preface

A picture is made up of numerous pixels, so we will fill these pixels with pictures

Knowledge points:

1. cv2

2. numpy

3. tqdm

4. argparse

Development environment:

Python 3.6

Pycharm

Ideas:

1. Input

2. Calculation and processing

3. Output

4. Display

Add Penguin Group 695185429 and you can get it for free. All the information is in the group file. Materials can be obtained, including but not limited to Python practice, PDF electronic documents, interview brochures, learning materials, etc

step

1, Import tool

import cv2
import glob
import argparse
import numpy as np
from tqdm import tqdm  # progress bar
from itertools import product  # iterator 

 

2, Picture file

def parseArgs():
    parser = argparse.ArgumentParser('Mosaic picture')
    parser.add_argument('--targetpath', type=str, default='examples/1.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 folder paths for stitching images')
    parser.add_argument('--blocksize', type=int, default=15, help='Mosaic block size')
    args = parser.parse_args()
    return args

 

3, Read all source images and calculate the corresponding color average

def readSourceImages(sourcepath,blocksize):
    print('Start reading image')
    # List of legal images
    sourceimages = []
    # Average color list
    avgcolors = []
    for path in tqdm(glob.glob("{}/*.jpg".format(sourcepath))):
        image = cv2.imread(path, cv2.IMREAD_COLOR)
        if image.shape[-1] != 3:
            continue
        image = cv2.resize(image, (blocksize, blocksize))
        avgcolor = np.sum(np.sum(image, axis=0), axis=0) / (blocksize * blocksize)
        sourceimages.append(image)
        avgcolors.append(avgcolor)
    print('End read')
    return sourceimages,np.array(avgcolors)

 

4, Call all functions

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('Production completed')

 

5, Call

if __name__ == '__main__':
    # run
    main(parseArgs())

 

The operation effect is as follows:

Comparison of final works and drawings