Morpheus is a neural network model used to generate pixel-level morphological classifications for astronomical sources. This model can be used to generate segmentation maps or to inform other photometric measurements with granular morphological information.
Morpheus is implemented using TensorFlow. TensorFlow is not listed in the dependencies for the package. So you need to install TensorFlow before you install Morpheus. It has to be done this way to support the GPU accelerated version of TensorFlow, which has a different package name. For more information on installing TensorFlow visit the TensorFlow website.
pip install morpheus-astroMorpheus has two main flavors of Docker Image: gpu for the GPU enabled version
of TensorFlow and cpu for the standard CPU implementation of TensorFlow.
Visit the Docker Hub page
for relevant tags.
For GPU support:
docker run --runtime=nvidia -it morpheusastro/morpheus:lastest-gpuFor CPU only:
docker run -it morpheusastro/morpheus:latest-cpuThere are two ways to use morpheus on images: the python API and the command line interface
The morpheus.classifier.Classifier class is the interface to the various functionalities of Morpheus.
To perform a pixel-level morphological classification, the image needs to be provided in the H, J, Z, and V bands. See classify for more information.
from morpheus.classifier import Classifier
from morpheus.data import example
h, j, v, z = example.get_sample()
classified = Classifier.classify(h=h, j=j, v=v, z=z)The classify function returns a dictionary where the keys indicate the output
for example spheroid, and the value is the corresponding numpy ndarray.
Using the output from classify you can:
- Make a segmap
- Make a morphgological catalog
- Make colorized version of the morphological classifications
To create a segmentation map using Morpheus, you need to provide the output from the classify function and a single flux band. In the below example we use H. For more information see segmap
from morpheus.classifier import Classifier
from morpheus.data import example
h, j, v, z = example.get_sample()
classified = Classifier.classify(h=h, j=j, v=v, z=z)
segmap = Classifier.segmap_from_classified(classified, h)To create a catalog using Morpheus, you need to provide the output from the classify function, the flux in a single band (we use H), and a segmentation map. The segmentation map doesn't have to be generated by Morpheus, but it must be similar in form. It should assign pixels values greater than 0 for all pixels that are associated with a source. Each source should be assigned a unique ID. Background should be set to 0 and excluded regions should be assigned -1. The catalog returned is a JSON compatible list of morphological classifications for each source in the segmap. For more information, see catalog.
from morpheus.classifier import Classifier
from morpheus.data import example
h, j, v, z = example.get_sample()
classified = Classifier.classify(h=h, j=j, v=v, z=z)
segmap = Classifier.segmap_from_classified(classified, h)
catalog = Classifier.catalog_from_classified(classified, h, segmap)A colorized classification is a way of making a single image to interpret the pixel level morphological classifications. For more information see colorize.
from morpheus.classifier import Classifier
from morpheus.data import example
h, j, v, z = example.get_sample()
classified = Classifier.classify(h=h, j=j, v=v, z=z)
color_rgb = Classifier.colorize_classified(classified)Morpheus supports simple parallelization by breaking an image into equally sized pieces along the y axis, classifying them in seperate processes, and stitching them back into a single image. Parallelization can be split into CPU jobs or GPU jobs. Importantly, you cannot specify both at the same time.
GPUS
The gpus argument should be a list of integers that are the ids assigned to
the GPUS to be used. These ids can be found by using nvidia-smi.
from morpheus.classifier import Classifier
from morpheus.data import example
h, j, v, z = example.get_sample()
classified = Classifier.classify(h=h, j=j, v=v, z=z, gpus=[0,1])CPUS
The cpus argument should be an integer indicating how many processes to
spin off.
from morpheus.classifier import Classifier
from morpheus.data import example
h, j, v, z = example.get_sample()
classified = Classifier.classify(h=h, j=j, v=v, z=z, cpus=2)Morpheus can be used from the terminal using the morpheus command. To
classify an image, it needs to be available in the H, J, V, and Z bands. From
the terminal the following actions can be performed:
- Per pixel morphological classification
- Make segmentation map
- Make a catalog of morphological classifications
- Make a colorized version of the morphological classifications
morpheus h.fits j.fits v.fits z.fitsOrder is important when calling the Morpheus from the terminal. The files
should be in the order H, J, V, and Z, as displayed in the above example. The
output classification will be saved in the current working directory unless
otherwise indicated by the --out_dir optional argument.
morpheus h.fits j.fits v.fits z.fits --action segmapTo create a segmap, append the optional --action flag with the argument
segmap. This will save both the classifications and the segmap to the
same directory.
morpheus h.fits j.fits v.fits z.fits --action catalogThis will create a catalog by classifying the input images, creating a segmap, and using both of those to generate a morphological catalog. The morphological classifications, segmap, and catalog are all saved to the same place.
morpheus h.fits j.fits v.fits z.fits --action colorizeUsing --action colorize will classify the image and then generate a
colorized version of that classification and save the classification and
colorized version to the same place.
Morpheus supports simple parallelization by breaking an image into equally sized pieces along the y axis, classifying them in separate processes, and stitching them back into a single image. Parallelization can be split into CPU jobs or GPU jobs. Importantly, you cannot specify both at the same time.
GPUS
The gpus optional flag should be a comma-separated list of ids for the
GPUS to be used. These ids can be found by using nvidia-smi.
morpheus h.fits j.fits v.fits z.fits --gpus 0,1CPUS
The cpus optional flag should be an integer indicating how many processes
to spin off.
morpheus h.fits j.fits v.fits z.fits --cpus 2Try it out on Google Colab!