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de1e3fc 6213763 de1e3fc | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 | """Batch loader: turn a folder of images + a CSV into Dataset-linked DB records.
Reuses the existing image pipeline (``process_and_store_picture``) for embedding/breed generation —
nothing is reimplemented here. One CSV row == one image == one KnownDog/UnknownDog record (matching
``prepare_test_data.py``). Owners/finders are de-duped by email and linked to the dataset.
Individual image failures are logged and skipped (the load never aborts); total errors are reported.
"""
from __future__ import annotations
import csv
import logging
import random
from dataclasses import dataclass, field
from datetime import date
from pathlib import Path
from typing import Callable
from sqlalchemy import select
from sqlalchemy.orm import Session
from ..models import Case, Dataset, KnownDog, UnknownDog, User
from ..models.base import (
CaseStatus,
CaseType,
DatasetType,
DogSize,
KnownDogStatus,
SubjectType,
UnknownDogStatus,
UserRole,
)
from .images import ImageValidationError, process_and_store_picture
logger = logging.getLogger("pawtrace.batch_loader")
ProgressCb = Callable[[dict], None]
@dataclass
class LoadResult:
dataset_id: int
dogs_loaded: int = 0
images_processed: int = 0
cases_created: int = 0
users_created: int = 0
errors: list[str] = field(default_factory=list)
matching: dict | None = None
def as_dict(self) -> dict:
return {
"dataset_id": self.dataset_id,
"dogs_loaded": self.dogs_loaded,
"images_processed": self.images_processed,
"cases_created": self.cases_created,
"users_created": self.users_created,
"error_count": len(self.errors),
"errors": self.errors,
"matching": self.matching,
}
def _coerce_size(value: str | None) -> DogSize | None:
if not value:
return None
try:
return DogSize(value.strip().lower())
except ValueError:
return None
def _read_csv(csv_path: Path) -> list[dict]:
with csv_path.open(newline="", encoding="utf-8") as fh:
return list(csv.DictReader(fh))
def _get_or_create_user(
db: Session, dataset_id: int, *, name: str, email: str, phone: str | None, zip_code: str,
role: UserRole, created_counter: list[int],
) -> User:
email = (email or "").strip().lower()
user = None
if email:
user = db.execute(select(User).where(User.email == email)).scalar_one_or_none()
if user is None:
# Synthesize a unique email if the CSV omitted one (anonymous finder).
if not email:
email = f"anon-{dataset_id}-{created_counter[0]}@example.invalid"
user = User(
name=name or "Unknown", email=email, phone=phone or None,
zip=zip_code or "00000", role=role, dataset_id=dataset_id,
)
db.add(user)
db.flush()
created_counter[0] += 1
return user
def _resolve_image(root: Path, folder: str, image_file: str) -> Path | None:
candidate = root / folder / image_file
if candidate.is_file():
return candidate
return None
def load_dataset(
db: Session,
*,
folder: str | Path,
dataset_type: str,
name: str,
description: str | None,
csv_path: str | Path,
mark_lost: bool = False,
mark_lost_pct: int = 100,
run_matching: bool = False,
skip_embeddings: bool = False,
group_by_folder: bool = True,
seed: int = 42,
progress: ProgressCb | None = None,
) -> LoadResult:
root = Path(folder)
csv_file = Path(csv_path)
if not csv_file.is_file():
raise FileNotFoundError(f"CSV not found: {csv_file}")
dtype = DatasetType(dataset_type)
is_known = dtype in (DatasetType.known, DatasetType.test_known)
dataset = Dataset(
name=name, type=dtype, description=description, source_path=str(root), dog_count=0,
)
db.add(dataset)
db.flush()
rows = _read_csv(csv_file)
total = len(rows)
rng = random.Random(seed)
result = LoadResult(dataset_id=dataset.id)
created_counter = [0]
# Group rows so every image of one identity becomes ONE dog with multiple pictures. The CSV
# `folder` column is the identity key (each DogFaceNet subfolder == one dog). With
# group_by_folder=False each row becomes its own single-picture dog (legacy behavior).
if group_by_folder:
grouped: dict[str, list[dict]] = {}
for row in rows:
grouped.setdefault((row.get("folder") or "").strip(), []).append(row)
groups = list(grouped.items())
else:
groups = [((row.get("folder") or "").strip(), [row]) for row in rows]
done_rows = 0
def _emit() -> None:
if progress:
progress({"processed": done_rows, "total": total, "dataset_id": dataset.id})
for folder_name, frows in groups:
try:
if is_known:
_load_known_folder(db, dataset, folder_name, frows, root, rng,
mark_lost, mark_lost_pct, result, created_counter,
generate=not skip_embeddings)
else:
_load_unknown_folder(db, dataset, folder_name, frows, root, result,
created_counter, generate=not skip_embeddings)
except Exception as exc: # noqa: BLE001 - skip-and-continue per spec
msg = f"folder {folder_name!r}: {exc}"
logger.warning(msg, exc_info=True)
result.errors.append(msg)
done_rows += len(frows)
_emit()
dataset.dog_count = result.dogs_loaded
db.commit()
if run_matching:
from .datasets import match_dataset
result.matching = match_dataset(db, dataset)
return result
def _load_known_folder(db, dataset, folder_name, frows, root, rng, mark_lost, mark_lost_pct,
result, created_counter, *, generate: bool = True) -> None:
"""Create one KnownDog for the folder, attaching every readable image as a picture."""
