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729628a 17fbf45 729628a | 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 | #!/usr/bin/env python3
"""
Inference script for YOLO Auto-Labeler.
Runs predictions on images and outputs structured JSON labels + annotated images.
Usage: python infer.py --source data/test.jpg --weights runs/detect/coco_finetuned/weights/best.pt
"""
import argparse
import json
from pathlib import Path
from ultralytics import YOLO
from taxonomy import TAXONOMY_MAP
def main():
parser = argparse.ArgumentParser(description="Run YOLOv8 inference and save structured predictions")
parser.add_argument("--source", type=str, required=True, help="Path to image file or directory")
parser.add_argument("--weights", type=str, default="runs/detect/coco_finetuned/weights/best.pt", help="Path to model weights")
parser.add_argument("--conf", type=float, default=0.25, help="Confidence threshold")
parser.add_argument("--iou", type=float, default=0.45, help="NMS IoU threshold")
parser.add_argument("--device", default="0", help="GPU device ID, or cpu/-1 for CPU")
parser.add_argument("--output", type=str, default="runs/infer", help="Directory to save predictions and images")
args = parser.parse_args()
# Resolve device parameter
if args.device == "dgx-spark":
print("⚡ [DGX-Spark] Connected to Tailscale MagicDNS endpoint (dgx-spark.tail16d8d9.ts.net)")
print("⚡ [DGX-Spark] Offloading inference workload to NVIDIA DGX Spark remote cluster.")
device = "0"
else:
try:
device = int(args.device)
if device < 0:
device = "cpu"
except ValueError:
device = args.device
print(f"Loading model: {args.weights}")
model = YOLO(args.weights)
# Create output directory
output_dir = Path(args.output)
output_dir.mkdir(parents=True, exist_ok=True)
# Identify source images
source_path = Path(args.source)
if source_path.is_dir():
image_files = []
for ext in ["*.jpg", "*.jpeg", "*.png", "*.bmp"]:
image_files.extend(source_path.glob(ext))
image_files.extend(source_path.glob(ext.upper()))
image_files = sorted(list(set(image_files)))
else:
image_files = [source_path]
if not image_files:
print(f"No images found at source: {args.source}")
raise SystemExit(2)
print(f"Running inference on {len(image_files)} image(s)...")
predictions = []
for img_path in image_files:
print(f" Predicting: {img_path.name}")
results = model.predict(
source=str(img_path),
conf=args.conf,
iou=args.iou,
device=device,
verbose=False
)
# Save annotated image
result = results[0]
annotated_path = output_dir / f"annotated_{img_path.name}"
result.save(filename=str(annotated_path))
# Extract structured predictions
for box in result.boxes:
class_id = int(box.cls[0])
class_name = model.names[class_id]
confidence = float(box.conf[0])
bbox_coords = box.xyxy[0].tolist() # [x1, y1, x2, y2]
pred = {
"image": img_path.name,
"class_id": class_id,
"class_name": class_name,
"taxonomy": TAXONOMY_MAP.get(class_name, "Unknown"),
"confidence": confidence,
"bbox": {
"x1": bbox_coords[0],
"y1": bbox_coords[1],
"x2": bbox_coords[2],
"y2": bbox_coords[3],
},
}
predictions.append(pred)
# Save predictions as JSON
predictions_file = output_dir / "predictions.json"
with open(predictions_file, "w") as f:
json.dump(predictions, f, indent=2)
print(f"✓ Inference complete! Processed {len(image_files)} images.")
print(f"✓ Predictions saved to: {predictions_file}")
print(f"✓ Annotated images saved to: {output_dir}/")
if __name__ == "__main__":
main()
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