Support for inference on LMDB

[gheinrich]

A (small) step towards support for non-image data: this makes it possible to perform inference on any 3D blob. Blobs may not be "proper" images (e.g. because they don't have a standard number of channels).

close #619
close #630

A new field and a new button were added to allow inference on LMDB database:

Results are shown as follows (using DB key instead of image path):

Progress

• Add "Test DB" form to generic model show page
• Create new route
• Update inference job to take in a path to a database
• Update inference tool to parse an LMDB database
• Add tests

[gheinrich]

Tests added in latest commit.

[ajcampisi]

I am interested in using Digits with my Multispectral images (4 image channels). Since most multi-spectral images can be output as a separate 8 bit gray-scale images (one for each channel), I was wondering if we could get Digits to accept each multi-spectral image as four separate channel images for both training and classification. For example the file-list could contain the image name, channel number and category.
Image1.jpg c1 1
Image1.jpg c2 1
Image1.jpg c3 1
Image1.jpg c4 1
Image2.jpg c1 1
Image2.jpg c2 1
Image2.jpg c3 1
Image2.jpg c4 1
Image3.jpg c1 2
Image3.jpg c2 2
Image3.jpg c3 2
Image3.jpg c4 2

[gheinrich]

Hi @ajcampisi DIGITS does not support the format you propose out of the box. Are you saying that each image channel - taken in isolation - is sufficient to classify the image? In that case why don't you convert the 4-channel image to grayscale with:

convert <input> -colorspace Gray <output>

and then feed those grayscale images to DIGITS?

[ajcampisi]

Hi Greg,
Thanks for the reply. No unfortunately it is not sufficient an individual channel image to be used for classification. All four channels images need to be feed into the network as four separate network inputs and the combined image data from all four network channels can then be used by the network for both classification and training.