<p align="center">
<a href="https://github.com/pydicom/pylibjpeg-rle/actions?query=workflow%3Aunit-tests"><img alt="Build status" src="https://github.com/pydicom/pylibjpeg-rle/workflows/unit-tests/badge.svg"></a>
<a href="https://codecov.io/gh/pydicom/pylibjpeg-rle"><img alt="Test coverage" src="https://codecov.io/gh/pydicom/pylibjpeg-rle/branch/main/graph/badge.svg"></a>
<a href="https://pypi.org/project/pylibjpeg-rle/"><img alt="PyPI versions" src="https://img.shields.io/pypi/v/pylibjpeg-rle"></a>
<a href="https://www.python.org/"><img alt="Python versions" src="https://img.shields.io/pypi/pyversions/pylibjpeg-rle.svg"></a>
<a href="https://github.com/psf/black"><img alt="Code style: black" src="https://img.shields.io/badge/code%20style-black-000000.svg"></a>
</p>
## pylibjpeg-rle
A fast DICOM ([PackBits](https://en.wikipedia.org/wiki/PackBits)) RLE plugin for [pylibjpeg](https://github.com/pydicom/pylibjpeg), written in Rust with a Python wrapper.
Linux, MacOS and Windows are all supported.
### Installation
#### Installing the current release
```bash
pip install pylibjpeg-rle
```
#### Installing the development version
Make sure [Python](https://www.python.org/), [Git](https://git-scm.com/) and
[Rust](https://www.rust-lang.org/) are installed. For Windows, you also need to install
[Microsoft's C++ Build Tools](https://visualstudio.microsoft.com/thank-you-downloading-visual-studio/?sku=BuildTools&rel=16).
```bash
git clone https://github.com/pydicom/pylibjpeg-rle
cd pylibjpeg-rle
python -m pip install .
```
### Supported Transfer Syntaxes
| UID | Description | Decoding | Encoding |
| --- | --- | --- | --- |
| 1.2.840.10008.1.2.5 | RLE Lossless | Yes | Yes |
### Usage
#### Decoding
##### With pylibjpeg
```python
from pydicom import dcmread
from pydicom.data import get_testdata_file
ds = dcmread(get_testdata_file("OBXXXX1A_rle.dcm"))
arr = ds.pixel_array
```
##### Standalone with pydicom
Alternatively you can use the included functions to decode a given dataset:
```python
from rle import pixel_array, generate_frames
# Return the entire Pixel Data as an ndarray
arr = pixel_array(ds)
# Generator function that only processes 1 frame at a time,
# may help reduce memory usage when dealing with large Pixel Data
for arr in generate_frames(ds):
print(arr.shape)
```
#### Encoding
##### Standalone with pydicom
Convert uncompressed pixel data to RLE encoding and save:
```python
from pydicom import dcmread
from pydicom.data import get_testdata_file
from pydicom.uid import RLELossless
from rle import pixel_data
# Get the uncompressed pixel data
ds = dcmread(get_testdata_file("OBXXXX1A.dcm"))
arr = ds.pixel_array
# RLE encode and encapsulate `arr`
ds.PixelData = pixel_data(arr, ds)
# Set the correct *Transfer Syntax UID*
ds.file_meta.TransferSyntaxUID = RLELossless
ds.save_as('as_rle.dcm')
```
### Benchmarks
#### Decoding
Time per 1000 decodes, pydicom's default RLE decoder vs. pylibjpeg-rle:
| Dataset | Pixels | Bytes | pydicom | pylibjpeg-rle |
| --- | --- | --- | --- | --- |
