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from __future__ import annotations
from pathlib import Path
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from PIL import Image, ImageDraw
from torchvision.transforms.functional import to_pil_image
try:
from ultralytics import YOLO
except ModuleNotFoundError:
print("Please install ultralytics using `pip install ultralytics`")
raise
def create_mask_from_bbox(
bboxes: np.ndarray, shape: tuple[int, int]
) -> list[Image.Image]:
"""
Parameters
----------
bboxes: list[list[float]]
list of [x1, y1, x2, y2]
bounding boxes
shape: tuple[int, int]
shape of the image (width, height)
Returns
-------
masks: list[Image.Image]
A list of masks
"""
masks = []
for bbox in bboxes:
mask = Image.new("L", shape, "black")
mask_draw = ImageDraw.Draw(mask)
mask_draw.rectangle(bbox, fill="white")
masks.append(mask)
return masks
def mask_to_pil(masks: torch.Tensor, shape: tuple[int, int]) -> list[Image.Image]:
"""
Parameters
----------
masks: torch.Tensor, dtype=torch.float32, shape=(N, H, W).
The device can be CUDA, but `to_pil_image` takes care of that.
shape: tuple[int, int]
(width, height) of the original image
Returns
-------
images: list[Image.Image]
"""
n = masks.shape[0]
return [to_pil_image(masks[i], mode="L").resize(shape) for i in range(n)]
def yolo_detector(
image: Image.Image, model_path: str | Path | None = None, confidence: float = 0.3
) -> list[Image.Image] | None:
if not model_path:
model_path = hf_hub_download("Bingsu/adetailer", "face_yolov8n.pt")
model = YOLO(model_path)
pred = model(image, conf=confidence)
bboxes = pred[0].boxes.xyxy.cpu().numpy()
if bboxes.size == 0:
return None
if pred[0].masks is None:
masks = create_mask_from_bbox(bboxes, image.size)
else:
masks = mask_to_pil(pred[0].masks.data, image.size)
return masks
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