diff --git a/docs/Makefile b/docs/Makefile deleted file mode 100644 index 269cadc..0000000 --- a/docs/Makefile +++ /dev/null @@ -1,20 +0,0 @@ -# Minimal makefile for Sphinx documentation -# - -# You can set these variables from the command line, and also -# from the environment for the first two. -SPHINXOPTS ?= -SPHINXBUILD ?= sphinx-build -SOURCEDIR = source -BUILDDIR = build - -# Put it first so that "make" without argument is like "make help". -help: - @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) - -.PHONY: help Makefile - -# Catch-all target: route all unknown targets to Sphinx using the new -# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). -%: Makefile - @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) \ No newline at end of file diff --git a/docs/index.html b/docs/index.html new file mode 100644 index 0000000..28126cd --- /dev/null +++ b/docs/index.html @@ -0,0 +1,313 @@ + + + + + + + + Accelerating Neural Field Training via Soft Mining + + + + + + + + + + + + + + + + + + + + +
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+ Accelerating Neural Field Training via Soft Mining +

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+ + Shakiba Kheradmand1, + + Daniel Rebain1, + + Gopal Sharma1, + + Hossam Isack2, + + + Abhishek Kar2 + +
+ + Andrea Tagliasacchi2, 3, 4 + + + Kwang Moo Yi1 + +
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+ 1University of British Columbia + 2Google DeepMind +
+ 3Simon Fraser University + 4University of Toronto +
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Abstract

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+ We present an approach to accelerate Neural Field training by efficiently selecting sampling locations. + While Neural Fields have recently become popular, it is often trained by uniformly sampling the training domain, or through handcrafted heuristics. + We show that improved convergence and final training quality can be achieved by a soft mining technique based on importance sampling: rather than either considering or ignoring a pixel completely, we weigh the corresponding loss by a scalar. + To implement our idea we use Langevin Monte-Carlo sampling. + We show that by doing so, regions with higher error are being selected more frequently, leading to more than 2x improvement in convergence speed. +

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+ Teaser – we introduce soft mining to accelerate neural field training. + When applied to Neural Radiance Field (NeRF) training, our method significantly improves convergence. + We visualize the error maps (blue denotes low error and red denotes high error) and the rendered novel views for uniform sampling and our method. + We plot the convergence showing the Peak Signal-to-Noise Ratio (PSNR) for the corresponding scene. + We render both images at 1k iterations of training, specified by the red dashed line in the (right) graph. + Our method achieves the same PSNR significantly faster than the baselines. +

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+ NeRF Example - Mic – + The evolution of test PSNR during the training of our Soft Mining Sampling technique compared to + the Uniform Sampling baseline is illustrated using the 'Microphone' image from the + NeRF Synthetic dataset. The orange curve indicates the test PSNR of the uniform sampling + baseline, while the blue curve represents the test PSNR of our method. The yellow arrow indicates the + current position on the curve. +

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+ NeRF Example - Lego – + The evolution of test PSNR during the training of our Soft Mining Sampling technique compared to + the Uniform Sampling baseline is illustrated using the 'Lego' image from the + NeRF Synthetic dataset. The orange curve indicates the test PSNR of the uniform sampling + baseline, while the blue curve represents the test PSNR of our method. The yellow arrow indicates the + current position on the curve. +

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+ NeRF Example - Chair – + The evolution of test PSNR during the training of our Soft Mining Sampling technique compared to + the Uniform Sampling baseline is illustrated using the 'Chair' image from the + NeRF Synthetic dataset. The orange curve indicates the test PSNR of the uniform sampling + baseline, while the blue curve represents the test PSNR of our method. The yellow arrow indicates the + current position on the curve. +

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+ NeRF Example - Trex – + The evolution of test PSNR during the training of our Soft Mining Sampling technique compared to + the Uniform Sampling baseline is illustrated using the 'Trex' image from the + LLFF dataset. The orange curve indicates the test PSNR of the uniform sampling + baseline, while the blue curve represents the test PSNR of our method. The yellow arrow indicates the + current position on the curve. +

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+ NeRF Example - Fortress – + The evolution of test PSNR during the training of our Soft Mining Sampling technique compared to + the Uniform Sampling baseline is illustrated using the 'Fortress' image from the + LLFF dataset. The orange curve indicates the test PSNR of the uniform sampling + baseline, while the blue curve represents the test PSNR of our method. The yellow arrow indicates the + current position on the curve. +

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+ NeRF Example - Fern - + The evolution of test PSNR during the training of our Soft Mining Sampling technique compared to + the Uniform Sampling baseline is illustrated using the 'Fern' image from the + LLFF dataset. The orange curve indicates the test PSNR of the uniform sampling + baseline, while the blue curve represents the test PSNR of our method. The yellow arrow indicates the + current position on the curve. +

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+ 2D Image Fitting Example - Tokyo – + The evolution of test PSNR during the training of our Soft Mining Sampling technique + compared to the Uniform Sampling baseline is illustrated using the 'Tokyo' image for + 2D image fitting application with a batch size of 512. The orange curve indicates the test PSNR of the uniform sampling + baseline, while the blue curve represents the test PSNR of our method. The yellow arrow indicates + the current position on the curve. The samples along with the error map is also provided. +

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+ 2D Image Fitting Example - Pluto – + The evolution of test PSNR during the training of our Soft Mining Sampling technique compared to + the Uniform Sampling baseline is illustrated using the 'Pluto' image for 2D image fitting application with a batch size of 512. + The orange curve indicates the test PSNR of the uniform sampling + baseline, while the blue curve represents the test PSNR of our method. The yellow arrow indicates + the current position on the curve. The samples along with the error map is also provided. +

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+ + + + + diff --git a/docs/requirements.txt b/docs/requirements.txt deleted file mode 100644 index cc62b5a..0000000 --- a/docs/requirements.txt +++ /dev/null @@ -1,4 +0,0 @@ -pytorch_sphinx_theme @ git+https://github.com/liruilong940607/pytorch_sphinx_theme.git#egg=pytorch_sphinx_theme -sphinx==5.2.1 -sphinx-copybutton==0.5.0 -sphinx-design==0.2.0 \ No newline at end of file diff --git a/docs/source/_static/css/readthedocs.css b/docs/source/_static/css/readthedocs.css deleted file mode 100644 index f49d191..0000000 --- a/docs/source/_static/css/readthedocs.css +++ /dev/null @@ -1,9 +0,0 @@ -.header-logo { - background-image: url("../images/logo4x.png"); - background-size: 156px 35px; - height: 35px; - width: 156px; -} -code { - word-break: normal; -} \ No newline at end of file diff --git a/docs/source/_static/images/coding/gpu_util.png b/docs/source/_static/images/coding/gpu_util.png deleted file mode 100644 index 5249dfc..0000000 Binary files a/docs/source/_static/images/coding/gpu_util.png and /dev/null differ diff --git a/docs/source/_static/images/illustration_occgrid.png b/docs/source/_static/images/illustration_occgrid.png deleted file mode 100644 index 100c2aa..0000000 Binary files a/docs/source/_static/images/illustration_occgrid.png and /dev/null differ diff --git a/docs/source/_static/images/illustration_propnet.png b/docs/source/_static/images/illustration_propnet.png deleted file mode 100644 index 55a1456..0000000 Binary files a/docs/source/_static/images/illustration_propnet.png and /dev/null differ diff --git a/docs/source/_static/images/logo.png b/docs/source/_static/images/logo.png deleted file mode 100644 index 1ee2c57..0000000 Binary files a/docs/source/_static/images/logo.png and /dev/null differ diff --git a/docs/source/_static/images/logo4x.png b/docs/source/_static/images/logo4x.png deleted file mode 100644 index dcfea0d..0000000 Binary files a/docs/source/_static/images/logo4x.png and /dev/null differ diff --git a/docs/source/_static/images/plot_occgrid.png b/docs/source/_static/images/plot_occgrid.png deleted file mode 100644 index 930a23f..0000000 Binary files a/docs/source/_static/images/plot_occgrid.png and /dev/null differ diff --git a/docs/source/_static/images/plot_propnet.png b/docs/source/_static/images/plot_propnet.png deleted file mode 100644 index 6d8d4f7..0000000 Binary files a/docs/source/_static/images/plot_propnet.png and /dev/null differ diff --git a/docs/source/_static/images/teaser.jpg b/docs/source/_static/images/teaser.jpg deleted file mode 100644 index 635cabc..0000000 Binary files a/docs/source/_static/images/teaser.jpg and /dev/null differ diff --git a/docs/source/apis/estimators.rst b/docs/source/apis/estimators.rst deleted file mode 100644 index 99cda38..0000000 --- a/docs/source/apis/estimators.rst +++ /dev/null @@ -1,12 +0,0 @@ -.. _`Estimators`: - -Estimators -=================================== - -.. currentmodule:: nerfacc - -.. autoclass:: OccGridEstimator - :members: - -.. autoclass:: PropNetEstimator - :members: diff --git a/docs/source/apis/generated/nerfacc.accumulate_along_rays.rst b/docs/source/apis/generated/nerfacc.accumulate_along_rays.rst deleted file mode 100644 index 0c34c82..0000000 --- a/docs/source/apis/generated/nerfacc.accumulate_along_rays.rst +++ /dev/null @@ -1,6 +0,0 @@ -nerfacc.accumulate\_along\_rays -=============================== - -.. currentmodule:: nerfacc - -.. autofunction:: accumulate_along_rays \ No newline at end of file diff --git a/docs/source/apis/generated/nerfacc.exclusive_prod.rst b/docs/source/apis/generated/nerfacc.exclusive_prod.rst deleted file mode 100644 index 8ce6c68..0000000 --- a/docs/source/apis/generated/nerfacc.exclusive_prod.rst +++ /dev/null @@ -1,6 +0,0 @@ -nerfacc.exclusive\_prod -======================= - -.. currentmodule:: nerfacc - -.. autofunction:: exclusive_prod \ No newline at end of file diff --git a/docs/source/apis/generated/nerfacc.exclusive_sum.rst b/docs/source/apis/generated/nerfacc.exclusive_sum.rst deleted file mode 100644 index c9d3e09..0000000 --- a/docs/source/apis/generated/nerfacc.exclusive_sum.rst +++ /dev/null @@ -1,6 +0,0 @@ -nerfacc.exclusive\_sum -====================== - -.. currentmodule:: nerfacc - -.. autofunction:: exclusive_sum \ No newline at end of file diff --git a/docs/source/apis/generated/nerfacc.importance_sampling.rst b/docs/source/apis/generated/nerfacc.importance_sampling.rst deleted file mode 100644 index d0ec106..0000000 --- a/docs/source/apis/generated/nerfacc.importance_sampling.rst +++ /dev/null @@ -1,6 +0,0 @@ -nerfacc.importance\_sampling -============================ - -.. currentmodule:: nerfacc - -.. autofunction:: importance_sampling \ No newline at end of file diff --git a/docs/source/apis/generated/nerfacc.inclusive_prod.rst b/docs/source/apis/generated/nerfacc.inclusive_prod.rst deleted file mode 100644 index e866483..0000000 --- a/docs/source/apis/generated/nerfacc.inclusive_prod.rst +++ /dev/null @@ -1,6 +0,0 @@ -nerfacc.inclusive\_prod -======================= - -.. currentmodule:: nerfacc - -.. autofunction:: inclusive_prod \ No newline at end of file diff --git a/docs/source/apis/generated/nerfacc.inclusive_sum.rst b/docs/source/apis/generated/nerfacc.inclusive_sum.rst deleted file mode 100644 index 1f23f1c..0000000 --- a/docs/source/apis/generated/nerfacc.inclusive_sum.rst +++ /dev/null @@ -1,6 +0,0 @@ -nerfacc.inclusive\_sum -====================== - -.. currentmodule:: nerfacc - -.. autofunction:: inclusive_sum \ No newline at end of file diff --git a/docs/source/apis/generated/nerfacc.pack_info.rst b/docs/source/apis/generated/nerfacc.pack_info.rst deleted file mode 100644 index 7c4515a..0000000 --- a/docs/source/apis/generated/nerfacc.pack_info.rst +++ /dev/null @@ -1,6 +0,0 @@ -nerfacc.pack\_info -================== - -.. currentmodule:: nerfacc - -.. autofunction:: pack_info \ No newline at end of file diff --git a/docs/source/apis/generated/nerfacc.ray_aabb_intersect.rst b/docs/source/apis/generated/nerfacc.ray_aabb_intersect.rst deleted file mode 100644 index 3b426e1..0000000 --- a/docs/source/apis/generated/nerfacc.ray_aabb_intersect.rst +++ /dev/null @@ -1,6 +0,0 @@ -nerfacc.ray\_aabb\_intersect -============================ - -.. currentmodule:: nerfacc - -.. autofunction:: ray_aabb_intersect \ No newline at end of file diff --git a/docs/source/apis/generated/nerfacc.render_transmittance_from_alpha.rst b/docs/source/apis/generated/nerfacc.render_transmittance_from_alpha.rst deleted file mode 100644 index c833e31..0000000 --- a/docs/source/apis/generated/nerfacc.render_transmittance_from_alpha.rst +++ /dev/null @@ -1,6 +0,0 @@ -nerfacc.render\_transmittance\_from\_alpha -========================================== - -.. currentmodule:: nerfacc - -.. autofunction:: render_transmittance_from_alpha \ No newline at end of file diff --git a/docs/source/apis/generated/nerfacc.render_transmittance_from_density.rst b/docs/source/apis/generated/nerfacc.render_transmittance_from_density.rst deleted file mode 100644 index 5715eca..0000000 --- a/docs/source/apis/generated/nerfacc.render_transmittance_from_density.rst +++ /dev/null @@ -1,6 +0,0 @@ -nerfacc.render\_transmittance\_from\_density -============================================ - -.. currentmodule:: nerfacc - -.. autofunction:: render_transmittance_from_density \ No newline at end of file diff --git a/docs/source/apis/generated/nerfacc.render_visibility_from_alpha.rst b/docs/source/apis/generated/nerfacc.render_visibility_from_alpha.rst deleted file mode 100644 index c7773d0..0000000 --- a/docs/source/apis/generated/nerfacc.render_visibility_from_alpha.rst +++ /dev/null @@ -1,6 +0,0 @@ -nerfacc.render\_visibility\_from\_alpha -======================================= - -.. currentmodule:: nerfacc - -.. autofunction:: render_visibility_from_alpha \ No newline at end of file diff --git a/docs/source/apis/generated/nerfacc.render_visibility_from_density.rst b/docs/source/apis/generated/nerfacc.render_visibility_from_density.rst deleted file mode 100644 index d39a4cf..0000000 --- a/docs/source/apis/generated/nerfacc.render_visibility_from_density.rst +++ /dev/null @@ -1,6 +0,0 @@ -nerfacc.render\_visibility\_from\_density -========================================= - -.. currentmodule:: nerfacc - -.. autofunction:: render_visibility_from_density \ No newline at end of file diff --git a/docs/source/apis/generated/nerfacc.render_weight_from_alpha.rst b/docs/source/apis/generated/nerfacc.render_weight_from_alpha.rst deleted file mode 100644 index 07dc885..0000000 --- a/docs/source/apis/generated/nerfacc.render_weight_from_alpha.rst +++ /dev/null @@ -1,6 +0,0 @@ -nerfacc.render\_weight\_from\_alpha -=================================== - -.. currentmodule:: nerfacc - -.. autofunction:: render_weight_from_alpha \ No newline at end of file diff --git a/docs/source/apis/generated/nerfacc.render_weight_from_density.rst b/docs/source/apis/generated/nerfacc.render_weight_from_density.rst deleted file mode 100644 index 26c0afb..0000000 --- a/docs/source/apis/generated/nerfacc.render_weight_from_density.rst +++ /dev/null @@ -1,6 +0,0 @@ -nerfacc.render\_weight\_from\_density -===================================== - -.. currentmodule:: nerfacc - -.. autofunction:: render_weight_from_density \ No newline at end of file diff --git a/docs/source/apis/generated/nerfacc.searchsorted.rst b/docs/source/apis/generated/nerfacc.searchsorted.rst deleted file mode 100644 index 77ab765..0000000 --- a/docs/source/apis/generated/nerfacc.searchsorted.rst +++ /dev/null @@ -1,6 +0,0 @@ -nerfacc.searchsorted -==================== - -.. currentmodule:: nerfacc - -.. autofunction:: searchsorted \ No newline at end of file diff --git a/docs/source/apis/generated/nerfacc.traverse_grids.rst b/docs/source/apis/generated/nerfacc.traverse_grids.rst deleted file mode 100644 index fecb800..0000000 --- a/docs/source/apis/generated/nerfacc.traverse_grids.rst +++ /dev/null @@ -1,6 +0,0 @@ -nerfacc.traverse\_grids -======================= - -.. currentmodule:: nerfacc - -.. autofunction:: traverse_grids \ No newline at end of file diff --git a/docs/source/apis/rendering.rst b/docs/source/apis/rendering.rst deleted file mode 100644 index b8063d8..0000000 --- a/docs/source/apis/rendering.rst +++ /dev/null @@ -1,6 +0,0 @@ -Rendering -=================================== - -.. currentmodule:: nerfacc - -.. autofunction:: rendering diff --git a/docs/source/apis/utils.rst b/docs/source/apis/utils.rst deleted file mode 100644 index 374639d..0000000 --- a/docs/source/apis/utils.rst +++ /dev/null @@ -1,32 +0,0 @@ -Utils -=================================== - -Below are the basic functions that supports sampling and rendering. - -.. currentmodule:: nerfacc - -.. autosummary:: - :nosignatures: - :toctree: generated/ - - inclusive_prod - exclusive_prod - inclusive_sum - exclusive_sum - - pack_info - - render_visibility_from_alpha - render_visibility_from_density - render_weight_from_alpha - render_weight_from_density - render_transmittance_from_alpha - render_transmittance_from_density - accumulate_along_rays - - importance_sampling - searchsorted - - ray_aabb_intersect - traverse_grids - \ No newline at end of file diff --git a/docs/source/conf.py b/docs/source/conf.py deleted file mode 100644 index 507d28e..0000000 --- a/docs/source/conf.py +++ /dev/null @@ -1,94 +0,0 @@ -import pytorch_sphinx_theme - -__version__ = None -exec(open("../../nerfacc/version.py", "r").read()) - -# -- Project information - -project = "nerfacc" -copyright = "2022, Ruilong" -author = "Ruilong" - -release = __version__ - -# -- General configuration - -extensions = [ - "sphinx.ext.napoleon", - "sphinx.ext.duration", - "sphinx.ext.doctest", - "sphinx.ext.autodoc", - "sphinx.ext.autosummary", - "sphinx.ext.intersphinx", -] - -intersphinx_mapping = { - "python": ("https://docs.python.org/3/", None), - "sphinx": ("https://www.sphinx-doc.org/en/master/", None), -} -intersphinx_disabled_domains = ["std"] - -templates_path = ["_templates"] - -# -- Options for HTML output - -# html_theme = "furo" - -html_theme = "pytorch_sphinx_theme" -html_theme_path = [pytorch_sphinx_theme.get_html_theme_path()] -html_static_path = ["_static"] -html_css_files = ["css/readthedocs.css"] - -# Ignore >>> when copying code -copybutton_prompt_text = r">>> |\.