利用Python处理图片，四种方案你会几种？(python做图,关键词优化)

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本篇本章介绍4种利用Python进行图片的灰度处理。

方法一，利用OpenCV种的imread

import cv2

img0 = cv2.imread('2.jpg',0)

img1 = cv2.imread('2.jpg',1)

print(img0.shape) #没有维度

print(img1.shape)

cv2.imshow('src',img0)

cv2.waitKey(0)

打印结果

(448, 400)(448, 400, 3)

这里可以发现，灰度处理的图片没有维度

方法二，利用OpenCV种的cvtColor 颜色空间转换

import cv2

img = cv2.imread('2.jpg',1)

#颜色空间转换 1 数据 2 BGR

dst = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

cv2.imshow('dst',dst)

cv2.waitKey(0)

方法3 用RGB 均值来做灰度

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 = r*0.299+g*0.587+b*0.114 利用这个公式来做

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)

最终效果图：