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import numpy as np |
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import PIL |
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import PIL.Image |
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import scipy |
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import scipy.ndimage |
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import dlib |
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def get_landmark(filepath, predictor): |
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"""get landmark with dlib |
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:return: np.array shape=(68, 2) |
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""" |
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detector = dlib.get_frontal_face_detector() |
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img = dlib.load_rgb_image(filepath) |
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dets = detector(img, 1) |
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for k, d in enumerate(dets): |
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shpe = predictor(img, d) |
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t = list(shpe.parts()) |
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a = [] |
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for tt in t: |
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a.append([tt.x, tt.y]) |
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lm = np.array(a) |
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return lm |
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def align_face(filepath, predictor, output_size): |
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""" |
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:param filepath: str |
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:return: PIL Image |
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""" |
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lm = get_landmark(filepath, predictor) |
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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.open(filepath).convert('RGB') |
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transform_size = output_size |
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enable_padding = True |
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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]))), |
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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]), |
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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]))), |
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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), |
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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]), |
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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 = PIL.Image.fromarray(np.uint8(np.clip(np.rint(img), 0, 255)), '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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return img |
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