Crane Zone Safety Video Pipeline Automation Script

Role: Senior Site Automation Engineer with deep expertise in edge computer vision pipelines for high-rise developments.

Context

• Camera Stream Endpoint: {{camera_rtsp_stream}}
• Frame Sampling Interval: {{target_frame_rate}}
• Critical Exclusion Polygon: {{bounding_zone_polygon}}
• Cloud Object Storage: {{storage_bucket_uri}}
• Hardware Acceleration ID: {{gpu_device_id}}
• Active Site Shift Hours: {{site_shift_schedule}}

Task

Write a Python automation script using OpenCV to ingest construction site RTSP camera streams, verify active shift operating hours, apply spatial masking to restricted crane swing zones, and upload sampled frames to object storage for automated safety inspection.

Method

1. Initialize hardware configuration binding to {{gpu_device_id}} and validating OpenCV video capture backend drivers.
2. Parse active shift boundaries from {{site_shift_schedule}} and set up a schedule check to pause execution during off-hours.
3. Open a stable video stream loop connecting to {{camera_rtsp_stream}} with automatic reconnect handling on timeout.
4. Sample frames selectively based on {{target_frame_rate}} rather than decoding every raw video frame to save compute.
5. Convert {{bounding_zone_polygon}} coordinates into a spatial mask and extract the designated high-risk region of interest.
6. Normalize and compress extracted region frames into JPEG format with standardized metadata headers.
7. Batch upload compressed frames to {{storage_bucket_uri}} using asynchronous cloud client operations.
8. Log throughput performance, frame drop counts, and connection latency on a recurring 5-minute schedule.

Constraints

• The script MUST drop corrupted video packets without blocking the main event thread.
• Frame uploads MUST include ISO-8601 timestamps and camera identifiers embedded in object key naming.
• CPU utilization MUST NOT exceed 40% when hardware acceleration via {{gpu_device_id}} is enabled.
• Raw full-resolution frames MUST NOT be saved to local gateway storage to avoid disk exhaustion.

Output format

Provide the complete response structured under these headings:

1. Dependency Configuration (required packages and OpenCV GPU compilation flags).
2. Complete Python Processing Pipeline (production-ready script with inline logging).
3. Operational Monitoring Commands (commands to verify pipeline health and test RTSP connectivity).

Self-review

• Confirm that {{bounding_zone_polygon}} handling accounts for varying stream resolutions dynamically.
• Ensure all 6 context variables ({{camera_rtsp_stream}}, {{target_frame_rate}}, {{bounding_zone_polygon}}, {{storage_bucket_uri}}, {{gpu_device_id}}, {{site_shift_schedule}}) are consumed.
• Verify stream reconnect logic implements exponential backoff to avoid flooding site network switches.