5ff4effddb
Background analysis on crop_0_0/0_1 showed wetland/embankment areas (dark grey-green, avg RGB 101,103,97) between water bodies not covered by any existing class. Add: - "muddy ground and wetland" (ID 14) - "embankment and levee" (ID 15) Total: 14 → 16 classes for UAV_VisLoc. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
109 lines
3.3 KiB
Python
109 lines
3.3 KiB
Python
#!/usr/bin/env python3
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"""Run annotation pipeline for UAV_VisLoc_processed dataset.
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Usage:
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python scripts/run_uav_visloc.py
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python scripts/run_uav_visloc.py --source db # only satellite
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python scripts/run_uav_visloc.py --source drone # only drone
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"""
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from __future__ import annotations
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import argparse
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import sys
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from pathlib import Path
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# Ensure project root is importable.
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_PROJECT_ROOT = Path(__file__).resolve().parent.parent
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sys.path.insert(0, str(_PROJECT_ROOT))
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import numpy as np
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import torch
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from src.conf.hardware_conf import HardwareConfig
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from src.conf.input_conf import InputConfig
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from src.conf.models_conf import ModelsConfig
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from src.conf.pipeline_conf import PipelineConfig
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from src.conf.seg_conf import SegConfig
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from src.augmentor.io_utils import setup_logging
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from src.main import run_pipeline
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INPUT_ROOT = "/home/servml/Документы/datasets/UAV_VisLoc_processed"
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OUTPUT_ROOT = "/home/servml/Документы/datasets/UAV_VisLoc_processed-aug"
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def main() -> None:
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parser = argparse.ArgumentParser(description="Annotate UAV_VisLoc_processed")
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parser.add_argument("--source", choices=["db", "drone", "all"], default="all",
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help="Process only db (satellite), drone, or all (default)")
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parser.add_argument("--stages", nargs="+",
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default=["depth", "edges", "segmentation", "chmv2"],
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help="Stages to run")
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args = parser.parse_args()
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import gin
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gin.clear_config()
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source = None if args.source == "all" else args.source
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# UAV_VisLoc uses "drone" folder, not "query". Map for filter compatibility.
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if source == "drone":
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source = "query"
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pipeline_conf = PipelineConfig(
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input_root=INPUT_ROOT,
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output_root=OUTPUT_ROOT,
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stages=args.stages,
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save_npy=False,
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save_vis=True,
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save_safetensors=True,
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cleanup_npy=True,
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resume=True,
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source=source,
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log_level="INFO",
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)
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hw_conf = HardwareConfig(
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profile_name="rtx4090",
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total_ram_gb=24.0,
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reserve_gb=2.0,
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use_fp16=True,
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batch_size=None, # auto
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num_workers=4,
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)
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input_conf = InputConfig(image_size=256)
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seg_conf = SegConfig(prompts=[
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"background", # 0
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"building", # 1
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"road", # 2
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"vegetation", # 3
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"water", # 4
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"sand and gravel ground", # 5
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"rocky terrain", # 6
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"farmland", # 7
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"railway", # 8
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"parking lot", # 9
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"sidewalk", # 10
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"bare soil and plowed field", # 11
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"roof and rooftop", # 12
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"sports field and playground", # 13
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"muddy ground and wetland", # 14
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"embankment and levee", # 15
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])
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models_conf = ModelsConfig(weights_dir=str(_PROJECT_ROOT / "in" / "weights"))
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setup_logging(
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pipeline_conf.log_level,
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log_file=Path(OUTPUT_ROOT) / "pipeline.log",
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)
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torch.manual_seed(42)
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np.random.seed(42)
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run_pipeline(pipeline_conf, hw_conf, models_conf, input_conf, seg_conf)
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if __name__ == "__main__":
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main()
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