Upload 9 files

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+example.png filter=lfs diff=lfs merge=lfs -text

class_names.txt

@@ -0,0 +1,20 @@
+barcode
+car
+cardboard box
+fire
+forklift
+freight container
+gloves
+helmet
+ladder
+license plate
+person
+qr code
+road sign
+safety vest
+smoke
+traffic cone
+traffic light
+truck
+van
+wood pallet

element.yaml

@@ -0,0 +1,25 @@
+version: 0.1.0
+element_type: Detect
+main: main.py
+source: https://universe.roboflow.com/large-benchmark-datasets/logistics-sz9jr
+objects:
+  - barcode
+  - car
+  - cardboard box
+  - fire
+  - forklift
+  - freight container
+  - gloves
+  - helmet
+  - ladder
+  - license plate
+  - person
+  - qr code
+  - road sign
+  - safety vest
+  - smoke
+  - traffic cone
+  - traffic light
+  - truck
+  - van
+  - wood pallet

environment.json

@@ -0,0 +1 @@
+{"BATCH_SIZE": -1, "CACHE_PATH": "/tmp/cache", "DATASET_ID": "zMLL75R2TfQEeWLSfGQG", "DATASET_LINK": "https://app.roboflow.com/ds/VphVagsykw?key=cFNYTypX5F", "DATASET_OWNER": "WStLbW6Owiz8aE9Z5N23", "DATASET_VERSION_ID": "2", "ENDPOINT": "zMLL75R2TfQEeWLSfGQG/2", "MODEL_NAME": "yolov8n", "PREPROCESSING": "{\"auto-orient\":{\"enabled\":true},\"resize\":{\"width\":640,\"format\":\"Stretch to\",\"enabled\":true,\"height\":640}}", "PROJECT": "roboflow-platform", "RESOLUTION": 640, "TRAINING_TIME": "2678400", "UID": "Q3duwiUbt8UlbotpPyNnQaayi353", "COLORS": {"barcode": "#C7FC00", "car": "#8622FF", "cardboard box": "#FE0056", "fire": "#00FFCE", "forklift": "#FF8000", "freight container": "#00B7EB", "gloves": "#FFFF00", "helmet": "#FF00FF", "ladder": "#0E7AFE", "license plate": "#FFABAB", "person": "#0000FF", "qr code": "#a0522d", "road sign": "#808000", "safety vest": "#483d8b", "smoke": "#8b008b", "traffic cone": "#ff4500", "traffic light": "#dc143c", "truck": "#00ffff", "van": "#d8bfd8", "wood pallet": "#ff1493"}}

main.py

@@ -0,0 +1,86 @@
+import argparse
+import json
+import sys
+from io import BytesIO
+from pathlib import Path
+from typing import Any, Dict, List
+
+import numpy as np
+from PIL import Image
+from ultralytics import YOLO
+
+
+def load_image(frame: Any, base_dir: Path) -> Image.Image:
+    if isinstance(frame, (bytes, bytearray, memoryview)):
+        return Image.open(BytesIO(frame)).convert("RGB")
+
+    path = Path(str(frame))
+    if not path.is_absolute():
+        path = (Path.cwd() / path).resolve()
+        if not path.exists():
+            candidate = (base_dir / str(frame)).resolve()
+            if candidate.exists():
+                path = candidate
+    return Image.open(path).convert("RGB")
+
+
+def load_model(*_args: Any, **_kwargs: Any):
+    base_dir = Path(__file__).resolve().parent
+    model_path = base_dir / "weights.onnx"
+    if not model_path.exists():
+        return None
+    return YOLO(str(model_path))
+
+
+def run_model(model, frame: "np.ndarray") -> List[Dict[str, Any]]:
+    image = Image.fromarray(frame)
+    results = model(image)
+    detections: List[Dict[str, Any]] = []
+    result = results[0]
+    names = result.names or model.names
+
+    for det_idx, box in enumerate(result.boxes):
+        xyxy = box.xyxy[0].tolist()
+        class_id = int(box.cls[0].item())
+        detections.append(
+            {
+                "frame_idx": 0,
+                "class": names.get(class_id, str(class_id)),
+                "bbox": [float(x) for x in xyxy],
+                "score": float(box.conf[0].item()),
+                "track_id": f"f0-d{det_idx}",
+            }
+        )
+
+    return detections
+
+
+def build_parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser(description="Run logistics detection (YOLOv8 ONNX).")
+    parser.add_argument(
+        "--stdin-raw",
+        action="store_true",
+        default=True,
+        help="Read raw image bytes from stdin.",
+    )
+    return parser
+
+
+if __name__ == "__main__":
+    build_parser().parse_args()
+
+    base_dir = Path(__file__).resolve().parent
+    model = load_model()
+    if model is None:
+        print("[]")
+        sys.exit(0)
+
+    try:
+        image = load_image(sys.stdin.buffer.read(), base_dir)
+    except Exception:
+        print("[]")
+        sys.exit(0)
+
+    frame = np.array(image)
+    output = run_model(model, frame)
+    print(json.dumps(output))

model_type.json

@@ -0,0 +1,4 @@
+{
+    "project_task_type": "object-detection",
+    "model_type": "yolov8n"
+}

pyproject.toml

@@ -0,0 +1,11 @@
+[project]
+name = "logistics-sz9jr-2"
+version = "0.1.0"
+requires-python = ">=3.11"
+dependencies = [
+  "numpy>=1.26",
+  "pillow>=10.0",
+  "ultralytics>=8.0.0",
+  "onnx>=1.16",
+  "onnxruntime>=1.17",
+]

weights.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f695550212d2dd2059928f543f3550690527bc0ed7071dca040e5f12c849e432
+size 12253107