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import numpy as np |
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import scipy.ndimage |
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import os |
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import PIL.Image |
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def image_align(image, face_landmarks, output_size=1024, transform_size=4096, enable_padding=True): |
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lm = np.array(face_landmarks) |
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lm_chin = lm[0 : 17] |
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lm_eyebrow_left = lm[17 : 22] |
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lm_eyebrow_right = lm[22 : 27] |
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lm_nose = lm[27 : 31] |
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lm_nostrils = lm[31 : 36] |
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lm_eye_left = lm[36 : 42] |
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lm_eye_right = lm[42 : 48] |
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lm_mouth_outer = lm[48 : 60] |
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lm_mouth_inner = lm[60 : 68] |
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eye_left = np.mean(lm_eye_left, axis=0) |
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eye_right = np.mean(lm_eye_right, axis=0) |
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eye_avg = (eye_left + eye_right) * 0.5 |
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eye_to_eye = eye_right - eye_left |
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mouth_left = lm_mouth_outer[0] |
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mouth_right = lm_mouth_outer[6] |
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mouth_avg = (mouth_left + mouth_right) * 0.5 |
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eye_to_mouth = mouth_avg - eye_avg |
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x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1] |
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x /= np.hypot(*x) |
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x *= max(np.hypot(*eye_to_eye) * 2.0, np.hypot(*eye_to_mouth) * 1.8) |
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y = np.flipud(x) * [-1, 1] |
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c = eye_avg + eye_to_mouth * 0.1 |
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quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y]) |
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qsize = np.hypot(*x) * 2 |
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img = PIL.Image.fromarray(image) |
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shrink = int(np.floor(qsize / output_size * 0.5)) |
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if shrink > 1: |
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rsize = (int(np.rint(float(img.size[0]) / shrink)), int(np.rint(float(img.size[1]) / shrink))) |
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img = img.resize(rsize, PIL.Image.ANTIALIAS) |
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quad /= shrink |
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qsize /= shrink |
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border = max(int(np.rint(qsize * 0.1)), 3) |
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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])))) |
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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])) |
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if crop[2] - crop[0] < img.size[0] or crop[3] - crop[1] < img.size[1]: |
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img = img.crop(crop) |
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quad -= crop[0:2] |
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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])))) |
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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)) |
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if enable_padding and max(pad) > border - 4: |
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pad = np.maximum(pad, int(np.rint(qsize * 0.3))) |
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img = np.pad(np.float32(img), ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect') |
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h, w, _ = img.shape |
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y, x, _ = np.ogrid[:h, :w, :1] |
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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])) |
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blur = qsize * 0.02 |
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img += (scipy.ndimage.gaussian_filter(img, [blur, blur, 0]) - img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0) |
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img += (np.median(img, axis=(0,1)) - img) * np.clip(mask, 0.0, 1.0) |
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img = np.uint8(np.clip(np.rint(img), 0, 255)) |
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img = PIL.Image.fromarray(img, 'RGB') |
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quad += pad[:2] |
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img = img.transform((transform_size, transform_size), PIL.Image.QUAD, (quad + 0.5).flatten(), PIL.Image.BILINEAR) |
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if output_size < transform_size: |
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img = img.resize((output_size, output_size), PIL.Image.ANTIALIAS) |
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img_np = np.array(img) |
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return img_np |
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