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| from typing import Iterator, List, Tuple | |
| import dlib | |
| import numpy as np | |
| import PIL.Image | |
| import scipy.ndimage | |
| class FaceAligner(object): | |
| def __init__( | |
| self, | |
| shape_predictor_path: str = 'shape_predictor_68_face_landmarks.dat', | |
| image_size: int = 512, | |
| ) -> None: | |
| self.image_size = image_size | |
| self.detector = dlib.get_frontal_face_detector() | |
| self.shape_predictor = dlib.shape_predictor(shape_predictor_path) | |
| def align(self, image: PIL.Image.Image) -> List[PIL.Image.Image]: | |
| landmarks = self.get_landmarks(image) | |
| return [image_align( | |
| image, | |
| face_landmarks, | |
| output_size=self.image_size, | |
| transform_size=self.image_size * 2, | |
| ) for face_landmarks in landmarks] | |
| def get_landmarks( | |
| self, | |
| image: PIL.Image.Image, | |
| ) -> Iterator[List[Tuple[int, int]]]: | |
| img = np.asarray(image.convert('L')) | |
| dets = self.detector(img, 1) | |
| for detection in dets: | |
| try: | |
| parts = self.shape_predictor(img, detection).parts() | |
| face_landmarks = [(point.x, point.y) for point in parts] | |
| yield face_landmarks | |
| except: | |
| print("Exception in get_landmarks()!") | |
| def image_align( | |
| img: PIL.Image.Image, | |
| face_landmarks: List[Tuple[int, int]], | |
| output_size: int = 1024, | |
| transform_size: int = 4096, | |
| enable_padding: bool = True, | |
| x_scale: float = 1, | |
| y_scale: float = 1, | |
| em_scale: float = 0.1, | |
| ) -> PIL.Image.Image: | |
| # Align function from FFHQ dataset pre-processing step | |
| # https://github.com/NVlabs/ffhq-dataset/blob/master/download_ffhq.py | |
| lm = np.array(face_landmarks) | |
| 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) | |
| x *= x_scale | |
| y = np.flipud(x) * [-y_scale, y_scale] | |
| c = eye_avg + eye_to_mouth * em_scale | |
| quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y]) | |
| qsize = np.hypot(*x) * 2 | |
| # 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 = np.uint8(np.clip(np.rint(img), 0, 255)) | |
| img = PIL.Image.fromarray(img, '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) | |
| return img | |