diff --git a/.gitignore b/.gitignore index 83311ac..08cc81d 100644 --- a/.gitignore +++ b/.gitignore @@ -7,6 +7,10 @@ __pycache__/ data/synthetic/ data/classifier/ +data/solver/ +data/real/ + +*.log checkpoints/ onnx_models/ diff --git a/main.py b/main.py deleted file mode 100644 index b76e696..0000000 --- a/main.py +++ /dev/null @@ -1,16 +0,0 @@ -# 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/pyproject.toml b/pyproject.toml index f770f7a..35f2131 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -23,6 +23,9 @@ server = [ cv = [ "opencv-python>=4.8.0", ] +dev = [ + "pytest>=7.0.0", +] [project.scripts] captcha = "cli:main" diff --git a/server.py b/server.py new file mode 100644 index 0000000..03b5270 --- /dev/null +++ b/server.py @@ -0,0 +1,88 @@ +""" +FastAPI HTTP 推理服务 + +提供 REST API 识别验证码: + POST /solve - 上传图片，返回识别结果 + GET /health - 健康检查 + +启动方式: + uv run python cli.py serve --port 8080 + +请求示例: + curl -X POST http://localhost:8080/solve -F "image=@captcha.png" + +响应示例: + {"type": "normal", "result": "A3B8", "confidence": 0.95, "time_ms": 45} +""" + +from __future__ import annotations + + +def create_app(): + """ + 创建 FastAPI 应用实例（工厂函数）。 + + cli.py 的 cmd_serve 依赖此签名。 + 需要安装 server 可选依赖: uv sync --extra server + """ + from fastapi import FastAPI, File, Query, UploadFile + from fastapi.responses import JSONResponse + + from inference.pipeline import CaptchaPipeline + + app = FastAPI( + title="CaptchaBreaker", + description="验证码识别多模型系统 - HTTP 推理服务", + version="0.1.0", + ) + + pipeline: CaptchaPipeline | None = None + + @app.on_event("startup") + def _load_models(): + nonlocal pipeline + try: + pipeline = CaptchaPipeline() + except FileNotFoundError: + # 模型未导出时允许启动，但 /solve 会返回 503 + pipeline = None + + @app.get("/health") + def health(): + models_loaded = pipeline is not None + return {"status": "ok" if models_loaded else "no_models", "models_loaded": models_loaded} + + @app.post("/solve") + async def solve( + image: UploadFile = File(...), + type: str | None = Query(None, description="指定类型跳过分类"), + ): + if pipeline is None: + return JSONResponse( + status_code=503, + content={"error": "模型未加载，请先训练并导出 ONNX 模型"}, + ) + + data = await image.read() + if not data: + return JSONResponse( + status_code=400, + content={"error": "空文件"}, + ) + + try: + result = pipeline.solve(data, captcha_type=type) + except RuntimeError as e: + return JSONResponse( + status_code=400, + content={"error": str(e)}, + ) + + return { + "type": result["type"], + "result": result["result"], + "raw": result["raw"], + "time_ms": result["time_ms"], + } + + return app diff --git a/tests/test_generators.py b/tests/test_generators.py new file mode 100644 index 0000000..4a6dff2 --- /dev/null +++ b/tests/test_generators.py @@ -0,0 +1,173 @@ +""" +测试所有验证码生成器。 + +每种生成器 generate() 1 张 → 验证返回类型、图片尺寸、标签格式。 +""" + +import re + +import pytest +from PIL import Image + +from config import GENERATE_CONFIG, NORMAL_CHARS, MATH_CHARS, THREED_CHARS, SOLVER_CONFIG +from generators import ( + NormalCaptchaGenerator, + MathCaptchaGenerator, + ThreeDCaptchaGenerator, + ThreeDRotateGenerator, + ThreeDSliderGenerator, + SlideDataGenerator, + RotateSolverDataGenerator, +) + + +class TestNormalCaptchaGenerator: + def setup_method(self): + self.gen = NormalCaptchaGenerator(seed=0) + self.cfg = GENERATE_CONFIG["normal"] + + def test_generate_returns_image_and_label(self): + img, label = self.gen.generate() + assert isinstance(img, Image.Image) + assert isinstance(label, str) + + def test_image_size(self): + img, _ = self.gen.generate() + w, h = self.cfg["image_size"] + assert img.size == (w, h) + + def test_label_chars_in_charset(self): + img, label = self.gen.generate() + assert len(label) >= 4 + for ch in label: + assert ch in NORMAL_CHARS, f"char {ch!r} not in NORMAL_CHARS" + + def test_generate_with_text(self): + img, label = self.gen.generate(text="AB12") + assert label == "AB12" + + +class TestMathCaptchaGenerator: + def setup_method(self): + self.gen = MathCaptchaGenerator(seed=0) + self.cfg = GENERATE_CONFIG["math"] + + def test_generate_returns_image_and_label(self): + img, label = self.gen.generate() + assert isinstance(img, Image.Image) + assert isinstance(label, str) + + def test_image_size(self): + img, _ = self.gen.generate() + w, h = self.cfg["image_size"] + assert img.size == (w, h) + + def test_label_is_expression(self): + """Label should be like '3+8' (expression without =? and without result).""" + img, label = self.gen.generate() + assert re.match(r"^\d+[+\-×÷]\d+$", label), f"unexpected label format: {label!r}" + + +class TestThreeDCaptchaGenerator: + def setup_method(self): + self.gen = ThreeDCaptchaGenerator(seed=0) + self.cfg = GENERATE_CONFIG["3d_text"] + + def test_generate_returns_image_and_label(self): + img, label = self.gen.generate() + assert isinstance(img, Image.Image) + assert isinstance(label, str) + + def test_image_size(self): + img, _ = self.gen.generate() + w, h = self.cfg["image_size"] + assert img.size == (w, h) + + def test_label_chars_in_charset(self): + img, label = self.gen.generate() + assert len(label) >= 4 + for ch in label: + assert ch in THREED_CHARS, f"char {ch!r} not in THREED_CHARS" + + +class TestThreeDRotateGenerator: + def setup_method(self): + self.gen = ThreeDRotateGenerator(seed=0) + self.cfg = GENERATE_CONFIG["3d_rotate"] + + def test_generate_returns_image_and_label(self): + img, label = self.gen.generate() + assert isinstance(img, Image.Image) + assert isinstance(label, str) + + def test_image_size(self): + img, _ = self.gen.generate() + w, h = self.cfg["image_size"] + assert img.size == (w, h) + + def test_label_is_angle(self): + img, label = self.gen.generate() + angle = int(label) + assert 0 <= angle <= 359 + + +class TestThreeDSliderGenerator: + def setup_method(self): + self.gen = ThreeDSliderGenerator(seed=0) + self.cfg = GENERATE_CONFIG["3d_slider"] + + def test_generate_returns_image_and_label(self): + img, label = self.gen.generate() + assert isinstance(img, Image.Image) + assert isinstance(label, str) + + def test_image_size(self): + img, _ = self.gen.generate() + w, h = self.cfg["image_size"] + assert img.size == (w, h) + + def test_label_is_offset(self): + img, label = self.gen.generate() + offset = int(label) + lo, hi = self.cfg["gap_x_range"] + assert lo <= offset <= hi + + +class TestSlideDataGenerator: + def setup_method(self): + self.gen = SlideDataGenerator(seed=0) + + def test_generate_returns_image_and_label(self): + img, label = self.gen.generate() + assert isinstance(img, Image.Image) + assert isinstance(label, str) + + def test_image_size(self): + img, _ = self.gen.generate() + h, w = SOLVER_CONFIG["slide"]["cnn_input_size"] + assert img.size == (w, h) + + def test_label_is_numeric(self): + img, label = self.gen.generate() + val = int(label) + assert val >= 0 + + +class TestRotateSolverDataGenerator: + def setup_method(self): + self.gen = RotateSolverDataGenerator(seed=0) + + def test_generate_returns_image_and_label(self): + img, label = self.gen.generate() + assert isinstance(img, Image.Image) + assert isinstance(label, str) + + def test_image_size(self): + img, _ = self.gen.generate() + h, w = SOLVER_CONFIG["rotate"]["input_size"] + assert img.size == (w, h) + + def test_label_is_angle(self): + img, label = self.gen.generate() + angle = int(label) + assert 0 <= angle <= 359 diff --git a/tests/test_models.py b/tests/test_models.py new file mode 100644 index 0000000..ea0d511 --- /dev/null +++ b/tests/test_models.py @@ -0,0 +1,177 @@ +""" +测试所有模型前向传播和输出形状。 + +每种模型构造 → forward → 验证输出 shape。 +""" + +import torch +import pytest + +from config import NORMAL_CHARS, MATH_CHARS, THREED_CHARS, IMAGE_SIZE, SOLVER_CONFIG +from models.classifier import CaptchaClassifier +from models.lite_crnn import LiteCRNN +from models.threed_cnn import ThreeDCNN +from models.regression_cnn import RegressionCNN +from models.gap_detector import GapDetectorCNN +from models.rotation_regressor import RotationRegressor + + +class TestCaptchaClassifier: + def setup_method(self): + self.model = CaptchaClassifier(num_types=5) + self.model.eval() + + def test_output_shape(self): + h, w = IMAGE_SIZE["classifier"] + x = torch.randn(2, 1, h, w) + out = self.model(x) + assert out.shape == (2, 5) + + def test_single_batch(self): + h, w = IMAGE_SIZE["classifier"] + x = torch.randn(1, 1, h, w) + out = self.model(x) + assert out.shape == (1, 5) + + def test_param_count_reasonable(self): + n = sum(p.numel() for p in self.model.parameters()) + # Should be < 500KB ≈ 125K float32 params + assert n < 200_000, f"too many params: {n}" + + +class TestLiteCRNN: + def setup_method(self): + self.model = LiteCRNN(chars=NORMAL_CHARS) + self.model.eval() + + def test_output_shape(self): + h, w = IMAGE_SIZE["normal"] + x = torch.randn(2, 1, h, w) + out = self.model(x) + num_classes = len(NORMAL_CHARS) + 1 # +1 for blank + seq_len = w // 4 + assert out.shape == (seq_len, 2, num_classes) + + def test_greedy_decode(self): + h, w = IMAGE_SIZE["normal"] + x = torch.randn(1, 1, h, w) + logits = self.model(x) + decoded = self.model.greedy_decode(logits) + assert isinstance(decoded, list) + assert len(decoded) == 1 + assert isinstance(decoded[0], str) + + def test_param_count_reasonable(self): + n = sum(p.numel() for p in self.model.parameters()) + # Should be < 2MB ≈ 500K float32 params + assert n < 600_000, f"too many params: {n}" + + def test_math_mode(self): + h, w = IMAGE_SIZE["math"] + model = LiteCRNN(chars=MATH_CHARS, img_h=h, img_w=w) + model.eval() + x = torch.randn(1, 1, h, w) + out = model(x) + num_classes = len(MATH_CHARS) + 1 + seq_len = w // 4 + assert out.shape == (seq_len, 1, num_classes) + + +class TestThreeDCNN: + def setup_method(self): + h, w = IMAGE_SIZE["3d_text"] + self.model = ThreeDCNN(chars=THREED_CHARS, img_h=h, img_w=w) + self.model.eval() + + def test_output_shape(self): + h, w = IMAGE_SIZE["3d_text"] + x = torch.randn(2, 1, h, w) + out = self.model(x) + num_classes = len(THREED_CHARS) + 1 + seq_len = w // 4 + assert out.shape == (seq_len, 2, num_classes) + + def test_greedy_decode(self): + h, w = IMAGE_SIZE["3d_text"] + x = torch.randn(1, 1, h, w) + logits = self.model(x) + decoded = self.model.greedy_decode(logits) + assert