具有不同图像尺寸的Keras CNN

Question

I'm trying to use the VOC2012 dataset for training a CNN. For my project, I require B&W data, so I extracted the R components. So far so good. The trouble is that the images are of different sizes, so I can't figure out how to pass it to the model. I compiled my model, and then created my mini-batches of size 32 as below (where X_train and Y_train are the paths to the files).

for x in X_train:
    img = plt.imread(x)
    img = img.reshape(*(img.shape), 1)
    X.append(img)

for y in Y_train:
    img = plt.imread(y)
    img = img.reshape(*(img.shape), 1)
    Y.append(img)

model.train_on_batch(np.array(X), np.array(Y))

However, I suspect that because the images are all of different sizes, the numpy array has a shape (32,) rather than (32, height, width, 1) as I'd expect. How do I take care of this?

Answer 1

According to some sources, one is indeed able to train at least some architectures with varying input sizes. ( Quora , Cross Validated )

When it comes to generating an array of arrays varying in size, one might simply use a Python list of NumPy arrays, or an ndarray of type object to collect all the image data. Then in the training process, the Quora answer mentioned that only batch size 1 can be used, or one might clump several images together based on the sizes. Even padding with zeros could be used to make the images evenly sized, but I can't say much about the validity of that approach.

Best of luck in your research!

Example code for illustration:

# Generate 10 "images" with different sizes
images = [np.zeros((i+5, i+10)) for i in range(10)]
images = np.array([np.zeros((i+5, i+10)) for i in range(10)])

# Or an empty array to append to
images = np.array([], dtype=object)