target_roi_yolo.py
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gcw_4spBpAfv
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test/test_triangle_one_image.py
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242
test/test_triangle_one_image.py
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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单张图片快速测试：三角形四角标记识别 + 单应性落点 + PnP 估距
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用法（在板子上）：
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python3 test/test_triangle_one_image.py --image /root/phot/xxx.jpg --out /root/phot/tri_out.jpg
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调参对比（不改代码，临时覆盖 config.TRIANGLE_*）：
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python3 test/test_triangle_one_image.py --image /root/phot/xxx.jpg --preset shadow
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python3 test/test_triangle_one_image.py --image /root/phot/xxx.jpg --max-interior-gray 160 --min-dark-ratio 0.20
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"""
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import argparse
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import json
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import os
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import time
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from typing import Any, Dict, Tuple
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import cv2
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import numpy as np
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import config
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import triangle_target as tri_mod
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from triangle_target import (
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detect_triangle_markers,
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load_camera_from_xml,
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load_triangle_positions,
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try_triangle_scoring,
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)
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def _apply_overrides(args) -> None:
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# 预设：阴影/低对比度场景更宽松（尽量保持速度：不启 CLAHE）
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if args.preset == "shadow":
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setattr(config, "TRIANGLE_ENABLE_CLAHE_FALLBACK", False)
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setattr(config, "TRIANGLE_MIN_CONTRAST_DIFF", 0)
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setattr(config, "TRIANGLE_MAX_INTERIOR_GRAY", 160)
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setattr(config, "TRIANGLE_DARK_PIXEL_GRAY", 160)
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setattr(config, "TRIANGLE_MIN_DARK_RATIO", 0.20)
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# adaptive 只在 Otsu 失败时尝试，保持尝试次数很少
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setattr(config, "TRIANGLE_ADAPTIVE_BLOCK_SIZES", (21,))
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# 手动覆盖（优先级高于 preset）
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if args.max_interior_gray is not None:
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setattr(config, "TRIANGLE_MAX_INTERIOR_GRAY", int(args.max_interior_gray))
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if args.dark_pixel_gray is not None:
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setattr(config, "TRIANGLE_DARK_PIXEL_GRAY", int(args.dark_pixel_gray))
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if args.min_dark_ratio is not None:
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setattr(config, "TRIANGLE_MIN_DARK_RATIO", float(args.min_dark_ratio))
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if args.min_contrast_diff is not None:
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setattr(config, "TRIANGLE_MIN_CONTRAST_DIFF", int(args.min_contrast_diff))
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if args.detect_scale is not None:
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setattr(config, "TRIANGLE_DETECT_SCALE", float(args.detect_scale))
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if args.adaptive_blocks is not None:
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bs = tuple(int(x) for x in args.adaptive_blocks.split(",") if x.strip())
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setattr(config, "TRIANGLE_ADAPTIVE_BLOCK_SIZES", bs)
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def _dump_config() -> Dict[str, Any]:
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keys = [
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"TRIANGLE_DETECT_SCALE",
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"TRIANGLE_SIZE_RANGE",
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"TRIANGLE_MAX_INTERIOR_GRAY",
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"TRIANGLE_DARK_PIXEL_GRAY",
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"TRIANGLE_MIN_DARK_RATIO",
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"TRIANGLE_MIN_CONTRAST_DIFF",
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"TRIANGLE_ADAPTIVE_BLOCK_SIZES",
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"TRIANGLE_MAX_FILTERED_FOR_COMBO",
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"TRIANGLE_EARLY_EXIT_CANDIDATES",
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"TRIANGLE_ENABLE_CLAHE_FALLBACK",
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]
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out = {}
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for k in keys:
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out[k] = getattr(config, k, None)
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return out
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def _draw_tri_debug(img_bgr: np.ndarray, tri: Dict[str, Any]) -> np.ndarray:
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out = img_bgr.copy()
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markers = tri.get("markers") or []
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# 画三角形轮廓 + center + id
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for m in markers:
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corners = np.array(m.get("corners", []), dtype=np.int32)
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if corners.size == 0:
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continue
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cv2.polylines(out, [corners], True, (0, 255, 0), 2)
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c = m.get("center") or (corners[:, 0].mean(), corners[:, 1].mean())
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cx, cy = int(c[0]), int(c[1])
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cv2.circle(out, (cx, cy), 4, (0, 0, 255), -1)
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mid = m.get("id", "?")