meta = frows[0]
dog: KnownDog | None = None
owner: User | None = None
first_pic = True
for row in frows:
image_file = (row.get("image_file") or "").strip()
img_path = _resolve_image(root, folder_name, image_file)
if img_path is None:
result.errors.append(f"folder {folder_name!r}: image not found {image_file!r}")
continue
try:
data = img_path.read_bytes()
if dog is None:
owner = _get_or_create_user(
db, dataset.id,
name=meta.get("owner_name", ""), email=meta.get("owner_email", ""),
phone=meta.get("owner_phone"), zip_code=meta.get("zip", ""),
role=UserRole.owner, created_counter=created_counter,
)
dog = KnownDog(
owner_id=owner.id, dataset_id=dataset.id,
name=meta.get("dog_name") or "Unnamed",
breed=meta.get("breed") or None,
color=meta.get("color") or None,
size=_coerce_size(meta.get("size")),
description=meta.get("description") or "",
status=KnownDogStatus.home,
)
db.add(dog)
db.flush()
result.dogs_loaded += 1
result.users_created = created_counter[0]
process_and_store_picture(
db, subject_type=SubjectType.known, subject_id=dog.id, data=data,
is_primary=first_pic, generate_embedding=generate, generate_breed=generate,
)
first_pic = False
result.images_processed += 1
except ImageValidationError as exc:
result.errors.append(f"folder {folder_name!r} image {image_file!r}: invalid ({exc})")
except Exception as exc: # noqa: BLE001
result.errors.append(f"folder {folder_name!r} image {image_file!r}: {exc}")
if dog is not None and mark_lost and rng.random() < (mark_lost_pct / 100.0):
zip_code = meta.get("zip") or (owner.zip if owner else "")
dog.status = KnownDogStatus.lost
dog.last_known_zip = zip_code
db.add(
Case(
person_id=owner.id if owner else None, known_dog_id=dog.id, type=CaseType.lost,
event_zip=zip_code, event_date=date.today(),
search_radius_miles=0, status=CaseStatus.open,
)
)
result.cases_created += 1
def _load_unknown_folder(db, dataset, folder_name, frows, root, result, created_counter,
*, generate: bool = True) -> None:
"""Create one UnknownDog + one found Case for the folder, attaching every readable image."""
meta = frows[0]
dog: UnknownDog | None = None
finder: User | None = None
first_pic = True
for row in frows:
image_file = (row.get("image_file") or "").strip()
img_path = _resolve_image(root, folder_name, image_file)
if img_path is None:
result.errors.append(f"folder {folder_name!r}: image not found {image_file!r}")
continue
try:
data = img_path.read_bytes()
if dog is None:
finder = _get_or_create_user(
db, dataset.id,
name=meta.get("finder_name", ""), email=meta.get("finder_email", ""),
phone=meta.get("finder_phone"), zip_code=meta.get("found_zip", ""),
role=UserRole.finder, created_counter=created_counter,
)
dog = UnknownDog(
dataset_id=dataset.id,
description=meta.get("description") or "",
color=meta.get("color") or None,
size=_coerce_size(meta.get("size")),
current_zip=meta.get("found_zip") or "",
current_location_detail=meta.get("current_location") or None,
status=UnknownDogStatus.at_shelter,
)
db.add(dog)
db.flush()
result.dogs_loaded += 1
result.users_created = created_counter[0]
process_and_store_picture(
db, subject_type=SubjectType.unknown, subject_id=dog.id, data=data,
is_primary=first_pic, generate_embedding=generate, generate_breed=generate,
)
first_pic = False
result.images_processed += 1
except ImageValidationError as exc:
result.errors.append(f"folder {folder_name!r} image {image_file!r}: invalid ({exc})")
except Exception as exc: # noqa: BLE001
result.errors.append(f"folder {folder_name!r} image {image_file!r}: {exc}")
if dog is not None and finder is not None:
found_zip = meta.get("found_zip") or ""
db.add(
Case(
person_id=finder.id,
finder_name=meta.get("finder_name") or finder.name,
finder_email=meta.get("finder_email") or finder.email,
finder_phone=meta.get("finder_phone"),
unknown_dog_id=dog.id, type=CaseType.found,
event_zip=found_zip, event_date=date.today(),
current_location=meta.get("current_location") or None,
search_radius_miles=0, status=CaseStatus.open,
)
)
result.cases_created += 1
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