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直方图均衡化-python实现


直方图均衡化-python实现_直方图均衡化

直方图均衡化-python实现_读取图像_02

直方图均衡化-python实现_直方图_03

直方图均衡化-python实现_读取图像_04

"""
@author: LiShiHang
@software: PyCharm
@file: 5.1.直方图均衡化.py
@time: 2018/12/24 16:02
@desc:
"""
import cv2 # 仅用于读取图像矩阵
import matplotlib.pyplot as plt
import numpy as np

gray_level = 256 # 灰度级


def pixel_probability(img):
"""
计算像素值出现概率
:param img:
:return:
"""
assert isinstance(img, np.ndarray)

prob = np.zeros(shape=(256))

for rv in img:
for cv in rv:
prob[cv] += 1

r, c = img.shape
prob = prob / (r * c)

return prob


def probability_to_histogram(img, prob):
"""
根据像素概率将原始图像直方图均衡化
:param img:
:param prob:
:return: 直方图均衡化后的图像
"""
prob = np.cumsum(prob) # 累计概率

img_map = [int(i * prob[i]) for i in range(256)] # 像素值映射

# 像素值替换
assert isinstance(img, np.ndarray)
r, c = img.shape
for ri in range(r):
for ci in range(c):
img[ri, ci] = img_map[img[ri, ci]]

return img


def plot(y, name):
"""
画直方图,len(y)==gray_level
:param y: 概率值
:param name:
:return:
"""
plt.figure(num=name)
plt.bar([i for i in range(gray_level)], y, width=1)


if __name__ == '__main__':

img = cv2.imread("source.jpg", 0) # 读取灰度图

prob = pixel_probability(img)
plot(prob, "原图直方图")

# 直方图均衡化
img = probability_to_histogram(img, prob)
cv2.imwrite("source_hist.jpg", img) # 保存图像

prob = pixel_probability(img)
plot(prob, "直方图均衡化结果")

plt.show()


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