[pull] main from kornia:main

.github/workflows/pr_test_cpu.yml

@@ -20,32 +20,32 @@ jobs:
         os: ['Ubuntu-latest', 'Windows-latest']
         pytorch-dtype: ['float32', 'float64']
 
-    uses: kornia/workflows/.github/workflows/tests.yml@v1.10.0
+    uses: kornia/workflows/.github/workflows/tests.yml@v1.10.1
     with:
       os: ${{ matrix.os }}
       python-version: '["3.8", "3.12"]'
       pytorch-version: '["1.9.1", "2.3.1"]'
       pytorch-dtype: ${{ matrix.pytorch-dtype }}
 
   tests-cpu-macos:
-    uses: kornia/workflows/.github/workflows/tests.yml@v1.10.0
+    uses: kornia/workflows/.github/workflows/tests.yml@v1.10.1
     with:
       os: 'MacOS-latest'
       python-version: '["3.8", "3.12"]'
       pytorch-dtype: 'float32'
 
 
   coverage:
-    uses: kornia/workflows/.github/workflows/coverage.yml@v1.10.0
+    uses: kornia/workflows/.github/workflows/coverage.yml@v1.10.1
 
   typing:
-    uses: kornia/workflows/.github/workflows/mypy.yml@v1.10.0
+    uses: kornia/workflows/.github/workflows/mypy.yml@v1.10.1
 
   tutorials:
-    uses: kornia/workflows/.github/workflows/tutorials.yml@v1.10.0
+    uses: kornia/workflows/.github/workflows/tutorials.yml@v1.10.1
 
   docs:
-    uses: kornia/workflows/.github/workflows/docs.yml@v1.10.0
+    uses: kornia/workflows/.github/workflows/docs.yml@v1.10.1
     with:
       python-version: "3.11"
 
@@ -68,7 +68,7 @@ jobs:
         os: ['Ubuntu-latest', 'Windows-latest'] #, 'MacOS-latest'] add it when https://github.com/pytorch/pytorch/pull/89262 be merged
         pytorch-dtype: ['float32', 'float64']
 
-    uses: kornia/workflows/.github/workflows/tests.yml@v1.10.0
+    uses: kornia/workflows/.github/workflows/tests.yml@v1.10.1
     with:
       os: ${{ matrix.os }}
       pytorch-version: '["nightly"]'

.github/workflows/scheduled_test_cpu.yml

@@ -18,7 +18,7 @@ jobs:
         # os: ['Ubuntu-latest', 'Windows-latest', 'MacOS-latest']
         pytorch-dtype: ['float32', 'float64']
 
-    uses: kornia/workflows/.github/workflows/tests.yml@v1.10.0
+    uses: kornia/workflows/.github/workflows/tests.yml@v1.10.1
     with:
       os: 'Ubuntu-latest'
       python-version: '["3.8", "3.9", "3.10", "3.11", "3.12"]'
@@ -34,7 +34,7 @@ jobs:
       matrix:
         pytorch-dtype: ['float32', 'float64']
 
-    uses: kornia/workflows/.github/workflows/tests.yml@v1.10.0
+    uses: kornia/workflows/.github/workflows/tests.yml@v1.10.1
     with:
       os: 'Windows-latest'
       python-version: '["3.12"]'
@@ -47,21 +47,21 @@ jobs:
       matrix:
         pytorch-dtype: ['float32', 'float64']
 
-    uses: kornia/workflows/.github/workflows/tests.yml@v1.10.0
+    uses: kornia/workflows/.github/workflows/tests.yml@v1.10.1
     with:
       os: 'MacOS-latest'
       pytorch-dtype: ${{ matrix.pytorch-dtype }}
 
   coverage:
-    uses: kornia/workflows/.github/workflows/coverage.yml@v1.10.0
+    uses: kornia/workflows/.github/workflows/coverage.yml@v1.10.1
 
   typing:
-    uses: kornia/workflows/.github/workflows/mypy.yml@v1.10.0
+    uses: kornia/workflows/.github/workflows/mypy.yml@v1.10.1
 
   tutorials:
-    uses: kornia/workflows/.github/workflows/tutorials.yml@v1.10.0
+    uses: kornia/workflows/.github/workflows/tutorials.yml@v1.10.1
 
   docs:
-    uses: kornia/workflows/.github/workflows/docs.yml@v1.10.0
+    uses: kornia/workflows/.github/workflows/docs.yml@v1.10.1
     with:
       python-version: "3.11"

