渐变图像叠加python处理

Posted 2021-10-21 良风抚旧

'''
Author: TuZhou
Version: 1.0
Date: 2021-08-09 10:46:45
LastEditTime: 2021-10-02 14:10:41
LastEditors: TuZhou
Description: 
FilePath: \\python_test\\test.py
'''
import cv2
import numpy as np
from PIL import Image

def opencv_process():
    #1:1原头像尺寸
    image_size = 960

    #需要渐变的guoqi图像，若宽比较长，可以用此处截取
    new_flag = cv2.imread('./images/flag2.png')
    flag_shape = new_flag.shape
    print('Image size', flag_shape)
    w = flag_shape[0]
    h = flag_shape[1]
    min_length = w if w < h else h
    new_flag = new_flag[0:min_length, 0:min_length]
    # print('New flag', new_flag.shape)
    # cv2.imshow('final', new_flag)
    # cv2.waitKey(0)

    #若是1:1guoqi渐变图像，用此处
    #img1 = cv2.resize(cv2.imread('./images/flag1.png'), (image_size, image_size)) #guoqi图片
    img2 = cv2.resize(cv2.imread('./images/1.jpg'), (image_size, image_size)) #微信头像
    img1 = cv2.resize(new_flag, (image_size, image_size))

    middle = int(image_size*0.8) #渐变截至位置(0-image_size)

    #np.linspace用于切分，如在1和0之间切割middle份，0-1之间与原图相乘使图像慢慢变淡，guoqi图像从左到右慢慢变淡
    #Numpy的tile()函数，就是将原矩阵横向、纵向地复制,参数2为横向复制多少倍，纵向复制多少倍，1表示不复制
    #np.newaxis相当于None,这里相当于扩维
    #np.repeat在指定维度复制3倍，这里是第2维度变为3，相当于RGB
    mask1 = np.repeat(np.tile(np.linspace(1, 0, middle), (img1.shape[1], 1))[:, :, np.newaxis], 3, axis=2)
    mask2 = np.repeat(np.tile(np.linspace(0, 1, middle), (img1.shape[1], 1))[:, :, np.newaxis], 3, axis=2)

    ones = np.ones((image_size, image_size-middle,3))
    zero = np.zeros((image_size, image_size-middle,3))

    #与全0矩阵拼接表示渐变图像右边部分消失
    mask1 = np.concatenate((mask1,zero), axis=1)
    mask2 = np.concatenate((mask2,ones), axis=1)

    #final = img1 + img2
    final = np.uint8(img1 * mask1 + img2 * mask2)
    #final = cv2.resize(final, (128,128))

    cv2.imwrite('final.png',final)
    cv2.imshow('final', final)
    cv2.waitKey(0)
    
    
if __name__ == '__main__':
    opencv_process()