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 = 64
COLS = 64
CHANNELS = 3

train_images = [TRAIN_DIR+i for i in os.listdir(TRAIN_DIR)] # use this for full dataset
train_dogs =   [TRAIN_DIR+i for i in os.listdir(TRAIN_DIR) if 'dog' in i]
train_cats =   [TRAIN_DIR+i for i in os.listdir(TRAIN_DIR) if 'cat' in i]

test_images =  [TEST_DIR+i for i in os.listdir(TEST_DIR)]


# slice datasets for memory efficiency on Kaggle Kernels, delete if using full dataset
train_images = train_dogs[:1000] + train_cats[:1000]
random.shuffle(train_images)
test_images =  test_images[:25]

def read_image(file_path):
    img = cv2.imread(file_path, cv2.IMREAD_COLOR) #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)

sns.countplot(labels)
sns.plt.title('Cats and Dogs')





def show_cats_and_dogs(idx):
    cat = read_image(train_cats[idx])
    dog = read_image(train_dogs[idx])
    pair = np.concatenate((cat, dog), axis=1)
    plt.figure(figsize=(10,5))
    plt.imshow(pair)
    plt.show()
    
for idx in range(0,5):
    show_cats_and_dogs(idx)
    
    
    
    
    
    
dog_avg = np.array([dog[0].T for i, dog in enumerate(train) if labels[i]==1]).mean(axis=0)
plt.imshow(dog_avg)
plt.title('Your Average Dog')