Update Docker configurations and dependencies for Jetson deployment

- Added image specifications for services in `docker-compose.demo-jetson.yml` and `docker-compose.jetson.yml` to streamline deployment.
- Updated `Dockerfile.gpu` to use the development version of the CUDA runtime for enhanced compatibility.
- Modified `Dockerfile.jetson` to switch to a newer JetPack base image and adjusted the requirements file to include additional dependencies for improved functionality.
- Removed obsolete deployment scripts and calibration cache generation script to clean up the project structure.

Made-with: Cursor

scripts/jetson/sample_calibration_images.py

@@ -0,0 +1,63 @@
+#!/usr/bin/env python3
+"""
+Sample a random subset of images from a YOLO dataset for INT8 calibration.
+
+Run locally (on your dev machine) before deploying to Jetson:
+
+  python3 scripts/jetson/sample_calibration_images.py \
+      --dataset /path/to/dataset-2025-05-22 \
+      --output  /tmp/calibration \
+      --num-samples 500
+
+The output directory can then be passed directly to deploy_demo_jetson.sh
+via --calibration-images, or to generate_int8_cache.py via --images-dir.
+"""
+import argparse
+import random
+import shutil
+import sys
+from pathlib import Path
+
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--dataset", required=True, help="Root of the YOLO dataset (must contain images/)")
+    parser.add_argument("--output", required=True, help="Destination directory for sampled images")
+    parser.add_argument("--num-samples", type=int, default=500)
+    parser.add_argument("--seed", type=int, default=42)
+    return parser.parse_args()
+
+
+def collect_images(dataset_root: Path) -> list[Path]:
+    images_dir = dataset_root / "images"
+    if not images_dir.is_dir():
+        print(f"ERROR: {images_dir} not found", file=sys.stderr)
+        sys.exit(1)
+    images: list[Path] = []
+    for pattern in ("**/*.jpg", "**/*.jpeg", "**/*.png"):
+        images += sorted(images_dir.glob(pattern))
+    return images
+
+
+def main():
+    args = parse_args()
+    dataset_root = Path(args.dataset)
+    output_dir = Path(args.output)
+
+    images = collect_images(dataset_root)
+    if not images:
+        print(f"ERROR: no images found in {dataset_root / 'images'}", file=sys.stderr)
+        sys.exit(1)
+
+    rng = random.Random(args.seed)
+    sample = rng.sample(images, min(args.num_samples, len(images)))
+
+    output_dir.mkdir(parents=True, exist_ok=True)
+    for src in sample:
+        shutil.copy2(src, output_dir / src.name)
+
+    print(f"Sampled {len(sample)} images → {output_dir}")
+
+
+if __name__ == "__main__":
+    main()