Histogram Equalization.py

import cv2
import numpy as np
import matplotlib.pyplot as plt

path = 'C:\\Users\\rabbear\\Desktop\\sky.jpg'

img = cv2.imread(path)
# plt.hist(img.ravel(),256,[0,256])
# plt.show()

def calcGrayHist(I):
    # 计算灰度直方图
    h, w = I.shape[:2]
    grayHist = np.zeros([256], np.uint64)
    for i in range(h):
        for j in range(w):
            grayHist[I[i][j]] += 1
    return grayHist

def equalHist(img):
    # 灰度图像矩阵的高、宽
    h, w = img.shape[0], img.shape[1]
    # 第一步：计算灰度直方图
    grayHist = calcGrayHist(img)
    # 第二步：计算累加灰度直方图
    zeroCumuMoment = np.zeros([256], np.uint32)
    for p in range(256):
        if p == 0:
            zeroCumuMoment[p] = grayHist[0]
        else:
            zeroCumuMoment[p] = zeroCumuMoment[p - 1] + grayHist[p]
    # 第三步：根据累加灰度直方图得到输入灰度级和输出灰度级之间的映射关系
    outPut_q = np.zeros([256], np.uint8)
    cofficient = 256.0 / (h * w)
    for p in range(256):
        q = cofficient * float(zeroCumuMoment[p]) - 1
        if q >= 0:
            outPut_q[p] = np.floor(q)
        else:
            outPut_q[p] = 0
    # 第四步：得到直方图均衡化后的图像
    equalHistImage = np.zeros(img.shape, np.uint8)
    for i in range(h):
        for j in range(w):
            equalHistImage[i][j] = outPut_q[img[i][j]]
    return equalHistImage


# 使用自己写的函数实现
equa = equalHist(img)

#plt.hist(equa.ravel(),256,[0,256])
plt.imshow(equa)
plt.show()