\.\. " -copybutton_prompt_is_regexp = True - -# Theme options are theme-specific and customize the look and feel of a theme -# further. For a list of options available for each theme, see the -# documentation. -html_theme_options = { - # The target url that the logo directs to. Unset to do nothing - "logo_url": "https://www.nerfacc.com/en/latest/index.html", - # "menu" is a list of dictionaries where you can specify the content and the - # behavior of each item in the menu. Each item can either be a link or a - # dropdown menu containing a list of links. - "menu": [ - # A link - {"name": "GitHub", "url": "https://github.com/KAIR-BAIR/nerfacc"}, - # A dropdown menu - # { - # "name": "Projects", - # "children": [ - # # A vanilla dropdown item - # { - # "name": "nerfstudio", - # "url": "https://docs.nerf.studio/", - # "description": "The all-in-one repo for NeRFs", - # }, - # ], - # # Optional, determining whether this dropdown menu will always be - # # highlighted. - # # "active": True, - # }, - ], -} -# html_theme_options = { -# "canonical_url": "", -# "analytics_id": "", -# "logo_only": False, -# "display_version": True, -# "prev_next_buttons_location": "bottom", -# "style_external_links": False, -# # Toc options -# "collapse_navigation": True, -# "sticky_navigation": True, -# "navigation_depth": 4, -# "includehidden": True, -# "titles_only": False -# } - -# -- Options for EPUB output -epub_show_urls = "footnote" - -# typehints -autodoc_typehints = "description" diff --git a/docs/source/examples/camera.rst b/docs/source/examples/camera.rst deleted file mode 100644 index 30de147..0000000 --- a/docs/source/examples/camera.rst +++ /dev/null @@ -1,24 +0,0 @@ -Camera Optimization NeRFs -=================================== - -Performance Overview --------------------- - -+----------------------+----------------+----------------------------------+-----------------------+--------------------------+------------------------+------------------------+ -| Methods | Dataset | Training Time :math:`\downarrow` | PSNR :math:`\uparrow` | LPIPS :math:`\downarrow` | E_R :math:`\downarrow` | E_T :math:`\downarrow` | -+======================+================+==================================+=======================+==========================+========================+========================+ -| BARF `[1]`_ | NeRF-Synthetic | 586min | 28.83 | 0.054 | 0.19 | 0.74 | -+----------------------+----------------+----------------------------------+-----------------------+--------------------------+------------------------+------------------------+ -| *+nerfacc (occgrid)* | | 130min | 30.11 | 0.044 | 0.07 | 0.35 | -+----------------------+----------------+----------------------------------+-----------------------+--------------------------+------------------------+------------------------+ - -Implementation Details ----------------------- - -.. toctree:: - :glob: - :maxdepth: 1 - - camera/* - -.. _`[1]`: https://arxiv.org/abs/2104.06405 diff --git a/docs/source/examples/camera/barf.rst b/docs/source/examples/camera/barf.rst deleted file mode 100644 index be05991..0000000 --- a/docs/source/examples/camera/barf.rst +++ /dev/null @@ -1,35 +0,0 @@ -BARF -==================== - -In this example we showcase how to plug the nerfacc library into the *official* codebase -of `BARF `_. See -`our forked repo `_ -for details. - - -Benchmark: NeRF-Synthetic Dataset ---------------------------------- -*updated on 2023-04-04 with nerfacc==0.5.0* - -Our experiments are conducted on a single NVIDIA GeForce RTX 2080 Ti. - -+-----------------------+-------+-------+---------+-------+-------+-------+-------+-------+-------+ -| PSNR | Lego | Mic |Materials| Chair |Hotdog | Ficus | Drums | Ship | MEAN | -| | | | | | | | | | | -+=======================+=======+=======+=========+=======+=======+=======+=======+=======+=======+ -| BARF | 31.16 | 23.87 | 26.28 | 34.48 | 28.4 | 27.86 | 31.07 | 27.55 | 28.83 | -+-----------------------+-------+-------+---------+-------+-------+-------+-------+-------+-------+ -| training time | 9.5hrs| 9.5hrs| 9.2hrs | 9.3hrs|12.3hrs| 9.3hrs| 9.3hrs| 9.5hrs| 9.8hrs| -+-----------------------+-------+-------+---------+-------+-------+-------+-------+-------+-------+ -| camera errors (R) | 0.105 | 0.047 | 0.085 | 0.226 | 0.071 | 0.846 | 0.068 | 0.089 | 0.192 | -+-----------------------+-------+-------+---------+-------+-------+-------+-------+-------+-------+ -| camera errors (T) | 0.0043| 0.0021| 0.0040 | 0.0120| 0.0026| 0.0272| 0.0025| 0.0044| 0.0074| -+-----------------------+-------+-------+---------+-------+-------+-------+-------+-------+-------+ -| Ours (occ) | 32.25 | 24.77 | 27.73 | 35.84 | 29.98 | 28.83 | 32.84 | 28.62 | 30.11 | -+-----------------------+-------+-------+---------+-------+-------+-------+-------+-------+-------+ -| training time | 1.5hrs| 2.0hrs| 2.0hrs | 2.3hrs| 2.2hrs| 1.9hrs| 2.2hrs| 2.3hrs| 2.0hrs| -+-----------------------+-------+-------+---------+-------+-------+-------+-------+-------+-------+ -| camera errors (R) | 0.081 | 0.036 | 0.056 | 0.171 | 0.058 | 0.039 | 0.039 | 0.079 | 0.070 | -+-----------------------+-------+-------+---------+-------+-------+-------+-------+-------+-------+ -| camera errors (T) | 0.0038| 0.0019| 0.0031 | 0.0106| 0.0021| 0.0013| 0.0014| 0.0041| 0.0035| -+-----------------------+-------+-------+---------+-------+-------+-------+-------+-------+-------+ diff --git a/docs/source/examples/dynamic.rst b/docs/source/examples/dynamic.rst deleted file mode 100644 index ce28109..0000000 --- a/docs/source/examples/dynamic.rst +++ /dev/null @@ -1,58 +0,0 @@ -Dynamic NeRFs -=================================== - - -The :class:`nerfacc.PropNetEstimator` can natually work with dynamic NeRFs. To make the -:class:`nerfacc.OccGridEstimator` also work with dynamic NeRFs, we need to make some compromises. -In these examples, we use the :class:`nerfacc.OccGridEstimator` to estimate the -`maximum` opacity at each area `over all the timestamps`. This allows us to share the same estimator -across all the timestamps, including those timestamps that are not in the training set. -In other words, we use it to cache the union of the occupancy at all timestamps. -It is not optimal but still makes the rendering very efficient if the motion is not crazyly significant. - - -Performance Overview --------------------- -*updated on 2023-04-04* - -+----------------------+-----------+----------------------------------+-----------------------+--------------------------+ -| Methods | Dataset | Training Time :math:`\downarrow` | PSNR :math:`\uparrow` | LPIPS :math:`\downarrow` | -+======================+===========+==================================+=======================+==========================+ -| TiNeuVox `[1]`_ | HyperNeRF | 56.3min | 24.19 | 0.425 | -+----------------------+-----------+----------------------------------+-----------------------+--------------------------+ -| *+nerfacc (occgrid)* | | 33.0min | 24.19 | 0.434 | -+----------------------+-----------+----------------------------------+-----------------------+--------------------------+ -| *+nerfacc (propnet)* | | 34.3min | 24.26 | 0.398 | -+----------------------+-----------+----------------------------------+-----------------------+--------------------------+ -| TiNeuVox `[1]`_ | D-NeRF | 11.8min | 31.14 | 0.050 | -+----------------------+-----------+----------------------------------+-----------------------+--------------------------+ -| *+nerfacc (occgrid)* | | 4.2min | 31.75 | 0.038 | -+----------------------+-----------+----------------------------------+-----------------------+--------------------------+ -| K-Planes `[2]`_ | D-NeRF | 63.9min | 30.28 | 0.043 | -+----------------------+-----------+----------------------------------+-----------------------+--------------------------+ -| *+nerfacc (occgrid)* | | 38.8min | 30.35 | 0.042 | -+----------------------+-----------+----------------------------------+-----------------------+--------------------------+ -| T-NeRF `[3]`_ | D-NeRF | 20hours | 28.78 | 0.069 | -+----------------------+-----------+----------------------------------+-----------------------+--------------------------+ -| *+nerfacc (occgrid)* | | 58min | 32.22 | 0.040 | -+----------------------+-----------+----------------------------------+-----------------------+--------------------------+ - -Implementation Details ----------------------- - -.. toctree:: - :glob: - :maxdepth: 1 - - dynamic/* - -| - -3rd-Party Use Cases -------------------- - -- `Representing Volumetric Videos as Dynamic MLP Maps, CVPR 2023 `_. - -.. _`[1]`: https://arxiv.org/abs/2205.15285 -.. _`[2]`: https://arxiv.org/abs/2301.10241 -.. _`[3]`: https://arxiv.org/abs/2011.13961 \ No newline at end of file diff --git a/docs/source/examples/dynamic/kplanes.rst b/docs/source/examples/dynamic/kplanes.rst deleted file mode 100644 index 20a593d..0000000 --- a/docs/source/examples/dynamic/kplanes.rst +++ /dev/null @@ -1,27 +0,0 @@ -K-Planes -==================== - -In this example we showcase how to plug the nerfacc library into the *official* codebase -of `K-Planes `_. See -`our forked repo `_ -for details. - - -Benchmark: D-NeRF Dataset ---------------------------------- -*updated on 2023-04-04 with nerfacc==0.5.0* - -Our experiments are conducted on a single NVIDIA GeForce RTX 2080 Ti. - -+----------------------+----------+---------+-------+---------+-------+--------+---------+-------+-------+ -| PSNR | bouncing | hell | hook | jumping | lego | mutant | standup | trex | MEAN | -| | balls | warrior | | jacks | | | | | | -+======================+==========+=========+=======+=========+=======+========+=========+=======+=======+ -| K-Planes | 39.10 | 23.95 | 27.76 | 31.11 | 25.18 | 32.44 | 32.51 | 30.25 | 30.29 | -+----------------------+----------+---------+-------+---------+-------+--------+---------+-------+-------+ -| training time | 68min | 70min | 70min | 70min | 70min | 71min | 72min | 71min | 70min | -+----------------------+----------+---------+-------+---------+-------+--------+---------+-------+-------+ -| Ours (occ) | 38.95 | 24.00 | 27.74 | 30.46 | 25.25 | 32.58 | 32.84 | 30.49 | 30.29 | -+----------------------+----------+---------+-------+---------+-------+--------+---------+-------+-------+ -| training time | 41min | 41min | 40min | 40min | 39min | 39min | 40min | 38min | 40min | -+----------------------+----------+---------+-------+---------+-------+--------+---------+-------+-------+ diff --git a/docs/source/examples/dynamic/tineuvox.rst b/docs/source/examples/dynamic/tineuvox.rst deleted file mode 100644 index b92705c..0000000 --- a/docs/source/examples/dynamic/tineuvox.rst +++ /dev/null @@ -1,53 +0,0 @@ -.. _`TiNeuVox Example`: - -TiNeuVox -==================== - -In this example we showcase how to plug the nerfacc library into the *official* codebase -of `TiNeuVox `_. See -`our forked repo `_ -for details. - - -Benchmark: D-NeRF Dataset ---------------------------------- -*updated on 2023-04-04 with nerfacc==0.5.0* - -Our experiments are conducted on a single NVIDIA GeForce RTX 2080 Ti. - -+----------------------+----------+---------+-------+---------+-------+--------+---------+-------+-------+ -| PSNR | bouncing | hell | hook | jumping | lego | mutant | standup | trex | MEAN | -| | balls | warrior | | jacks | | | | | | -+======================+==========+=========+=======+=========+=======+========+=========+=======+=======+ -| TiNeuVox | 39.37 | 27.05 | 29.61 | 32.92 | 24.32 | 31.47 | 33.59 | 30.01 | 31.04 | -+----------------------+----------+---------+-------+---------+-------+--------+---------+-------+-------+ -| Training Time | 832s | 829s | 833s | 841s | 824s | 833s | 827s | 840s | 833s | -+----------------------+----------+---------+-------+---------+-------+--------+---------+-------+-------+ -| Ours (occ) | 40.56 | 27.17 | 31.35 | 33.44 | 25.17 | 34.05 | 35.35 | 32.29 | 32.42 | -+----------------------+----------+---------+-------+---------+-------+--------+---------+-------+-------+ -| Training Time | 378s | 302s | 342s | 325s | 355s | 360s | 346s | 362s | 346s | -+----------------------+----------+---------+-------+---------+-------+--------+---------+-------+-------+ - - -Benchmark: HyperNeRF Dataset ---------------------------------- -*updated on 2023-04-04 with nerfacc==0.5.0* - -Our experiments are conducted on a single NVIDIA GeForce RTX 2080 Ti. - -+----------------------+----------+---------+-------+-------------+-------+ -| PSNR | 3dprinter| broom |chicken| peel-banana | MEAN | -| | | | | | | -+======================+==========+=========+=======+=============+=======+ -| TiNeuVox | 22.77 | 21.30 | 28.29 | 24.50 | 24.22 | -+----------------------+----------+---------+-------+-------------+-------+ -| Training Time | 3253s | 2811s | 3933s | 2705s | 3175s | -+----------------------+----------+---------+-------+-------------+-------+ -| Ours (occ) | 22.72 | 21.27 | 28.27 | 24.54 | 24.20 | -+----------------------+----------+---------+-------+-------------+-------+ -| Training Time | 2265s | 2221s | 2157s | 2101s | 2186s | -+----------------------+----------+---------+-------+-------------+-------+ -| Ours (prop) | 22.75 | 21.17 | 28.27 | 24.97 | 24.29 | -+----------------------+----------+---------+-------+-------------+-------+ -| Training Time | 2307s | 2281s | 2267s | 2510s | 2341s | -+----------------------+----------+---------+-------+-------------+-------+ diff --git a/docs/source/examples/dynamic/tnerf.rst b/docs/source/examples/dynamic/tnerf.rst deleted file mode 100644 index eaaeec6..0000000 --- a/docs/source/examples/dynamic/tnerf.rst +++ /dev/null @@ -1,35 +0,0 @@ -.. _`T-NeRF Example`: - -T-NeRF -==================== -See code `examples/train_mlp_dnerf.py` at our `github repository`_ for details. - -Radiance Field --------------- -Here we implement a very basic time-conditioned NeRF (T-NeRF) model (`examples/radiance_fields/mlp.py`) -for dynamic scene reconstruction. -The implementation is mostly follow the T-NeRF described in the `D-NeRF`_ paper, with a 8-layer-MLP -for the radiance field and a 4-layer-MLP for the warping field. The only major difference is that -we reduce the max frequency of the positional encoding from 10 to 4, to respect the fact that the -motion of the object is relatively smooth. - - -Benchmarks: D-NeRF Dataset ---------------------------- -*updated on 2022-10-08* - -Our experiments are conducted on a single NVIDIA TITAN RTX GPU. -The training memory footprint is about 11GB. - -+----------------------+----------+---------+-------+---------+-------+--------+---------+-------+-------+ -| PSNR | bouncing | hell | hook | jumping | lego | mutant | standup | trex | MEAN | -| | balls | warrior | | jacks | | | | | | -+======================+==========+=========+=======+=========+=======+========+=========+=======+=======+ -| D-NeRF (~ days) | 32.80 | 25.02 | 29.25 | 32.80 | 21.64 | 31.29 | 32.79 | 31.75 | 29.67 | -+----------------------+----------+---------+-------+---------+-------+--------+---------+-------+-------+ -| Ours (~ 1 hr) | 39.49 | 25.58 | 31.86 | 32.73 | 24.32 | 35.55 | 35.90 | 32.33 | 32.22 | -+----------------------+----------+---------+-------+---------+-------+--------+---------+-------+-------+ - -.. _`D-NeRF`: https://arxiv.org/abs/2011.13961 -.. _`github repository`: https://github.com/KAIR-BAIR/nerfacc/ - diff --git a/docs/source/examples/static.rst b/docs/source/examples/static.rst deleted file mode 100644 index cf91963..0000000 --- a/docs/source/examples/static.rst +++ /dev/null @@ -1,56 +0,0 @@ -Static NeRFs -=================================== - -Performance Overview --------------------- - -+----------------------+----------------+----------------------------------+-----------------------+--------------------------+ -| Methods | Dataset | Training Time :math:`\downarrow` | PSNR :math:`\uparrow` | LPIPS :math:`\downarrow` | -+======================+================+==================================+=======================+==========================+ -| TensoRF `[1]`_ | Tanks&Temple | 19min | 28.11 | 0.167 | -+----------------------+----------------+----------------------------------+-----------------------+--------------------------+ -| *+nerfacc (occgrid)* | | 14min | 28.06 | 0.174 | -+----------------------+----------------+----------------------------------+-----------------------+--------------------------+ -| TensoRF `[1]`_ | NeRF-Synthetic | 10.3min | 32.73 | 0.049 | -+----------------------+----------------+----------------------------------+-----------------------+--------------------------+ -| *+nerfacc (occgrid)* | | 7.1min | 32.52 | 0.054 | -+----------------------+----------------+----------------------------------+-----------------------+--------------------------+ -| NeRF `[2]`_ | NeRF-Synthetic | days | 31.00 | 0.047 | -+----------------------+----------------+----------------------------------+-----------------------+--------------------------+ -| *+nerfacc (occgrid)* | | 1hr | 31.55 | 0.072 | -+----------------------+----------------+----------------------------------+-----------------------+--------------------------+ -| Instant-NGP `[3]`_ | NeRF-Synthetic | 4.4min | 32.35 | | -+----------------------+----------------+----------------------------------+-----------------------+--------------------------+ -| *+nerfacc (occgrid)* | | 4.5min | 33.11 | 0.053 | -+----------------------+----------------+----------------------------------+-----------------------+--------------------------+ -| *+nerfacc (propnet)* | | 4.0min | 31.76 | 0.062 | -+----------------------+----------------+----------------------------------+-----------------------+--------------------------+ -| Instant-NGP `[3]`_ | Mip-NeRF 360 | 5.3min | 25.93 | | -+----------------------+----------------+----------------------------------+-----------------------+--------------------------+ -| *+nerfacc (occgrid)* | | 5.0min | 26.41 | 0.353 | -+----------------------+----------------+----------------------------------+-----------------------+--------------------------+ -| *+nerfacc (propnet)* | | 4.9min | 27.58 | 0.292 | -+----------------------+----------------+----------------------------------+-----------------------+--------------------------+ - -Implementation Details ----------------------- - -.. toctree:: - :glob: - :maxdepth: 1 - - static/* - -| - -3rd-Party Use Cases -------------------- - -- `nerfstudio `_: A collaboration friendly studio for NeRFs. -- `modelscope `_: A collection of deep-learning algorithms. -- `sdfstudio `_: A Unified Framework for Surface Reconstruction. -- `instant-nsr-pl `_: Train NeuS in 10min. - -.. _`[1]`: https://arxiv.org/abs/2203.09517 -.. _`[2]`: