From 760b80ee5eef3b36a75a887ec78f3744d4adf766 Mon Sep 17 00:00:00 2001 From: Hua Date: Tue, 10 Mar 2026 18:47:29 +0800 Subject: [PATCH] Initialize repository --- .gitignore | 14 + AGENTS.md | 28 + CLAUDE.md | 391 +++++++++++ cli.py | 228 +++++++ config.py | 195 ++++++ data/real/3d/.gitkeep | 0 data/real/math/.gitkeep | 0 data/real/normal/.gitkeep | 0 generators/__init__.py | 20 + generators/base.py | 61 ++ generators/math_gen.py | 186 +++++ generators/normal_gen.py | 154 +++++ generators/threed_gen.py | 211 ++++++ inference/__init__.py | 18 + inference/export_onnx.py | 121 ++++ inference/math_eval.py | 66 ++ inference/pipeline.py | 231 +++++++ main.py | 16 + models/__init__.py | 18 + models/classifier.py | 72 ++ models/lite_crnn.py | 141 ++++ models/threed_cnn.py | 155 +++++ pyproject.toml | 25 + tests/__init__.py | 3 + training/__init__.py | 10 + training/dataset.py | 159 +++++ training/train_3d.py | 40 ++ training/train_classifier.py | 232 +++++++ training/train_math.py | 40 ++ training/train_normal.py | 40 ++ training/train_utils.py | 232 +++++++ uv.lock | 1236 ++++++++++++++++++++++++++++++++++ 32 files changed, 4343 insertions(+) create mode 100644 .gitignore create mode 100644 AGENTS.md create mode 100644 CLAUDE.md create mode 100644 cli.py create mode 100644 config.py create mode 100644 data/real/3d/.gitkeep create mode 100644 data/real/math/.gitkeep create mode 100644 data/real/normal/.gitkeep create mode 100644 generators/__init__.py create mode 100644 generators/base.py create mode 100644 generators/math_gen.py create mode 100644 generators/normal_gen.py create mode 100644 generators/threed_gen.py create mode 100644 inference/__init__.py create mode 100644 inference/export_onnx.py create mode 100644 inference/math_eval.py create mode 100644 inference/pipeline.py create mode 100644 main.py create mode 100644 models/__init__.py create mode 100644 models/classifier.py create mode 100644 models/lite_crnn.py create mode 100644 models/threed_cnn.py create mode 100644 pyproject.toml create mode 100644 tests/__init__.py create mode 100644 training/__init__.py create mode 100644 training/dataset.py create mode 100644 training/train_3d.py create mode 100644 training/train_classifier.py create mode 100644 training/train_math.py create mode 100644 training/train_normal.py create mode 100644 training/train_utils.py create mode 100644 uv.lock diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..83311ac --- /dev/null +++ b/.gitignore @@ -0,0 +1,14 @@ +.venv/ +__pycache__/ +*.py[cod] + +.idea/ +.claude/ + +data/synthetic/ +data/classifier/ + +checkpoints/ +onnx_models/ + +.DS_Store diff --git a/AGENTS.md b/AGENTS.md new file mode 100644 index 0000000..14e4833 --- /dev/null +++ b/AGENTS.md @@ -0,0 +1,28 @@ +# Repository Guidelines + +## Project Structure & Module Organization +Use `cli.py` as the main entrypoint and keep shared settings in `config.py`. `generators/` builds synthetic captchas, `models/` contains the classifier and expert OCR models, `training/` owns datasets and training scripts, and `inference/` contains the ONNX pipeline, export code, and math post-processing. Runtime artifacts live in `data/`, `checkpoints/`, and `onnx_models/`. + +## Build, Test, and Development Commands +Use `uv` for environment and dependency management. + +- `uv sync` installs the base runtime dependencies from `pyproject.toml`. +- `uv sync --extra server` installs HTTP service dependencies. +- `uv run captcha generate --type normal --num 1000` generates synthetic training data. +- `uv run captcha train --model normal` trains one model; `uv run captcha train --all` runs the full order: `normal -> math -> 3d -> classifier`. +- `uv run captcha export --all` exports all trained models to ONNX. +- `uv run captcha predict image.png` runs auto-routing inference; add `--type normal` to skip classification. +- `uv run captcha predict-dir ./test_images` runs batch inference on a directory. +- `uv run captcha serve --port 8080` starts the optional HTTP API when `server.py` is implemented. + +## Coding Style & Naming Conventions +Target Python 3.10+ and follow existing style: 4-space indentation, snake_case for functions/modules, PascalCase for classes, and short docstrings on public entrypoints. Keep captcha-type ids exactly `normal`, `math`, `3d`, and `classifier`. Preserve the design rules from `CLAUDE.md`: float32 training/export, CPU-safe ops, and greedy CTC decoding unless the pipeline is intentionally redesigned. `normal` uses the local configured charset and currently includes confusing characters; math captchas must be recognized as strings and then evaluated in `inference/math_eval.py`. + +## Data & Testing Guidelines +Synthetic generator output should use `{label}_{index:06d}.png`; real labeled samples should use `{label}_{anything}.png`. Save best checkpoints to `checkpoints/` and export matching ONNX files to `onnx_models/`. Use `pytest`, place tests under `tests/` as `test_.py`, and run them with `uv run pytest`. For model, data, or routing changes, add a fast smoke test for shapes, decoding, CLI behavior, or pipeline routing. + +## Commit & Pull Request Guidelines +Git history is not available in this workspace snapshot, so use short imperative commit subjects such as `Add classifier export smoke test`. Keep pull requests focused, describe affected modules, list the commands you ran, and attach sample outputs when prediction behavior changes. + +## Documentation Sync +Do not commit large generated datasets unless explicitly required. When a change affects project structure, commands, config, architecture, artifact paths, supported captcha types, or workflow rules, update `AGENTS.md` and `CLAUDE.md` in the same patch. diff --git a/CLAUDE.md b/CLAUDE.md new file mode 100644 index 0000000..9ffbaa0 --- /dev/null +++ b/CLAUDE.md @@ -0,0 +1,391 @@ +# CLAUDE.md - 验证码识别多模型系统 (CaptchaBreaker) + +## 项目概述 + +构建一个本地验证码识别系统，采用 **调度模型 + 多专家模型** 的两级架构。调度模型负责分类验证码类型，专家模型负责具体识别。所有模型轻量化设计，最终导出 ONNX 用于部署。 + +## 技术栈 + +- Python 3.10+ +- uv (包管理，依赖定义在 pyproject.toml) +- PyTorch 2.x (训练) +- ONNX + ONNXRuntime (推理部署) +- Pillow (图像处理) +- FastAPI (可选，提供 HTTP 识别服务) + +## 项目结构 + +``` +captcha-breaker/ +├── CLAUDE.md +├── pyproject.toml # 项目配置与依赖 (uv 管理) +├── config.py # 全局配置 (字符集、图片尺寸、路径等) +├── data/ +│ ├── synthetic/ # 合成训练数据 (自动生成，不入 git) +│ │ ├── normal/ # 普通字符型 +│ │ ├── math/ # 算式型 +│ │ └── 3d/ # 3D立体型 +│ ├── real/ # 真实验证码样本 (手动标注) +│ │ ├── normal/ +│ │ ├── math/ +│ │ └── 3d/ +│ └── classifier/ # 调度分类器训练数据 (混合各类型) +├── generators/ +│ ├── __init__.py +│ ├── base.py # 生成器基类 +│ ├── normal_gen.py # 普通字符验证码生成器 +│ ├── math_gen.py # 算式验证码生成器 (如 3+8=?) +│ └── threed_gen.py # 3D立体验证码生成器 +├── models/ +│ ├── __init__.py +│ ├── lite_crnn.py # 轻量 CRNN (用于普通字符和算式) +│ ├── classifier.py # 调度分类模型 +│ └── threed_cnn.py # 3D验证码专用模型 (更深的CNN) +├── training/ +│ ├── __init__.py +│ ├── train_classifier.py # 训练调度模型 +│ ├── train_normal.py # 训练普通字符识别 +│ ├── train_math.py # 训练算式识别 +│ ├── train_3d.py # 训练3D识别 +│ └── dataset.py # 通用 Dataset 类 +├── inference/ +│ ├── __init__.py +│ ├── pipeline.py # 核心推理流水线 (调度+识别) +│ ├── export_onnx.py # PyTorch → ONNX 导出脚本 +│ └── math_eval.py # 算式计算模块 +├── checkpoints/ # 训练产出的模型文件 +│ ├── classifier.pth +│ ├── normal.pth +│ ├── math.pth +│ └── threed.pth +├── onnx_models/ # 导出的 ONNX 模型 +│ ├── classifier.onnx +│ ├── normal.onnx +│ ├── math.onnx +│ └── threed.onnx +├── server.py # FastAPI 推理服务 (可选) +├── cli.py # 命令行入口 +└── tests/ + ├── test_generators.py + ├── test_models.py + └── test_pipeline.py +``` + +## 核心架构设计 + +### 推理流水线 + +``` +输入图片 → 预处理 → 调度分类器 → 路由到专家模型 → 后处理 → 输出结果 + │ + ┌────────┼────────┐ + ▼ ▼ ▼ + normal math 3d + (CRNN) (CRNN) (CNN) + │ │ │ + ▼ ▼ ▼ + "A3B8" "3+8=?"→11 "X9K2" +``` + +### 调度分类器 (classifier.py) + +- 任务: 图像分类，判断验证码属于哪个类型 +- 架构: 轻量 CNN，3-4 层卷积 + 全局平均池化 + 全连接 +- 输入: 灰度图 1x64x128 +- 输出: softmax 概率分布，类别数 = 验证码类型数 +- 要求: 准确率 99%+，推理 < 5ms +- 模型体积目标: < 500KB + +```python +class CaptchaClassifier(nn.Module): + """ + 轻量分类器，几层卷积即可区分不同类型验证码。 + 不同类型验证码视觉差异大（有无运算符、3D效果等），分类很容易。 + """ + def __init__(self, num_types=3): + # 4层卷积 + GAP + FC + # Conv2d(1,16) -> Conv2d(16,32) -> Conv2d(32,64) -> Conv2d(64,64) + # AdaptiveAvgPool2d(1) -> Linear(64, num_types) + pass +``` + +### 普通字符识别专家 (lite_crnn.py - normal 模式) + +- 任务: 识别彩色字符验证码 (数字+字母混合) +- 架构: CRNN + CTC +- 字符集: `0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ` (36个，包含易混淆字符，按本地配置训练) +- 输入: 灰度图 1x40x120 +- 输出: 字符序列，通过 CTC 贪心解码 +- 验证码特征: 浅色背景、彩色字符、轻微干扰线、字符有倾斜 +- 模型体积目标: < 2MB + +### 算式识别专家 (lite_crnn.py - math 模式) + +- 任务: 识别算式验证码并计算结果 +- 架构: 复用 CRNN + CTC，字符集不同 +- 字符集: `0123456789+-×÷=?` (数字+运算符) +- 输入: 灰度图 1x40x160 (算式通常更宽) +- 输出: 识别出算式字符串，然后交给 math_eval.py 计算 +- 分两步: (1) OCR 识别 → "3+8=?" (2) 正则解析并计算 → 11 +- 模型体积目标: < 2MB + +```python +# math_eval.py 核心逻辑 +def eval_captcha_math(expr: str) -> str: + """ + 解析并计算验证码算式。 + 支持: 加减乘除，个位到两位数运算。 + 输入: "3+8=?" 或 "12×3=?" 或 "15-7=?" + 输出: "11" 或 "36" 或 "8" + 用正则提取数字和运算符，不要用 eval()。 + """ + pass +``` + +### 3D立体识别专家 (threed_cnn.py) + +- 任务: 识别带 3D 透视/阴影效果的验证码 +- 架构: 更深的 CNN + CRNN，或 ResNet-lite backbone +- 输入: 灰度图 1x60x160 +- 需要更强的特征提取能力来处理透视变形和阴影 +- 模型体积目标: < 5MB + +## 数据生成器规范 + +### 基类 (base.py) + +```python +class BaseCaptchaGenerator: + def generate(self, text=None) -> tuple[Image.Image, str]: + """生成一张验证码，返回 (图片, 标签文本)""" + raise NotImplementedError + + def generate_dataset(self, num_samples: int, output_dir: str): + """批量生成，文件名格式: {label}_{index:06d}.png""" + pass +``` + +### 普通字符生成器 (normal_gen.py) + +模拟目标风格: +- 浅色随机背景 (RGB 各通道 230-255) +- 每个字符随机颜色 (深色: 蓝/红/绿/紫/棕等) +- 字符数量: 4-5 个 +- 字符有 ±15° 随机旋转 +- 2-5 条浅色干扰线 +- 少量噪点 +- 可选轻微高斯模糊 + +### 算式生成器 (math_gen.py) + +- 生成形如 `A op B = ?` 的算式图片 +- A, B 范围: 1-30 的整数 +- op: +, -, × (除法只生成能整除的) +- 确保结果为非负整数 +- 标签格式: `3+8` (存储算式本身，不存结果) +- 视觉风格: 与目标算式验证码一致 + +### 3D生成器 (threed_gen.py) + +- 使用 Pillow 的仿射变换模拟 3D 透视 +- 添加阴影效果 +- 字符有深度感和倾斜 +- 标签: 纯字符内容 + +## 训练规范 + +### 通用训练配置 + +```python +# config.py 中定义 +TRAIN_CONFIG = { + 'classifier': { + 'epochs': 30, + 'batch_size': 128, + 'lr': 1e-3, + 'scheduler': 'cosine', + 'synthetic_samples': 30000, # 每类 10000 + }, + 'normal': { + 'epochs': 50, + 'batch_size': 128, + 'lr': 1e-3, + 'scheduler': 'cosine', + 'synthetic_samples': 60000, + 'loss': 'CTCLoss', + }, + 'math': { + 'epochs': 50, + 'batch_size': 128, + 'lr': 1e-3, + 'scheduler': 'cosine', + 'synthetic_samples': 60000, + 'loss': 'CTCLoss', + }, + 'threed': { + 'epochs': 80, + 'batch_size': 64, + 'lr': 5e-4, + 'scheduler': 'cosine', + 'synthetic_samples': 80000, + 'loss': 'CTCLoss', + }, +} +``` + +### 训练脚本要求 + +每个训练脚本必须: +1. 检查合成数据是否已生成，没有则自动调用生成器 +2. 支持混合真实数据 (如果 data/real/{type}/ 有文件) +3. 使用数据增强: RandomAffine, ColorJitter, GaussianBlur, RandomErasing +4. 输出训练日志: epoch, loss, 整体准确率, 字符级准确率 +5. 保存最佳模型到 checkpoints/ +6. 