直方图均衡化
图像增强
马赛克算法
import cv2
import numpy as np
import random
img = cv2.imread('Resources/1.jpg', 1)
imgInfo = img.shape
height = imgInfo[0]
width = imgInfo[1]
k = 0
for m in range(500, 600):
for n in range(300, 450):
if m % 10 == 0 and n % 10 == 0:
for i in range(10):
for j in range(10):
(b, g, r) = img[m, n]
img[i+m, j+n] = (b, g, r)
cv2.imshow('image', img)
cv2.waitKey(0)
毛玻璃算法
import cv2
import numpy as np
import random
img = cv2.imread('Resources/1.jpg', 1)
imgInfo = img.shape
height = imgInfo[0]
width = imgInfo[1]
dst = np.zeros([height, width, 3], np.uint8)
mm = 8
for m in range(height - mm):
for n in range(width - mm):
index = int(random.random() * 8)
(b, g, r) = img[m+index, n+index]
dst[m, n] = (b, g, r)
cv2.imshow('image', dst)
cv2.waitKey(0)
图像增强算法
滤波算法
高斯滤波算法
# encoding:utf-8
import cv2
import numpy as np
import matplotlib.pyplot as plt
# 读取图片
img = cv2.imread('1.jpg')
source = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# 高斯滤波
result = cv2.GaussianBlur(source, (3,3), 0)
# 显示图形
titles = ['Source Image', 'medianBlur Image']
images = [source, result]
for i in range(2):
plt.subplot(1, 2, i + 1), plt.imshow(images[i], 'gray')
plt.title(titles[i])
plt.xticks([]), plt.yticks([])
plt.show()
中值滤波算法
# encoding:utf-8
import cv2
import numpy as np
import matplotlib.pyplot as plt
# 读取图片
img = cv2.imread('1.jpg')
source = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# 中值滤波
result = cv2.medianBlur(source, 3)
# 显示图形
titles = ['Source Image', 'medianBlur Image']
images = [source, result]
for i in range(2):
plt.subplot(1, 2, i + 1), plt.imshow(images[i], 'gray')
plt.title(titles[i])
plt.xticks([]), plt.yticks([])
plt.show()
均值滤波算法
# encoding:utf-8
import cv2
import numpy as np
import matplotlib.pyplot as plt
# 读取图片
img = cv2.imread('1.jpg')
source = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# 均值滤波
result = cv2.blur(source, (5, 5))
# 显示图形
titles = ['Source Image', 'Blur Image']
images = [source, result]
for i in range(2):
plt.subplot(1, 2, i + 1), plt.imshow(images[i], 'gray')
plt.title(titles[i])
plt.xticks([]), plt.yticks([])
plt.show()