"""仅检测缺口(修正版) + ASCII 验证, 不 swipe。"""
from __future__ import annotations
import subprocess
from io import BytesIO
import numpy as np
from PIL import Image

PHOTO_LEFT = 66


def cap():
    d = subprocess.run(["adb", "exec-out", "screencap", "-p"],
                       capture_output=True, timeout=30).stdout
    return np.asarray(Image.open(BytesIO(d)).convert("L"))


def detect_notch(img, x_min_local=230, col_thr=25):
    reg = img[267:819, PHOTO_LEFT:1013]            # 552 x 947
    col = (reg < 90).sum(axis=0)
    col_r = col[x_min_local:]                       # 只看 home 右侧
    hot = np.where(col_r > col_thr)[0]
    if len(hot) == 0:
        return None
    br = np.where(np.diff(hot) > 12)[0]
    segs = [s for s in np.split(hot, br + 1) if len(s) > 10]
    seg = max(segs, key=lambda s: col_r[s].sum())   # 缺口实心簇
    segL = x_min_local + int(seg[0])
    segR = x_min_local + int(seg[-1])
    # 在 seg 列范围内取 <95 像素的面积加权质心 = 缺口中心
    block = (reg[:, segL:segR + 1] < 95)
    ys, xs = np.where(block)
    if len(xs) == 0:
        return None
    cx_local = int(round(np.average(xs + segL)))
    cy_local = int(round(np.average(ys)))
    return dict(center=PHOTO_LEFT + cx_local, seg_local=(segL, segR),
                body_local=(segL, segR), cy=267 + cy_local)


def main():
    img = cap()
    n = detect_notch(img)
    print("notch:", n)
    if not n:
        return
    cl = n["center"] - PHOTO_LEFT
    L, R = max(0, cl - 170), min(947, cl + 170)
    reg = img[267:819, PHOTO_LEFT:1013]
    for y in range(0, 552, 8):
        line = "".join("#" if reg[y, x] < 90 else ("+" if reg[y, x] < 140 else ".")
                       for x in range(int(L), int(R)))
        if any(c in line for c in "#+"):
            print(f"y{267+y:4d} {line}")
    print(f"(window local[{int(L)},{int(R)}] center_screen={n['center']})")


if __name__ == "__main__":
    main()