本篇本章介紹4種方法來進行圖片的灰度處理。

方法一，利用OpenCV種的imread

import cv2
img0 = cv2.imread('2.jpg',0)
img1 = cv2.imread('2.jpg',1)
print(img0.shape)  #沒有維度
print(img1.shape)
cv2.imshow('src',img0)
cv2.waitKey(0)
打印結果
(448, 400)
(448, 400, 3)

這里可以發(fā)現(xiàn)，灰度處理的圖片沒有維度

方法二，利用OpenCV種的cvtColor 顏色空間轉換

import cv2
img = cv2.imread('2.jpg',1)
#顏色空間轉換  1 數(shù)據(jù)  2 BGR 
dst = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) 
cv2.imshow('dst',dst)
cv2.waitKey(0)

方法3 用RGB 均值來做灰度

import cv2
import numpy as np
img = cv2.imread('2.jpg',1)
imgInfo = img.shape
height = imgInfo[0]
width = imgInfo[1]
# RGB R=G=B = gray （R+G+B）/3  求RGB的平均值
dst = np.zeros((height,width,3),np.uint8)
for i in range(0,height):
    for j in range(0,width):
        (b,g,r) = img[i,j]
        gray = (int(b)+int(g)+int(r))/3
        dst[i,j] = np.uint8(gray)
cv2.imshow('dst',dst)
cv2.waitKey(0)

方法4 gray = r0.299+g0.587+b*0.114 利用這個公式來做

import cv2
import numpy as np
img = cv2.imread('2.jpg',1)
imgInfo = img.shape
height = imgInfo[0]
width = imgInfo[1]
# RGB R=G=B = gray （R+G+B）/3
dst = np.zeros((height,width,3),np.uint8)
for i in range(0,height):
    for j in range(0,width):
        (b,g,r) = img[i,j]
        b = int(b)
        g = int(g)
        r = int(r)
        gray =  r*0.299+g*0.587+b*0.114
        dst[i,j] = np.uint8(gray)
cv2.imshow('dst',dst)
cv2.waitKey(0)

最終效果圖

效果圖.png