import numpy as np 
import pandas as pd 

from subprocess import check_output

import os
import cv2 
import 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

#find the directories for train and test
TRAIN_DIR = '../input/train/'
TEST_DIR = '../input/test/'

#Will not work for anything above 32x32
ROWS = 32
COLS = 32
CHANNELS = 1

#get images from both train and tests
train_images = [TRAIN_DIR+i for i in os.listdir(TRAIN_DIR)] 
test_images =  [TEST_DIR+i for i in os.listdir(TEST_DIR)]

#prep images and data
def read_image(file_path):
    img = cv2.imread(file_path, cv2.IMREAD_GRAYSCALE) #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%250 == 0: print('Processed {} of {}'.format(i, count))
    
    return data

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

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]

#Create a sequential model
model = Sequential()
model.add(Convolution2D(16, 3, 3, border_mode='same', input_shape=(ROWS, COLS, CHANNELS), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Convolution2D(32, 3, 3, border_mode='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(100, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(100, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('sigmoid'))
                                 
model.compile(loss='binary_crossentropy', optimizer='adam',metrics=['accuracy'])

model.fit(X_train, Y_train,
          batch_size=128, nb_epoch=8,
          #show_accuracy=True, 
          verbose=1,
          validation_data=(X_valid, Y_valid))
          
#Setup a submission for a prediction set
CvD_submission = model.predict_proba(X_test, verbose=1)
test_id = range(1,12501)
predict = pd.DataFrame({'id': test_id, 'label': CvD_submission[:,0]})

#create a CSV
predict.to_csv("CvD_submission.csv", index=False)