photo-to-replan

name	photo-to-replan
description	Use this skill when the user wants to run the end-to-end all-sky photo pipeline from a raw all-sky image to a replanned observing sequence, using the packaged run_pipeline.py entrypoint in this shared directory.

Photo To Replan

Use this skill when the task is to go from a raw all-sky image to replanned observing outputs.

Workflow

1. Locate the shared photo_to_replan_pipeline directory.

2. Run the single-entry pipeline script from that directory:

python run_pipeline.py --image /abs/path/to/image.jpg

Optional:

• Override parameter group with --position-name position_X
• Save segmentation overlay with --save-overlay

3. Read the generated report:

• output/<image_stem>/pipeline_report.json

4. From the report, inspect these outputs when needed:

• scenario.json
• replanned_schedule.json
• deferred_targets.json
• replanned_sequence.ninaTargetSet
• sky_replan_plot.png

Input Requirements

• The input must be a raw all-sky camera photo.
• The filename should contain Beijing time, preferably in YYYY_MM_DD_HH_MM_SS.jpg form.

Notes

• The mask stage uses the packaged Pic2mask/full_image_infer/infer_full_image.py.
• The replan stage uses the packaged sequence_adjust_tool/scheme_horizon_to_image/replan.py.
• The pipeline configuration is stored in configs/default.json.
• The packaged configuration uses a single conda environment for the whole pipeline.

Validation

A run counts as pipeline-successful if these files exist and are readable:

• pipeline_report.json
• scenario.json
• replanned_schedule.json
• replanned_sequence.ninaTargetSet

Business success is separate. Check deferred_targets.json and replanned_schedule.json to see whether future slots were actually filled.