LETTER_IMAGES_FOLDER = "datasets"
MODEL_FILENAME = "fonts_model.hdf5"
MODEL_LABELS_FILENAME = "model_labels.dat"


data = pd.read_csv('annotations.csv')

paths = list(data['Path'].values)
Y = list(data['Font'].values)

encoder = LabelEncoder()
encoder.fit(Y)
Y = encoder.transform(Y)
Y = np_utils.to_categorical(Y)

data = []

# loop over the input images
for image_file in paths:
    # Load the image and convert it to grayscale
    image = cv2.imread(image_file)
    image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

    # Add a third channel dimension to the image to make Keras happy
    image = np.expand_dims(image, axis=2)

    # Add the letter image and it's label to our training data
    data.append(image)

data = np.array(data, dtype="float") / 255.0

train_x, test_x, train_y, test_y = model_selection.train_test_split(data,Y,test_size = 0.1, random_state = 0)


# Save the mapping from labels to one-hot encodings.
# We'll need this later when we use the model to decode what it's predictions mean
with open(MODEL_LABELS_FILENAME, "wb") as f:
    pickle.dump(encoder, f)

# Build the neural network!
model = Sequential()

# First convolutional layer with max pooling
model.add(Conv2D(20, (5, 5), padding="same", input_shape=(100, 100, 1), activation="relu"))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))

# Second convolutional layer with max pooling
model.add(Conv2D(50, (5, 5), padding="same", activation="relu"))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
model.add(Flatten())
model.add(Dense(500, activation="relu"))
print (len(encoder.classes_))
model.add(Dense(len(encoder.classes_), activation="softmax"))

# Ask Keras to build the TensorFlow model behind the scenes
model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])

# Train the neural network
model.fit(train_x, train_y, validation_data=(test_x, test_y), batch_size=32, epochs=2, verbose=1)

# Save the trained model to disk
model.save(MODEL_FILENAME)

predictions = model.predict(letter_image)
print (predictions) # this has the length of 1