https://arxiv.org/abs/2003.08934 -.. _`[3]`: https://arxiv.org/abs/2201.05989 \ No newline at end of file diff --git a/docs/source/examples/static/nerf.rst b/docs/source/examples/static/nerf.rst deleted file mode 100644 index b6650b0..0000000 --- a/docs/source/examples/static/nerf.rst +++ /dev/null @@ -1,38 +0,0 @@ -.. _`Vanilla NeRF Example`: - -Vanilla NeRF -==================== -See code `examples/train_mlp_nerf.py` at our `github repository`_ for details. - - -Radiance Field --------------- -We follow the original `NeRF`_ paper to implement a 8-layer-MLP radiance field (`examples/radiance_fields/mlp.py`) -with positional encoding. - -.. note:: - The vanilla Nerf paper uses two MLPs for course-to-fine sampling. Instead here we only use a - single MLP with more samples (1024). Both ways share the same spirit to do dense sampling - around the surface. Our fast rendering inheritly skip samples away from the surface - so we can simplly increase the number of samples with a single MLP, to achieve the same goal - with the coarse-to-fine sampling, without runtime or memory issue. - - -Benchmark: Nerf-Synthetic Dataset ---------------------------------- -*updated on 2022-10-08* - -Our experiments are conducted on a single NVIDIA TITAN RTX GPU. -The training memory footprint is about 10GB. - -+----------------------+-------+-------+---------+-------+-------+-------+-------+-------+-------+ -| PSNR | Lego | Mic |Materials| Chair |Hotdog | Ficus | Drums | Ship | MEAN | -| | | | | | | | | | | -+======================+=======+=======+=========+=======+=======+=======+=======+=======+=======+ -| NeRF (~ days) | 32.54 | 32.91 | 29.62 | 33.00 | 36.18 | 30.13 | 25.01 | 28.65 | 31.00 | -+----------------------+-------+-------+---------+-------+-------+-------+-------+-------+-------+ -| Ours (~ 1 hr) | 33.69 | 33.76 | 29.73 | 33.32 | 35.80 | 32.52 | 25.39 | 28.18 | 31.55 | -+----------------------+-------+-------+---------+-------+-------+-------+-------+-------+-------+ - -.. _`github repository`: https://github.com/KAIR-BAIR/nerfacc/ -.. _`NeRF`: https://arxiv.org/abs/2003.08934 diff --git a/docs/source/examples/static/ngp.rst b/docs/source/examples/static/ngp.rst deleted file mode 100644 index c5ac7b5..0000000 --- a/docs/source/examples/static/ngp.rst +++ /dev/null @@ -1,88 +0,0 @@ -.. _`Instant-NGP Example`: - -Instant-NGP -==================== - -See code `examples/train_ngp_nerf_occ.py` and `examples/train_ngp_nerf_prop.py` at our -`github repository`_ for details. - - -Radiance Field --------------- -We follow the `Instant-NGP`_ paper to implement the radiance field (`examples/radiance_fields/ngp.py`), -and aligns the hyperparameters (e.g., hashencoder, mlp) with the paper. It is build on top of the -`tiny-cuda-nn`_ library. - - -Benchmark: Nerf-Synthetic Dataset ---------------------------------- -*updated on 2023-04-04 with nerfacc==0.5.0* - -Our experiments are conducted on a single NVIDIA TITAN RTX GPU. -The training memory footprint is about 3GB. - -.. note:: - - The Instant-NGP paper makes use of the alpha channel in the images to apply random background - augmentation during training. For fair comparision, we rerun their code with a constant white - background during both training and testing. Also it is worth to mention that we didn't strictly - follow the training receipe in the Instant-NGP paper, such as the learning rate schedule etc, as - the purpose of this benchmark is to showcase instead of reproducing the paper. - -+-----------------------+-------+-------+---------+-------+-------+-------+-------+-------+-------+ -| PSNR | Lego | Mic |Materials| Chair |Hotdog | Ficus | Drums | Ship | MEAN | -| | | | | | | | | | | -+=======================+=======+=======+=========+=======+=======+=======+=======+=======+=======+ -|Instant-NGP 35k steps | 35.87 | 36.22 | 29.08 | 35.10 | 37.48 | 30.61 | 23.85 | 30.62 | 32.35 | -+-----------------------+-------+-------+---------+-------+-------+-------+-------+-------+-------+ -| training time | 309s | 258s | 256s | 316s | 292s | 207s | 218s | 250s | 263s | -+-----------------------+-------+-------+---------+-------+-------+-------+-------+-------+-------+ -|Ours (occ) 20k steps | 35.67 | 36.85 | 29.60 | 35.71 | 37.37 | 33.95 | 25.44 | 30.29 | 33.11 | -+-----------------------+-------+-------+---------+-------+-------+-------+-------+-------+-------+ -| training time | 288s | 260s | 253s | 326s | 272s | 249s | 252s | 251s | 269s | -+-----------------------+-------+-------+---------+-------+-------+-------+-------+-------+-------+ -|Ours (prop) 20k steps | 34.04 | 34.56 | 28.76 | 34.21 | 36.44 | 31.41 | 24.81 | 29.85 | 31.76 | -+-----------------------+-------+-------+---------+-------+-------+-------+-------+-------+-------+ -| training time | 225s | 235s | 235s | 240s | 239s | 242s | 258s | 247s | 240s | -+-----------------------+-------+-------+---------+-------+-------+-------+-------+-------+-------+ - - -Benchmark: Mip-NeRF 360 Dataset ---------------------------------- -*updated on 2023-04-04 with nerfacc==0.5.0* - -Our experiments are conducted on a single NVIDIA TITAN RTX GPU. - -.. note:: - `Ours (prop)` combines the proposal network (:class:`nerfacc.PropNetEstimator`) with the - Instant-NGP radiance field. This is exactly what the `Nerfacto`_ model is doing in the - `nerfstudio`_ project. In fact, the hyperparameters for `Ours (prop)` in this experiment - are aligned with the `Nerfacto`_ model. - -+-----------------------+-------+-------+---------+-------+-------+-------+-------+-------+ -| PSNR |Bicycle| Garden| Stump | Bonsai|Counter|Kitchen| Room | MEAN | -| | | | | | | | | | -+=======================+=======+=======+=========+=======+=======+=======+=======+=======+ -|NeRF++ (~days) | 22.64 | 24.32 | 23.34 | 29.15 | 26.38 | 27.80 | 28.87 | 26.21 | -+-----------------------+-------+-------+---------+-------+-------+-------+-------+-------+ -|Mip-NeRF 360 (~days) | 24.37 | 26.98 | 26.40 | 33.46 | 29.55 | 32.23 | 31.63 | 29.23 | -+-----------------------+-------+-------+---------+-------+-------+-------+-------+-------+ -|Instant-NGP 35k steps | 22.40 | 24.86 | 23.17 | 24.41 | 27.38 | 29.07 | 30.24 | 25.93 | -+-----------------------+-------+-------+---------+-------+-------+-------+-------+-------+ -| training time | 301s | 339s | 295s | 279s | 339s | 366s | 317s | 319s | -+-----------------------+-------+-------+---------+-------+-------+-------+-------+-------+ -|Ours (occ) 20k steps | 22.40 | 23.94 | 22.98 | 30.09 | 26.84 | 28.03 | 30.60 | 26.41 | -+-----------------------+-------+-------+---------+-------+-------+-------+-------+-------+ -| training time | 277s | 302s | 299s | 278s | 315s | 331s | 301s | 300s | -+-----------------------+-------+-------+---------+-------+-------+-------+-------+-------+ -|Ours (prop) 20k steps | 23.23 | 25.42 | 25.24 | 30.71 | 26.74 | 30.70 | 30.99 | 27.58 | -+-----------------------+-------+-------+---------+-------+-------+-------+-------+-------+ -| training time | 276s | 293s | 291s | 291s | 291s | 295s | 287s | 289s | -+-----------------------+-------+-------+---------+-------+-------+-------+-------+-------+ - - -.. _`github repository`: https://github.com/KAIR-BAIR/nerfacc/ -.. _`Instant-NGP`: https://arxiv.org/abs/2201.05989 -.. _`tiny-cuda-nn`: https://github.com/NVlabs/tiny-cuda-nn -.. _`Nerfacto`: https://docs.nerf.studio/en/latest/nerfology/methods/nerfacto.html -.. _`nerfstudio`: https://docs.nerf.studio/en/latest/ \ No newline at end of file diff --git a/docs/source/examples/static/tensorf.rst b/docs/source/examples/static/tensorf.rst deleted file mode 100644 index 83a5147..0000000 --- a/docs/source/examples/static/tensorf.rst +++ /dev/null @@ -1,49 +0,0 @@ -.. _`TensoRF Example`: - -TensoRF -==================== - -In this example we showcase how to plug the nerfacc library into the *official* codebase -of `TensoRF `_. See -`our forked repo `_ -for details. - - -Benchmark: NeRF-Synthetic Dataset ---------------------------------- -*updated on 2023-04-04 with nerfacc==0.5.0* - -Our experiments are conducted on a single NVIDIA GeForce RTX 2080 Ti. - -+-----------------------+-------+-------+---------+-------+-------+-------+-------+-------+-------+ -| PSNR | Lego | Mic |Materials| Chair |Hotdog | Ficus | Drums | Ship | MEAN | -| | | | | | | | | | | -+=======================+=======+=======+=========+=======+=======+=======+=======+=======+=======+ -| TensoRF | 35.14 | 25.70 | 33.69 | 37.03 | 36.04 | 29.77 | 34.35 | 30.12 | 32.73 | -+-----------------------+-------+-------+---------+-------+-------+-------+-------+-------+-------+ -| training time | 504s | 522s | 633s | 648s | 584s | 824s | 464s | 759s | 617s | -+-----------------------+-------+-------+---------+-------+-------+-------+-------+-------+-------+ -| Ours (occ) | 35.05 | 25.70 | 33.54 | 36.99 | 35.62 | 29.76 | 34.08 | 29.39 | 32.52 | -+-----------------------+-------+-------+---------+-------+-------+-------+-------+-------+-------+ -| training time | 310s | 312s | 463s | 433s | 363s | 750s | 303s | 468s | 425s | -+-----------------------+-------+-------+---------+-------+-------+-------+-------+-------+-------+ - - -Benchmark: Tanks&Temples Dataset ---------------------------------- -*updated on 2023-04-04 with nerfacc==0.5.0* - -Our experiments are conducted on a single NVIDIA GeForce RTX 2080 Ti. - -+-----------------------+-------+-------------+--------+-------+-------+ -| PSNR | Barn | Caterpillar | Family | Truck | MEAN | -| | | | | | | -+=======================+=======+=============+========+=======+=======+ -| TensoRF | 26.88 | 25.48 | 33.48 | 26.59 | 28.11 | -+-----------------------+-------+-------------+--------+-------+-------+ -| training time | 24min | 19min | 15min | 18min | 19min | -+-----------------------+-------+-------------+--------+-------+-------+ -| Ours (occ) | 26.74 | 25.64 | 33.16 | 26.70 | 28.06 | -+-----------------------+-------+-------------+--------+-------+-------+ -| training time | 19min | 15min | 11min | 13min | 14min | -+-----------------------+-------+-------------+--------+-------+-------+ diff --git a/docs/source/index.rst b/docs/source/index.rst deleted file mode 100644 index 8ef1b7f..0000000 --- a/docs/source/index.rst +++ /dev/null @@ -1,169 +0,0 @@ -NerfAcc Documentation -=================================== - -NerfAcc is a PyTorch Nerf acceleration toolbox for both training and inference. It focus on -efficient sampling in the volumetric rendering pipeline of radiance fields, which is -universal and plug-and-play for most of the NeRFs. -With minimal modifications to the existing codebases, Nerfacc provides significant speedups -in training various recent NeRF papers. -**And it is pure Python interface with flexible APIs!** - -| - -.. image:: _static/images/teaser.jpg - :align: center - -| - -| Github: https://github.com/KAIR-BAIR/nerfacc -| Paper: https://arxiv.org/pdf/2305.04966.pdf -| Authors: `Ruilong Li`_, `Hang Gao`_, `Matthew Tancik`_, `Angjoo Kanazawa`_ - -.. note:: - - Though this repo only contains examples for single scene optimization, - we believe generalizable NeRFs across multiple scenes can also be accelerate with our - :class:`nerfacc.PropNetEstimator`. Examples will be added soon. - - -Installation: -------------- - -**Dependence**: Please install `Pytorch`_ first. - -The easist way is to install from PyPI. In this way it will build the CUDA code **on the first run** (JIT). - -.. code-block:: console - - $ pip install nerfacc - -Or install from source. In this way it will build the CUDA code during installation. - -.. code-block:: console - - $ pip install git+https://github.com/KAIR-BAIR/nerfacc.git - -We also provide pre-built wheels covering major combinations of Pytorch + CUDA versions. -See our `Github README`_ for what we support. - -.. code-block:: console - - // e.g. torch 1.13.0 + CUDA 11.7 - $ pip install nerfacc -f https://nerfacc-bucket.s3.us-west-2.amazonaws.com/whl/torch-1.13.0_cu117.html - - -Usage: -------------- - -The idea of NerfAcc is to perform efficient volumetric sampling with a computationally cheap estimator to discover surfaces. -So NerfAcc can work with any user-defined radiance field. To plug the NerfAcc rendering pipeline into your code and enjoy -the acceleration, you only need to define two functions with your radience field. - -- `sigma_fn`: Compute density at each sample. It will be used by the estimator - (e.g., :class:`nerfacc.OccGridEstimator`, :class:`nerfacc.PropNetEstimator`) to discover surfaces. -- `rgb_sigma_fn`: Compute color and density at each sample. It will be used by - :func:`nerfacc.rendering` to conduct differentiable volumetric rendering. This function - will receive gradients to update your radiance field. - -An simple example is like this: - -.. code-block:: python - - import torch - from torch import Tensor - import nerfacc - - radiance_field = ... # network: a NeRF model - rays_o: Tensor = ... # ray origins. (n_rays, 3) - rays_d: Tensor = ... # ray normalized directions. (n_rays, 3) - optimizer = ... # optimizer - - estimator = nerfacc.OccGridEstimator(...) - - def sigma_fn( - t_starts: Tensor, t_ends:Tensor, ray_indices: Tensor - ) -> Tensor: - """ Define how to query density for the estimator.""" - t_origins = rays_o[ray_indices] # (n_samples, 3) - t_dirs = rays_d[ray_indices] # (n_samples, 3) - positions = t_origins + t_dirs * (t_starts + t_ends)[:, None] / 2.0 - sigmas = radiance_field.query_density(positions) - return sigmas # (n_samples,) - - def rgb_sigma_fn( - t_starts: Tensor, t_ends: Tensor, ray_indices: Tensor - ) -> Tuple[Tensor, Tensor]: - """ Query rgb and density values from a user-defined radiance field. """ - t_origins = rays_o[ray_indices] # (n_samples, 3) - t_dirs = rays_d[ray_indices] # (n_samples, 3) - positions = t_origins + t_dirs * (t_starts + t_ends)[:, None] / 2.0 - rgbs, sigmas = radiance_field(positions, condition=t_dirs) - return rgbs, sigmas # (n_samples, 3), (n_samples,) - - # Efficient Raymarching: - # ray_indices: (n_samples,). t_starts: (n_samples,). t_ends: (n_samples,). - ray_indices, t_starts, t_ends = estimator.sampling( - rays_o, rays_d, sigma_fn=sigma_fn, near_plane=0.2, far_plane=1.0, - early_stop_eps=1e-4, alpha_thre=1e-2, - ) - - # Differentiable Volumetric Rendering. - # colors: (n_rays, 3). opaicity: (n_rays, 1). depth: (n_rays, 1). - color, opacity, depth, extras = nerfacc.rendering( - t_starts, t_ends, ray_indices, n_rays=rays_o.shape[0], rgb_sigma_fn=rgb_sigma_fn - ) - - # Optimize: Both the network and rays will receive gradients - optimizer.zero_grad() - loss = F.mse_loss(color, color_gt) - loss.backward() - optimizer.step() - - -Links: -------------- - -.. toctree:: - :glob: - :maxdepth: 1 - :caption: Methodology - - methodology/* - -.. toctree:: - :glob: - :maxdepth: 1 - :caption: Python API - - apis/* - -.. toctree:: - :glob: - :maxdepth: 1 - :caption: Example Usages and Benchmarks - - examples/* - -.. toctree:: - :maxdepth: 1 - :caption: Projects - - nerfstudio - sdfstudio - instant-nsr-pl - -.. _`vanilla Nerf`: https://arxiv.org/abs/2003.08934 -.. _`Instant-NGP Nerf`: https://arxiv.org/abs/2201.05989 -.. _`D-Nerf`: https://arxiv.org/abs/2011.13961 -.. _`MipNerf360`: https://arxiv.org/abs/2111.12077 -.. _`pixel-Nerf`: https://arxiv.org/abs/2012.02190 -.. _`Nerf++`: https://arxiv.org/abs/2010.07492 - -.. _`Ruilong Li`: https://www.liruilong.cn/ -.. _`Hang Gao`: https://hangg7.com/ -.. _`Matthew Tancik`: https://www.matthewtancik.com/ -.. _`Angjoo Kanazawa`: https://people.eecs.berkeley.edu/~kanazawa/ - -.. _`Github README`: https://github.com/KAIR-BAIR/nerfacc#readme - -.. _`PyTorch`: https://pytorch.org/get-started/locally/ diff --git a/docs/source/methodology/coding.rst b/docs/source/methodology/coding.rst deleted file mode 100644 index 2d40972..0000000 --- a/docs/source/methodology/coding.rst +++ /dev/null @@ -1,107 +0,0 @@ -.. _`Efficient Coding`: - -Efficient Coding -================ - -Monitor the GPU Utilization ----------------------------- - -The rule of thumb is to maximize the computation power you have. When working with GPU, -that means to maximize the percentage of GPU kernels are running at the same time. This -can be monitored by the GPU utilization (last column) reported by the `nvidia-smi` command: - -.. image:: ../_static/images/coding/gpu_util.png - :align: center - -| - -If the GPU utilization is less than 100%, it means there are some GPU kernels are idling -from time to time (if not all the time). This is of course not good for the performance. -Under the hood, Pytorch will try to use all the GPU kernels to parellelize the computation, -but in most of the case you don't see 100%. Why is that? - - -The first reason is simplly that there might not be too much to parellelize, for which -we don't have too much to do other than increasing batch size. For example - -.. code-block:: python - - # Only 1000 threads are running at the same time to create this tensor. - # So we see 28% GPU utilization. - while True: torch.zeros((1000), device="cuda") - # Now we see 100% GPU utilization. - while True: torch.zeros((10000000), device="cuda") - -The second reason, which is more common, is that there is *CPU-GPU synchronization* -happening in the code. For example: - -.. code-block:: python - - data = torch.rand((10000000), device="cuda") - mask = data > 0.5 - ids = torch.where(mask)[0] - assert torch.all(data[mask] == data[ids]) - - # 100% GPU utilization. - while True: data[ids] - # 95% GPU utilization. - while True: data[mask] - -Besides, if there are many cpu operations in the pipeline, such as data loading and -preprocessing, it might also cause the GPU utilization to be low. In this case, you -can try to use `torch.utils.data.DataLoader` to overlap the data processing time -with the GPU computation time. - -Avoid CPU-GPU Synchronization ------------------------------ - -In the above example, if you time your code, you will see a significant difference: - -.. code-block:: python - - # 177 µs ± 15.4 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each) - %timeit data[ids] - # 355 µs ± 466 ns per loop (mean ± std. dev. of 7 runs, 1,000 loops each) - %timeit data[mask] - -Explanation: In this case, the mask operation needs to decide the size of the output tensor, which -lives on CPU, based on the number of `True` values in the mask that lives on GPU. So -a synchronization is required. The index selection operation, on the other hand, -already knows the size of the output tensor based the size of the `ids`, so no -synchronization is required. - -You may argue in this case we can't really improve things because the `ids` are computed -from the `mask`, which would require the synchronization anyway (`torch.where`). However, -in many cases we can avoid the synchronization by carefully writing the code. For example: - -.. code-block:: python - - # no sync. 67.3 µs ± 5.01 ns per loop - while True: torch.zeros((10000000), device="cuda") - # sync. 13.7 ms ± 320 µs per loop - while True: torch.zeros((10000000)).to("cuda") - -Operations that require synchronization including `torch.where`, `tensor.item()`, -`print(tensor)`, `tensor.to(device)`, `torch.nonzero` etc. Just imagine those functions -have a inherit `torch.cuda.synchronize()` called under the hood. See the -`official guide `_ -if with more interests. - -In our library, :func:`nerfacc.traverse_grids` is a function that requires synchronization, -because it needs to know the size of the output tensor when traversing the grids. As a result, -sampling with :class:`nerfacc.OccGridEstimator` also requires synchronization. But there is -no walkaround in this case so just be aware of it. - - -Profiling ------------------------------ - -There are plenty of tools for profiling. My personal favorite is -`line_profiler `_ which will give you *per-line* runtime -of a function with a simple decorator `@profile`. It is very useful for finding where the bottleneck -is in your code. It is worth to note that due to the asynchronized nature of Pytorch code, you would -need to set `CUDA_LAUNCH_BLOCKING=1` when profiling your code (no matter which profiling tool you are using). -This variable will force CPU-GPU synchronization for every torch function (equavalent to add -`torch.cuda.synchronize()` everywhere), which can reveal the true runtime of each line of code. -And of course, with `CUDA_LAUNCH_BLOCKING=1` you would get slower total runtime, so don't forget to -remove it when you are done profiling. \ No newline at end of file diff --git a/docs/source/methodology/sampling.rst b/docs/source/methodology/sampling.rst deleted file mode 100644 index cbb191d..0000000 --- a/docs/source/methodology/sampling.rst +++ /dev/null @@ -1,127 +0,0 @@ -.. _`Efficient Sampling`: - -Efficient Sampling -=================================== - -Importance Sampling via Transmittance Estimator. -------------------------------------------------- - -Efficient sampling is a well-explored problem in Graphics, wherein the -emphasis is on identifying regions that make the most significant -contribution to the final rendering. This objective is generally accomplished -through importance sampling, which aims to distribute samples based on the -probability density function (PDF), denoted as :math:`p(t)`, between the range -of :math:`[t_n, t_f]`. By computing the cumulative distribution function (CDF) -through integration, *i.e.