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This article will explain in detail how Python can synthesize multiple images into a mosaic image. Xiaobian thinks it is quite practical, so share it with you as a reference. I hope you can gain something after reading this article.

picture material

4K HD original

development environment

Python 3.6

Pycharm

import cv2import globimport argparseimport numpy as npfrom tqdm import tqdm #progress bar from itertools import product #iterator reads image file def parsArgs(): parser = argparse.ArgumentParser ('mosaic picture') parser.add_argument ('--targetpath', type=str, default ='examples/3.jpg ', help ='target image path') parser.add_argument ('--outputpath', type=str, default ='output.jpg ', help ='output image path') parser.add_argument ('--sourcepath', type=str, default ='sourceimages ', help ='all source image file paths used to stitch images') parser.add_argument ('--blocksize', type=int, default=15, help ='mosaic block size') args = parser.parse_args() return args Read all source images and calculate the color average def readSourceImages(sourcepath,blocksize): print ('Start reading image') List of legal images

Set up a list of color images that meet the requirements

sourceimages = [] averagecolorlistavgcolors = [] traversal

Every time you traverse, the progress bar goes once

for path in tqdm(glob.glob("{}/*.jpg".format(sourcepath))): image = cv2.imread(path, cv2.IMREAD_COLOR) if image.shape[-1] != 3: continue #Scale size image = cv2.resize(image, (blocksize, blocksize)) #Image Color Average avgcolor = np.sum(np.sum(image, axis=0), axis=0) / (blocksize * blocksize) sourceimages.append(image) avgcolors.append(avgcolor)print ('finish reading') return sourceimages,np.array(avgcolors) main function def main(args): targetimage = cv2.imread(args.targetpath) outputimage = np.zeros(targetimage.shape,np.uint8) # int8 int16 int32 int64 sourceimages,avgcolors = readSourceImages(args.sourcepath,args.blocksize) print ('Start Production') for i, j in tqdm(product(range(int(targetimage.shape[1]/args.blocksize)), range(int(targetimage.shape[0]/args.blocksize)))): block = targetimage[j * args.blocksize: (j + 1) * args.blocksize, i * args.blocksize: (i + 1) * args.blocksize,:] avgcolor = np.sum(np.sum(block, axis=0), axis=0) / (args.blocksize * args.blocksize) distances = np.linalg.norm(avgcolor - avgcolors, axis=1) idx = np.argmin(distances) outputimage[j * args.blocksize: (j + 1) * args.blocksize, i * args.blocksize: (i + 1) * args.blocksize, :] = \ sourceimages[idx] cv2.imwrite(args.outputpath, outputimage) cv2.imshow('result', outputimage) print ('finished ') module call execution if __name__= '__main__': # run main(parseArgs()) Full effect

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