训练结束自动导出 ONNX 到 onnx_models/ + +### 数据增强策略 + +```python +# 训练时增强 +train_augment = transforms.Compose([ + transforms.Grayscale(), + transforms.Resize((H, W)), + transforms.RandomAffine(degrees=8, translate=(0.05, 0.05), scale=(0.95, 1.05)), + transforms.ColorJitter(brightness=0.3, contrast=0.3), + transforms.GaussianBlur(3, sigma=(0.1, 0.5)), + transforms.ToTensor(), + transforms.Normalize([0.5], [0.5]), + transforms.RandomErasing(p=0.15, scale=(0.01, 0.05)), +]) +``` + +## 推理流水线 (pipeline.py) + +```python +class CaptchaPipeline: + """ + 核心推理流水线。 + 加载调度模型和所有专家模型 (ONNX 格式)。 + 提供统一的 solve(image) 接口。 + """ + + def __init__(self, models_dir='onnx_models/'): + """ + 初始化加载所有 ONNX 模型。 + 使用 onnxruntime.InferenceSession。 + """ + pass + + def preprocess(self, image: Image.Image, target_size: tuple) -> np.ndarray: + """图片预处理: resize, grayscale, normalize, 转 numpy""" + pass + + def classify(self, image: Image.Image) -> str: + """调度分类，返回类型名: 'normal' / 'math' / '3d'""" + pass + + def solve(self, image) -> str: + """ + 完整识别流程: + 1. 分类验证码类型 + 2. 路由到对应专家模型 + 3. 后处理 (算式型需要计算结果) + 4. 返回最终答案字符串 + + image: PIL.Image 或文件路径或 bytes + """ + pass +``` + +## ONNX 导出 (export_onnx.py) + +```python +def export_model(model, model_name, input_shape, onnx_dir='onnx_models/'): + """ + 导出单个模型为 ONNX。 + - 使用 opset_version=18 + - 开启 dynamic_axes 支持动态 batch + - 导出后用 onnxruntime 验证推理一致性 + - 可选: onnx 模型简化 (onnxsim) + """ + pass + +def export_all(): + """依次导出 classifier, normal, math, threed 四个模型""" + pass +``` + +## CLI 入口 (cli.py) + +```bash +# 安装依赖 +uv sync # 核心依赖 +uv sync --extra server # 含 HTTP 服务依赖 + +# 生成训练数据 +uv run python cli.py generate --type normal --num 60000 +uv run python cli.py generate --type math --num 60000 +uv run python cli.py generate --type 3d --num 80000 +uv run python cli.py generate --type classifier --num 30000 + +# 训练模型 +uv run python cli.py train --model classifier +uv run python cli.py train --model normal +uv run python cli.py train --model math +uv run python cli.py train --model 3d +uv run python cli.py train --all # 按依赖顺序全部训练 + +# 导出 ONNX +uv run python cli.py export --all + +# 推理 +uv run python cli.py predict image.png # 自动分类+识别 +uv run python cli.py predict image.png --type normal # 跳过分类直接识别 +uv run python cli.py predict-dir ./test_images/ # 批量识别 + +# 启动 HTTP 服务 (需先安装 server 可选依赖) +uv run python cli.py serve --port 8080 +``` + +## HTTP 服务 (server.py，可选) + +```python +# FastAPI 服务，提供 REST API +# POST /solve - 上传图片，返回识别结果 +# 请求: multipart/form-data，字段名 image +# 响应: {"type": "normal", "result": "A3B8", "confidence": 0.95, "time_ms": 45} +``` + +## 关键约束和注意事项 + +1. **所有模型用 float32 训练，导出 ONNX 时不做量化**，先保证精度 +2. **CTC 解码统一用贪心解码**，不需要 beam search，验证码场景贪心够用 +3. **字符集由 config.py 统一定义**: 当前 normal 保留易混淆字符，3d 继续使用去混淆字符集 +4. **算式识别分两步**: 先 OCR 识别字符串，再用规则计算，不要让模型直接输出数值 +5. **生成器的随机种子**: 生成数据时设置 seed 保证可复现 +6. **真实数据文件名格式**: `{label}_{任意}.png`，label 部分是标注内容 +7. **模型保存格式**: PyTorch checkpoint 包含 model_state_dict, chars, best_acc, epoch +8. **不使用 GPU 特有功能**，确保 CPU 也能训练和推理 (只是慢一些) +9. **类型扩展**: 新增验证码类型时，只需 (1) 加生成器 (2) 加专家模型 (3) 调度器加一个类别重新训练 +10. **文档同步**: 对项目结构、配置、架构等做出变更时，必须同步更新 CLAUDE.md 中的对应内容，保持文档与代码一致 + +## 目标指标 + +| 模型 | 准确率目标 | 推理延迟 | 模型体积 | +|------|-----------|---------|---------| +| 调度分类器 | > 99% | < 5ms | < 500KB | +| 普通字符 | > 95% | < 30ms | < 2MB | +| 算式识别 | > 93% | < 30ms | < 2MB | +| 3D立体 | > 85% | < 50ms | < 5MB | +| 全流水线 | - | < 80ms | < 10MB 总计 | + +## 开发顺序 + +1. 先实现 config.py 和 generators/ +2. 实现 models/ 中所有模型定义 +3. 实现 training/dataset.py 通用数据集类 +4. 按顺序训练: normal → math → 3d → classifier +5. 实现 inference/pipeline.py 和 export_onnx.py +6. 实现 cli.py 统一入口 +7. 可选: server.py HTTP 服务 +8. 编写 tests/ diff --git a/cli.py b/cli.py new file mode 100644 index 0000000..ff5aaff --- /dev/null +++ b/cli.py @@ -0,0 +1,228 @@ +""" +CaptchaBreaker 命令行入口 + +用法: + python cli.py generate --type normal --num 60000 + python cli.py train --model normal + python cli.py train --all + python cli.py export --all + python cli.py predict image.png + python cli.py predict image.png --type normal + python cli.py predict-dir ./test_images/ + python cli.py serve --port 8080 +""" + +import argparse +import sys +from pathlib import Path + + +def cmd_generate(args): + """生成训练数据。""" + from config import ( + SYNTHETIC_NORMAL_DIR, SYNTHETIC_MATH_DIR, SYNTHETIC_3D_DIR, + CLASSIFIER_DIR, TRAIN_CONFIG, CAPTCHA_TYPES, NUM_CAPTCHA_TYPES, + ) + from generators import NormalCaptchaGenerator, MathCaptchaGenerator, ThreeDCaptchaGenerator + + gen_map = { + "normal": (NormalCaptchaGenerator, SYNTHETIC_NORMAL_DIR), + "math": (MathCaptchaGenerator, SYNTHETIC_MATH_DIR), + "3d": (ThreeDCaptchaGenerator, SYNTHETIC_3D_DIR), + } + + captcha_type = args.type + num = args.num + + if captcha_type == "classifier": + # 分类器数据: 各类型各生成 num // num_types + per_class = num // NUM_CAPTCHA_TYPES + print(f"生成分类器训练数据: 每类 {per_class} 张") + for cls_name in CAPTCHA_TYPES: + gen_cls, out_dir = gen_map[cls_name] + cls_dir = CLASSIFIER_DIR / cls_name + cls_dir.mkdir(parents=True, exist_ok=True) + gen = gen_cls() + gen.generate_dataset(per_class, str(cls_dir)) + elif captcha_type in gen_map: + gen_cls, out_dir = gen_map[captcha_type] + print(f"生成 {captcha_type} 数据: {num} 张 → {out_dir}") + gen = gen_cls() + gen.generate_dataset(num, str(out_dir)) + else: + print(f"未知类型: {captcha_type} 可选: normal, math, 3d, classifier") + sys.exit(1) + + +def cmd_train(args): + """训练模型。""" + if args.all: + # 按依赖顺序: normal → math → 3d → classifier + print("按顺序训练全部模型: normal → math → 3d → classifier\n") + from training.train_normal import main as train_normal + from training.train_math import main as train_math + from training.train_3d import main as train_3d + from training.train_classifier import main as train_classifier + + train_normal() + print("\n") + train_math() + print("\n") + train_3d() + print("\n") + train_classifier() + return + + model = args.model + if model == "normal": + from training.train_normal import main as train_fn + elif model == "math": + from training.train_math import main as train_fn + elif model == "3d": + from training.train_3d import main as train_fn + elif model == "classifier": + from training.train_classifier import main as train_fn + else: + print(f"未知模型: {model} 可选: normal, math, 3d, classifier") + sys.exit(1) + + train_fn() + + +def cmd_export(args): + """导出 ONNX 模型。""" + from inference.export_onnx import export_all, _load_and_export + + if args.all: + export_all() + elif args.model: + _load_and_export(args.model) + else: + print("请指定 --all 或 --model ") + sys.exit(1) + + +def cmd_predict(args): + """单张图片推理。""" + from inference.pipeline import CaptchaPipeline + + image_path = args.image + if not Path(image_path).exists(): + print(f"文件不存在: {image_path}") + sys.exit(1) + + pipeline = CaptchaPipeline() + result = pipeline.solve(image_path, captcha_type=args.type) + + print(f"文件: {image_path}") + print(f"类型: {result['type']}") + print(f"识别: {result['raw']}") + print(f"结果: {result['result']}") + print(f"耗时: {result['time_ms']:.1f} ms") + + +def cmd_predict_dir(args): + """批量目录推理。""" + from inference.pipeline import CaptchaPipeline + + dir_path = Path(args.directory) + if not dir_path.is_dir(): + print(f"目录不存在: {dir_path}") + sys.exit(1) + + pipeline = CaptchaPipeline() + images = sorted(dir_path.glob("*.png")) + sorted(dir_path.glob("*.jpg")) + if not images: + print(f"目录中未找到图片: {dir_path}") + sys.exit(1) + + print(f"批量识别: {len(images)} 张图片\n") + print(f"{'文件名':<30} {'类型':<8} {'结果':<15} {'耗时(ms)':>8}") + print("-" * 65) + + total_ms = 0.0 + for img_path in images: + result = pipeline.solve(str(img_path), captcha_type=args.type) + total_ms += result["time_ms"] + print( + f"{img_path.name:<30} {result['type']:<8} " + f"{result['result']:<15} {result['time_ms']:>8.1f}" + ) + + print("-" * 65) + print(f"总计: {len(images)} 张 平均: {total_ms / len(images):.1f} ms 总耗时: {total_ms:.1f} ms") + + +def cmd_serve(args): + """启动 HTTP 服务。""" + try: + from server import create_app + except ImportError: + # server.py 尚未实现或缺少依赖 + print("HTTP 服务需要 FastAPI 和 uvicorn。") + print("安装: uv sync --extra server") + print("并确保 server.py 已实现。") + sys.exit(1) + + import uvicorn + app = create_app() + uvicorn.run(app, host=args.host, port=args.port) + + +def main(): + parser = argparse.ArgumentParser( + prog="captcha-breaker", + description="验证码识别多模型系统 - 调度模型 + 多专家模型", + ) + subparsers = parser.add_subparsers(dest="command", help="子命令") + + # ---- generate ---- + p_gen = subparsers.add_parser("generate", help="生成训练数据") + p_gen.add_argument("--type", required=True, help="验证码类型: normal, math, 3d, classifier") + p_gen.add_argument("--num", type=int, required=True, help="生成数量") + + # ---- train ---- + p_train = subparsers.add_parser("train", help="训练模型") + p_train.add_argument("--model", help="模型名: normal, math, 3d, classifier") + p_train.add_argument("--all", action="store_true", help="按依赖顺序训练全部模型") + + # ---- export ---- + p_export = subparsers.add_parser("export", help="导出 ONNX 模型") + p_export.add_argument("--model", help="模型名: normal, math, 3d, classifier, threed") + p_export.add_argument("--all", action="store_true", help="导出全部模型") + + # ---- predict ---- + p_pred = subparsers.add_parser("predict", help="识别单张验证码") + p_pred.add_argument("image", help="图片路径") + p_pred.add_argument("--type", default=None, help="指定类型跳过分类: normal, math, 3d") + + # ---- predict-dir ---- + p_pdir = subparsers.add_parser("predict-dir", help="批量识别目录中的验证码") + p_pdir.add_argument("directory", help="图片目录路径") + p_pdir.add_argument("--type", default=None, help="指定类型跳过分类: normal, math, 3d") + + # ---- serve ---- + p_serve = subparsers.add_parser("serve", help="启动 HTTP 识别服务") + p_serve.add_argument("--host", default="0.0.0.0", help="监听地址 (默认 0.0.0.0)") + p_serve.add_argument("--port", type=int, default=8080, help="监听端口 (默认 8080)") + + args = parser.parse_args() + + if args.command is None: + parser.print_help() + sys.exit(0) + + cmd_map = { + "generate": cmd_generate, + "train": cmd_train, + "export": cmd_export, + "predict": cmd_predict, + "predict-dir": cmd_predict_dir, + "serve": cmd_serve, + } + + cmd_map[args.command](args) + + +if __name__ == "__main__": + main() diff --git a/config.py b/config.py new file mode 100644 index 0000000..7f7d280 --- /dev/null +++ b/config.py @@ -0,0 +1,195 @@ +""" +全局配置 - 验证码识别多模型系统 (CaptchaBreaker) + +定义字符集、图片尺寸、路径、训练超参等所有全局常量。 +""" + +import os +from pathlib import Path + +# ============================================================ +# 项目根目录 +# ============================================================ +PROJECT_ROOT = Path(__file__).resolve().parent + +# ============================================================ +# 数据目录 +# ============================================================ +DATA_DIR = PROJECT_ROOT / "data" +SYNTHETIC_DIR = DATA_DIR / "synthetic" +REAL_DIR = DATA_DIR / "real" +CLASSIFIER_DIR = DATA_DIR / "classifier" + +# 合成数据子目录 +SYNTHETIC_NORMAL_DIR = SYNTHETIC_DIR / "normal" +SYNTHETIC_MATH_DIR = SYNTHETIC_DIR / "math" +SYNTHETIC_3D_DIR = SYNTHETIC_DIR / "3d" + +# 真实数据子目录 +REAL_NORMAL_DIR = REAL_DIR / "normal" +REAL_MATH_DIR = REAL_DIR / "math" +REAL_3D_DIR = REAL_DIR / "3d" + +# ============================================================ +# 模型输出目录 +# ============================================================ +CHECKPOINTS_DIR = PROJECT_ROOT / "checkpoints" +ONNX_DIR = PROJECT_ROOT / "onnx_models" + +# 确保关键目录存在 +for _dir in [ + SYNTHETIC_NORMAL_DIR, SYNTHETIC_MATH_DIR, SYNTHETIC_3D_DIR, + REAL_NORMAL_DIR, REAL_MATH_DIR, REAL_3D_DIR, + CLASSIFIER_DIR, CHECKPOINTS_DIR, ONNX_DIR, +]: + _dir.mkdir(parents=True, exist_ok=True) + +# ============================================================ +# 字符集定义 +# ============================================================ +# 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 +NORMAL_CHARS = "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ" + +# 算式验证码: 数字 + 运算符 +MATH_CHARS = "0123456789+-×÷=?" + +# 3D 验证码: 继续使用去掉易混淆字符的精简字符集 +THREED_CHARS = "23456789ABCDEFGHJKMNPQRSTUVWXYZ" + +# 验证码类型列表 (调度分类器输出) +CAPTCHA_TYPES = ["normal", "math", "3d"] +NUM_CAPTCHA_TYPES = len(CAPTCHA_TYPES) + +# ============================================================ +# 图片尺寸配置 (H, W) +# ============================================================ +IMAGE_SIZE = { + "classifier": (64, 128), # 调度分类器输入 + "normal": (40, 120), # 普通字符识别 + "math": (40, 160), # 算式识别 (更宽) + "3d": (60, 160), # 3D 立体识别 +} + +# ============================================================ +# 验证码生成参数 +# ============================================================ +GENERATE_CONFIG = { + "normal": { + "char_count_range": (4, 5), # 字符数量: 4-5 个 + "bg_color_range": (230, 255), # 浅色背景 RGB 各通道 + "rotation_range": (-15, 15), # 字符旋转角度 + "noise_line_range": (2, 5), # 干扰线数量 + "noise_point_num": 100, # 噪点数量 + "blur_radius": 0.8, # 高斯模糊半径 + "image_size": (120, 40), # 生成图片尺寸 (W, H) + }, + "math": { + "operand_range": (1, 30), # 操作数范围 + "operators": ["+", "-", "×"], # 支持的运算符 (除法只生成能整除的) + "image_size": (160, 40), # 生成图片尺寸 (W, H) + "bg_color_range": (230, 255), + "rotation_range": (-10, 10), + "noise_line_range": (2, 4), + }, + "3d": { + "char_count_range": (4, 5), + "image_size": (160, 60), # 生成图片尺寸 (W, H) + "shadow_offset": (3, 3), # 阴影偏移 + "perspective_intensity": 0.3, # 透视变换强度 + }, +} + +# ============================================================ +# 训练配置 +# ============================================================ +TRAIN_CONFIG = { + "classifier": { + "epochs": 30, + "batch_size": 128, + "lr": 1e-3, + "scheduler": "cosine", + "synthetic_samples": 30000, # 每类 10000 + "val_split": 0.1, # 验证集比例 + }, + "normal": { + "epochs": 50, + "batch_size": 128, + "lr": 1e-3, + "scheduler": "cosine", + "synthetic_samples": 60000, + "loss": "CTCLoss", + "val_split": 0.1, + }, + "math": { + "epochs": 50, + "batch_size": 128, + "lr": 1e-3, + "scheduler": "cosine", + "synthetic_samples": 60000, + "loss": "CTCLoss", + "val_split": 0.1, + }, + "threed": { + "epochs": 80, + "batch_size": 64, + "lr": 5e-4, + "scheduler": "cosine", + "synthetic_samples": 80000, + "loss": "CTCLoss", + "val_split": 0.1, + }, +} + +# ============================================================ +# 数据增强参数 (训练时使用) +# ============================================================ +AUGMENT_CONFIG = { + "degrees": 8, # RandomAffine 旋转范围 + "translate": (0.05, 0.05), # 平移范围 + "scale": (0.95, 1.05), # 缩放范围 + "brightness": 0.3, # ColorJitter 亮度 + "contrast": 0.3, # ColorJitter 对比度 + "blur_kernel": 3, # GaussianBlur 核大小 + "blur_sigma": (0.1, 0.5), # GaussianBlur sigma + "erasing_prob": 0.15, # RandomErasing 概率 + "erasing_scale": (0.01, 0.05), # RandomErasing 面积比 +} + +# ============================================================ +# ONNX 导出配置 +# ============================================================ +ONNX_CONFIG = { + "opset_version": 18, + "dynamic_batch": True, # 支持动态 batch size +} + +# ============================================================ +# 推理配置 +# ============================================================ +INFERENCE_CONFIG = { + "default_models_dir": str(ONNX_DIR), + "normalize_mean": 0.5, + "normalize_std": 0.5, +} + +# ============================================================ +# 随机种子 (保证数据生成可复现) +# ============================================================ +RANDOM_SEED = 42 + +# ============================================================ +# 设备配置 (优先 GPU，回退 CPU) +# 延迟导入 torch，避免仅使用生成器时必须安装 torch +# ============================================================ +def get_device(): + """返回可用的 torch 设备，优先 GPU。""" + import torch + return torch.device("cuda" if torch.cuda.is_available() else "cpu") + +# ============================================================ +# 服务配置 (可选 HTTP 服务) +# ============================================================ +SERVER_CONFIG = { + "host": "0.0.0.0", + "port": 8080, +} diff --git a/data/real/3d/.gitkeep b/data/real/3d/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/data/real/math/.gitkeep b/data/real/math/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/data/real/normal/.gitkeep b/data/real/normal/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/generators/__init__.py b/generators/__init__.py new file mode 100644 index 0000000..d59cf50 --- /dev/null +++ b/generators/__init__.py @@ -0,0 +1,20 @@ +""" +数据生成器包 + +提供三种验证码类型的数据生成器： +- NormalCaptchaGenerator: 普通字符验证码 +- MathCaptchaGenerator: 算式验证码 +- ThreeDCaptchaGenerator: 3D 立体验证码 +""" + +from generators.base import BaseCaptchaGenerator +from generators.normal_gen import NormalCaptchaGenerator +from generators.math_gen import MathCaptchaGenerator +from generators.threed_gen import ThreeDCaptchaGenerator + +__all__ = [ + "BaseCaptchaGenerator", + "NormalCaptchaGenerator", + "MathCaptchaGenerator", + "ThreeDCaptchaGenerator", +] diff --git a/generators/base.py b/generators/base.py new file mode 100644 index 0000000..4c13c3c --- /dev/null +++ b/generators/base.py @@ -0,0 +1,61 @@ +""" +验证码生成器基类 + +所有验证码生成器继承此基类，实现 generate() 方法。 +基类提供通用的 generate_dataset() 批量生成能力。 +""" + +import os +import random +from pathlib import Path +from PIL import Image +from tqdm import tqdm + +from config import RANDOM_SEED + + +class BaseCaptchaGenerator: + """验证码生成器基类。""" + + def __init__(self, seed: int = RANDOM_SEED): + """ + 初始化生成器。 + + Args: + seed: 随机种子，保证数据生成可复现。 + """ + self.seed = seed + self.rng = random.Random(seed) + + def generate(self, text: str | None = None) -> tuple[Image.Image, str]: + """ + 生成一张验证码图片。 + + Args: + text: 指定标签文本。为 None 时随机生成。 + + Returns: + (图片, 标签文本) + """ + raise NotImplementedError + + def generate_dataset(self, num_samples: int, output_dir: str) -> None: + """ + 批量生成验证码数据集。 + + 文件名格式: {label}_{index:06d}.png + + Args: + num_samples: 生成数量。 + output_dir: 输出目录路径。 + """ + output_path = Path(output_dir) + output_path.mkdir(parents=True, exist_ok=True) + + # 重置随机种子，保证每次批量生成结果一致 + self.rng = random.Random(self.seed) + + for i in tqdm(range(num_samples), desc=f"Generating → {output_path.name}"): + img, label = self.generate() + filename = f"{label}_{i:06d}.png" + img.save(output_path / filename) diff --git a/generators/math_gen.py b/generators/math_gen.py new file mode 100644 index 0000000..a59dd2f --- /dev/null +++ b/generators/math_gen.py @@ -0,0 +1,186 @@ +""" +算式验证码生成器 + +生成形如 A op B = ? 的算式图片： +- A, B 范围: 1-30 的整数 +- op: +, -, × (除法只生成能整除的) +- 确保结果为非负整数 +- 标签格式: "3+8" (存储算式本身，不存结果) +- 视觉风格: 浅色背景、深色字符、干扰线 +""" + +import random + +from PIL import Image, ImageDraw, ImageFilter, ImageFont + +from config import GENERATE_CONFIG +from generators.base import BaseCaptchaGenerator + +# 字体 +_FONT_PATHS = [ + "/usr/share/fonts/TTF/DejaVuSans-Bold.ttf", + "/usr/share/fonts/TTF/DejaVuSerif-Bold.ttf", + "/usr/share/fonts/TTF/DejaVuSansMono-Bold.ttf", + "/usr/share/fonts/liberation/LiberationSans-Bold.ttf", + "/usr/share/fonts/liberation/LiberationMono-Bold.ttf", + "/usr/share/fonts/gnu-free/FreeSansBold.otf", +] + +# 深色调色板 +_DARK_COLORS = [ + (0, 0, 180), + (180, 0, 0), + (0, 130, 0), + (130, 0, 130), + (120, 60, 0), + (0, 0, 0), + (50, 50, 150), +] + +# 运算符显示映射（用于渲染） +_OP_DISPLAY = { + "+": "+", + "-": "-", + "×": "×", + "÷": "÷", +} + + +class MathCaptchaGenerator(BaseCaptchaGenerator): + """算式验证码生成器。""" + + def __init__(self, seed: int | None = None): + from config import RANDOM_SEED + super().__init__(seed=seed if seed is not None else RANDOM_SEED) + + self.cfg = GENERATE_CONFIG["math"] + self.width, self.height = self.cfg["image_size"] + self.operators = self.cfg["operators"] + self.op_lo, self.op_hi = self.cfg["operand_range"] + + # 预加载可用字体 + self._fonts: list[str] = [] + for p in _FONT_PATHS: + try: + ImageFont.truetype(p, 20) + self._fonts.append(p) + except OSError: + continue + if not self._fonts: + raise RuntimeError("未找到任何可用字体，无法生成验证码") + + # ---------------------------------------------------------- + # 公共接口 + # ---------------------------------------------------------- + def generate(self, text: str | None = None) -> tuple[Image.Image, str]: + rng = self.rng + + # 1. 生成算式 + if text is None: + a, op, b = self._random_expression(rng) + text = f"{a}{op}{b}" + else: + a, op, b = self._parse_expression(text) + + # 显示文本: "3+8=?" + display = f"{a}{_OP_DISPLAY.get(op, op)}{b}=?" + + # 2. 浅色背景 + bg_lo, bg_hi = self.cfg["bg_color_range"] + bg = tuple(rng.randint(bg_lo, bg_hi) for _ in range(3)) + img = Image.new("RGB", (self.width, self.height), bg) + + # 3. 绘制算式文本 + self._draw_expression(img, display, rng) + + # 4. 干扰线 + self._draw_noise_lines(img, rng) + + # 5. 