* Bump minimum Python version to 3.8
* typing_extensions.Literal -> typing.Literal
* ns-dev-test
* typing_extensions.get_args => typing.get_args
* Format with older version of black
* Bump `black`
* Run black
* Copy in `viser` communication infrastructure
* Update background image
* Add build directory to fix viser HTTP server
May want to remove these later
* Copy in `viser` communication infrastructure
* Update background image
* Pass dataset vis using viser
* initial impl of ViewerParameter
* idk what i did but brent and matt know
* Suppress eslint typescript warnings
* implement ViewerElement base class, then ViewerButton, ViewerSlider, etc
* client -> server message setup
* multi folder stuff
* Stream controls over viser
* import
* add types to viewer_param.py
* Move another message to viser
* Update folder color
* Use non throttled messages
* multiple folders works
* Cleanup, more porting
* starting impl of control panel in viser
* More porting
* scenebox to viser
* Cleanup
* Fix render updates
* More cleanup, docs, typing
* start control_panel.py
* set_hidden logic but not merged in
* control_panel replicates behavior of existing control panel (visually), except for setting the list of output options. not yet integrated with viewer_utils.py for logic flow. also implemented set_disabled and set_hidden in javscript/ViewerElement side
* Remove some original leva components
* add some train util logic, attempt set_value, doesn't work on javascript side fully yet (value updates but not slider visually
* bye zmq
* missing call to decoder
* 'gui_set' works properly, control panel now replicates behavior of original
* remove print
* Fix status viewer message
* More cleanup
* support set_options in dropdown
* hiding buttons works correctly
* fix set_options, switch from cur_value to get_value()
* Fix slowdown
* output options
* colormap logic converted
* add handle to cb_hook
* fix some lambda bugs
* cleanup
* Save checkpoint logic readded
* Cleanup and minor fixes
* snappier ui
* Refactoring
* Fix state update bugs
* Bug fixes
* Write less to redux
* added comments to reducer code
* Render state machine (#1727)
* state machine works, but train step throttling not implemented
* more step logic, cleanup
* remove random try catcH
* mutex logic for render
* Update lock logic
* types and docs
* added some state machine logic to help rerenders
* merge
---------
Co-authored-by: Justin <justin.g.kerr@gmail.com>
* Refactor and cleanup
* More cleanup and docs
* docstring to gui_utils
* docstrings, rename viewer_param.py to viewer_elements.py
* Fix viewer, remove bridge stuff
* Render on connect
* Fix lint errors
* More eslint fixes
* render_freq logic
* better rerender logic for crop
* More cleanup
* Purge
* Cleanup
* Lint fixes
* Fix cache
* better parsing logic
* Fix infinite test
* More cleanup
* typo
* Update webdocs
* Update docs
* rerender on custom hooks
* add more docs
* Use shared utilities in `viser.infra`
* rework callback to use elements instead of handles
* Fix ESlint errors
* change naming of handle to element, add setter for value
* cleanup
---------
Co-authored-by: Matt Tancik <mattt4001@gmail.com>
Co-authored-by: Justin <justin.g.kerr@gmail.com>
Co-authored-by: Ethan Weber <eweb0124@gmail.com>
Co-authored-by: Matthew Tancik <tancik@berkeley.edu>
* Extending ns-render to support loop rendering using interpolated cameras.
* Adding docstring for render.py. Applying formating.
* Expanding docstrings for class. Renaming trajectory type.
* Removing mentions of loop from render.py.
* use max resolution when not moving or training
* work around mismatch to allow rgb depth side-by-side output
* Update render.py
Co-authored-by: Matthew Tancik <tancik@berkeley.edu>
* when camera_type is equirectangular, add spherical metadata to rendered video
* remove unnecessary CONSOLE.print
* refactor to make pylint happier
* get os.stat size properly; document camera_type argument
* write video on the fly to avoid holding all images in memory
Note: video render width and height were unspecified for spiral trajectory; this uses values for Nerfacto
* fix images output
* doc fix
This branch gives us the flexibility to use Cameras as a tensordataclass, giving arbitrary batch dimensions, slicing, etc all for free. It tests this with the phototourism dataset.
Tensordataclass changes:
Originally, the tensordataclass would look at tensors in an object and take everything in front of the last dimension to be the batch dimensions, but in the case of the camera_to_world matrices, everything except for the last TWO are the important dimensions, so we needed to have a way for the tensordataclass to acknowledge these specific fields where there are multiple data dimensions behind the batch dimensions. This is important for things like reshaping where we need to know which dimensions get reshaped and which dimensions get left alone.
Cameras changes:
Now subclass of tensordataclass, which required a change to the tensordataclass itself.
The possibility of extra batch dimensions is the hairy part that requires the majority of these changes. The extra batch dimensions aren't a hassle in every function except in ray generation. The ray generation is where this operates different relative to the old cameras. The reason for this is before, for each ray we wanted to generate, we could specify a single index into our batch of cameras, but if you have multiple camera dimensions, for each ray, you need to now specify one index per camera batch dimension, meaning our camera_indices we specify need to have one extra dimension at the end, a dimension of size Cameras.ndim (number of batch dimensions in the camera object).
I also added some neat logic in generate_rays where if you want your output raybundle to be batched, if you specify batched camera_indices, you will receive the same batch size for the output raybundle, meaning you can easily generate raybundles corresponding to things like image patches and whatnot.
Phototourism changes:
The difficulty here is that images in the phototourism dataset are of different sizes, which is difficult because we can no longer batch the image tensors. We also need to better utilize caching in the CacheDataloader since the vanilladatamanager defaults don't really use it as well as they could, and without properly utilizing it, the rays / sec are maybe 25% of the standard nerfstudio dataset speeds (which are 10% of the size of the phototourism datasets). To do this, we specify a collate function in the existing datamanager configs, and we make a subclass of the vanilladatamanagerconfig named VariableResDataManagerConfig which will have a custom collate function storing the images in a list, and using better defaults for caching in the dataloader, caching ~40 images at a time. The slowest part of using a list is pixel sampling, where we added a custom if case for if our pixel sampler receives a batch of images that are in a list, and the lack of ability to vectorize operations over a list means that caching in the dataloader is even more important.
