TensorFlow+OpenCV图像处理（ 三 图片特效——灰度处理、颜色反转、马赛克、毛玻璃、图片融合）

3.1用API实现灰度处理（cv2.imread）

import cv2
img = cv2.imread('image00.jpg', 1) #参数为 1 表示彩色
img1 = cv2.imread('image00.jpg', 0)  #参数为 0 表示灰度
print(img.shape)
print(img1.shape)
cv2.waitKey()
cv2.destroyAllWindows()

结果为：
(164, 219, 3)
(164, 219)

3.2调用API实现灰度处理（cv2.cvtColor）

图像类型转换详解

import cv2
img = cv2.imread("image00.jpg",1)
dst = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)  #颜色空间转换
cv2.imshow('dst',dst)
cv2.waitKey()
cv2.destroyAllWindows()

3.3源码实现图像灰度处理（gray=（r+b+g）/3）

import cv2
import numpy as np
img = cv2.imread('image00.jpg',1)
info = img.shape
height = info[0]
width = info[1]
dst = np.zeros((height,width,3),np.uint8)
for i in range(height):
    for j in range(width):
        (b,g,r) = img[i,j]
        gray = (int(b)+int(g)+int(r))/3  #结果如下图左
        #gray = (b+g+r) / 3              #结果如下图右
        dst[i,j] = int(gray)
cv2.imshow('dst',dst)
cv2.waitKey()
cv2.destroyAllWindows()

3.4源码实现图像灰度处理（gray = r0.299 + g0.587 + b*0.114）

import cv2
import numpy as np
img = cv2.imread('image00.jpg',1)
info = img.shape
height = info[0]
width = info[1]
dst = np.zeros((height,width,3),np.uint8)
# gray = r*0.299 + g*0.587 + b*0.114
for i in range(height):
    for j in range(width):
        (b,g,r) = img[i,j]
        gray = int(b)*0.299 + int(g)*0.587 + int(r)*0.114
        dst[i,j] = int(gray)
cv2.imshow('dst',dst)
cv2.waitKey()
cv2.destroyAllWindows()

3.5灰度图像颜色反转

import cv2
import numpy as np
img = cv2.imread('image00.jpg',1)
info = img.shape
height = info[0]
width = info[1]
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
#dst = np.zeros(img.shape,np.uint8)
dst = np.zeros((height,width,1),np.uint8)
for i in range(height):
    for j in range(width):
        graypixel = gray[i,j]
        dst[i,j] = 255 - graypixel
cv2.imshow('dst',dst)
cv2.waitKey()
cv2.destroyAllWindows()

3.6彩色图像颜色反转

import cv2
import numpy as np
img = cv2.imread('image00.jpg',1)
info = img.shape
height = info[0]
width = info[1]
dst = np.zeros(img.shape,np.uint8)
for i in range(height):
    for j in range(width):
        (b,g,r) = img[i,j]
        dst[i,j] = (255-b,255-g,255-r)
cv2.imshow('dst',dst)
cv2.waitKey()
cv2.destroyAllWindows()

3.7马赛克效果

import cv2
import numpy as np
img = cv2.imread('image0.jpg',1)
info = img.shape
height = info[0]
width = info[1]
for m in range(100,300):
    for n in range(100,200):
        # pixel -> 10*10
        if m%10 == 0 and n%10 == 0:
            for i in range(0,10):
                for j in range(0,10):
                    (b,g,r) = img[m,n]
                    img[i+m,j+n] = (b,g,r)
cv2.imshow('dst',img)
cv2.waitKey()
cv2.destroyAllWindows()

3.8毛玻璃效果

import cv2
import numpy as np
import random
img = cv2.imread('image0.jpg',1)
info = img.shape
height = info[0]
width = info[1]
dst = np.zeros(img.shape,np.uint8)
#cv2.imshow('dst',dst)
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('dst',dst)
cv2.waitKey()
cv2.destroyAllWindows()

3.9图片融合

import cv2
import numpy as np
img1 = cv2.imread('image0.jpg',1)
img2 = cv2.imread('image1.jpg',1)
info = img1.shape
height = info[0]
width = info[1]
#ROI
roiH = int(height/2)
roiW = int(width/2)
img1ROI = img1[0:roiH,0:roiW]
img2ROI = img2[0:roiH,0:roiW]
dst = np.zeros((roiH,roiW,3),np.uint8)
dst = cv2.addWeighted(img1ROI,0.5,img2ROI,0.5,0)
cv2.imshow('dst',dst)
cv2.waitKey()
cv2.destroyAllWindows()