refactor(halcon): single-skill plugin layout → invoke as /halcon (not /halcon:halcon)

Move SKILL.md + references/ scripts/ evals/ from skills/halcon/ up to the plugin root. Per Claude Code plugins-reference, a plugin with SKILL.md at its root and no skills/ subdir is auto-loaded as a single-skill plugin (v2.1.142+), so the invocation name = frontmatter name = halcon → clean /halcon. Bump plugin.json 2.0.0 → 2.0.1 so existing installs receive the update. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
2026-07-10 23:14:39 +08:00
parent 36eadffa87
commit 3fe919e37c
44 changed files with 1 additions and 1 deletions
@@ -0,0 +1,79 @@
+"""HDevEngine 全自动驱动:读 roi.hobj,循环所有 png 跑 find_circles,
+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。"""
+import math
+import csv
+from halcon.hdevengine import HDevEngine, HDevProcedure, HDevProcedureCall, HDevEngineError
+import halcon as ha
+
+PROJ = r"C:\workspace\agent-studio\halcon-001"
+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"
+ROI_FILE = PROJ + r"\roi.hobj"
+CSV_OUT = PROJ + r"\compare_result.csv"
+
+def as_list(v):
+    return list(v) if isinstance(v, (list, tuple)) else [v]
+
+# 引擎 + procedure
+eng = HDevEngine()
+eng.set_procedure_path(PROJ)
+proc = HDevProcedure.load_external("find_circles")
+call = HDevProcedureCall(proc)
+
+# 图片列表
+files = ha.list_files(IMG_DIR, "files")
+pngs = sorted(ha.tuple_regexp_select(files, r"\.png$"))
+print(f"待处理图片: {len(pngs)} 张")
+
+all_dists = []          # 所有 图×圆 的中心欧氏距离
+per_image_mean = []     # 每张图的平均偏差
+skipped = 0
+rows_csv = [("image", "circle_idx", "fit_row", "fit_col", "metro_row", "metro_col", "dist_px")]
+
+for path in pngs:
+    call.reset()
+    call.set_input_control_param_by_name("ImageFile", path)
+    call.set_input_control_param_by_name("RoiFile", ROI_FILE)
+    try:
+        call.execute()
+    except HDevEngineError as e:
+        skipped += 1
+        if skipped <= 3:
+            print(f"[skip] {path.rsplit(chr(92),1)[-1]}: {e}")
+        continue
+    fr = as_list(call.get_output_control_param_by_name("FitRow"))
+    fc = as_list(call.get_output_control_param_by_name("FitCol"))
+    mr = as_list(call.get_output_control_param_by_name("MetroRow"))
+    mc = as_list(call.get_output_control_param_by_name("MetroCol"))
+
+    name = path.rsplit("\\", 1)[-1]
+    dists = []
+    for i in range(16):
+        d = math.hypot(fr[i] - mr[i], fc[i] - mc[i])
+        dists.append(d)
+        all_dists.append(d)
+        rows_csv.append((name, i, f"{fr[i]:.4f}", f"{fc[i]:.4f}",
+                         f"{mr[i]:.4f}", f"{mc[i]:.4f}", f"{d:.4f}"))
+    per_image_mean.append(sum(dists) / len(dists))
+
+# 统计
+def stats(xs):
+    n = len(xs)
+    m = sum(xs) / n
+    sd = math.sqrt(sum((x - m) ** 2 for x in xs) / n)
+    return n, m, sd, min(xs), max(xs)
+
+with open(CSV_OUT, "w", newline="", encoding="utf-8-sig") as f:
+    csv.writer(f).writerows(rows_csv)
+
+print("=" * 60)
+print(f"成功处理: {len(per_image_mean)} 张   跳过(圆数!=16): {skipped} 张")
+if all_dists:
+    n, m, sd, lo, hi = stats(all_dists)
+    print(f"两算法圆心偏差 (像素),样本 {n} 个圆:")
+    print(f"  均值   = {m:.4f}")
+    print(f"  标准差 = {sd:.4f}")
+    print(f"  最小   = {lo:.4f}")
+    print(f"  最大   = {hi:.4f}")
+    _, mm, _, _, _ = stats(per_image_mean)
+    print(f"  每图平均偏差的均值 = {mm:.4f}")
+print(f"明细已写: {CSV_OUT}")