"""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}")