File size: 4,251 Bytes
11a414e | 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 | """Dataset Splitter Engine.
Stratified, cluster-aware train / val / test dataset partitioning:
- Prevents data leakage by ensuring all images in the same visual `cluster_id`
are assigned to the SAME split (train, val, or test).
- Maintains balanced class distributions across splits according to target ratios (e.g. 70 / 15 / 15).
- Exports organized image & label directories under `data/splits/` for YOLOv8 model training.
"""
from __future__ import annotations
import logging
import random
import shutil
from collections import defaultdict
from pathlib import Path
from typing import Sequence
from .manifest import Manifest
log = logging.getLogger(__name__)
def create_cluster_splits(
manifest: Manifest,
train_ratio: float = 0.70,
val_ratio: float = 0.15,
test_ratio: float = 0.15,
seed: int = 42,
) -> dict[str, list[dict]]:
"""Partition valid images into cluster-isolated train/val/test splits."""
random.seed(seed)
rows = manifest.images(statuses=["deduped", "kept", "validated", "normalized"])
if not rows:
log.warning("splitter: no valid images found to split")
return {"train": [], "val": [], "test": []}
clusters: dict[str, list[dict]] = defaultdict(list)
for r in rows:
cid = r["cluster_id"] or r["image_id"]
clusters[cid].append(r)
cluster_items = list(clusters.items())
random.shuffle(cluster_items)
total_count = len(rows)
target_train = int(total_count * train_ratio)
target_val = int(total_count * val_ratio)
splits: dict[str, list[dict]] = {"train": [], "val": [], "test": []}
current_train = 0
current_val = 0
for cid, members in cluster_items:
mem_len = len(members)
if current_train < target_train:
splits["train"].extend(members)
current_train += mem_len
elif current_val < target_val:
splits["val"].extend(members)
current_val += mem_len
else:
splits["test"].extend(members)
return splits
def run(
manifest: Manifest,
cfg: dict,
data_dir: Path,
splits_dir: Path,
train_ratio: float = 0.70,
val_ratio: float = 0.15,
test_ratio: float = 0.15,
copy_files: bool = True,
seed: int = 42,
) -> dict[str, int]:
"""Execute cluster-isolated dataset split and write image & label files to splits directory."""
splits = create_cluster_splits(
manifest, train_ratio=train_ratio, val_ratio=val_ratio, test_ratio=test_ratio, seed=seed
)
stats = {
"train": len(splits["train"]),
"val": len(splits["val"]),
"test": len(splits["test"]),
"total_split": sum(len(s) for s in splits.values()),
}
if not stats["total_split"]:
log.warning("splitter: 0 images split")
return stats
with manifest.transaction():
for split_name, members in splits.items():
for m in members:
manifest.update_image(m["image_id"], split=split_name)
auto_labels_dir = data_dir / "annotated_auto" / "labels"
if copy_files:
for split_name, members in splits.items():
dst_img_dir = splits_dir / split_name / "images"
dst_lbl_dir = splits_dir / split_name / "labels"
dst_img_dir.mkdir(parents=True, exist_ok=True)
dst_lbl_dir.mkdir(parents=True, exist_ok=True)
for m in members:
src_rel = m.get("norm_path") or m.get("orig_path")
if not src_rel:
continue
src_path = data_dir / src_rel
if src_path.exists():
dst_path = dst_img_dir / src_path.name
if not dst_path.exists():
shutil.copy2(src_path, dst_path)
# Copy matching label file if available
label_name = f"{src_path.stem}.txt"
src_label = auto_labels_dir / label_name
if src_label.exists():
dst_label = dst_lbl_dir / label_name
if not dst_label.exists():
shutil.copy2(src_label, dst_label)
log.info("splitter split summary: %s", stats)
return stats
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