4c15d5ff5d
- sam31server를 SAM3.1 서버로 전환 (x-anylabeling01 대체) - detect_raamen.py: B/C 분류 기반 라멘형 전철주 검출 파이프라인 정비 - sam3_everything_explore.py: Discovery Sweep 탐색 모드 정리 - detect_all_objects.py: 타일 검출 개선 - docs/railway-client-guide.html: 서버·도구·파이프라인 전체 가이드 추가 - tools 추가: detect_control_box, group_ramen_poles, render_everything_by_label, render_label_polygons, debug_vh Closes #1 Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
84 lines
2.9 KiB
Python
84 lines
2.9 KiB
Python
"""LabelMe JSON에서 특정 레이블 폴리곤만 원본 이미지에 표시.
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사용:
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python tools/render_label_polygons.py \
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--image data/역사구간/.../DJI_20260306100900_0034.JPG \
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--json output/detect/DJI_20260306100900_0034/tiles1to24_railway_zone_001.json \
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--label catenary_pole \
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--output output/detect/DJI_20260306100900_0034/catenary_pole_only.jpg
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# 여러 레이블:
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python tools/render_label_polygons.py ... --label catenary_pole bracket
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# 레이블 생략 시 전체 표시:
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python tools/render_label_polygons.py ... --json foo.json --image foo.jpg
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"""
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import argparse
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import json
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import cv2
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import numpy as np
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from pathlib import Path
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COLORS = [
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(0, 0, 255), # red
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(0, 200, 0), # green
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(255, 100, 0), # blue-orange
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(0, 200, 255), # yellow
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(200, 0, 200), # magenta
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(0, 165, 255), # orange
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(255, 0, 100), # pink-blue
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(100, 255, 100),# light green
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]
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def render(image_path: Path, json_path: Path, output_path: Path, labels: list[str] | None):
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buf = np.fromfile(str(image_path), dtype=np.uint8)
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img = cv2.imdecode(buf, cv2.IMREAD_COLOR)
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if img is None:
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raise FileNotFoundError(f"이미지 읽기 실패: {image_path}")
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with open(json_path, encoding="utf-8") as f:
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data = json.load(f)
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shapes = data.get("shapes", [])
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if labels:
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shapes = [s for s in shapes if s["label"] in labels]
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# 레이블별 색상 매핑
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label_set = sorted({s["label"] for s in shapes})
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color_map = {lbl: COLORS[i % len(COLORS)] for i, lbl in enumerate(label_set)}
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for s in shapes:
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pts = np.array(s["points"], dtype=np.int32).reshape((-1, 1, 2))
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color = color_map[s["label"]]
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cv2.polylines(img, [pts], isClosed=True, color=color, thickness=8)
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x, y = int(s["points"][0][0]), int(s["points"][0][1])
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score = s.get("score")
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text = f"{s['label']} {score:.2f}" if score is not None else s["label"]
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cv2.putText(img, text, (x, max(y - 10, 20)),
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cv2.FONT_HERSHEY_SIMPLEX, 2.5, color, 5)
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output_path.parent.mkdir(parents=True, exist_ok=True)
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ret, buf_out = cv2.imencode(".jpg", img, [cv2.IMWRITE_JPEG_QUALITY, 85])
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if not ret:
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raise RuntimeError("JPEG 인코딩 실패")
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output_path.write_bytes(buf_out.tobytes())
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print(f"저장: {output_path} ({len(shapes)}개 폴리곤)")
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def main():
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p = argparse.ArgumentParser(description="LabelMe JSON 폴리곤 시각화")
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p.add_argument("--image", required=True, type=Path)
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p.add_argument("--json", required=True, type=Path)
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p.add_argument("--output", required=True, type=Path)
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p.add_argument("--label", nargs="*", default=None,
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help="표시할 레이블 (생략 시 전체)")
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args = p.parse_args()
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render(args.image, args.json, args.output, args.label)
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if __name__ == "__main__":
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main()
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