80 lines
2.9 KiB
Python
80 lines
2.9 KiB
Python
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"""HDevEngine 全自动驱动:读 roi.hobj,循环所有 png 跑 find_circles,
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对比 拟合法 vs 计量模型法 两种找圆结果,输出偏差统计,并写 CSV。"""
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import math
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import csv
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from halcon.hdevengine import HDevEngine, HDevProcedure, HDevProcedureCall, HDevEngineError
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import halcon as ha
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PROJ = r"C:\workspace\agent-studio\halcon-001"
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IMG_DIR = r"C:\工作文档\2025.10.03_精度实验数据分析\两种找圆算法对比\初始状态-往复-右侧上视左标定片阵列Mark定位"
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ROI_FILE = PROJ + r"\roi.hobj"
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CSV_OUT = PROJ + r"\compare_result.csv"
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def as_list(v):
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return list(v) if isinstance(v, (list, tuple)) else [v]
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# 引擎 + procedure
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eng = HDevEngine()
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eng.set_procedure_path(PROJ)
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proc = HDevProcedure.load_external("find_circles")
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call = HDevProcedureCall(proc)
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# 图片列表
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files = ha.list_files(IMG_DIR, "files")
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pngs = sorted(ha.tuple_regexp_select(files, r"\.png$"))
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print(f"待处理图片: {len(pngs)} 张")
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all_dists = [] # 所有 图×圆 的中心欧氏距离
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per_image_mean = [] # 每张图的平均偏差
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skipped = 0
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rows_csv = [("image", "circle_idx", "fit_row", "fit_col", "metro_row", "metro_col", "dist_px")]
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for path in pngs:
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call.reset()
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call.set_input_control_param_by_name("ImageFile", path)
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call.set_input_control_param_by_name("RoiFile", ROI_FILE)
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try:
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call.execute()
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except HDevEngineError as e:
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skipped += 1
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if skipped <= 3:
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print(f"[skip] {path.rsplit(chr(92),1)[-1]}: {e}")
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continue
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fr = as_list(call.get_output_control_param_by_name("FitRow"))
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fc = as_list(call.get_output_control_param_by_name("FitCol"))
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mr = as_list(call.get_output_control_param_by_name("MetroRow"))
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mc = as_list(call.get_output_control_param_by_name("MetroCol"))
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name = path.rsplit("\\", 1)[-1]
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dists = []
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for i in range(16):
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d = math.hypot(fr[i] - mr[i], fc[i] - mc[i])
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dists.append(d)
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all_dists.append(d)
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rows_csv.append((name, i, f"{fr[i]:.4f}", f"{fc[i]:.4f}",
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f"{mr[i]:.4f}", f"{mc[i]:.4f}", f"{d:.4f}"))
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per_image_mean.append(sum(dists) / len(dists))
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# 统计
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def stats(xs):
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n = len(xs)
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m = sum(xs) / n
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sd = math.sqrt(sum((x - m) ** 2 for x in xs) / n)
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return n, m, sd, min(xs), max(xs)
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with open(CSV_OUT, "w", newline="", encoding="utf-8-sig") as f:
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csv.writer(f).writerows(rows_csv)
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print("=" * 60)
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print(f"成功处理: {len(per_image_mean)} 张 跳过(圆数!=16): {skipped} 张")
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if all_dists:
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n, m, sd, lo, hi = stats(all_dists)
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print(f"两算法圆心偏差 (像素),样本 {n} 个圆:")
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print(f" 均值 = {m:.4f}")
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print(f" 标准差 = {sd:.4f}")
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print(f" 最小 = {lo:.4f}")
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print(f" 最大 = {hi:.4f}")
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_, mm, _, _, _ = stats(per_image_mean)
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print(f" 每图平均偏差的均值 = {mm:.4f}")
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print(f"明细已写: {CSV_OUT}")
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