isinstance(decoded, list) + assert len(decoded) == 1 + + def test_param_count_reasonable(self): + n = sum(p.numel() for p in self.model.parameters()) + # Should be < 5MB ≈ 1.25M float32 params + assert n < 1_500_000, f"too many params: {n}" + + +class TestRegressionCNN: + def test_3d_rotate_shape(self): + h, w = IMAGE_SIZE["3d_rotate"] + model = RegressionCNN(img_h=h, img_w=w) + model.eval() + x = torch.randn(2, 1, h, w) + out = model(x) + assert out.shape == (2, 1) + # Output should be sigmoid [0, 1] + assert out.min() >= 0.0 + assert out.max() <= 1.0 + + def test_3d_slider_shape(self): + h, w = IMAGE_SIZE["3d_slider"] + model = RegressionCNN(img_h=h, img_w=w) + model.eval() + x = torch.randn(2, 1, h, w) + out = model(x) + assert out.shape == (2, 1) + + def test_param_count_reasonable(self): + h, w = IMAGE_SIZE["3d_rotate"] + model = RegressionCNN(img_h=h, img_w=w) + n = sum(p.numel() for p in model.parameters()) + # Should be ~1MB ≈ 250K float32 params + assert n < 400_000, f"too many params: {n}" + + +class TestGapDetectorCNN: + def setup_method(self): + h, w = SOLVER_CONFIG["slide"]["cnn_input_size"] + self.model = GapDetectorCNN(img_h=h, img_w=w) + self.model.eval() + + def test_output_shape(self): + h, w = SOLVER_CONFIG["slide"]["cnn_input_size"] + x = torch.randn(2, 1, h, w) + out = self.model(x) + assert out.shape == (2, 1) + assert out.min() >= 0.0 + assert out.max() <= 1.0 + + def test_param_count_reasonable(self): + n = sum(p.numel() for p in self.model.parameters()) + assert n < 400_000, f"too many params: {n}" + + +class TestRotationRegressor: + def setup_method(self): + h, w = SOLVER_CONFIG["rotate"]["input_size"] + self.model = RotationRegressor(img_h=h, img_w=w) + self.model.eval() + + def test_output_shape(self): + h, w = SOLVER_CONFIG["rotate"]["input_size"] + x = torch.randn(2, 3, h, w) # RGB, 3 channels + out = self.model(x) + assert out.shape == (2, 2) # (sin, cos) + + def test_output_range_tanh(self): + h, w = SOLVER_CONFIG["rotate"]["input_size"] + x = torch.randn(4, 3, h, w) + out = self.model(x) + assert out.min() >= -1.0 + assert out.max() <= 1.0 + + def test_param_count_reasonable(self): + n = sum(p.numel() for p in self.model.parameters()) + # Should be ~2MB ≈ 500K float32 params + assert n < 600_000, f"too many params: {n}" diff --git a/tests/test_pipeline.py b/tests/test_pipeline.py new file mode 100644 index 0000000..eba6bb1 --- /dev/null +++ b/tests/test_pipeline.py @@ -0,0 +1,176 @@ +""" +测试推理流水线组件。 + +- math_eval: 加减乘除正确性 + 异常输入 +- CTC greedy decode (构造 logits) +- SlideSolver (合成图 → OpenCV 检测) +- generate_slide_track 轨迹合理性 +""" + +import math + +import numpy as np +import pytest + +from inference.math_eval import eval_captcha_math +from inference.pipeline import CaptchaPipeline + + +# ============================================================ +# math_eval 测试 +# ============================================================ +class TestMathEval: + def test_addition(self): + assert eval_captcha_math("3+8=?") == "11" + assert eval_captcha_math("12+5") == "17" + assert eval_captcha_math("0+0=?") == "0" + + def test_subtraction(self): + assert eval_captcha_math("15-7=?") == "8" + assert eval_captcha_math("20-20") == "0" + + def test_multiplication(self): + assert eval_captcha_math("12×3=?") == "36" + assert eval_captcha_math("5*4") == "20" + assert eval_captcha_math("6x7") == "42" + assert eval_captcha_math("6X7") == "42" + + def