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

import matplotlib.pyplot as plt
from matplotlib import ticker
import seaborn as sns


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) #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)

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]

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))
          
submission = model.predict_proba(X_test, verbose=1)
test_id = range(1,12501)
predictions = pd.DataFrame({'id': test_id, 'label': submission[:,0]})

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