import os from pathlib import Path import PIL import dlib import numpy as np import scipy import scipy.ndimage import torch from PIL import Image from torchvision import transforms as T from utils.drive import open_url """ 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 """ def get_landmark(filepath, predictor): """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) filepath = Path(filepath) print(f"{filepath.name}: Number of faces detected: {len(dets)}") shapes = [predictor(img, d) for k, d in enumerate(dets)] lms = [np.array([[tt.x, tt.y] for tt in shape.parts()]) for shape in shapes] return lms def get_landmark_from_tensors(tensors: list[torch.Tensor | Image.Image | np.ndarray], predictor): detector = dlib.get_frontal_face_detector() transform = T.ToPILImage() images = [] lms = [] for k, tensor in enumerate(tensors): if isinstance(tensor, torch.Tensor): img_pil = transform(tensor) else: img_pil = tensor img = np.array(img_pil) images.append(img_pil) dets = detector(img, 1) if len(dets) == 0: raise ValueError(f"No faces detected in the image {k}.") elif len(dets) == 1: print(f"Number of faces detected: {len(dets)}") else: print(f"Number of faces detected: {len(dets)}, get largest face") # Find the largest face dets = sorted(dets, key=lambda det: det.width() * det.height(), reverse=True) shape = predictor(img, dets[0]) lm = np.array([[tt.x, tt.y] for tt in shape.parts()]) lms.append(lm) return images, lms def align_face(data, predictor=None, is_filepath=False, return_tensors=True): """ :param data: filepath or list torch Tensors :return: list of PIL Images """ if predictor is None: predictor_path = 'shape_predictor_68_face_landmarks.dat' if not os.path.isfile(predictor_path): print("Downloading Shape Predictor") data_io = open_url("https://drive.google.com/uc?id=1huhv8PYpNNKbGCLOaYUjOgR1pY5pmbJx") with open(predictor_path, 'wb') as f: f.write(data_io.getbuffer()) predictor = dlib.shape_predictor(predictor_path) if is_filepath: lms = get_landmark(data, predictor) else: if not isinstance(data, list): data = [data] images, lms = get_landmark_from_tensors(data, predictor) imgs = [] for num_img, lm in enumerate(lms): 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 if is_filepath: img = PIL.Image.open(data) else: img = images[num_img] output_size = 1024 # output_size = 256 transform_size = 4096 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.LANCZOS) # Save aligned image. imgs.append(img) if return_tensors: transform = T.ToTensor() tensors = [transform(img).clamp(0, 1) for img in imgs] return tensors return imgs