sigrecog.py

import cv2
import os
import numpy as np
import network
import preprocessor


def main():
    print('OpenCV version {} '.format(cv2.__version__))

    current_dir = os.path.dirname(__file__)

    author = '021'
    training_folder = os.path.join(current_dir, 'data/training/', author)
    test_folder = os.path.join(current_dir, 'data/test/', author)

    training_data = []
    for filename in os.listdir(training_folder):
        img = cv2.imread(os.path.join(training_folder, filename), 0)
        if img is not None:
            data = np.array(preprocessor.prepare(img))
            data = np.reshape(data, (901, 1))
            result = [[0], [1]] if "genuine" in filename else [[1], [0]]
            result = np.array(result)
            result = np.reshape(result, (2, 1))
            training_data.append((data, result))

    test_data = []
    for filename in os.listdir(test_folder):
        img = cv2.imread(os.path.join(test_folder, filename), 0)
        if img is not None:
            data = np.array(preprocessor.prepare(img))
            data = np.reshape(data, (901, 1))
            result = 1 if "genuine" in filename else 0
            test_data.append((data, result))

    net = network.NeuralNetwork([901, 500, 500, 2])
    net.sgd(training_data, 10, 50, 0.01, test_data)


if __name__ == '__main__':
    main()