iscat.py

# -*- coding: utf-8 -*-
"""isCat.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1FKoEyoMkIAxj5ihj30-oRoOfskXg7sfi
"""

import tensorflow as tf
import cv2
import numpy as np
print("tensorflow version: " + tf.__version__)

# pretrained model from imagenet
model = tf.keras.applications.MobileNetV2(weights='imagenet')

# store image path in src variable
src = 'cat.png'

# store image
image = cv2.imread(src)

#resize image
image = cv2.resize(image, (224,224))

# convert color of image
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

# expand dimensions of image array
image = np.expand_dims(image, axis=0)

#preprocess image
image = tf.keras.applications.mobilenet_v2.preprocess_input(image)

# get predictions from model
predict = model.predict(image)

#decode predictions, get 5
decode = tf.keras.applications.mobilenet_v2.decode_predictions(predict, top=5)

# iterate through first 5 predictions, check if any of them contain 'cat'
isCat = False
for tup in decode[0]:
  if 'cat' in tup[1]:
    isCat = True

# print message to user
if isCat:
  print('There is a cat in the image')
else:
  print('There is no cat in the image')