about the data bits depth and normalization

[Jam-G]

As far as I am concerned， the huawei P20's raw image is 10bits dng files. but the data downloaded form the link http://people.ee.ethz.ch/~ihnatova/pynet.html#dataset are mixed with 8bits and 10bits png files. And the normalization code for training and testting in load_dataset.py at 21th line is shown below where the divisor is 4*255:

RAW_norm = RAW_combined.astype(np.float32) / (4 * 255)

Since there are part of data are 8bits depth, the divisor is too big for them.

And I tryed to used the pretrained model to test my own data captuered by huawei P20, the result is slightly overlighted.

So Is that a bug？ or something far from my understand?

I try to fix this by replace the code in load_dataset.py at 21th line and re-train the model:

RAW_norm = RAW_combined.astype(np.float32) / (4 * 255)

by

if raw.dtype == np.uint16:

    # 10bits

   RAW_norm = RAW_combined.astype(np.float32) / (1023)

elif raw.dtype == np.uint8:

    # 8bits

    RAW_norm = RAW_combined.astype(np.float32) / (255)

Is that correct？

[aiff22]

Hi @Jam-G,

All image crops were saved as 4-channel 10-bit png files, though some of the crops correspond to dark image areas and thus their values might lie between 0 and 255. Therefore, some libraries might show that they are just 8-bit images since the values are lying in the 8-bit interval.

[Jam-G]

@aiff22
Thanks for your reply!
I try to save a ndarray with shape 448x448 which values lies between 0 and 255 by imageio.imwrite, no matter the ndarray's dtype is uint8 or uint16 , both of the png file saved have 8bit depth.
Thanks again!