sigrecogtf.py

import cv2
import os
import tensorflow as tf
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 = []
    training_labels = []
    for filename in os.listdir(training_folder):
        img = cv2.imread(os.path.join(training_folder, filename), 0)
        if img is not None:
            data = preprocessor.prepare(img)
            training_data.append(data)
            training_labels.append([0, 1] if "genuine" in filename else [1, 0])

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

    sgd(training_data, training_labels, test_data, test_labels)


# Softmax Regression Model
def regression(x):
    W = tf.Variable(tf.zeros([901, 2]), name="W")
    b = tf.Variable(tf.zeros([2]), name="b")
    y = tf.nn.softmax(tf.matmul(x, W) + b)
    return y, [W, b]


def sgd(training_data, training_labels, test_data, test_labels):
    # model
    with tf.variable_scope("regression"):
        x = tf.placeholder(tf.float32, [None, 901])
        y, variables = regression(x)

    # train
    y_ = tf.placeholder("float", [None, 2])
    cross_entropy = -tf.reduce_sum(y_ * tf.log(y))
    train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
    correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        sess.run(train_step, feed_dict={x: training_data, y_: training_labels})
        print(sess.run(accuracy, feed_dict={x: test_data, y_: test_labels}))

if __name__ == '__main__':
    main()