CODEWITHSUNDEEP
pythontensorflowkerasconv-neural-networkmask
Mark.F
Mark.F

Reputation: 1694

Define a binary mask in Keras

I have an input image of shape [X,Y,3] and I have 2 coordinates (x,y). Now I want to create a mask with these coordinates and then multiply it with the input image. The mask should be a binary matrix with the same size as the image, with ones at coordinates [x:x+p_size,y:y+p_size] and zeros elsewhere.

My question is how to define the mask in Keras (tensorflow backend)?

note that this operation happens within the model (so simply using numpy won't help).

img = Input(shape=(32,32,3))
xy = Input(shape=(2)) # x and y coordinates for the mask
mask = ?
output = keras.layers.Multiply()([img, mask])

Upvotes: 2

Views: 3694

Answers (2)

Steve Brown
Steve Brown

Reputation: 449

Looks like https://keras.io/api/layers/merging_layers/multiply/ is the answer, I am going to try and post results. Like this:

>>> tf.keras.layers.Multiply()([np.arange(5).reshape(5, 1),
...                             np.arange(5, 10).reshape(5, 1)])
<tf.Tensor: shape=(5, 1), dtype=int64, numpy=
array([[ 0],
     [ 6],
     [14],
     [24],
     [36]])>

Upvotes: 0

javidcf
javidcf

Reputation: 59701

You can do the whole thing with a Lambda layer implementing a custom function:

from keras.models import Model
from keras.layers import Input, Lambda
from keras import backend as K
import numpy as np

# Masking function factory
def mask_img(x_size, y_size=None):
    if y_size is None:
        y_size = x_size
    # Masking function
    def mask_func(tensors):
        img, xy = tensors
        img_shape = K.shape(img)
        # Make indexing arrays
        xx = K.arange(img_shape[1])
        yy = K.arange(img_shape[2])
        # Get coordinates
        xy = K.cast(xy, img_shape.dtype)
        x = xy[:, 0:1]
        y = xy[:, 1:2]
        # Make X and Y masks
        mask_x = (xx >= x) & (xx < x + x_size)
        mask_y = (yy >= y) & (yy < y + y_size)
        # Make full mask
        mask = K.expand_dims(mask_x, 2) & K.expand_dims(mask_y, 1)
        # Add channels dimension
        mask = K.expand_dims(mask, -1)
        # Multiply image and mask
        mask = K.cast(mask, img.dtype)
        return img * mask
    return mask_func

# Model
img = Input(shape=(10, 10, 3))  # Small size for test
xy = Input(shape=(2,))
output = Lambda(mask_img(3))([img, xy])
model = Model(inputs=[img, xy], outputs=output)

# Test
img_test = np.arange(100).reshape((1, 10, 10, 1)).repeat(3, axis=-1)
xy_test = np.array([[2, 4]])
output_test = model.predict(x=[img_test, xy_test])
print(output_test[0, :, :, 0])

Output:

[[ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]
 [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]
 [ 0.  0.  0.  0. 24. 25. 26.  0.  0.  0.]
 [ 0.  0.  0.  0. 34. 35. 36.  0.  0.  0.]
 [ 0.  0.  0.  0. 44. 45. 46.  0.  0.  0.]
 [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]
 [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]
 [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]
 [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]
 [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]]

Upvotes: 4

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