利用各种小元素构建图片

#pip install -r requirement.txt #git clone https://github.com/nuno-faria/tiler.git opencv-python numpy tqdm 

Github

1. 下载项目文件

2. 准备项目图片 放上准备好的图片./images文件夹下

3. 选择像素图块

这里选择line

python3 .\tiler.py .\images\333.png .\tiles\lines\gen_line_h\ 

tiler.py

import cv2 import numpy as np import os import sys from collections import defaultdict from tqdm import tqdm from multiprocessing import Pool import math import pickle import conf from time import sleep   # number of colors per image COLOR_DEPTH = conf.COLOR_DEPTH # tiles scales RESIZING_SCALES = conf.RESIZING_SCALES # number of pixels shifted to create each box (x,y) PIXEL_SHIFT = conf.PIXEL_SHIFT # multiprocessing pool size POOL_SIZE = conf.POOL_SIZE # if tiles can overlap OVERLAP_TILES = conf.OVERLAP_TILES   # reduces the number of colors in an image def color_quantization(img, n_colors):     return np.round(img / 255 * n_colors) / n_colors * 255 # returns an image given its path def read_image(path): img = cv2.imread(path, cv2.IMREAD_UNCHANGED) if img.shape[2] == 3: img = cv2.cvtColor(img, cv2.COLOR_BGR2BGRA) img = color_quantization(img.astype('float'), COLOR_DEPTH) return img.astype('uint8') # scales an image def resize_image(img, ratio): img = cv2.resize(img, (int(img.shape[1] * ratio), int(img.shape[0] * ratio))) return img # the most frequent color in an image and its relative frequency def mode_color(img, ignore_alpha=False): counter = defaultdict(int) total = 0 for y in img: for x in y: if len(x) < 4 or ignore_alpha or x[3] != 0: counter[tuple(x[:3])] += 1 else: counter[(-1,-1,-1)] += 1 total += 1 if total > 0: mode_color = max(counter, key=counter.get) if mode_color == (-1,-1,-1): return None, None else: return mode_color, counter[mode_color] / total else: return None, None # displays an image def show_image(img, wait=True): cv2.imshow('img', img) if wait: cv2.waitKey(0) else: cv2.waitKey(1) # load and process the tiles def load_tiles(paths): print('Loading tiles') tiles = defaultdict(list) for path in paths: if os.path.isdir(path): for tile_name in tqdm(os.listdir(path)): tile = read_image(os.path.join(path, tile_name)) mode, rel_freq = mode_color(tile, ignore_alpha=True) if mode is not None: for scale in RESIZING_SCALES: t = resize_image(tile, scale) res = tuple(t.shape[:2]) tiles[res].append({ 
          'tile': t, 'mode': mode, 'rel_freq': rel_freq }) with open('tiles.pickle', 'wb') as f: pickle.dump(tiles, f) # load pickle with tiles (one file only) else: with open(path, 'rb') as f: tiles = pickle.load(f) return tiles # returns the boxes (image and start pos) from an image, with 'res' resolution def image_boxes(img, res): if not PIXEL_SHIFT: shift = np.flip(res) else: shift = PIXEL_SHIFT boxes = [] for y in range(0, img.shape[0], shift[1]): for x in range(0, img.shape[1], shift[0]): boxes.append({ 
          'img': img[y:y+res[0], x:x+res[1]], 'pos': (x,y) }) return boxes # euclidean distance between two colors def color_distance(c1, c2): c1_int = [int(x) for x in c1] c2_int = [int(x) for x in c2] return math.sqrt((c1_int[0] - c2_int[0])**2 + (c1_int[1] - c2_int[1])**2 + (c1_int[2] - c2_int[2])**2) # returns the most similar tile to a box (in terms of color) def most_similar_tile(box_mode_freq, tiles): if not box_mode_freq[0]: return (0, np.zeros(shape=tiles[0]['tile'].shape)) else: min_distance = None min_tile_img = None for t in tiles: dist = (1 + color_distance(box_mode_freq[0], t['mode'])) / box_mode_freq[1] if min_distance is None or dist < min_distance: min_distance = dist min_tile_img = t['tile'] return (min_distance, min_tile_img) # builds the boxes and finds the best tile for each one def get_processed_image_boxes(image_path, tiles): print('Getting and processing boxes') img = read_image(image_path) pool = Pool(POOL_SIZE) all_boxes = [] for res, ts in tqdm(sorted(tiles.items(), reverse=True)): boxes = image_boxes(img, res) modes = pool.map(mode_color, [x['img'] for x in boxes]) most_similar_tiles = pool.starmap(most_similar_tile, zip(modes, [ts for x in range(len(modes))])) i = 0 for min_dist, tile in most_similar_tiles: boxes[i]['min_dist'] = min_dist boxes[i]['tile'] = tile i += 1 all_boxes += boxes return all_boxes, img.shape # places a tile in the image def place_tile(img, box): p1 = np.flip(box['pos']) p2 = p1 + box['img'].shape[:2] img_box = img[p1[0]:p2[0], p1[1]:p2[1]] mask = box['tile'][:, :, 3] != 0 mask = mask[:img_box.shape[0], :img_box.shape[1]] if OVERLAP_TILES or not np.any(img_box[mask]): img_box[mask] = box['tile'][:img_box.shape[0], :img_box.shape[1], :][mask] # tiles the image def create_tiled_image(boxes, res, render=False): print('Creating tiled image') img = np.zeros(shape=(res[0], res[1], 4), dtype=np.uint8) for box in tqdm(sorted(boxes, key=lambda x: x['min_dist'], reverse=OVERLAP_TILES)): place_tile(img, box) if render: show_image(img, wait=False) sleep(0.025) return img # main def main(): if len(sys.argv) > 1: image_path = sys.argv[1] else: image_path = conf.IMAGE_TO_TILE if len(sys.argv) > 2: tiles_paths = sys.argv[2:] else: tiles_paths = conf.TILES_FOLDER.split(' ') if not os.path.exists(image_path): print('Image not found') exit(-1) for path in tiles_paths: if not os.path.exists(path): print('Tiles folder not found') exit(-1) tiles = load_tiles(tiles_paths) boxes, original_res = get_processed_image_boxes(image_path, tiles) img = create_tiled_image(boxes, original_res, render=conf.RENDER) cv2.imwrite(conf.OUT, img) if __name__ == "__main__": main()