02_opencv_python_图像处理进阶

import cv2  # opencv读取的格式是BGR
import numpy as np
import matplotlib.pyplot as plt  # Matplotlib是RGB
%matplotlib inline 

img=cv2.imread(‘cat.jpg‘)
img_gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
img_gray.shape

? cv2.imshow("img_gray",img_gray)
? cv2.waitKey(0)
? cv2.destroyAllWindows()

hsv = cv2.cvtColor(img,cv2.COLOR_BGR2HSV)

cv2.imshow("hsv",hsv)
cv2.waitKey(0)    
cv2.destroyAllWindows()

# 1.将RGB转换成HSV色彩空间
hsv = cv2.cvtColor(img,cv2.COLOR_BGR2HSV)

# 2.定义数组，说明你要提取（过滤）的颜色目标
# 三通道，所以是三个参数
# 红色
lower_hsv_r = np.array([156,43,46])
upper_hsv_r = np.array([180,255,255]) 
    
# 3.进行过滤，提取，得到二值图像
mask_red = cv2.inRange(hsv,lower_hsv_r,upper_hsv_r)  # 通道数是 1 

ret,thresh1 = cv2.threshold(img_gray,127,255,cv2.THRESH_BINARY)
ret,thresh2 = cv2.threshold(img_gray,cv2.THRESH_BINARY_INV)
ret,thresh3 = cv2.threshold(img_gray,cv2.THRESH_TRUNC)
ret,thresh4 = cv2.threshold(img_gray,cv2.THRESH_TOZERO)
ret,thresh5 = cv2.threshold(img_gray,cv2.THRESH_TOZERO_INV)

titles = [‘Original Image‘,‘BINARY‘,‘BINARY_INV‘,‘TRUNC‘,‘TOZERO‘,‘TOZERO_INV‘]
images = [img,thresh1,thresh2,thresh3,thresh4,thresh5]

for i in range(6):
    plt.subplot(2,3,i + 1),plt.imshow(images[i],‘gray‘)
    plt.title(titles[i])
    plt.xticks([]),plt.yticks([])
plt.show()

img = cv2.imread(‘lenaNoise.png‘)  # 椒盐噪音

cv2.imshow(‘img‘,img)
cv2.waitKey(0)
cv2.destroyAllWindows()

# 均值滤波
# 简单的平均卷积操作
blur = cv2.blur(img,(3,3))

cv2.imshow(‘blur‘,blur)
cv2.waitKey(0)
cv2.destroyAllWindows()

# 方框滤波
# 基本和均值一样，可以选择归一化
box = cv2.boxFilter(img,-1,3),normalize=True)  

cv2.imshow(‘box‘,box)
cv2.waitKey(0)
cv2.destroyAllWindows()

# 高斯滤波
# 高斯模糊的卷积核里的数值是满足高斯分布，相当于更重视中间的
aussian = cv2.GaussianBlur(img,(5,5),1)  

cv2.imshow(‘aussian‘,aussian)
cv2.waitKey(0)
cv2.destroyAllWindows()

# 中值滤波
# 相当于用中值代替
median = cv2.medianBlur(img,5)  # 中值滤波

cv2.imshow(‘median‘,median)
cv2.waitKey(0)
cv2.destroyAllWindows()

# 展示所有的
res = np.hstack((blur,aussian,median))
#print (res)
cv2.imshow(‘median vs average‘,res)
cv2.waitKey(0)
cv2.destroyAllWindows()