python直⽅图规定化_OpenCVpython彩⾊图像的直⽅图规定
化
⾸先是opencv在图像处理⽅⾯的基础应⽤，彩⾊图像的相关知识和技术以及直⽅图的均衡化和规定化的原理是必须提前掌握，这些我就不做过多的介绍了。 本次实验也是基于python下完成的。
⽼师要求的是在hsv模型⾥对直⽅图进⾏规定化，所以如果你是在rgb模型下进⾏直⽅图规定化，只需要将rgb和hsv互相转化的部分删除即可，重点并不是这⾥。
import cv2
手工扇子import numpy as np三只小猪ppt
from matplotlib import pyplot as plt
img1 = cv2.imread('E:\\picSource\\Fig7A.jpg')
img2 = cv2.imread('E:\\picSource\\Fig7B.jpg')
img_hsv1 = cv2.cvtColor(img1, cv2.COLOR_BGR2HSV) # bgr转hsv
img_hsv2 = cv2.cvtColor(img2, cv2.COLOR_BGR2HSV)
color = ('h', 's', 'v')王四代
for i, col in enumerate(color):
# histr = cv2.calcHist([img_hsv1], [i], None, [256], [0, 256])
hist1, bins = np.histogram(img_hsv1[:, :, i].ravel(), 256, [0, 256])
hist2, bins = np.histogram(img_hsv2[:, :, i].ravel(), 256, [0, 256])
cdf1 = hist1.cumsum() # 灰度值0-255的累计值数组
咖啡怎么煮cdf2 = hist2.cumsum()
cdf1_hist = hist1.cumsum() / cdf1.max() # 灰度值的累计值的⽐率
cdf2_hist = hist2.cumsum() / cdf2.max()
海口必去的五大景点diff_cdf = [[0 for j in range(256)] for k in range(256)] # diff_cdf ⾥是每2个灰度值⽐率间的差值
for j in range(256):
for k in range(256):
diff_cdf[j][k] = abs(cdf1_hist[j] - cdf2_hist[k])
lut = [0 for j in range(256)] # 映射表
for j in range(256):
min = diff_cdf[j][0]
index = 0
for k in range(256): # 直⽅图规定化的映射原理
if min > diff_cdf[j][k]:
楷书入门
min = diff_cdf[j][k]
index = k
lut[j] = ([j, index])
h = int(img_hsv1.shape[0])
w = int(img_hsv1.shape[1])
for j in range(h): # 对原图像进⾏灰度值的映射
for k in range(w):
img_hsv1[j, k, i] = lut[img_hsv1[j, k, i]][1]
hsv_img1 = cv2.cvtColor(img_hsv1, cv2.COLOR_HSV2BGR) # hsv转bgr hsv_img2 = cv2.cvtColor(img_hsv2, cv2.COLOR_HSV2BGR)
cv2.namedWindow('firstpic', 0)阿城棋王
cv2.namedWindow('targetpic', 0)
荤段子大全
cv2.namedWindow('defpic', 0)
cv2.imshow('firstpic', img1)
cv2.imshow('targetpic',img2)
# cv2.imshow('img1', img_hsv1)
cv2.imshow('defpic', hsv_img1)
cv2.waitKey(0)
cv2.destroyAllWindows()
以下分别是原图像 ⽬标图像 和 经过直⽅图规定化后的图像：