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cv2.putText(out, f"T{mid}", (cx - 18, cy - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.55, (0, 255, 0), 1)
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# 若有 homography，画靶心（把 (0,0) 反投影到图像）
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H = tri.get("homography")
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if H is not None:
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try:
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H = np.array(H, dtype=np.float64)
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H_inv = np.linalg.inv(H)
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c_img = cv2.perspectiveTransform(np.array([[[0.0, 0.0]]], dtype=np.float32), H_inv)[0][0]
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ocx, ocy = int(c_img[0]), int(c_img[1])
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cv2.circle(out, (ocx, ocy), 5, (0, 0, 255), -1)
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cv2.circle(out, (ocx, ocy), 10, (0, 0, 255), 1)
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except Exception:
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pass
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# 叠加结果信息
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lines = []
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if tri.get("ok"):
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lines.append("tri_ok=True")
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if tri.get("dx_cm") is not None and tri.get("dy_cm") is not None:
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lines.append(f"dx,dy=({tri['dx_cm']:.2f},{tri['dy_cm']:.2f})cm")
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if tri.get("distance_m") is not None:
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lines.append(f"dist={float(tri['distance_m']):.2f}m")
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else:
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lines.append("tri_ok=False")
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y0 = 22
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for i, t in enumerate(lines):
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cv2.putText(out, t, (10, y0 + i * 18), cv2.FONT_HERSHEY_SIMPLEX, 0.55, (0, 255, 0), 1)
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return out
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def main():
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ap = argparse.ArgumentParser()
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ap.add_argument("--image", required=True, help="输入图片路径（jpg/png）")
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ap.add_argument("--out", default="", help="输出标注图片路径（可选）")
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ap.add_argument("--laser-x", type=int, default=-1, help="激光点 x（像素），默认用图像中心")
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ap.add_argument("--laser-y", type=int, default=-1, help="激光点 y（像素），默认用图像中心")
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ap.add_argument("--preset", choices=["", "shadow"], default="", help="调参预设（shadow=阴影更鲁棒，不启 CLAHE）")
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ap.add_argument("--max-interior-gray", type=int, default=None)
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ap.add_argument("--dark-pixel-gray", type=int, default=None)
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ap.add_argument("--min-dark-ratio", type=float, default=None)
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ap.add_argument("--min-contrast-diff", type=int, default=None)
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ap.add_argument("--detect-scale", type=float, default=None)
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ap.add_argument("--adaptive-blocks", default=None, help="例如: 11,21 ；为空表示不改")
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ap.add_argument("--verbose", action="store_true", help="输出更多检测阶段信息")
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args = ap.parse_args()
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_apply_overrides(args)
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# triangle_target.py 的日志默认写到 logger_manager；在离线脚本里 logger 可能未初始化。
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# verbose 模式下把 _log 重定向为 print，方便直接看到诊断信息。
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if args.verbose:
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try:
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tri_mod._log = lambda msg: print(str(msg))
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except Exception:
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pass
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img_bgr = cv2.imread(args.image, cv2.IMREAD_COLOR)
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if img_bgr is None:
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raise SystemExit(f"读图失败：{args.image}")
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# triangle_target.try_triangle_scoring 约定输入为 RGB；OpenCV imread 返回 BGR
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img_rgb = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)
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h, w = img_bgr.shape[:2]
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if args.laser_x >= 0 and args.laser_y >= 0:
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laser_point = (int(args.laser_x), int(args.laser_y))
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else:
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laser_point = (w // 2, h // 2)
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K, dist = load_camera_from_xml(getattr(config, "CAMERA_CALIB_XML", ""))
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pos = load_triangle_positions(getattr(config, "TRIANGLE_POSITIONS_JSON", ""))
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print("[tri-test] image:", args.image, "shape:", (h, w))
