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Zero
Running
on
Zero
| import numpy as np | |
| from PIL import Image | |
| import scipy.ndimage | |
| import insightface | |
| import torch | |
| import scipy | |
| # Initialize InsightFace model | |
| face_analyzer = insightface.app.FaceAnalysis(name='buffalo_l', providers=['CPUExecutionProvider']) | |
| face_analyzer.prepare(ctx_id=0) | |
| def image_grid(imgs, rows, cols): | |
| assert len(imgs) == rows*cols | |
| w, h = imgs[0].size | |
| grid = Image.new('RGB', size=(cols*w, rows*h)) | |
| grid_w, grid_h = grid.size | |
| for i, img in enumerate(imgs): | |
| grid.paste(img, box=(i%cols*w, i//cols*h)) | |
| return grid | |
| def get_generator(seed, device): | |
| if seed is not None: | |
| if isinstance(seed, list): | |
| generator = [ | |
| torch.Generator(device).manual_seed(seed_item) for seed_item in seed | |
| ] | |
| else: | |
| generator = torch.Generator(device).manual_seed(seed) | |
| else: | |
| generator = None | |
| return generator | |
| def get_landmark_pil_insight(pil_image): | |
| """Get 68 facial landmarks using InsightFace.""" | |
| img_np = np.array(pil_image.convert("RGB")) | |
| faces = face_analyzer.get(img_np) | |
| if not faces: | |
| return None | |
| landmarks = faces[0].kps # shape: (5, 2) or (68, 2) depending on model | |
| if landmarks.shape[0] < 68: | |
| # InsightFace returns only 5 points: [left_eye, right_eye, nose, left_mouth, right_mouth] | |
| left_eye, right_eye, nose, left_mouth, right_mouth = landmarks | |
| # Approximate 68 landmarks (basic heuristic or fallback) | |
| return np.array([ | |
| left_eye, right_eye, nose, left_mouth, right_mouth | |
| ]) | |
| return landmarks | |
| def align_face(pil_image): | |
| """Align a face from a PIL.Image, returning an aligned PIL.Image of size 512x512.""" | |
| lm = get_landmark_pil_insight(pil_image) | |
| if lm is None or lm.shape[0] < 5: | |
| return pil_image | |
| eye_left, eye_right = lm[0], lm[1] | |
| eye_avg = (eye_left + eye_right) * 0.5 | |
| eye_to_eye = eye_right - eye_left | |
| mouth_left, mouth_right = lm[3], lm[4] | |
| mouth_avg = (mouth_left + mouth_right) * 0.5 | |
| eye_to_mouth = mouth_avg - eye_avg | |
| # The rest is your original alignment logic | |
| 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 | |
| img = pil_image.convert("RGB") | |
| transform_size = 512 | |
| output_size = 512 | |
| enable_padding = True | |
| shrink = int(np.floor(qsize / output_size * 0.5)) | |
| if shrink > 1: | |
| rsize = (int(np.rint(img.size[0] / shrink)), int(np.rint(img.size[1] / shrink))) | |
| img = img.resize(rsize, Image.Resampling.LANCZOS) | |
| quad /= shrink | |
| qsize /= shrink | |
| 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[:2] | |
| 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 = Image.fromarray(np.uint8(np.clip(np.rint(img), 0, 255)), 'RGB') | |
| quad += pad[:2] | |
| img = img.transform((transform_size, transform_size), Image.QUAD, (quad + 0.5).flatten(), Image.BILINEAR) | |
| if output_size < transform_size: | |
| img = img.resize((output_size, output_size), Image.Resampling.LANCZOS) | |
| return img | |