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class_names.txt

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+QR_CODE
+qr_code
+qrcode

element.yaml

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+version: 0.1.0
+element_type: Detect
+main: main.py
+source: https://universe.roboflow.com/krkkale-niversitesi-pnmrg/qr-code-ee1km
+objects:
+  - QR_CODE
+  - qr_code
+  - qrcode

environment.json

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+{"BATCH_SIZE": -1, "CACHE_PATH": "/tmp/cache", "DATASET_ID": "prsg2ggNaUDK4kKNjZ8y", "DATASET_LINK": "https://app.roboflow.com/ds/9f5oc5pEBM?key=LsBUYgkzRT", "DATASET_OWNER": "2w1FkkGDTNVbv5xYuZ8XbYezIBu1", "DATASET_VERSION_ID": "3", "ENDPOINT": "prsg2ggNaUDK4kKNjZ8y/3", "MODEL_NAME": "yolov8n", "PREPROCESSING": "{\"auto-orient\":{\"enabled\":true},\"resize\":{\"format\":\"Stretch to\",\"width\":640,\"enabled\":true,\"height\":640}}", "PROJECT": "roboflow-platform", "RESOLUTION": 640, "TRAINING_TIME": "2678400", "UID": "2w1FkkGDTNVbv5xYuZ8XbYezIBu1", "COLORS": {"QR_CODE": "#C7FC00", "qr_code": "#8622FF", "qrcode": "#FE0056"}}

main.py

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+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 QR code 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

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+{
+    "project_task_type": "object-detection",
+    "model_type": "yolov8n"
+}

pyproject.toml

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+[project]
+name = "qr-code-ee1km-3"
+version = "0.1.0"
+requires-python = ">=3.11"
+dependencies = [
+  "numpy>=1.26",
+  "pillow>=10.0",
+  "ultralytics>=8.0.0",
+  "onnxruntime>=1.17",
+]

weights.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:fd54085a60f46d449378f6208e2f12c449e8c642d4eac98ca6626a9cca1e5484
+size 12239568