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| """ | |
| brief: face alignment with FFHQ method (https://github.com/NVlabs/ffhq-dataset) | |
| author: lzhbrian (https://lzhbrian.me) | |
| date: 2020.1.5 | |
| note: code is heavily borrowed from | |
| https://github.com/NVlabs/ffhq-dataset | |
| http://dlib.net/face_landmark_detection.py.html | |
| requirements: | |
| apt install cmake | |
| conda install Pillow numpy scipy | |
| pip install dlib | |
| # download face landmark model from: | |
| # http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2 | |
| """ | |
| from argparse import ArgumentParser | |
| import time | |
| import numpy as np | |
| import PIL | |
| import PIL.Image | |
| import os | |
| import scipy | |
| import scipy.ndimage | |
| import dlib | |
| import multiprocessing as mp | |
| import math | |
| SHAPE_PREDICTOR_PATH = "shape_predictor_68_face_landmarks.dat" | |
| def get_landmark(filepath, predictor, i=None): | |
| """get landmark with dlib | |
| :return: np.array shape=(68, 2) | |
| """ | |
| detector = dlib.get_frontal_face_detector() | |
| img = dlib.load_rgb_image(filepath) | |
| dets = detector(img, 1) | |
| #for k, d in enumerate(dets): | |
| if i is None: | |
| i = len(dets) - 1 | |
| try: | |
| shape = predictor(img, dets[i]) | |
| except IndexError: | |
| print("Face not found") | |
| return | |
| t = list(shape.parts()) | |
| a = [] | |
| for tt in t: | |
| a.append([tt.x, tt.y]) | |
| lm = np.array(a) | |
| return lm | |
| def align_face(filepath, predictor, idx=None): | |
| """ | |
| :param filepath: str | |
| :return: PIL Image | |
| """ | |
| lm = get_landmark(filepath, predictor, i=idx) | |
| lm_chin = lm[0:17] # left-right | |
| lm_eyebrow_left = lm[17:22] # left-right | |
| lm_eyebrow_right = lm[22:27] # left-right | |
| lm_nose = lm[27:31] # top-down | |
| lm_nostrils = lm[31:36] # top-down | |
| lm_eye_left = lm[36:42] # left-clockwise | |
| lm_eye_right = lm[42:48] # left-clockwise | |
| lm_mouth_outer = lm[48:60] # left-clockwise | |
| lm_mouth_inner = lm[60:68] # left-clockwise | |
| # Calculate auxiliary vectors. | |
| eye_left = np.mean(lm_eye_left, axis=0) | |
| eye_right = np.mean(lm_eye_right, axis=0) | |
| eye_avg = (eye_left + eye_right) * 0.5 | |
| eye_to_eye = eye_right - eye_left | |
| mouth_left = lm_mouth_outer[0] | |
| mouth_right = lm_mouth_outer[6] | |
| mouth_avg = (mouth_left + mouth_right) * 0.5 | |
| eye_to_mouth = mouth_avg - eye_avg | |
| # Choose oriented crop rectangle. | |
| x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1] | |
| x /= np.hypot(*x) | |
| x *= max(np.hypot(*eye_to_eye) * 2.0, np.hypot(*eye_to_mouth) * 1.8) | |
| y = np.flipud(x) * [-1, 1] | |
| c = eye_avg + eye_to_mouth * 0.1 | |
| quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y]) | |
| qsize = np.hypot(*x) * 2 | |
| # read image | |
| img = PIL.Image.open(filepath) | |
| output_size = 256 | |
| transform_size = 256 | |
| enable_padding = True | |
| # Shrink. | |
| shrink = int(np.floor(qsize / output_size * 0.5)) | |
| if shrink > 1: | |
| rsize = ( | |
| int(np.rint(float(img.size[0]) / shrink)), | |
| int(np.rint(float(img.size[1]) / shrink)), | |
| ) | |
| img = img.resize(rsize, PIL.Image.ANTIALIAS) | |
| quad /= shrink | |
| qsize /= shrink | |
| # Crop. | |
| border = max(int(np.rint(qsize * 0.1)), 3) | |
| crop = ( | |
| int(np.floor(min(quad[:, 0]))), | |
| int(np.floor(min(quad[:, 1]))), | |
| int(np.ceil(max(quad[:, 0]))), | |
| int(np.ceil(max(quad[:, 1]))), | |
| ) | |
| crop = ( | |
| max(crop[0] - border, 0), | |
| max(crop[1] - border, 0), | |
| min(crop[2] + border, img.size[0]), | |
| min(crop[3] + border, img.size[1]), | |
| ) | |