*, :math:`F(t) = \int_{t_n}^{t} p(v)\,dv`, -samples are generated using the inverse transform sampling method: - -.. math:: - - t = F^{-1}(u) \quad \text{where} \quad u \sim \mathcal{U}[0,1]. - -In volumetric rendering, the contribution of each sample to the final -rendering is expressed by the accumulation weights :math:`T(t)\sigma(t)`: - -.. math:: - - C(\mathbf{r}) = \int_{t_n}^{t_f} T(t)\,\sigma(t)\,c(t)\,dt - -Hence, the PDF for volumetric rendering is :math:`p(t) = T(t)\sigma(t)` -and the CDF is: - -.. math:: - - F(t) = \int_{t_n}^{t} T(v)\sigma(v)\,dv = 1 - T(t) - -Therefore, inverse sampling the CDF :math:`F(t)` is equivalent to inverse -sampling the transmittance :math:`T(t)`. A transmittance estimator is sufficient -to determine the optimal samples. We refer readers to the -`SIGGRAPH 2017 Course: Production Volume Rendering`_ for more details about this -concept if within interests. - -Occupancy Grid Estimator. ----------------------------- - -.. image:: ../_static/images/illustration_occgrid.png - :class: float-right - :width: 200px - -The idea of Occupancy Grid is to cache the density in the scene with a binaraized voxel grid. When -sampling, the ray marches through the grid with a preset step sizes, and skip the empty regions by querying -the voxel grid. Intuitively, the binaraized voxel grid is an *estimator* of the radiance field, with much -faster readout. This technique is proposed in `Instant-NGP`_ with highly optimized CUDA implementations. -More formally, The estimator describes a binaraized density distribution :math:`\hat{\sigma}` along -the ray with a conservative threshold :math:`\tau`: - -.. math:: - - \hat{\sigma}(t_i) = \mathbb{1}\big[\sigma(t_i) > \tau\big] - -Consequently, the piece-wise constant PDF can be expressed as - -.. math:: - - p(t_i) = \hat{\sigma}(t_i) / \sum_{j=1}^{n} \hat{\sigma}(t_j) - -and the piece-wise linear transmittance estimator is - -.. math:: - - T(t_i) = 1 - \sum_{j=1}^{i-1}\hat{\sigma}(t_j) / \sum_{j=1}^{n} \hat{\sigma}(t_j) - -See the figure below for an illustration. - -.. rst-class:: clear-both - -.. image:: ../_static/images/plot_occgrid.png - :align: center - -| - -In `nerfacc`, this is implemented via the :class:`nerfacc.OccGridEstimator` class. - -Proposal Network Estimator. ------------------------------ - -.. image:: ../_static/images/illustration_propnet.png - :class: float-right - :width: 200px - -Another type of approach is to directly estimate the PDF along the ray with discrete samples. -In `vanilla NeRF`_, the coarse MLP is trained using volumetric rendering loss to output a set of -densities :math:`{\sigma(t_i)}`. This allows for the creation of a piece-wise constant PDF: - -.. math:: - - p(t_i) = \sigma(t_i)\exp(-\sigma(t_i)\,dt) - -and a piece-wise linear transmittance estimator: - -.. math:: - - T(t_i) = \exp(-\sum_{j=1}^{i-1}\sigma(t_i)\,dt) - -This approach was further improved in `Mip-NeRF 360`_ with a PDF matching loss, which allows for -the use of a much smaller MLP in the coarse level, namely Proposal Network, to speedup the -PDF construction. - -See the figure below for an illustration. - -.. image:: ../_static/images/plot_propnet.png - :align: center - -| - -In `nerfacc`, this is implemented via the :class:`nerfacc.PropNetEstimator` class. - -Which Estimator to use? ------------------------ -- :class:`nerfacc.OccGridEstimator` is a generally more efficient when most of the space in the scene is empty, such as in the case of `NeRF-Synthetic`_ dataset. But it still places samples within occluded areas that contribute little to the final rendering (e.g., the last sample in the above illustration). - -- :class:`nerfacc.PropNetEstimator` generally provide more accurate transmittance estimation, enabling samples to concentrate more on high-contribution areas (e.g., surfaces) and to be more spread out in both empty and occluded regions. Also this method works nicely on unbouned scenes as it does not require a preset bounding box of the scene. 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+ margin: 0 !important; +} + +#interpolation-image-wrapper { + width: 100%; +} +#interpolation-image-wrapper img { + border-radius: 5px; +} + +.hero.teaser { + flex: 1; /* Each section takes equal width */ + text-align: center; /* Center the text */ +} + +.videos-container { + display: flex; + justify-content: center; /* Adjust this as needed */ + margin-bottom: 30px; /* Add space after the videos container */ +} + +.video-container { + width: 29%; /* Adjusted to slightly less than 30% */ + margin-right: 2%; /* Space between the videos */ + display: flex; + flex-direction: column; + align-items: center; +} + +.video-container:last-child { + margin-right: 0; /* Remove margin for the last video container */ +} + +.video-container video { + width: 100%; /* Make video take full width of its container */ + height: auto; /* Maintain aspect ratio */ + margin-bottom: 20px; /* Add space below the video */ +} + + +video { + width: 100%; /* Make video take full width of its container */ + height: auto; /* Maintain aspect ratio */ +} + + +.videos-container-2 { + display: flex; + justify-content: center; + align-items: center; + width: 100%; /* Set the desired width */ + height: 100%; /* Set the desired height */ + margin: 0 auto; /* Center the container horizontally */ +} + +.video-container-2 { +width: 60%; /* Adjusted to slightly less than 30% */ +margin-right: 2%; /* Space between the videos */ +display: flex; +flex-direction: column; +align-items: center; +margin-bottom: 20px; +} + +.video-container-2:last-child { +margin-right: 0; /* Remove margin for the last video container */ +} + +.video-container-2 video { +width: 100%; +height: 100%; +object-fit: cover; +} + +.centered-line { + width: 75%; /* Adjust the width as needed */ + height: 10px; /* This controls the thickness of the line */ + background-color: blue; /* Choose the color of the line */ + margin: 0 auto; /* This centers the line horizontally */ + margin-top: 10px; /* Space above the line */ + margin-bottom: 50px; /* Space below the line */ + box-shadow: 0px 2px 5px rgba(0, 0, 0, 0.3); + background: linear-gradient(to right, #1e5799, #2989d8, #207cca, #7db9e8, #207cca, #2989d8, #1e5799); + border-radius: 5px; +} + + +.centered-line2 { + width: 75%; /* Adjust the width as needed */ + height: 10px; /* This controls the thickness of the line */ + background-color: black; /* Choose the color of the line */ + margin: 0 auto; /* This centers the line horizontally */ + margin-top: 1px; /* Space above the line */ + margin-bottom: 1px; /* Space below the line */ + box-shadow: 0px 2px 5px rgba(0, 0, 0, 0.3); + border-radius: 5px; + background: linear-gradient(to right, white, black, white); +} + +.subtitle { + margin-top: 10px; /* Adjust the value to create more or less space */ +} diff --git a/docs/static/css/slide.css b/docs/static/css/slide.css new file mode 100644 index 0000000..5655e57 --- /dev/null +++ b/docs/static/css/slide.css @@ -0,0 +1,37 @@ +.slider-wrapper { + display: flex; + justify-content: center; + align-items: center; + height: 400px; /* Adjust to your preference */ + margin: 0 auto; /* Center horizontally */ + } + + .img-comp-container { + position: relative; + width: 500px; /* Adjust to your preference */ + } + + .img-comp-img { + position: absolute; + width: auto; + height: 100%; + overflow: hidden; + } + + .img-comp-img img { + display: block; + width: auto; + height: 100%; + } + + .img-comp-slider { + position: absolute; + z-index: 9; + cursor: ew-resize; + /* Style the slider (optional) */ + background-color: #2196F3; + opacity: 0.7; + height: 40px; + width: 10px; + border-radius: 5px; + } \ No newline at end of file diff --git a/docs/static/images/sgld-uni_35000_512_0.png b/docs/static/images/sgld-uni_35000_512_0.png new file mode 100644 index 0000000..52b2e61 Binary files /dev/null and b/docs/static/images/sgld-uni_35000_512_0.png differ diff --git a/docs/static/images/teaser.png b/docs/static/images/teaser.png new file mode 100644 index 0000000..f52e168 Binary files /dev/null and b/docs/static/images/teaser.png differ diff --git a/docs/static/images/uniform_35000_512_0.png b/docs/static/images/uniform_35000_512_0.png new file mode 100644 index 0000000..7144a0b Binary files /dev/null and b/docs/static/images/uniform_35000_512_0.png differ diff --git a/docs/static/js/bulma-carousel.js b/docs/static/js/bulma-carousel.js new file mode 100644 index 0000000..66ef3de --- /dev/null +++ b/docs/static/js/bulma-carousel.js @@ -0,0 +1,2371 @@ +(function webpackUniversalModuleDefinition(root, factory) { + if(typeof exports === 'object' && typeof module === 'object') + module.exports = factory(); + else if(typeof define === 'function' && define.amd) + define([], factory); + else if(typeof exports === 'object') + exports["bulmaCarousel"] = factory(); + else + root["bulmaCarousel"] = factory(); +})(typeof self !== 'undefined' ? self : this, function() { +return /******/ (function(modules) { // webpackBootstrap +/******/ // The module cache +/******/ var installedModules = {}; +/******/ +/******/ // The require function +/******/ function __webpack_require__(moduleId) { +/******/ +/******/ // Check if module is in cache +/******/ if(installedModules[moduleId]) { +/******/ return installedModules[moduleId].exports; +/******/ } +/******/ // Create a new module (and put it into the cache) +/******/ var module = installedModules[moduleId] = { +/******/ i: moduleId, +/******/ l: false, +/******/ exports: {} +/******/ }; +/******/ +/******/ // Execute the module function +/******/ modules[moduleId].call(module.exports, module, module.exports, __webpack_require__); +/******/ +/******/ // Flag the module as loaded +/******/ module.l = true; +/******/ +/******/ // Return the exports of the module +/******/ return module.exports; +/******/ } +/******/ +/******/ +/******/ // expose the modules object (__webpack_modules__) +/******/ __webpack_require__.m = modules; +/******/ +/******/ // expose the module cache +/******/ __webpack_require__.c = installedModules; +/******/ +/******/ // define getter function for harmony exports +/******/ __webpack_require__.d = function(exports, name, getter) { +/******/ if(!__webpack_require__.o(exports, name)) { +/******/ Object.defineProperty(exports, name, { +/******/ configurable: false, +/******/ enumerable: true, +/******/ get: getter +/******/ }); +/******/ } +/******/ }; +/******/ +/******/ // getDefaultExport function for compatibility with non-harmony modules +/******/ __webpack_require__.n = function(module) { +/******/ var getter = module && module.__esModule ? +/******/ function getDefault() { return module['default']; } : +/******/ function getModuleExports() { return module; }; +/******/ __webpack_require__.d(getter, 'a', getter); +/******/ return getter; +/******/ }; +/******/ +/******/ // Object.prototype.hasOwnProperty.call +/******/ __webpack_require__.o = function(object, property) { return Object.prototype.hasOwnProperty.call(object, property); }; +/******/ +/******/ // __webpack_public_path__ +/******/ __webpack_require__.p = ""; +/******/ +/******/ // Load entry module and return exports +/******/ return __webpack_require__(__webpack_require__.s = 5); +/******/ }) +/************************************************************************/ +/******/ ([ +/* 0 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* unused harmony export addClasses */ +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "d", function() { return removeClasses; }); +/* unused harmony export show */ +/* unused harmony export hide */ +/* unused harmony export offset */ +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "e", function() { return width; }); +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "b", function() { return height; }); +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "c", function() { return outerHeight; }); +/* unused harmony export outerWidth */ +/* unused harmony export position */ +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "a", function() { return css; }); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__type__ = __webpack_require__(2); + + +var addClasses = function addClasses(element, classes) { + classes = Array.isArray(classes) ? classes : classes.split(' '); + classes.forEach(function (cls) { + element.classList.add(cls); + }); +}; + +var removeClasses = function removeClasses(element, classes) { + classes = Array.isArray(classes) ? classes : classes.split(' '); + classes.forEach(function (cls) { + element.classList.remove(cls); + }); +}; + +var show = function show(elements) { + elements = Array.isArray(elements) ? elements : [elements]; + elements.forEach(function (element) { + element.style.display = ''; + }); +}; + +var hide = function hide(elements) { + elements = Array.isArray(elements) ? elements : [elements]; + elements.forEach(function (element) { + element.style.display = 'none'; + }); +}; + +var offset = function offset(element) { + var rect = element.getBoundingClientRect(); + return { + top: rect.top + document.body.scrollTop, + left: rect.left + document.body.scrollLeft + }; +}; + +// returns an element's width +var width = function width(element) { + return element.getBoundingClientRect().width || element.offsetWidth; +}; +// returns an element's height +var height = function height(element) { + return element.getBoundingClientRect().height || element.offsetHeight; +}; + +var outerHeight = function outerHeight(element) { + var withMargin = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : false; + + var height = element.offsetHeight; + if (withMargin) { + var style = window.getComputedStyle(element); + height += parseInt(style.marginTop) + parseInt(style.marginBottom); + } + return height; +}; + +var outerWidth = function outerWidth(element) { + var withMargin = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : false; + + var width = element.offsetWidth; + if (withMargin) { + var style = window.getComputedStyle(element); + width += parseInt(style.marginLeft) + parseInt(style.marginRight); + } + return width; +}; + +var position = function position(element) { + return { + left: element.offsetLeft, + top: element.offsetTop + }; +}; + +var css = function css(element, obj) { + if (!obj) { + return window.getComputedStyle(element); + } + if (Object(__WEBPACK_IMPORTED_MODULE_0__type__["b" /* isObject */])(obj)) { + var style = ''; + Object.keys(obj).forEach(function (key) { + style += key + ': ' + obj[key] + ';'; + }); + + element.style.cssText += style; + } +}; + +/***/ }), +/* 1 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony export (immutable) */ __webpack_exports__["a"] = detectSupportsPassive; +function detectSupportsPassive() { + var supportsPassive = false; + + try { + var opts = Object.defineProperty({}, 'passive', { + get: function get() { + supportsPassive = true; + } + }); + + window.addEventListener('testPassive', null, opts); + window.removeEventListener('testPassive', null, opts); + } catch (e) {} + + return supportsPassive; +} + +/***/ }), +/* 2 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "a", function() { return isFunction; }); +/* unused harmony export isNumber */ +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "c", function() { return isString; }); +/* unused harmony export isDate */ +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "b", function() { return isObject; }); +/* unused harmony export isEmptyObject */ +/* unused harmony export isNode */ +/* unused harmony export isVideo */ +/* unused harmony export isHTML5 */ +/* unused harmony export isIFrame */ +/* unused harmony export isYoutube */ +/* unused harmony export isVimeo */ +var _typeof = typeof Symbol === "function" && typeof Symbol.iterator === "symbol" ? function (obj) { return typeof obj; } : function (obj) { return obj && typeof Symbol === "function" && obj.constructor === Symbol && obj !== Symbol.prototype ? "symbol" : typeof obj; }; + +var isFunction = function isFunction(unknown) { + return typeof unknown === 'function'; +}; +var isNumber = function isNumber(unknown) { + return typeof unknown === "number"; +}; +var isString = function isString(unknown) { + return typeof unknown === 'string' || !!unknown && (typeof unknown === 'undefined' ? 'undefined' : _typeof(unknown)) === 'object' && Object.prototype.toString.call(unknown) === '[object String]'; +}; +var isDate = function isDate(unknown) { + return (Object.prototype.toString.call(unknown) === '[object Date]' || unknown instanceof Date) && !isNaN(unknown.valueOf()); +}; +var isObject = function isObject(unknown) { + return (typeof unknown === 'function' || (typeof unknown === 'undefined' ? 