CODE_OF_CONDUCT.md

@@ -6,7 +6,7 @@ In the interest of fostering an open and welcoming environment, we as
 contributors and maintainers pledge to making participation in our project and
 our community a harassment-free experience for everyone, regardless of age, body
 size, disability, ethnicity, sex characteristics, gender identity and expression,
-level of experience, education, socio-economic status, nationality, personal
+level of experience, education, socioeconomic status, nationality, personal
 appearance, race, religion, or sexual identity and orientation.
 
 ## Our Standards

README.md

@@ -1,119 +1,71 @@
 <div align="center">
 <p align="center">
-  <img width="75%" src="https://github.com/kornia/data/raw/main/kornia_banner_pixie.png" />
+  <img width="55%" src="https://github.com/kornia/data/raw/main/kornia_banner_pixie.png" />
 </p>
 
 ---
 
 English | [简体中文](README_zh-CN.md)
 
 <!-- prettier-ignore -->
-<a href="https://kornia.org">Website</a> •
 <a href="https://kornia.readthedocs.io">Docs</a> •
-<a href="https://colab.research.google.com/github/kornia/tutorials/blob/master/source/hello_world_tutorial.ipynb">Try it Now</a> •
+<a href="https://colab.sandbox.google.com/github/kornia/tutorials/blob/master/nbs/hello_world_tutorial.ipynb">Try it Now</a> •
 <a href="https://kornia.github.io/tutorials/">Tutorials</a> •
 <a href="https://github.com/kornia/kornia-examples">Examples</a> •
 <a href="https://kornia.github.io//kornia-blog">Blog</a> •
 <a href="https://join.slack.com/t/kornia/shared_invite/zt-csobk21g-CnydWe5fmvkcktIeRFGCEQ">Community</a>
 
-[![PyPI python](https://img.shields.io/pypi/pyversions/kornia)](https://pypi.org/project/kornia)
-[![pytorch](https://img.shields.io/badge/PyTorch_1.9.1+-ee4c2c?logo=pytorch&logoColor=white)](https://pytorch.org/get-started/locally/)
-[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](LICENCE)
-
 [![PyPI version](https://badge.fury.io/py/kornia.svg)](https://pypi.org/project/kornia)
 [![Downloads](https://static.pepy.tech/badge/kornia)](https://pepy.tech/project/kornia)
 [![Slack](https://img.shields.io/badge/Slack-4A154B?logo=slack&logoColor=white)](https://join.slack.com/t/kornia/shared_invite/zt-csobk21g-2AQRi~X9Uu6PLMuUZdvfjA)
 [![Twitter](https://img.shields.io/twitter/follow/kornia_foss?style=social)](https://twitter.com/kornia_foss)
-
-[![tests-cpu](https://github.com/kornia/kornia/actions/workflows/scheduled_test_cpu.yml/badge.svg?event=schedule&&branch=main)](https://github.com/kornia/kornia/actions/workflows/scheduled_test_cpu.yml)
-[![codecov](https://codecov.io/gh/kornia/kornia/branch/main/graph/badge.svg?token=FzCb7e0Bso)](https://codecov.io/gh/kornia/kornia)
-[![Documentation Status](https://readthedocs.org/projects/kornia/badge/?version=latest)](https://kornia.readthedocs.io/en/latest/?badge=latest)
-[![pre-commit.ci status](https://results.pre-commit.ci/badge/github/kornia/kornia/main.svg)](https://results.pre-commit.ci/latest/github/kornia/kornia/main)
-
-<a href="https://www.producthunt.com/posts/kornia?utm_source=badge-featured&utm_medium=badge&utm_souce=badge-kornia" target="_blank"><img src="https://api.producthunt.com/widgets/embed-image/v1/featured.svg?post_id=306439&theme=light" alt="Kornia - Computer vision library for deep learning | Product Hunt" style="width: 250px; height: 54px;" width="250" height="54" /></a>
+[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](LICENCE)
 
 </p>
 </div>
 
-*Kornia* is a differentiable computer vision library for [PyTorch](https://pytorch.org).
+**Kornia** is a differentiable computer vision library for [PyTorch](https://pytorch.org).
 
 It consists of a set of routines and differentiable modules to solve generic computer vision problems. At its core, the package uses *PyTorch* as its main backend both for efficiency and to take advantage of the reverse-mode auto-differentiation to define and compute the gradient of complex functions.
 
-<div align="center">
-  <img src="https://github.com/kornia/kornia/raw/main/docs/source/_static/img/hakuna_matata.gif" width="75%" height="75%">
-</div>
-
-<!--<div align="center">
-  <img src="http://drive.google.com/uc?export=view&id=1KNwaanUdY1MynF0EYfyXjDM3ti09tzaq">
-</div>-->
+Inspired by existing packages, this library is composed by a subset of packages containing operators that can be inserted within neural networks to train models to perform image transformations, epipolar geometry, depth estimation, and low-level image processing such as filtering and edge detection that operate directly on tensors.
 
-## Overview
+## Sponsorship
 
-Inspired by existing packages, this library is composed by a subset of packages containing operators that can be inserted within neural networks to train models to perform image transformations, epipolar geometry, depth estimation, and low-level image processing such as filtering and edge detection that operate directly on tensors.
+Kornia is an open-source project that is developed and maintained by volunteers. Whether you're using it for research or commercial purposes, consider sponsoring or collaborating with us. Your support will help ensure Kornia's growth and ongoing innovation. Reach out to us today and be a part of shaping the future of this exciting initiative!
 
-At a granular level, Kornia is a library that consists of the following components:
-
-| **Component**                                                                    | **Description**                                                                                                                       |
-|----------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------|
-| [kornia](https://kornia.readthedocs.io/en/latest/index.html)                     | a Differentiable Computer Vision library, with strong GPU support                                                                     |
-| [kornia.augmentation](https://kornia.readthedocs.io/en/latest/augmentation.html) | a module to perform data augmentation in the GPU                                                                                      |
-| [kornia.color](https://kornia.readthedocs.io/en/latest/color.html)               | a set of routines to perform color space conversions                                                                                  |
-| [kornia.contrib](https://kornia.readthedocs.io/en/latest/contrib.html)           | a compilation of user contrib and experimental operators                                                                              |
-| [kornia.enhance](https://kornia.readthedocs.io/en/latest/enhance.html)           | a module to perform normalization and intensity transformation                                                                        |
-| [kornia.feature](https://kornia.readthedocs.io/en/latest/feature.html)           | a module to perform feature detection                                                                                                 |
-| [kornia.filters](https://kornia.readthedocs.io/en/latest/filters.html)           | a module to perform image filtering and edge detection                                                                                |
-| [kornia.geometry](https://kornia.readthedocs.io/en/latest/geometry.html)         | a geometric computer vision library to perform image transformations, 3D linear algebra and conversions using different camera models |
-| [kornia.losses](https://kornia.readthedocs.io/en/latest/losses.html)             | a stack of loss functions to solve different vision tasks                                                                             |
-| [kornia.morphology](https://kornia.readthedocs.io/en/latest/morphology.html)     | a module to perform morphological operations                                                                                          |
-| [kornia.utils](https://kornia.readthedocs.io/en/latest/utils.html)               | image to tensor utilities and metrics for vision problems                                                                             |
+<a href="https://opencollective.com/kornia/donate" target="_blank">
+  <img src="https://opencollective.com/webpack/donate/button@2x.png?color=blue" width=300 />
+</a>
 
 ## Installation
 
-### From pip:
+[![PyPI python](https://img.shields.io/pypi/pyversions/kornia)](https://pypi.org/project/kornia)
+[![pytorch](https://img.shields.io/badge/PyTorch_1.9.1+-ee4c2c?logo=pytorch&logoColor=white)](https://pytorch.org/get-started/locally/)
+
+### From pip
 
   ```bash
   pip install kornia
-  pip install kornia[x]  # to get the training API !
   ```
 
 <details>
   <summary>Other installation options</summary>
 
-  #### From source:
-
-  ```bash
-  python setup.py install
-  ```
-
-  #### From source with symbolic links:
+#### From source with editable mode
 
   ```bash
   pip install -e .
   ```
 
-  #### From source using pip:
+#### From Github url (latest version)
 
