194 lines
7.3 KiB
Python
194 lines
7.3 KiB
Python
"""真机 人性化拖动 (sendevent 注入) 测试: 350014 是 bot 拖拽模式 还是 号码标记?
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adb input swipe = 恒速线性 = 最像 bot。sendevent 注入可模拟人手:
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ease-in-out 加速曲线 + 高斯抖动 + 不规则时间间隔。
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若人性化拖动到正确落点 => close(result=1) => 是拖拽模式被检测, 路径=人性化自动化/人工。
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仍刷新 => 号码被标记(或人性化仍不够, 需人工确认)。
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MT-B 协议 (touchpanel /dev/input/event7):
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X[0,23040] Y[0,50688], ABS_MT_SLOT=002f TRACKING_ID=0039 X=0035 Y=0036
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PRESSURE=0030 TOUCH_MAJOR=0031, BTN_TOUCH=014a BTN_TOOL_FINGER=0145, SYN=0000
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"""
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from __future__ import annotations
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import subprocess
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import time
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import math
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import random
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from io import BytesIO
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import numpy as np
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from PIL import Image
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DEV = "/dev/input/event7"
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SX = 23040 / 1080.0
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SY = 50688 / 2376.0
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PHOTO_LEFT, PHOTO_RIGHT = 66, 1013
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PHOTO_TOP, PHOTO_BOT = 267, 819
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HOME_TRUE = 66 + (24 + 61) * (947 / 686.0)
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OFFSET = 23.0 # v7 收敛到的偏移修正
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TRACK_Y = 950
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def adb_text(*a):
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r = subprocess.run(["adb", *a], capture_output=True, text=True,
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encoding="utf-8", errors="replace", timeout=60)
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return r.stdout
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def cap():
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d = subprocess.run(["adb", "exec-out", "screencap", "-p"],
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capture_output=True, timeout=30).stdout
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return np.asarray(Image.open(BytesIO(d)).convert("L"))
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def photo(img):
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return img[PHOTO_TOP:PHOTO_BOT, PHOTO_LEFT:PHOTO_RIGHT]
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def detect_notch(img, x_min_local=230, col_thr=25):
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reg = photo(img); col = (reg < 90).sum(axis=0); col_r = col[x_min_local:]
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hot = np.where(col_r > col_thr)[0]
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if len(hot) == 0:
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return None
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br = np.where(np.diff(hot) > 12)[0]
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segs = [s for s in np.split(hot, br + 1) if len(s) > 10]
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seg = max(segs, key=lambda s: col_r[s].sum())
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segL = x_min_local + int(seg[0]); segR = x_min_local + int(seg[-1])
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ys, xs = np.where((reg[:, segL:segR + 1] < 95))
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if len(xs) == 0:
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return None
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return PHOTO_LEFT + int(round(np.average(xs + segL)))
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def detect_handle(img, lo=80, hi=340):
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band = img[932:968, lo:hi]; col = (band < 200).sum(axis=0).astype(float)
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hot = np.where(col > 12)[0]
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if len(hot) == 0:
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return None
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br = np.where(np.diff(hot) > 6)[0]
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cands = [s for s in np.split(hot, br + 1) if 10 <= len(s) <= 80]
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if not cands:
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return None
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seg = max(cands, key=lambda s: col[s].max())
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return lo + int(round(np.average(seg, weights=col[seg])))
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def piece_moved(home, cur):
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d = np.abs(photo(cur).astype(np.int16) - photo(home).astype(np.int16))
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return int((d > 45).sum())
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def piece_pos(home, cur):
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"""diff 右簇 cx(拼图块当前位置, screen x)。"""
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d = np.abs(photo(cur).astype(np.int16) - photo(home).astype(np.int16))
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mask = d > 45; col = mask.sum(axis=0); hot = np.where(col > 6)[0]
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if len(hot) == 0:
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return None, None
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w = np.where(col > 6, col, 0); br = np.where(np.diff(hot) > 12)[0]
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c = [s for s in np.split(hot, br + 1) if len(s) > 12]
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seg = max(c, key=lambda s: s[-1])
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return (PHOTO_LEFT + int(round(np.average(seg, weights=w[seg])))), (PHOTO_LEFT + int(seg[-1]))
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def top_activity():
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out = adb_text("shell", "dumpsys", "activity", "activities")
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return next((l.strip() for l in out.splitlines() if "topResumedActivity" in l), "(none)")
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def gen_human_path(x0, y0, x1, y1, n=56, total_s=1.7, seed=1):
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"""ease-in-out + 抖动 + 不规则 dt 的人手路径。返回 [(screen_x,screen_y,sleep_ms)]。"""
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rnd = random.Random(seed)
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pts = []
