335 lines
12 KiB
Python
335 lines
12 KiB
Python
"""
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全局配置 - 验证码识别多模型系统 (CaptchaBreaker)
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定义字符集、图片尺寸、路径、训练超参等所有全局常量。
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"""
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import os
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from pathlib import Path
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# ============================================================
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# 项目根目录
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# ============================================================
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PROJECT_ROOT = Path(__file__).resolve().parent
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# ============================================================
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# 数据目录
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# ============================================================
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DATA_DIR = PROJECT_ROOT / "data"
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SYNTHETIC_DIR = DATA_DIR / "synthetic"
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REAL_DIR = DATA_DIR / "real"
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CLASSIFIER_DIR = DATA_DIR / "classifier"
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SERVER_TASKS_DIR = DATA_DIR / "server_tasks"
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# 合成数据子目录
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SYNTHETIC_NORMAL_DIR = SYNTHETIC_DIR / "normal"
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SYNTHETIC_MATH_DIR = SYNTHETIC_DIR / "math"
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SYNTHETIC_3D_TEXT_DIR = SYNTHETIC_DIR / "3d_text"
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SYNTHETIC_3D_ROTATE_DIR = SYNTHETIC_DIR / "3d_rotate"
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SYNTHETIC_3D_SLIDER_DIR = SYNTHETIC_DIR / "3d_slider"
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# 真实数据子目录
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REAL_NORMAL_DIR = REAL_DIR / "normal"
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REAL_MATH_DIR = REAL_DIR / "math"
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REAL_3D_TEXT_DIR = REAL_DIR / "3d_text"
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REAL_3D_ROTATE_DIR = REAL_DIR / "3d_rotate"
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REAL_3D_SLIDER_DIR = REAL_DIR / "3d_slider"
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REAL_FUN_CAPTCHA_DIR = REAL_DIR / "funcaptcha"
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REAL_FUN_CAPTCHA_ROLLBALL_DIR = REAL_FUN_CAPTCHA_DIR / "4_3d_rollball_animals"
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# Solver 数据目录
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SOLVER_DATA_DIR = DATA_DIR / "solver"
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SLIDE_DATA_DIR = SOLVER_DATA_DIR / "slide"
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ROTATE_SOLVER_DATA_DIR = SOLVER_DATA_DIR / "rotate"
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# ============================================================
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# 模型输出目录
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# ============================================================
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CHECKPOINTS_DIR = PROJECT_ROOT / "checkpoints"
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ONNX_DIR = PROJECT_ROOT / "onnx_models"
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# 确保关键目录存在
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for _dir in [
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SYNTHETIC_NORMAL_DIR, SYNTHETIC_MATH_DIR,
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SYNTHETIC_3D_TEXT_DIR, SYNTHETIC_3D_ROTATE_DIR, SYNTHETIC_3D_SLIDER_DIR,
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REAL_NORMAL_DIR, REAL_MATH_DIR,
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REAL_3D_TEXT_DIR, REAL_3D_ROTATE_DIR, REAL_3D_SLIDER_DIR,
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REAL_FUN_CAPTCHA_DIR, REAL_FUN_CAPTCHA_ROLLBALL_DIR,
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CLASSIFIER_DIR, CHECKPOINTS_DIR, ONNX_DIR,
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SLIDE_DATA_DIR, ROTATE_SOLVER_DATA_DIR,
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SERVER_TASKS_DIR,
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]:
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_dir.mkdir(parents=True, exist_ok=True)
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# ============================================================
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# 字符集定义
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# ============================================================
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# 普通字符验证码: 按当前本地配置保留易混淆字符，覆盖完整数字 + 大写字母
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NORMAL_CHARS = "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ"
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# 算式验证码: 数字 + 运算符
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MATH_CHARS = "0123456789+-×÷=?"
