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)

@tharindu326 Fixed, thanks!

why i got this error?
OSError: Unable to open file (unable to open file: name = '/model/model.h5', errno = 2, error message = 'No such file or directory', flags = 0, o_flags = 0)

The code could use
img = img/255 after line 24
or
images = images/255 after line 28

Hi,
I have a scenario where I have to predict images into Crack and Non-Crack. I have prepared the model for that and used your code to predict the images, but unable to save them into folders where images with Crack should be saved in "/Crack" folder and images with Non-Crack should be saved in "/Non-Crack" folder.
Can anyone please help me on how to save the predicted images.

@BhagyasriYella

i just copied the code i have used. this is the way to do it! plz rearrange saving formats as you want

model.compile(loss='binary_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])

###############change the code as follow ################

    img = image.load_img(filename, target_size=(img_width, img_height))
    img = image.img_to_array(img)
    img = np.expand_dims(img, axis=0)
    classes = model.predict_classes(img, batch_size=16)
    print(classes)
    if classes == 0:
        filename1 = result_negative_folder + "/image_" + str(int(frameId)) + ".jpg"  
        cv2.imwrite(filename1, img)
    elif classes == 1:
        filename2 = result_possitive_folder + "/image_" + str(int(frameId)) + ".jpg"
        cv2.imwrite(filename2, img)

use the directories as follow
result_possitive_folder = 'G:/Uni/7th sem/load_model_test01//Crack'
result_negative_folder = 'G:/Uni/7th sem/load_model_test01//non_Crack'

or if it is a folder inside your code folder (envi), "/crack" and "/non _crack" will enough
Note: use a loop for 'frameId' or else all images will replace!! if not just save by the default image name that you loaded

@tharindu326

Hey, thanks for the code. My code ran successfully :)

Thank You for your code please how to plot or show the predicting result as images using pyplot or another library

Did you get any reply regarding it?

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>