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| import numpy as np | |
| import torch | |
| from PIL import Image | |
| import dlib | |
| import numpy as np | |
| import PIL | |
| import PIL.Image | |
| import scipy | |
| import scipy.ndimage | |
| class ImageProcessor(): | |
| def __init__(self, predictor_path=None) -> None: | |
| self.predictor = None | |
| if predictor_path: | |
| self.predictor = dlib.shape_predictor(predictor_path) | |
| def preprocess_image(image, is_batch=True): | |
| image = image.resize( (256, 256)) | |
| image = np.asarray(image).transpose(2, 0, 1).astype(np.float32) # C,H,W -> H,W,C | |
| image = torch.FloatTensor(image.copy()) | |
| image = (image - 127.5) / 127.5 # Normalize | |
| if not is_batch: | |
| image = image.unsqueeze(0) | |
| return image | |
| """ | |
| Input: A numpy image with shape NxCxHxW. | |
| Output: Output image with NxHxWxC with values between 0-255 | |
| """ | |
| def postprocess_image(image, min_val=-1.0, max_val=1.0, is_batch=True): | |
| image = image.astype(np.float64) | |
| image = (image - min_val) * 255 / (max_val - min_val) | |
| image = np.clip(image + 0.5, 0, 255).astype(np.uint8) | |
| image = image.transpose(0, 2, 3, 1) | |
| if not is_batch: | |
| image = Image.fromarray(image[0]).resize((256,256)) | |
| return image | |
| """ | |
| 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(self, image): | |
| """get landmark with dlib | |
| :return: np.array shape=(68, 2) | |
| """ | |
| detector = dlib.get_frontal_face_detector() | |
| # img = dlib.load_rgb_image(filepath) | |
| img = np.asarray(image) | |
| dets = detector(img, 1) | |
| for k, d in enumerate(dets): | |
| shape = self.predictor(img, d) | |
| t = list(shape.parts()) | |
| a = [] | |
| for tt in t: | |
| a.append([tt.x, tt.y]) | |
| lm = np.array(a) | |
| return lm | |
| def align_face(self, img): | |
| """ | |
| :param image: PIL image | |
| :return: PIL Image | |
| """ | |
| if self.predictor is None: | |
| return img | |
| lm = self.get_landmark(img) | |
| 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 = 512 | |
| transform_size = 1024 | |
| 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. | |
| img.save('aligned.png') | |
| return img | |