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print("[tri-test] laser_point:", laser_point)
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print("[tri-test] calib_ok:", bool(K is not None and dist is not None), "pos_ok:", bool(pos))
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print("[tri-test] config:", json.dumps(_dump_config(), ensure_ascii=False))
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# 先单独跑一次三角形候选检测，便于区分“没找到候选” vs “找到候选但评分/单应性失败”
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scale = float(getattr(config, "TRIANGLE_DETECT_SCALE", 0.5) or 0.5)
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if not (0.05 <= scale <= 1.0):
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scale = 0.5
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long_side = max(h, w)
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max_dim = max(64, int(long_side * scale))
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if long_side > max_dim:
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det_scale = max_dim / long_side
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det_w = int(w * det_scale)
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det_h = int(h * det_scale)
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img_det = cv2.resize(img_bgr, (det_w, det_h), interpolation=cv2.INTER_LINEAR)
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inv_scale = 1.0 / det_scale
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size_range_det = (
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max(4, int(getattr(config, "TRIANGLE_SIZE_RANGE", (8, 500))[0] * det_scale)),
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max(8, int(getattr(config, "TRIANGLE_SIZE_RANGE", (8, 500))[1] * det_scale)),
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)
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else:
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img_det = img_bgr
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inv_scale = 1.0
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size_range_det = getattr(config, "TRIANGLE_SIZE_RANGE", (8, 500))
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gray = cv2.cvtColor(img_det, cv2.COLOR_BGR2GRAY)
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markers_det = detect_triangle_markers(
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gray,
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orig_gray=gray,
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size_range=size_range_det,
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verbose=bool(args.verbose),
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)
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if inv_scale != 1.0 and markers_det:
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for m in markers_det:
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m["center"] = [m["center"][0] * inv_scale, m["center"][1] * inv_scale]
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m["corners"] = [[c[0] * inv_scale, c[1] * inv_scale] for c in m["corners"]]
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print("[tri-test] markers_found:", len(markers_det), "ids:", [m.get("id") for m in markers_det])
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t0 = time.time()
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tri = try_triangle_scoring(
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img_rgb, # try_triangle_scoring 期望 RGB
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laser_point,
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pos,
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K,
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dist,
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size_range=getattr(config, "TRIANGLE_SIZE_RANGE", (8, 500)),
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)
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dt_ms = int(round((time.time() - t0) * 1000))
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print("[tri-test] elapsed_ms:", dt_ms)
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print(json.dumps(tri, ensure_ascii=False, indent=2))
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if args.out:
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out_path = args.out
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# 允许传目录（如 ./），自动生成文件名；未带扩展名时默认 .jpg
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if out_path.endswith("/") or out_path.endswith("\\") or os.path.isdir(out_path):
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out_path = os.path.join(out_path, "tri_out.jpg")
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root, ext = os.path.splitext(out_path)
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if not ext:
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out_path = root + ".jpg"
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# 若 try_triangle_scoring 失败且没带回 markers，至少把候选 markers 画出来，方便肉眼判断
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tri_for_draw = tri if isinstance(tri, dict) else {"ok": False}
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if not tri_for_draw.get("markers") and markers_det:
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tri_for_draw = dict(tri_for_draw)
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tri_for_draw["markers"] = markers_det
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out_img = _draw_tri_debug(img_bgr, tri_for_draw)
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ok = cv2.imwrite(out_path, out_img)
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if not ok:
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raise SystemExit(f"写图失败（可能是不支持的扩展名）：{out_path}")
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print("[tri-test] wrote:", out_path)
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if __name__ == "__main__":
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main()
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