   ```bash
   pip install git+https://github.com/kornia/kornia
   ```
 
 </details>
 
-
-## Examples
-
-Run our Jupyter notebooks [tutorials](https://kornia.github.io/tutorials) to learn to use the library.
-
-<div align="center">
-  <a href="https://colab.research.google.com/github/kornia/tutorials/blob/master/source/hello_world_tutorial.ipynb" target="_blank">
-    <img src="https://raw.githubusercontent.com/kornia/data/main/hello_world_arturito.png" width="75%" height="75%">
-  </a>
-</div>
-
-:triangular_flag_on_post: **Updates**
-- :white_check_mark: [Image Matching](https://kornia.readthedocs.io/en/latest/applications/image_matching.html) Integrated to [Huggingface Spaces](https://huggingface.co/spaces). See [Gradio Web Demo](https://huggingface.co/spaces/akhaliq/Kornia-LoFTR).
-- :white_check_mark: [Face Detection](https://kornia.readthedocs.io/en/latest/applications/face_detection.html) Integrated to [Huggingface Spaces](https://huggingface.co/spaces). See [Gradio Web Demo](https://huggingface.co/spaces/frapochetti/blurry-faces).
-
 ## Cite
 
 If you are using kornia in your research-related documents, it is recommended that you cite the paper. See more in [CITATION](./CITATION.md).
@@ -129,17 +81,21 @@ If you are using kornia in your research-related documents, it is recommended th
   ```
 
 ## Contributing
-We appreciate all contributions. If you are planning to contribute back bug-fixes, please do so without any further discussion. If you plan to contribute new features, utility functions or extensions, please first open an issue and discuss the feature with us. Please, consider reading the [CONTRIBUTING](./CONTRIBUTING.md) notes. The participation in this open source project is subject to [Code of Conduct](./CODE_OF_CONDUCT.md).
 
+We appreciate all contributions. If you are planning to contribute back bug-fixes, please do so without any further discussion. If you plan to contribute new features, utility functions or extensions, please first open an issue and discuss the feature with us. Please, consider reading the [CONTRIBUTING](./CONTRIBUTING.md) notes. The participation in this open source project is subject to [Code of Conduct](./CODE_OF_CONDUCT.md).
 
 ## Community
+
 - **Forums:** discuss implementations, research, etc. [GitHub Forums](https://github.com/kornia/kornia/discussions)
 - **GitHub Issues:** bug reports, feature requests, install issues, RFCs, thoughts, etc. [OPEN](https://github.com/kornia/kornia/issues/new/choose)
 - **Slack:** Join our workspace to keep in touch with our core contributors and be part of our community. [JOIN HERE](https://join.slack.com/t/kornia/shared_invite/zt-csobk21g-2AQRi~X9Uu6PLMuUZdvfjA)
-- For general information, please visit our website at www.kornia.org
 
 <a href="https://github.com/Kornia/kornia/graphs/contributors">
-  <img src="https://contrib.rocks/image?repo=Kornia/kornia" width="75%" height="75%" />
+  <img src="https://contrib.rocks/image?repo=Kornia/kornia" width="60%" />
 </a>
 
 Made with [contrib.rocks](https://contrib.rocks).
+
+## License
+
+Kornia is released under the Apache 2.0 license. See the [LICENSE](./LICENSE) file for more information.

docs/source/models/segment_anything.rst

@@ -263,7 +263,7 @@ This is a simple example, of how to directly use the SAM model loaded. We recomm
     image = image[None, ...]
 
     # Resize the image to have the maximum size 1024 on its largest side
-    inpt = resize(image, 1024, side='long')
+    data = resize(image, 1024, side='long')
 
     # Embed prompts -- ATTENTION: should match the coordinates after the resize of the image
     sparse_embeddings, dense_embeddings = sam_model.prompt_encoder(points=None, boxes=None, masks=None)
@@ -273,16 +273,16 @@ This is a simple example, of how to directly use the SAM model loaded. We recomm
     pixel_std = torch.tensor(...)
 
     # Preprocess input
-    inpt = normalize(inpt, pixel_mean, pixel_std)
+    data = normalize(data, pixel_mean, pixel_std)
     padh = model_sam.image_encoder.img_size - h
