Datasets:
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"""
Script to split the lines_dataset into train/validation/test splits.
- 80% train
- 10% validation
- 10% test
The split is done by source image to prevent data leakage - all crops from the
same source image go into the same split.
"""
import json
import shutil
import random
from pathlib import Path
from collections import defaultdict
# Configuration
RANDOM_SEED = 42
TRAIN_RATIO = 0.8
VAL_RATIO = 0.1
TEST_RATIO = 0.1
BASE_DIR = Path(__file__).parent
CURRENT_TRAIN_DIR = BASE_DIR / "train"
METADATA_FILE = CURRENT_TRAIN_DIR / "metadata.jsonl"
def load_metadata():
"""Load all metadata from the jsonl file."""
if not METADATA_FILE.exists():
print(f"Metadata file not found: {METADATA_FILE}")
return []
metadata = []
with open(METADATA_FILE, 'r') as f:
for line in f:
if line.strip():
metadata.append(json.loads(line))
return metadata
def group_by_source(metadata):
"""Group samples by their source image index."""
groups = defaultdict(list)
for item in metadata:
source_idx = item.get("source_image_idx", 0)
groups[source_idx].append(item)
return groups
def split_sources(source_indices, train_ratio, val_ratio, test_ratio):
"""Split source indices into train/val/test sets."""
random.shuffle(source_indices)
n = len(source_indices)
n_train = int(n * train_ratio)
n_val = int(n * val_ratio)
train_sources = source_indices[:n_train]
val_sources = source_indices[n_train:n_train + n_val]
test_sources = source_indices[n_train + n_val:]
return train_sources, val_sources, test_sources
def create_split_directory(split_name, samples, base_dir, source_dir):
"""Create a split directory with images and metadata."""
split_dir = base_dir / split_name
split_dir.mkdir(parents=True, exist_ok=True)
# Copy images and prepare metadata
metadata_entries = []
for sample in samples:
file_name = sample["file_name"]
src_path = source_dir / file_name
dst_path = split_dir / file_name
if src_path.exists():
shutil.copy2(src_path, dst_path)
metadata_entries.append(sample)
else:
print(f"Warning: Image not found: {src_path}")
# Write metadata
metadata_path = split_dir / "metadata.jsonl"
with open(metadata_path, 'w') as f:
for entry in metadata_entries:
f.write(json.dumps(entry) + '\n')
return len(metadata_entries)
def main():
random.seed(RANDOM_SEED)
print("Loading metadata...")
metadata = load_metadata()
print(f"Total samples: {len(metadata)}")
# Group by source image
print("Grouping by source image...")
groups = group_by_source(metadata)
source_indices = list(groups.keys())
print(f"Total source images: {len(source_indices)}")
# Split source indices
print(f"\nSplitting sources ({TRAIN_RATIO:.0%} train, {VAL_RATIO:.0%} val, {TEST_RATIO:.0%} test)...")
train_sources, val_sources, test_sources = split_sources(
source_indices, TRAIN_RATIO, VAL_RATIO, TEST_RATIO
)
print(f" Train sources: {len(train_sources)}")
print(f" Validation sources: {len(val_sources)}")
print(f" Test sources: {len(test_sources)}")
# Gather samples for each split
train_samples = []
val_samples = []
test_samples = []
for src_idx in train_sources:
train_samples.extend(groups[src_idx])
for src_idx in val_sources:
val_samples.extend(groups[src_idx])
for src_idx in test_sources:
test_samples.extend(groups[src_idx])
print(f"\nSamples per split:")
print(f" Train: {len(train_samples)} ({len(train_samples)/len(metadata)*100:.1f}%)")
print(f" Validation: {len(val_samples)} ({len(val_samples)/len(metadata)*100:.1f}%)")
print(f" Test: {len(test_samples)} ({len(test_samples)/len(metadata)*100:.1f}%)")
# Create temporary directory for new structure
temp_dir = BASE_DIR / "_temp_splits"
temp_dir.mkdir(exist_ok=True)
print("\nCreating split directories...")
# Create each split
n_train = create_split_directory("train", train_samples, temp_dir, CURRENT_TRAIN_DIR)
print(f" Created train split: {n_train} samples")
n_val = create_split_directory("validation", val_samples, temp_dir, CURRENT_TRAIN_DIR)
print(f" Created validation split: {n_val} samples")
n_test = create_split_directory("test", test_samples, temp_dir, CURRENT_TRAIN_DIR)
print(f" Created test split: {n_test} samples")
# Remove old train directory and move new splits
print("\nReorganizing directory structure...")
# Remove old train directory
shutil.rmtree(CURRENT_TRAIN_DIR)
# Move new splits from temp to base
for split_name in ["train", "validation", "test"]:
src = temp_dir / split_name
dst = BASE_DIR / split_name
shutil.move(str(src), str(dst))
# Remove temp directory
temp_dir.rmdir()
print("\nDone! New directory structure:")
print(f" {BASE_DIR}/train/ ({n_train} samples)")
print(f" {BASE_DIR}/validation/ ({n_val} samples)")
print(f" {BASE_DIR}/test/ ({n_test} samples)")
# Count total lines per split
def count_lines(samples):
return sum(len(s.get("lines", {}).get("segments", [])) for s in samples)
print(f"\nTotal lines per split:")
print(f" Train: {count_lines(train_samples)}")
print(f" Validation: {count_lines(val_samples)}")
print(f" Test: {count_lines(test_samples)}")
if __name__ == "__main__":
main()
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