Python库 | CaptchaCracker-0.0.5.tar.gz

# CaptchaCracker   [](https://www.linkedin.com/in/wooil/) [한국어 문서](https://github.com/WooilJeong/CaptchaCracker/blob/main/README-ko.md) ## Introduction CaptchaCracker is an open source Python library that provides functions to create and apply deep learning models for Captcha Image recognition. You can create a deep learning model that recognizes numbers in the Captcha Image as shown below and outputs a string of numbers, or you can try the model yourself. ### Input  ### Output ``` 023062 ``` ## Installation ```bash pip install CaptchaCracker ``` ## Dependency ``` pip install numpy==1.19.5 tensorflow==2.5.0 ``` ## Examples ### Train and save the model Before executing model training, training data image files in which the actual value of the Captcha image is indicated in the file name should be prepared as shown below. - [Download Sample Dataset](https://github.com/WooilJeong/CaptchaCracker/raw/main/sample.zip)  ```python import glob import CaptchaCracker as cc # Training image data path train_img_path_list = glob.glob("../data/train_numbers_only/*.png") # Training image data size img_width = 200 img_height = 50 # Creating an instance that creates a model CM = cc.CreateModel(train_img_path_list, img_width, img_height) # Performing model training model = CM.train_model(epochs=100) # Saving the weights learned by the model to a file model.save_weights("../model/weights.h5") ``` ### Load a saved model to make predictions ```python import CaptchaCracker as cc # Training image data size img_width = 200 img_height = 50 # Training image label length max_length = 5 # Training image label component characters = {'0', '1', '2', '3', '4', '5', '6', '7', '8', '9'} # Model weight file path weights_path = "../model/weights.h5" # Creating a model application instance AM = cc.ApplyModel(weights_path, img_width, img_height, max_length, characters) # Target image path target_img_path = "../data/target.png" # Predicted value pred = AM.predict(target_img_path) print(pred) ``` ## References - https://keras.io/examples/vision/captcha_ocr/