Tukukkk

I am using pre-trained VGG16 model to classify images located in the folder. Currently, I am able to classify only one single image.

1. How can I modify the code to classify all the images in the folder


2. How can I save the prediction values for each image ?

Below is my code :

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



filename = 'cat.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 / images into batch format

# expand_dims will add an extra dimension to the data at a particular axis

# We want the input matrix to the network to be of the form (batchsize, height, width, channels)

# Thus we add the extra dimension to the axis 0.

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)

Thank you

You just have to list filenames and loop over them. You could use i.e. os.listdir, specifying the input folder. I removed the various imshow or plot chunks of code.

Code below:

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

import os

nfiles = os.listdir("./inputfolder") # List filenames



for filename in nfiles: # Enter the loop

    # load an image in PIL format

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

    # 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)

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



    # Convert the image / images into batch format

    # expand_dims will add an extra dimension to the data at a particular 

axis

    # We want the input matrix to the network to be of the form (batchsize, 

height, width, channels)

    # Thus we add the extra dimension to the axis 0.

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

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



    # 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)