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624fba0 | 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 | import cv2
import logging
from PIL import Image
from ultralytics import YOLO
logger = logging.getLogger(__name__)
def get_yolo_damage_boxes(image_path, yolo_model: YOLO, output_path: str):
"""Runs object detection using the YOLO ONNX model."""
logger.info("Starting YOLO ONNX damage detection...")
try:
image = Image.open(image_path).convert("RGB")
# Run ONNX inference through Ultralytics wrapper
results = yolo_model.predict(
source=image,
conf=0.05,
imgsz=640,
verbose=False
)
result = results[0]
boxes = result.boxes
detections = []
if boxes is not None and len(boxes) > 0:
logger.info(f"{len(boxes)} damage detections found.")
for box in boxes:
conf = float(box.conf[0])
cls_id = int(box.cls[0])
label = yolo_model.names[cls_id]
x1, y1, x2, y2 = map(int, box.xyxy[0])
detections.append({
"label": label,
"confidence": round(conf, 4),
"box": [x1, y1, x2, y2]
})
else:
logger.info("No damage detections found.")
# Save bounding box visual overlay
plotted = result.plot()
cv2.imwrite(output_path, plotted)
logger.info(f"YOLO ONNX output saved to: {output_path}")
return {
"detections": detections,
"total_detections": len(detections),
"message": (
"No damage detected"
if len(detections) == 0
else "Detections found"
)
}
except Exception as e:
logger.exception("YOLO ONNX detection failed.")
raise RuntimeError(f"YOLO detection failed: {str(e)}") |