用opencv给图片换背景色的示例代码

img = cv2.imread('lena.jpg')
# 定义卷积核
kernel = np.ones((3, 3), np.float32) / 10
# 卷积操作，-1表示通道数与原图相同
dst = cv2.filter2D(img, -1, kernel)

dst = cv2.filter2D(img, -1, kernel)

kernel = np.ones((3, 3), np.float32) / 10

dst = cv2.filter2D(img, -1, kernel)

img = cv2.imread('lena.jpg')
# 均值模糊
blur = cv2.blur(img,(3,3)

img = cv2.imread(‘gaussian_noise.bmp')

blur = cv2.blur(img, (5, 5)) # 均值滤波
gaussian = cv2.GaussianBlur(img, (5, 5), 1) # 高斯滤波

img = cv2.imread(‘salt_noise.bmp', 0)

img = cv2.imread(‘lena.jpg')

import cv2
import numpy as np
img = cv2.imread(‘j.bmp', 0)
kernel = np.ones((5, 5), np.uint8)
erosion = cv2.erode(img, kernel) # 腐蚀

kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5)) # 矩形结构
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) # 椭圆结构
kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (5, 5)) # 十字结构

dilation = cv2.dilate(img, kernel) # 膨胀

kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
img = cv2.imread(‘j_noise_out.bmp', 0)

opening = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel)

closing = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel)

gradient = cv2.morphologyEx(img, cv2.MORPH_GRADIENT, kernel)

tophat = cv2.morphologyEx(img, cv2.MORPH_TOPHAT, kernel)

blackhat = cv2.morphologyEx(img, cv2.MORPH_BLACKHAT, kernel)

#imagechuli
import cv2
import time
import numpy as np

#图片名子
name = "1.jpg"
#程序计时
start = time.perf_counter()
#显示图片
img=cv2.imread("./input_image/3.jpg")
#图片缩放
img = cv2.resize(img,None,fx = 0.5,fy = 0.5)
rows,cols,channels = img.shape
#print(rows,cols,channels)
cv2.resizeWindow("origin", 0, 0);
#cv2.imshow("origin",img)
#转换为二值化图像
hsv = cv2.cvtColor(img,cv2.COLOR_BGR2HSV)
rows,cols,channels = hsv.shape
 
#图片的二值化处理
lower_blue = np.array([90,70,70])
upper_blue = np.array([110,255,255])
mask = cv2.inRange(hsv,lower_blue,upper_blue)

def shap():
 
 #图像的腐蚀
 kernel = np.ones((4, 4), np.uint8)
 erode=cv2.erode(mask,kernel,iterations=1)
 #cv2.imshow("erode",erode)
 
 #膨胀操作
 kernel = np.ones((2, 2), np.uint8)
 dilate=cv2.dilate(erode,None,iterations=3)
 #cv2.imshow("dilate",dilate)
 #循环遍历
 for i in range(rows):
  for j in range(cols):
   if dilate[i,j]==255:
    img[i,j]=(0,0,255)#注意是BGR通道，不是RGB
 #cv2.imshow("res",img)
 bianyuanchuli()
 
 #cv2.destroyAllWindows()
 
def bianyuanchuli():
 #图像边缘检测的内核大小
 data = (900,1100)
 img_copy = img.copy()
 imgCanny = cv2.Canny(img, *data)
 #cv2.imshow("imgcanny",imgCanny)
 # 创建矩形结构
 g = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
 g2 = cv2.getStructuringElement(cv2.MORPH_RECT, (7, 7))
 # 膨化处理
 # 更细腻
 img_dilate = cv2.dilate(imgCanny, g,iterations=1)
 #cv2.imshow("img_dilate",img_dilate)
 # 更粗大
 img_dilate2 = cv2.dilate(imgCanny, g2)

 shape = img_dilate.shape
 # 提取
 for i in range(shape[0]):
  for j in range(shape[1]):
   if img_dilate2[i, j] == 0: # 二维定位到三维
    img[i, j] = [0, 0, 0]
 #cv2.imshow('dst1', img)
 
 dst = cv2.GaussianBlur(img, (3, 3), 0, 0, cv2.BORDER_DEFAULT)

 for i in range(shape[0]):
  for j in range(shape[1]):
   if img_dilate[i, j] != 0: # 二维定位到三维
    img_copy[i, j] = dst[i, j]

 #cv2.imshow('dst', img_copy)
 cv2.imwrite("./out_image/3.jpg",img_copy) 
 shap()
# 窗口等待的命令，0表示无限等待
cv2.destroyAllWindows()
cv2.waitKey(0)
dur = time.perf_counter() - start
print("程序总用时:{:.2f}s".format(dur))