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Zero
Running
on
Zero
File size: 1,978 Bytes
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import logging
from PIL import Image, ImageDraw
from huggingface_hub import hf_hub_download
from ultralytics import YOLO
import os
logger = logging.getLogger(__name__)
class ObjectDetector:
def __init__(self, model_key="yolov8n.pt", device="cpu"):
self.device = device
self.model = None
self.model_key = model_key.lower().replace(".pt", "")
self.repo_map = {
"yolov8n": ("ultralytics/yolov8", "yolov8n.pt"),
"yolov8s": ("ultralytics/yolov8", "yolov8s.pt"),
"yolov8l": ("ultralytics/yolov8", "yolov8l.pt"),
"yolov11b": ("Ultralytics/YOLO11", "yolov11b.pt"),
}
def load_model(self):
if self.model is not None:
return
if self.model_key not in self.repo_map:
raise ValueError(f"Unsupported model key: {self.model_key}")
repo_id, filename = self.repo_map[self.model_key]
weights_path = hf_hub_download(repo_id=repo_id, filename=filename, cache_dir="models/detection/weights")
self.model = YOLO(weights_path) # ✅ ZeroGPU-safe: runtime only
def predict(self, image: Image.Image, conf_threshold=0.25):
self.load_model()
results = self.model(image)
detections = []
for r in results:
for box in r.boxes:
detections.append({
"class_name": r.names[int(box.cls)],
"confidence": float(box.conf),
"bbox": box.xyxy[0].tolist()
})
return detections
def draw(self, image: Image.Image, detections, alpha=0.5):
overlay = image.copy()
draw = ImageDraw.Draw(overlay)
for det in detections:
bbox = det["bbox"]
label = f'{det["class_name"]} {det["confidence"]:.2f}'
draw.rectangle(bbox, outline="red", width=2)
draw.text((bbox[0], bbox[1]), label, fill="red")
return Image.blend(image, overlay, alpha)
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