python - How to use .predict_generator() on new Images - Keras

Question

I've used ImageDataGenerator and flow_from_directory for training and validation.

These are my directories:

train_dir = Path('D:/Datasets/Trell/images/new_images/training')
test_dir = Path('D:/Datasets/Trell/images/new_images/validation')
pred_dir = Path('D:/Datasets/Trell/images/new_images/testing')

ImageGenerator Code:

img_width, img_height = 28, 28
batch_size=32
train_datagen = ImageDataGenerator(
    rescale=1. / 255,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True)

test_datagen = ImageDataGenerator(rescale=1. / 255)

train_generator = train_datagen.flow_from_directory(
    train_dir,
    target_size=(img_height, img_width),
    batch_size=batch_size,
    class_mode='categorical')

validation_generator = test_datagen.flow_from_directory(
    test_dir,
    target_size=(img_height, img_width),
    batch_size=batch_size,
    class_mode='categorical')

Found 1852 images belonging to 4 classes

Found 115 images belonging to 4 classes

This is my model training code:

history = cnn.fit_generator(
        train_generator,
        steps_per_epoch=1852 // batch_size,
        epochs=20,
        validation_data=validation_generator,
        validation_steps=115 // batch_size)

Now I have some new images in a test folder (all images are inside the same folder only), on which I want to predict. But when I use .predict_generator I get:

Found 0 images belonging to 0 class

So I tried these solutions:

1) Keras: How to use predict_generator with ImageDataGenerator? This didn't work out, because its trying on validation set only.

2) How to predict the new image by using model.predict? module image not found

3) How to get predictions with predict_generator on streaming test data in Keras? This also didn't work out.

My train data is basically stored in 4 separate folders, i.e. 4 specific classes, validation also stored in same way and works out pretty well.

So in my test folder I have around 300 images, on which I want to predict and make a dataframe, like this:

image_name    class
gghh.jpg       1
rrtq.png       2
1113.jpg       1
44rf.jpg       4
tyug.png       1
ssgh.jpg       3

I have also used this following code:

img = image.load_img(pred_dir, target_size=(28, 28))
img_tensor = image.img_to_array(img)
img_tensor = np.expand_dims(img_tensor, axis=0)
img_tensor /= 255.

cnn.predict(img_tensor)

But I get this error: [Errno 13] Permission denied: 'D:\Datasets\Trell\images\new_images\testing'

But I haven't been able to predict_generator on my test images. So how can I predict on my new images using Keras. I have googled a lot, searched on Kaggle Kernels also but haven't been able to get a solution.

Answer 1

So first of all the test images should be placed inside a separate folder inside the test folder. So in my case I made another folder inside test folder and named it all_classes. Then ran the following code:

test_generator = test_datagen.flow_from_directory(
    directory=pred_dir,
    target_size=(28, 28),
    color_mode="rgb",
    batch_size=32,
    class_mode=None,
    shuffle=False
)

The above code gives me an output:

Found 306 images belonging to 1 class

And most importantly you've to write the following code:

test_generator.reset()

else weird outputs will come. Then using the .predict_generator() function:

pred=cnn.predict_generator(test_generator,verbose=1,steps=306/batch_size)

Running the above code will give output in probabilities so at first I need to convert them to class number. In my case it was 4 classes, so class numbers were 0,1,2 and 3.

Code written:

predicted_class_indices=np.argmax(pred,axis=1)

Next step is I want the name of the classes:

labels = (train_generator.class_indices)
labels = dict((v,k) for k,v in labels.items())
predictions = [labels[k] for k in predicted_class_indices]

Where by class numbers will be replaced by the class names. One final step if you want to save it to a csv file, arrange it in a dataframe with the image names appended with the class predicted.

filenames=test_generator.filenames
results=pd.DataFrame({"Filename":filenames,
                      "Predictions":predictions})

Display your dataframe. Everything is done now. You get all the predicted class for your images.