Rotate-Captcha-Crack

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Predict the rotation angle of given picture through CNN. This project can be used for rotate-captcha cracking.

Test result:

Three kinds of model are implemented, as shown in the table below.

Name	Backbone	Cross-Domain Loss (less is better)	Params	FLOPs
RotNet	ResNet50	71.7920°	24.246M	4.132G
RotNetR	RegNetY 3.2GFLOPs	19.1594°	18.468M	3.223G
RCCNet_v0_5	RegNetY 3.2GFLOPs	42.7774°	17.923M	3.223G

RotNet is the implementation of d4nst/RotNet over PyTorch. RotNetR is based on RotNet, with RegNet as its backbone and class number of 180. The average prediction error is 19.1594°, obtained by 64 epochs of training (2 hours) on the Google Street View dataset.

The Cross-Domain Test uses Google Street View and Landscape-Dataset for training, and Captcha Pictures from Baidu (thanks to @xiangbei1997) for testing.

The captcha picture used in the demo above comes from RotateCaptchaBreak

Try it!

Prepare

• Device supporting CUDA10+ (mem>=4G for training)

• Python>=3.8,<3.12

• PyTorch>=1.11

• Clone the repository and install all requiring dependencies

git clone --depth=1 https://github.com/Starry-OvO/rotate-captcha-crack.git
cd ./rotate-captcha-crack
pip install .

DO NOT miss the . after install

• Or, if you prefer venv

git clone --depth=1 https://github.com/Starry-OvO/rotate-captcha-crack.git
python -m venv ./rotate-captcha-crack --system-site-packages
cd ./rotate-captcha-crack
# Choose the proper script to acivate venv according to your shell type. e.g. `./Script/active*`
python -m pip install -U pip
pip install .

Download the Pretrained Models

Download the *.zip files in Release and unzip them all to the ./models dir.

The directory structure will be like ./models/RCCNet_v0_5/230228_20_07_25_000/best.pth

The names of models will change frequently as the project is still in beta status. So, if any FileNotFoundError occurs, please try to rollback to the corresponding tag first.

Test the Rotation Effect by a Single Captcha Picture

If no GUI is presented, try to change the debugging behavior from showing images to saving them.

python test_captcha.py

Use HTTP Server

• Install extra dependencies

pip install aiohttp httpx[cli]

• Launch server

python server.py

• Another Shell to Send Images

 httpx -m POST http://127.0.0.1:4396 -f img ./test.jpg

Train Your Own Model

Prepare Datasets

• For this project I'm using Google Street View and Landscape-Dataset for training. You can collect some photos and leave them in one directory. Without any size or shape requirement.

• Modify the dataset_root variable in train.py, let it points to the directory containing images.

• No manual labeling is required. All the cropping, rotation and resizing will be done soon after the image is loaded.

Train

python train_RotNetR.py

Validate the Model on Test Set

python test_RotNetR.py

Details of Design

Most of the rotate-captcha cracking methods are based on d4nst/RotNet, with ResNet50 as its backbone. RotNet treat the angle prediction as a classification task with 360 classes, then use CrossEntropy to compute the loss.

Yet CrossEntropy will bring a significant metric distance of about $358°$ between $1°$ and $359°$, clearly defies common sense, it should be a small value like $2°$. Meanwhile, the angle_error_regression proposed by d4nst/RotNet is less effective. That's because when dealing with outliers, the gradient leads to a non-convergence result. You can easily understand this through the subsequent comparison between loss functions.

My regression loss function RotationLoss is based on MSELoss, with an extra cosine-correction to decrease the metric distance between $±k*360°$.

$$ \mathcal{L}(dist) = {dist}^{2} + \lambda_{cos} (1 - \cos(2\pi*{dist})) $$

Why MSELoss here? Because the label generated by self-supervised method is guaranteed not to contain any outliers. So our design does not need to consider the outliers. Also, MSELoss won't break the derivability of loss function.

The loss function is derivable and almost convex over the entire $\mathbb{R}$. Why say almost? Because there will be local minimum at $predict = \pm 1$ when $\lambda_{cos} \gt 0.25$.

Finally, let's take a look at the figure of two loss functions: