DoImageSegmentation

Algorithm

The ImageSegmentationTask class implements the image segmentation task. The algorithm is implemented in the do_image_segmentation() function. Depending on the parameters passed as input, different strategies for the image segmentation are followed.

Parameters

• method sets the segmentation method

  • ‘dbscan’ : the dbscan method is used
  • ‘dbscan_binned’ the binned dbscan method is used(default)
  • ‘connected’ the nearest neighbor method is used

• bkg_threshold: the value of estimated background threshold . All the points below this value are masked (** not present in conf file because this value in the process is determined by the bkg task**)

• K: sets the segmentation threshold as K*bkg_threshold

• K_pix: if True, total flux in the dbscan bursh is divided by the number of pixels in the brush, and the compared to K*bkg_threshold

• dbscan_eps: the scanning brush radius for the dbscan. (In the case of binned, it is the size of the scanning box)

• seg_map_bkg_val: value for the bkg pixels in the seg map (default 0)

• max_image_size: if is not None, the image is segmented in substamps, according to bkg_threshold

• min_sub_stamp_size: the minimum substamp size, if smaller the sub stamp will be rejected

• input_seg_map: a 2dim numpy array storing the segmentation map. In this case the segmentation will come directly from the seg_map (** in the do_IO_conf section of the conf file **)

• use_only_central_sub_stamp: if True, only the central source is returned

conf file section

The configuration file section will read: .. code-block:

[ task: image_segmentation: start]
    method = dbscan_binned
    K = 1.5
    K_pix = False
    dbscan_eps = 1.0
    seg_map_bkg_val = 0
    verbose = False
    plot = False
    max_image_size = 1000000
    use_only_central_sub_stamp = False
    min_sub_stamp_size = 25
[ task: image_segmentation: stop]

Accessing through the Task class

from asterism import data_dir
from asterism.core.image_manager.image import Image
from asterism.analysis_tasks.source_detection.background_estimation.background_estimation import DoSetBkgThreshTask
from asterism.analysis_tasks.source_detection.image_segmentation.image_segmentation import DoImageSegmentationTask
import  numpy as np

image=Image.from_fits_file(data_dir+'/galaxy.fits')

bkg_task=DoSetBkgThreshTask()
bkg_task.list_parameters()
bkg_task.set_par('plot',value=False)
bkg_task.set_par('skewness_range',value=0.1)
bkg_task.set_par('sub_block_frac_size',value=0.1)

bkg_threshold,bkg_mode,bkg_sig=bkg_task.run(image=image)
image_seg_task=DoImageSegmentationTask()
image_seg_task.list_parameters()
image_seg_task.set_par('bkg_threshold',value=float(bkg_threshold))
image_seg_task.set_par('K',value=1.5)
image_seg_task.set_par('K_pix',value=True)
image_seg_task.set_par('plot',value=True)
image_seg_task.list_parameters()

image_seg_task.run(image=image)

Direct access to do_image_segmentation()

from asterism import data_dir
from asterism.core.image_manager.image import Image
from asterism.analysis_tasks.source_detection.background_estimation.background_estimation import set_thresh
from asterism.analysis_tasks.source_detection.image_segmentation.image_segmentation import do_image_segmentation

image=Image.from_fits_file(data_dir+'/galaxy.fits')
bkg_threshold,bkg_mode,bkg_sig=set_thresh(image,sub_block_frac_size=0.1,skewness_range=0.1,plot=False)
do_image_segmentation(image,bkg_threshold=bkg_threshold,K=np.float_(1.5),K_pix=True,plot=True)