opencv图像灰度化-算法优化

之前的灰度图像进行处理的时候是浮点型运算，运算时间较长，现在可以对灰度图像进行算法优化，将浮点型运算转换成整型运算。提高运算速度。
方法一：

import cv2
import numpy as np

img1 = cv2.imread(r"C:\Users\lenovo\Desktop\python\python_vision\image.jpg",1)

imgInfo = img1.shape
height = imgInfo[0]
width = imgInfo[1]
#  huidutuxiang   suanfayouhua
#  fudianxing>>>dingdian
dst = np.zeros((height,width,3),np.uint8)
for i in range(0,height):
    for j in range(0,width):
        (b,g,r) = img1[i,j]
        b = int(b)
        g = int(g)
        r = int(r)
        gray = (r*1+g*2+b*1)/4
        dst[i,j] = np.uint8(gray)
cv2.imshow("dst",dst)
cv2.waitKey()

方法二：
整型运算之后，还可以左移右移的方式。

import cv2
import numpy as np

img1 = cv2.imread(r"C:\Users\lenovo\Desktop\python\python_vision\image.jpg",1)

imgInfo = img1.shape
height = imgInfo[0]
width = imgInfo[1]
#  huidutuxiang   suanfayouhua
#  fudianxing>>>dingdian
dst = np.zeros((height,width,3),np.uint8)
for i in range(0,height):
    for j in range(0,width):
        (b,g,r) = img1[i,j]
        b = int(b)
        g = int(g)
        r = int(r)

        gray = (r*1+(g<<1)+b*1)>>2
        dst[i,j] = np.uint8(gray)
cv2.imshow("dst",dst)
cv2.waitKey()