Hua
9b5f29083e
New solver subsystem with independent models: - GapDetectorCNN (1x128x256 grayscale → sigmoid) for slide gap detection - RotationRegressor (3x128x128 RGB → sin/cos via tanh) for rotation angle prediction - SlideSolver with 3-tier strategy: template match → edge detect → CNN fallback - RotateSolver with ONNX sin/cos → atan2 inference - Generators, training scripts, CLI commands, and slide track utility Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
76 lines
2.0 KiB
Python
76 lines
2.0 KiB
Python
"""
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滑块轨迹生成工具
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生成模拟人类操作的滑块拖拽轨迹。
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"""
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import math
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import random
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def generate_slide_track(
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distance: int,
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duration: float = 1.0,
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seed: int | None = None,
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) -> list[dict]:
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"""
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生成滑块拖拽轨迹。
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使用贝塞尔曲线 ease-out 加速减速，末尾带微小过冲回退。
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y 轴 ±1~3px 随机抖动，时间间隔不均匀。
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Args:
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distance: 滑动距离 (像素)
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duration: 总时长 (秒)
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seed: 随机种子
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Returns:
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[{"x": float, "y": float, "t": float}, ...]
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"""
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rng = random.Random(seed)
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if distance <= 0:
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return [{"x": 0.0, "y": 0.0, "t": 0.0}]
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track = []
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# 采样点数
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num_points = rng.randint(30, 60)
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total_ms = duration * 1000
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# 生成不均匀时间点
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raw_times = sorted([rng.random() for _ in range(num_points - 2)])
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times = [0.0] + raw_times + [1.0]
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# 过冲距离
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overshoot = rng.uniform(2, 6)
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overshoot_start = 0.85 # 85% 时到达目标 + 过冲
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for t_norm in times:
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t_ms = round(t_norm * total_ms, 1)
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if t_norm <= overshoot_start:
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# ease-out: 快速启动，缓慢减速
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progress = t_norm / overshoot_start
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eased = 1 - (1 - progress) ** 3 # cubic ease-out
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x = eased * (distance + overshoot)
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else:
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# 过冲回退段
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retract_progress = (t_norm - overshoot_start) / (1 - overshoot_start)
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eased_retract = retract_progress ** 2 # ease-in 回退
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x = (distance + overshoot) - overshoot * eased_retract
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# y 轴随机抖动
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y_jitter = rng.uniform(-3, 3) if t_norm > 0.05 else 0.0
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track.append({
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"x": round(x, 1),
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"y": round(y_jitter, 1),
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"t": t_ms,
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})
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# 确保最后一个点精确到达目标
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track[-1]["x"] = float(distance)
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track[-1]["y"] = round(rng.uniform(-0.5, 0.5), 1)
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return track
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