"""v6: 修正 R=1.0 (1:1 联动, 同浏览器) + 轨迹确认拼图块到达缺口 + 观察释放。

v4/v5 用 R=1.27 (受低 handle 处有偏 piece_x 污染) => target 714 => 拼图块实际到 760,
错过缺口 916 共 156px => 静默回弹(有效区假设: 错位回弹, 不算尝试)。
轨迹标定证实 R≈0.99 (handle/piece 同步 ~88/步), home_true=183 自洽。

target = handle_home + (notch - 183) / 1.0 = 137 + 733 = 870。
慢速 swipe 到 870, 轨迹跟踪 piece 确认到达 ~916, 释放后观察:
  close => result=1 (真机指纹过!); refresh+350014 => 号码标记; snapback => 仍错位或bot。
handle 预测跟踪(左移窗, 跟住左侧按钮, 不抓 x727 元素)。
"""
from __future__ import annotations
import subprocess
import time
from io import BytesIO
import numpy as np
from PIL import Image

PHOTO_LEFT, PHOTO_RIGHT = 66, 1013
PHOTO_TOP, PHOTO_BOT = 267, 819
HOME_TRUE = 66 + (24 + 61) * (947 / 686.0)   # 183.2
R = 1.0
TRACK_Y = 950


def adb_text(*a):
    r = subprocess.run(["adb", *a], capture_output=True, text=True,
                       encoding="utf-8", errors="replace", timeout=60)
    return r.stdout


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 photo(img):
    return img[PHOTO_TOP:PHOTO_BOT, PHOTO_LEFT:PHOTO_RIGHT]


def detect_notch(img, x_min_local=230, col_thr=25):
    reg = photo(img)
    col = (reg < 90).sum(axis=0)
    col_r = col[x_min_local:]
    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())
    segL = x_min_local + int(seg[0]); segR = x_min_local + int(seg[-1])
    ys, xs = np.where((reg[:, segL:segR + 1] < 95))
    if len(xs) == 0:
        return None
    return PHOTO_LEFT + int(round(np.average(xs + segL)))


def detect_handle(img, lo=80, hi=340):
    band = img[932:968, lo:hi]
    col = (band < 200).sum(axis=0).astype(float)
    hot = np.where(col > 12)[0]
    if len(hot) == 0:
        return None
    br = np.where(np.diff(hot) > 6)[0]
    cands = [s for s in np.split(hot, br + 1) if 10 <= len(s) <= 80]
    if not cands:
        return None
    seg = max(cands, key=lambda s: col[s].max())
    w = col[seg]
    return lo + int(round(np.average(seg, weights=w)))


def track_handle(img, center):
    """预测跟踪: 在 center±70 内找最密紧簇(左移窗跟随左侧按钮)。"""
    lo = max(40, center - 70); hi = min(940, center + 70)
    return detect_handle(img, lo=lo, hi=hi) or center


def piece_blob(home, cur):
    d = np.abs(photo(cur).astype(np.int16) - photo(home).astype(np.int16))
    mask = d > 45; col = mask.sum(axis=0); hot = np.where(col > 6)[0]
    if len(hot) == 0:
        return None, None, None
    w = np.where(col > 6, col, 0); br = np.where(np.diff(hot) > 12)[0]
    c = [s for s in np.split(hot, br + 1) if len(s) > 12]
    if not c:
        return None, None, None
    seg = max(c, key=lambda s: s[-1])     # rightmost = piece current
    cx = PHOTO_LEFT + int(round(np.average(seg, weights=w[seg])))
    return cx, PHOTO_LEFT + int(seg[0]), PHOTO_LEFT + int(seg[-1])


def top_activity():
    out = adb_text("shell", "dumpsys", "activity", "activities")
    return next((l.strip() for l in out.splitlines() if "topResumedActivity" in l), "(none)")


def main():
    home = cap()
    notch = detect_notch(home)
    hh = detect_handle(home)
    print(f"[init] notch={notch} handle_home={hh} HOME_TRUE={HOME_TRUE:.0f} R={R}", flush=True)
    if notch is None or hh is None:
        print("[!] 检测失败"); return 2

    target = int(round(hh + (notch - HOME_TRUE) / R))
    print(f"[plan] target = {hh} + ({notch}-{HOME_TRUE:.0f})/{R} = {target}", flush=True)

    adb_text("logcat", "-c")
    reached = None
    p = subprocess.Popen(["adb", "shell", "input", "swipe",
                          str(hh), str(TRACK_Y), str(target), str(TRACK_Y), "3000"])
    hc = hh; t0 = time.monotonic()
    traj = []
    for _ in range(9):
        time.sleep(0.32)
        c = cap()
        hc = track_handle(c, hc)
        cx, bl, br = piece_blob(home, c)
        traj.append((round(time.monotonic() - t0, 1), hc, cx, br))
        if cx is not None:
            reached = cx
    p.wait()
    print("[traj] (t, handle, piece_cx, piece_right):", flush=True)
    for t in traj:
        print(f"       t={t[0]} h={t[1]} pcx={t[2]} pr={t[3]}", flush=True)
    print(f"[traj] piece max_cx reached ~ {reached} (notch={notch}, diff={reached-notch if reached else '?'})",
          flush=True)

    time.sleep(4.0)
    aft = cap()
    after = top_activity()
    closed = "KwaiWebViewActivity" not in after
    changed = not np.array_equal(photo(home), photo(aft))
    print(f"[post] closed={closed} photo_changed={changed} top={after[:60]}", flush=True)

    code = None
    hits = []
    for ln in adb_text("logcat", "-d").splitlines():
        low = ln.lower()
        if any(k in low for k in ("350014", "350002", "350005", "\"result\"", "ksecretapi", "captcha/sliding", "verify")):
            hits.append(ln[:170])
            for tok in ("350014", "350002", "350005"):
                if tok in ln:
                    code = tok
    print("----- logcat hits -----", flush=True)
    for h in hits[:25]:
        print(h, flush=True)

    if closed:
        print("\n>>> VERDICT: PASS (result=1) — 真机 WebView 指纹过验证! 路径=真机/模拟器。", flush=True)
    elif code == "350014":
        print("\n>>> VERDICT: 350014 — 号码被永久标记, 真机正确落点也过不去 => 只能换号。", flush=True)
    elif code == "350002":
        print("\n>>> VERDICT: 350002 — 触发 verify 但缺口错位, 微调 target。", flush=True)
    elif changed:
        print("\n>>> VERDICT: 刷新(无码) — 触发了 verify, 落点接近, 待 logcat 细节。", flush=True)
    else:
        print(f"\n>>> VERDICT: 静默回弹(nochange) — 落点 max={reached} 仍错位缺口{notch}, "
              f"或 bot 检测。检查轨迹 max 是否到达缺口。", flush=True)
    return 0


if __name__ == "__main__":
    raise SystemExit(main())