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Running
on
Zero
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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
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