How to get list of values in ImageDataGenerator.flow_from_directory Keras?

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We can generate image dataset using ImageDataGenerator with flow_from_directory method. For calling list of class, we can use oject.classes. But, how to call list of values? I've searched and still not found any.

Thanks :)

conv-neural-network keras deep-learning python
2 Answers

'But, how to call list of values' - If I understood correctly, I guess you wish to know what all files are there in your data set - if that's correct, (or if not), there are various ways you can get values from your generator:

  1. use object.filenames.

Object.filenames returns the list of all files in your target folder. I just use the len(object.filename) function to get the total number of files in my test folder. Then pass that number back into my generator and run it again.

  1. generator.n

Other way to get number of all items in your test folder is generator.n

  1. x , y = test_generator.next() to load my array and classes ( if inferred). Or a = test_generator.next(), where your array and classes will be returned as tuple.

I only used this as my test data set was really small ( 60 images) and I was using extracted features to train and predict my model( that is feature array and not the image array). If you are building a normal model, using generator to yield batches is much better way.

  1. Create a function using generator
def generate_test_data_from_directory(folder_path, image_target_size = 224, batch_size = 5, channels = 3, class_mode = 'sparse' ): 

'''fetch all out test data from directory'''
 
test_datagen = ImageDataGenerator(rescale=1./255)
test_generator = test_datagen.flow_from_directory(
        folder_path ,
        target_size = (image_target_size, image_target_size),
        batch_size  = batch_size,
        class_mode  = class_mode)

total_images = test_generator.n  
steps = total_images//batch_size 
#iterations to cover all data, so if batch is 5, it will take total_images/5  iteration 

x , y = [] , []
for i in range(steps):
    a , b = test_generator.next()
    x.extend(a) 
    y.extend(b)
    
return np.array(x), np.array(y)
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