Plant Tracer Movie Processing

This page describes the current upload, first-frame, playback, and retrace workflow. Historical phase plans have been collapsed into the live behavior.

Upload

  1. The browser computes a SHA-256 digest for the selected movie.

  2. The browser posts metadata to POST /api/new-movie.

  3. Flask creates a DynamoDB movie row with status uploading and returns a presigned S3 POST for the final object key.

  4. The browser uploads the movie directly to S3/MinIO with the returned form fields.

  5. The upload page requests the first frame from lambda-resize and links the user to Analyze.

There is no S3 uploads/ staging prefix and no S3 bucket notification path. Lambda is invoked by HTTP or SQS/local queue.

First Frame

The browser requests:

GET LAMBDA_API_BASE/resize-api/v1/first-frame?api_key=...&movie_id=...

lambda-resize validates the API key, signs the movie S3 URL, extracts frame 0, applies saved rotation, scales the frame to the analysis size, and returns a JPEG.

Playback

The movie list requests:

GET LAMBDA_API_BASE/api/v1/movie-data?api_key=...&movie_id=...&format=json

The compatibility route returns signed movie and optional ZIP URLs. The browser sets the video element source to the signed movie URL so playback bytes come from S3/MinIO.

Retracing

  1. The user edits markers on frame N.

  2. The browser saves frame N trackpoints through Flask POST /api/put-frame-trackpoints.

  3. The browser posts to lambda-resize:

    POST /resize-api/v1/trace-movie
x-api-key: <api_key>

    { "movie_id": "m...", "frame_start": 12, "frame_end": 200 }

  4. lambda-resize clears trackpoints after frame N, marks the movie as tracing, and queues the work.

  5. In local mode, TRACING_QUEUE_MODE=local sends work to the in-process queue. In deployed mode, TRACING_QUEUE_URL sends work to SQS.

  6. The worker traces from frame N + 1 through optional frame_end, writes trackpoints to DynamoDB, writes a ZIP and traced movie to S3/MinIO, and marks status as tracing completed.

  7. The browser polls /api/get-movie-metadata until completion.

Local Process Model

Use two local processes:

make run-local-lambda-debug
make run-local-debug

run-local-debug starts Flask on port 8080 and ensures a local Lambda endpoint is available. run-local-lambda-debug starts the local HTTP bridge on port 9811 and the local retrace worker.

Video Processing Library

Current lambda-resize frame extraction, scaling, JPEG generation, and tracing use OpenCV (cv2) and Pillow. ffmpeg is legacy/local tooling, not the Lambda runtime path.