Initial commit

dagster_project/assets/net_detection.py

@@ -0,0 +1,72 @@
+"""Asset: Detect tennis/pickleball net using Roboflow"""
+
+import os
+import cv2
+import numpy as np
+from pathlib import Path
+from typing import Dict
+from dagster import asset, AssetExecutionContext
+from inference_sdk import InferenceHTTPClient
+
+
+@asset(
+    io_manager_key="json_io_manager",
+    compute_kind="roboflow",
+    description="Detect pickleball/tennis net using Roboflow model"
+)
+def detect_net(
+    context: AssetExecutionContext,
+    extract_video_frames: Dict,
+    detect_court_keypoints: Dict
+) -> Dict:
+    """
+    Detect net on first frame using Roboflow model
+
+    NO FALLBACKS - if model doesn't detect net, this will fail
+
+    Inputs:
+        - extract_video_frames: frame metadata
+        - detect_court_keypoints: court corners (for visualization)
+
+    Outputs:
+        - data/{run_id}/net_detection_preview.jpg: visualization
+        - JSON with net detection results
+
+    Returns:
+        Dict with net detection data
+    """
+    run_id = context.run_id
+    frames_dir = Path(extract_video_frames['frames_dir'])
+    first_frame_path = frames_dir / "frame_0000.jpg"
+
+    context.log.info(f"Loading first frame: {first_frame_path}")
+
+    # Load frame
+    frame = cv2.imread(str(first_frame_path))
+    h, w = frame.shape[:2]
+    context.log.info(f"Frame dimensions: {w}x{h}")
+
+    # Get API key
+    api_key = os.getenv("ROBOFLOW_API_KEY")
+    if not api_key:
+        raise ValueError("ROBOFLOW_API_KEY environment variable is not set")
+
+    context.log.info("Detecting net using Roboflow model...")
+
+    client = InferenceHTTPClient(
+        api_url="https://serverless.roboflow.com",
+        api_key=api_key
+    )
+
+    # Call Roboflow model - MODEL_ID WILL BE PROVIDED BY USER
+    # Placeholder - user will provide correct model
+    model_id = "MODEL_ID_PLACEHOLDER"
+
+    result = client.infer(str(first_frame_path), model_id=model_id)
+
+    context.log.info(f"Roboflow response: {result}")
+
+    # TODO: Parse result based on actual model output format
+    # User will provide correct model and we'll update parsing logic
+
+    raise NotImplementedError("Waiting for correct Roboflow model from user")