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import os
from PIL import Image
import numpy as np
import torch
import cv2
import logging
import hashlib
from insightface.app.common import Face
from safetensors.torch import save_file, safe_open
from tqdm import tqdm
import urllib.request
def tensor_to_pil(img_tensor, batch_index=0):
# Convert tensor of shape [batch_size, channels, height, width] at the batch_index to PIL Image
img_tensor = img_tensor[batch_index].unsqueeze(0)
i = 255. * img_tensor.cpu().numpy()
img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8).squeeze())
return img
def batch_tensor_to_pil(img_tensor):
# Convert tensor of shape [batch_size, channels, height, width] to a list of PIL Images
return [tensor_to_pil(img_tensor, i) for i in range(img_tensor.shape[0])]
def pil_to_tensor(image):
# Takes a PIL image and returns a tensor of shape [1, height, width, channels]
image = np.array(image).astype(np.float32) / 255.0
image = torch.from_numpy(image).unsqueeze(0)
if len(image.shape) == 3: # If the image is grayscale, add a channel dimension
image = image.unsqueeze(-1)
return image
def batched_pil_to_tensor(images):
# Takes a list of PIL images and returns a tensor of shape [batch_size, height, width, channels]
return torch.cat([pil_to_tensor(image) for image in images], dim=0)
def img2tensor(imgs, bgr2rgb=True, float32=True):
def _totensor(img, bgr2rgb, float32):
if img.shape[2] == 3 and bgr2rgb:
if img.dtype == 'float64':
img = img.astype('float32')
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = torch.from_numpy(img.transpose(2, 0, 1))
if float32:
img = img.float()
return img
if isinstance(imgs, list):
return [_totensor(img, bgr2rgb, float32) for img in imgs]
else:
return _totensor(imgs, bgr2rgb, float32)
def tensor2img(tensor, rgb2bgr=True, out_type=np.uint8, min_max=(0, 1)):
if not (torch.is_tensor(tensor) or (isinstance(tensor, list) and all(torch.is_tensor(t) for t in tensor))):
raise TypeError(f'tensor or list of tensors expected, got {type(tensor)}')
if torch.is_tensor(tensor):
tensor = [tensor]
result = []
for _tensor in tensor:
_tensor = _tensor.squeeze(0).float().detach().cpu().clamp_(*min_max)
_tensor = (_tensor - min_max[0]) / (min_max[1] - min_max[0])
n_dim = _tensor.dim()
if n_dim == 4:
img_np = make_grid(_tensor, nrow=int(math.sqrt(_tensor.size(0))), normalize=False).numpy()
img_np = img_np.transpose(1, 2, 0)
if rgb2bgr:
img_np = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR)
elif n_dim == 3:
img_np = _tensor.numpy()
img_np = img_np.transpose(1, 2, 0)
if img_np.shape[2] == 1: # gray image
img_np = np.squeeze(img_np, axis=2)
else:
if rgb2bgr:
img_np = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR)
elif n_dim == 2:
img_np = _tensor.numpy()
else:
raise TypeError('Only support 4D, 3D or 2D tensor. ' f'But received with dimension: {n_dim}')
if out_type == np.uint8:
# Unlike MATLAB, numpy.unit8() WILL NOT round by default.
img_np = (img_np * 255.0).round()
img_np = img_np.astype(out_type)
result.append(img_np)
if len(result) == 1:
result = result[0]
return result
def download(url, path, name):
request = urllib.request.urlopen(url)
total = int(request.headers.get('Content-Length', 0))
with tqdm(total=total, desc=f'[ReActor] Downloading {name} to {path}', unit='B', unit_scale=True, unit_divisor=1024) as progress:
urllib.request.urlretrieve(url, path, reporthook=lambda count, block_size, total_size: progress.update(block_size))
def move_path(old_path, new_path):
if os.path.exists(old_path):
try:
models = os.listdir(old_path)
for model in models:
move_old_path = os.path.join(old_path, model)
move_new_path = os.path.join(new_path, model)
os.rename(move_old_path, move_new_path)
os.rmdir(old_path)
except Exception as e:
print(f"Error: {e}")
new_path = old_path
def addLoggingLevel(levelName, levelNum, methodName=None):
if not methodName:
methodName = levelName.lower()
def logForLevel(self, message, *args, **kwargs):
if self.isEnabledFor(levelNum):
self._log(levelNum, message, args, **kwargs)
def logToRoot(message, *args, **kwargs):
logging.log(levelNum, message, *args, **kwargs)
logging.addLevelName(levelNum, levelName)
setattr(logging, levelName, levelNum)
setattr(logging.getLoggerClass(), methodName, logForLevel)
setattr(logging, methodName, logToRoot)
def get_image_md5hash(image: Image.Image):
md5hash = hashlib.md5(image.tobytes())
return md5hash.hexdigest()
def save_face_model(face: Face, filename: str) -> None:
try:
tensors = {
"bbox": torch.tensor(face["bbox"]),
"kps": torch.tensor(face["kps"]),
"det_score": torch.tensor(face["det_score"]),
"landmark_3d_68": torch.tensor(face["landmark_3d_68"]),
"pose": torch.tensor(face["pose"]),
"landmark_2d_106": torch.tensor(face["landmark_2d_106"]),
"embedding": torch.tensor(face["embedding"]),
"gender": torch.tensor(face["gender"]),
"age": torch.tensor(face["age"]),
}
save_file(tensors, filename)
print(f"Face model has been saved to '{filename}'")
except Exception as e:
print(f"Error: {e}")
def load_face_model(filename: str):
face = {}
with safe_open(filename, framework="pt") as f:
for k in f.keys():
face[k] = f.get_tensor(k).numpy()
return Face(face)
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