code/public_common.py

#!/usr/bin/env python3
"""
Shared helpers for public ophthalmology dataset adapters.

Aligns each public cohort with the private (shanghai_drioct_triton) manifest schema
in build_manifest.py so a single training pipeline can pd.concat the two parquets.

Adapter output layout (under {output_root}):
    extracted/{cohort}/{hash[:2]}/{hash}/
        {image_slot}.{ext}      e.g. fundus_color.jpg, oct_bscan/000.png
        *_mask.png              auxiliary segmentation masks (not in manifest rows)
        meta.json               raw per-study metadata
    manifest/{cohort}_images.parquet
    captions/{cohort}_captions.parquet
    manifest/{cohort}_sidecar.parquet     (optional, IDRiD localization etc.)

build_public_manifest.py concatenates all per-cohort parquets into:
    manifest/public_images_v1.parquet
    manifest/public_studies_v1.parquet
    captions/public_captions_v1.parquet
    schema_v1.json

file_path in public manifest is relative to {output_root}/extracted/ and always starts
with the cohort name. Private file_path is relative to {private_root}/extracted/{cohort}/
(no leading cohort) — training code reads each manifest with its own root prefix.
"""
import hashlib
import json
from pathlib import Path


# Column order must match build_manifest.py emission for clean pd.concat.
IMAGE_SCHEMA_COLUMNS = [
    "cohort", "study_id", "patient_hash", "visit_date", "eye",
    "device_vendor", "device_model", "device_serial_hash", "device_software_version",
    "hospital_domain", "ethnicity",
    "image_quality_score", "image_quality_band",
    "diagnosis_group", "lesion_tags", "lesion_location", "layer_involvement", "severity",
    "diagnosis_source", "label_confidence", "schema_version",
    "image_id", "file_path", "file_format",
    "modality", "anatomy", "device_technology", "scan_protocol",
    "scan_x_mm", "bscan_index",
    "image_height_px", "image_width_px", "axial_resolution_um",
    "has_segmentation", "n_layers_visible",
    "fovea_x_norm", "crt_um", "choroid_thickness_um",
    "oct_footprint_bbox_fundus", "oct_footprint_bbox_slo",
    "is_valid",
]

CAPTION_SCHEMA_COLUMNS = [
    "caption_id", "image_id", "level", "prompt_text",
    "language", "generator", "grounded_in",
]


def study_hash_for(cohort: str, basename: str) -> str:
    """Per-cohort namespaced hash. Salt = "{cohort}_v1" — distinct from private
    salt "shdoct_v1" so even identical basenames cannot collide across cohorts."""
    salt = f"{cohort}_v1"
    return hashlib.sha256(f"{salt}:{basename}".encode()).hexdigest()[:16]


def default_base_fields(cohort: str, study_id: str, *, patient_hash: str = None,
                        eye: str = "unknown", ethnicity: str = "unknown",
                        hospital_domain: str = None) -> dict:
    """Returns the 21 cohort/patient/label fields that are constant across all
    images of a study. Adapter merges this with the 20 per-image fields."""
    return {
        "cohort": cohort,
        "study_id": study_id,
        "patient_hash": patient_hash or study_id,
        "visit_date": None,
        "eye": eye,
        "device_vendor": "unknown",
        "device_model": "unknown",
        "device_serial_hash": None,
        "device_software_version": None,
        "hospital_domain": hospital_domain or f"{cohort}_v1",
        "ethnicity": ethnicity,
        "image_quality_score": None,
        "image_quality_band": "unknown",
        "diagnosis_group": [],
        "lesion_tags": [],
        "lesion_location": [],
        "layer_involvement": [],
        "severity": "unknown",
        "diagnosis_source": "none",
        "label_confidence": None,
        "schema_version": "v1",
    }


# ============================================================
# Caption templates (public, device-agnostic — never mentions Topcon)
# ============================================================

_MOD_PHRASE = {
    "fundus_color": ("color fundus photograph", "color fundus photo"),
    "slo_gray": ("grayscale scanning laser ophthalmoscopy image", "grayscale SLO"),
    "oct_bscan": ("OCT B-scan", "OCT B-scan"),
}


def _eye_long(eye):
    return {"OD": "right", "OS": "left"}.get(eye, "unspecified")


def _eye_short(eye):
    return eye if eye in ("OD", "OS") else "unspecified eye"


def caption_l1_public(image_id: str, cohort_phrase: str, modality: str,
                      eye: str = "unknown") -> list:
    """4 L1 variants (v1_factual/v2_style/v3_prefix/v4_short), grounded only in
    manifest fields. Mirrors private caption_l1_variants in build_manifest.py."""
    mod_long, mod_short = _MOD_PHRASE.get(modality, (modality, modality))
    eye_l = _eye_long(eye)
    article = "An" if mod_long[0].lower() in "aeiou" else "A"
    common = {"image_id": image_id, "language": "en",
              "generator": "template_v1", "grounded_in": "manifest_fields_only"}
    return [
        {**common, "caption_id": f"{image_id}_L1_v1_factual", "level": "L1_v1_factual",
         "prompt_text": f"{article} {mod_long} of the {eye_l} eye, from the {cohort_phrase}."},
        {**common, "caption_id": f"{image_id}_L1_v2_style", "level": "L1_v2_style",
         "prompt_text": f"{article} {mod_long} of the {eye_l} eye, public dataset style."},
        {**common, "caption_id": f"{image_id}_L1_v3_prefix", "level": "L1_v3_prefix",
         "prompt_text": f"{cohort_phrase}, {mod_long}, {eye_l} eye."},
        {**common, "caption_id": f"{image_id}_L1_v4_short", "level": "L1_v4_short",
         "prompt_text": f"A {mod_short}, {_eye_short(eye)}, public dataset."},
    ]


def caption_l3_public(image_id: str, prompt_text: str,
                      grounded_in: str = "manifest_fields_only") -> dict:
    return {
        "caption_id": f"{image_id}_L3_derived",
        "image_id": image_id, "level": "L3_derived",
        "prompt_text": prompt_text,
        "language": "en", "generator": "template_v1",
        "grounded_in": grounded_in,
    }


# ============================================================
# IO helpers
# ============================================================

def study_dir_for(out_root: Path, cohort: str, study_hash: str) -> Path:
    """{out_root}/extracted/{cohort}/{hash[:2]}/{hash}/"""
    return out_root / "extracted" / cohort / study_hash[:2] / study_hash


def rel_file_path(cohort: str, study_hash: str, filename: str) -> str:
    """Manifest file_path: '{cohort}/{hash[:2]}/{hash}/{filename}', relative to
    {out_root}/extracted/. Same root works for all public cohorts."""
    return f"{cohort}/{study_hash[:2]}/{study_hash}/{filename}"


def write_meta(study_dir: Path, meta: dict):
    """Atomic write of meta.json — readers see either a complete prior meta or
    the new one, never a half-written file (extractor crash recovery)."""
    tmp = study_dir / "meta.json.tmp"
    tmp.write_text(json.dumps(meta, indent=2, ensure_ascii=False))
    tmp.rename(study_dir / "meta.json")


def coerce_image_row(row: dict) -> dict:
    """Ensure all 41 columns present (fill missing with None) and column order
    matches IMAGE_SCHEMA_COLUMNS. Called by adapters before DataFrame creation."""
    out = {}
    for col in IMAGE_SCHEMA_COLUMNS:
        out[col] = row.get(col, None)
    return out