mediapy `display_images()` downsamples images seemingly arbitrarily.... about mediapy

[hhoppe]

Hi Eric!

I can't seem to be able to reproduce it.
In Colab, I tried:

import mediapy as media
images = {f'image{index}': media.color_ramp((512, 512)) for index in range(32)}
media.show_images(images)

and it shows 32 images of size (512, 512, 3).
When I right-click Save_as_image on one of them, it does save as a 512x512 image.

In Jupyter Lab, it shows 32 shrunk images such that all of them appear in the window width.
But again when I shift-right-click Save_as_image it is natively 512x512.
(The scaling there might be adjustable using CSS styles.)

Please give us more details. Perhaps try Chrome Developer Tools (Control-Shift-i) to examine the native image resolution?
Is there any OS display scaling (e.g., Windows "Make everything bigger") or Chrome Zoom scaling?

[erictabellion]

Hi Hugues!

All the input image resolutions I reported were obtained printing the np.ndarray.shape.
All the display resolutions I reported were obtained after right-clicking on the image displayed by mediapy and selecting "Open in a new tab" in Google Chrome. I then hover the mouse over the new tab and a tooltip appears showing the native resolution of the image being displayed among other things. right-click+save the image produces an image file of that resolution.

In my use-case I get an image_batch from my ML pipeline data-loader, with shape (1, 32, 512, 512, 3) and dtype=np.float32. I display it with:

def display_image_row(image_row):
  image_dict = {}
  for col in range(image_row.shape[0]):
    image_dict[f'{col}'] = image_row[col]
  media.show_images(image_dict, height=512, downsample=False, ylabel='row0')

display_image_row(image_batch[0])

Whether or not I convert the input image_batch first to uint8, I get the same issue.

image_batch = image_batch * 255
image_batch = image_batch.astype(np.uint8)

[hhoppe]

Are you using Colab? Please see my shared Colab notebook in https://colab.research.google.com/drive/1pkTsS0v40kbf66d6a_rfMS1V6VrS247d?usp=sharing
which seems to work fine with both np.uint8 and np.float32 and with/without the height and downsample flags.