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import numpy as np
from PIL import Image, ImageDraw
from facenet_pytorch import MTCNN
class FaceCropper:
"""Detect faces and return (cropped_face, annotated_image)."""
def __init__(self, device: str | None = None, image_size: int = 224):
import torch
self.device = device or ("cuda" if torch.cuda.is_available() else "cpu")
self.mtcnn = MTCNN(keep_all=True, device=self.device)
self.image_size = image_size
def _to_pil(self, img):
if isinstance(img, Image.Image):
return img.convert("RGB")
return Image.fromarray(img).convert("RGB")
def detect_and_crop(self, img, select="largest"):
pil = self._to_pil(img)
boxes, probs = self.mtcnn.detect(pil)
annotated = pil.copy()
draw = ImageDraw.Draw(annotated)
if boxes is None or len(boxes) == 0:
return None, annotated, {"boxes": np.empty((0,4)), "scores": np.empty((0,))}
for b, p in zip(boxes, probs):
x1, y1, x2, y2 = map(float, b)
draw.rectangle([x1, y1, x2, y2], outline=(255, 0, 0), width=3)
draw.text((x1, y1-10), f"{p:.2f}", fill=(255, 0, 0))
idx = 0
if select == "largest":
areas = [(b[2]-b[0])*(b[3]-b[1]) for b in boxes]
idx = int(np.argmax(areas))
elif isinstance(select, int) and 0 <= select < len(boxes):
idx = select
x1, y1, x2, y2 = boxes[idx].astype(int)
face = pil.crop((x1, y1, x2, y2))
return face, annotated, {"boxes": boxes, "scores": probs}
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