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>
83 lines
2.5 KiB
Python
83 lines
2.5 KiB
Python
"""
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滑块缺口检测 CNN (GapDetectorCNN)
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用于检测滑块验证码中缺口的 x 坐标位置。
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输出 sigmoid 归一化到 [0,1]，推理时按图片宽度缩放回像素坐标。
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架构:
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Conv(1→32) + BN + ReLU + Pool
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Conv(32→64) + BN + ReLU + Pool
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Conv(64→128) + BN + ReLU + Pool
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Conv(128→128) + BN + ReLU + Pool
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AdaptiveAvgPool2d(1) → FC(128→64) → ReLU → Dropout(0.2) → FC(64→1) → Sigmoid
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约 250K 参数，~1MB。
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"""
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import torch
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import torch.nn as nn
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class GapDetectorCNN(nn.Module):
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"""
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滑块缺口检测 CNN，输出缺口 x 坐标的归一化百分比 [0,1]。
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与 RegressionCNN 架构相同，但语义上专用于滑块缺口检测，
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默认输入尺寸 1x128x256 (灰度)。
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"""
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def __init__(self, img_h: int = 128, img_w: int = 256):
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super().__init__()
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self.img_h = img_h
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self.img_w = img_w
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self.features = nn.Sequential(
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# block 1: 1 → 32, H/2, W/2
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nn.Conv2d(1, 32, kernel_size=3, padding=1, bias=False),
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nn.BatchNorm2d(32),
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nn.ReLU(inplace=True),
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nn.MaxPool2d(2, 2),
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# block 2: 32 → 64, H/4, W/4
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nn.Conv2d(32, 64, kernel_size=3, padding=1, bias=False),
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nn.BatchNorm2d(64),
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nn.ReLU(inplace=True),
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nn.MaxPool2d(2, 2),
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# block 3: 64 → 128, H/8, W/8
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nn.Conv2d(64, 128, kernel_size=3, padding=1, bias=False),
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nn.BatchNorm2d(128),
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nn.ReLU(inplace=True),
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nn.MaxPool2d(2, 2),
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# block 4: 128 → 128, H/16, W/16
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nn.Conv2d(128, 128, kernel_size=3, padding=1, bias=False),
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nn.BatchNorm2d(128),
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nn.ReLU(inplace=True),
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nn.MaxPool2d(2, 2),
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)
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self.pool = nn.AdaptiveAvgPool2d(1)
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self.regressor = nn.Sequential(
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nn.Linear(128, 64),
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nn.ReLU(inplace=True),
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nn.Dropout(0.2),
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nn.Linear(64, 1),
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nn.Sigmoid(),
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)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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"""
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Args:
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x: (batch, 1, H, W) 灰度图
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Returns:
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output: (batch, 1) sigmoid 输出 [0, 1]，表示缺口 x 坐标百分比
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"""
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feat = self.features(x)
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feat = self.pool(feat) # (B, 128, 1, 1)
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feat = feat.flatten(1) # (B, 128)
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out = self.regressor(feat) # (B, 1)
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return out
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