     padw = model_sam.image_encoder.img_size - w
-    inpt = pad(inpt, (0, padw, 0, padh))
+    data = pad(data, (0, padw, 0, padh))
 
     #--------------------------------------------------------------------
     # Option A: Manually calling each API
     #--------------------------------------------------------------------
     low_res_logits, iou_predictions = sam_model.mask_decoder(
-        image_embeddings=sam_model.image_encoder(inpt),
+        image_embeddings=sam_model.image_encoder(data),
         image_pe=sam_model.prompt_encoder.get_dense_pe(),
         sparse_prompt_embeddings=sparse_embeddings,
         dense_prompt_embeddings=dense_embeddings,
@@ -294,13 +294,13 @@ This is a simple example, of how to directly use the SAM model loaded. We recomm
     #--------------------------------------------------------------------
     # Option B: Calling the model itself
     #--------------------------------------------------------------------
-    prediction = sam_model(inpt[None, ...], [{}], multimask_output=True)
+    prediction = sam_model(data[None, ...], [{}], multimask_output=True)
 
     #--------------------------------------------------------------------
     # Post processing
     #--------------------------------------------------------------------
     # Upscale the masks to the original image resolution
-    input_size = (inpt.shape[-2], inpt.shape[-1])
+    input_size = (data.shape[-2], data.shape[-1])
     original_size = (image.shape[-2], image.shape[-1])
     image_size_encoder = (model_sam.image_encoder.img_size, model_sam.image_encoder.img_size)
     prediction.original_res_logits(input_size, original_size, image_size_encoder)

kornia/__init__.py

@@ -5,11 +5,32 @@
 from . import grad_estimator
 
 # import the other modules for convenience
-from . import augmentation, color, contrib, core, enhance, feature, io, losses, metrics, morphology, tracking, utils, x
+from . import (
+    augmentation,
+    color,
+    contrib,
+    core,
+    enhance,
+    feature,
+    io,
+    losses,
+    metrics,
+    morphology,
+    tracking,
+    utils,
+    x,
+)
 
 # NOTE: we are going to expose to top level very few things
 from kornia.constants import pi
-from kornia.utils import eye_like, vec_like, create_meshgrid, image_to_tensor, tensor_to_image, xla_is_available
+from kornia.utils import (
+    eye_like,
+    vec_like,
+    create_meshgrid,
+    image_to_tensor,
+    tensor_to_image,
+    xla_is_available,
+)
 
 # Version variable
-__version__ = "0.7.3-dev"
+__version__ = "0.7.4-dev"

kornia/filters/bilateral.py

@@ -54,7 +54,7 @@ def _bilateral_blur(
     elif color_distance_type == "l2":
         color_distance_sq = diff.square().sum(1, keepdim=True)
     else:
-        raise ValueError("color_distance_type only acceps l1 or l2")
+        raise ValueError("color_distance_type only accepts l1 or l2")
     color_kernel = (-0.5 / sigma_color**2 * color_distance_sq).exp()  # (B, 1, H, W, Ky x Kx)
 
     space_kernel = get_gaussian_kernel2d(kernel_size, sigma_space, device=input.device, dtype=input.dtype)

kornia/geometry/epipolar/triangulation.py

@@ -1,4 +1,4 @@
-"""Module with the functionalites for triangulation."""
+"""Module with the functionalities for triangulation."""
 
 import torch
 

requirements/requirements-dev.txt

@@ -1,6 +1,6 @@
 coverage
 mypy
-numpy
+numpy<2
 onnx
 pre-commit>=2
 pytest==8.2.2

testing/geometry/create.py

@@ -7,10 +7,10 @@
 from kornia.utils.misc import eye_like
 
 
-def create_random_homography(inpt: Tensor, eye_size: int, std_val: float = 1e-3) -> Tensor:
+def create_random_homography(data: Tensor, eye_size: int, std_val: float = 1e-3) -> Tensor:
     """Create a batch of random homographies of shape Bx3x3."""
-    std = zeros(inpt.shape[0], eye_size, eye_size, device=inpt.device, dtype=inpt.dtype)
-    eye = eye_like(eye_size, inpt)
+    std = zeros(data.shape[0], eye_size, eye_size, device=data.device, dtype=data.dtype)
+    eye = eye_like(eye_size, data)
     return eye + std.uniform_(-std_val, std_val)