f0c876dfc7
Two heuristic rules applied after SegEarth-OV3 inference: 1. Dark water: if background pixels have mean_rgb < 0.24 and std < 0.08, reclassify as water. Fixes GTA-UAV satellite dark ocean (57% → ~15% bg). 2. Wetland reclassify (GTA-UAV only): split false-positive wetland pixels by color — green-dominant → vegetation, else → bare soil. Fixes 14.3% muddy/wetland false positives on GTA-V hillside terrain. Config flags: seg_fix_dark_water (default True), seg_reclassify_wetland (default False, enabled in run_gta_uav.py). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
105 lines
3.1 KiB
Python
105 lines
3.1 KiB
Python
#!/usr/bin/env python3
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"""Run annotation pipeline for GTA-UAV-LR dataset.
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GTA-UAV-LR: synthetic dataset from GTA V engine.
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- drone/images/: 33763 images, 512x384, RGB PNG
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- satellite/: 14640 images, 256x256, RGBA PNG (alpha = map boundary)
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- Total: 48403 images
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- 6 flight heights: 100, 200, 300, 400, 500, 600 meters
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Usage:
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python scripts/run_gta_uav.py
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python scripts/run_gta_uav.py --source db # only satellite (14.6K)
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python scripts/run_gta_uav.py --source drone # only drone (33.8K)
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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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_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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from scripts.seg_classes import UNIFIED_PROMPTS
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INPUT_ROOT = "/home/servml/Документы/datasets/GTA-UAV-LR"
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OUTPUT_ROOT = "/home/servml/Документы/datasets/GTA-UAV-LR-aug"
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def main() -> None:
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parser = argparse.ArgumentParser(description="Annotate GTA-UAV-LR")
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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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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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seg_fix_dark_water=True,
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seg_reclassify_wetland=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,
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num_workers=4,
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)
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# GTA-UAV: satellite 256x256, drone 512x384
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# Use 256 for satellite, 512 for drone (non-square → resize to square)
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input_conf = InputConfig(image_size=256, query_image_size=512)
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# GTA V synthetic scenes: urban, suburban, rural, coastal, mountainous
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# 11 base classes + pool (swimming pools common in GTA suburbs)
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seg_conf = SegConfig(threshold=0.15, prompts=UNIFIED_PROMPTS)
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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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