Keras predicting on all images in a directory

folder_predict.py

from keras.models import load_model
from keras.preprocessing import image
import numpy as np
import os

# image folder
folder_path = '/path/to/folder/'
# path to model
model_path = '/path/to/saved/model.h5'
# dimensions of images
img_width, img_height = 320, 240

# load the trained model
model = load_model(model_path)
model.compile(loss='binary_crossentropy',
              optimizer='rmsprop',
              metrics=['accuracy'])

# load all images into a list
images = []
for img in os.listdir(folder_path):
    img = os.path.join(folder_path, img)
    img = image.load_img(img, target_size=(img_width, img_height))
    img = image.img_to_array(img)
    img = np.expand_dims(img, axis=0)
    images.append(img)

# stack up images list to pass for prediction
images = np.vstack(images)
classes = model.predict_classes(images, batch_size=10)
print(classes)

Hello,
I have a similar problem like @BhagyasriYella, only that my classifier uses rescaled images. I solved this in the code via

    img /= 255.
    classes = model.predict_classes(img, batch_size=10)
    img *= 255.

However, with the rescaling and without, I do get my original images (.jpg) classified correctly (as seen on the names) but somehow I cannot open them and they are 0KB. Any ideas on why that is?

Hello,
can you please help me with my issue:
I want to show the results on a table showing the correct and incorrect predictions but after several tries I have not been able to implement the prettytable correctly in your code.
Can you please help me!
Thank you in advance

Hello
can you please help me !
if I want to save the predicted images into a folder ,how should I do it!

It worked Thanks..

Thank you for the code! I just had to substitute img_to_array(img) to np.asarray(img), then it worked.

I am getting following error while running above code
AttributeError: 'Model' object has no attribute 'predict_classes'

Hello, please help. I am getting this error:

cannot identify image file <_io.BytesIO object at 0x7fbe54eb8090>