| """
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| Convert YOLO-segmentation-format labels (polygons) to YOLO-detection-format
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| labels (bounding boxes), so a segmentation-labeled dataset can be dropped
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| into a standard detection training pipeline.
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|
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| YOLO segmentation label line format:
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| class_id x1 y1 x2 y2 x3 y3 ... xn yn (all normalized 0-1)
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|
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| YOLO detection label line format:
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| class_id x_center y_center width height (all normalized 0-1)
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|
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| USAGE:
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| Just set SRC_LABEL_DIR below to the folder containing the segmentation
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| .txt files, and DST_LABEL_DIR to where you want the converted detection
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| .txt files written. The script handles the rest -- multiple objects per
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| file, multiple classes, etc.
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|
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| If you want to convert in place (overwrite the same files), set
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| DST_LABEL_DIR equal to SRC_LABEL_DIR -- but a backup is strongly
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| recommended first since this is a destructive format change.
|
| """
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|
|
| import os
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| import shutil
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|
|
|
|
|
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| SRC_LABEL_DIR = r"C:\Users\shrey\Downloads\project1.yolo26 (1)\valid\labels"
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| DST_LABEL_DIR = r"C:\Users\shrey\Downloads\dataset\labels_1"
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|
|
|
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|
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| SKIP_INVALID_LINES = True
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|
|
|
|
|
|
|
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| def polygon_to_bbox(coords):
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| """coords: flat list [x1, y1, x2, y2, ...] normalized 0-1. Returns (xc, yc, w, h)."""
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| xs = coords[0::2]
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| ys = coords[1::2]
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| x_min, x_max = min(xs), max(xs)
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| y_min, y_max = min(ys), max(ys)
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| x_center = (x_min + x_max) / 2
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| y_center = (y_min + y_max) / 2
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| width = x_max - x_min
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| height = y_max - y_min
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| return x_center, y_center, width, height
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|
|
|
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| def convert_file(src_path, dst_path):
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| converted_lines = []
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| skipped = 0
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|
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| with open(src_path) as f:
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| for line in f:
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| line = line.strip()
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| if not line:
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| continue
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|
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| parts = line.split()
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| class_id = parts[0]
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| coords = list(map(float, parts[1:]))
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|
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| if len(coords) < 6 or len(coords) % 2 != 0:
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|
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| skipped += 1
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| if not SKIP_INVALID_LINES:
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| raise ValueError(f"Invalid polygon in {src_path}: {line}")
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| continue
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|
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| xc, yc, w, h = polygon_to_bbox(coords)
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| converted_lines.append(f"{class_id} {xc:.6f} {yc:.6f} {w:.6f} {h:.6f}")
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|
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| with open(dst_path, "w") as f:
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| f.write("\n".join(converted_lines) + ("\n" if converted_lines else ""))
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|
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| return len(converted_lines), skipped
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|
|
|
|
| def main():
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| if not os.path.isdir(SRC_LABEL_DIR):
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| print(f"Source folder not found: {SRC_LABEL_DIR}")
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| return
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|
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| os.makedirs(DST_LABEL_DIR, exist_ok=True)
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|
|
| total_files = 0
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| total_boxes = 0
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| total_skipped = 0
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|
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| for fname in os.listdir(SRC_LABEL_DIR):
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| if not fname.endswith(".txt"):
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| continue
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|
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| src_path = os.path.join(SRC_LABEL_DIR, fname)
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| dst_path = os.path.join(DST_LABEL_DIR, fname)
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|
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| n_boxes, n_skipped = convert_file(src_path, dst_path)
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|
|
| total_files += 1
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| total_boxes += n_boxes
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| total_skipped += n_skipped
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|
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| print(f"Converted {total_files} label file(s).")
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| print(f"Total bounding boxes written: {total_boxes}")
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| if total_skipped:
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| print(f"Skipped {total_skipped} invalid/malformed polygon line(s) -- check these manually if the count seems high.")
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| print(f"\nOutput written to: {DST_LABEL_DIR}")
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| print("Next: point your data.yaml / dataset folder at these converted labels "
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| "(alongside the original images) before training.")
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|
|
|
|
| if __name__ == "__main__":
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| main() |