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# 3D 验证码: 继续使用去掉易混淆字符的精简字符集
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THREED_CHARS = "23456789ABCDEFGHJKMNPQRSTUVWXYZ"
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# 验证码类型列表 (调度分类器输出)
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CAPTCHA_TYPES = ["normal", "math", "3d_text", "3d_rotate", "3d_slider"]
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NUM_CAPTCHA_TYPES = len(CAPTCHA_TYPES)
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# ============================================================
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# 图片尺寸配置 (H, W)
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# ============================================================
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IMAGE_SIZE = {
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"classifier": (64, 128), # 调度分类器输入
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"normal": (40, 120), # 普通字符识别
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"math": (40, 160), # 算式识别 (更宽)
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"3d_text": (60, 160), # 3D 立体文字识别
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"3d_rotate": (80, 80), # 3D 旋转角度回归 (正方形)
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"3d_slider": (80, 240), # 3D 滑块偏移回归
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"funcaptcha_rollball_animals": (48, 48), # FunCaptcha 专项 Siamese 输入
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}
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# ============================================================
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# 验证码生成参数
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# ============================================================
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GENERATE_CONFIG = {
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"normal": {
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"char_count_range": (4, 5), # 字符数量: 4-5 个
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"bg_color_range": (230, 255), # 浅色背景 RGB 各通道
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"rotation_range": (-15, 15), # 字符旋转角度
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"noise_line_range": (2, 5), # 干扰线数量
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"noise_point_num": 100, # 噪点数量
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"blur_radius": 0.8, # 高斯模糊半径
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"image_size": (120, 40), # 生成图片尺寸 (W, H)
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},
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"math": {
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"operand_range": (1, 30), # 操作数范围
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"operators": ["+", "-", "×", "÷"], # 支持的运算符 (除法只生成能整除的)
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"image_size": (160, 40), # 生成图片尺寸 (W, H)
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"bg_color_range": (230, 255),
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"rotation_range": (-10, 10),
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"noise_line_range": (2, 4),
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},
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"3d_text": {
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"char_count_range": (4, 5),
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"image_size": (160, 60), # 生成图片尺寸 (W, H)
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"shadow_offset": (3, 3), # 阴影偏移
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"perspective_intensity": 0.3, # 透视变换强度
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"rotation_range": (-20, 20), # 字符旋转角度
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},
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"3d_rotate": {
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"image_size": (80, 80), # 生成图片尺寸 (W, H)
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"disc_radius": 35, # 圆盘半径
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"marker_size": 8, # 方向标记大小
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"bg_color_range": (200, 240), # 背景色范围
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},
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"3d_slider": {
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"image_size": (240, 80), # 生成图片尺寸 (W, H)
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"puzzle_size": (40, 40), # 拼图块大小 (W, H)
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"gap_x_range": (50, 200), # 缺口 x 坐标范围
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"piece_left_margin": 5, # 拼图块左侧留白
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"bg_noise_intensity": 30, # 背景纹理噪声强度
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},
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}
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# ============================================================
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# 训练配置
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# ============================================================
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TRAIN_CONFIG = {
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"classifier": {
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"epochs": 30,
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"batch_size": 128,
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"lr": 1e-3,
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"scheduler": "cosine",
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"synthetic_samples": 50000, # 每类 10000 × 5 类
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"val_split": 0.1, # 验证集比例
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},
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"normal": {
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"epochs": 50,
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"batch_size": 128,
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"lr": 1e-3,
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"scheduler": "cosine",
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"synthetic_samples": 60000,
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"loss": "CTCLoss",
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"val_split": 0.1,
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},
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"math": {
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"epochs": 50,
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"batch_size": 128,
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"lr": 1e-3,
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"scheduler": "cosine",
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"synthetic_samples": 60000,
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"loss": "CTCLoss",
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"val_split": 0.1,
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},
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"3d_text": {
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"epochs": 80,
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"batch_size": 64,
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"lr": 5e-4,
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"scheduler": "cosine",
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"synthetic_samples": 80000,
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"loss": "CTCLoss",
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"val_split": 0.1,
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},
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"3d_rotate": {
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"epochs": 60,
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"batch_size": 128,
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"lr": 1e-3,
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"scheduler": "cosine",
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"synthetic_samples": 60000,
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"loss": "SmoothL1",
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"val_split": 0.1,
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},
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"3d_slider": {
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"epochs": 60,
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"batch_size": 128,
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"lr": 1e-3,
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"scheduler": "cosine",
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"synthetic_samples": 60000,
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"loss": "SmoothL1",
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"val_split": 0.1,
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},
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"funcaptcha_rollball_animals": {
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"epochs": 30,
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"batch_size": 64,
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"lr": 1e-3,
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"scheduler": "cosine",
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"loss": "BCEWithLogits",
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"val_split": 0.1,
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},
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}
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# ============================================================
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# 数据增强参数 (训练时使用)
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# ============================================================
