experiments
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main.py
123
main.py
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@ -4,33 +4,42 @@ from dotenv import load_dotenv
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from base64 import b64decode
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import re
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import requests
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import tf2onnx
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import cv2
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import keras
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import numpy as np
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from keras.callbacks import EarlyStopping, ModelCheckpoint
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load_dotenv()
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DOWNLOAD_PATH=environ.get("DOWNLOAD_PATH")
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TESTING_PATH=environ.get("TESTING_PATH")
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TRAINING_PATH=environ.get("TRAINING_PATH")
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# Constants
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IMAGE_HEIGHT = 70
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IMAGE_WIDTH = 200
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DOWNLOAD_PATH = environ.get("DOWNLOAD_PATH")
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TESTING_PATH = environ.get("TESTING_PATH")
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TRAINING_PATH = environ.get("TRAINING_PATH")
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PERCENT_OF_TESTING = int(environ.get("PERCENT_OF_TESTING"))
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def prepare_dirs():
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"""Create necessary directories for downloading and storing images."""
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makedirs(DOWNLOAD_PATH, exist_ok=True)
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makedirs(TESTING_PATH, exist_ok=True)
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makedirs(TRAINING_PATH, exist_ok=True)
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def fetch_captcha(id):
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# print(f"Fetching captcha with id {id}")
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captcha = requests.get(f"{environ.get('CAPTCHA_AGGREGATOR_API')}/captcha/{id}").json()["captcha"]
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with open(f"{DOWNLOAD_PATH}/{captcha['hash']}_{captcha['solution']}.jpeg", 'wb') as captcha_file:
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captcha_file.write(b64decode(captcha['image']))
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"""Fetch a captcha image by its ID and save it to the download path."""
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try:
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response = requests.get(f"{environ.get('CAPTCHA_AGGREGATOR_API')}/captcha/{id}")
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response.raise_for_status()
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captcha = response.json()["captcha"]
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captcha_file_path = path.join(DOWNLOAD_PATH, f"{captcha['hash']}_{captcha['solution']}.jpeg")
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with open(captcha_file_path, 'wb') as captcha_file:
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captcha_file.write(b64decode(captcha['image']))
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except requests.RequestException as e:
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print(f"Error fetching captcha {id}: {e}")
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def search_saved_captcha(hash, path):
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# print(f"searching captcha with hash {hash} in {path}")
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regex = re.compile(hash + '_\\w{6}\\.jpeg')
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"""Check if a captcha with the given hash exists in the specified path."""
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regex = re.compile(f"{hash}_\\w{{6}}\\.jpeg")
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for _, _, files in walk(path):
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for file in files:
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if regex.match(file):
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@ -38,41 +47,45 @@ def search_saved_captcha(hash, path):
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return False
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def search_and_download_new(captchas):
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# print(f"Searching and downloading new captchas")
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"""Search for new captchas and download them if they don't already exist."""
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for captcha in captchas:
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id = captcha["id"]
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hash = captcha["hash"]
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training_exists = search_saved_captcha(hash, TRAINING_PATH)
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testing_exists = search_saved_captcha(hash, TESTING_PATH)
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new_exists = search_saved_captcha(hash, DOWNLOAD_PATH)
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if not training_exists and not testing_exists and not new_exists:
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if not (search_saved_captcha(hash, TRAINING_PATH) or
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search_saved_captcha(hash, TESTING_PATH) or
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search_saved_captcha(hash, DOWNLOAD_PATH)):
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fetch_captcha(id)
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def sort_datasets():
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# print(f"Sorting datasets")
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percent_of_testing = int(environ.get("PERCENT_OF_TESTING"))
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amount_of_new_data = len([file for file in listdir(DOWNLOAD_PATH) if path.isfile(f'{DOWNLOAD_PATH}/{file}')])
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amount_to_send_to_test = round(amount_of_new_data * (percent_of_testing / 100))
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for _, _, files in walk(DOWNLOAD_PATH):
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for index, file in enumerate(files):
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if index < amount_to_send_to_test:
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move(f"{DOWNLOAD_PATH}/{file}", TESTING_PATH)
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else:
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move(f"{DOWNLOAD_PATH}/{file}", TRAINING_PATH)
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"""Sort downloaded captchas into training and testing datasets."""
