import cv2
from PIL import Image
import matplotlib.pyplot as plt
import numpy as np
from matplotlib import cm
def convolution(img1):
n,m = img1.shape
img_new = [[0 for i in range(m)] for i in range(n)]
for i in range(n-3):
for j in range(m-3):
img2 = img1.copy()
a = img2[i:i+3,j:j+3] #定义滑动窗口
w,u=a.shape
ave=np.sum(a)/(w*u)#计算窗口总平均灰度值
k=9
value=0
for p in range(w):
for q in range(u):
if a[p][q]<ave:
a[p][q]=0
else:
a[p][q]=1
value=value+np.power(2,k-1) #由二进制转化为10进制
k=k-1
img_new[i+1][j+1]=value
return img_new
img = cv2.imread(“fish.png”)
img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB) #opencv默认通道是BGR,但大多数其他图像处理库使用RGB排序
gray = cv2.cvtColor(img,cv2.COLOR_RGB2GRAY)
plt.imshow(img)#原图
plt.show()
plt.imshow(gray,cmap=cm.gray)#灰度图
plt.show()
n,m = gray.shape
print(“n=%d,m=%d”%(n,m))
img_new = convolution(gray) #MCT修正统计变换
卷积结果可视化
plt.imshow(img_new,cmap=cm.gray)
plt.show()
结果如下：
原图：
灰度图：
MCT变换后的图：