轻微模糊 + img = img.filter(ImageFilter.GaussianBlur(radius=0.6)) + + return img, text + + # ---------------------------------------------------------- + # 私有方法 + # ---------------------------------------------------------- + def _random_expression(self, rng: random.Random) -> tuple[int, str, int]: + """随机生成一个合法算式 (a, op, b)，确保结果为非负整数。""" + while True: + op = rng.choice(self.operators) + a = rng.randint(self.op_lo, self.op_hi) + b = rng.randint(self.op_lo, self.op_hi) + + if op == "+": + return a, op, b + elif op == "-": + # 确保 a >= b，结果非负 + if a < b: + a, b = b, a + return a, op, b + elif op == "×": + # 限制乘积不过大，保持合理 + if a * b <= 900: + return a, op, b + elif op == "÷": + # 只生成能整除的 + if b != 0 and a % b == 0: + return a, op, b + + @staticmethod + def _parse_expression(text: str) -> tuple[int, str, int]: + """解析标签文本，如 '3+8' -> (3, '+', 8)。""" + for op in ["×", "÷", "+", "-"]: + if op in text: + parts = text.split(op, 1) + return int(parts[0]), op, int(parts[1]) + raise ValueError(f"无法解析算式: {text}") + + def _draw_expression(self, img: Image.Image, display: str, rng: random.Random) -> None: + """将算式文本绘制到图片上，每个字符单独渲染并带轻微旋转。""" + n = len(display) + slot_w = self.width // n + font_size = int(min(slot_w * 0.85, self.height * 0.65)) + font_size = max(font_size, 14) + + for i, ch in enumerate(display): + font_path = rng.choice(self._fonts) + + # 对于 × 等特殊符号，某些字体可能不支持，回退到 DejaVu + try: + font = ImageFont.truetype(font_path, font_size) + bbox = font.getbbox(ch) + if bbox[2] - bbox[0] <= 0: + raise ValueError + except (OSError, ValueError): + font = ImageFont.truetype(self._fonts[0], font_size) + bbox = font.getbbox(ch) + + color = rng.choice(_DARK_COLORS) + + cw = bbox[2] - bbox[0] + 4 + ch_h = bbox[3] - bbox[1] + 4 + char_img = Image.new("RGBA", (cw, ch_h), (0, 0, 0, 0)) + ImageDraw.Draw(char_img).text((-bbox[0] + 2, -bbox[1] + 2), ch, fill=color, font=font) + + # 轻微旋转 + angle = rng.randint(*self.cfg["rotation_range"]) + char_img = char_img.rotate(angle, resample=Image.BICUBIC, expand=True) + + x = slot_w * i + (slot_w - char_img.width) // 2 + y = (self.height - char_img.height) // 2 + rng.randint(-2, 2) + x = max(0, min(x, self.width - char_img.width)) + y = max(0, min(y, self.height - char_img.height)) + + img.paste(char_img, (x, y), char_img) + + def _draw_noise_lines(self, img: Image.Image, rng: random.Random) -> None: + """绘制浅色干扰线。""" + draw = ImageDraw.Draw(img) + lo, hi = self.cfg["noise_line_range"] + num = rng.randint(lo, hi) + for _ in range(num): + x1, y1 = rng.randint(0, self.width), rng.randint(0, self.height) + x2, y2 = rng.randint(0, self.width), rng.randint(0, self.height) + color = tuple(rng.randint(150, 220) for _ in range(3)) + draw.line([(x1, y1), (x2, y2)], fill=color, width=rng.randint(1, 2)) diff --git a/generators/normal_gen.py b/generators/normal_gen.py new file mode 100644 index 0000000..d6da9bd --- /dev/null +++ b/generators/normal_gen.py @@ -0,0 +1,154 @@ +""" +普通字符验证码生成器 + +生成风格： +- 浅色随机背景 (RGB 各通道 230-255) +- 每个字符随机深色 (蓝/红/绿/紫/棕等) +- 字符数量 4-5 个 +- 字符有 ±15° 随机旋转 +- 2-5 条浅色干扰线 +- 少量噪点 +- 可选轻微高斯模糊 +""" + +import random + +from PIL import Image, ImageDraw, ImageFilter, ImageFont + +from config import GENERATE_CONFIG, NORMAL_CHARS +from generators.base import BaseCaptchaGenerator + +# 系统可用字体列表（粗体/常规混合，增加多样性） +_FONT_PATHS = [ + "/usr/share/fonts/TTF/DejaVuSans-Bold.ttf", + "/usr/share/fonts/TTF/DejaVuSerif-Bold.ttf", + "/usr/share/fonts/TTF/DejaVuSansMono-Bold.ttf", + "/usr/share/fonts/liberation/LiberationSans-Bold.ttf", + "/usr/share/fonts/liberation/LiberationMono-Bold.ttf", + "/usr/share/fonts/liberation/LiberationSerif-Bold.ttf", + "/usr/share/fonts/gnu-free/FreeSansBold.otf", + "/usr/share/fonts/gnu-free/FreeMonoBold.otf", +] + +# 深色调色板 (R, G, B) +_DARK_COLORS = [ + (0, 0, 180), # 蓝 + (180, 0, 0), # 红 + (0, 130, 0), # 绿 + (130, 0, 130), # 紫 + (120, 60, 0), # 棕 + (0, 100, 100), # 青 + (80, 80, 0), # 橄榄 + (0, 0, 0), # 黑 + (100, 0, 50), # 暗玫红 + (50, 50, 150), # 钢蓝 +] + + +class NormalCaptchaGenerator(BaseCaptchaGenerator): + """普通字符验证码生成器。""" + + def __init__(self, seed: int | None = None): + from config import RANDOM_SEED + super().__init__(seed=seed if seed is not None else RANDOM_SEED) + + self.cfg = GENERATE_CONFIG["normal"] + self.chars = NORMAL_CHARS + self.width, self.height = self.cfg["image_size"] + + # 预加载可用字体 + self._fonts: list[str] = [] + for p in _FONT_PATHS: + try: + ImageFont.truetype(p, 20) + self._fonts.append(p) + except OSError: + continue + if not self._fonts: + raise RuntimeError("未找到任何可用字体，无法生成验证码") + + # ---------------------------------------------------------- + # 公共接口 + # ---------------------------------------------------------- + def generate(self, text: str | None = None) -> tuple[Image.Image, str]: + rng = self.rng + + # 1. 随机文本 + if text is None: + length = rng.randint(*self.cfg["char_count_range"]) + text = "".join(rng.choices(self.chars, k=length)) + + # 2. 浅色背景 + bg_lo, bg_hi = self.cfg["bg_color_range"] + bg = tuple(rng.randint(bg_lo, bg_hi) for _ in range(3)) + img = Image.new("RGB", (self.width, self.height), bg) + + # 3. 逐字符绘制（旋转后粘贴） + self._draw_text(img, text, rng) + + # 4. 干扰线 + self._draw_noise_lines(img, rng) + + # 5. 噪点 + self._draw_noise_points(img, rng) + + # 6. 轻微高斯模糊 + if self.cfg["blur_radius"] > 0: + img = img.filter(ImageFilter.GaussianBlur(radius=self.cfg["blur_radius"])) + + return img, text + + # ---------------------------------------------------------- + # 私有方法 + # ---------------------------------------------------------- + def _draw_text(self, img: Image.Image, text: str, rng: random.Random) -> None: + """逐字符旋转并粘贴到画布上。""" + n = len(text) + # 每个字符的水平可用宽度 + slot_w = self.width // n + font_size = int(min(slot_w * 0.9, self.height * 0.7)) + font_size = max(font_size, 12) + + for i, ch in enumerate(text): + font_path = rng.choice(self._fonts) + font = ImageFont.truetype(font_path, font_size) + color = rng.choice(_DARK_COLORS) + + # 绘制单字符到临时透明图层 + bbox = font.getbbox(ch) + cw = bbox[2] - bbox[0] + 4 + ch_h = bbox[3] - bbox[1] + 4 + char_img = Image.new("RGBA", (cw, ch_h), (0, 0, 0, 0)) + ImageDraw.Draw(char_img).text((-bbox[0] + 2, -bbox[1] + 2), ch, fill=color, font=font) + + # 随机旋转 + angle = rng.randint(*self.cfg["rotation_range"]) + char_img = char_img.rotate(angle, resample=Image.BICUBIC, expand=True) + + # 粘贴位置 + x = slot_w * i + (slot_w - char_img.width) // 2 + y = (self.height - char_img.height) // 2 + rng.randint(-3, 3) + x = max(0, min(x, self.width - char_img.width)) + y = max(0, min(y, self.height - char_img.height)) + + img.paste(char_img, (x, y), char_img) + + def _draw_noise_lines(self, img: Image.Image, rng: random.Random) -> None: + """绘制浅色干扰线。""" + draw = ImageDraw.Draw(img) + lo, hi = self.cfg["noise_line_range"] + num = rng.randint(lo, hi) + for _ in range(num): + x1, y1 = rng.randint(0, self.width), rng.randint(0, self.height) + x2, y2 = rng.randint(0, self.width), rng.randint(0, self.height) + color = tuple(rng.randint(150, 220) for _ in range(3)) + draw.line([(x1, y1), (x2, y2)], fill=color, width=rng.randint(1, 2)) + + def _draw_noise_points(self, img: Image.Image, rng: random.Random) -> None: + """绘制噪点。""" + draw = ImageDraw.Draw(img) + for _ in range(self.cfg["noise_point_num"]): + x = rng.randint(0, self.width - 1) + y = rng.randint(0, self.height - 1) + color = tuple(rng.randint(0, 200) for _ in range(3)) + draw.point((x, y), fill=color) diff --git a/generators/threed_gen.py b/generators/threed_gen.py new file mode 100644 index 0000000..18f2924 --- /dev/null +++ b/generators/threed_gen.py @@ -0,0 +1,211 @@ +""" +3D 立体验证码生成器 + +生成具有 3D 透视/阴影效果的验证码： +- 使用仿射变换模拟 3D 透视 +- 添加阴影效果 (偏移的深色副本) +- 字符有深度感和倾斜 +- 渐变背景增强立体感 +- 标签: 纯字符内容 +""" + +import math +import random + +from PIL import Image, ImageDraw, ImageFilter, ImageFont + +from config import GENERATE_CONFIG, THREED_CHARS +from generators.base import BaseCaptchaGenerator + +# 字体 (粗体效果更好渲染 3D) +_FONT_PATHS = [ + "/usr/share/fonts/TTF/DejaVuSans-Bold.ttf", + "/usr/share/fonts/TTF/DejaVuSerif-Bold.ttf", + "/usr/share/fonts/liberation/LiberationSans-Bold.ttf", + "/usr/share/fonts/liberation/LiberationSerif-Bold.ttf", + "/usr/share/fonts/gnu-free/FreeSansBold.otf", +] + +# 前景色 — 鲜艳、对比度高 +_FRONT_COLORS = [ + (220, 50, 50), # 红 + (50, 100, 220), # 蓝 + (30, 160, 30), # 绿 + (200, 150, 0), # 金 + (180, 50, 180), # 紫 + (0, 160, 160), # 青 + (220, 100, 0), # 橙 +] + + +class ThreeDCaptchaGenerator(BaseCaptchaGenerator): + """3D 立体验证码生成器。""" + + def __init__(self, seed: int | None = None): + from config import RANDOM_SEED + super().__init__(seed=seed if seed is not None else RANDOM_SEED) + + self.cfg = GENERATE_CONFIG["3d"] + self.chars = THREED_CHARS + self.width, self.height = self.cfg["image_size"] + + # 预加载可用字体 + self._fonts: list[str] = [] + for p in _FONT_PATHS: + try: + ImageFont.truetype(p, 20) + self._fonts.append(p) + except OSError: + continue + if not self._fonts: + raise RuntimeError("未找到任何可用字体，无法生成验证码") + + # ---------------------------------------------------------- + # 公共接口 + # ---------------------------------------------------------- + def generate(self, text: str | None = None) -> tuple[Image.Image, str]: + rng = self.rng + + # 1. 随机文本 + if text is None: + length = rng.randint(*self.cfg["char_count_range"]) + text = "".join(rng.choices(self.chars, k=length)) + + # 2. 渐变背景 (增强立体感) + img = self._gradient_background(rng) + + # 3. 逐字符绘制 (阴影 + 透视 + 前景) + self._draw_3d_text(img, text, rng) + + # 4. 干扰线 (较粗、有深度感) + self._draw_depth_lines(img, rng) + + # 5. 轻微高斯模糊 + img = img.filter(ImageFilter.GaussianBlur(radius=0.7)) + + return img, text + + # ---------------------------------------------------------- + # 私有方法 + # ---------------------------------------------------------- + def _gradient_background(self, rng: random.Random) -> Image.Image: + """生成从上到下的浅色渐变背景。""" + img = Image.new("RGB", (self.width, self.height)) + draw = ImageDraw.Draw(img) + + # 随机两个浅色 + c1 = tuple(rng.randint(200, 240) for _ in range(3)) + c2 = tuple(rng.randint(180, 220) for _ in range(3)) + + for y in range(self.height): + ratio = y / max(self.height - 1, 1) + r = int(c1[0] + (c2[0] - c1[0]) * ratio) + g = int(c1[1] + (c2[1] - c1[1]) * ratio) + b = int(c1[2] + (c2[2] - c1[2]) * ratio) + draw.line([(0, y), (self.width, y)], fill=(r, g, b)) + + return img + + def _draw_3d_text(self, img: Image.Image, text: str, rng: random.Random) -> None: + """逐字符绘制 3D 效果: 阴影层 + 透视变换 + 前景层。""" + n = len(text) + slot_w = self.width // n + font_size = int(min(slot_w * 0.8, self.height * 0.65)) + font_size = max(font_size, 16) + + shadow_dx, shadow_dy = self.cfg["shadow_offset"] + + for i, ch in enumerate(text): + font_path = rng.choice(self._fonts) + font = ImageFont.truetype(font_path, font_size) + front_color = rng.choice(_FRONT_COLORS) + # 阴影色: 对应前景色的暗化版本 + shadow_color = tuple(max(0, c - 80) for c in front_color) + + # 渲染单字符 + bbox = font.getbbox(ch) + cw = bbox[2] - bbox[0] + 8 + ch_h = bbox[3] - bbox[1] + 8 + pad = max(shadow_dx, shadow_dy) + 4 # 额外空间给阴影 + + canvas_w = cw + pad * 2 + canvas_h = ch_h + pad * 2 + + # --- 阴影层 --- + shadow_img = Image.new("RGBA", (canvas_w, canvas_h), (0, 0, 0, 0)) + ImageDraw.Draw(shadow_img).text( + (-bbox[0] + pad + shadow_dx, -bbox[1] + pad + shadow_dy), + ch, fill=shadow_color + (180,), font=font + ) + + # --- 前景层 --- + front_img = Image.new("RGBA", (canvas_w, canvas_h), (0, 0, 0, 0)) + ImageDraw.Draw(front_img).text( + (-bbox[0] + pad, -bbox[1] + pad), + ch, fill=front_color + (255,), font=font + ) + + # 合并: 先阴影后前景 + char_img = Image.new("RGBA", (canvas_w, canvas_h), (0, 0, 0, 0)) + char_img = Image.alpha_composite(char_img, shadow_img) + char_img = Image.alpha_composite(char_img, front_img) + + # 透视变换 (仿射) + char_img = self._perspective_transform(char_img, rng) + + # 随机旋转 + angle = rng.randint(-20, 20) + char_img = char_img.rotate(angle, resample=Image.BICUBIC, expand=True) + + # 粘贴到画布 + x = slot_w * i + (slot_w - char_img.width) // 2 + y = (self.height - char_img.height) // 2 + rng.randint(-4, 4) + x = max(0, min(x, self.width - char_img.width)) + y = max(0, min(y, self.height - char_img.height)) + + img.paste(char_img, (x, y), char_img) + + def _perspective_transform(self, img: Image.Image, rng: random.Random) -> Image.Image: + """对单个字符图片施加仿射变换模拟 3D 透视。""" + w, h = img.size + intensity = self.cfg["perspective_intensity"] + + # 随机 shear / scale 参数 + shear_x = rng.uniform(-intensity, intensity) + shear_y = rng.uniform(-intensity * 0.5, intensity * 0.5) + scale_x = rng.uniform(1.0 - intensity * 0.3, 1.0 + intensity * 0.3) + scale_y = rng.uniform(1.0 - intensity * 0.3, 1.0 + intensity * 0.3) + + # 仿射变换矩阵 (a, b, c, d, e, f) -> (x', y') = (a*x+b*y+c, d*x+e*y+f) + # Pillow transform 需要逆变换系数 + a = scale_x + b = shear_x + d = shear_y + e = scale_y + # 计算偏移让中心不变 + c = (1 - a) * w / 2 - b * h / 2 + f = -d * w / 2 + (1 - e) * h / 2 + + return img.transform( + (w, h), Image.AFFINE, + (a, b, c, d, e, f), + resample=Image.BICUBIC + ) + + def _draw_depth_lines(self, img: Image.Image, rng: random.Random) -> None: + """绘制有深度感的干扰线 (较粗、带阴影)。""" + draw = ImageDraw.Draw(img) + num = rng.randint(2, 4) + for _ in range(num): + x1, y1 = rng.randint(0, self.width), rng.randint(0, self.height) + x2, y2 = rng.randint(0, self.width), rng.randint(0, self.height) + + # 阴影线 + shadow_color = tuple(rng.randint(80, 130) for _ in range(3)) + dx, dy = self.cfg["shadow_offset"] + draw.line([(x1 + dx, y1 + dy), (x2 + dx, y2 + dy)], + fill=shadow_color, width=rng.randint(2, 3)) + + # 前景线 + color = tuple(rng.randint(120, 200) for _ in range(3)) + draw.line([(x1, y1), (x2, y2)], fill=color, width=rng.randint(1, 2)) diff --git a/inference/__init__.py b/inference/__init__.py new file mode 100644 index 0000000..1c69e07 --- /dev/null +++ b/inference/__init__.py @@ -0,0 +1,18 @@ +""" +推理包 + +- pipeline.py: CaptchaPipeline 核心推理流水线 +- export_onnx.py: PyTorch → ONNX 导出 +- math_eval.py: 算式计算模块 +""" + +from inference.pipeline import CaptchaPipeline +from inference.math_eval import eval_captcha_math +from inference.export_onnx import export_model, export_all + +__all__ = [ + "CaptchaPipeline", + "eval_captcha_math", + "export_model", + "export_all", +] diff --git a/inference/export_onnx.py b/inference/export_onnx.py new file mode 100644 index 0000000..838652e --- /dev/null +++ b/inference/export_onnx.py @@ -0,0 +1,121 @@ +""" +ONNX 导出脚本 + +从 checkpoints/ 加载训练好的 PyTorch 模型，导出为 ONNX 格式到 onnx_models/。 +支持逐个导出或一次导出全部。 +""" + +import torch +import torch.nn as nn + +from config import ( + CHECKPOINTS_DIR, + ONNX_DIR, + ONNX_CONFIG, + IMAGE_SIZE, + NORMAL_CHARS, + MATH_CHARS, + THREED_CHARS, + NUM_CAPTCHA_TYPES, +) +from models.classifier import CaptchaClassifier +from models.lite_crnn import LiteCRNN +from models.threed_cnn import ThreeDCNN + + +def export_model( + model: nn.Module, + model_name: str, + input_shape: tuple, + onnx_dir: str | None = None, +): + """ + 导出单个模型为 ONNX。 + + Args: + model: 已加载权重的 PyTorch 模型 + model_name: 模型名 (classifier / normal / math / threed) + input_shape: 输入形状 (C, H, W) + onnx_dir: 输出目录 (默认使用 config.ONNX_DIR) + """ + from pathlib import Path + + out_dir = Path(onnx_dir) if onnx_dir else ONNX_DIR + out_dir.mkdir(parents=True, exist_ok=True) + onnx_path = out_dir / f"{model_name}.onnx" + + model.eval() + model.cpu() + + dummy = torch.randn(1, *input_shape) + + # 分类器和识别器的 dynamic_axes 不同 + if model_name == "classifier": + dynamic_axes = {"input": {0: "batch"}, "output": {0: "batch"}} + else: + # CTC 模型: output shape = (T, B, C) + dynamic_axes = {"input": {0: "batch"}, "output": {1: "batch"}} + + torch.onnx.export( + model, + dummy, + str(onnx_path), + opset_version=ONNX_CONFIG["opset_version"], + input_names=["input"], + output_names=["output"], + dynamic_axes=dynamic_axes if ONNX_CONFIG["dynamic_batch"] else None, + ) + + size_kb = onnx_path.stat().st_size / 1024 + print(f"[ONNX] 导出完成: {onnx_path} ({size_kb:.1f} KB)") + + +def _load_and_export(model_name: str): + """从 checkpoint 加载模型并导出 ONNX。""" + ckpt_path = CHECKPOINTS_DIR / f"{model_name}.pth" + if not ckpt_path.exists(): + print(f"[跳过] {model_name}: checkpoint 不存在 ({ckpt_path})") + return + + ckpt = torch.load(ckpt_path, map_location="cpu", weights_only=True) + print(f"[加载] {model_name}: epoch={ckpt.get('epoch', '?')} acc={ckpt.get('best_acc', '?')}") + + if model_name == "classifier": + model = CaptchaClassifier(num_types=NUM_CAPTCHA_TYPES) + h, w = IMAGE_SIZE["classifier"] + input_shape = (1, h, w) + elif model_name == "normal": + chars = ckpt.get("chars", NORMAL_CHARS) + h, w = IMAGE_SIZE["normal"] + model = LiteCRNN(chars=chars, img_h=h, img_w=w) + input_shape = (1, h, w) + elif model_name == "math": + chars = ckpt.get("chars", MATH_CHARS) + h, w = IMAGE_SIZE["math"] + model = LiteCRNN(chars=chars, img_h=h, img_w=w) + input_shape = (1, h, w) + elif model_name == "threed": + chars = ckpt.get("chars", THREED_CHARS) + h, w = IMAGE_SIZE["3d"] + model = ThreeDCNN(chars=chars, img_h=h, img_w=w) + input_shape = (1, h, w) + else: + print(f"[错误] 未知模型: {model_name}") + return + + model.load_state_dict(ckpt["model_state_dict"]) + export_model(model, model_name, input_shape) + + +def export_all(): + """依次导出 classifier, normal, math, threed 四个模型。""" + print("=" * 50) + print("导出全部 ONNX 模型") + print("=" * 50) + for name in ["classifier", "normal", "math", "threed"]: + _load_and_export(name) + print("\n全部导出完成。") + + +if __name__ == "__main__": + export_all() diff --git a/inference/math_eval.py b/inference/math_eval.py new file mode 100644 index 0000000..8f99486 --- /dev/null +++ b/inference/math_eval.py @@ -0,0 +1,66 @@ +""" +算式计算模块 + +解析并计算验证码中的算式表达式。 +用正则提取数字和运算符，不使用 eval()。 + +支持: 加减乘除，个位到两位数运算。 +""" + +import re + + +# 匹配: 数字 运算符 数字 (后面可能跟 =? 等) +_EXPR_PATTERN = re.compile( + r"(\d+)\s*([+\-×÷xX*])\s*(\d+)" +) + +# 运算符归一化映射 +_OP_MAP = { + "+": "+", + "-": "-", + "×": "×", + "÷": "÷", + "x": "×", + "X": "×", + "*": "×", +} + + +def eval_captcha_math(expr: str) -> str: + """ + 解析并计算验证码算式。 + + 支持: 加减乘除，个位到两位数运算。 + 输入: "3+8=?" 或 "12×3=?" 或 "15-7=?" 或 "3+8" + 输出: "11" 或 "36" 或 "8" + + 用正则提取数字和运算符，不使用 eval()。 + + Raises: + ValueError: 无法解析表达式 + """ + match = _EXPR_PATTERN.search(expr) + if not match: + raise ValueError(f"无法解析算式: {expr!r}") + + a = int(match.group(1)) + op_raw = match.group(2) + b = int(match.group(3)) + + op = _OP_MAP.get(op_raw, op_raw) + + if op == "+": + result = a + b + elif op == "-": + result = a - b + elif op == "×": + result = a * b + elif op == "÷": + if b == 0: + raise ValueError(f"除数为零: {expr!r}") + result = a // b + else: + raise ValueError(f"不支持的运算符: {op!r} 原式: {expr!r}") + + return str(result) diff --git a/inference/pipeline.py b/inference/pipeline.py new file mode 100644 index 0000000..c537718 --- /dev/null +++ b/inference/pipeline.py @@ -0,0 +1,231 @@ +""" +核心推理流水线 + +加载调度模型和所有专家模型 (ONNX 格式)，提供统一的 solve(image) 接口。 + +推理流程: + 输入图片 → 预处理 → 调度分类器 → 路由到专家模型 → CTC 解码 → 后处理 → 输出 + +对算式类型，解码后还会调用 math_eval 计算结果。 +""" + +import io +import time +from pathlib import Path + +import numpy as np +from PIL import Image + +from config import ( + CAPTCHA_TYPES, + IMAGE_SIZE, + INFERENCE_CONFIG, + NORMAL_CHARS, + MATH_CHARS, + THREED_CHARS, +) +from inference.math_eval import eval_captcha_math + + +def _try_import_ort(): + """延迟导入 onnxruntime，给出友好错误提示。""" + try: + import onnxruntime as ort + return ort + except ImportError: + raise ImportError( + "推理需要 onnxruntime，请安装: uv pip install onnxruntime" + ) + + +class CaptchaPipeline: + """ + 核心推理流水线。 + + 加载调度模型和所有专家模型 (ONNX 格式)。 + 提供统一的 solve(image) 接口。 + """ + + def __init__(self, models_dir: str | None = None): + """ + 初始化加载所有 ONNX 模型。 + + Args: + models_dir: ONNX 模型目录，默认使用 config 中的路径 + """ + ort = _try_import_ort() + + self.models_dir = Path(models_dir or INFERENCE_CONFIG["default_models_dir"]) + self.mean = INFERENCE_CONFIG["normalize_mean"] + self.std = INFERENCE_CONFIG["normalize_std"] + + # 字符集映射 + self._chars = { + "normal": NORMAL_CHARS, + "math": MATH_CHARS, + "3d": THREED_CHARS, + } + + # 专家模型名 → ONNX 文件名 + self._model_files = { + "classifier": "classifier.onnx", + "normal": "normal.onnx", + "math": "math.onnx", + "3d": "threed.onnx", + } + + # 加载所有可用模型 + opts = ort.SessionOptions() + opts.inter_op_num_threads = 1 + opts.intra_op_num_threads = 2 + + self._sessions: dict[str, "ort.InferenceSession"] = {} + for name, fname in self._model_files.items(): + path = self.models_dir / fname + if path.exists(): + self._sessions[name] = ort.InferenceSession( + str(path), sess_options=opts, + providers=["CPUExecutionProvider"], + ) + + loaded = list(self._sessions.keys()) + if not loaded: + raise FileNotFoundError( + f"未找到任何 ONNX 模型，请先训练并导出模型到 {self.models_dir}" + ) + + # ---------------------------------------------------------- + # 公共接口 + # ---------------------------------------------------------- + def preprocess(self, image: Image.Image, target_size: tuple[int, int]) -> np.ndarray: + """ + 图片预处理: resize, grayscale, normalize, 转 numpy。 + + Args: + image: PIL Image + target_size: (H, W) + + Returns: + (1, 1, H, W) float32 ndarray + """ + h, w = target_size + img = image.convert("L").resize((w, h), Image.BILINEAR) + arr = np.array(img, dtype=np.float32) / 255.0 + arr = (arr - self.mean) / self.std + return arr.reshape(1, 1, h, w) + + def classify(self, image: Image.Image) -> str: + """ + 调度分类，返回类型名: 'normal' / 'math' / '3d'。 + + Raises: + RuntimeError: 分类器模型未加载 + """ + if "classifier" not in self._sessions: + raise RuntimeError("分类器模型未加载，请先训练并导出 classifier.onnx") + + inp = self.preprocess(image, IMAGE_SIZE["classifier"]) + session = self._sessions["classifier"] + input_name = session.get_inputs()[0].name + logits = session.run(None, {input_name: inp})[0] # (1, num_types) + idx = int(np.argmax(logits, axis=1)[0]) + return CAPTCHA_TYPES[idx] + + def solve( + self, + image, + captcha_type: str | None = None, + ) -> dict: + """ + 完整识别流程。 + + Args: + image: PIL.Image 或文件路径 (str/Path) 或 bytes + captcha_type: 指定类型可跳过分类 ('normal'/'math'/'3d') + + Returns: + dict: { + "type": str, # 验证码类型 + "raw": str, # OCR 原始识别结果 + "result": str, # 最终答案 (算式型为计算结果) + "time_ms": float, # 推理耗时 (毫秒) + } + """ + t0 = time.perf_counter() + + # 1. 解析输入 + img = self._load_image(image) + + # 2. 分类 + if captcha_type is None: + captcha_type = self.classify(img) + + # 3. 路由到专家模型 + if captcha_type not in self._sessions: + raise RuntimeError( + f"专家模型 '{captcha_type}' 未加载，请先训练并导出对应 ONNX 模型" + ) + + size_key = captcha_type # "normal"/"math"/"3d" + inp = self.preprocess(img, IMAGE_SIZE[size_key]) + session = self._sessions[captcha_type] + input_name = session.get_inputs()[0].name + logits = session.run(None, {input_name: inp})[0] # (T, 1, C) + + # 4. CTC 贪心解码 + chars = self._chars[captcha_type] + raw_text = self._ctc_greedy_decode(logits, chars) + + # 5. 后处理 + if captcha_type == "math": + try: + result = eval_captcha_math(raw_text) + except ValueError: + result = raw_text # 解析失败则返回原始文本 + else: + result = raw_text + + elapsed = (time.perf_counter() - t0) * 1000 + + return { + "type": captcha_type, + "raw": raw_text, + "result": result, + "time_ms": round(elapsed, 2), + } + + # ---------------------------------------------------------- + # 私有方法 + # ---------------------------------------------------------- + @staticmethod + def _load_image(image) -> Image.Image: + """将多种输入类型统一转为 PIL Image。""" + if isinstance(image, Image.Image): + return image + if isinstance(image, (str, Path)): + return Image.open(image).convert("RGB") + if isinstance(image, bytes): + return Image.open(io.BytesIO(image)).convert("RGB") + raise TypeError(f"不支持的图片输入类型: {type(image)}") + + @staticmethod + def _ctc_greedy_decode(logits: np.ndarray, chars: str) -> str: + """ + CTC 贪心解码 (numpy 版本)。 + + Args: + logits: (T, B, C) ONNX 输出 + chars: 字符集 (不含 blank, blank=index 0) + + Returns: + 解码后的字符串 + """ + # 取 batch=0 + preds = np.argmax(logits[:, 0, :], axis=1) # (T,) + decoded = [] + prev = -1 + for idx in preds: + if idx != 0 and idx != prev: + decoded.append(chars[idx - 1]) + prev = idx + return "".join(decoded) diff --git a/main.py b/main.py new file mode 100644 index 0000000..b76e696 --- /dev/null +++ b/main.py @@ -0,0 +1,16 @@ +# 这是一个示例 Python 脚本。 + +# 按 Shift+F10 执行或将其替换为您的代码。 +# 按 双击 Shift 在所有地方搜索类、文件、工具窗口、操作和设置。 + + +def print_hi(name): + # 在下面的代码行中使用断点来调试脚本。 + print(f'Hi, {name}') # 按 Ctrl+8 切换断点。 + + +# 按装订区域中的绿色按钮以运行脚本。 +if __name__ == '__main__': + print_hi('PyCharm') + +# 访问 https://www.jetbrains.com/help/pycharm/ 获取 PyCharm 帮助 diff --git a/models/__init__.py b/models/__init__.py new file mode 100644 index 0000000..cc521af --- /dev/null +++ b/models/__init__.py @@ -0,0 +1,18 @@ +""" +模型定义包 + +提供三种模型： +- CaptchaClassifier: 调度分类器 (轻量 CNN, < 500KB) +- LiteCRNN: 轻量 CRNN (普通字符 + 算式, < 2MB) +- ThreeDCNN: 3D 验证码专用模型 (ResNet-lite + BiLSTM, < 5MB) +""" + +from models.classifier import CaptchaClassifier +from models.lite_crnn import LiteCRNN +from models.threed_cnn import ThreeDCNN + +__all__ = [ + "CaptchaClassifier", + "LiteCRNN", + "ThreeDCNN", +] diff --git a/models/classifier.py b/models/classifier.py new file mode 100644 index 0000000..2600537 --- /dev/null +++ b/models/classifier.py @@ -0,0 +1,72 @@ +""" +调度分类器模型 + +轻量 CNN 分类器，用于判断验证码类型 (normal / math / 3d)。 +不同类型验证码视觉差异大，分类任务简单。 + +架构: 4 层卷积 + GAP + FC +输入: 灰度图 1×64×128 +输出: softmax 概率分布 (num_types 个类别) +体积目标: < 500KB +""" + +import torch +import torch.nn as nn + + +class CaptchaClassifier(nn.Module): + """ + 轻量分类器。 + + 4 层卷积 (每层 Conv + BN + ReLU + MaxPool) + → 全局平均池化 → 全连接 → 输出类别数。 + """ + + def __init__(self, num_types: int = 3): + super().__init__() + self.num_types = num_types + + self.features = nn.Sequential( + # block 1: 1 -> 16, 64x128 -> 32x64 + nn.Conv2d(1, 16, kernel_size=3, padding=1), + nn.BatchNorm2d(16), + nn.ReLU(inplace=True), + nn.MaxPool2d(2, 2), + + # block 2: 16 -> 32, 32x64 -> 16x32 + nn.Conv2d(16, 32, kernel_size=3, padding=1), + nn.BatchNorm2d(32), + nn.ReLU(inplace=True), + nn.MaxPool2d(2, 2), + + # block 3: 32 -> 64, 16x32 -> 8x16 + nn.Conv2d(32, 64, kernel_size=3, padding=1), + nn.BatchNorm2d(64), + nn.ReLU(inplace=True), + nn.MaxPool2d(2, 2), + + # block 4: 64 -> 64, 8x16 -> 4x8 + nn.Conv2d(64, 64, kernel_size=3, padding=1), + nn.BatchNorm2d(64), + nn.ReLU(inplace=True), + nn.MaxPool2d(2, 2), + ) + + # 全局平均池化 → 输出 (batch, 64, 1, 1) + self.gap = nn.AdaptiveAvgPool2d(1) + + self.classifier = nn.Linear(64, num_types) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """ + Args: + x: (batch, 1, 64, 128) 灰度图 + + Returns: + logits: (batch, num_types) 未经 softmax 的原始输出 + """ + x = self.features(x) + x = self.gap(x) # (B, 64, 1, 1) + x = x.view(x.size(0), -1) # (B, 64) + x = self.classifier(x) # (B, num_types) + return x diff --git a/models/lite_crnn.py b/models/lite_crnn.py new file mode 100644 index 0000000..1b99c0c --- /dev/null +++ b/models/lite_crnn.py @@ -0,0 +1,141 @@ +""" +轻量 CRNN 模型 (Convolutional Recurrent Neural Network) + +用于普通字符验证码和算式验证码的 OCR 识别。 +两种模式通过不同的字符集和输入尺寸区分，共享同一网络架构。 + +架构: CNN 特征提取 → 序列映射 → BiLSTM → 全连接 → CTC 解码 +CTC 输出长度 = 特征图宽度 (经过若干次宽度方向 pool 后) +CTC blank 位于 index 0，字符从 index 1 开始映射。 + +- normal 模式: 输入 1×40×120, 字符集 30 字符, 体积 < 2MB +- math 模式: 输入 1×40×160, 字符集 16 字符, 体积 < 2MB +""" + +import torch +import torch.nn as nn + + +class LiteCRNN(nn.Module): + """ + 轻量 CRNN + CTC。 + + CNN 部分对高度做 4 次 pool (40→20→10→5→1 via AdaptivePool)， + 宽度做 2 次 pool (保留足够序列长度给 CTC)。 + RNN 部分使用单层 BiLSTM。 + """ + + def __init__(self, chars: str, img_h: int = 40, img_w: int = 120): + """ + Args: + chars: 字符集字符串 (不含 CTC blank) + img_h: 输入图片高度 + img_w: 输入图片宽度 + """ + super().__init__() + self.chars = chars + self.img_h = img_h + self.img_w = img_w + # CTC 类别数 = 字符数 + 1 (blank at index 0) + self.num_classes = len(chars) + 1 + + # ---- CNN 特征提取 ---- + self.cnn = nn.Sequential( + # block 1: 1 -> 32, H/2, W不变 + nn.Conv2d(1, 32, kernel_size=3, padding=1), + nn.BatchNorm2d(32), + nn.ReLU(inplace=True), + nn.MaxPool2d(kernel_size=(2, 1)), # H/2, W不变 + + # block 2: 32 -> 64, H/2, W/2 + nn.Conv2d(32, 64, kernel_size=3, padding=1), + nn.BatchNorm2d(64), + nn.ReLU(inplace=True), + nn.MaxPool2d(2, 2), # H/2, W/2 + + # block 3: 64 -> 128, H/2, W不变 + nn.Conv2d(64, 128, kernel_size=3, padding=1), + nn.BatchNorm2d(128), + nn.ReLU(inplace=True), + nn.MaxPool2d(kernel_size=(2, 1)), # H/2, W不变 + + # block 4: 128 -> 128, H/2, W/2 + nn.Conv2d(128, 128, kernel_size=3, padding=1), + nn.BatchNorm2d(128), + nn.ReLU(inplace=True), + nn.MaxPool2d(2, 2), # H/2, W/2 + ) + + # 经过 4 次高度 pool: img_h / 16 (如 40 → 2, 不够整除时用自适应) + # 用 AdaptiveAvgPool 把高度压到 1 + self.adaptive_pool = nn.AdaptiveAvgPool2d((1, None)) # (B, 128, 1, W') + + # ---- RNN 序列建模 ---- + self.rnn_input_size = 128 + self.rnn_hidden = 96 + self.rnn = nn.LSTM( + input_size=self.rnn_input_size, + hidden_size=self.rnn_hidden, + num_layers=1, + batch_first=True, + bidirectional=True, + ) + + # ---- 输出层 ---- + self.fc = nn.Linear(self.rnn_hidden * 2, self.num_classes) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """ + Args: + x: (batch, 1, H, W) 灰度图 + + Returns: + logits: (seq_len, batch, num_classes) + 即 CTC 所需的 (T, B, C) 格式 + """ + # CNN + conv = self.cnn(x) # (B, 128, H', W') + conv = self.adaptive_pool(conv) # (B, 128, 1, W') + conv = conv.squeeze(2) # (B, 128, W') + conv = conv.permute(0, 2, 1) # (B, W', 128) — batch_first 序列 + + # RNN + rnn_out, _ = self.rnn(conv) # (B, W', 256) + + # FC + logits = self.fc(rnn_out) # (B, W', num_classes) + logits = logits.permute(1, 0, 2) # (T, B, C) — CTC 格式 + + return logits + + @property + def seq_len(self) -> int: + """根据输入宽度计算 CTC 序列长度 (特征图宽度)。""" + # 宽度经过 2 次 /2 的 pool + return self.img_w // 4 + + # ---------------------------------------------------------- + # CTC 贪心解码 + # ---------------------------------------------------------- + def greedy_decode(self, logits: torch.Tensor) -> list[str]: + """ + CTC 贪心解码。 + + Args: + logits: (T, B, C) 模型原始输出 + + Returns: + 解码后的字符串列表，长度 = batch size + """ + # (T, B, C) -> (B, T) + preds = logits.argmax(dim=2).permute(1, 0) # (B, T) + results = [] + for pred in preds: + chars = [] + prev = -1 + for idx in pred.tolist(): + if idx != 0 and idx != prev: # 0 = blank + chars.append(self.chars[idx - 1]) # 字符从 index 1 开始 + prev = idx + results.append("".join(chars)) + return results diff --git a/models/threed_cnn.py b/models/threed_cnn.py new file mode 100644 index 0000000..3f374b3 --- /dev/null +++ b/models/threed_cnn.py @@ -0,0 +1,155 @@ +""" +3D 立体验证码专用模型 + +采用更深的 CNN backbone（类 ResNet 残差块）+ CRNN 序列建模， +以更强的特征提取能力处理透视变形和阴影效果。 + +架构: ResNet-lite backbone → 自适应池化 → BiLSTM → FC → CTC +输入: 灰度图 1×60×160 +体积目标: < 5MB +""" + +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class ResidualBlock(nn.Module): + """简化残差块: Conv-BN-ReLU-Conv-BN + shortcut。""" + + def __init__(self, channels: int): + super().__init__() + self.conv1 = nn.Conv2d(channels, channels, kernel_size=3, padding=1, bias=False) + self.bn1 = nn.BatchNorm2d(channels) + self.conv2 = nn.Conv2d(channels, channels, kernel_size=3, padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(channels) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + residual = x + out = F.relu(self.bn1(self.conv1(x)), inplace=True) + out = self.bn2(self.conv2(out)) + out = F.relu(out + residual, inplace=True) + return out + + +class ThreeDCNN(nn.Module): + """ + 3D 验证码识别专用模型。 + + backbone 使用 5 层卷积（含 2 个残差块），通道数逐步增长: + 1 → 32 → 64 → 64(res) → 128 → 128(res) + 高度通过 pool 压缩后再用自适应池化归一，宽度保留序列长度。 + 之后接 BiLSTM + FC 做 CTC 序列输出。 + """ + + def __init__(self, chars: str, img_h: int = 60, img_w: int = 160): + """ + Args: + chars: 字符集字符串 (不含 CTC blank) + img_h: 输入图片高度 + img_w: 输入图片宽度 + """ + super().__init__() + self.chars = chars + self.img_h = img_h + self.img_w = img_w + self.num_classes = len(chars) + 1 # +1 for CTC blank + + # ---- ResNet-lite backbone ---- + self.backbone = nn.Sequential( + # stage 1: 1 -> 32, H/2, W不变 + nn.Conv2d(1, 32, kernel_size=3, padding=1, bias=False), + nn.BatchNorm2d(32), + nn.ReLU(inplace=True), + nn.MaxPool2d(kernel_size=(2, 1)), + + # stage 2: 32 -> 64, H/2, W/2 + nn.Conv2d(32, 64, kernel_size=3, padding=1, bias=False), + nn.BatchNorm2d(64), + nn.ReLU(inplace=True), + nn.MaxPool2d(2, 2), + + # stage 3: 残差块 64 -> 64 + ResidualBlock(64), + + # stage 4: 64 -> 128, H/2, W/2 + nn.Conv2d(64, 128, kernel_size=3, padding=1, bias=False), + nn.BatchNorm2d(128), + nn.ReLU(inplace=True), + nn.MaxPool2d(2, 2), + + # stage 5: 残差块 128 -> 128 + ResidualBlock(128), + + # stage 6: 128 -> 128, H/2, W不变 + nn.Conv2d(128, 128, kernel_size=3, padding=1, bias=False), + nn.BatchNorm2d(128), + nn.ReLU(inplace=True), + nn.MaxPool2d(kernel_size=(2, 1)), + ) + + # 高度方向自适应压到 1，宽度保持 + self.adaptive_pool = nn.AdaptiveAvgPool2d((1, None)) + + # ---- RNN 序列建模 ---- + self.rnn_input_size = 128 + self.rnn_hidden = 128 + self.rnn = nn.LSTM( + input_size=self.rnn_input_size, + hidden_size=self.rnn_hidden, + num_layers=2, + batch_first=True, + bidirectional=True, + dropout=0.2, + ) + + # ---- 输出层 ---- + self.fc = nn.Linear(self.rnn_hidden * 2, self.num_classes) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """ + Args: + x: (batch, 1, H, W) 灰度图 + + Returns: + logits: (seq_len, batch, num_classes) CTC 格式 (T, B, C) + """ + conv = self.backbone(x) # (B, 128, H', W') + conv = self.adaptive_pool(conv) # (B, 128, 1, W') + conv = conv.squeeze(2) # (B, 128, W') + conv = conv.permute(0, 2, 1) # (B, W', 128) + + rnn_out, _ = self.rnn(conv) # (B, W', 256) + logits = self.fc(rnn_out) # (B, W', num_classes) + logits = logits.permute(1, 0, 2) # (T, B, C) + return logits + + @property + def seq_len(self) -> int: + """CTC 序列长度 = 输入宽度经过 2 次 W/2 pool 后的宽度。""" + return self.img_w // 4 + + # ---------------------------------------------------------- + # CTC 贪心解码 + # ---------------------------------------------------------- + def greedy_decode(self, logits: torch.Tensor) -> list[str]: + """ + CTC 贪心解码。 + + Args: + logits: (T, B, C) 模型原始输出 + + Returns: + 解码后的字符串列表 + """ + preds = logits.argmax(dim=2).permute(1, 0) # (B, T) + results = [] + for pred in preds: + chars = [] + prev = -1 + for idx in pred.tolist(): + if idx != 0 and idx != prev: + chars.append(self.chars[idx - 1]) + prev = idx + results.append("".join(chars)) + return results diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..c671713 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,25 @@ +[project] +name = "captchbreaker" +version = "0.1.0" +description = "验证码识别多模型系统 - 调度模型 + 多专家模型两级架构" +requires-python = ">=3.10" +dependencies = [ + "torch>=2.0.0", + "torchvision>=0.15.0", + "onnx>=1.14.0", + "onnxscript>=0.6.0", + "onnxruntime>=1.15.0", + "pillow>=10.0.0", + "numpy>=1.24.0", + "tqdm>=4.65.0", +] + +[project.optional-dependencies] +server = [ + "fastapi>=0.100.0", + "uvicorn>=0.23.0", + "python-multipart>=0.0.6", +] + +[project.scripts] +captcha = "cli:main" diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000..f8966cc --- /dev/null +++ b/tests/__init__.py @@ -0,0 +1,3 @@ +""" +测试包 +""" diff --git a/training/__init__.py b/training/__init__.py new file mode 100644 index 0000000..8d05557 --- /dev/null +++ b/training/__init__.py @@ -0,0 +1,10 @@ +""" +训练脚本包 + +- dataset.py: CRNNDataset / CaptchaDataset 通用数据集类 +- train_utils.py: CTC 训练通用逻辑 (train_ctc_model) +- train_normal.py: 训练普通字符识别 (LiteCRNN - normal) +- train_math.py: 训练算式识别 (LiteCRNN - math) +- train_3d.py: 训练 3D 立体识别 (ThreeDCNN) +- train_classifier.py: 训练调度分类器 (CaptchaClassifier) +""" diff --git a/training/dataset.py b/training/dataset.py new file mode 100644 index 0000000..ef44790 --- /dev/null +++ b/training/dataset.py @@ -0,0 +1,159 @@ +""" +通用 Dataset 类 + +提供两种数据集： +- CaptchaDataset: 用于分类器训练 (图片 → 类别标签) +- CRNNDataset: 用于 CRNN/CTC 识别训练 (图片 → 字符序列编码) + +文件名格式约定: {label}_{任意}.png + - 分类器: label 可为任意字符，所在子目录名即为类别 + - 识别器: label 即标注内容 (如 "A3B8" 或 "3+8") +""" + +import os +from pathlib import Path + +from PIL import Image +from torch.utils.data import Dataset +from torchvision import transforms + +from config import AUGMENT_CONFIG + + +# ============================================================ +# 增强 / 推理 transform 工厂函数 +# ============================================================ +def build_train_transform(img_h: int, img_w: int) -> transforms.Compose: + """训练时数据增强 transform。""" + aug = AUGMENT_CONFIG + return transforms.Compose([ + transforms.Grayscale(), + transforms.Resize((img_h, img_w)), + transforms.RandomAffine( + degrees=aug["degrees"], + translate=aug["translate"], + scale=aug["scale"], + ), + transforms.ColorJitter(brightness=aug["brightness"], contrast=aug["contrast"]), + transforms.GaussianBlur(aug["blur_kernel"], sigma=aug["blur_sigma"]), + transforms.ToTensor(), + transforms.Normalize([0.5], [0.5]), + transforms.RandomErasing(p=aug["erasing_prob"], scale=aug["erasing_scale"]), + ]) + + +def build_val_transform(img_h: int, img_w: int) -> transforms.Compose: + """验证 / 推理时 transform (无增强)。""" + return transforms.Compose([ + transforms.Grayscale(), + transforms.Resize((img_h, img_w)), + transforms.ToTensor(), + transforms.Normalize([0.5], [0.5]), + ]) + + +# ============================================================ +# CRNN / CTC 识别用数据集 +# ============================================================ +class CRNNDataset(Dataset): + """ + CTC 识别数据集。 + + 从目录中读取 {label}_{xxx}.png 文件， + 将 label 编码为整数序列 (CTC target)。 + """ + + def __init__( + self, + dirs: list[str | Path], + chars: str, + transform: transforms.Compose | None = None, + ): + """ + Args: + dirs: 数据目录列表 (会合并所有目录下的 .png 文件) + chars: 字符集字符串 (不含 CTC blank) + transform: 图片预处理/增强 + """ + self.chars = chars + self.char_to_idx = {c: i + 1 for i, c in enumerate(chars)} # blank=0 + self.transform = transform + + self.samples: list[tuple[str, str]] = [] # (文件路径, 标签文本) + for d in dirs: + d = Path(d) + if not d.exists(): + continue + for f in sorted(d.glob("*.png")): + label = f.stem.rsplit("_", 1)[0] # "A3B8_000001" -> "A3B8" + self.samples.append((str(f), label)) + + def __len__(self) -> int: + return len(self.samples) + + def __getitem__(self, idx: int): + path, label = self.samples[idx] + img = Image.open(path).convert("RGB") + if self.transform: + img = self.transform(img) + + # 编码标签为整数序列 + target = [self.char_to_idx[c] for c in label if c in self.char_to_idx] + return img, target, label + + @staticmethod + def collate_fn(batch): + """自定义 collate: 图片堆叠为 tensor，标签拼接为 1D tensor。""" + import torch + images, targets, labels = zip(*batch) + images = torch.stack(images, 0) + target_lengths = torch.IntTensor([len(t) for t in targets]) + targets_flat = torch.IntTensor([idx for t in targets for idx in t]) + return images, targets_flat, target_lengths, list(labels) + + +# ============================================================ +# 分类器用数据集 +# ============================================================ +class CaptchaDataset(Dataset): + """ + 分类器训练数据集。 + + 每个子目录名为类别名 (如 "normal", "math", "3d")， + 目录内所有 .png 文件属于该类。 + """ + + def __init__( + self, + root_dir: str | Path, + class_names: list[str], + transform: transforms.Compose | None = None, + ): + """ + Args: + root_dir: 根目录，包含以类别名命名的子文件夹 + class_names: 类别名列表 (顺序即标签索引) + transform: 图片预处理/增强 + """ + self.class_names = class_names + self.class_to_idx = {c: i for i, c in enumerate(class_names)} + self.transform = transform + + self.samples: list[tuple[str, int]] = [] # (文件路径, 类别索引) + root = Path(root_dir) + for cls_name in class_names: + cls_dir = root / cls_name + if not cls_dir.exists(): + continue + for f in sorted(cls_dir.glob("*.png")): + self.samples.append((str(f), self.class_to_idx[cls_name])) + + def __len__(self) -> int: + return len(self.samples) + + def __getitem__(self, idx: int): + path, label = self.samples[idx] + img = Image.open(path).convert("RGB") + if self.transform: + img = self.transform(img) + return img, label diff --git a/training/train_3d.py b/training/train_3d.py new file mode 100644 index 0000000..8a20cdd --- /dev/null +++ b/training/train_3d.py @@ -0,0 +1,40 @@ +""" +训练 3D 立体验证码识别模型 (ThreeDCNN) + +用法: python -m training.train_3d +""" + +from config import ( + THREED_CHARS, + IMAGE_SIZE, + SYNTHETIC_3D_DIR, + REAL_3D_DIR, +) +from generators.threed_gen import ThreeDCaptchaGenerator +from models.threed_cnn import ThreeDCNN +from training.train_utils import train_ctc_model + + +def main(): + img_h, img_w = IMAGE_SIZE["3d"] + model = ThreeDCNN(chars=THREED_CHARS, img_h=img_h, img_w=img_w) + + print("=" * 60) + print("训练 3D 立体验证码识别模型 (ThreeDCNN)") + print(f" 字符集: {THREED_CHARS} ({len(THREED_CHARS)} 字符)") + print(f" 输入尺寸: {img_h}×{img_w}") + print("=" * 60) + + train_ctc_model( + model_name="threed", + model=model, + chars=THREED_CHARS, + synthetic_dir=SYNTHETIC_3D_DIR, + real_dir=REAL_3D_DIR, + generator_cls=ThreeDCaptchaGenerator, + config_key="threed", + ) + + +if __name__ == "__main__": + main() diff --git a/training/train_classifier.py b/training/train_classifier.py new file mode 100644 index 0000000..49bec3e --- /dev/null +++ b/training/train_classifier.py @@ -0,0 +1,232 @@ +""" +训练调度分类器 (CaptchaClassifier) + +从各类型验证码数据中混合采样，训练分类器区分 normal / math / 3d。 +数据来源: data/classifier/ 目录 (按类型子目录组织) + +用法: python -m training.train_classifier +""" + +import os +import shutil +from pathlib import Path + +import torch +import torch.nn as nn +from torch.utils.data import DataLoader, random_split +from tqdm import tqdm + +from config import ( + CAPTCHA_TYPES, + NUM_CAPTCHA_TYPES, + IMAGE_SIZE, + TRAIN_CONFIG, + CLASSIFIER_DIR, + SYNTHETIC_NORMAL_DIR, + SYNTHETIC_MATH_DIR, + SYNTHETIC_3D_DIR, + CHECKPOINTS_DIR, + ONNX_DIR, + ONNX_CONFIG, + get_device, +) +from generators.normal_gen import NormalCaptchaGenerator +from generators.math_gen import MathCaptchaGenerator +from generators.threed_gen import ThreeDCaptchaGenerator +from models.classifier import CaptchaClassifier +from training.dataset import CaptchaDataset, build_train_transform, build_val_transform + + +def _prepare_classifier_data(): + """ + 准备分类器训练数据。 + + 策略：从各类型的合成数据目录中软链接 / 复制到 data/classifier/{type}/ 下， + 每类取相同数量，保证类别平衡。 + 如果各类型合成数据不存在，先自动生成。 + """ + cfg = TRAIN_CONFIG["classifier"] + per_class = cfg["synthetic_samples"] // NUM_CAPTCHA_TYPES + + # 各类型: (类名, 合成目录, 生成器类) + type_info = [ + ("normal", SYNTHETIC_NORMAL_DIR, NormalCaptchaGenerator), + ("math", SYNTHETIC_MATH_DIR, MathCaptchaGenerator), + ("3d", SYNTHETIC_3D_DIR, ThreeDCaptchaGenerator), + ] + + for cls_name, syn_dir, gen_cls in type_info: + syn_dir = Path(syn_dir) + existing = sorted(syn_dir.glob("*.png")) + + # 如果合成数据不够，生成一些 + if len(existing) < per_class: + print(f"[数据] {cls_name} 合成数据不足 ({len(existing)}/{per_class})，开始生成...") + gen = gen_cls() + gen.generate_dataset(per_class, str(syn_dir)) + existing = sorted(syn_dir.glob("*.png")) + + # 复制到 classifier 目录 + cls_dir = CLASSIFIER_DIR / cls_name + cls_dir.mkdir(parents=True, exist_ok=True) + already = len(list(cls_dir.glob("*.png"))) + if already >= per_class: + print(f"[数据] {cls_name} 分类器数据已就绪: {already} 张") + continue + + # 清空后重新链接 + for f in cls_dir.glob("*.png"): + f.unlink() + + selected = existing[:per_class] + for f in tqdm(selected, desc=f"准备 {cls_name}", leave=False): + dst = cls_dir / f.name + # 使用符号链接节省空间，失败则复制 + try: + dst.symlink_to(f.resolve()) + except OSError: + shutil.copy2(f, dst) + + print(f"[数据] {cls_name} 分类器数据就绪: {len(selected)} 张") + + +def main(): + cfg = TRAIN_CONFIG["classifier"] + img_h, img_w = IMAGE_SIZE["classifier"] + device = get_device() + + print("=" * 60) + print("训练调度分类器 (CaptchaClassifier)") + print(f" 类别: {CAPTCHA_TYPES}") + print(f" 输入尺寸: {img_h}×{img_w}") + print("=" * 60) + + # ---- 1. 准备数据 ---- + _prepare_classifier_data() + + # ---- 2. 构建数据集 ---- + train_transform = build_train_transform(img_h, img_w) + val_transform = build_val_transform(img_h, img_w) + + full_dataset = CaptchaDataset( + root_dir=CLASSIFIER_DIR, + class_names=CAPTCHA_TYPES, + transform=train_transform, + ) + total = len(full_dataset) + val_size = int(total * cfg["val_split"]) + train_size = total - val_size + train_ds, val_ds = random_split(full_dataset, [train_size, val_size]) + + # 验证集无增强 + val_ds_clean = CaptchaDataset( + root_dir=CLASSIFIER_DIR, + class_names=CAPTCHA_TYPES, + transform=val_transform, + ) + val_ds_clean.samples = [full_dataset.samples[i] for i in val_ds.indices] + + train_loader = DataLoader( + train_ds, batch_size=cfg["batch_size"], shuffle=True, + num_workers=2, pin_memory=True, + ) + val_loader = DataLoader( + val_ds_clean, batch_size=cfg["batch_size"], shuffle=False, + num_workers=2, pin_memory=True, + ) + + print(f"[数据] 训练: {train_size} 验证: {val_size}") + + # ---- 3. 模型 / 优化器 / 调度器 ---- + model = CaptchaClassifier(num_types=NUM_CAPTCHA_TYPES).to(device) + optimizer = torch.optim.Adam(model.parameters(), lr=cfg["lr"]) + scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=cfg["epochs"]) + criterion = nn.CrossEntropyLoss() + + best_acc = 0.0 + ckpt_path = CHECKPOINTS_DIR / "classifier.pth" + + # ---- 4. 训练循环 ---- + for epoch in range(1, cfg["epochs"] + 1): + model.train() + total_loss = 0.0 + num_batches = 0 + + pbar = tqdm(train_loader, desc=f"Epoch {epoch}/{cfg['epochs']}", leave=False) + for images, labels in pbar: + images = images.to(device) + labels = labels.to(device) + + logits = model(images) + loss = criterion(logits, labels) + + optimizer.zero_grad() + loss.backward() + optimizer.step() + + total_loss += loss.item() + num_batches += 1 + pbar.set_postfix(loss=f"{loss.item():.4f}") + + scheduler.step() + avg_loss = total_loss / max(num_batches, 1) + + # ---- 5. 