How to use OpenCV functions in Keras Lambda Layer?

Question

I am trying to use a function that uses some OpenCV function on the image. But the data I am getting is a tensor and I am not able to convert it into an image.

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Answer 1

Im going to assume image_func function does what you want (resize) and image. Note that an image is represent by a numpy array. Since you are using the tensorflow backend you are operating over Tensors (this you knew).

The job now is to convert a Tensor to a numpy array. To do that we need to evaluate the Tensor using its evaluate the tensor. But inorder to do that we need a to grab a tensor flow session.

Use the get_session() method of the keras backend module to grab the current tensorflow session.

Here is the docstring for get_session()

def get_session():
    """Returns the TF session to be used by the backend.
    If a default TensorFlow session is available, we will return it.
    Else, we will return the global Keras session.
    If no global Keras session exists at this point:
    we will create a new global session.
    Note that you can manually set the global session
    via `K.set_session(sess)`.
    # Returns
        A TensorFlow session.
    """

So try:

def image_func(img)

    from keras import backend as K

    sess  = K.get_session()
    img = sess.run(img) # now img is a proper numpy array 

    img=cv2.cvtColor(img,cv2.COLOR_BGR2YUV) 
    img=cv2.resize(img,(200,66))
    return img

Note, I haven't been able to test this

EDIT: Just tested this and it won't work (as you noticed). The lambda function needs to return Tensor. Computation flows throw a Tensor so it also needs to be to be smooth in the sense of differentiation.

I see that essentially the lambda is changing the color and resizing the image, why don't you do this in pre-processing step?