CaptchBreaker/cli.py

"""
CaptchaBreaker 命令行入口

用法:
    python cli.py generate --type normal --num 60000
    python cli.py generate --type 3d_text --num 80000
    python cli.py generate --type 3d_rotate --num 60000
    python cli.py generate --type 3d_slider --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
    python cli.py generate-solver slide --num 30000
    python cli.py train-solver slide
    python cli.py train-solver rotate
    python cli.py solve slide --bg bg.png [--tpl tpl.png]
    python cli.py solve rotate --image img.png
"""

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_TEXT_DIR, SYNTHETIC_3D_ROTATE_DIR, SYNTHETIC_3D_SLIDER_DIR,
        CLASSIFIER_DIR, TRAIN_CONFIG, CAPTCHA_TYPES, NUM_CAPTCHA_TYPES,
    )
    from generators import (
        NormalCaptchaGenerator, MathCaptchaGenerator, ThreeDCaptchaGenerator,
        ThreeDRotateGenerator, ThreeDSliderGenerator,
    )

    gen_map = {
        "normal": (NormalCaptchaGenerator, SYNTHETIC_NORMAL_DIR),
        "math": (MathCaptchaGenerator, SYNTHETIC_MATH_DIR),
        "3d_text": (ThreeDCaptchaGenerator, SYNTHETIC_3D_TEXT_DIR),
        "3d_rotate": (ThreeDRotateGenerator, SYNTHETIC_3D_ROTATE_DIR),
        "3d_slider": (ThreeDSliderGenerator, SYNTHETIC_3D_SLIDER_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:
        valid = ", ".join(list(gen_map.keys()) + ["classifier"])
        print(f"未知类型: {captcha_type}  可选: {valid}")
        sys.exit(1)


def cmd_train(args):
    """训练模型。"""
    if args.all:
        print("按顺序训练全部模型: normal → math → 3d_text → 3d_rotate → 3d_slider → classifier\n")
        from training.train_normal import main as train_normal
        from training.train_math import main as train_math
        from training.train_3d_text import main as train_3d_text
        from training.train_3d_rotate import main as train_3d_rotate
        from training.train_3d_slider import main as train_3d_slider
        from training.train_classifier import main as train_classifier

        train_normal()
        print("\n")
        train_math()
        print("\n")
        train_3d_text()
        print("\n")
        train_3d_rotate()
        print("\n")
        train_3d_slider()
        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_text":
        from training.train_3d_text import main as train_fn
    elif model == "3d_rotate":
        from training.train_3d_rotate import main as train_fn
    elif model == "3d_slider":
        from training.train_3d_slider 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_text, 3d_rotate, 3d_slider, 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:
        # 别名映射
        alias = {
            "3d_text": "threed_text",
            "3d_rotate": "threed_rotate",
            "3d_slider": "threed_slider",
        }
        name = alias.get(args.model, args.model)
        _load_and_export(name)
    else:
        print("请指定 --all 或 --model <name>")
        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':<10} {'结果':<15} {'耗时(ms)':>8}")
    print("-" * 67)

    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']:<10} "
            f"{result['result']:<15} {result['time_ms']:>8.1f}"
        )

    print("-" * 67)
    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 cmd_generate_solver(args):
    """生成 solver 训练数据。"""
    from config import SLIDE_DATA_DIR, ROTATE_SOLVER_DATA_DIR
    from generators.slide_gen import SlideDataGenerator
    from generators.rotate_solver_gen import RotateSolverDataGenerator

    solver_type = args.type
    num = args.num

    gen_map = {
        "slide": (SlideDataGenerator, SLIDE_DATA_DIR),
        "rotate": (RotateSolverDataGenerator, ROTATE_SOLVER_DATA_DIR),
    }

    if solver_type not in gen_map:
        print(f"未知 solver 类型: {solver_type}  可选: {', '.join(gen_map.keys())}")
        sys.exit(1)

    gen_cls, out_dir = gen_map[solver_type]
    out_dir.mkdir(parents=True, exist_ok=True)
    print(f"生成 solver/{solver_type} 数据: {num} 张 → {out_dir}")
    gen = gen_cls()
    gen.generate_dataset(num, str(out_dir))


def cmd_train_solver(args):
    """训练 solver 模型。"""
    solver_type = args.type

    if solver_type == "slide":
        from training.train_slide import main as train_fn
    elif solver_type == "rotate":
        from training.train_rotate_solver import main as train_fn
    else:
        print(f"未知 solver 类型: {solver_type}  可选: slide, rotate")
        sys.exit(1)

    train_fn()


def cmd_solve(args):
    """求解验证码。"""
    solver_type = args.type

    if solver_type == "slide":
        from solvers.slide_solver import SlideSolver

        bg_path = args.bg
        tpl_path = getattr(args, "tpl", None)
        if not Path(bg_path).exists():
            print(f"文件不存在: {bg_path}")
            sys.exit(1)

        solver = SlideSolver()
        result = solver.solve(bg_path, template_image=tpl_path)

        print(f"背景图: {bg_path}")
        if tpl_path:
            print(f"模板图: {tpl_path}")
        print(f"缺口 x:     {result['gap_x']} px")
        print(f"缺口 x%:    {result['gap_x_percent']:.4f}")
        print(f"置信度:     {result['confidence']:.4f}")
        print(f"方法:       {result['method']}")

    elif solver_type == "rotate":
        from solvers.rotate_solver import RotateSolver

        image_path = args.image
        if not Path(image_path).exists():
            print(f"文件不存在: {image_path}")
            sys.exit(1)

        solver = RotateSolver()
        result = solver.solve(image_path)

        print(f"图片:   {image_path}")
        print(f"角度:   {result['angle']}°")
        print(f"置信度: {result['confidence']}")

    else:
        print(f"未知 solver 类型: {solver_type}  可选: slide, rotate")
        sys.exit(1)


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_text, 3d_rotate, 3d_slider, 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_text, 3d_rotate, 3d_slider, 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_text, 3d_rotate, 3d_slider, classifier",
    )
    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_text, 3d_rotate, 3d_slider",
    )

    # ---- 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_text, 3d_rotate, 3d_slider",
    )

    # ---- 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)")

    # ---- generate-solver ----
    p_gen_solver = subparsers.add_parser("generate-solver", help="生成 solver 训练数据")
    p_gen_solver.add_argument("type", help="solver 类型: slide, rotate")
    p_gen_solver.add_argument("--num", type=int, required=True, help="生成数量")

    # ---- train-solver ----
    p_train_solver = subparsers.add_parser("train-solver", help="训练 solver 模型")
    p_train_solver.add_argument("type", help="solver 类型: slide, rotate")

    # ---- solve ----
    p_solve = subparsers.add_parser("solve", help="求解交互式验证码")
    p_solve.add_argument("type", help="solver 类型: slide, rotate")
    p_solve.add_argument("--bg", help="背景图路径 (slide 必需)")
    p_solve.add_argument("--tpl", default=None, help="模板图路径 (slide 可选)")
    p_solve.add_argument("--image", help="图片路径 (rotate 必需)")

    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,
        "generate-solver": cmd_generate_solver,
        "train-solver": cmd_train_solver,
        "solve": cmd_solve,
    }

    cmd_map[args.command](args)


if __name__ == "__main__":
    main()