import numpy as np
import pandas as pd
import keras

img_rows, img_cols = 30, 20
num_classes = 36

def data_prep(raw):
    out_y = raw.label.values.reshape(-1, 1)

    num_images = raw.shape[0]
    x_as_array = raw.values[:,1:]
    x_shaped_array = x_as_array.reshape(num_images, img_rows, img_cols, 1)
    out_x = x_shaped_array / 255
    return out_x[:,:,:,0], out_y

def load_data():

    df = pd.read_csv("data/Mercosul_56_30x20.csv")
    df = df.sample(frac=1, random_state=10)
    x, y = data_prep(df)

    train_samples = int(y.shape[0] * 0.75)
    x_train, x_test = x[:train_samples, :], x[train_samples:, :]
    y_train, y_test = y[:train_samples], y[train_samples:]
    return (x_train, y_train), (x_test, y_test)