| OBXXXX1A_rle.dcm | 480,000 | 480,000 | 5.7 s | 1.1 s |
| OBXXXX1A_rle_2frame.dcm | 960,000 | 960,000 | 11.5 s | 2.1 s |
| SC_rgb_rle.dcm | 10,000 | 30,000 | 0.28 s | 0.19 s |
| SC_rgb_rle_2frame.dcm | 20,000 | 60,000 | 0.45 s | 0.28 s |
| MR_small_RLE.dcm | 4,096 | 8,192 | 0.46 s | 0.15 s |
| emri_small_RLE.dcm | 40,960 | 81,920 | 1.8 s | 0.67 s |
| SC_rgb_rle_16bit.dcm | 10,000 | 60,000 | 0.48 s | 0.25 s |
| SC_rgb_rle_16bit_2frame.dcm | 20,000 | 120,000 | 0.86 s | 0.39 s |
| rtdose_rle_1frame.dcm | 100 | 400 | 0.16 s | 0.13 s |
| rtdose_rle.dcm | 1,500 | 6,000 | 1.0 s | 0.64 s |
| SC_rgb_rle_32bit.dcm | 10,000 | 120,000 | 0.82 s | 0.35 s |
| SC_rgb_rle_32bit_2frame.dcm | 20,000 | 240,000 | 1.5 s | 0.60 s |
#### Encoding
Time per 1000 encodes, pydicom's default RLE encoder vs. pylibjpeg-rle and [python-gdcm](https://github.com/tfmoraes/python-gdcm):
| Dataset | Pixels | Bytes | pydicom | pylibjpeg-rle | python-gdcm |
| --- | --- | --- | --- | --- | --- |
| OBXXXX1A.dcm | 480,000 | 480,000 | 30.6 s | 1.4 s | 1.5 s |
| SC_rgb.dcm | 10,000 | 30,000 | 1.9 s | 0.11 s | 0.21 s |
| MR_small.dcm | 4,096 | 8,192 | 3.0 s | 0.11 s | 0.29 s |
| SC_rgb_16bit.dcm | 10,000 | 60,000 | 3.6 s | 0.18 s | 0.28 s |
| rtdose_1frame.dcm | 100 | 400 | 0.28 s | 0.04 s | 0.14 s |
| SC_rgb_32bit.dcm | 10,000 | 120,000 | 7.1 s | 0.32 s | 0.43 s |
Raw data
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"description": "<p align=\"center\">\n<a href=\"https://github.com/pydicom/pylibjpeg-rle/actions?query=workflow%3Aunit-tests\"><img alt=\"Build status\" src=\"https://github.com/pydicom/pylibjpeg-rle/workflows/unit-tests/badge.svg\"></a>\n<a href=\"https://codecov.io/gh/pydicom/pylibjpeg-rle\"><img alt=\"Test coverage\" src=\"https://codecov.io/gh/pydicom/pylibjpeg-rle/branch/main/graph/badge.svg\"></a>\n<a href=\"https://pypi.org/project/pylibjpeg-rle/\"><img alt=\"PyPI versions\" src=\"https://img.shields.io/pypi/v/pylibjpeg-rle\"></a>\n<a href=\"https://www.python.org/\"><img alt=\"Python versions\" src=\"https://img.shields.io/pypi/pyversions/pylibjpeg-rle.svg\"></a>\n<a href=\"https://github.com/psf/black\"><img alt=\"Code style: black\" src=\"https://img.shields.io/badge/code%20style-black-000000.svg\"></a>\n</p>\n\n## pylibjpeg-rle\n\nA fast DICOM ([PackBits](https://en.wikipedia.org/wiki/PackBits)) RLE plugin for [pylibjpeg](https://github.com/pydicom/pylibjpeg), written in Rust with a Python wrapper.\n\nLinux, MacOS and Windows are all supported.\n\n### Installation\n#### Installing the current release\n```bash\npip install pylibjpeg-rle\n```\n#### Installing the development version\n\nMake sure [Python](https://www.python.org/), [Git](https://git-scm.com/) and\n[Rust](https://www.rust-lang.org/) are installed. For Windows, you also need to install\n[Microsoft's C++ Build Tools](https://visualstudio.microsoft.com/thank-you-downloading-visual-studio/?sku=BuildTools&rel=16).\n```bash\ngit clone https://github.com/pydicom/pylibjpeg-rle\ncd pylibjpeg-rle\npython -m pip install .