Image classifier using CNN

# Python program for creating
# Image classifier using CNN

 
# Import required libraries

import cv2

import os

import numpy as np

from random import shuffle

from tqdm import tqdm

 
& # 39; & # 39; & # 39; Setting env & # 39; & # 39; & # 39;

TRAIN_DIR = ’E: / dataset / Cats_vs_Dogs / train’

TEST_DIR = ’ E: / dataset / Cats_vs_Dogs / test1’

IMG_SIZE = 50

LR = 1e - 3

 
& # 39; & # 39; & # 39; Model setup to help with tensorflow models & # 39; & # 39; & # 39;

MODEL_NAME = ’ dogsvscats - {} - {}. model’ . format (LR, ’ 6conv-basic’ )

 
& # 39; & # 39; & # 39; Dataset Marking & # 39; & # 39; & # 39;

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 ]

 
& # 39; & # 39; & # 39; Create training data & # 39; & # 39; & # 39;

def create_train_data ():

# Create an empty list where we should store training data

# after a little preprocessing of the data

training_data = []

# tqdm is used for interactive download only

# load training data

  for img in tqdm (os.listdir (TRAIN_DIR)):

# labeling images

  label = label_img (img)

path = os.path.join ( (TRAIN_DIR, img)

# load images from path and then convert them to

# grayscale for easier covnet search

img = cv2.imread (path, cv2.IMREAD_GRAYSCALE)

# resize the image for processing them in covnet

img = cv2.resize (img, (IMG_SIZE, IMG_SIZE))

# the last step is to generate a training data list with an array of images

training_data.append ([np.array (img), np.array (label)])

# shuffle the training data to keep our random state data

shuffle (training_data)

  # save our trained data for later use as needed

np.save ( ’train_data.npy’ , training_data)

return training_data

 
& # 39; & # 39; & # 39; Data processing of this test & # 39; & # 39; & # 39;
# Almost the same as processing training data, but
# we shouldn’t label this.

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 ]

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’ )

 
# Separate test and training data

train = train_data [: - 500 ]

test = train_data [ - 500 :]

 
& # 39; & # 39; & # 39; Customizing Features & Tags & # 39; & # 39; & # 39;
# X-Features & amp; Y -Labels

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]

  
& # 39; & # 39; & # 39; Fitting data into our model & # 39; & # 39; & # 39;
# era = 5 accepted

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)

 
& # 39; & # 39; & # 39; Data Testing & # 39; & # 39; & # 39;

import matplotlib.pyplot as plt

# if you need to create 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 ()