验证 ---- + model.eval() + correct = 0 + total_val = 0 + with torch.no_grad(): + for images, labels in val_loader: + images = images.to(device) + labels = labels.to(device) + logits = model(images) + preds = logits.argmax(dim=1) + correct += (preds == labels).sum().item() + total_val += labels.size(0) + + val_acc = correct / max(total_val, 1) + lr = scheduler.get_last_lr()[0] + + print( + f"Epoch {epoch:3d}/{cfg['epochs']} " + f"loss={avg_loss:.4f} " + f"acc={val_acc:.4f} " + f"lr={lr:.6f}" + ) + + # ---- 6. 保存最佳模型 ---- + if val_acc > best_acc: + best_acc = val_acc + torch.save({ + "model_state_dict": model.state_dict(), + "class_names": CAPTCHA_TYPES, + "best_acc": best_acc, + "epoch": epoch, + }, ckpt_path) + print(f" → 保存最佳模型 acc={best_acc:.4f} {ckpt_path}") + + # ---- 7. 导出 ONNX ---- + print(f"\n[训练完成] 最佳准确率: {best_acc:.4f}") + ckpt = torch.load(ckpt_path, map_location="cpu", weights_only=True) + model.load_state_dict(ckpt["model_state_dict"]) + model.eval() + onnx_path = ONNX_DIR / "classifier.onnx" + dummy = torch.randn(1, 1, img_h, img_w) + torch.onnx.export( + model.cpu(), + dummy, + str(onnx_path), + opset_version=ONNX_CONFIG["opset_version"], + input_names=["input"], + output_names=["output"], + dynamic_axes={"input": {0: "batch"}, "output": {0: "batch"}} + if ONNX_CONFIG["dynamic_batch"] + else None, + ) + print(f"[ONNX] 导出完成: {onnx_path} ({onnx_path.stat().st_size / 1024:.1f} KB)") + + return best_acc + + +if __name__ == "__main__": + main() diff --git a/training/train_math.py b/training/train_math.py new file mode 100644 index 0000000..82dfc2d --- /dev/null +++ b/training/train_math.py @@ -0,0 +1,40 @@ +""" +训练算式识别模型 (LiteCRNN - math 模式) + +用法: python -m training.train_math +""" + +from config import ( + MATH_CHARS, + IMAGE_SIZE, + SYNTHETIC_MATH_DIR, + REAL_MATH_DIR, +) +from generators.math_gen import MathCaptchaGenerator +from models.lite_crnn import LiteCRNN +from training.train_utils import train_ctc_model + + +def main(): + img_h, img_w = IMAGE_SIZE["math"] + model = LiteCRNN(chars=MATH_CHARS, img_h=img_h, img_w=img_w) + + print("=" * 60) + print("训练算式识别模型 (LiteCRNN - math)") + print(f" 字符集: {MATH_CHARS} ({len(MATH_CHARS)} 字符)") + print(f" 输入尺寸: {img_h}×{img_w}") + print("=" * 60) + + train_ctc_model( + model_name="math", + model=model, + chars=MATH_CHARS, + synthetic_dir=SYNTHETIC_MATH_DIR, + real_dir=REAL_MATH_DIR, + generator_cls=MathCaptchaGenerator, + config_key="math", + ) + + +if __name__ == "__main__": + main() diff --git a/training/train_normal.py b/training/train_normal.py new file mode 100644 index 0000000..473480e --- /dev/null +++ b/training/train_normal.py @@ -0,0 +1,40 @@ +""" +训练普通字符识别模型 (LiteCRNN - normal 模式) + +用法: python -m training.train_normal +""" + +from config import ( + NORMAL_CHARS, + IMAGE_SIZE, + SYNTHETIC_NORMAL_DIR, + REAL_NORMAL_DIR, +) +from generators.normal_gen import NormalCaptchaGenerator +from models.lite_crnn import LiteCRNN +from training.train_utils import train_ctc_model + + +def main(): + img_h, img_w = IMAGE_SIZE["normal"] + model = LiteCRNN(chars=NORMAL_CHARS, img_h=img_h, img_w=img_w) + + print("=" * 60) + print("训练普通字符识别模型 (LiteCRNN - normal)") + print(f" 字符集: {NORMAL_CHARS} ({len(NORMAL_CHARS)} 字符)") + print(f" 输入尺寸: {img_h}×{img_w}") + print("=" * 60) + + train_ctc_model( + model_name="normal", + model=model, + chars=NORMAL_CHARS, + synthetic_dir=SYNTHETIC_NORMAL_DIR, + real_dir=REAL_NORMAL_DIR, + generator_cls=NormalCaptchaGenerator, + config_key="normal", + ) + + +if __name__ == "__main__": + main() diff --git a/training/train_utils.py b/training/train_utils.py new file mode 100644 index 0000000..0a10fbe --- /dev/null +++ b/training/train_utils.py @@ -0,0 +1,232 @@ +""" +CTC 训练通用逻辑 + +提供 train_ctc_model() 函数，被 train_normal / train_math / train_3d 共用。 +职责： +1. 检查合成数据，不存在则自动调用生成器 +2. 构建 Dataset / DataLoader（含真实数据混合） +3. CTC 训练循环 + cosine scheduler +4. 输出日志: epoch, loss, 整体准确率, 字符级准确率 +5. 保存最佳模型到 checkpoints/ +6. 训练结束导出 ONNX +""" + +import os +from pathlib import Path + +import torch +import torch.nn as nn +from torch.utils.data import DataLoader, random_split +from tqdm import tqdm + +from config import ( + CHECKPOINTS_DIR, + ONNX_DIR, + ONNX_CONFIG, + TRAIN_CONFIG, + IMAGE_SIZE, + get_device, +) +from training.dataset import CRNNDataset, build_train_transform, build_val_transform + + +# ============================================================ +# 准确率计算 +# ============================================================ +def _calc_accuracy(preds: list[str], labels: list[str]): + """返回 (整体准确率, 字符级准确率)。""" + total_samples = len(preds) + correct_samples = 0 + total_chars = 0 + correct_chars = 0 + + for pred, label in zip(preds, labels): + if pred == label: + correct_samples += 1 + # 字符级: 逐位比较 (取较短长度) + max_len = max(len(pred), len(label)) + if max_len == 0: + continue + for i in range(max_len): + total_chars += 1 + if i < len(pred) and i < len(label) and pred[i] == label[i]: + correct_chars += 1 + + sample_acc = correct_samples / max(total_samples, 1) + char_acc = correct_chars / max(total_chars, 1) + return sample_acc, char_acc + + +# ============================================================ +# ONNX 导出 +# ============================================================ +def _export_onnx(model: nn.Module, model_name: str, img_h: int, img_w: int): + """导出模型为 ONNX 格式。""" + model.eval() + onnx_path = ONNX_DIR / f"{model_name}.onnx" + dummy = torch.randn(1, 1, img_h, img_w) + torch.onnx.export( + model.cpu(), + dummy, + str(onnx_path), + opset_version=ONNX_CONFIG["opset_version"], + input_names=["input"], + output_names=["output"], + dynamic_axes={"input": {0: "batch"}, "output": {1: "batch"}} + if ONNX_CONFIG["dynamic_batch"] + else None, + ) + print(f"[ONNX] 导出完成: {onnx_path} ({onnx_path.stat().st_size / 1024:.1f} KB)") + + +# ============================================================ +# 核心训练函数 +# ============================================================ +def train_ctc_model( + model_name: str, + model: nn.Module, + chars: str, + synthetic_dir: str | Path, + real_dir: str | Path, + generator_cls, + config_key: str, +): + """ + 通用 CTC 训练流程。 + + Args: + model_name: 模型名称 (用于保存文件: normal / math / threed) + model: PyTorch 模型实例 (LiteCRNN 或 ThreeDCNN) + chars: 字符集字符串 + synthetic_dir: 合成数据目录 + real_dir: 真实数据目录 + generator_cls: 生成器类 (用于自动生成数据) + config_key: TRAIN_CONFIG 中的键名 + """ + cfg = TRAIN_CONFIG[config_key] + img_h, img_w = IMAGE_SIZE[config_key if config_key != "threed" else "3d"] + device = get_device() + + # ---- 1. 检查 / 生成合成数据 ---- + syn_path = Path(synthetic_dir) + existing = list(syn_path.glob("*.png")) + if len(existing) < cfg["synthetic_samples"]: + print(f"[数据] 合成数据不足 ({len(existing)}/{cfg['synthetic_samples']})，开始生成...") + gen = generator_cls() + gen.generate_dataset(cfg["synthetic_samples"], str(syn_path)) + else: + print(f"[数据] 合成数据已就绪: {len(existing)} 张") + + # ---- 2. 构建数据集 ---- + data_dirs = [str(syn_path)] + real_path = Path(real_dir) + if real_path.exists() and list(real_path.glob("*.png")): + data_dirs.append(str(real_path)) + print(f"[数据] 混合真实数据: {len(list(real_path.glob('*.png')))} 张") + + train_transform = build_train_transform(img_h, img_w) + val_transform = build_val_transform(img_h, img_w) + + full_dataset = CRNNDataset(dirs=data_dirs, chars=chars, transform=train_transform) + total = len(full_dataset) + val_size = int(total * cfg["val_split"]) + train_size = total - val_size + train_ds, val_ds = random_split(full_dataset, [train_size, val_size]) + + # 验证集使用无增强 transform + val_ds_clean = CRNNDataset(dirs=data_dirs, chars=chars, transform=val_transform) + val_ds_clean.samples = [full_dataset.samples[i] for i in val_ds.indices] + + train_loader = DataLoader( + train_ds, batch_size=cfg["batch_size"], shuffle=True, + num_workers=2, collate_fn=CRNNDataset.collate_fn, pin_memory=True, + ) + val_loader = DataLoader( + val_ds_clean, batch_size=cfg["batch_size"], shuffle=False, + num_workers=2, collate_fn=CRNNDataset.collate_fn, pin_memory=True, + ) + + print(f"[数据] 训练: {train_size} 验证: {val_size}") + + # ---- 3. 优化器 / 调度器 / 损失 ---- + model = model.to(device) + optimizer = torch.optim.Adam(model.parameters(), lr=cfg["lr"]) + scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=cfg["epochs"]) + ctc_loss = nn.CTCLoss(blank=0, zero_infinity=True) + + best_acc = 0.0 + ckpt_path = CHECKPOINTS_DIR / f"{model_name}.pth" + + # ---- 4. 训练循环 ---- + for epoch in range(1, cfg["epochs"] + 1): + model.train() + total_loss = 0.0 + num_batches = 0 + + pbar = tqdm(train_loader, desc=f"Epoch {epoch}/{cfg['epochs']}", leave=False) + for images, targets, target_lengths, _ in pbar: + images = images.to(device) + targets = targets.to(device) + target_lengths = target_lengths.to(device) + + logits = model(images) # (T, B, C) + T, B, C = logits.shape + input_lengths = torch.full((B,), T, dtype=torch.int32, device=device) + + log_probs = logits.log_softmax(2) + loss = ctc_loss(log_probs, targets, input_lengths, target_lengths) + + optimizer.zero_grad() + loss.backward() + torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=5.0) + optimizer.step() + + total_loss += loss.item() + num_batches += 1 + pbar.set_postfix(loss=f"{loss.item():.4f}") + + scheduler.step() + avg_loss = total_loss / max(num_batches, 1) + + # ---- 5. 验证 ---- + model.eval() + all_preds = [] + all_labels = [] + with torch.no_grad(): + for images, _, _, labels in val_loader: + images = images.to(device) + logits = model(images) + preds = model.greedy_decode(logits) + all_preds.extend(preds) + all_labels.extend(labels) + + sample_acc, char_acc = _calc_accuracy(all_preds, all_labels) + lr = scheduler.get_last_lr()[0] + + print( + f"Epoch {epoch:3d}/{cfg['epochs']} " + f"loss={avg_loss:.4f} " + f"acc={sample_acc:.4f} " + f"char_acc={char_acc:.4f} " + f"lr={lr:.6f}" + ) + + # ---- 6. 保存最佳模型 ---- + if sample_acc >= best_acc: + best_acc = sample_acc + torch.save({ + "model_state_dict": model.state_dict(), + "chars": chars, + "best_acc": best_acc, + "epoch": epoch, + }, ckpt_path) + print(f" → 保存最佳模型 acc={best_acc:.4f} {ckpt_path}") + + # ---- 7. 导出 ONNX ---- + print(f"\n[训练完成] 最佳准确率: {best_acc:.4f}") + # 加载最佳权重再导出 + ckpt = torch.load(ckpt_path, map_location="cpu", weights_only=True) + model.load_state_dict(ckpt["model_state_dict"]) + _export_onnx(model, model_name, img_h, img_w) + + return best_acc diff --git a/uv.lock b/uv.lock new file mode 100644 index 0000000..2cdf474 --- /dev/null +++ b/uv.lock @@ -0,0 +1,1236 @@ +version = 1 +revision = 3 +requires-python = ">=3.10" +resolution-markers = [ + "python_full_version >= '3.13'", + "python_full_version == '3.12.*'", + "python_full_version == '3.11.*'", + "python_full_version < '3.11'", +] + +[[package]] +name = "annotated-doc" +version = "0.0.4" +source = 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