test_division(self): + assert eval_captcha_math("20÷4=?") == "5" + assert eval_captcha_math("9÷3") == "3" + + def test_division_by_zero(self): + with pytest.raises(ValueError, match="除数为零"): + eval_captcha_math("5÷0=?") + + def test_invalid_expression(self): + with pytest.raises(ValueError, match="无法解析"): + eval_captcha_math("abc") + + def test_with_spaces(self): + assert eval_captcha_math("3 + 8 = ?") == "11" + + +# ============================================================ +# CTC greedy decode 测试 +# ============================================================ +class TestCTCGreedyDecode: + """Test the static _ctc_greedy_decode method from CaptchaPipeline.""" + + def test_simple_decode(self): + chars = "ABC" # index 0=blank, 1=A, 2=B, 3=C + T = 6 + C = 4 # blank + 3 chars + logits = np.full((T, 1, C), -10.0, dtype=np.float32) + # Spell out "AB": A, A, blank, B, B, B + logits[0, 0, 1] = 10.0 # A + logits[1, 0, 1] = 10.0 # A (dup, collapsed) + logits[2, 0, 0] = 10.0 # blank + logits[3, 0, 2] = 10.0 # B + logits[4, 0, 2] = 10.0 # B (dup) + logits[5, 0, 2] = 10.0 # B (dup) + result = CaptchaPipeline._ctc_greedy_decode(logits, chars) + assert result == "AB" + + def test_all_blank(self): + chars = "ABC" + T = 5 + C = 4 + logits = np.full((T, 1, C), -10.0, dtype=np.float32) + for t in range(T): + logits[t, 0, 0] = 10.0 + result = CaptchaPipeline._ctc_greedy_decode(logits, chars) + assert result == "" + + def test_repeated_chars_with_blank_separator(self): + chars = "ABC" + T = 5 + C = 4 + logits = np.full((T, 1, C), -10.0, dtype=np.float32) + # Spell "AA": A, blank, A, blank, blank + logits[0, 0, 1] = 10.0 # A + logits[1, 0, 0] = 10.0 # blank + logits[2, 0, 1] = 10.0 # A + logits[3, 0, 0] = 10.0 # blank + logits[4, 0, 0] = 10.0 # blank + result = CaptchaPipeline._ctc_greedy_decode(logits, chars) + assert result == "AA" + + +# ============================================================ +# SlideSolver 测试 +# ============================================================ +class TestSlideSolver: + def test_solve_with_synthetic_image(self): + """Generate a synthetic slide image and verify the solver detects a gap.""" + try: + import cv2 + except ImportError: + pytest.skip("OpenCV not installed") + + from generators.slide_gen import SlideDataGenerator + from solvers.slide_solver import SlideSolver + + gen = SlideDataGenerator(seed=42) + img, label = gen.generate() + expected_gap_x = int(label) + + solver = SlideSolver() + result = solver.solve(img) + + assert "gap_x" in result + assert "gap_x_percent" in result + assert "confidence" in result + assert "method" in result + assert isinstance(result["gap_x"], int) + assert 0.0 <= result["gap_x_percent"] <= 1.0 + + +# ============================================================ +# generate_slide_track 测试 +# ============================================================ +class TestSlideTrack: + def test_track_basic(self): + from utils.slide_utils import generate_slide_track + + track = generate_slide_track(100, seed=42) + assert isinstance(track, list) + assert len(track) >= 10 + + def test_track_point_structure(self): + from utils.slide_utils import generate_slide_track + + track = generate_slide_track(150, seed=0) + for pt in track: + assert "x" in pt + assert "y" in pt + assert "t" in pt + + def test_track_starts_at_origin(self): + from utils.slide_utils import generate_slide_track + + track = generate_slide_track(100, seed=1) + assert track[0]["x"] == 0.0 or abs(track[0]["x"]) < 1e-6 + + def test_track_ends_near_distance(self): + from utils.slide_utils import generate_slide_track + + distance = 120 + track = generate_slide_track(distance, seed=2) + final_x = track[-1]["x"] + assert abs(final_x - distance) < 1.0, f"final x={final_x}, expected ~{distance}" + + def test_track_time_increases(self): + from utils.slide_utils import generate_slide_track + + track = generate_slide_track(100, seed=3) + for i in range(1, len(track)): + assert track[i]["t"] >= track[i - 1]["t"] + + def test_track_y_has_jitter(self): + from utils.slide_utils import generate_slide_track + + track = generate_slide_track(200, seed=4) + y_vals = [pt["y"] for pt in track] + # At least some y values should be non-zero (jitter) + assert any(abs(y) > 0 for y in y_vals) diff --git a/training/__init__.py b/training/__init__.py index 232f3b0..5344949 100644 --- a/training/__init__.py +++ b/training/__init__.py @@ -10,4 +10,6 @@ - train_3d_rotate.py: 训练 3D 旋转回归 (RegressionCNN) - train_3d_slider.py: 训练 3D 滑块回归 (RegressionCNN) - train_classifier.py: 训练调度分类器 (CaptchaClassifier) +- train_slide.py: 训练滑块缺口检测 (GapDetectorCNN) +- train_rotate_solver.py: 训练旋转角度回归 (RotationRegressor) """ diff --git a/training/train_regression_utils.py b/training/train_regression_utils.py index d0a2dd8..adf0106 100644 --- a/training/train_regression_utils.py +++ b/training/train_regression_utils.py @@ -175,10 +175,26 @@ def train_regression_model( best_mae = float("inf") best_tol_acc = 0.0 + start_epoch = 1 ckpt_path = CHECKPOINTS_DIR / f"{model_name}.pth" + # ---- 3.5 断点续训 ---- + if ckpt_path.exists(): + ckpt = torch.load(ckpt_path, map_location=device, weights_only=True) + model.load_state_dict(ckpt["model_state_dict"]) + best_tol_acc = ckpt.get("best_tol_acc", 0.0) + best_mae = ckpt.get("best_mae", float("inf")) + start_epoch = ckpt.get("epoch", 0) + 1 + # 快进 scheduler 到对应 epoch + for _ in range(start_epoch - 1): + scheduler.step() + print( + f"[续训] 从 epoch {start_epoch} 继续, " + f"best_tol_acc={best_tol_acc:.4f}, best_mae={best_mae:.2f}" + ) + # ---- 4. 训练循环 ---- - for epoch in range(1, cfg["epochs"] + 1): + for epoch in range(start_epoch, cfg["epochs"] + 1): model.train() total_loss = 0.0 num_batches = 0 diff --git a/training/train_utils.py b/training/train_utils.py index 7377acc..b22a3c3 100644 --- a/training/train_utils.py +++ b/training/train_utils.py @@ -170,10 +170,25 @@ def train_ctc_model( ctc_loss = nn.CTCLoss(blank=0, zero_infinity=True) best_acc = 0.0 + start_epoch = 1 ckpt_path = CHECKPOINTS_DIR / f"{model_name}.pth" + # ---- 3.5 断点续训 ---- + if ckpt_path.exists(): + ckpt = torch.load(ckpt_path, map_location=device, weights_only=True) + model.load_state_dict(ckpt["model_state_dict"]) + best_acc = ckpt.get("best_acc", 0.0) + start_epoch = ckpt.get("epoch", 0) + 1 + # 快进 scheduler 到对应 epoch + for _ in range(start_epoch - 1): + scheduler.step() + print( + f"[续训] 从 epoch {start_epoch} 继续, " + f"best_acc={best_acc:.4f}" + ) + # ---- 4. 训练循环 ---- - for epoch in range(1, cfg["epochs"] + 1): + for epoch in range(start_epoch, cfg["epochs"] + 1): model.train() total_loss = 0.0 num_batches = 0 @@ -181,11 +196,12 @@ def train_ctc_model( 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 - # cuDNN CTC requires targets/lengths on CPU - input_lengths = 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