# InferCode: Single-Pair Interface-B Full Pipeline

This directory provides an inference entry that runs the full Interface-B pipeline on one image pair (`OCTA + wfCFP`): Step1, Step2, and Step3.

Pipeline:

1. Step1 vessel segmentation (`CARe-VesselSeg` or `Broad domain retinal vessel segmentation [ICASSP 2025]`)
2. Step2 optic-disc/fovea detection (`wfCFP + OCTA`)
3. Step3 registration (primary + retry route selection)

## Run

Activate environment first:

```bash
source "$(conda info --base)/etc/profile.d/conda.sh"
conda activate MMR
```

Then run:

```bash
cd /path/to/CAReReleaseProject/Huggingface/InferCode
bash run_interface_b_single_pair.sh
```

`run_interface_b_single_pair.sh` is configured in-script. Edit variables inside the shell script instead of appending many CLI flags.

## Key Parameters

- `SEG_MODEL={CARe-VesselSeg,Broad domain retinal vessel segmentation [ICASSP 2025]}`
  - `CARe-VesselSeg`: original vessel segmentation model
  - `Broad domain retinal vessel segmentation [ICASSP 2025]`: broad-domain vessel segmentation model
- `REGISTRATION_MODE={interface_b_retry,direct_no_crop}`
  - `interface_b_retry`: Interface-B flow (Step2 coordinates + retry routes)
  - `direct_no_crop`: skip Step2 and cropping, go Step1 -> Step3 directly (suitable for CFP-CFP)
- `QUERY_MODALITY / REFER_MODALITY={AUTO,CFP,OCTA}`
  - Used when `SEG_MODEL=Broad domain retinal vessel segmentation [ICASSP 2025]`
  - `AUTO`: infer from filename (`OCTA` or `_001` -> OCTA, otherwise CFP)
- Auto-switch rule:
  - If `interface_b_retry` is requested but detected as `CFP-CFP`, it auto-switches to `direct_no_crop`
- `INLIER_METHOD={RANSAC,LMEDS}`: Step3 inlier filtering and polynomial fitting method
- `RETRY_FILTERED_INLIER_THRESHOLD`: trigger retry when primary filtered inliers drop below this value (default `20`)
- `DEVICE`: `auto/cuda:0/cpu` (default `auto`)

## Main Outputs

Default output root is `InferCode/Output/single_pair_<timestamp>`, including:

- `results/vessels/`
  - `octa_vessel.png`
  - `wfcfp_vessel.png`
- `results/keypoints/`
  - `filtered_pairs_norm.txt`
  - `filtered_pairs_input_space_pixels.txt`
  - `filtered_pairs_restored_space_pixels.txt`
  - `filtered_pairs_input_space_vis.png`
  - `filtered_pairs_restored_space_vis.png`
- `results/overlays/`
  - `vessel_overlay.png`
  - `raw_overlay.png`
- `results/polynomial/quadratic_polynomial_params.json`
  - quadratic polynomial coefficients
- `results/visualizations/summary_board.png`
  - summary visualization board
- `summary.json`
  - run summary (routing, metrics, and output paths)

## Weight Loading via Hugging Face Snapshot

For lightweight deployment, weights can be hosted in Hugging Face Model repos.

Recommended: separate repos for each stage.

```bash
export CARE_WEIGHT_STEP1_REPO_ID="Hongyang-Li/CARe_VesselSeg"
export CARE_WEIGHT_STEP2_REPO_ID="Hongyang-Li/CARe_OpticDiscFoveaDetection"
export CARE_WEIGHT_STEP3_REPO_ID="Hongyang-Li/CARe_Reg"
# Optional: override broad-domain vessel model repo
export CARE_WEIGHT_BROAD_REPO_ID="AIMClab-RUC/UNet_DCP_1024"
```

Optional:

- `CARE_WEIGHT_STEP1_REVISION`
- `CARE_WEIGHT_STEP2_REVISION`
- `CARE_WEIGHT_STEP3_REVISION`
- `CARE_WEIGHT_BROAD_REVISION`
- `CARE_WEIGHTS_CACHE_DIR`
- `CARE_WEIGHT_*_REL` for per-file relative path overrides

Backward-compatible single bundled repo mode is also supported through:

- `CARE_WEIGHTS_REPO_ID`
- `CARE_WEIGHTS_REVISION`