File size: 5,661 Bytes
2adb41a | 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 | """
TrackChain SAHI Dataset Slicer (tc.v1 SOTA).
Slices high-resolution or standard railway defect imagery into overlapping crops,
recomputing normalized YOLO bounding boxes for each tile.
Acts as a dataset multiplier and small-object detection booster (fasteners, clips, micro-cracks).
"""
import argparse
import os
import sys
from pathlib import Path
from typing import List, Dict, Tuple, Optional, Any
import cv2
import numpy as np
from tqdm import tqdm
import yaml
# Ensure project root is in sys.path
repo_root = Path(__file__).resolve().parent.parent.parent
if str(repo_root) not in sys.path:
sys.path.insert(0, str(repo_root))
from ml.data.synthetic_vision import sanitize_bbox, CLASS_NAMES
from ml.scripts.generate_synthetic_defects import slice_image_and_boxes
def slice_yolo_dataset(
input_dir: str = "data/external/rail_defects",
output_dir: str = "data/external/rail_defects_sahi_sliced",
slice_size: int = 480,
overlap_ratio: float = 0.20,
min_visibility: float = 0.25,
) -> Dict[str, Any]:
"""
Load an existing YOLO dataset, slice each image into overlapping tiles,
update bounding boxes, and save to output_dir.
"""
abs_input = Path(input_dir) if Path(input_dir).is_absolute() else repo_root / input_dir
abs_output = Path(output_dir) if Path(output_dir).is_absolute() else repo_root / output_dir
abs_output.mkdir(parents=True, exist_ok=True)
print("=" * 75)
print("TrackChain SAHI Dataset Multiplier & Slicer (tc.v1 SOTA)")
print("=" * 75)
print(f"Input Dataset: {abs_input}")
print(f"Output Dataset: {abs_output}")
print(f"Slice Tile Size: {slice_size}x{slice_size}")
print(f"Overlap Ratio: {overlap_ratio:.1%}")
print(f"Min Visibility: {min_visibility:.1%}")
stats = {"original_images": 0, "sliced_tiles": 0, "splits": {}}
for split in ["train", "valid", "test"]:
in_img_dir = abs_input / split / "images"
in_lbl_dir = abs_input / split / "labels"
out_img_dir = abs_output / split / "images"
out_lbl_dir = abs_output / split / "labels"
out_img_dir.mkdir(parents=True, exist_ok=True)
out_lbl_dir.mkdir(parents=True, exist_ok=True)
if not in_img_dir.exists():
continue
img_files = sorted(list(in_img_dir.glob("*.jpg")) + list(in_img_dir.glob("*.png")) + list(in_img_dir.glob("*.jpeg")))
tile_count = 0
for img_p in tqdm(img_files, desc=f"Slicing {split} split"):
stats["original_images"] += 1
lbl_p = in_lbl_dir / (img_p.stem + ".txt")
boxes: List[Dict[str, Any]] = []
if lbl_p.exists():
with open(lbl_p, "r", encoding="utf-8") as f:
for line in f:
parts = line.strip().split()
if len(parts) >= 5:
cls_id = int(parts[0])
clean_b = sanitize_bbox([float(x) for x in parts[1:5]])
if clean_b:
boxes.append({"class_id": cls_id, "bbox": clean_b})
img = cv2.imread(str(img_p))
if img is None:
continue
sliced_items = slice_image_and_boxes(
img,
boxes,
slice_size=slice_size,
overlap_ratio=overlap_ratio,
min_visibility=min_visibility,
)
for t_idx, (t_img, t_boxes) in enumerate(sliced_items):
tile_count += 1
base_name = f"{img_p.stem}_sahi_{t_idx:02d}"
out_img_path = out_img_dir / f"{base_name}.jpg"
out_lbl_path = out_lbl_dir / f"{base_name}.txt"
cv2.imwrite(str(out_img_path), t_img)
with open(out_lbl_path, "w", encoding="utf-8") as f:
for b in t_boxes:
bx = b["bbox"]
f.write(f"{b['class_id']} {bx[0]:.6f} {bx[1]:.6f} {bx[2]:.6f} {bx[3]:.6f}\n")
stats["splits"][split] = tile_count
stats["sliced_tiles"] += tile_count
# Generate data.yaml
data_yaml = {
"path": abs_output.resolve().as_posix(),
"train": "train/images",
"val": "valid/images",
"test": "test/images",
"nc": 4,
"names": CLASS_NAMES,
}
yaml_path = abs_output / "data.yaml"
with open(yaml_path, "w", encoding="utf-8") as f:
yaml.dump(data_yaml, f, sort_keys=False)
print("\n" + "=" * 75)
print("SAHI Slicing Complete")
print("=" * 75)
print(f"Original Images: {stats['original_images']}")
print(f"Total Sliced Tiles: {stats['sliced_tiles']}")
for sp, cnt in stats["splits"].items():
print(f" - {sp:10s}: {cnt} tiles")
print(f"Dataset YAML: {yaml_path}")
print("=" * 75)
return stats
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Slice YOLO dataset using SAHI overlapping tiling.")
parser.add_argument("--input-dir", default="data/external/rail_defects", help="Source YOLO dataset directory")
parser.add_argument("--output-dir", default="data/external/rail_defects_sahi_sliced", help="Output sliced directory")
parser.add_argument("--slice-size", type=int, default=480, help="Tile slice size in pixels")
parser.add_argument("--overlap-ratio", type=float, default=0.20, help="Tile overlap fraction")
args = parser.parse_args()
slice_yolo_dataset(
input_dir=args.input_dir,
output_dir=args.output_dir,
slice_size=args.slice_size,
overlap_ratio=args.overlap_ratio,
)
|