'undefined' : _typeof(unknown)) === 'object' && !!unknown) && !Array.isArray(unknown); +}; +var isEmptyObject = function isEmptyObject(unknown) { + for (var name in unknown) { + if (unknown.hasOwnProperty(name)) { + return false; + } + } + return true; +}; + +var isNode = function isNode(unknown) { + return !!(unknown && unknown.nodeType === HTMLElement | SVGElement); +}; +var isVideo = function isVideo(unknown) { + return isYoutube(unknown) || isVimeo(unknown) || isHTML5(unknown); +}; +var isHTML5 = function isHTML5(unknown) { + return isNode(unknown) && unknown.tagName === 'VIDEO'; +}; +var isIFrame = function isIFrame(unknown) { + return isNode(unknown) && unknown.tagName === 'IFRAME'; +}; +var isYoutube = function isYoutube(unknown) { + return isIFrame(unknown) && !!unknown.src.match(/\/\/.*?youtube(-nocookie)?\.[a-z]+\/(watch\?v=[^&\s]+|embed)|youtu\.be\/.*/); +}; +var isVimeo = function isVimeo(unknown) { + return isIFrame(unknown) && !!unknown.src.match(/vimeo\.com\/video\/.*/); +}; + +/***/ }), +/* 3 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _toConsumableArray(arr) { if (Array.isArray(arr)) { for (var i = 0, arr2 = Array(arr.length); i < arr.length; i++) { arr2[i] = arr[i]; } return arr2; } else { return Array.from(arr); } } + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + +var EventEmitter = function () { + function EventEmitter() { + var events = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : []; + + _classCallCheck(this, EventEmitter); + + this.events = new Map(events); + } + + _createClass(EventEmitter, [{ + key: "on", + value: function on(name, cb) { + var _this = this; + + this.events.set(name, [].concat(_toConsumableArray(this.events.has(name) ? this.events.get(name) : []), [cb])); + + return function () { + return _this.events.set(name, _this.events.get(name).filter(function (fn) { + return fn !== cb; + })); + }; + } + }, { + key: "emit", + value: function emit(name) { + for (var _len = arguments.length, args = Array(_len > 1 ? _len - 1 : 0), _key = 1; _key < _len; _key++) { + args[_key - 1] = arguments[_key]; + } + + return this.events.has(name) && this.events.get(name).map(function (fn) { + return fn.apply(undefined, args); + }); + } + }]); + + return EventEmitter; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (EventEmitter); + +/***/ }), +/* 4 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + +var Coordinate = function () { + function Coordinate() { + var x = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : 0; + var y = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : 0; + + _classCallCheck(this, Coordinate); + + this._x = x; + this._y = y; + } + + _createClass(Coordinate, [{ + key: 'add', + value: function add(coord) { + return new Coordinate(this._x + coord._x, this._y + coord._y); + } + }, { + key: 'sub', + value: function sub(coord) { + return new Coordinate(this._x - coord._x, this._y - coord._y); + } + }, { + key: 'distance', + value: function distance(coord) { + var deltaX = this._x - coord._x; + var deltaY = this._y - coord._y; + + return Math.sqrt(Math.pow(deltaX, 2) + Math.pow(deltaY, 2)); + } + }, { + key: 'max', + value: function max(coord) { + var x = Math.max(this._x, coord._x); + var y = Math.max(this._y, coord._y); + + return new Coordinate(x, y); + } + }, { + key: 'equals', + value: function equals(coord) { + if (this == coord) { + return true; + } + if (!coord || coord == null) { + return false; + } + return this._x == coord._x && this._y == coord._y; + } + }, { + key: 'inside', + value: function inside(northwest, southeast) { + if (this._x >= northwest._x && this._x <= southeast._x && this._y >= northwest._y && this._y <= southeast._y) { + + return true; + } + return false; + } + }, { + key: 'constrain', + value: function constrain(min, max) { + if (min._x > max._x || min._y > max._y) { + return this; + } + + var x = this._x, + y = this._y; + + if (min._x !== null) { + x = Math.max(x, min._x); + } + if (max._x !== null) { + x = Math.min(x, max._x); + } + if (min._y !== null) { + y = Math.max(y, min._y); + } + if (max._y !== null) { + y = Math.min(y, max._y); + } + + return new Coordinate(x, y); + } + }, { + key: 'reposition', + value: function reposition(element) { + element.style['top'] = this._y + 'px'; + element.style['left'] = this._x + 'px'; + } + }, { + key: 'toString', + value: function toString() { + return '(' + this._x + ',' + this._y + ')'; + } + }, { + key: 'x', + get: function get() { + return this._x; + }, + set: function set() { + var value = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : 0; + + this._x = value; + return this; + } + }, { + key: 'y', + get: function get() { + return this._y; + }, + set: function set() { + var value = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : 0; + + this._y = value; + return this; + } + }]); + + return Coordinate; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (Coordinate); + +/***/ }), +/* 5 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +Object.defineProperty(__webpack_exports__, "__esModule", { value: true }); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__utils_index__ = __webpack_require__(6); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_1__utils_css__ = __webpack_require__(0); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_2__utils_type__ = __webpack_require__(2); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_3__utils_eventEmitter__ = __webpack_require__(3); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_4__components_autoplay__ = __webpack_require__(7); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_5__components_breakpoint__ = __webpack_require__(9); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_6__components_infinite__ = __webpack_require__(10); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_7__components_loop__ = __webpack_require__(11); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_8__components_navigation__ = __webpack_require__(13); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_9__components_pagination__ = __webpack_require__(15); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_10__components_swipe__ = __webpack_require__(18); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_11__components_transitioner__ = __webpack_require__(19); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_12__defaultOptions__ = __webpack_require__(22); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_13__templates__ = __webpack_require__(23); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_14__templates_item__ = __webpack_require__(24); +var _extends = Object.assign || function (target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i]; for (var key in source) { if (Object.prototype.hasOwnProperty.call(source, key)) { target[key] = source[key]; } } } return target; }; + +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _defineProperty(obj, key, value) { if (key in obj) { Object.defineProperty(obj, key, { value: value, enumerable: true, configurable: true, writable: true }); } else { obj[key] = value; } return obj; } + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + +function _possibleConstructorReturn(self, call) { if (!self) { throw new ReferenceError("this hasn't been initialised - super() hasn't been called"); } return call && (typeof call === "object" || typeof call === "function") ? call : self; } + +function _inherits(subClass, superClass) { if (typeof superClass !== "function" && superClass !== null) { throw new TypeError("Super expression must either be null or a function, not " + typeof superClass); } subClass.prototype = Object.create(superClass && superClass.prototype, { constructor: { value: subClass, enumerable: false, writable: true, configurable: true } }); if (superClass) Object.setPrototypeOf ? Object.setPrototypeOf(subClass, superClass) : subClass.__proto__ = superClass; } + + + + + + + + + + + + + + + + + + + +var bulmaCarousel = function (_EventEmitter) { + _inherits(bulmaCarousel, _EventEmitter); + + function bulmaCarousel(selector) { + var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {}; + + _classCallCheck(this, bulmaCarousel); + + var _this = _possibleConstructorReturn(this, (bulmaCarousel.__proto__ || Object.getPrototypeOf(bulmaCarousel)).call(this)); + + _this.element = Object(__WEBPACK_IMPORTED_MODULE_2__utils_type__["c" /* isString */])(selector) ? document.querySelector(selector) : selector; + // An invalid selector or non-DOM node has been provided. + if (!_this.element) { + throw new Error('An invalid selector or non-DOM node has been provided.'); + } + _this._clickEvents = ['click', 'touch']; + + // Use Element dataset values to override options + var elementConfig = _this.element.dataset ? Object.keys(_this.element.dataset).filter(function (key) { + return Object.keys(__WEBPACK_IMPORTED_MODULE_12__defaultOptions__["a" /* default */]).includes(key); + }).reduce(function (obj, key) { + return _extends({}, obj, _defineProperty({}, key, _this.element.dataset[key])); + }, {}) : {}; + // Set default options - dataset attributes are master + _this.options = _extends({}, __WEBPACK_IMPORTED_MODULE_12__defaultOptions__["a" /* default */], options, elementConfig); + + _this._id = Object(__WEBPACK_IMPORTED_MODULE_0__utils_index__["a" /* uuid */])('slider'); + + _this.onShow = _this.onShow.bind(_this); + + // Initiate plugin + _this._init(); + return _this; + } + + /** + * Initiate all DOM element containing datePicker class + * @method + * @return {Array} Array of all datePicker instances + */ + + + _createClass(bulmaCarousel, [{ + key: '_init', + + + /**************************************************** + * * + * PRIVATE FUNCTIONS * + * * + ****************************************************/ + /** + * Initiate plugin instance + * @method _init + * @return {Slider} Current plugin instance + */ + value: function _init() { + this._items = Array.from(this.element.children); + + // Load plugins + this._breakpoint = new __WEBPACK_IMPORTED_MODULE_5__components_breakpoint__["a" /* default */](this); + this._autoplay = new __WEBPACK_IMPORTED_MODULE_4__components_autoplay__["a" /* default */](this); + this._navigation = new __WEBPACK_IMPORTED_MODULE_8__components_navigation__["a" /* default */](this); + this._pagination = new __WEBPACK_IMPORTED_MODULE_9__components_pagination__["a" /* default */](this); + this._infinite = new __WEBPACK_IMPORTED_MODULE_6__components_infinite__["a" /* default */](this); + this._loop = new __WEBPACK_IMPORTED_MODULE_7__components_loop__["a" /* default */](this); + this._swipe = new __WEBPACK_IMPORTED_MODULE_10__components_swipe__["a" /* default */](this); + + this._build(); + + if (Object(__WEBPACK_IMPORTED_MODULE_2__utils_type__["a" /* isFunction */])(this.options.onReady)) { + this.options.onReady(this); + } + + return this; + } + + /** + * Build Slider HTML component and append it to the DOM + * @method _build + */ + + }, { + key: '_build', + value: function _build() { + var _this2 = this; + + // Generate HTML Fragment of template + this.node = document.createRange().createContextualFragment(Object(__WEBPACK_IMPORTED_MODULE_13__templates__["a" /* default */])(this.id)); + // Save pointers to template parts + this._ui = { + wrapper: this.node.firstChild, + container: this.node.querySelector('.slider-container') + + // Add slider to DOM + };this.element.appendChild(this.node); + this._ui.wrapper.classList.add('is-loading'); + this._ui.container.style.opacity = 0; + + this._transitioner = new __WEBPACK_IMPORTED_MODULE_11__components_transitioner__["a" /* default */](this); + + // Wrap all items by slide element + this._slides = this._items.map(function (item, index) { + return _this2._createSlide(item, index); + }); + + this.reset(); + + this._bindEvents(); + + this._ui.container.style.opacity = 1; + this._ui.wrapper.classList.remove('is-loading'); + } + + /** + * Bind all events + * @method _bindEvents + * @return {void} + */ + + }, { + key: '_bindEvents', + value: function _bindEvents() { + this.on('show', this.onShow); + } + }, { + key: '_unbindEvents', + value: function _unbindEvents() { + this.off('show', this.onShow); + } + }, { + key: '_createSlide', + value: function _createSlide(item, index) { + var slide = document.createRange().createContextualFragment(Object(__WEBPACK_IMPORTED_MODULE_14__templates_item__["a" /* default */])()).firstChild; + slide.dataset.sliderIndex = index; + slide.appendChild(item); + return slide; + } + + /** + * Calculate slider dimensions + */ + + }, { + key: '_setDimensions', + value: function _setDimensions() { + var _this3 = this; + + if (!this.options.vertical) { + if (this.options.centerMode) { + this._ui.wrapper.style.padding = '0px ' + this.options.centerPadding; + } + } else { + this._ui.wrapper.style.height = Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["c" /* outerHeight */])(this._slides[0]) * this.slidesToShow; + if (this.options.centerMode) { + this._ui.wrapper.style.padding = this.options.centerPadding + ' 0px'; + } + } + + this._wrapperWidth = Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["e" /* width */])(this._ui.wrapper); + this._wrapperHeight = Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["c" /* outerHeight */])(this._ui.wrapper); + + if (!this.options.vertical) { + this._slideWidth = Math.ceil(this._wrapperWidth / this.slidesToShow); + this._containerWidth = Math.ceil(this._slideWidth * this._slides.length); + this._ui.container.style.width = this._containerWidth + 'px'; + } else { + this._slideWidth = Math.ceil(this._wrapperWidth); + this._containerHeight = Math.ceil(Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["c" /* outerHeight */])(this._slides[0]) * this._slides.length); + this._ui.container.style.height = this._containerHeight + 'px'; + } + + this._slides.forEach(function (slide) { + slide.style.width = _this3._slideWidth + 'px'; + }); + } + }, { + key: '_setHeight', + value: function _setHeight() { + if (this.options.effect !== 'translate') { + this._ui.container.style.height = Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["c" /* outerHeight */])(this._slides[this.state.index]) + 'px'; + } + } + + // Update slides classes + + }, { + key: '_setClasses', + value: function _setClasses() { + var _this4 = this; + + this._slides.forEach(function (slide) { + Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["d" /* removeClasses */])(slide, 'is-active is-current is-slide-previous is-slide-next'); + if (Math.abs((_this4.state.index - 1) % _this4.state.length) === parseInt(slide.dataset.sliderIndex, 10)) { + slide.classList.add('is-slide-previous'); + } + if (Math.abs(_this4.state.index % _this4.state.length) === parseInt(slide.dataset.sliderIndex, 10)) { + slide.classList.add('is-current'); + } + if (Math.abs((_this4.state.index + 1) % _this4.state.length) === parseInt(slide.dataset.sliderIndex, 10)) { + slide.classList.add('is-slide-next'); + } + }); + } + + /**************************************************** + * * + * GETTERS and SETTERS * + * * + ****************************************************/ + + /** + * Get id of current datePicker + */ + + }, { + key: 'onShow', + + + /**************************************************** + * * + * EVENTS FUNCTIONS * + * * + ****************************************************/ + value: function onShow(e) { + this._navigation.refresh(); + this._pagination.refresh(); + this._setClasses(); + } + + /**************************************************** + * * + * PUBLIC FUNCTIONS * + * * + ****************************************************/ + + }, { + key: 'next', + value: function next() { + if (!this.options.loop && !this.options.infinite && this.state.index + this.slidesToScroll > this.state.length - this.slidesToShow && !this.options.centerMode) { + this.state.next = this.state.index; + } else { + this.state.next = this.state.index + this.slidesToScroll; + } + this.show(); + } + }, { + key: 'previous', + value: function previous() { + if (!this.options.loop && !this.options.infinite && this.state.index === 0) { + this.state.next = this.state.index; + } else { + this.state.next = this.state.index - this.slidesToScroll; + } + this.show(); + } + }, { + key: 'start', + value: function start() { + this._autoplay.start(); + } + }, { + key: 'pause', + value: function pause() { + this._autoplay.pause(); + } + }, { + key: 'stop', + value: function stop() { + this._autoplay.stop(); + } + }, { + key: 'show', + value: function show(index) { + var force = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : false; + + // If all slides are already visible then return + if (!this.state.length || this.state.length <= this.slidesToShow) { + return; + } + + if (typeof index === 'Number') { + this.state.next = index; + } + + if (this.options.loop) { + this._loop.apply(); + } + if (this.options.infinite) { + this._infinite.apply(); + } + + // If new slide is already the current one then return + if (this.state.index === this.state.next) { + return; + } + + this.emit('before:show', this.state); + this._transitioner.apply(force, this._setHeight.bind(this)); + this.emit('after:show', this.state); + + this.emit('show', this); + } + }, { + key: 'reset', + value: function reset() { + var _this5 = this; + + this.state = { + length: this._items.length, + index: Math.abs(this.options.initialSlide), + next: Math.abs(this.options.initialSlide), + prev: undefined + }; + + // Fix options + if (this.options.loop && this.options.infinite) { + this.options.loop = false; + } + if (this.options.slidesToScroll > this.options.slidesToShow) { + this.options.slidesToScroll = this.slidesToShow; + } + this._breakpoint.init(); + + if (this.state.index >= this.state.length && this.state.index !== 0) { + this.state.index = this.state.index - this.slidesToScroll; + } + if (this.state.length <= this.slidesToShow) { + this.state.index = 0; + } + + this._ui.wrapper.appendChild(this._navigation.init().render()); + this._ui.wrapper.appendChild(this._pagination.init().render()); + + if (this.options.navigationSwipe) { + this._swipe.bindEvents(); + } else { + this._swipe._bindEvents(); + } + + this._breakpoint.apply(); + // Move all created slides into slider + this._slides.forEach(function (slide) { + return _this5._ui.container.appendChild(slide); + }); + this._transitioner.init().apply(true, this._setHeight.bind(this)); + + if (this.options.autoplay) { + this._autoplay.init().start(); + } + } + + /** + * Destroy Slider + * @method destroy + */ + + }, { + key: 'destroy', + value: function destroy() { + var _this6 = this; + + this._unbindEvents(); + this._items.forEach(function (item) { + _this6.element.appendChild(item); + }); + this.node.remove(); + } + }, { + key: 'id', + get: function get() { + return this._id; + } + }, { + key: 'index', + set: function set(index) { + this._index = index; + }, + get: function get() { + return this._index; + } + }, { + key: 'length', + set: function set(length) { + this._length = length; + }, + get: function get() { + return this._length; + } + }, { + key: 'slides', + get: function get() { + return this._slides; + }, + set: function set(slides) { + this._slides = slides; + } + }, { + key: 'slidesToScroll', + get: function get() { + return this.options.effect === 'translate' ? this._breakpoint.getSlidesToScroll() : 1; + } + }, { + key: 'slidesToShow', + get: function get() { + return this.options.effect === 'translate' ? this._breakpoint.getSlidesToShow() : 1; + } + }, { + key: 'direction', + get: function get() { + return this.element.dir.toLowerCase() === 'rtl' || this.element.style.direction === 'rtl' ? 