut[i] = 255
    else:
        lut[i] = int(255.0*(i-minBinNo)/(maxBinNo-minBinNo)+0.5)

#计算
result = cv2.LUT(image, lut)

import cv2
import numpy as np
 
image = cv2.imread('wiki.jpg', 0)
# 创建空的查找表
lut = np.zeros(256, dtype=image.dtype)
# OpenCV提供了cv.calcHist()函数来获取直方图
hist = cv2.calcHist([image],  # 计算图像的直方图
                    [0],  # 使用的通道
                    None,  # 没有使用mask
                    [256],  # it is a 2D histogram
                    [0.0, 255.0])
# print(hist.shape)  # (256, 1)
minBinNo, maxBinNo = 0, 255
 
# 计算从左起第一个不为0的直方图位置
for binNo, binValue in enumerate(hist):
    if binValue != 0:
        minBinNo = binNo
        break
 
# 计算从右起第一个不为0的直方图位置
for binNo, binValue in enumerate(reversed(hist)):
    if binValue != 0:
        maxBinNo = 255 - binNo
        break
 
# 生成查找表
for i, v in enumerate(lut):
    if i < minBinNo:
        lut[i] = 0
    elif i > maxBinNo:
        lut[i] = 255
    else:
        lut[i] = int(255.0 * (i - minBinNo) / (maxBinNo - minBinNo) + 0.5)
 
# 计算
result = cv2.LUT(image, lut)
print(result.shape)   # (534, 800)
cv2.imshow('result', result)
cv2.waitKey(0)
cv2.destroyAllWindows()

def opencv_equalizeHist(image_path):
    img = cv2.imread(image_path, 0)
    equ = cv2.equalizeHist(img)
    cv2.imshow('equ', equ)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

def numpy_equalizeHist(image_path):
    img = cv2.imread(image_path, 0)
    # 创建空的查询表
    lut = np.zeros(256, dtype=img.dtype)
 
    hist, bins = np.histogram(img.flatten(), 256, [0, 256])
    # 计算累计直方图
    cdf = hist.cumsum()
    # 除以直方图中的0值
    cdf_m = np.ma.masked_equal(cdf, 0)
    #等同于前面介绍的lut[i] = int(255.0 *p[i])公式
    cdf_m = (cdf_m - cdf_m.min())*255/(cdf_m.max()-cdf_m.min())
    #将掩模处理掉的元素补为0
    cdf = np.ma.filled(cdf_m, 0).astype('uint8')
    # 计算
    result2 = cdf[img]
    # result = cv2.LUT(img, cdf)
 
    # cv2.imshow("OpenCVLUT", result)
    cv2.imshow("NumPyLUT", result2)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

import cv2
import numpy as np
import matplotlib.pyplot as plt
 
 
def LookUpTable(photo_path):
    image = cv2.imread(photo_path, 0)
    # 创建空的查找表
    lut = np.zeros(256, dtype=image.dtype)
    # OpenCV提供了cv.calcHist()函数来获取直方图
    hist = cv2.calcHist([image],  # 计算图像的直方图
                        [0],  # 使用的通道
                        None,  # 没有使用mask
                        [256],  # it is a 2D histogram
                        [0.0, 255.0])
    # print(hist.shape)  # (256, 1)
    minBinNo, maxBinNo = 0, 255
 
    # 计算从左起第一个不为0的直方图位置
    for binNo, binValue in enumerate(hist):
        if binValue != 0:
            minBinNo = binNo
            break
 
    # 计算从右起第一个不为0的直方图位置
    for binNo, binValue in enumerate(reversed(hist)):
        if binValue != 0:
            maxBinNo = 255 - binNo
            break
 
    # 生成查找表
    for i, v in enumerate(lut):
        if i < minBinNo:
            lut[i] = 0
        elif i > maxBinNo:
            lut[i] = 255
        else:
            lut[i] = int(255.0 * (i - minBinNo) / (maxBinNo - minBinNo) + 0.5)
 
    # 计算
    lut = cv2.LUT(image, lut)
    cv2.imwrite('lut.jpg', lut)
    # cv2.imshow('lut', lut)
    # cv2.waitKey(0)
    # cv2.destroyAllWindows()
    return lut
 
 
def opencv_equalizeHist(image_path):
    img = cv2.imread(image_path, 0)
    equ = cv2.equalizeHist(img)
    cv2.imwrite('equ.jpg', equ)
    # cv2.imshow('equ', equ)
    # cv2.waitKey(0)
    # cv2.destroyAllWindows()
    return equ
 
 
def numpy_equalizeHist(image_path):
    img = cv2.imread(image_path, 0)
    hist, bins = np.histogram(img.flatten(), 256, [0, 256])
    # 计算累计直方图
    cdf = hist.cumsum()
    # 除以直方图中的0值
    cdf_m = np.ma.masked_equal(cdf, 0)
    # 等同于前面介绍的lut[i] = int(255.0 *p[i])公式
    cdf_m = (cdf_m - cdf_m.min()) * 255 / (cdf_m.max() - cdf_m.min())
    # 将掩模处理掉的元素补为0
    cdf = np.ma.filled(cdf_m, 0).astype('uint8')
    # 计算
    numpy_lut = cdf[img]
    cv2.imwrite('numpy_lut.jpg', numpy_lut)
    # cv2.imshow("NumPyLUT", numpy_lut)
    # cv2.waitKey(0)
    # cv2.destroyAllWindows()
    return numpy_lut
 
 
def show_allphoto():
    lut = cv2.imread('lut.jpg', 0)
    np_equ = cv2.imread('numpy_lut.jpg', 0)
    opencv_equ = cv2.imread('equ.jpg', 0)
    print(lut.shape,  np_equ.shape, opencv_equ.shape)
 
    lut = cv2.calcHist([lut], [0], None, [256], [0.0, 256.0])
    np_equ = cv2.calcHist([np_equ], [0], None, [256], [0.0, 256.0])
    opencv_equ = cv2.calcHist([opencv_equ], [0], None, [256], [0.0, 256.0])
 
    plt.subplot(311), plt.plot(lut)
    plt.subplot(312), plt.plot(np_equ)
    plt.subplot(313), plt.plot(opencv_equ)
    plt.show()
 
 
 
if __name__ == '__main__':
    photo_path = 'wiki.jpg'
    # lut = LookUpTable(photo_path)
    # np_equ = numpy_equalizeHist(photo_path)
    # opencv_equ = opencv_equalizeHist(photo_path)
    show_allphoto()