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https://github.com/yynps737/dnf-auto-cloud.git
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119 lines
3.4 KiB
Python
119 lines
3.4 KiB
Python
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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"""
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人类行为模拟工具，提供模拟人类操作的功能
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"""
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import random
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import time
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import math
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import numpy as np
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from config.settings import BEHAVIOR
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def generate_human_delay():
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"""
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生成人类化的延迟时间
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返回:
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float: 延迟时间（秒）
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"""
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# 使用正态分布生成随机延迟
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mean_delay = (BEHAVIOR["min_delay"] + BEHAVIOR["max_delay"]) / 2
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std_dev = (BEHAVIOR["max_delay"] - BEHAVIOR["min_delay"]) / 6 # 使99.7%的值在范围内
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delay = random.normalvariate(mean_delay, std_dev)
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# 确保在设定范围内
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return max(BEHAVIOR["min_delay"], min(BEHAVIOR["max_delay"], delay))
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def generate_human_movement_path(start_pos, end_pos, steps=None):
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"""
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生成模拟人类的鼠标移动路径
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参数:
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start_pos (list): 起始位置 [x, y]
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end_pos (list): 目标位置 [x, y]
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steps (int): 路径点数量，如果为None则自动计算
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返回:
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list: 路径点列表 [[x1, y1], [x2, y2], ...]
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"""
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# 计算距离
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distance = math.sqrt((end_pos[0] - start_pos[0])**2 + (end_pos[1] - start_pos[1])**2)
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# 如果未指定步数，根据距离计算
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if steps is None:
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steps = max(int(distance / 20), 5) # 每20像素一个点，至少5个点
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# 生成基础路径
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t = np.linspace(0, 1, steps)
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path = []
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for i in range(steps):
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# 基础线性插值
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x = start_pos[0] + (end_pos[0] - start_pos[0]) * t[i]
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y = start_pos[1] + (end_pos[1] - start_pos[1]) * t[i]
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# 添加随机偏移（越靠近中间偏移越大）
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mid_factor = 4 * t[i] * (1 - t[i]) # 在中间最大
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max_offset = distance * 0.05 * mid_factor # 最大偏移为距离的5%
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offset_x = random.normalvariate(0, max_offset / 3)
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offset_y = random.normalvariate(0, max_offset / 3)
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# 添加到路径
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path.append([x + offset_x, y + offset_y])
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# 确保起点和终点准确
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path[0] = start_pos.copy()
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path[-1] = end_pos.copy()
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return path
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def generate_human_click_offset(target_pos, target_size=(50, 50)):
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"""
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生成人类化的点击位置偏移
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参数:
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target_pos (list): 目标中心位置 [x, y]
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target_size (tuple): 目标大小 (width, height)
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返回:
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list: 带偏移的点击位置 [x, y]
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"""
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# 计算最大偏移（不超过目标大小的一半）
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max_offset_x = min(target_size[0] / 2 * 0.8, 10) # 最大偏移不超过10像素
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max_offset_y = min(target_size[1] / 2 * 0.8, 10)
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# 生成偏移（中心位置概率更高）
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offset_x = random.normalvariate(0, max_offset_x / 3)
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offset_y = random.normalvariate(0, max_offset_y / 3)
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# 应用偏移
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click_pos = [
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target_pos[0] + offset_x,
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target_pos[1] + offset_y
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]
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return click_pos
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def generate_typing_speed(base_cps=5.0, variance=0.2):
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"""
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生成人类化的打字速度
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参数:
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base_cps (float): 基础字符每秒速度
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variance (float): 速度方差
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返回:
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float: 字符间延迟时间（秒）
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"""
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# 添加随机波动
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speed = random.normalvariate(base_cps, base_cps * variance)
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speed = max(speed, base_cps * 0.5) # 确保速度不会太慢
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# 将速度转换为延迟
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delay = 1.0 / speed
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return delay |