stack86/CodeFormer

1"""2brief: face alignment with FFHQ method (https://github.com/NVlabs/ffhq-dataset)3author: lzhbrian (https://lzhbrian.me)4link: https://gist.github.com/lzhbrian/bde87ab23b499dd02ba4f588258f57d55date: 2020.1.56note: code is heavily borrowed from7    https://github.com/NVlabs/ffhq-dataset8    http://dlib.net/face_landmark_detection.py.html9requirements:10    conda install Pillow numpy scipy11    conda install -c conda-forge dlib12    # download face landmark model from:13    # http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz214"""15 16import cv217import dlib18import glob19import numpy as np20import os21import PIL22import PIL.Image23import scipy24import scipy.ndimage25import sys26import argparse27 28# download model from: http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz229predictor = dlib.shape_predictor('weights/dlib/shape_predictor_68_face_landmarks-fbdc2cb8.dat')30 31 32def get_landmark(filepath, only_keep_largest=True):33    """get landmark with dlib34    :return: np.array shape=(68, 2)35    """36    detector = dlib.get_frontal_face_detector()37 38    img = dlib.load_rgb_image(filepath)39    dets = detector(img, 1)40 41    # Shangchen modified42    print("Number of faces detected: {}".format(len(dets)))43    if only_keep_largest:44        print('Detect several faces and only keep the largest.')45        face_areas = []46        for k, d in enumerate(dets):47            face_area = (d.right() - d.left()) * (d.bottom() - d.top())48            face_areas.append(face_area)49 50        largest_idx = face_areas.index(max(face_areas))51        d = dets[largest_idx]52        shape = predictor(img, d)53        print("Part 0: {}, Part 1: {} ...".format(54            shape.part(0), shape.part(1)))55    else:56        for k, d in enumerate(dets):57            print("Detection {}: Left: {} Top: {} Right: {} Bottom: {}".format(58                k, d.left(), d.top(), d.right(), d.bottom()))59            # Get the landmarks/parts for the face in box d.60            shape = predictor(img, d)61            print("Part 0: {}, Part 1: {} ...".format(62                shape.part(0), shape.part(1)))63 64    t = list(shape.parts())65    a = []66    for tt in t:67        a.append([tt.x, tt.y])68    lm = np.array(a)69    # lm is a shape=(68,2) np.array70    return lm71 72def align_face(filepath, out_path):73    """74    :param filepath: str75    :return: PIL Image76    """77    try:78        lm = get_landmark(filepath)79    except:80        print('No landmark ...')81        return82 83    lm_chin = lm[0:17]  # left-right84    lm_eyebrow_left = lm[17:22]  # left-right85    lm_eyebrow_right = lm[22:27]  # left-right86    lm_nose = lm[27:31]  # top-down87    lm_nostrils = lm[31:36]  # top-down88    lm_eye_left = lm[36:42]  # left-clockwise89    lm_eye_right = lm[42:48]  # left-clockwise90    lm_mouth_outer = lm[48:60]  # left-clockwise91    lm_mouth_inner = lm[60:68]  # left-clockwise92 93    # Calculate auxiliary vectors.94    eye_left = np.mean(lm_eye_left, axis=0)95    eye_right = np.mean(lm_eye_right, axis=0)96    eye_avg = (eye_left + eye_right) * 0.597    eye_to_eye = eye_right - eye_left98    mouth_left = lm_mouth_outer[0]99    mouth_right = lm_mouth_outer[6]100    mouth_avg = (mouth_left + mouth_right) * 0.5101    eye_to_mouth = mouth_avg - eye_avg102 103    # Choose oriented crop rectangle.104    x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1]105    x /= np.hypot(*x)106    x *= max(np.hypot(*eye_to_eye) * 2.0, np.hypot(*eye_to_mouth) * 1.8)107    y = np.flipud(x) * [-1, 1]108    c = eye_avg + eye_to_mouth * 0.1109    quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y])110    qsize = np.hypot(*x) * 2111 112    # read image113    img = PIL.Image.open(filepath)114 115    output_size = 512116    transform_size = 4096117    enable_padding = False118 119    # Shrink.120    shrink = int(np.floor(qsize / output_size * 0.5))121    if shrink > 1:122        rsize = (int(np.rint(float(img.size[0]) / shrink)),123                 int(np.rint(float(img.size[1]) / shrink)))124        img = img.resize(rsize, PIL.Image.ANTIALIAS)125        quad /= shrink126        qsize /= shrink127 128    # Crop.129    border = max(int(np.rint(qsize * 0.1)), 3)130    crop = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))),131            int(np.ceil(max(quad[:, 0]))), int(np.ceil(max(quad[:, 1]))))132    crop = (max(crop[0] - border, 0), max(crop[1] - border, 0),133            min(crop[2] + border,134                img.size[0]), min(crop[3] + border, img.size[1]))135    if crop[2] - crop[0] < img.size[0] or crop[3] - crop[1] < img.size[1]:136        img = img.crop(crop)137        quad -= crop[0:2]138 139    # Pad.140    pad = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))),141           int(np.ceil(max(quad[:, 0]))), int(np.ceil(max(quad[:, 1]))))142    pad = (max(-pad[0] + border,143               0), max(-pad[1] + border,144                       0), max(pad[2] - img.size[0] + border,145                               0), max(pad[3] - img.size[1] + border, 0))146    if enable_padding and max(pad) > border - 4:147        pad = np.maximum(pad, int(np.rint(qsize * 0.3)))148        img = np.pad(149            np.float32(img), ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)),150            'reflect')151        h, w, _ = img.shape152        y, x, _ = np.ogrid[:h, :w, :1]153        mask = np.maximum(154            1.0 -155            np.minimum(np.float32(x) / pad[0],156                       np.float32(w - 1 - x) / pad[2]), 1.0 -157            np.minimum(np.float32(y) / pad[1],158                       np.float32(h - 1 - y) / pad[3]))159        blur = qsize * 0.02160        img += (scipy.ndimage.gaussian_filter(img, [blur, blur, 0]) -161                img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0)162        img += (np.median(img, axis=(0, 1)) - img) * np.clip(mask, 0.0, 1.0)163        img = PIL.Image.fromarray(164            np.uint8(np.clip(np.rint(img), 0, 255)), 'RGB')165        quad += pad[:2]166 167    img = img.transform((transform_size, transform_size), PIL.Image.QUAD,168                        (quad + 0.5).flatten(), PIL.Image.BILINEAR)169 170    if output_size < transform_size:171        img = img.resize((output_size, output_size), PIL.Image.ANTIALIAS)172 173    # Save aligned image.174    print('saveing: ', out_path)175    img.save(out_path)176 177    return img, np.max(quad[:, 0]) - np.min(quad[:, 0])178 179 180if __name__ == '__main__':181    parser = argparse.ArgumentParser()182    parser.add_argument('--in_dir', type=str, default='./inputs/whole_imgs')183    parser.add_argument('--out_dir', type=str, default='./inputs/cropped_faces')184    args = parser.parse_args()185 186    img_list = sorted(glob.glob(f'{args.in_dir}/*.png'))187    img_list = sorted(img_list)188 189    for in_path in img_list:190        out_path = os.path.join(args.out_dir, in_path.split("/")[-1])        191        out_path = out_path.replace('.jpg', '.png')192        size_ = align_face(in_path, out_path)