Download data/Data-report/scripts/crop.py from ibrahim80876/Change-Detection-LiDAR-Dataset: direct link, hf CLI and curl.
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- Download file 2.68 kB
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https://huggingface.co/datasets/ibrahim80876/Change-Detection-LiDAR-Dataset/resolve/main/data/Data-report/scripts/crop.py
- Command line
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hf download hf://datasets/ibrahim80876/Change-Detection-LiDAR-Dataset/data/Data-report/scripts/crop.py
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curl -L -o crop.py https://huggingface.co/datasets/ibrahim80876/Change-Detection-LiDAR-Dataset/resolve/main/data/Data-report/scripts/crop.py
2.68 kB
| """Crop several XY boxes from one huge ASCII PLY in a single streaming pass. | |
| Usage: python crop.py boxes.json (boxes.json: list of {name, src, xmin, xmax, ymin, ymax, out})""" | |
| import sys, os, json, time | |
| import polars as pl, numpy as np | |
| def header_len(path): | |
| n = 0 | |
| with open(path, 'rb') as f: | |
| for line in f: | |
| n += 1 | |
| if line.strip() == b'end_header': return n | |
| def write_ply(path, df, comments): | |
| pts = df.select(['x', 'y', 'z']).to_numpy() | |
| rgb = df.select(['r', 'g', 'b']).to_numpy() | |
| with open(path, 'w', newline='\n') as f: | |
| f.write('ply\nformat ascii 1.0\n') | |
| for c in comments: f.write(f'comment {c}\n') | |
| f.write(f'element vertex {len(pts)}\nproperty float x\nproperty float y\nproperty float z\n' | |
| 'property uchar red\nproperty uchar green\nproperty uchar blue\nend_header\n') | |
| np.savetxt(f, np.c_[pts, rgb], fmt='%.6f %.6f %.6f %d %d %d') | |
| def run(boxes): | |
| by_src = {} | |
| for b in boxes: by_src.setdefault(b['src'], []).append(b) | |
| for src, bl in by_src.items(): | |
| t0 = time.time(); hl = header_len(src) | |
| lf = pl.scan_csv(src, separator=' ', has_header=False, skip_rows=hl, new_columns=['x','y','z','r','g','b'], | |
| schema_overrides={'x': pl.Float64, 'y': pl.Float64, 'z': pl.Float64, 'r': pl.UInt8, 'g': pl.UInt8, 'b': pl.UInt8}, | |
| low_memory=True) | |
| preds = [(pl.col('x') >= b['xmin']) & (pl.col('x') < b['xmax']) & (pl.col('y') >= b['ymin']) & (pl.col('y') < b['ymax']) for b in bl] | |
| anyp = preds[0] | |
| for p in preds[1:]: anyp = anyp | p | |
| tag = pl.lit(None, dtype=pl.Int32) | |
| for i, p in reversed(list(enumerate(preds))): | |
| tag = pl.when(p).then(pl.lit(i, dtype=pl.Int32)).otherwise(tag) | |
| df = lf.filter(anyp).with_columns(tag.alias('box')).collect(engine='streaming') | |
| for i, b in enumerate(bl): | |
| sub = df.filter(pl.col('box') == i) | |
| os.makedirs(os.path.dirname(b['out']), exist_ok=True) | |
| write_ply(b['out'], sub, [f"Subiaco change-detection raw crop {b['name']} (source-map coordinates, uncorrected)", | |
| f"source {os.path.basename(src)}", | |
| f"crop box x[{b['xmin']},{b['xmax']}) y[{b['ymin']},{b['ymax']}) all z", | |
| 'Generated 2026-09-27 by crop.py']) | |
| print(f"{b['name']}: {os.path.basename(src)} -> {len(sub):,} pts -> {b['out']}", flush=True) | |
| print(f" pass over {os.path.basename(src)}: {time.time()-t0:.1f}s", flush=True) | |
| if __name__ == '__main__': | |
| run(json.load(open(sys.argv[1]))) | |