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a42ddbb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 | import torch
from transformers import AutoModelForImageSegmentation
from torchvision import transforms
from PIL import Image
import numpy as np
# Device setup
_device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Global models
_birefnet = None
_birefnet_lite = None
# Preprocessing transform
_transform = transforms.Compose(
[
transforms.Resize((768, 768)),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
]
)
def load_model(fast_mode=False):
global _birefnet, _birefnet_lite
if fast_mode:
if _birefnet_lite is None:
_birefnet_lite = AutoModelForImageSegmentation.from_pretrained(
"ZhengPeng7/BiRefNet_lite", trust_remote_code=True
).to(_device)
return _birefnet_lite
else:
if _birefnet is None:
_birefnet = AutoModelForImageSegmentation.from_pretrained(
"ZhengPeng7/BiRefNet", trust_remote_code=True
).to(_device)
return _birefnet
def unload_model():
global _birefnet, _birefnet_lite
_birefnet = None
_birefnet_lite = None
if torch.cuda.is_available():
torch.cuda.empty_cache()
def _apply_mask(image: Image.Image, mask_tensor, bg, bg_type: str):
size = image.size
mask = transforms.ToPILImage()(mask_tensor.squeeze()).resize(size)
if bg_type == "Color" and isinstance(bg, str) and bg.startswith("#"):
color_rgb = tuple(int(bg[i : i + 2], 16) for i in (1, 3, 5))
bg_image = Image.new("RGBA", size, color_rgb + (255,))
elif bg_type in ["Image", "Video"]:
bg_image = bg.convert("RGBA").resize(size)
else:
raise ValueError("Invalid background type or format")
return Image.composite(image.convert("RGBA"), bg_image, mask)
def _get_transform(image_size: tuple[int, int] | None = None):
if image_size:
resize = transforms.Resize(image_size)
else:
resize = transforms.Resize((768, 768)) # default fallback
return transforms.Compose(
[
resize,
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
]
)
def predict(
input: Image.Image | list[Image.Image],
bg: str | Image.Image,
bg_type: str = "Color",
fast_mode: bool = True,
bg_frames: list[np.ndarray] | None = None,
image_size_mode: str = "default", # "default", "origin", or "custom"
custom_size: tuple[int, int] | None = None,
) -> Image.Image | list[Image.Image]:
model = _birefnet_lite if fast_mode else _birefnet
if model is None:
raise RuntimeError("Model not loaded. Please call `load_model()` first.")
def get_transform_for_img(img: Image.Image):
if image_size_mode == "origin":
return _get_transform(img.size)
elif image_size_mode == "custom":
if not custom_size:
raise ValueError("`custom_size` must be provided for 'custom' mode.")
return _get_transform(custom_size)
else:
return _get_transform() # default to (768, 768)
if isinstance(input, Image.Image):
_transform = get_transform_for_img(input)
input_tensor = _transform(input).unsqueeze(0).to(_device)
with torch.no_grad():
pred = model(input_tensor)[-1].sigmoid().cpu()
bg_image = (
Image.fromarray(bg_frames[0]) if bg_type == "Video" and bg_frames else bg
)
return _apply_mask(input, pred[0], bg_image, bg_type)
elif isinstance(input, list):
results = []
for idx, frame in enumerate(input):
_transform = get_transform_for_img(frame)
input_tensor = _transform(frame).unsqueeze(0).to(_device)
with torch.no_grad():
pred = model(input_tensor)[-1].sigmoid().cpu()
if bg_type == "Video" and bg_frames:
bg_image = Image.fromarray(bg_frames[idx % len(bg_frames)])
else:
bg_image = bg
results.append(_apply_mask(frame, pred[0], bg_image, bg_type))
return results
else:
raise TypeError("`input` must be a PIL.Image or a list of PIL.Image.")
# # Default behavior (768x768)
# predict(img, "#ffffff", "Color")
# # Keep original image size
# predict(img, "#ffffff", "Color", image_size_mode="origin")
# # Resize to 1024x1024
# predict(img, "#ffffff", "Color", image_size_mode="custom", custom_size=(1024, 1024))
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