在keras中创建自定义生成器以进行预测

Question

I am working on doing prediction for my large database of ~1 million images. For each image, I have code that can chop the image up into ~200 smaller images and pass them into keras as a numpy array for prediction.

I want to avoid unnecessary reading and writing to the hard drive, so I don't want to save all these smaller images and use flow_from_directory. Instead, I am looking to read in an image, chop it up with my existing code, and pass the smaller images into my network as a batch all in memory, and then repeat this process for many images.

Is this something Keras can handle? If so, I suspect I will need to make my own custom generator, but I'm not sure how to do this, and I couldn't find any good examples. Does anyone have an example of how to implement a custom generator?

Answer 1

Try something like this:

dpath='path to test folder'
ids=os.listdir(dpath+"test/")
for id in ids:
    x_batch=[]
    img = cv2.imread(dpath+'test/{}.jpg'.format(id))  #jpg if image in jpg format
    img = cv2.resize(img, (224, 224), interpolation = cv2.INTER_CUBIC) #if resize is needed

    chopped_image= your code that chops image
        x_batch.append(chopped_image)

    x_batch = np.array(x_batch, np.float32) 
    preds=(model.predict_on_batch(x_batch))
    if first==1:
        predsA=preds.copy()
        first=0
    else:
        predsA=np.append(predsA,preds,axis=0)