| if crop[2] - crop[0] < img.size[0] or crop[3] - crop[1] < img.size[1]: | |
| img = img.crop(crop) | |
| quad -= crop[0:2] | |
| # Pad. | |
| pad = ( | |
| int(np.floor(min(quad[:, 0]))), | |
| int(np.floor(min(quad[:, 1]))), | |
| int(np.ceil(max(quad[:, 0]))), | |
| int(np.ceil(max(quad[:, 1]))), | |
| ) | |
| pad = ( | |
| max(-pad[0] + border, 0), | |
| max(-pad[1] + border, 0), | |
| max(pad[2] - img.size[0] + border, 0), | |
| max(pad[3] - img.size[1] + border, 0), | |
| ) | |
| if enable_padding and max(pad) > border - 4: | |
| pad = np.maximum(pad, int(np.rint(qsize * 0.3))) | |
| img = np.pad( | |
| np.float32(img), ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), "reflect" | |
| ) | |
| h, w, _ = img.shape | |
| y, x, _ = np.ogrid[:h, :w, :1] | |
| mask = np.maximum( | |
| 1.0 - np.minimum(np.float32(x) / pad[0], np.float32(w - 1 - x) / pad[2]), | |
| 1.0 - np.minimum(np.float32(y) / pad[1], np.float32(h - 1 - y) / pad[3]), | |
| ) | |
| blur = qsize * 0.02 | |
| img += (scipy.ndimage.gaussian_filter(img, [blur, blur, 0]) - img) * np.clip( | |
| mask * 3.0 + 1.0, 0.0, 1.0 | |
| ) | |
| img += (np.median(img, axis=(0, 1)) - img) * np.clip(mask, 0.0, 1.0) | |
| img = PIL.Image.fromarray(np.uint8(np.clip(np.rint(img), 0, 255)), "RGB") | |
| quad += pad[:2] | |
| # Transform. | |
| img = img.transform( | |
| (transform_size, transform_size), | |
| PIL.Image.QUAD, | |
| (quad + 0.5).flatten(), | |
| PIL.Image.BILINEAR, | |
| ) | |
| if output_size < transform_size: | |
| img = img.resize((output_size, output_size), PIL.Image.ANTIALIAS) | |
| # Save aligned image. | |
| return img | |
| def chunks(lst, n): | |
| """Yield successive n-sized chunks from lst.""" | |
| for i in range(0, len(lst), n): | |
| yield lst[i : i + n] | |
| def extract_on_paths(file_paths): | |
| predictor = dlib.shape_predictor(SHAPE_PREDICTOR_PATH) | |
| pid = mp.current_process().name | |
| print(f"\t{pid} is starting to extract on #{len(file_paths)} images") | |
| tot_count = len(file_paths) | |
| count = 0 | |
| for file_path, res_path in file_paths: | |
| count += 1 | |
| if count % 100 == 0: | |
| print(f"{pid} done with {count}/{tot_count}") | |
| try: | |
| res = align_face(file_path, predictor) | |
| res = res.convert("RGB") | |
| os.makedirs(os.path.dirname(res_path), exist_ok=True) | |
| res.save(res_path) | |
| except Exception: | |
| continue | |
| print("\tDone!") | |
| def parse_args(): | |
| parser = ArgumentParser(add_help=False) | |
| parser.add_argument("--num_threads", type=int, default=1) | |
| parser.add_argument("--root_path", type=str, default="") | |
| args = parser.parse_args() | |
| return args | |
| def run(args): | |
| root_path = args.root_path | |
| out_crops_path = root_path + "_crops" | |
| if not os.path.exists(out_crops_path): | |
| os.makedirs(out_crops_path, exist_ok=True) | |
| file_paths = [] | |
| for root, dirs, files in os.walk(root_path): | |
| for file in files: | |
| file_path = os.path.join(root, file) | |
| fname = os.path.join(out_crops_path, os.path.relpath(file_path, root_path)) | |
| res_path = f"{os.path.splitext(fname)[0]}.jpg" | |
| if os.path.splitext(file_path)[1] == ".txt" or os.path.exists(res_path): | |
| continue | |
| file_paths.append((file_path, res_path)) | |
| file_chunks = list( | |
| chunks(file_paths, int(math.ceil(len(file_paths) / args.num_threads))) | |
| ) | |
| print(len(file_chunks)) | |
| pool = mp.Pool(args.num_threads) | |
| print(f"Running on {len(file_paths)} paths\nHere we goooo") | |
| tic = time.time() | |
| pool.map(extract_on_paths, file_chunks) | |
| toc = time.time() | |
| print(f"Mischief managed in {str(toc - tic)}s") | |
| if __name__ == "__main__": | |
| args = parse_args() | |
| run(args) | |