Python+OpenCV实现六种常用图像特效

import cv2
import cv2 as cv

img = cv.imread("img/lena.jpg")
tony = cv.imread("img/tony.jpg", )

# 修改lena图片的宽高  融合图像之前两个图片的宽高要保持一样
height, width = img.shape[0:2]
new_height = int(height * 1.5)
new_width = int(width * 2)
new_img = cv2.resize(img, (new_width, new_height))

# 进行叠加时的插值
dst = cv.addWeighted(new_img, 0.5, tony, 0.5, 0)
cv.imshow("dst", dst)

cv.waitKey(0)
cv.destroyAllWindows()

import cv2
img = cv2.imread("img/lena.jpg", cv.IMREAD_GRAYSCALE)

import cv2

img = cv2.imread("img/lena.jpg", cv.IMREAD_COLOR)
# 将原图的所有颜色转成灰色
dstImg = cv2.cvtColor(img, cv.COLOR_BGR2GRAY)
cv.imshow("dstImg", dstImg)
cv.waitKey(0)

import cv2 as cv

img = cv.imread("img/lena.jpg", cv.IMREAD_COLOR)
# 将原图的所有颜色转成灰色
dstImg = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
# 获取高度和宽度
height, width = dstImg.shape[0:2]
# 遍历每一个像素点
for row in range(height):
    for col in range(width):
        # 255 - 每一个像素点 = 反转后的颜色
        dstImg[row, col] = 255 - dstImg[row, col]

cv.imshow("dstImg", dstImg)
cv.waitKey(0)

import cv2 as cv

img = cv.imread("img/lena.jpg", cv.IMREAD_COLOR)
# 将原图的所有颜色转成灰色
dstImg = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
# 获取高度和宽度
height, width = dstImg.shape[0:2]
# 遍历每一个像素点
for row in range(height):
    for col in range(width):
        # 255 - 每一个像素点 = 反转后的颜色
        dstImg[row, col] = 255 - dstImg[row, col]

cv.imshow("dstImg", dstImg)
cv.waitKey(0)

import cv2

# 读取图片  cv2读取出的图片都是一个二维矩阵
img = cv2.imread('./img/lena.jpg', cv2.IMREAD_COLOR)
# 切片 两个点的坐标可以截取图片
# x1:x2,y1:y2  截取眼睛部分
img1 = img[180:250, 180:310]
# 获取到高度和宽度
height, width = img1.shape[0:2]
# 遍历每一个像素点
for row in range(height):
    for col in range(width):
        # 如果正好为10的倍数的行并且是10的倍数的列
        if row % 10 == 0 and col % 10 == 0:
            # 获取到这个像素点的bgr三原色
            b, g, r = img1[row, col]
            # 遍历这个像素点旁边的100个像素点 都等于中间这个像素点
            for i in range(10):
                for j in range(10):
                    img1[row + i, col + j] = b, g, r

cv2.imshow('img', img)

cv2.imwrite('msk_lena.jpg', img)
cv2.waitKey()

import random

import cv2
import numpy as np

img = cv2.imread('./lena.jpg')
height, width = img.shape[0:2]
new_img = np.zeros_like(img, np.uint8)
# 定义偏移量
offset = 6
# 遍历每一个像素点
for row in range(height):
    for col in range(width):
        # 定义不超过1的随机值与offset相乘
        index = int(random.random() * offset)
        # 获取到随机完的行号和列号   如果不超过总高度就使用随机的行  如果超过就使用高度-1
        random_row = row + index if row + index < height else height - 1
        random_col = col + index if col + index < width else width - 1
        # 赋值颜色
        b, g, r = img[random_row, random_col]
        new_img[row, col] = b, g, r

cv2.imshow('img', img)
cv2.imshow('new_img', new_img)

cv2.waitKey()

import cv2
import numpy as np

img = cv2.imread('./lena.jpg')
# 获取高度宽度
height, width = img.shape[0:2]
# 转为灰度图
gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

new_img = np.zeros_like(gray_img, np.uint8)
# 遍历每一个像素点
for row in range(height):
    # 因为要获取相邻的像素点 防止下标越界提前遍历的时候宽度-1
    for col in range(width - 1):
        # 获取像素点的像素值
        gray0 = gray_img[row, col]
        # 获取相邻像素点的像素值
        gray1 = gray_img[row, col + 1]
        # 使用浮雕效果的公式
        new_gray = int(gray0) - int(gray1) + 120
        # 判断新的灰度值是否越界
        if new_gray > 255:
            new_gray = 255
        elif new_gray < 0:
            new_gray = 0
        # 赋值
        new_img[row, col] = new_gray

cv2.imshow('img', img)
cv2.imshow('new_img', new_img)

cv2.waitKey()