yolov26_3d/tools/feishu_project/SKILL_fp.md

name, description

name

description

feishu-project-issue-data

Use when working in the yolo26-3d repository and the user wants to read Feishu Project issue views, export a view to structured JSON, classify issue data addresses, download issue data, repair affected standard-path downloads, validate case completeness, or run batch inference on downloaded issue data. Covers fp CLI plus tools/feishu_project/export_feishu_view_issues.py, download_issue_data.py/sh, and run_issue_data_inference.py/sh, including the 董颖-G1Q3 workflow.

Feishu Project Issue Data

Use the repo dev env for Python commands:

/root/.codex/skills/use-dongying-dev-env/scripts/with-dev-env.sh python ...

or:

/deeplearning_team/ydong/dongying/miniconda/envs/dev/bin/python ...

Current repo defaults are documented in ../../feishu_project.md.

When to use

• Read or verify a Feishu Project issue view with fp
• Export a view such as 董颖-G1Q3 to structured JSON
• Work with 问题数据地址 / 问题数据地址_PDCL
• Download issue data into the local workspace
• Repair historical bad copies that only copied sigmastar.1
• Check whether downloaded cases are inference-ready
• Batch-run exported-model inference on downloaded issue data

Core workflow

1. Verify Feishu access and view contents

Use fp directly when the user wants current data.

fp view list -p <project_key> -u <user_key> -t issue --name "<view_name>"
fp workitem list -o json -p <project_key> -u <user_key> --view "<view_name>" --all
fp workitem get <issue_id> -o json -p <project_key> -u <user_key> -t issue

2. Export structured issue JSON

Use ../../export_feishu_view_issues.py.

python ../../export_feishu_view_issues.py \
  --project-key <project_key> \
  --user-key <user_key> \
  --view-name "<view_name>" \
  --output ../../dongying_g1q3_issue_list.json

The export should include:

• 缺陷标签池
• 问题数据地址
• 问题数据地址_PDCL
• 问题发生frameid

3. Interpret data-address fields

Treat these as the same download class:

• pure ADAS_...::... clip references
• mdi raw -r ... commands

Use this rule:

• ADAS_xxx::yyy is equivalent to mdi raw -r ADAS_xxx::yyy -s .

Standard paths use these normalization rules:

• rewrite hfs/project-G1M3 or project-G1M3 to G1M3 when needed
• if the path ends with sigmastar.1, copy the parent case dir
• if the path ends with sigmastar.1/camera4.bin, copy the case dir above it
• if the copied case has no local test_data/calibs/camera4.json, sync a shared parent test_data directory when present

4. Download or repair issue data

Use ../../download_issue_data.sh or ../../download_issue_data.py.

Common modes:

DRY_RUN=1 bash ../../download_issue_data.sh

ONLY_REDOWNLOAD_AFFECTED_CASES=1 bash ../../download_issue_data.sh

SKIP_MDI=1 bash ../../download_issue_data.sh --issue-id <id>

Defaults:

• download root: /data1/dongying/Mono3d/G1Q3/feishu_project/downloaded_issue_data
• manifest: <download_root>/download_manifest.json

Use ONLY_REDOWNLOAD_AFFECTED_CASES=1 to repair old standard-path copies that previously kept only sigmastar.1.

5. Validate inference readiness

Use the same case-resolution rules as ../../../model_inference/adapters/video_dir_inference_utils.py.

A valid case must resolve:

• */sigmastar.1/camera4.bin
• a reachable camera4.json from one of:
  • case_dir/test_data/calibs/camera4.json
  • case_dir.parent/test_data/calibs/camera4.json
  • case_dir/sigmastar.1/calibs/camera4.json
  • case_dir/calibs/camera4.json

Prefer validating with the actual inference path-resolution logic instead of ad hoc file checks.

6. Run batch inference on downloaded issue data

Use ../../run_issue_data_inference.sh or ../../run_issue_data_inference.py.

DRY_RUN=1 bash ../../run_issue_data_inference.sh

bash ../../run_issue_data_inference.sh

Behavior:

• recursively scans the download root for */sigmastar.1/camera4.bin
• calls ../../../model_inference/core/run_two_roi_exported_onnx_infer.py with --video-case-dir
• mirrors the download-tree relative layout into the inference output root

Defaults:

• inference root: /data1/dongying/Mono3d/G1Q3/feishu_project/inference_issue_data
• manifest: <inference_root>/inference_manifest.json

Useful flags:

• SKIP_EXISTING=1
• ENABLE_ATTR=1
• SAVE_AGGREGATE_PREDICTIONS=1
• VIDEO_STRIDE=<n>
• MAX_IMAGES=<n>

Current repo artifacts

These files are useful outputs, but they are not the source of truth for latest Feishu data:

• ../../dongying_g1q3_issue_list.json
• ../../dongying_g1q3_data_address_summary.md
• ../../dongying_g1q3_data_address_catalog.md

If the user asks for latest status, re-query Feishu with fp and regenerate outputs instead of trusting stale local exports.