import numpy as np 
import pandas as pd 

from subprocess import check_output

import os, cv2, random
import numpy as np
import pandas as pd

from keras.models import Sequential
from keras.layers import Input, Dropout, Flatten, Convolution2D, MaxPooling2D, Dense, Activation
from keras.optimizers import RMSprop
from keras.callbacks import ModelCheckpoint, Callback, EarlyStopping
from keras.utils import np_utils

TRAIN_DIR = '../input/train/'
TEST_DIR  = '../input/test/'

ROWS = 32
COLS = 32
CHANNELS = 1

train_images = [TRAIN_DIR + i for i in os.listdir(TRAIN_DIR)] # use this for full dataset
test_images =  [TEST_DIR + i for i in os.listdir(TEST_DIR)]

def read_image(file_path):
    img = cv2.imread(file_path, cv2.IMREAD_GRAYSCALE)
    return cv2.resize(img, (ROWS, COLS), interpolation=cv2.INTER_CUBIC)


def prep_data(images):
    count = len(images)
    data = np.ndarray((count, CHANNELS, ROWS, COLS), dtype=np.uint8)

    for i, image_file in enumerate(images):
        image = read_image(image_file)
        data[i] = image.T
        if i % 2500 == 0: print('Processed {} of {}'.format(i, count))
    
    return data

train = prep_data(train_images)
test = prep_data(test_images)

print("Train shape: {}".format(train.shape))
print("Test shape: {}".format(test.shape))

labels = []

for i in train_images:
    if 'dog' in i:
        labels.append(1)
    else:
        labels.append(0)

train = train.reshape(-1, 32,32,1)
test = test.reshape(-1, 32,32,1)

X_train = train.astype('float32')
X_test = test.astype('float32')

X_train /= 255
X_test /= 255
Y_train = labels

X_valid = X_train[:5000, :, :, :]
Y_valid =   Y_train[:5000]
X_train = X_train[5001:25000, :, :, :]
Y_train  = Y_train[5001:25000]

print("Training matrix shape", X_train.shape)
print("Testing matrix shape", X_test.shape)

def CatDog():
    
    #Neural network model object
    model = Sequential()
    
    #First convolution
    model.add(Convolution2D(
        16, 3, 3,
        border_mode = 'same',
        input_shape = (ROWS, COLS, CHANNELS),
        activation = 'relu'
    ))
    
    #First dimensionality reduction
    model.add(
        MaxPooling2D( pool_size = (2, 2) )
    )
    
    #Second convolution
    model.add(Convolution2D(
        32, 3, 3,
        border_mode = 'same',
        activation = 'relu'
    ))
    
    #Second dimensionality reduction
    model.add(
        MaxPooling2D( pool_size = (2, 2) )
    )
    
    #Dimensionality reduction to single array
    model.add(Flatten())
    
    #Dense layers - linear model on flattened array
    model.add(Dense(100, activation = 'relu'))
    
    #Prevent overfitting by randomly setting some model coeffecients to zero
    model.add(Dropout(0.5))
    
    #More dense layers
    model.add(Dense(100, activation = 'relu'))
    
    #More overfitting prevention
    model.add(Dropout(0.5))
    
    #Last dense layer
    model.add(Dense(1))
    
    #Add output to model
    model.add(Activation('sigmoid'))
    
    #Compile model and return
    model.compile(
        loss = 'binary_crossentropy',
        optimizer = 'adam',
        metrics=['accuracy']
    )
    
    return model

model = CatDog()

model.fit(
    X_train,
    Y_train,
    batch_size = 128,
    nb_epoch = 8,
    verbose = 1,
    validation_data = (X_valid, Y_valid)
)

submission = model.predict_proba(X_test, verbose = 1)

test_id = range(1, 12501)

predictions_df = pd.DataFrame({
    'id': test_id,
    'label': submission[:, 0]
})

predictions_df.to_csv("submission.csv", index = False)