Input format for the network about deepgaze HOT 4 CLOSED

Hi @santo4ul

1. Correct, this is using the OpenCV convention.
2. Yes, you have to subtract 127 to center the values, image must be of size 64x64 pixels.
3. Not sure what you mean by this, the images once loaded in tensorflow become tf.float32 tensors.

from deepgaze.

Hi @mpatacchiola

On 3. above, Originally the R, G and B channels are 8 bit signed values. We do mean subtraction and just pass it as is to the network. So the channels are still 8bit signed values.

On the Tensorflow side, at the input layer (Reshape), the 8bit signed values we pass is used as tf.float32.

For example,

Original Input: Signed 8 bit

B = 128
G = 129
R = 130

After mean subtraction:

B = 1
G = 2
R = 3

At Tensorflow input layer:
B = 1.0
G = 2.0
R = 3.0

What I mean is, there is no other pre-processing involved other than the mean subtraction of 127. Am I right?

from deepgaze.

Yes that is correct. You just have to normalize by subtracting 127.
The other thing you should do is to resize the image if it is larger that 64x64 pixels.
This is done using an inter area interpolation, in OpenCV you can do:

image_resized = cv2.resize(image, (64, 64), interpolation = cv2.INTER_AREA)

from deepgaze.

Thanks a lot @mpatacchiola for your prompt reply!

Yes, I'm resizing the input accordingly.

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