import numpy as np
import pandas as pd
import cv2
from random import shuffle
from tqdm import tqdm
import os

TRAIN_DIR = 'C:\\Users\\SID\\Desktop\\Python Codes\\DogVCat\\DogVCat_train'
TEST_DIR = 'C:\\Users\\SID\\Desktop\\Python Codes\\DogVCat\\DogVCat_test'
IMG_SIZE = 100
LR = 1e-3
MODEL_NAME = "dogsvscats-{}-{}.modelfinal".format(LR, '6conv-basic')

def label_image(img):
    world_label = img.split('.')[0]
    if world_label == "dog": return [1, 0]
    elif world_label == 'cat': return [0, 1]

def create_training_data():
    training_data = []
    for img in tqdm(os.listdir(TRAIN_DIR)):
        label = label_image(img)
        path = os.path.join(TRAIN_DIR, img)
        img = cv2.resize(cv2.imread(path, cv2.IMREAD_GRAYSCALE), (IMG_SIZE, IMG_SIZE))
        training_data.append([np.array(img), np.array(label)])
    shuffle(training_data)
    np.save("training_data.npy", training_data)
    return training_data

def create_test_data():
    testing_data = []
    for img in tqdm(os.listdir(TEST_DIR)):
        path = os.path.join(TEST_DIR, img)
        #img_num = img.split('.')[0]
        label = label_image(img)
        img = cv2.resize(cv2.imread(path, cv2.IMREAD_GRAYSCALE), (IMG_SIZE, IMG_SIZE))
        testing_data.append([np.array(img), np.array(label)])
    np.save("testing_data.npy", testing_data)
    return testing_data

train_data = create_training_data()
#train_data = np.load('C:\\Users\\SID\\Desktop\\Python Codes\\training_data.npy')
test_data = create_test_data()
import tensorflow as tf
tf.reset_default_graph()
import tflearn
from tflearn.layers.conv import conv_2d, max_pool_2d
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.estimator import regression

convnet = input_data(shape=[None, IMG_SIZE, IMG_SIZE, 1], name='input')

convnet = conv_2d(convnet, 32, 5, activation='relu')
convnet = max_pool_2d(convnet, 5)

convnet = conv_2d(convnet, 64, 5, activation='relu')
convnet = max_pool_2d(convnet, 5)

convnet = conv_2d(convnet, 128, 5, activation='relu')
convnet = max_pool_2d(convnet, 5)

convnet = conv_2d(convnet, 64, 5, activation='relu')
convnet = max_pool_2d(convnet, 5)

convnet = conv_2d(convnet, 32, 5, activation='relu')
convnet = max_pool_2d(convnet, 5)

convnet = fully_connected(convnet, 1024, activation='relu')
convnet = dropout(convnet, 0.8)

convnet = fully_connected(convnet, 2, activation='softmax')
convnet = regression(convnet, optimizer='adam', learning_rate=LR, loss='categorical_crossentropy', name='targets')

model = tflearn.DNN(convnet, tensorboard_dir='log')

if os.path.exists('{}.meta'.format(MODEL_NAME)):
    model.load(MODEL_NAME)
    print('model loaded!')

train = train_data[:-500]
test = train_data[-500:]

X = np.array([i[0] for i in train]).reshape(-1,IMG_SIZE,IMG_SIZE,1)
Y = [i[1] for i in train]

test_x = np.array([i[0] for i in test]).reshape(-1,IMG_SIZE,IMG_SIZE,1)
test_y = [i[1] for i in test]

model.fit({'input': X}, {'targets': Y}, n_epoch=10, validation_set=({'input': test_x}, {'targets': test_y}),
    snapshot_step=500, show_metric=True, run_id=MODEL_NAME)
model.save(MODEL_NAME)

import matplotlib.pyplot as plt
fig=plt.figure()
#test_data = np.load('C:\\Users\\SID\\Desktop\\Python Codes\\testing_data.npy')
for num,data in enumerate(test_data[:12]):
    img_num = data[1]
    img_data = data[0]

    y = fig.add_subplot(3,4,num+1)
    orig = img_data
    data = img_data.reshape(IMG_SIZE,IMG_SIZE,1)
    model_out = model.predict([data])[0]

    if np.argmax(model_out) == 1: str_label='Dog'
    else: str_label='Cat'

    y.imshow(orig,cmap='gray')
    plt.title(str_label)
    y.axes.get_xaxis().set_visible(False)
    y.axes.get_yaxis().set_visible(False)
plt.show()
