Hey friends, Welcome to CodeWithNepal today in this blog you’ll learn how to make a photomosaic generator using python. There are a few different ones out there, but we are going to make our own Photomosaic Generator from scratch. In the earlier blog, I shared OpenCV C++ Program for Face Detection which I recommend you to watch.
What is Photo mosaic ?
A photomosaic is an image made up of many smaller images. The small images are called tiles, and when put together, they create a larger image.
Photomosaics can be made from any type of image, but are often made from photographs. The tiles can be any size but are usually small.
To create a photomosaic, the first step is to choose the tiles. The tiles can be chosen randomly, or they can be chosen to create a specific effect. For example, the tiles can be chosen to create a gradient effect.
Once the tiles are chosen, they are placed on a grid. The grid can be any size but is typically square or rectangular.
The next step is to determine the position of each tile. The position of each tile is determined by its colour. The tiles are placed so that the colours of adjacent tiles are similar.
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A photomosaic is a type of image composed of many smaller images, each of which is typically a photograph. The smaller images are arranged in a grid, and the overall image appears as a single image when viewed from a distance.
There are a number of photomosaic generators available online, which allow users to create their own photomosaics from a selection of images. These generators typically provide a number of options for customizing the photo mosaic, such as the size of the grid, the spacing between images, and the type of background image.
Creating a photomosaic can be a fun and creative way to turn a collection of images into a single work of art. And with the help of a photomosaic generator, anyone can create their own photo mosaic.
Source code of photo mosaic
#Importing the required libraries import os, random, argparse from PIL import Image import imghdr import numpy as np def getAverageRGBOld(image): """ Given PIL Image, return average value of color as (r, g, b) """ # no. of pixels in image npixels = image.size*image.size # get colors as [(cnt1, (r1, g1, b1)), ...] cols = image.getcolors(npixels) # get [(c1*r1, c1*g1, c1*g2),...] sumRGB = [(x*x, x*x, x*x) for x in cols] # calculate (sum(ci*ri)/np, sum(ci*gi)/np, sum(ci*bi)/np) # the zip gives us [(c1*r1, c2*r2, ..), (c1*g1, c1*g2,...)...] avg = tuple([int(sum(x)/npixels) for x in zip(*sumRGB)]) return avg def getAverageRGB(image): """ Given PIL Image, return average value of color as (r, g, b) """ # get image as numpy array im = np.array(image) # get shape w,h,d = im.shape # get average return tuple(np.average(im.reshape(w*h, d), axis=0)) def splitImage(image, size): """ Given Image and dims (rows, cols) returns an m*n list of Images """ W, H = image.size, image.size m, n = size w, h = int(W/n), int(H/m) # image list imgs =  # generate list of dimensions for j in range(m): for i in range(n): # append cropped image imgs.append(image.crop((i*w, j*h, (i+1)*w, (j+1)*h))) return imgs def getImages(imageDir): """ given a directory of images, return a list of Images """ files = os.listdir(imageDir) images =  for file in files: filePath = os.path.abspath(os.path.join(imageDir, file)) try: # explicit load so we don't run into resource crunch fp = open(filePath, "rb") im = Image.open(fp) images.append(im) # force loading image data from file im.load() # close the file fp.close() except: # skip print("Invalid image: %s" % (filePath,)) return images def getImageFilenames(imageDir): """ given a directory of images, return a list of Image file names """ files = os.listdir(imageDir) filenames =  for file in files: filePath = os.path.abspath(os.path.join(imageDir, file)) try: imgType = imghdr.what(filePath) if imgType: filenames.append(filePath) except: # skip print("Invalid image: %s" % (filePath,)) return filenames def getBestMatchIndex(input_avg, avgs): """ return index of best Image match based on RGB value distance """ # input image average avg = input_avg # get the closest RGB value to input, based on x/y/z distance index = 0 min_index = 0 min_dist = float("inf") for val in avgs: dist = ((val - avg)*(val - avg) + (val - avg)*(val - avg) + (val - avg)*(val - avg)) if dist < min_dist: min_dist = dist min_index = index index += 1 return min_index def createImageGrid(images, dims): """ Given a list of images and a grid size (m, n), create a grid of images. """ m, n = dims # sanity check assert m*n == len(images) # get max height and width of images # ie, not assuming they are all equal width = max([img.size for img in images]) height = max([img.size for img in images]) # create output image grid_img = Image.new('RGB', (n*width, m*height)) # paste images for index in range(len(images)): row = int(index/n) col = index - n*row grid_img.paste(images[index], (col*width, row*height)) return grid_img def createPhotomosaic(target_image, input_images, grid_size, reuse_images=True): """ Creates photomosaic given target and input images. """ print('splitting input image...') # split target image target_images = splitImage(target_image, grid_size) print('finding image matches...') # for each target image, pick one from input output_images =  # for user feedback count = 0 batch_size = int(len(target_images)/10) # calculate input image averages avgs =  for img in input_images: avgs.append(getAverageRGB(img)) for img in target_images: # target sub-image average avg = getAverageRGB(img) # find match index match_index = getBestMatchIndex(avg, avgs) output_images.append(input_images[match_index]) # user feedback if count > 0 and batch_size > 10 and count % batch_size is 0: print('processed %d of %d...' %(count, len(target_images))) count += 1 # remove selected image from input if flag set if not reuse_images: input_images.remove(match) print('creating mosaic...') # draw mosaic to image mosaic_image = createImageGrid(output_images, grid_size) # return mosaic return mosaic_image # Gather our code in a main() function def main(): # Command line args are in sys.argv, sys.argv .. # sys.argv is the script name itself and can be ignored # parse arguments parser = argparse.ArgumentParser (description='Creates a photomosaic from input images') # add arguments parser.add_argument('--target-image', dest='target_image', required=True) parser.add_argument('--input-folder', dest='input_folder', required=True) parser.add_argument('--grid-size', nargs=2, dest='grid_size', required=True) parser.add_argument('--output-file', dest='outfile', required=False) args = parser.parse_args() ###### INPUTS ###### # target image target_image = Image.open(args.target_image) # input images print('reading input folder...') input_images = getImages(args.input_folder) # check if any valid input images found if input_images == : print('No input images found in %s. Exiting.' % (args.input_folder, )) exit() # shuffle list - to get a more varied output? random.shuffle(input_images) # size of grid grid_size = (int(args.grid_size), int(args.grid_size)) # output output_filename = 'mosaic.png' if args.outfile: output_filename = args.outfile # re-use any image in input reuse_images = True # resize the input to fit original image size? resize_input = True ##### END INPUTS ##### print('starting photomosaic creation...') # if images can't be reused, ensure m*n <= num_of_images if not reuse_images: if grid_size*grid_size > len(input_images): print('grid size less than number of images') exit() # resizing input if resize_input: print('resizing images...') # for given grid size, compute max dims w,h of tiles dims = (int(target_image.size/grid_size), int(target_image.size/grid_size)) print("max tile dims: %s" % (dims,)) # resize for img in input_images: img.thumbnail(dims) # create photomosaic mosaic_image = createPhotomosaic(target_image, input_images, grid_size, reuse_images) # write out mosaic mosaic_image.save(output_filename, 'PNG') print("saved output to %s" % (output_filename,)) print('done.') # Standard boilerplate to call the main() function to begin # the program. if __name__ == '__main__': main()