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t = 0.0
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# 时间间隔: 主体均匀 + 抖动, 末端略慢(减速)
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for i in range(n):
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s = i / (n - 1)
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# smoothstep ease-in-out
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e = s * s * (3 - 2 * s)
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x = x0 + (x1 - x0) * e
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y = y0 + (y1 - y0) * e
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# 抖动: 平行 ±2px, 垂直 ±1.5px (末端减小)
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jx = rnd.gauss(0, 2.0) * (1 - 0.6 * s)
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jy = rnd.gauss(0, 1.2) * (1 - 0.6 * s)
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x += jx; y += jy
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# dt: 基础 + 不规则, 起步快末端慢
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base = total_s / n * 1000
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dt = base * (0.7 + 0.6 * s + rnd.gauss(0, 0.12))
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pts.append((x, y, max(8, dt)))
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# 末点精确到目标
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pts[-1] = (x1, y1, 30)
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return pts
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def build_script(pts):
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"""生成 input motionevent 人性化拖拽脚本(屏幕坐标, 无需缩放; 有权限)。"""
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L = ["#!/system/bin/sh"]
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x0, y0, _ = pts[0]
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L.append(f"input motionevent DOWN {int(round(x0))} {int(round(y0))}")
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L.append("sleep 0.04")
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for (x, y, dt) in pts[1:]:
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L.append(f"input motionevent MOVE {int(round(x))} {int(round(y))}")
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L.append(f"sleep {dt/1000.0:.3f}")
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xn, yn, _ = pts[-1]
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L.append(f"input motionevent UP {int(round(xn))} {int(round(yn))}")
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return "\n".join(L)
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def main():
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home = cap()
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notch = detect_notch(home)
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hh = detect_handle(home)
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print(f"[init] notch={notch} handle={hh}", flush=True)
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if notch is None or hh is None:
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print("[!] 检测失败"); return 2
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target = hh + (notch - HOME_TRUE) + OFFSET
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print(f"[plan] humanized drag {hh},{TRACK_Y} -> {target:.0f},{TRACK_Y}", flush=True)
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pts = gen_human_path(hh, TRACK_Y, target, TRACK_Y)
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script = build_script(pts)
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import os
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local = os.path.join(os.path.dirname(__file__), "hdrag.sh")
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with open(local, "w", newline="\n") as f:
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f.write(script)
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subprocess.run(["adb", "push", local, "/sdcard/hdrag.sh"],
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capture_output=True, timeout=30)
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adb_text("logcat", "-c")
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# 后台跑脚本, 多帧跟踪 piece 是否到达缺口
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sp = subprocess.Popen(["adb", "shell", "sh", "/sdcard/hdrag.sh"],
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stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
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reached_cx = 0; reached_r = 0
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for k in range(7):
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time.sleep(0.42)
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c = cap()
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cx, r = piece_pos(home, c)
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if r and r > reached_r:
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reached_cx = cx or 0; reached_r = r
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print(f" [t~{0.42*(k+1):.1f}s] piece_cx={cx} piece_right={r}", flush=True)
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sp.wait()
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print(f"[max] piece_cx={reached_cx} piece_right={reached_r} (notch={notch}, "
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f"want_right~{notch+84})", flush=True)
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time.sleep(4.0)
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aft = cap()
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after = top_activity()
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closed = "KwaiWebViewActivity" not in after
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changed = not np.array_equal(photo(home), photo(aft))
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print(f"[post] closed={closed} photo_changed={changed} top={after[:60]}", flush=True)
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near = reached_r >= notch + 84 - 30 # 落点接近缺口右缘(±30)
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if closed:
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print("\n>>> VERDICT: PASS (result=1)! 人性化拖动过验证。", flush=True)
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print(">>> 350014 是 bot 拖拽模式被检测(非号码标记)。可行路径 = 人性化 motionevent/人工。", flush=True)
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elif changed:
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print("\n>>> 刷新(verify 触发但未过)。落点正确(near) => 号码被标记; 落点偏 => 错位。", flush=True)
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print(">>> 下一步: 请人工手指滑一次(决定性判据)确认号码是否标记。", flush=True)
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elif near:
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print("\n>>> 落点接近缺口却静默回弹(no verify) => bot 拖拽模式仍被检测。需更人性或人工。", flush=True)
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else:
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print(f"\n>>> 落点未到缺口(max_right={reached_r} < {notch+84}) => 欠滑, 增大 target。", flush=True)
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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