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AUGMENT_CONFIG = {
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"degrees": 8, # RandomAffine 旋转范围
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"translate": (0.05, 0.05), # 平移范围
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"scale": (0.95, 1.05), # 缩放范围
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"brightness": 0.3, # ColorJitter 亮度
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"contrast": 0.3, # ColorJitter 对比度
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"blur_kernel": 3, # GaussianBlur 核大小
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"blur_sigma": (0.1, 0.5), # GaussianBlur sigma
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"erasing_prob": 0.15, # RandomErasing 概率
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"erasing_scale": (0.01, 0.05), # RandomErasing 面积比
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}
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# ============================================================
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# ONNX 导出配置
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# ============================================================
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ONNX_CONFIG = {
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"opset_version": 18,
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"dynamic_batch": True, # 支持动态 batch size
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}
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# ============================================================
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# 回归模型标签范围
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# ============================================================
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REGRESSION_RANGE = {
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"3d_rotate": (0, 360), # 旋转角度 0-359°
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"3d_slider": (10, 200), # 滑块 x 偏移 (像素)
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}
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# ============================================================
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# 推理配置
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# ============================================================
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INFERENCE_CONFIG = {
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"default_models_dir": str(ONNX_DIR),
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"normalize_mean": 0.5,
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"normalize_std": 0.5,
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}
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# ============================================================
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# FunCaptcha 专项任务配置
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# ============================================================
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FUN_CAPTCHA_TASKS = {
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"4_3d_rollball_animals": {
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"artifact_name": "funcaptcha_rollball_animals",
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"checkpoint_name": "funcaptcha_rollball_animals",
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"data_dir": REAL_FUN_CAPTCHA_ROLLBALL_DIR,
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"input_size": IMAGE_SIZE["funcaptcha_rollball_animals"], # (H, W)
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"tile_size": (200, 200), # (W, H)
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"reference_box": (0, 200, 200, 400), # (x1, y1, x2, y2)
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"num_candidates": 4,
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"answer_index_base": 0,
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"channels": 3,
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"external_model_env": "FUNCAPTCHA_ROLLBALL_MODEL_PATH",
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"fallback_model_paths": [
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str(PROJECT_ROOT.parent / "funcaptcha-server" / "model" / "4_3d_rollball_animals.onnx"),
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],
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},
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}
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# ============================================================
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# 随机种子 (保证数据生成可复现)
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# ============================================================
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RANDOM_SEED = 42
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# ============================================================
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# 设备配置 (优先 GPU，回退 CPU)
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# 延迟导入 torch，避免仅使用生成器时必须安装 torch
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# ============================================================
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def get_device():
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"""返回可用的 torch 设备，优先 GPU。"""
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import torch
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return torch.device("cuda" if torch.cuda.is_available() else "cpu")
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# ============================================================
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# 服务配置 (可选 HTTP 服务)
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# ============================================================
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SERVER_CONFIG = {
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"host": "0.0.0.0",
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"port": 8080,
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"task_ttl_seconds": 600,
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"task_workers": 2,
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"tasks_dir": str(SERVER_TASKS_DIR),
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"callback_timeout_seconds": 10,
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"callback_max_retries": 2,
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"callback_retry_delay_seconds": 1.0,
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"callback_retry_backoff": 2.0,
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"callback_signing_secret": os.getenv("CALLBACK_SIGNING_SECRET")
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or os.getenv("CAPTCHA_CALLBACK_SIGNING_SECRET"),
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"balance": 999999.0,
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"task_cost": 0.0,
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"client_key": os.getenv("CLIENT_KEY") or os.getenv("CAPTCHA_CLIENT_KEY"),
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}
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# ============================================================
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# Solver 配置 (交互式验证码求解)
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# ============================================================
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SOLVER_CONFIG = {
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"slide": {
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"canny_low": 50,
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"canny_high": 150,
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"cnn_input_size": (128, 256), # H, W
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},
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"rotate": {
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"input_size": (128, 128), # H, W
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"channels": 3, # RGB
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},
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}
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SOLVER_TRAIN_CONFIG = {
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"slide_cnn": {
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"epochs": 50,
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"batch_size": 64,
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"lr": 1e-3,
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"synthetic_samples": 30000,
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"val_split": 0.1,
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},
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"rotate": {
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"epochs": 80,
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"batch_size": 64,
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"lr": 5e-4,
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"synthetic_samples": 50000,
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"val_split": 0.1,
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},
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}
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SOLVER_REGRESSION_RANGE = {
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# 在 solver 输入宽度空间内预测缺口中心 x 坐标，再通过 sigmoid 归一化到 [0, 1]
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"slide": (0, SOLVER_CONFIG["slide"]["cnn_input_size"][1]),
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"rotate": (0, 360), # 角度
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}
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