SourceDetectionTask#

SourceDetectionTask detects positive and negative sources on an exposure and produces a lsst.afw.table.SourceCatalog of detected sources. This task expects the image to have been background subtracted first, for example via :lsst-task:`~lsst.meas.algorithms.subtractBackground.SubtractBackgroundTask`. Running detection on images with a non-zero-centered background may result in a single source detected on the entire image containing thousands of peaks, or other pathological outputs. This task convolves the image with a Gaussian approximation to the PSF, matched to the sigma of the input exposure, because this is separable and fast.

Python API summary#

Retargetable subtasks#

Configuration fields#

Examples#

This code is in measAlgTasks.py in the examples directory, and can be run as e.g.

examples/measAlgTasks.py --doDisplay

The example also runs the SingleFrameMeasurementTask; see meas_algorithms_measurement_Example for more explanation.

Import the task (there are some other standard imports; read the file if you’re confused)

from lsst.meas.algorithms.detection import SourceDetectionTask

We need to create our task before processing any data as the task constructor can add an extra column to the schema, but first we need an almost-empty Schema

schema = afwTable.SourceTable.makeMinimalSchema()

after which we can call the constructor:

config = SourceDetectionTask.ConfigClass()
config.thresholdPolarity = "both"
config.background.isNanSafe = True
config.thresholdValue = 3
detectionTask = SourceDetectionTask(config=config, schema=schema)

We’re now ready to process the data (we could loop over multiple exposure/catalogues using the same task objects). First create the output table:

table = afwTable.SourceTable.make(schema)

And process the image

result = detectionTask.run(table, exposure)

(You may not be happy that the threshold was set in the config before creating the Task rather than being set separately for each exposure. You can reset it just before calling the run method if you must, but we should really implement a better solution).

We can then unpack the results:

sources = result.sources
print("Found %d sources (%d +ve, %d -ve)" % (len(sources), result.numPos,
result.numNeg))

Debugging#

The pipetask run command-line interface supports a flag --debug to to import debug.py from your PYTHONPATH; see lsstDebug for more about debug.py files.

The available variables in SourceDetectionTask are:

display

  1. If True, display the exposure of afwDisplay.Display’s frame 0. Positive detections in blue, negative detections in cyan.

  2. If display > 1, display the convolved exposure on frame 1