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amount_of_new_data = len([file for file in listdir(DOWNLOAD_PATH) if path.isfile(path.join(DOWNLOAD_PATH, file))])
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amount_to_send_to_test = round(amount_of_new_data * (PERCENT_OF_TESTING / 100))
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files = listdir(DOWNLOAD_PATH)
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for index, file in enumerate(files):
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if index < amount_to_send_to_test:
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move(path.join(DOWNLOAD_PATH, file), TESTING_PATH)
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else:
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move(path.join(DOWNLOAD_PATH, file), TRAINING_PATH)
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def download_dataset():
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"""Download the dataset of captchas and sort them into training and testing sets."""
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prepare_dirs()
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captchas = requests.get(f"{environ.get('CAPTCHA_AGGREGATOR_API')}/captcha/all").json()["captchas"]
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search_and_download_new(captchas)
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sort_datasets()
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try:
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response = requests.get(f"{environ.get('CAPTCHA_AGGREGATOR_API')}/captcha/all")
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response.raise_for_status()
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captchas = response.json()["captchas"]
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search_and_download_new(captchas)
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sort_datasets()
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except requests.RequestException as e:
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print(f"Error downloading dataset: {e}")
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def load_dataset(dataset_path):
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"""Load images and their corresponding solutions from the specified dataset path."""
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images = []
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solutions = []
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for filename in listdir(dataset_path):
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img = cv2.imread(f"{dataset_path}/{filename}")
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img = cv2.imread(path.join(dataset_path, filename))
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img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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img = img / 255.0
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images.append(img)
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@ -83,39 +96,59 @@ def load_dataset(dataset_path):
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solution_to_label = {solution: i for i, solution in enumerate(unique_solutions)}
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labels = [solution_to_label[solution] for solution in solutions]
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return images, labels, unique_solutions
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return np.array(images), np.array(labels), unique_solutions
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def load_training_dataset():
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"""Load the training dataset."""
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return load_dataset(TRAINING_PATH)
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def load_testing_dataset():
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"""Load the testing dataset."""
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return load_dataset(TESTING_PATH)
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def train_nn():
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"""Train the neural network on the training dataset."""
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training_images, training_labels, unique_solutions = load_training_dataset()
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if int(environ.get("PERCENT_OF_TESTING")) > 0:
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testing_images, testing_labels = (None, None)
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if PERCENT_OF_TESTING > 0:
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testing_images, testing_labels, _ = load_testing_dataset()
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model = keras.Sequential([
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keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(70, 200, 1)),
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keras.layers.Conv2D(128, (3, 3), activation='relu', input_shape=(IMAGE_HEIGHT, IMAGE_WIDTH, 1)),
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keras.layers.MaxPooling2D((2, 2)),
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keras.layers.Conv2D(64, (3, 3), activation='relu'),
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keras.layers.Conv2D(256, (3, 3), activation='relu'),
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keras.layers.MaxPooling2D((2, 2)),
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keras.layers.Conv2D(64, (3, 3), activation='relu'),
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keras.layers.Conv2D(256, (3, 3), activation='relu'),
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keras.layers.Flatten(),
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keras.layers.Dense(64, activation='relu'),
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keras.layers.Dense(len(unique_solutions), activation='softmax')
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keras.layers.Dense(128, activation='relu'),
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keras.layers.Dropout(0.5), # Dropout for regularization
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keras.layers.Dense(len(unique_solutions), activation='softmax') # Output layer
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])
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model.summary()
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model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
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if int(environ.get("PERCENT_OF_TESTING")) > 0:
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model.fit(np.array(training_images), np.array(training_labels), epochs=10, batch_size=128, validation_data=(np.array(testing_images), np.array(testing_labels)))
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callbacks = [
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EarlyStopping(monitor='accuracy', patience=3),
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ModelCheckpoint('best_model.keras', save_best_only=True)
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]
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EPOCHS = 100
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BATCH_SIZE = 8
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if PERCENT_OF_TESTING > 0:
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model.fit(np.array(training_images), np.array(training_labels),
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epochs=EPOCHS, batch_size=BATCH_SIZE, callbacks=callbacks,
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validation_data=(np.array(testing_images), np.array(testing_labels)),
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)
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else:
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model.fit(np.array(training_images), np.array(training_labels), epochs=10, batch_size=128)
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model.fit(np.array(training_images), np.array(training_labels),
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epochs=EPOCHS, batch_size=BATCH_SIZE, callbacks=callbacks
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)
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keras.saving.save_model(model, 'captcha_solver.keras')
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# model.save('model.h5')
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# tf2onnx.convert.from_keras(model, opset=13, output_path='model_onnx')
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if __name__ == "__main__":
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