update readme before release 0.0.6

.github/workflows/release.yml

@@ -1,6 +1,8 @@
 name: Release
 
 on:
+  release:
+    types: [published]
   workflow_dispatch:
 
 env:

README.md

@@ -1,46 +1,75 @@
 # kornia-rs: Low level implementations for Computer Vision in Rust.
 
-## (🚨 Warning: Unstable Prototype 🚨)
-
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-The expectation of this project is to provide low level functionality
-for Computer Vision written in [Rust](https://www.rust-lang.org/) to be consumed by deep learning frameworks, specially those working with images. We mainly provide I/O for images (future: video, cameras) and visualisation.
+This project provides low level functionality for Computer Vision written in [Rust](https://www.rust-lang.org/) to be consumed by machine learning and data-science frameworks, specially those working with images. We mainly aim to provide I/O functionality for images (future: video, cameras), and visualisation in future.
 
-The library is written in [Rust](https://www.rust-lang.org/) and wrapped to Python (potentially later to C/C++) using [PyO3/Maturin](https://github.com/PyO3/maturin). The library can also be used as a standalone Rust crate.
+- The library is written in [Rust](https://www.rust-lang.org/).
+- Python bindings are created with [PyO3/Maturin](https://github.com/PyO3/maturin).
+- We package with support for Linux [amd64/arm64], Macos and WIndows.
+- Supported Python versions are 3.7/3.8/3.9/3.10/3.11
 
 ## Basic Usage
 
-Load an image, that is converted to `cv::Tensor` wich is designed centric
-to the DLPack protocol to share tensor data across deep learning frameworks with a zero-copy cost.
-
-The visualisation API is based on `vviz`: https://github.com/strasdat/vviz
+Load an image, that is converted to `cv::Tensor` wich is a centric structure to the DLPack protocol to share tensor data across frameworks with a zero-copy cost.
 
 ```python
     import kornia_rs as K
     from kornia_rs import Tensor as cvTensor
 
+    # load an image with Rust `image-rs` as backend library
     cv_tensor: cvTensor = K.read_image_rs("dog.jpeg")
     assert cv_tensor.shape == [195, 258, 3]
 
-    # convert to dlpack to import to torch and numpy
-    # NOTE: later we will support to jax and mxnet.
+    # convert to dlpack to import to torch
     th_tensor = torch.utils.dlpack.from_dlpack(cv_tensor)
-    np_tensor = np.from_dlpack(cv_tensor)
     assert th_tensor.shape == (195, 258, 3)
     assert np_tensor.shape == (195, 258, 3)
+
+    # or to numpy with same interface
+    np_tensor = np.from_dlpack(cv_tensor)
+```
+
+## Advanced usage
+
+Encode or decoda image streams using the `turbojpeg` backend
+
+```python
+# load image using turbojpeg
+cv_tensor = K.read_image_jpeg("dog.jpeg")
+image: np.ndarray = np.from_dlpack(cv_tensor)  # HxWx3
+
+# encode the image with jpeg
+image_encoder = K.ImageEncoder()
+image_encoder.set_quality(95)  # set the encoding quality
+
+# get the encoded stream
+image_encoded: List[int] = image_encoder.encode(image.tobytes(), image.shape)
+
+# write to disk the encoded stream
+K.write_image_jpeg("dog_encoded.jpeg", image_encoded)
+
+# decode back the image
+image_decoder = K.ImageDecoder()
+
+decoded_tensor = image_decoder.decode(bytes(image_encoded))
+decoded_image: np.ndarray = np.from_dlpack(decoded_tensor)  # HxWx3
 ```
 
-## TODO
+## TODO: short/mid-terrm
 
 - [x] [infra] Automate packaging for manywheels.
 - [x] [kornia] integrate with the new `Image` API
 - [x] [dlpack] move dlpack implementation to dlpack-rs.
-- [ ] [dlpack] implement test for numpy, jax and mxnet.
+- [x] [dlpack] implement test for torch and numpy.
+- [ ] [dlpack] update dlpack version >=0.8
 - [ ] [dlpack] implement `DLPack` to `cv::Tensor`.
+
+## TODO: not priority for now
+
 - [ ] [io] Implement image encoding and explore video.
 - [ ] [viz] Fix minor issues and implement a full `VizManager` to work on the browser.
 - [ ] [tensor] implement basic functionality to test: add, sub, mul, etc.
@@ -91,7 +120,6 @@ maturin develop --extras dev
 pytest test/
 ```
 
-
 ## Contributing
 
 This is a child project of [Kornia](https://github.com/kornia/kornia). Join the community to get in touch with us, or just sponsor the project: https://opencollective.com/kornia