## using Keras pretrained models

import keras

import numpy as np

from keras.applications import vgg16, inception_v3, resnet50, mobilenet

def load_model(model_name):

return model

#%%

from keras.preprocessing.image import load_img

from keras.preprocessing.image import img_to_array

from keras.applications.imagenet_utils import decode_predictions

import matplotlib.pyplot as plt

#matplotlib inline

filename = 'D:/AG/Keras/Lamborghini.jpg'

# load an image in PIL format

original = load_img(filename, target_size=(224, 224))

print('PIL image size',original.size)

plt.imshow(original)

plt.show()

# convert the PIL image to a numpy array

# IN PIL - image is in (width, height, channel)

# In Numpy - image is in (height, width, channel)

numpy_image = img_to_array(original)

plt.imshow(np.uint8(numpy_image))

plt.show()

print('numpy array size',numpy_image.shape)

# Convert the image into batch format

image_batch = np.expand_dims(numpy_image, axis=0)

print('image batch size', image_batch.shape)

plt.imshow(np.uint8(image_batch[0]))

#%%

# prepare the image for the VGG model

processed_image = vgg16.preprocess_input(image_batch.copy())

# get the predicted probabilities for each class

predictions = vgg_model.predict(processed_image)

#print(predictions)

# convert the probabilities to class labels

# We will get top 5 predictions which is the default

label = decode_predictions(predictions)

print(label)

#%%

# prepare the image for the Resnet model

processed_image = resnet50.preprocess_input(image_batch.copy())

# get the predicted probabilities for each class

predictions = resnet_model.predict(processed_image)

#print(predictions)

# convert the probabilities to class labels

# We will get top 5 predictions which is the default

label = decode_predictions(predictions)

print(label)

#%%

# prepare the image for the Resnet model

processed_image = mobilenet.preprocess_input(image_batch.copy())

# get the predicted probabilities for each class

predictions = mobilenet_model.predict(processed_image)

#print(predictions)

# convert the probabilities to class labels

# We will get top 5 predictions which is the default

label = decode_predictions(predictions)

print(label)