Python opencv 图像特效处理

Posted 2022-11-14

最近处理视觉相关的项目，分享一下通过opencv的图像特效处理，

原图镇楼

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1、灰度处理

将cv2.imread()方法的第二参数设为0即可得到灰色图像。

import cv2
img0 = cv2.imread(1.jpg, 0)
img1 = cv2.imread(1.jpg, 1)
print(img0.shape)
print(img1.shape)
cv2.imshow(img0, img0)
cv2.waitKey(0)

通过​​cv2.cvtColor​​方法对图像进行灰度转换

​​cv2.cvtColor(src, code, dst, dstCN):​​

-src: 目标图像
-code: 颜色转换方式
-dst: 图像大小
-dstCN: 颜色通道大小

import cv2
img = cv2.imread(1.jpg, 1)
dat = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY )
cv2.imshow(dat, dat)
cv2.waitKey(0)

灰色图像的所有颜色通道的值相等，所以要想将彩色图像变为灰色图像，只需将他们颜色通道的值相等即可。

通过设置图片的颜色均值来使彩色图像变为灰色图像

import cv2
import numpy as np
img = cv2.imread(1.jpg, 1)
dat = np.zeros((img.shape[0], img.shape[1]), np.uint8)
for i in range(0, img.shape[0]):
    for j in range(0, img.shape[1]):
        (b, g, r) = img[i, j]
        gray = (int(b) + int(g) + int(r)) / 3
        dat[i, j] = np.uint(gray)
cv2.imshow(dat, dat)
cv2.waitKey(0)

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2、颜色反转

将图像的颜色反转也就是让255减去当前的颜色值

import cv2
import numpy as np
img = cv2.imread(1.jpg, 1)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
dat = np.zeros((img.shape[0], img.shape[1]), np.uint8)
for i in range(gray.shape[0]):
    for j in range(gray.shape[1]):
        dat[i, j] = 255 - gray[i, j]
cv2.imshow(dat, dat)
cv2.waitKey(0)

彩色图像的颜色反转也是一样的道理

import cv2
import numpy as np
img = cv2.imread(1.jpg, 1)


dat = np.zeros((img.shape[0], img.shape[1], img.shape[2]), np.uint8)
for i in range(img.shape[0]):
    for j in range(img.shape[1]):
        (b, g, r) = img[i, j]
        dat[i, j] = (255 - b, 255 - g, 255 - r)
cv2.imshow(src, img)
cv2.imshow(dat, dat)
cv2.waitKey(0)

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3、马赛克

原理: 取一个指定大小的窗口，将该窗口填充为一个颜色

下面将窗口设置为10*10来生成一个马赛克图像

import cv2
img = cv2.imread(1.jpg, 1)
height = img.shape[0]
width = img.shape[1]
for m in range(100, 200):
for n in range(200, 300):
if m % 10 == 0 and n % 10 == 0:
for i in range(10):
    for j in range(10):
        (b, g, r) = img[m, n]
        img[m + i, n + j] = (b, g, r)
cv2.imshow(img, img)
cv2.waitKey(0)

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4、毛玻璃效果

原理: 将当前的像素颜色随机设置为窗口中的一个颜色

这里设置窗口为8*8来生成一个毛玻璃图像

import cv2
import random
import numpy as np
img = cv2.imread(1.jpg, 1)
height = img.shape[0]
width = img.shape[1]
dat = np.zeros(img.shape, np.uint8)
for m in range(height - 8):
    for n in range(width - 8):
        index = int(random.random() * 8)
        (b, g, r) = img[m + index, n + index]
        dat[m, n] = (b, g, r)
cv2.imshow(dat, dat)
cv2.waitKey(0)

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5、图像融合

图片乘以比例系数相加得到俩张图片的融合效果，公式如下

target = src1 * a +src2 * (1 - a)
cv2.addWeighted(src1, alpha, src2, beta, gamma):

import cv2
import numpy as np
img1 = cv2.imread(1.jpg, 1)
img2 = cv2.imread("2.jpg", 1)
img3 = cv2.resize(img2, (img1.shape[1], img1.shape[0]), interpolatinotallow=cv2.INTER_AREA)
dat = np.zeros(img1.shape, np.uint8)
dat = cv2.addWeighted(img1, 0.8, img3, 0.2, 0)


cv2.imshow(dat, dat)
cv2.waitKey(0)