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6903746 | 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 | """
Create a clean train/test split with no data leakage.
Rules:
- Internet classes (exaltata, garganica, incubacea, sphegodes):
50 images from internet_cropped → test, rest → train
All private images → train
- Private-only classes (majellensis, sphegodes_Palena):
50 images from private → test, rest → train
Result:
dataset/train_clean/{class}/ — training images (no overlap with test)
dataset/test_clean/{class}/ — test images (50 per class, balanced)
Uses fixed seed for reproducibility. Non-destructive (copies, not moves).
"""
import os
import json
import shutil
import hashlib
from pathlib import Path
from datetime import datetime
import numpy as np
SEED = 42
TEST_PER_CLASS = 50
BASE_DIR = Path(__file__).parent.parent / "dataset"
# Source directories
PRIVATE_DIR = BASE_DIR / "raw"
INTERNET_CROPPED_DIR = BASE_DIR / "internet_cropped"
# Output directories
TRAIN_CLEAN_DIR = BASE_DIR / "train_clean"
TEST_CLEAN_DIR = BASE_DIR / "test_clean"
# All 6 classes
ALL_CLASSES = [
"O. exaltata",
"O. garganica",
"O. incubacea",
"O. majellensis",
"O. sphegodes",
"O. sphegodes_Palena",
]
# Classes that have internet images
INTERNET_CLASSES = [
"O. exaltata",
"O. garganica",
"O. incubacea",
"O. sphegodes",
]
# Classes that only have private images
PRIVATE_ONLY_CLASSES = [
"O. majellensis",
"O. sphegodes_Palena",
]
def list_images(directory):
"""List image files in a directory."""
if not os.path.exists(directory):
return []
return sorted([
f for f in os.listdir(directory)
if f.lower().endswith(('.jpg', '.jpeg', '.png'))
])
def md5(path):
"""Compute MD5 hash of a file."""
with open(path, 'rb') as f:
return hashlib.md5(f.read()).hexdigest()
def split_class(source_dir, n_test, rng):
"""Split images in source_dir into test (n_test) and train (rest)."""
files = list_images(source_dir)
if len(files) < n_test:
raise ValueError(
f"Not enough images in {source_dir}: {len(files)} < {n_test}"
)
indices = rng.permutation(len(files))
test_files = [files[i] for i in indices[:n_test]]
train_files = [files[i] for i in indices[n_test:]]
return train_files, test_files
def main():
rng = np.random.default_rng(SEED)
# Create output directories
for cls in ALL_CLASSES:
os.makedirs(TRAIN_CLEAN_DIR / cls, exist_ok=True)
os.makedirs(TEST_CLEAN_DIR / cls, exist_ok=True)
manifest = {
"seed": SEED,
"test_per_class": TEST_PER_CLASS,
"timestamp": datetime.now().isoformat(),
"classes": {},
}
total_train = 0
total_test = 0
for cls in ALL_CLASSES:
cls_manifest = {"train_sources": {}, "test_source": None}
# --- Test set ---
if cls in INTERNET_CLASSES:
# Test from internet_cropped
source_dir = INTERNET_CROPPED_DIR / cls
_, test_files = split_class(source_dir, TEST_PER_CLASS, rng)
cls_manifest["test_source"] = f"internet_cropped/{cls}"
cls_manifest["test_count"] = len(test_files)
for f in test_files:
shutil.copy2(source_dir / f, TEST_CLEAN_DIR / cls / f)
# Train: all private + remaining internet
train_files_priv = list_images(PRIVATE_DIR / cls)
internet_all = list_images(source_dir)
train_files_inet = [f for f in internet_all if f not in test_files]
for f in train_files_priv:
shutil.copy2(PRIVATE_DIR / cls / f, TRAIN_CLEAN_DIR / cls / f)
for f in train_files_inet:
shutil.copy2(source_dir / f, TRAIN_CLEAN_DIR / cls / f)
cls_manifest["train_sources"] = {
"private": len(train_files_priv),
"internet_remaining": len(train_files_inet),
}
cls_manifest["train_count"] = len(train_files_priv) + len(train_files_inet)
else:
# Private-only class: test from private, rest to train
source_dir = PRIVATE_DIR / cls
train_files, test_files = split_class(source_dir, TEST_PER_CLASS, rng)
cls_manifest["test_source"] = f"raw/{cls}"
cls_manifest["test_count"] = len(test_files)
for f in test_files:
shutil.copy2(source_dir / f, TEST_CLEAN_DIR / cls / f)
for f in train_files:
shutil.copy2(source_dir / f, TRAIN_CLEAN_DIR / cls / f)
cls_manifest["train_sources"] = {"private": len(train_files)}
cls_manifest["train_count"] = len(train_files)
total_train += cls_manifest["train_count"]
total_test += cls_manifest["test_count"]
manifest["classes"][cls] = cls_manifest
print(f"{cls}:")
print(f" Train: {cls_manifest['train_count']} "
f"({', '.join(f'{k}={v}' for k, v in cls_manifest['train_sources'].items())})")
print(f" Test: {cls_manifest['test_count']} (from {cls_manifest['test_source']})")
manifest["total_train"] = total_train
manifest["total_test"] = total_test
# Save manifest
manifest_path = BASE_DIR / "split_manifest.json"
with open(manifest_path, "w") as f:
json.dump(manifest, f, indent=2)
print(f"\nManifest saved: {manifest_path}")
print(f"Total: {total_train} train + {total_test} test = {total_train + total_test} images")
if __name__ == "__main__":
main()
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