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| import numpy as np | |
| import scipy.ndimage | |
| import os | |
| import PIL.Image | |
| def image_align(src, face_landmarks, output_size=1024, transform_size=4096, enable_padding=True, x_scale=1, y_scale=1, em_scale=0.1, alpha=False): | |
| # 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 | |
| # Load in-the-wild image. | |
| img = PIL.Image.fromarray(src) | |
| # 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)) | |
| if alpha: | |
| mask = 1-np.clip(3.0 * mask, 0.0, 1.0) | |
| mask = np.uint8(np.clip(np.rint(mask*255), 0, 255)) | |
| img = np.concatenate((img, mask), axis=2) | |
| img = PIL.Image.fromarray(img, 'RGBA') | |
| else: | |
| 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 np.array(img) |