'rtl' : 'ltr'; + } + }, { + key: 'wrapper', + get: function get() { + return this._ui.wrapper; + } + }, { + key: 'wrapperWidth', + get: function get() { + return this._wrapperWidth || 0; + } + }, { + key: 'container', + get: function get() { + return this._ui.container; + } + }, { + key: 'containerWidth', + get: function get() { + return this._containerWidth || 0; + } + }, { + key: 'slideWidth', + get: function get() { + return this._slideWidth || 0; + } + }, { + key: 'transitioner', + get: function get() { + return this._transitioner; + } + }], [{ + key: 'attach', + value: function attach() { + var _this7 = this; + + var selector = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : '.slider'; + var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {}; + + var instances = new Array(); + + var elements = Object(__WEBPACK_IMPORTED_MODULE_2__utils_type__["c" /* isString */])(selector) ? document.querySelectorAll(selector) : Array.isArray(selector) ? selector : [selector]; + [].forEach.call(elements, function (element) { + if (typeof element[_this7.constructor.name] === 'undefined') { + var instance = new bulmaCarousel(element, options); + element[_this7.constructor.name] = instance; + instances.push(instance); + } else { + instances.push(element[_this7.constructor.name]); + } + }); + + return instances; + } + }]); + + return bulmaCarousel; +}(__WEBPACK_IMPORTED_MODULE_3__utils_eventEmitter__["a" /* default */]); + +/* harmony default export */ __webpack_exports__["default"] = (bulmaCarousel); + +/***/ }), +/* 6 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "a", function() { return uuid; }); +/* unused harmony export isRtl */ +/* unused harmony export defer */ +/* unused harmony export getNodeIndex */ +/* unused harmony export camelize */ +function _toConsumableArray(arr) { if (Array.isArray(arr)) { for (var i = 0, arr2 = Array(arr.length); i < arr.length; i++) { arr2[i] = arr[i]; } return arr2; } else { return Array.from(arr); } } + +var uuid = function uuid() { + var prefix = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : ''; + return prefix + ([1e7] + -1e3 + -4e3 + -8e3 + -1e11).replace(/[018]/g, function (c) { + return (c ^ crypto.getRandomValues(new Uint8Array(1))[0] & 15 >> c / 4).toString(16); + }); +}; +var isRtl = function isRtl() { + return document.documentElement.getAttribute('dir') === 'rtl'; +}; + +var defer = function defer() { + this.promise = new Promise(function (resolve, reject) { + this.resolve = resolve; + this.reject = reject; + }.bind(this)); + + this.then = this.promise.then.bind(this.promise); + this.catch = this.promise.catch.bind(this.promise); +}; + +var getNodeIndex = function getNodeIndex(node) { + return [].concat(_toConsumableArray(node.parentNode.children)).indexOf(node); +}; +var camelize = function camelize(str) { + return str.replace(/-(\w)/g, toUpper); +}; + +/***/ }), +/* 7 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__utils_eventEmitter__ = __webpack_require__(3); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_1__utils_device__ = __webpack_require__(8); +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + +function _possibleConstructorReturn(self, call) { if (!self) { throw new ReferenceError("this hasn't been initialised - super() hasn't been called"); } return call && (typeof call === "object" || typeof call === "function") ? call : self; } + +function _inherits(subClass, superClass) { if (typeof superClass !== "function" && superClass !== null) { throw new TypeError("Super expression must either be null or a function, not " + typeof superClass); } subClass.prototype = Object.create(superClass && superClass.prototype, { constructor: { value: subClass, enumerable: false, writable: true, configurable: true } }); if (superClass) Object.setPrototypeOf ? Object.setPrototypeOf(subClass, superClass) : subClass.__proto__ = superClass; } + + + + +var onVisibilityChange = Symbol('onVisibilityChange'); +var onMouseEnter = Symbol('onMouseEnter'); +var onMouseLeave = Symbol('onMouseLeave'); + +var defaultOptions = { + autoplay: false, + autoplaySpeed: 3000 +}; + +var Autoplay = function (_EventEmitter) { + _inherits(Autoplay, _EventEmitter); + + function Autoplay(slider) { + _classCallCheck(this, Autoplay); + + var _this = _possibleConstructorReturn(this, (Autoplay.__proto__ || Object.getPrototypeOf(Autoplay)).call(this)); + + _this.slider = slider; + + _this.onVisibilityChange = _this.onVisibilityChange.bind(_this); + _this.onMouseEnter = _this.onMouseEnter.bind(_this); + _this.onMouseLeave = _this.onMouseLeave.bind(_this); + return _this; + } + + _createClass(Autoplay, [{ + key: 'init', + value: function init() { + this._bindEvents(); + return this; + } + }, { + key: '_bindEvents', + value: function _bindEvents() { + document.addEventListener('visibilitychange', this.onVisibilityChange); + if (this.slider.options.pauseOnHover) { + this.slider.container.addEventListener(__WEBPACK_IMPORTED_MODULE_1__utils_device__["a" /* pointerEnter */], this.onMouseEnter); + this.slider.container.addEventListener(__WEBPACK_IMPORTED_MODULE_1__utils_device__["b" /* pointerLeave */], this.onMouseLeave); + } + } + }, { + key: '_unbindEvents', + value: function _unbindEvents() { + document.removeEventListener('visibilitychange', this.onVisibilityChange); + this.slider.container.removeEventListener(__WEBPACK_IMPORTED_MODULE_1__utils_device__["a" /* pointerEnter */], this.onMouseEnter); + this.slider.container.removeEventListener(__WEBPACK_IMPORTED_MODULE_1__utils_device__["b" /* pointerLeave */], this.onMouseLeave); + } + }, { + key: 'start', + value: function start() { + var _this2 = this; + + this.stop(); + if (this.slider.options.autoplay) { + this.emit('start', this); + this._interval = setInterval(function () { + if (!(_this2._hovering && _this2.slider.options.pauseOnHover)) { + if (!_this2.slider.options.centerMode && _this2.slider.state.next >= _this2.slider.state.length - _this2.slider.slidesToShow && !_this2.slider.options.loop && !_this2.slider.options.infinite) { + _this2.stop(); + } else { + _this2.slider.next(); + } + } + }, this.slider.options.autoplaySpeed); + } + } + }, { + key: 'stop', + value: function stop() { + this._interval = clearInterval(this._interval); + this.emit('stop', this); + } + }, { + key: 'pause', + value: function pause() { + var _this3 = this; + + var speed = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : 0; + + if (this.paused) { + return; + } + if (this.timer) { + this.stop(); + } + this.paused = true; + if (speed === 0) { + this.paused = false; + this.start(); + } else { + this.slider.on('transition:end', function () { + if (!_this3) { + return; + } + _this3.paused = false; + if (!_this3.run) { + _this3.stop(); + } else { + _this3.start(); + } + }); + } + } + }, { + key: 'onVisibilityChange', + value: function onVisibilityChange(e) { + if (document.hidden) { + this.stop(); + } else { + this.start(); + } + } + }, { + key: 'onMouseEnter', + value: function onMouseEnter(e) { + this._hovering = true; + if (this.slider.options.pauseOnHover) { + this.pause(); + } + } + }, { + key: 'onMouseLeave', + value: function onMouseLeave(e) { + this._hovering = false; + if (this.slider.options.pauseOnHover) { + this.pause(); + } + } + }]); + + return Autoplay; +}(__WEBPACK_IMPORTED_MODULE_0__utils_eventEmitter__["a" /* default */]); + +/* harmony default export */ __webpack_exports__["a"] = (Autoplay); + +/***/ }), +/* 8 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* unused harmony export isIE */ +/* unused harmony export isIETouch */ +/* unused harmony export isAndroid */ +/* unused harmony export isiPad */ +/* unused harmony export isiPod */ +/* unused harmony export isiPhone */ +/* unused harmony export isSafari */ +/* unused harmony export isUiWebView */ +/* unused harmony export supportsTouchEvents */ +/* unused harmony export supportsPointerEvents */ +/* unused harmony export supportsTouch */ +/* unused harmony export pointerDown */ +/* unused harmony export pointerMove */ +/* unused harmony export pointerUp */ +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "a", function() { return pointerEnter; }); +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "b", function() { return pointerLeave; }); +var isIE = window.navigator.pointerEnabled || window.navigator.msPointerEnabled; +var isIETouch = window.navigator.msPointerEnabled && window.navigator.msMaxTouchPoints > 1 || window.navigator.pointerEnabled && window.navigator.maxTouchPoints > 1; +var isAndroid = navigator.userAgent.match(/(Android);?[\s\/]+([\d.]+)?/); +var isiPad = navigator.userAgent.match(/(iPad).*OS\s([\d_]+)/); +var isiPod = navigator.userAgent.match(/(iPod)(.*OS\s([\d_]+))?/); +var isiPhone = !navigator.userAgent.match(/(iPad).*OS\s([\d_]+)/) && navigator.userAgent.match(/(iPhone\sOS)\s([\d_]+)/); +var isSafari = navigator.userAgent.toLowerCase().indexOf('safari') >= 0 && navigator.userAgent.toLowerCase().indexOf('chrome') < 0 && navigator.userAgent.toLowerCase().indexOf('android') < 0; +var isUiWebView = /(iPhone|iPod|iPad).*AppleWebKit(?!.*Safari)/i.test(navigator.userAgent); + +var supportsTouchEvents = !!('ontouchstart' in window); +var supportsPointerEvents = !!('PointerEvent' in window); +var supportsTouch = supportsTouchEvents || window.DocumentTouch && document instanceof DocumentTouch || navigator.maxTouchPoints; // IE >=11 +var pointerDown = !supportsTouch ? 'mousedown' : 'mousedown ' + (supportsTouchEvents ? 'touchstart' : 'pointerdown'); +var pointerMove = !supportsTouch ? 'mousemove' : 'mousemove ' + (supportsTouchEvents ? 'touchmove' : 'pointermove'); +var pointerUp = !supportsTouch ? 'mouseup' : 'mouseup ' + (supportsTouchEvents ? 'touchend' : 'pointerup'); +var pointerEnter = supportsTouch && supportsPointerEvents ? 'pointerenter' : 'mouseenter'; +var pointerLeave = supportsTouch && supportsPointerEvents ? 'pointerleave' : 'mouseleave'; + +/***/ }), +/* 9 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + +var onResize = Symbol('onResize'); + +var Breakpoints = function () { + function Breakpoints(slider) { + _classCallCheck(this, Breakpoints); + + this.slider = slider; + this.options = slider.options; + + this[onResize] = this[onResize].bind(this); + + this._bindEvents(); + } + + _createClass(Breakpoints, [{ + key: 'init', + value: function init() { + this._defaultBreakpoint = { + slidesToShow: this.options.slidesToShow, + slidesToScroll: this.options.slidesToScroll + }; + this.options.breakpoints.sort(function (a, b) { + return parseInt(a.changePoint, 10) > parseInt(b.changePoint, 10); + }); + this._currentBreakpoint = this._getActiveBreakpoint(); + + return this; + } + }, { + key: 'destroy', + value: function destroy() { + this._unbindEvents(); + } + }, { + key: '_bindEvents', + value: function _bindEvents() { + window.addEventListener('resize', this[onResize]); + window.addEventListener('orientationchange', this[onResize]); + } + }, { + key: '_unbindEvents', + value: function _unbindEvents() { + window.removeEventListener('resize', this[onResize]); + window.removeEventListener('orientationchange', this[onResize]); + } + }, { + key: '_getActiveBreakpoint', + value: function _getActiveBreakpoint() { + //Get breakpoint for window width + var _iteratorNormalCompletion = true; + var _didIteratorError = false; + var _iteratorError = undefined; + + try { + for (var _iterator = this.options.breakpoints[Symbol.iterator](), _step; !(_iteratorNormalCompletion = (_step = _iterator.next()).done); _iteratorNormalCompletion = true) { + var point = _step.value; + + if (point.changePoint >= window.innerWidth) { + return point; + } + } + } catch (err) { + _didIteratorError = true; + _iteratorError = err; + } finally { + try { + if (!_iteratorNormalCompletion && _iterator.return) { + _iterator.return(); + } + } finally { + if (_didIteratorError) { + throw _iteratorError; + } + } + } + + return this._defaultBreakpoint; + } + }, { + key: 'getSlidesToShow', + value: function getSlidesToShow() { + return this._currentBreakpoint ? this._currentBreakpoint.slidesToShow : this._defaultBreakpoint.slidesToShow; + } + }, { + key: 'getSlidesToScroll', + value: function getSlidesToScroll() { + return this._currentBreakpoint ? this._currentBreakpoint.slidesToScroll : this._defaultBreakpoint.slidesToScroll; + } + }, { + key: 'apply', + value: function apply() { + if (this.slider.state.index >= this.slider.state.length && this.slider.state.index !== 0) { + this.slider.state.index = this.slider.state.index - this._currentBreakpoint.slidesToScroll; + } + if (this.slider.state.length <= this._currentBreakpoint.slidesToShow) { + this.slider.state.index = 0; + } + + if (this.options.loop) { + this.slider._loop.init().apply(); + } + + if (this.options.infinite) { + this.slider._infinite.init().apply(); + } + + this.slider._setDimensions(); + this.slider._transitioner.init().apply(true, this.slider._setHeight.bind(this.slider)); + this.slider._setClasses(); + + this.slider._navigation.refresh(); + this.slider._pagination.refresh(); + } + }, { + key: onResize, + value: function value(e) { + var newBreakPoint = this._getActiveBreakpoint(); + if (newBreakPoint.slidesToShow !== this._currentBreakpoint.slidesToShow) { + this._currentBreakpoint = newBreakPoint; + this.apply(); + } + } + }]); + + return Breakpoints; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (Breakpoints); + +/***/ }), +/* 10 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _toConsumableArray(arr) { if (Array.isArray(arr)) { for (var i = 0, arr2 = Array(arr.length); i < arr.length; i++) { arr2[i] = arr[i]; } return arr2; } else { return Array.from(arr); } } + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + +var Infinite = function () { + function Infinite(slider) { + _classCallCheck(this, Infinite); + + this.slider = slider; + } + + _createClass(Infinite, [{ + key: 'init', + value: function init() { + if (this.slider.options.infinite && this.slider.options.effect === 'translate') { + if (this.slider.options.centerMode) { + this._infiniteCount = Math.ceil(this.slider.slidesToShow + this.slider.slidesToShow / 2); + } else { + this._infiniteCount = this.slider.slidesToShow; + } + + var frontClones = []; + var slideIndex = 0; + for (var i = this.slider.state.length; i > this.slider.state.length - 1 - this._infiniteCount; i -= 1) { + slideIndex = i - 1; + frontClones.unshift(this._cloneSlide(this.slider.slides[slideIndex], slideIndex - this.slider.state.length)); + } + + var backClones = []; + for (var _i = 0; _i < this._infiniteCount + this.slider.state.length; _i += 1) { + backClones.push(this._cloneSlide(this.slider.slides[_i % this.slider.state.length], _i + this.slider.state.length)); + } + + this.slider.slides = [].concat(frontClones, _toConsumableArray(this.slider.slides), backClones); + } + return this; + } + }, { + key: 'apply', + value: function apply() {} + }, { + key: 'onTransitionEnd', + value: function onTransitionEnd(e) { + if (this.slider.options.infinite) { + if (this.slider.state.next >= this.slider.state.length) { + this.slider.state.index = this.slider.state.next = this.slider.state.next - this.slider.state.length; + this.slider.transitioner.apply(true); + } else if (this.slider.state.next < 0) { + this.slider.state.index = this.slider.state.next = this.slider.state.length + this.slider.state.next; + this.slider.transitioner.apply(true); + } + } + } + }, { + key: '_cloneSlide', + value: function _cloneSlide(slide, index) { + var newSlide = slide.cloneNode(true); + newSlide.dataset.sliderIndex = index; + newSlide.dataset.cloned = true; + var ids = newSlide.querySelectorAll('[id]') || []; + ids.forEach(function (id) { + id.setAttribute('id', ''); + }); + return newSlide; + } + }]); + + return Infinite; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (Infinite); + +/***/ }), +/* 11 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__utils_dom__ = __webpack_require__(12); +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + + + +var Loop = function () { + function Loop(slider) { + _classCallCheck(this, Loop); + + this.slider = slider; + } + + _createClass(Loop, [{ + key: "init", + value: function init() { + return this; + } + }, { + key: "apply", + value: function apply() { + if (this.slider.options.loop) { + if (this.slider.state.next > 0) { + if (this.slider.state.next < this.slider.state.length) { + if (this.slider.state.next > this.slider.state.length - this.slider.slidesToShow && Object(__WEBPACK_IMPORTED_MODULE_0__utils_dom__["a" /* isInViewport */])(this.slider._slides[this.slider.state.length - 1], this.slider.wrapper)) { + this.slider.state.next = 0; + } else { + this.slider.state.next = Math.min(Math.max(this.slider.state.next, 0), this.slider.state.length - this.slider.slidesToShow); + } + } else { + this.slider.state.next = 0; + } + } else { + if (this.slider.state.next <= 0 - this.slider.slidesToScroll) { + this.slider.state.next = this.slider.state.length - this.slider.slidesToShow; + } else { + this.slider.state.next = 0; + } + } + } + } + }]); + + return Loop; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (Loop); + +/***/ }), +/* 12 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "a", function() { return isInViewport; }); +var isInViewport = function isInViewport(element, html) { + var rect = element.getBoundingClientRect(); + html = html || document.documentElement; + return rect.top >= 0 && rect.left >= 0 && rect.bottom <= (window.innerHeight || html.clientHeight) && rect.right <= (window.innerWidth || html.clientWidth); +}; + +/***/ }), +/* 13 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__templates_navigation__ = __webpack_require__(14); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_1__utils_detect_supportsPassive__ = __webpack_require__(1); +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + + + + +var Navigation = function () { + function Navigation(slider) { + _classCallCheck(this, Navigation); + + this.slider = slider; + + this._clickEvents = ['click', 'touch']; + this._supportsPassive = Object(__WEBPACK_IMPORTED_MODULE_1__utils_detect_supportsPassive__["a" /* default */])(); + + this.onPreviousClick = this.onPreviousClick.bind(this); + this.onNextClick = this.onNextClick.bind(this); + this.onKeyUp = this.onKeyUp.bind(this); + } + + _createClass(Navigation, [{ + key: 'init', + value: function init() { + this.node = document.createRange().createContextualFragment(Object(__WEBPACK_IMPORTED_MODULE_0__templates_navigation__["a" /* default */])(this.slider.options.icons)); + this._ui = { + previous: this.node.querySelector('.slider-navigation-previous'), + next: this.node.querySelector('.slider-navigation-next') + }; + + this._unbindEvents(); + this._bindEvents(); + + this.refresh(); + + return this; + } + }, { + key: 'destroy', + value: function destroy() { + this._unbindEvents(); + } + }, { + key: '_bindEvents', + value: function _bindEvents() { + var _this = this; + + this.slider.wrapper.addEventListener('keyup', this.onKeyUp); + this._clickEvents.forEach(function (clickEvent) { + _this._ui.previous.addEventListener(clickEvent, _this.onPreviousClick); + _this._ui.next.addEventListener(clickEvent, _this.onNextClick); + }); + } + }, { + key: '_unbindEvents', + value: function _unbindEvents() { + var _this2 = this; + + this.slider.wrapper.removeEventListener('keyup', this.onKeyUp); + this._clickEvents.forEach(function (clickEvent) { + _this2._ui.previous.removeEventListener(clickEvent, _this2.onPreviousClick); + _this2._ui.next.removeEventListener(clickEvent, _this2.onNextClick); + }); + } + }, { + key: 'onNextClick', + value: function onNextClick(e) { + if (!this._supportsPassive) { + e.preventDefault(); + } + + if (this.slider.options.navigation) { + this.slider.next(); + } + } + }, { + key: 'onPreviousClick', + value: function onPreviousClick(e) { + if (!this._supportsPassive) { + e.preventDefault(); + } + + if (this.slider.options.navigation) { + this.slider.previous(); + } + } + }, { + key: 'onKeyUp', + value: function onKeyUp(e) { + if (this.slider.options.keyNavigation) { + if (e.key === 'ArrowRight' || e.key === 'Right') { + this.slider.next(); + } else if (e.key === 'ArrowLeft' || e.key === 'Left') { + this.slider.previous(); + } + } + } + }, { + key: 'refresh', + value: function refresh() { + // let centerOffset = Math.floor(this.options.slidesToShow / 2); + if (!this.slider.options.loop && !this.slider.options.infinite) { + if (this.slider.options.navigation && this.slider.state.length > this.slider.slidesToShow) { + this._ui.previous.classList.remove('is-hidden'); + this._ui.next.classList.remove('is-hidden'); + if (this.slider.state.next === 0) { + this._ui.previous.classList.add('is-hidden'); + this._ui.next.classList.remove('is-hidden'); + } else if (this.slider.state.next >= this.slider.state.length - this.slider.slidesToShow && !this.slider.options.centerMode) { + this._ui.previous.classList.remove('is-hidden'); + this._ui.next.classList.add('is-hidden'); + } else if (this.slider.state.next >= this.slider.state.length - 1 && this.slider.options.centerMode) { + this._ui.previous.classList.remove('is-hidden'); + this._ui.next.classList.add('is-hidden'); + } + } else { + this._ui.previous.classList.add('is-hidden'); + this._ui.next.classList.add('is-hidden'); + } + } + } + }, { + key: 'render', + value: function render() { + return this.node; + } + }]); + + return Navigation; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (Navigation); + +/***/ }), +/* 14 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony default export */ __webpack_exports__["a"] = (function (icons) { + return "
" + icons.previous + "
\n
" + icons.next + "
"; +}); + +/***/ }), +/* 15 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__templates_pagination__ = __webpack_require__(16); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_1__templates_pagination_page__ = __webpack_require__(17); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_2__utils_detect_supportsPassive__ = __webpack_require__(1); +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + + + + + +var Pagination = function () { + function Pagination(slider) { + _classCallCheck(this, Pagination); + + this.slider = slider; + + this._clickEvents = ['click', 'touch']; + this._supportsPassive = Object(__WEBPACK_IMPORTED_MODULE_2__utils_detect_supportsPassive__["a" /* default */])(); + + this.onPageClick = this.onPageClick.bind(this); + this.onResize = this.onResize.bind(this); + } + + _createClass(Pagination, [{ + key: 'init', + value: function init() { + this._pages = []; + this.node = document.createRange().createContextualFragment(Object(__WEBPACK_IMPORTED_MODULE_0__templates_pagination__["a" /* default */])()); + this._ui = { + container: this.node.firstChild + }; + + this._count = Math.ceil((this.slider.state.length - this.slider.slidesToShow) / this.slider.slidesToScroll); + + this._draw(); + this.refresh(); + + return this; + } + }, { + key: 'destroy', + value: function destroy() { + this._unbindEvents(); + } + }, { + key: '_bindEvents', + value: function _bindEvents() { + var _this = this; + + window.addEventListener('resize', this.onResize); + window.addEventListener('orientationchange', this.onResize); + + this._clickEvents.forEach(function (clickEvent) { + _this._pages.forEach(function (page) { + return page.addEventListener(clickEvent, _this.onPageClick); + }); + }); + } + }, { + key: '_unbindEvents', + value: function _unbindEvents() { + var _this2 = this; + + window.removeEventListener('resize', this.onResize); + window.removeEventListener('orientationchange', this.onResize); + + this._clickEvents.forEach(function (clickEvent) { + _this2._pages.forEach(function (page) { + return page.removeEventListener(clickEvent, _this2.onPageClick); + }); + }); + } + }, { + key: '_draw', + value: function _draw() { + this._ui.container.innerHTML = ''; + if (this.slider.options.pagination && this.slider.state.length > this.slider.slidesToShow) { + for (var i = 0; i <= this._count; i++) { + var newPageNode = document.createRange().createContextualFragment(Object(__WEBPACK_IMPORTED_MODULE_1__templates_pagination_page__["a" /* default */])()).firstChild; + newPageNode.dataset.index = i * this.slider.slidesToScroll; + this._pages.push(newPageNode); + this._ui.container.appendChild(newPageNode); + } + this._bindEvents(); + } + } + }, { + key: 'onPageClick', + value: function onPageClick(e) { + if (!this._supportsPassive) { + e.preventDefault(); + } + + this.slider.state.next = e.currentTarget.dataset.index; + this.slider.show(); + } + }, { + key: 'onResize', + value: function onResize() { + this._draw(); + } + }, { + key: 'refresh', + value: function refresh() { + var _this3 = this; + + var newCount = void 0; + + if (this.slider.options.infinite) { + newCount = Math.ceil(this.slider.state.length - 1 / this.slider.slidesToScroll); + } else { + newCount = Math.ceil((this.slider.state.length - this.slider.slidesToShow) / this.slider.slidesToScroll); + } + if (newCount !== this._count) { + this._count = newCount; + this._draw(); + } + + this._pages.forEach(function (page) { + page.classList.remove('is-active'); + if (parseInt(page.dataset.index, 10) === _this3.slider.state.next % _this3.slider.state.length) { + page.classList.add('is-active'); + } + }); + } + }, { + key: 'render', + value: function render() { + return this.node; + } + }]); + + return Pagination; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (Pagination); + +/***/ }), +/* 16 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony default export */ __webpack_exports__["a"] = (function () { + return "
"; +}); + +/***/ }), +/* 17 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony default export */ __webpack_exports__["a"] = (function () { + return "
"; +}); + +/***/ }), +/* 18 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__ = __webpack_require__(4); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_1__utils_detect_supportsPassive__ = __webpack_require__(1); +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + + + + +var Swipe = function () { + function Swipe(slider) { + _classCallCheck(this, Swipe); + + this.slider = slider; + + this._supportsPassive = Object(__WEBPACK_IMPORTED_MODULE_1__utils_detect_supportsPassive__["a" /* default */])(); + + this.onStartDrag = this.onStartDrag.bind(this); + this.onMoveDrag = this.onMoveDrag.bind(this); + this.onStopDrag = this.onStopDrag.bind(this); + + this._init(); + } + + _createClass(Swipe, [{ + key: '_init', + value: function _init() {} + }, { + key: 'bindEvents', + value: function bindEvents() { + var _this = this; + + this.slider.container.addEventListener('dragstart', function (e) { + if (!_this._supportsPassive) { + e.preventDefault(); + } + }); + this.slider.container.addEventListener('mousedown', this.onStartDrag); + this.slider.container.addEventListener('touchstart', this.onStartDrag); + + window.addEventListener('mousemove', this.onMoveDrag); + window.addEventListener('touchmove', this.onMoveDrag); + + window.addEventListener('mouseup', this.onStopDrag); + window.addEventListener('touchend', this.onStopDrag); + window.addEventListener('touchcancel', this.onStopDrag); + } + }, { + key: 'unbindEvents', + value: function unbindEvents() { + var _this2 = this; + + this.slider.container.removeEventListener('dragstart', function (e) { + if (!_this2._supportsPassive) { + e.preventDefault(); + } + }); + this.slider.container.removeEventListener('mousedown', this.onStartDrag); + this.slider.container.removeEventListener('touchstart', this.onStartDrag); + + window.removeEventListener('mousemove', this.onMoveDrag); + window.removeEventListener('touchmove', this.onMoveDrag); + + window.removeEventListener('mouseup', this.onStopDrag); + window.removeEventListener('mouseup', this.onStopDrag); + window.removeEventListener('touchcancel', this.onStopDrag); + } + + /** + * @param {MouseEvent|TouchEvent} + */ + + }, { + key: 'onStartDrag', + value: function onStartDrag(e) { + if (e.touches) { + if (e.touches.length > 1) { + return; + } else { + e = e.touches[0]; + } + } + + this._origin = new __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__["a" /* default */](e.screenX, e.screenY); + this.width = this.slider.wrapperWidth; + this.slider.transitioner.disable(); + } + + /** + * @param {MouseEvent|TouchEvent} + */ + + }, { + key: 'onMoveDrag', + value: function onMoveDrag(e) { + if (this._origin) { + var point = e.touches ? e.touches[0] : e; + this._lastTranslate = new __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__["a" /* default */](point.screenX - this._origin.x, point.screenY - this._origin.y); + if (e.touches) { + if (Math.abs(this._lastTranslate.x) > Math.abs(this._lastTranslate.y)) { + if (!this._supportsPassive) { + e.preventDefault(); + } + e.stopPropagation(); + } + } + } + } + + /** + * @param {MouseEvent|TouchEvent} + */ + + }, { + key: 'onStopDrag', + value: function onStopDrag(e) { + if (this._origin && this._lastTranslate) { + if (Math.abs(this._lastTranslate.x) > 0.2 * this.width) { + if (this._lastTranslate.x < 0) { + this.slider.next(); + } else { + this.slider.previous(); + } + } else { + this.slider.show(true); + } + } + this._origin = null; + this._lastTranslate = null; + } + }]); + + return Swipe; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (Swipe); + +/***/ }), +/* 19 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__transitions_fade__ = __webpack_require__(20); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_1__transitions_translate__ = __webpack_require__(21); +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + + + + +var Transitioner = function () { + function Transitioner(slider) { + _classCallCheck(this, Transitioner); + + this.slider = slider; + this.options = slider.options; + + this._animating = false; + this._animation = undefined; + + this._translate = new __WEBPACK_IMPORTED_MODULE_1__transitions_translate__["a" /* default */](this, slider, slider.options); + this._fade = new __WEBPACK_IMPORTED_MODULE_0__transitions_fade__["a" /* default */](this, slider, slider.options); + } + + _createClass(Transitioner, [{ + key: 'init', + value: function init() { + this._fade.init(); + this._translate.init(); + return this; + } + }, { + key: 'isAnimating', + value: function isAnimating() { + return this._animating; + } + }, { + key: 'enable', + value: function enable() { + this._animation && this._animation.enable(); + } + }, { + key: 'disable', + value: function disable() { + this._animation && this._animation.disable(); + } + }, { + key: 'apply', + value: function apply(force, callback) { + // If we don't force refresh and animation in progress then return + if (this._animating && !force) { + return; + } + + switch (this.options.effect) { + case 'fade': + this._animation = this._fade; + break; + case 'translate': + default: + this._animation = this._translate; + break; + } + + this._animationCallback = callback; + + if (force) { + this._animation && this._animation.disable(); + } else { + this._animation && this._animation.enable(); + this._animating = true; + } + + this._animation && this._animation.apply(); + + if (force) { + this.end(); + } + } + }, { + key: 'end', + value: function end() { + this._animating = false; + this._animation = undefined; + this.slider.state.index = this.slider.state.next; + if (this._animationCallback) { + this._animationCallback(); + } + } + }]); + + return Transitioner; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (Transitioner); + +/***/ }), +/* 20 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__utils_css__ = __webpack_require__(0); +var _extends = Object.assign || function (target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i]; for (var key in source) { if (Object.prototype.hasOwnProperty.call(source, key)) { target[key] = source[key]; } } } return target; }; + +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + + + +var Fade = function () { + function Fade(transitioner, slider) { + var options = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : {}; + + _classCallCheck(this, Fade); + + this.transitioner = transitioner; + this.slider = slider; + this.options = _extends({}, options); + } + + _createClass(Fade, [{ + key: 'init', + value: function init() { + var _this = this; + + if (this.options.effect === 'fade') { + this.slider.slides.forEach(function (slide, index) { + Object(__WEBPACK_IMPORTED_MODULE_0__utils_css__["a" /* css */])(slide, { + position: 'absolute', + left: 0, + top: 0, + bottom: 0, + 'z-index': slide.dataset.sliderIndex == _this.slider.state.index ? 0 : -2, + opacity: slide.dataset.sliderIndex == _this.slider.state.index ? 1 : 0 + }); + }); + } + return this; + } + }, { + key: 'enable', + value: function enable() { + var _this2 = this; + + this._oldSlide = this.slider.slides.filter(function (slide) { + return slide.dataset.sliderIndex == _this2.slider.state.index; + })[0]; + this._newSlide = this.slider.slides.filter(function (slide) { + return slide.dataset.sliderIndex == _this2.slider.state.next; + })[0]; + if (this._newSlide) { + this._newSlide.addEventListener('transitionend', this.onTransitionEnd.bind(this)); + this._newSlide.style.transition = this.options.duration + 'ms ' + this.options.timing; + if (this._oldSlide) { + this._oldSlide.addEventListener('transitionend', this.onTransitionEnd.bind(this)); + this._oldSlide.style.transition = this.options.duration + 'ms ' + this.options.timing; + } + } + } + }, { + key: 'disable', + value: function disable() { + var _this3 = this; + + this._oldSlide = this.slider.slides.filter(function (slide) { + return slide.dataset.sliderIndex == _this3.slider.state.index; + })[0]; + this._newSlide = this.slider.slides.filter(function (slide) { + return slide.dataset.sliderIndex == _this3.slider.state.next; + })[0]; + if (this._newSlide) { + this._newSlide.removeEventListener('transitionend', this.onTransitionEnd.bind(this)); + this._newSlide.style.transition = 'none'; + if (this._oldSlide) { + this._oldSlide.removeEventListener('transitionend', this.onTransitionEnd.bind(this)); + this._oldSlide.style.transition = 'none'; + } + } + } + }, { + key: 'apply', + value: function apply(force) { + var _this4 = this; + + this._oldSlide = this.slider.slides.filter(function (slide) { + return slide.dataset.sliderIndex == _this4.slider.state.index; + })[0]; + this._newSlide = this.slider.slides.filter(function (slide) { + return slide.dataset.sliderIndex == _this4.slider.state.next; + })[0]; + + if (this._oldSlide && this._newSlide) { + Object(__WEBPACK_IMPORTED_MODULE_0__utils_css__["a" /* css */])(this._oldSlide, { + opacity: 0 + }); + Object(__WEBPACK_IMPORTED_MODULE_0__utils_css__["a" /* css */])(this._newSlide, { + opacity: 1, + 'z-index': force ? 