\n```\n\n### Supported Transfer Syntaxes\n\n| UID | Description | Decoding | Encoding |\n| --- | --- | --- | --- |\n| 1.2.840.10008.1.2.5 | RLE Lossless | Yes | Yes |\n\n### Usage\n#### Decoding\n##### With pylibjpeg\n\n```python\nfrom pydicom import dcmread\nfrom pydicom.data import get_testdata_file\n\nds = dcmread(get_testdata_file(\"OBXXXX1A_rle.dcm\"))\narr = ds.pixel_array\n```\n\n##### Standalone with pydicom\nAlternatively you can use the included functions to decode a given dataset:\n```python\nfrom rle import pixel_array, generate_frames\n\n# Return the entire Pixel Data as an ndarray\narr = pixel_array(ds)\n\n# Generator function that only processes 1 frame at a time,\n# may help reduce memory usage when dealing with large Pixel Data\nfor arr in generate_frames(ds):\n print(arr.shape)\n```\n\n#### Encoding\n##### Standalone with pydicom\n\nConvert uncompressed pixel data to RLE encoding and save:\n```python\nfrom pydicom import dcmread\nfrom pydicom.data import get_testdata_file\nfrom pydicom.uid import RLELossless\n\nfrom rle import pixel_data\n\n# Get the uncompressed pixel data\nds = dcmread(get_testdata_file(\"OBXXXX1A.dcm\"))\narr = ds.pixel_array\n\n# RLE encode and encapsulate `arr`\nds.PixelData = pixel_data(arr, ds)\n# Set the correct *Transfer Syntax UID*\nds.file_meta.TransferSyntaxUID = RLELossless\nds.save_as('as_rle.dcm')\n```\n\n### Benchmarks\n#### Decoding\n\nTime per 1000 decodes, pydicom's default RLE decoder vs. pylibjpeg-rle:\n\n| Dataset | Pixels | Bytes | pydicom | pylibjpeg-rle |\n| --- | --- | --- | --- | --- |\n| OBXXXX1A_rle.dcm | 480,000 | 480,000 | 5.7 s | 1.1 s |\n| OBXXXX1A_rle_2frame.dcm | 960,000 | 960,000 | 11.5 s | 2.1 s |\n| SC_rgb_rle.dcm | 10,000 | 30,000 | 0.28 s | 0.19 s |\n| SC_rgb_rle_2frame.dcm | 20,000 | 60,000 | 0.45 s | 0.28 s |\n| MR_small_RLE.dcm | 4,096 | 8,192 | 0.46 s | 0.15 s |\n| emri_small_RLE.dcm | 40,960 | 81,920 | 1.8 s | 0.67 s |\n| SC_rgb_rle_16bit.dcm | 10,000 | 60,000 | 0.48 s | 0.25 s |\n| SC_rgb_rle_16bit_2frame.dcm | 20,000 | 120,000 | 0.86 s | 0.39 s |\n| rtdose_rle_1frame.dcm | 100 | 400 | 0.16 s | 0.13 s |\n| rtdose_rle.dcm | 1,500 | 6,000 | 1.0 s | 0.64 s |\n| SC_rgb_rle_32bit.dcm | 10,000 | 120,000 | 0.82 s | 0.35 s |\n| SC_rgb_rle_32bit_2frame.dcm | 20,000 | 240,000 | 1.5 s | 0.60 s |\n\n#### Encoding\n\nTime per 1000 encodes, pydicom's default RLE encoder vs. pylibjpeg-rle and [python-gdcm](https://github.com/tfmoraes/python-gdcm):\n\n| Dataset | Pixels | Bytes | pydicom | pylibjpeg-rle | python-gdcm |\n| --- | --- | --- | --- | --- | --- |\n| OBXXXX1A.dcm | 480,000 | 480,000 | 30.6 s | 1.4 s | 1.5 s |\n| SC_rgb.dcm | 10,000 | 30,000 | 1.9 s | 0.11 s | 0.21 s |\n| MR_small.dcm | 4,096 | 8,192 | 3.0 s | 0.11 s | 0.29 s |\n| SC_rgb_16bit.dcm | 10,000 | 60,000 | 3.6 s | 0.18 s | 0.28 s |\n| rtdose_1frame.dcm | 100 | 400 | 0.28 s | 0.04 s | 0.14 s |\n| SC_rgb_32bit.dcm | 10,000 | 120,000 | 7.1 s | 0.32 s | 0.43 s |\n\n",
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