[](https://badge.fury.io/py/compressed-segmentation)
# Compress Seg [](http://www.janelia.org)
## Library for compressing and decompressing image segmentation (adapted from [neuroglancer](https://github.com/google/neuroglancer))
```python
import compressed_segmentation as cseg
sx, sy, sz = (128,128,128)
dtype = np.uint64
order = 'C'
labels = np.arange(0, sx*sy*sz, dtype=dtype).reshape((sx,sy,sz), order=order)
compressed = cseg.compress(labels, order=order)
recovered = cseg.decompress(
compressed, (sx,sy,sz) dtype=dtype, order=order
)
arr = CompressedSegmentationArray(
compressed, shape=(sx,sy,sz), dtype=dtype
)
label = arr[54,32,103] # random access to single voxels w/o decompressing
uniq_labels = arr.labels() # get all distinct values w/o decompressing
binary2 = arr.remap({ 1: 2 }, preserve_missing_labels=False) # remap labels in segmentation w/o decompressing
recovered = arr.numpy() # decompress to a numpy array, same as decompress
124213 in arr # test if a value is in the array
```
```bash
cseg compress connectomics.npy
cseg decompress connectomics.npy.cseg --volume-size 512,512,512 --bytes 4
````
NOTE: This repository is the PyPI distribution repo but is based on work done by Jeremy Maitin-Shepard (Google), Stephen Plaza (Janelia Research Campus), and William Silversmith (Princeton) here: https://github.com/janelia-flyem/compressedseg
This library contains routined to decompress and compress segmentation and to manipulate compressed segmentation data defined by the [neuroglancer project](https://github.com/google/neuroglancer/blob/master/src/sliceview/compressed_segmentation/README.md). compressed_segmentation essentially renumbers large bit width labels to smaller ones in chunks. This provides for large reductions in memory usage and higher compression.
Note that limitations in the compressed_segmentation format restrict the size of the chunk that can be compressed. As this limitation is data dependent, for example a random array with 1024 labels passes testing at 256x256x128, but 256x256x256 often does not.
### Features
* Compression and decompression
* Random access to voxels without decompression
* Read out unique values without decompression
* Remap labels without decompression
* Command line interface for numpy files
* (TBD) Interface to relabel and manipulate segmentation from the compressed data
* C++, Python, and Go interface (see original repo for Golang)
### C++ Compilation
Compiling as a shared library. Feel free to subsititute e.g. clang for the C++ compiler.
```bash
g++ -std=c++11 -O3 -fPIC -shared -I./include src/compress_segmentation.cc src/decompress_segmentation.cc -o compress_segmentation.so
```
### Python Installation
#### `pip` Binary Installation
```bash
$ pip install compressed-segmentation
$ python
>>> import compressed_segmentation as cseg
>>> help(cseg)
```
If there are pre-built binaries available for your architecture this should just work.
#### `pip` Source Installation
If you need to build from source, you will need to have a C++ compiler installed:
```bash
$ sudo apt-get install g++ python3-dev
$ pip install numpy
$ pip install compressed-segmentation
$ python
>>> import compressed_segmentation as cseg
>>> help(cseg)
```
#### Direct Installation
_Requires a C++ compiler such as g++ or clang._
Works with both Python 2 and 3. Encodes from / decodes to 3D or 4D numpy ndarrays.
```bash
$ sudo apt-get install g++ python3-dev
$ pip install -r requirements.txt
$ python setup.py install
$ python
>>> import compressed_segmentation as cseg
>>> help(cseg)
```
### License
Please see the licenses in this repo.
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"description": "[](https://badge.fury.io/py/compressed-segmentation) \n\n# Compress Seg [](http://www.janelia.org)\n## Library for compressing and decompressing image segmentation (adapted from [neuroglancer](https://github.com/google/neuroglancer))\n\n```python\nimport compressed_segmentation as cseg\n\nsx, sy, sz = (128,128,128)\ndtype = np.uint64\norder = 'C'\n\nlabels = np.arange(0, sx*sy*sz, dtype=dtype).reshape((sx,sy,sz), order=order)\ncompressed = cseg.compress(labels, order=order)\nrecovered = cseg.decompress(\n compressed, (sx,sy,sz) dtype=dtype, order=order\n)\n\narr = CompressedSegmentationArray(\n compressed, shape=(sx,sy,sz), dtype=dtype\n)\nlabel = arr[54,32,103] # random access to single voxels w/o decompressing\nuniq_labels = arr.labels() # get all distinct values w/o decompressing\nbinary2 = arr.remap({ 1: 2 }, preserve_missing_labels=False) # remap labels in segmentation w/o decompressing\nrecovered = arr.numpy() # decompress to a numpy array, same as decompress\n124213 in arr # test if a value is in the array\n```\n\n```bash\ncseg compress connectomics.npy\ncseg decompress connectomics.npy.cseg --volume-size 512,512,512 --bytes 4\n````\n\n\nNOTE: This repository is the PyPI distribution repo but is based on work done by Jeremy Maitin-Shepard (Google), Stephen Plaza (Janelia Research Campus), and William Silversmith (Princeton) here: https://github.com/janelia-flyem/compressedseg\n\nThis library contains routined to decompress and compress segmentation and to manipulate compressed segmentation data defined by the [neuroglancer project](https://github.com/google/neuroglancer/blob/master/src/sliceview/compressed_segmentation/README.md). compressed_segmentation essentially renumbers large bit width labels to smaller ones in chunks. This provides for large reductions in memory usage and higher compression.\n\nNote that limitations in the compressed_segmentation format restrict the size of the chunk that can be compressed. As this limitation is data dependent, for example a random array with 1024 labels passes testing at 256x256x128, but 256x256x256 often does not.\n\n\n### Features\n\n* Compression and decompression\n* Random access to voxels without decompression\n* Read out unique values without decompression\n* Remap labels without decompression\n* Command line interface for numpy files\n* (TBD) Interface to relabel and manipulate segmentation from the compressed data\n* C++, Python, and Go interface (see original repo for Golang)\n\n### C++ Compilation\n\nCompiling as a shared library. Feel free to subsititute e.g. clang for the C++ compiler.\n\n```bash\ng++ -std=c++11 -O3 -fPIC -shared -I./include src/compress_segmentation.cc src/decompress_segmentation.cc -o compress_segmentation.so\n```\n\n### Python Installation\n\n#### `pip` Binary Installation \n\n```bash\n$ pip install compressed-segmentation\n\n$ python\n>>> import compressed_segmentation as cseg\n>>> help(cseg)\n```\n\nIf there are pre-built binaries available for your architecture this should just work. \n\n#### `pip` Source Installation \n\nIf you need to build from source, you will need to have a C++ compiler installed:\n\n```bash\n$ sudo apt-get install g++ python3-dev \n$ pip install numpy\n$ pip install compressed-segmentation\n\n$ python\n>>> import compressed_segmentation as cseg\n>>> help(cseg)\n```\n\n#### Direct Installation \n\n_Requires a C++ compiler such as g++ or clang._\n\nWorks with both Python 2 and 3. Encodes from / decodes to 3D or 4D numpy ndarrays. \n\n```bash\n$ sudo apt-get install g++ python3-dev \n$ pip install -r requirements.txt\n$ python setup.py install\n\n$ python\n>>> import compressed_segmentation as cseg\n>>> help(cseg)\n```\n\n### License\n\nPlease see the licenses in this repo.\n\n",
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