0 : -1 + }); + } + } + }, { + key: 'onTransitionEnd', + value: function onTransitionEnd(e) { + if (this.options.effect === 'fade') { + if (this.transitioner.isAnimating() && e.target == this._newSlide) { + if (this._newSlide) { + Object(__WEBPACK_IMPORTED_MODULE_0__utils_css__["a" /* css */])(this._newSlide, { + 'z-index': 0 + }); + this._newSlide.removeEventListener('transitionend', this.onTransitionEnd.bind(this)); + } + if (this._oldSlide) { + Object(__WEBPACK_IMPORTED_MODULE_0__utils_css__["a" /* css */])(this._oldSlide, { + 'z-index': -2 + }); + this._oldSlide.removeEventListener('transitionend', this.onTransitionEnd.bind(this)); + } + } + this.transitioner.end(); + } + } + }]); + + return Fade; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (Fade); + +/***/ }), +/* 21 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__ = __webpack_require__(4); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_1__utils_css__ = __webpack_require__(0); +var _extends = Object.assign || function (target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i]; for (var key in source) { if (Object.prototype.hasOwnProperty.call(source, key)) { target[key] = source[key]; } } } return target; }; + +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + + + + +var Translate = function () { + function Translate(transitioner, slider) { + var options = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : {}; + + _classCallCheck(this, Translate); + + this.transitioner = transitioner; + this.slider = slider; + this.options = _extends({}, options); + + this.onTransitionEnd = this.onTransitionEnd.bind(this); + } + + _createClass(Translate, [{ + key: 'init', + value: function init() { + this._position = new __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__["a" /* default */](this.slider.container.offsetLeft, this.slider.container.offsetTop); + this._bindEvents(); + return this; + } + }, { + key: 'destroy', + value: function destroy() { + this._unbindEvents(); + } + }, { + key: '_bindEvents', + value: function _bindEvents() { + this.slider.container.addEventListener('transitionend', this.onTransitionEnd); + } + }, { + key: '_unbindEvents', + value: function _unbindEvents() { + this.slider.container.removeEventListener('transitionend', this.onTransitionEnd); + } + }, { + key: 'enable', + value: function enable() { + this.slider.container.style.transition = this.options.duration + 'ms ' + this.options.timing; + } + }, { + key: 'disable', + value: function disable() { + this.slider.container.style.transition = 'none'; + } + }, { + key: 'apply', + value: function apply() { + var _this = this; + + var maxOffset = void 0; + if (this.options.effect === 'translate') { + var slide = this.slider.slides.filter(function (slide) { + return slide.dataset.sliderIndex == _this.slider.state.next; + })[0]; + var slideOffset = new __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__["a" /* default */](slide.offsetLeft, slide.offsetTop); + if (this.options.centerMode) { + maxOffset = new __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__["a" /* default */](Math.round(Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["e" /* width */])(this.slider.container)), Math.round(Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["b" /* height */])(this.slider.container))); + } else { + maxOffset = new __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__["a" /* default */](Math.round(Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["e" /* width */])(this.slider.container) - Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["e" /* width */])(this.slider.wrapper)), Math.round(Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["b" /* height */])(this.slider.container) - Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["b" /* height */])(this.slider.wrapper))); + } + var nextOffset = new __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__["a" /* default */](Math.min(Math.max(slideOffset.x * -1, maxOffset.x * -1), 0), Math.min(Math.max(slideOffset.y * -1, maxOffset.y * -1), 0)); + if (this.options.loop) { + if (!this.options.vertical && Math.abs(this._position.x) > maxOffset.x) { + nextOffset.x = 0; + this.slider.state.next = 0; + } else if (this.options.vertical && Math.abs(this._position.y) > maxOffset.y) { + nextOffset.y = 0; + this.slider.state.next = 0; + } + } + + this._position.x = nextOffset.x; + this._position.y = nextOffset.y; + if (this.options.centerMode) { + this._position.x = this._position.x + this.slider.wrapperWidth / 2 - Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["e" /* width */])(slide) / 2; + } + + if (this.slider.direction === 'rtl') { + this._position.x = -this._position.x; + this._position.y = -this._position.y; + } + this.slider.container.style.transform = 'translate3d(' + this._position.x + 'px, ' + this._position.y + 'px, 0)'; + + /** + * update the index with the nextIndex only if + * the offset of the nextIndex is in the range of the maxOffset + */ + if (slideOffset.x > maxOffset.x) { + this.slider.transitioner.end(); + } + } + } + }, { + key: 'onTransitionEnd', + value: function onTransitionEnd(e) { + if (this.options.effect === 'translate') { + + if (this.transitioner.isAnimating() && e.target == this.slider.container) { + if (this.options.infinite) { + this.slider._infinite.onTransitionEnd(e); + } + } + this.transitioner.end(); + } + } + }]); + + return Translate; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (Translate); + +/***/ }), +/* 22 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +var defaultOptions = { + initialSlide: 0, + slidesToScroll: 1, + slidesToShow: 1, + + navigation: true, + navigationKeys: true, + navigationSwipe: true, + + pagination: true, + + loop: false, + infinite: false, + + effect: 'translate', + duration: 300, + timing: 'ease', + + autoplay: false, + autoplaySpeed: 3000, + pauseOnHover: true, + breakpoints: [{ + changePoint: 480, + slidesToShow: 1, + slidesToScroll: 1 + }, { + changePoint: 640, + slidesToShow: 3, + slidesToScroll: 3 + }, { + changePoint: 768, + slidesToShow: 1, + slidesToScroll: 1 + }], + + onReady: null, + icons: { + 'previous': '\n \n ', + 'next': '\n \n ' + } +}; + +/* harmony default export */ __webpack_exports__["a"] = (defaultOptions); + +/***/ }), +/* 23 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony default export */ __webpack_exports__["a"] = (function (id) { + return "
\n
\n
"; +}); + +/***/ }), +/* 24 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony default export */ __webpack_exports__["a"] = (function () { + return "
"; +}); + +/***/ }) +/******/ ])["default"]; +}); \ No newline at end of file diff --git a/docs/static/js/bulma-carousel.min.js b/docs/static/js/bulma-carousel.min.js new file mode 100644 index 0000000..8fec782 --- /dev/null +++ b/docs/static/js/bulma-carousel.min.js @@ -0,0 +1 @@ +!function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof exports?exports.bulmaCarousel=e():t.bulmaCarousel=e()}("undefined"!=typeof self?self:this,function(){return function(i){var n={};function s(t){if(n[t])return n[t].exports;var e=n[t]={i:t,l:!1,exports:{}};return i[t].call(e.exports,e,e.exports,s),e.l=!0,e.exports}return s.m=i,s.c=n,s.d=function(t,e,i){s.o(t,e)||Object.defineProperty(t,e,{configurable:!1,enumerable:!0,get:i})},s.n=function(t){var e=t&&t.__esModule?function(){return t.default}:function(){return t};return s.d(e,"a",e),e},s.o=function(t,e){return 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"symbol" : typeof obj; }; + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + +function _possibleConstructorReturn(self, call) { if (!self) { throw new ReferenceError("this hasn't been initialised - super() hasn't been called"); } return call && (typeof call === "object" || typeof call === "function") ? call : self; } + +function _inherits(subClass, superClass) { if (typeof superClass !== "function" && superClass !== null) { throw new TypeError("Super expression must either be null or a function, not " + typeof superClass); } subClass.prototype = Object.create(superClass && superClass.prototype, { constructor: { value: subClass, enumerable: false, writable: true, configurable: true } }); if (superClass) Object.setPrototypeOf ? Object.setPrototypeOf(subClass, superClass) : subClass.__proto__ = superClass; } + + + +var isString = function isString(unknown) { + return typeof unknown === 'string' || !!unknown && (typeof unknown === 'undefined' ? 'undefined' : _typeof(unknown)) === 'object' && Object.prototype.toString.call(unknown) === '[object String]'; +}; + +var bulmaSlider = function (_EventEmitter) { + _inherits(bulmaSlider, _EventEmitter); + + function bulmaSlider(selector) { + var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {}; + + _classCallCheck(this, bulmaSlider); + + var _this = _possibleConstructorReturn(this, (bulmaSlider.__proto__ || Object.getPrototypeOf(bulmaSlider)).call(this)); + + _this.element = typeof selector === 'string' ? document.querySelector(selector) : selector; + // An invalid selector or non-DOM node has been provided. + if (!_this.element) { + throw new Error('An invalid selector or non-DOM node has been provided.'); + } + + _this._clickEvents = ['click']; + /// Set default options and merge with instance defined + _this.options = _extends({}, options); + + _this.onSliderInput = _this.onSliderInput.bind(_this); + + _this.init(); + return _this; + } + + /** + * Initiate all DOM element containing selector + * @method + * @return {Array} Array of all slider instances + */ + + + _createClass(bulmaSlider, [{ + key: 'init', + + + /** + * Initiate plugin + * @method init + * @return {void} + */ + value: function init() { + this._id = 'bulmaSlider' + new Date().getTime() + Math.floor(Math.random() * Math.floor(9999)); + this.output = this._findOutputForSlider(); + + this._bindEvents(); + + if (this.output) { + if (this.element.classList.contains('has-output-tooltip')) { + // Get new output position + var newPosition = this._getSliderOutputPosition(); + + // Set output position + this.output.style['left'] = newPosition.position; + } + } + + this.emit('bulmaslider:ready', this.element.value); + } + }, { + key: '_findOutputForSlider', + value: function _findOutputForSlider() { + var _this2 = this; + + var result = null; + var outputs = document.getElementsByTagName('output') || []; + + Array.from(outputs).forEach(function (output) { + if (output.htmlFor == _this2.element.getAttribute('id')) { + result = output; + return true; + } + }); + return result; + } + }, { + key: '_getSliderOutputPosition', + value: function _getSliderOutputPosition() { + // Update output position + var newPlace, minValue; + + var style = window.getComputedStyle(this.element, null); + // Measure width of range input + var sliderWidth = parseInt(style.getPropertyValue('width'), 10); + + // Figure out placement percentage between left and right of input + if (!this.element.getAttribute('min')) { + minValue = 0; + } else { + minValue = this.element.getAttribute('min'); + } + var newPoint = (this.element.value - minValue) / (this.element.getAttribute('max') - minValue); + + // Prevent bubble from going beyond left or right (unsupported browsers) + if (newPoint < 0) { + newPlace = 0; + } else if (newPoint > 1) { + newPlace = sliderWidth; + } else { + newPlace = sliderWidth * newPoint; + } + + return { + 'position': newPlace + 'px' + }; + } + + /** + * Bind all events + * @method _bindEvents + * @return {void} + */ + + }, { + key: '_bindEvents', + value: function _bindEvents() { + if (this.output) { + // Add event listener to update output when slider value change + this.element.addEventListener('input', this.onSliderInput, false); + } + } + }, { + key: 'onSliderInput', + value: function onSliderInput(e) { + e.preventDefault(); + + if (this.element.classList.contains('has-output-tooltip')) { + // Get new output position + var newPosition = this._getSliderOutputPosition(); + + // Set output position + this.output.style['left'] = newPosition.position; + } + + // Check for prefix and postfix + var prefix = this.output.hasAttribute('data-prefix') ? this.output.getAttribute('data-prefix') : ''; + var postfix = this.output.hasAttribute('data-postfix') ? this.output.getAttribute('data-postfix') : ''; + + // Update output with slider value + this.output.value = prefix + this.element.value + postfix; + + this.emit('bulmaslider:ready', this.element.value); + } + }], [{ + key: 'attach', + value: function attach() { + var _this3 = this; + + var selector = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : 'input[type="range"].slider'; + var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {}; + + var instances = new Array(); + + var elements = isString(selector) ? document.querySelectorAll(selector) : Array.isArray(selector) ? selector : [selector]; + elements.forEach(function (element) { + if (typeof element[_this3.constructor.name] === 'undefined') { + var instance = new bulmaSlider(element, options); + element[_this3.constructor.name] = instance; + instances.push(instance); + } else { + instances.push(element[_this3.constructor.name]); + } + }); + + return instances; + } + }]); + + return bulmaSlider; +}(__WEBPACK_IMPORTED_MODULE_0__events__["a" /* default */]); + +/* harmony default export */ __webpack_exports__["default"] = (bulmaSlider); + +/***/ }), +/* 1 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + +var EventEmitter = function () { + function EventEmitter() { + var listeners = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : []; + + _classCallCheck(this, EventEmitter); + + this._listeners = new Map(listeners); + this._middlewares = new Map(); + } + + _createClass(EventEmitter, [{ + key: "listenerCount", + value: function listenerCount(eventName) { + if (!this._listeners.has(eventName)) { + return 0; + } + + var eventListeners = this._listeners.get(eventName); + return eventListeners.length; + } + }, { + key: "removeListeners", + value: function removeListeners() { + var _this = this; + + var eventName = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : null; + var middleware = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : false; + + if (eventName !== null) { + if (Array.isArray(eventName)) { + name.forEach(function (e) { + return _this.removeListeners(e, middleware); + }); + } else { + this._listeners.delete(eventName); + + if (middleware) { + this.removeMiddleware(eventName); + } + } + } else { + this._listeners = new Map(); + } + } + }, { + key: "middleware", + value: function middleware(eventName, fn) { + var _this2 = this; + + if (Array.isArray(eventName)) { + name.forEach(function (e) { + return _this2.middleware(e, fn); + }); + } else { + if (!Array.isArray(this._middlewares.get(eventName))) { + this._middlewares.set(eventName, []); + } + + this._middlewares.get(eventName).push(fn); + } + } + }, { + key: "removeMiddleware", + value: function removeMiddleware() { + var _this3 = this; + + var eventName = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : null; + + if (eventName !== null) { + if (Array.isArray(eventName)) { + name.forEach(function (e) { + return _this3.removeMiddleware(e); + }); + } else { + this._middlewares.delete(eventName); + } + } else { + this._middlewares = new Map(); + } + } + }, { + key: "on", + value: function on(name, callback) { + var _this4 = this; + + var once = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : false; + + if (Array.isArray(name)) { + name.forEach(function (e) { + return _this4.on(e, callback); + }); + } else { + name = name.toString(); + var split = name.split(/,|, | /); + + if (split.length > 1) { + split.forEach(function (e) { + return _this4.on(e, callback); + }); + } else { + if (!Array.isArray(this._listeners.get(name))) { + this._listeners.set(name, []); + } + + this._listeners.get(name).push({ once: once, callback: callback }); + } + } + } + }, { + key: "once", + value: function once(name, callback) { + this.on(name, callback, true); + } + }, { + key: "emit", + value: function emit(name, data) { + var _this5 = this; + + var silent = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : false; + + name = name.toString(); + var listeners = this._listeners.get(name); + var middlewares = null; + var doneCount = 0; + var execute = silent; + + if (Array.isArray(listeners)) { + listeners.forEach(function (listener, index) { + // Start Middleware checks unless we're doing a silent emit + if (!silent) { + middlewares = _this5._middlewares.get(name); 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+ setTimeout(function () { + slider.val(0).trigger("input"); + resetAutoScrollTimer(); // Reset timer with standard interval + }, 3000); + } else { + // Not yet at the end, just increment + slider.val(nextValue).trigger("input"); + } + } +} + +$(document).ready(function () { + resetAutoScrollTimer(); // Start auto-scrolling the slider + + // Event handler for starting slider interaction + $("#interpolation-slider").on("mousedown touchstart", function () { + clearInterval(autoScrollTimer); + isDragging = true; // Start dragging + }); + + // Event handler for ending slider interaction + $("#interpolation-slider").on("mouseup touchend", function () { + isDragging = false; // Stop dragging + if (!isHovering) { + resetAutoScrollTimer(); // Reset timer if not hovering + } + }); + + // Event handlers for mouse hover + $("#interpolation-slider").on("mouseenter", function () { + clearInterval(autoScrollTimer); // Pause on hover + isHovering = true; // Start hovering + }); + + $("#interpolation-slider").on("mouseleave", function () { + isHovering = false; // Stop hovering + if (!isDragging) { + // Resume only if not dragging + resetAutoScrollTimer(); + } + }); + + // Check for click events on the navbar burger icon + $(".navbar-burger").click(function () { + // Toggle the "is-active" class on both the "navbar-burger" and the "navbar-menu" + $(".navbar-burger").toggleClass("is-active"); + $(".navbar-menu").toggleClass("is-active"); + }); + + var options = { + slidesToScroll: 1, + slidesToShow: 1, + loop: true, + infinite: true, + autoplay: true, + autoplaySpeed: 5000, + }; + + // Initialize all div with carousel class + var carousels = bulmaCarousel.attach(".carousel", options); + + // Loop on each carousel initialized + for (var i = 0; i < carousels.length; i++) { + // Add listener to event + carousels[i].on("before:show", (state) => { + console.log(state); + }); + } + + // Access to bulmaCarousel instance of an element + var element = document.querySelector("#my-element"); + if (element && element.bulmaCarousel) { + // bulmaCarousel instance is available as element.bulmaCarousel + element.bulmaCarousel.on("before-show", function (state) { + console.log(state); + }); + } + + /*var player = document.getElementById('interpolation-video'); + player.addEventListener('loadedmetadata', function() { + $('#interpolation-slider').on('input', function(event) { + console.log(this.value, player.duration); + player.currentTime = player.duration / 50 * this.value; + }) + }, false);*/ + preloadInterpolationImages(); + + $("#interpolation-slider").on("input", function (event) { + setInterpolationImage(this.value); + }); + setInterpolationImage(0); + $("#interpolation-slider").prop("max", NUM_INTERP_FRAMES - 1); + + bulmaSlider.attach(); +}); diff --git a/docs/static/js/slide.js b/docs/static/js/slide.js new file mode 100644 index 0000000..393e51f --- /dev/null +++ b/docs/static/js/slide.js @@ -0,0 +1,16 @@ +document.addEventListener("DOMContentLoaded", function() { + const observer = new IntersectionObserver((entries, observer) => { + entries.forEach(entry => { + if (entry.isIntersecting) { + entry.target.play(); + } else { + entry.target.pause(); + } + }); + }, { threshold: 0.6 }); // Trigger when 50% of the video is visible + + const videos = document.querySelectorAll('video'); + videos.forEach(video => { + observer.observe(video); + }); +}); \ No newline at end of file diff --git a/docs/static/videos/._Icon b/docs/static/videos/._Icon new file mode 100644 index 0000000..f114948 Binary files /dev/null and b/docs/static/videos/._Icon differ diff --git a/docs/static/videos/all_pluto_512_1.mov b/docs/static/videos/all_pluto_512_1.mov new file mode 100644 index 0000000..3b5df6e Binary files /dev/null and b/docs/static/videos/all_pluto_512_1.mov differ diff --git a/docs/static/videos/all_tokyo_512_1.mov b/docs/static/videos/all_tokyo_512_1.mov new file mode 100644 index 0000000..f91dc14 Binary files /dev/null and b/docs/static/videos/all_tokyo_512_1.mov differ diff --git a/docs/static/videos/chair_good_video_3.mov b/docs/static/videos/chair_good_video_3.mov new file mode 100644 index 0000000..7640f53 Binary files /dev/null and b/docs/static/videos/chair_good_video_3.mov differ diff --git a/docs/static/videos/fern_good_video_3.mov b/docs/static/videos/fern_good_video_3.mov new file mode 100644 index 0000000..1f84b22 Binary files /dev/null and b/docs/static/videos/fern_good_video_3.mov differ diff --git a/docs/static/videos/fortress_good_video_3.mov b/docs/static/videos/fortress_good_video_3.mov new file mode 100644 index 0000000..7031a36 Binary files /dev/null and b/docs/static/videos/fortress_good_video_3.mov differ diff --git a/docs/static/videos/lego_good_video_3.mov b/docs/static/videos/lego_good_video_3.mov new file mode 100644 index 0000000..0876987 Binary files /dev/null and b/docs/static/videos/lego_good_video_3.mov differ diff --git a/docs/static/videos/mic_good_video_3.mov b/docs/static/videos/mic_good_video_3.mov new file mode 100644 index 0000000..279a945 Binary files /dev/null and b/docs/static/videos/mic_good_video_3.mov differ diff --git a/docs/static/videos/trex_good_video_3.mov b/docs/static/videos/trex_good_video_3.mov new file mode 100644 index 0000000..1872a18 Binary files /dev/null and b/docs/static/videos/trex_good_video_3.mov differ