Introduction
A photomosaic is an image split into a grid of rectangles, with each replaced by another image that matches the target (the image you ultimately want to appear in the photomosaic). In other words, if you look at a photomosaic from a distance, you see the target image; but if you come closer, you will see that the image actually consists of many smaller images. This works because of how the human eye works.
There are two kinds of mosaic, depending on how the matching is done. In simpler kind, each part of the target image is averaged down to a single color. Each of the library images is also reduced to a single color. Each part of the target image is then replaced with one from the library where these colors are as similar as possible. In effect, the target image is reduced in resolution (by downsampling), and then each of the resulting pixels is replaced with an image whose average color matches that pixel.
In the more advanced kind of photographic mosaic, the target image is not downsampled, and the matching is done by comparing each pixel in the rectangle to the corresponding pixel from each library image. The rectangle in the target is then replaced with the library image that minimizes the total difference. This requires much more computation than the simple kind, but the results can be much better since the pixel-by-pixel matching can preserve the resolution of the target image.
How to create Photomosaics?
- Read the tile images, which will replace the tiles in the original image.
- Read the target image and split it into an M×N grid of tiles.
- For each tile, find the best match from the input images.
- Create the final mosaic by arranging the selected input images in an M×N grid.
Splitting the images into tiles
Now let’s look at how to calculate the coordinates for a single tile from this grid. The tile with index (i, j) has a top-left corner coordinate of (i*w, i*j) and a bottom-right corner coordinate of ((i+1)*w, (j+1)*h), where w and h stand for the width and height of a tile, respectively. These can be used with the PIL to crop and create a tile from this image.
Averaging Color Values
Every pixel in an image has a color that can be represented by its red, green, and blue values. In this case, you are using 8-bit images, so each of these components has an 8-bit value in the range [0, 255]. Given an image with a total of N pixels, the average RGB is calculated as follows:
Matching Images
For each tile in the target image, you need to find a matching image from the images in the input folder specified by the user. To determine whether two images match, use the average RGB values. The closest match is the image with the closest average RGB value.
The simplest way to do this is to calculate the distance between the RGB values in a pixel to find the best match among the input images. You can use the following distance calculation for 3D points from geometry:
Now lets try to code this out
Python3
#Importing the required librariesimport os, random, argparsefrom PIL import Imageimport imghdrimport numpy as npdef getAverageRGBOld(image): """ Given PIL Image, return average value of color as (r, g, b) """ # no. of pixels in image npixels = image.size[0]*image.size[1] # get colors as [(cnt1, (r1, g1, b1)), ...] cols = image.getcolors(npixels) # get [(c1*r1, c1*g1, c1*g2),...] sumRGB = [(x[0]*x[1][0], x[0]*x[1][1], x[0]*x[1][2]) 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 avgdef 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[0], image.size[1] 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 imgsdef 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 imagesdef 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 filenamesdef 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[0] - avg[0])*(val[0] - avg[0]) + (val[1] - avg[1])*(val[1] - avg[1]) + (val[2] - avg[2])*(val[2] - avg[2])) if dist < min_dist: min_dist = dist min_index = index index += 1 return min_indexdef 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[0] for img in images]) height = max([img.size[1] 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_imgdef 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() functiondef main(): # Command line args are in sys.argv[1], sys.argv[2] .. # sys.argv[0] 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[0]), int(args.grid_size[1])) # 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[0]*grid_size[1] > 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[0]/grid_size[1]), int(target_image.size[1]/grid_size[0])) 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() |
python test.py --target-image test-data/a.jpg --input-folder test-data/set1/ --grid-size 128 128
Output:
Reference Links:
1) Python Playground by Mahesh Venkitachalam.
2) PILLOW docs
3) Wikipedia – Photomosaics
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