Need additional down viewpoints for wild dogs

What Wildbook should this feature be in? ACW

What would you like to see?
Currently, the list of viewpoints available to choose from when adding manual annotations has only one “down” option. We regularly have images that contain dogs lying on one side or another where their bellies are viewable along with one or other side. Currently, because we only have 1 “down” option, we have to choose straight “right” or “left” in order to try to get a match that includes that viewpoint. So “down” becomes almost un-usable and yet it’s a common viewpoint - in the image below, there are 3 “down” related viewpoints, only one of which is “down” only (in front of the tree):

How would this functionality help you?
Patterns on their bellies could be captured and matched against as separate from any left or right viewpoints. Also, it makes the viewpoint much more accurate.

We’d like to add: downfront, downback, downleft and downright, please.


cc: @PaulK

Hi, @ACWadmin1
Tagging @Drew and @parham on this one, as I think they might be able to respond more knowledgeably.

The machine learning (ML) system supports up to 26 possible viewpoints (plus one more for “unknown”): wildbook-ia/ at ac433d4f2a47b1d905c421a36c497f787003afc3 · WildMeOrg/wildbook-ia · GitHub. When the ML models are trained, we look at the distribution of the viewpoints and intentionally collapse some smaller categories into a single bucket in order to generate more examples in total.

For example, this is the code that maps the ground-truth viewpoints we have into the appropriate bucket:

label_two_species_mapping = {
    'wild_dog'                    : 'wild_dog',
    'wild_dog_dark'               : 'wild_dog',
    'wild_dog_light'              : 'wild_dog',
    'wild_dog_puppy'              : 'wild_dog_puppy',
    'wild_dog_standard'           : 'wild_dog',
    'wild_dog_tan'                : 'wild_dog',
    'wild_dog+'                   : 'wild_dog+tail_general',
    'wild_dog+____'               : 'wild_dog+tail_general',
    'wild_dog+double_black_brown' : 'wild_dog+tail_multi_black',
    'wild_dog+double_black_white' : 'wild_dog+tail_multi_black',
    'wild_dog+long_black'         : 'wild_dog+tail_long',
    'wild_dog+long_white'         : 'wild_dog+tail_long',
    'wild_dog+short_black'        : 'wild_dog+tail_short_black',
    'wild_dog+standard'           : 'wild_dog+tail_standard',
    'wild_dog+triple_black'       : 'wild_dog+tail_multi_black',
label_two_species_list = sorted(list(set(label_two_species_mapping.values())))

label_two_viewpoint_mapping = {
    species: {
        None         : None,
        'unknown'    : None,
        'left'       : 'left',
        'right'      : 'right',
        'front'      : 'front',
        'frontleft'  : 'frontleft',
        'frontright' : 'frontright',
        'back'       : 'back',
        'backleft'   : 'backleft',
        'backright'  : 'backright',
        'up'         : 'up',
        'upfront'    : 'upfront',
        'upleft'     : 'upleft',
        'upright'    : 'upright',
        'upback'     : 'upback',
        'down'       : 'down',
        'downfront'  : 'down',
        'downleft'   : 'down',
        'downright'  : 'down',
        'downback'   : 'down',
    for species in label_two_species_list
for viewpoint in label_two_viewpoint_mapping['wild_dog_puppy']:
    label_two_viewpoint_mapping['wild_dog_puppy'][viewpoint] = 'ignore'

We can see in the above mapping that down, downfront, downleft, downback, and downright are all mapped to a single down viewpoint. The reason we do this is because the model will have too few examples of downfront compared to the overwhelming examples of left and right to properly train the model. This mapping only applies to the automated outputs of the ML models that predict viewpoint, but this restriction should not be a problem in Wildbook’s interface. We may need to make a ticket in Wildbook to allow this feature in the interface, separate from what the ML system is producing.

We also need to quickly clarify the terminology that is used by the ML system. We have 6 possible primary viewpoints: front, back, left, right, up, down. These viewpoints are defined where it is the answer to the question, “what side of the animal are you looking at?” For example, if you are seeing the underneath belly of the animal, you are seeing the bottom of the animal, and therefore the viewpoint is “down”. At first, you’ll ask why we use “up” and “down” instead of “top” and “bottom”. We do this for a very specific and boring reason: abbreviation. If we use up and down, we can abbreviate all of the 6 primary viewpoints as F, B, L, R, U, and D. If we used “top” and “bottom”, there would be a conflict between “back” and “bottom” when we abbreviate, so we use “down” instead.

Beyond the 6 primary viewpoints, we can have secondary viewpoints as well as the combination of two primary viewpoints. There are also tertiary viewpoints that are the combination of three of the primary viewpoints. For example, the viewpoints front-right (or FR) is the viewpoint seeing the front of the animal and the right side of the animal. A viewpoint of DBR (or down-back-left) is a viewpoint where you see the bottom, back, and left-side of the animal all at the same time. In general, tertiary viewpoints are fairly rare and we almost exclusively use only primary and secondary viewpoints in all of our ML models.

Going back to the original example image, there are only one animal where only the bottom is being seen. Below are my annotated viewpoints for each of the boxes:


I agree that the Wildbook platform can add the ability to annotate viewpoints beyond the ones produced by the ML models. We will likely cross this threshold after the migration to the new Codex framework.

Hi @parham - I never thanked you for theis detailed explanation, so firstly - thank you! And sincerest apologies for the rude delay.

This came up again today with our BPC wild dog folks and we came away with a question. I think it’s implied in your response but I just want to be sure so I’m gonna ask.

With all Down variations collapsed into the Down category, will these get matched only to other Down only viewpoints? The concern is around getting matches to exposed right or left sides of the animal which can be visible when the dog is lying on the opposite side, such as with your 2 BottomRight examples in the annotated image above. Would these “bottoms” be compared to Right viewpoints in the database? Or only other Bottom viewpoints.

If the latter, does that then include BottomRight and BottomLeft by default (as part of the collapsed Bottom category) but not Right or Left or Back or Front?

To us, it seems logical that these other viewpoints should be included in the comparison to a Bottom viewpoint, which is why we’re asking.

Thanks for your help!


Hi @ACWadmin1

@parham Is teaching at the CV4E Summer School this month, so I am answering for him.

When we build the match-against set for an ID comparison, we look for annotations of the same type (i.e. same IA class) and at the same and adjacent viewpoints. Which viewpoints those are depends on the query annotation, and we have a matrix that builds that list. A down viewpoint is going to be matched against any other viewpoint with “down” in it, such as down, downfront, downback, downleft, downright, etc. A downleft starting viewpoint will be matched against other viewpoints containing either down or left in them. A front viewpoint is going to be matched to the corresponding frontleft, frontright,frontup,frontdown, etc. So we are by default looking for adjacent viewpoints when considering matching. I hope this helps answer the question.


1 Like

Thanks @jason. That make sense - so even though the variety of down viewpoints (down, downfront, downback, downleft, downright, etc) have been collapsed into just a “down” category, they are still detected with a variable like down+left so they’ll be matched against all other downs & lefts.

Which is great and what we hoped for.

thanks and have a great weekend!