Hua/CaptchBreaker
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Expand 3D captcha into three subtypes: 3d_text, 3d_rotate, 3d_slider

Browse Source 
Split the single "3d" captcha type into three independent expert models:
- 3d_text: 3D perspective text OCR (renamed from old "3d", CTC-based ThreeDCNN)
- 3d_rotate: rotation angle regression (new RegressionCNN, circular loss)
- 3d_slider: slider offset regression (new RegressionCNN, SmoothL1 loss)

CAPTCHA_TYPES expanded from 3 to 5 classes. Classifier samples updated
to 50000 (10000 per class). New generators, model, dataset, training
utilities, and full pipeline/export/CLI support for all subtypes.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
Hua
2026-03-11 13:55:53 +08:00
parent 760b80ee5e
commit f5be7671bc
20 changed files with 1109 additions and 142 deletions
Download Patch File Download Diff File Expand all files Collapse all files

25
inference/export_onnx.py
View File

@@ -17,10 +17,12 @@ from config import (
MATH_CHARS,
THREED_CHARS,
NUM_CAPTCHA_TYPES,
REGRESSION_RANGE,
)
from models.classifier import CaptchaClassifier
from models.lite_crnn import LiteCRNN
from models.threed_cnn import ThreeDCNN
from models.regression_cnn import RegressionCNN
def export_model(
@@ -34,7 +36,7 @@ def export_model(
Args:
model: 已加载权重的 PyTorch 模型
model_name: 模型名 (classifier / normal / math / threed)
model_name: 模型名 (classifier / normal / math / threed_text / threed_rotate / threed_slider)
input_shape: 输入形状 (C, H, W)
onnx_dir: 输出目录 (默认使用 config.ONNX_DIR)
"""
@@ -50,7 +52,7 @@ def export_model(
dummy = torch.randn(1, *input_shape)
# 分类器和识别器的 dynamic_axes 不同
if model_name == "classifier":
if model_name == "classifier" or model_name in ("threed_rotate", "threed_slider"):
dynamic_axes = {"input": {0: "batch"}, "output": {0: "batch"}}
else:
# CTC 模型: output shape = (T, B, C)
@@ -78,7 +80,8 @@ def _load_and_export(model_name: str):
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', '?')}")
acc_info = ckpt.get('best_acc') or ckpt.get('best_tol_acc', '?')
print(f"[加载] {model_name}: epoch={ckpt.get('epoch', '?')} acc={acc_info}")
if model_name == "classifier":
model = CaptchaClassifier(num_types=NUM_CAPTCHA_TYPES)
@@ -94,11 +97,19 @@ def _load_and_export(model_name: str):
h, w = IMAGE_SIZE["math"]
model = LiteCRNN(chars=chars, img_h=h, img_w=w)
input_shape = (1, h, w)
elif model_name == "threed":
elif model_name == "threed_text":
chars = ckpt.get("chars", THREED_CHARS)
h, w = IMAGE_SIZE["3d"]
h, w = IMAGE_SIZE["3d_text"]
model = ThreeDCNN(chars=chars, img_h=h, img_w=w)
input_shape = (1, h, w)
elif model_name == "threed_rotate":
h, w = IMAGE_SIZE["3d_rotate"]
model = RegressionCNN(img_h=h, img_w=w)
input_shape = (1, h, w)
elif model_name == "threed_slider":
h, w = IMAGE_SIZE["3d_slider"]
model = RegressionCNN(img_h=h, img_w=w)
input_shape = (1, h, w)
else:
print(f"[错误] 未知模型: {model_name}")
return
@@ -108,11 +119,11 @@ def _load_and_export(model_name: str):
def export_all():
"""依次导出 classifier, normal, math, threed 四个模型。"""
"""依次导出 classifier, normal, math, threed_text, threed_rotate, threed_slider 六个模型。"""
print("=" * 50)
print("导出全部 ONNX 模型")
print("=" * 50)
for name in ["classifier", "normal", "math", "threed"]:
for name in ["classifier", "normal", "math", "threed_text", "threed_rotate", "threed_slider"]:
_load_and_export(name)
print("\n全部导出完成。")

54
inference/pipeline.py
View File

@@ -4,7 +4,7 @@
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
输入图片 → 预处理 → 调度分类器 → 路由到专家模型 → CTC 解码 / 回归缩放 → 后处理 → 输出
对算式类型，解码后还会调用 math_eval 计算结果。
"""
@@ -23,6 +23,7 @@ from config import (
NORMAL_CHARS,
MATH_CHARS,
THREED_CHARS,
REGRESSION_RANGE,
)
from inference.math_eval import eval_captcha_math
@@ -59,19 +60,24 @@ class CaptchaPipeline:
self.mean = INFERENCE_CONFIG["normalize_mean"]
self.std = INFERENCE_CONFIG["normalize_std"]
# 字符集映射
# 字符集映射 (仅 CTC 模型需要)
self._chars = {
"normal": NORMAL_CHARS,
"math": MATH_CHARS,
"3d": THREED_CHARS,
"3d_text": THREED_CHARS,
}
# 回归模型类型
self._regression_types = {"3d_rotate", "3d_slider"}
# 专家模型名 → ONNX 文件名
self._model_files = {
"classifier": "classifier.onnx",
"normal": "normal.onnx",
"math": "math.onnx",
"3d": "threed.onnx",
"3d_text": "threed_text.onnx",
"3d_rotate": "threed_rotate.onnx",
"3d_slider": "threed_slider.onnx",
}
# 加载所有可用模型
@@ -116,7 +122,7 @@ class CaptchaPipeline:
def classify(self, image: Image.Image) -> str:
"""
调度分类，返回类型名: 'normal' / 'math' / '3d'。
调度分类，返回类型名。
Raises:
RuntimeError: 分类器模型未加载
@@ -141,7 +147,7 @@ class CaptchaPipeline:
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')
captcha_type: 指定类型可跳过分类 ('normal'/'math'/'3d_text'/'3d_rotate'/'3d_slider')
Returns:
dict: {
@@ -166,24 +172,34 @@ class CaptchaPipeline:
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"
size_key = captcha_type
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
# 4. 分支: CTC 解码 vs 回归
if captcha_type in self._regression_types:
# 回归模型: 输出 (batch, 1) sigmoid 值
output = session.run(None, {input_name: inp})[0] # (1, 1)
sigmoid_val = float(output[0, 0])
lo, hi = REGRESSION_RANGE[captcha_type]
real_val = sigmoid_val * (hi - lo) + lo
raw_text = f"{real_val:.1f}"
result = str(int(round(real_val)))
else:
result = raw_text
# CTC 模型
logits = session.run(None, {input_name: inp})[0] # (T, 1, C)
chars = self._chars[captcha_type]
raw_text = self._ctc_greedy_decode(logits, chars)
# 后处理
if captcha_type == "math":
try:
result = eval_captcha_math(raw_text)
except ValueError:
result = raw_text
else:
result = raw_text
elapsed = (time.perf_counter() - t0) * 1000