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e2f75d1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 | #!/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
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