# Python program to create
# Image Classifier using CNN
# Importing the required libraries
import cv2
import os
import numpy as np
from random import shuffle
from tqdm import tqdm
'''Setting up the env'''
TRAIN_DIR = 'E:/dataset / Cats_vs_Dogs / train'
TEST_DIR = 'E:/dataset / Cats_vs_Dogs / test1'
IMG_SIZE = 50
LR = 1e-3
'''Setting up the model which will help with tensorflow models'''
MODEL_NAME = 'dogsvscats-{}-{}.model'.format(LR, '6conv-basic')
'''Labelling the dataset'''
def label_img(img):
word_label = img.split('.')[-3]
# DIY One hot encoder
if word_label == 'cat': return [1, 0]
elif word_label == 'dog': return [0, 1]
'''Creating the training data'''
def create_train_data():
# Creating an empty list where we should store the training data
# after a little preprocessing of the data
training_data = []
# tqdm is only used for interactive loading
# loading the training data
for img in tqdm(os.listdir(TRAIN_DIR)):
# labeling the images
label = label_img(img)
path = os.path.join(TRAIN_DIR, img)
# loading the image from the path and then converting them into
# grayscale for easier covnet prob
img = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
# resizing the image for processing them in the covnet
img = cv2.resize(img, (IMG_SIZE, IMG_SIZE))
# final step-forming the training data list with numpy array of the images
training_data.append([np.array(img), np.array(label)])
# shuffling of the training data to preserve the random state of our data
shuffle(training_data)
# saving our trained data for further uses if required
np.save('train_data.npy', training_data)
return training_data
'''Processing the given test data'''
# Almost same as processing the training data but
# we dont have to label it.
def process_test_data():
testing_data = []
for img in tqdm(os.listdir(TEST_DIR)):
path = os.path.join(TEST_DIR, img)
img_num = img.split('.')[0]
img = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
img = cv2.resize(img, (IMG_SIZE, IMG_SIZE))
testing_data.append([np.array(img), img_num])
shuffle(testing_data)
np.save('test_data.npy', testing_data)
return testing_data
'''Running the training and the testing in the dataset for our model'''
train_data = create_train_data()
test_data = process_test_data()
# train_data = np.load('train_data.npy')
# test_data = np.load('test_data.npy')
'''Creating the neural network using tensorflow'''
# Importing the required libraries
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
import tensorflow as tf
tf.compat.v1.reset_default_graph()
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')
# Splitting the testing data and training data
train = train_data[:-500]
test = train_data[-500:]
'''Setting up the features and labels'''
# X-Features & Y-Labels
X = np.array([i[0] for i in train]).reshape(-1, IMG_SIZE, IMG_SIZE, 1)
Y = np.array([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 = np.array([i[1] for i in test])
'''Fitting the data into our model'''
# epoch = 5 taken
model.fit({'input': X}, {'targets': Y}, n_epoch = 5,
validation_set =({'input': test_x}, {'targets': test_y}),
snapshot_step = 500, show_metric = True, run_id = MODEL_NAME)
model.save(MODEL_NAME)
'''Testing the data'''
import matplotlib.pyplot as plt
# if you need to create the data:
# test_data = process_test_data()
# if you already have some saved:
test_data = np.load('test_data.npy')
fig = plt.figure()
for num, data in enumerate(test_data[:20]):
# cat: [1, 0]
# dog: [0, 1]
img_num = data[1]
img_data = data[0]
y = fig.add_subplot(4, 5, num + 1)
orig = img_data
data = img_data.reshape(IMG_SIZE, IMG_SIZE, 1)
# model_out = model.predict([data])[0]
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()