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Self.wants_minimum_lifetime = cps.Binary( Splits continue the lifetime count from their parents, so the minimum The filtered object's per-object measurements are retained.Īn object can be filtered only if it is tracked as an unique object. This operation does not actually delete the filtered object,īut merely removes its label from the tracked object list Marking objects which transiently appear and disappear, such Lifetime, i.e., total duration in frames. Select %(YES)s if you want objects to be filtered by their Self.wants_lifetime_filtering = cps.Binary( 'Filter objects by lifetime?', False, doc = ''' Set the maximum gap lower to reduce the chance of erroneously connecting to the wrong object afterĬorrectly losing the original object (e.g., if the cell dies or moves off-screen). Set the maximum gap higher in order to have more chance of correctly recapturing an object afterĮrroneously losing the original for a few frames. These gaps occur when an image is mis-segmented and identificationįails to find an object in one or more frames. This setting controls the maximum number of frames that canīe skipped when merging a gap caused by an unsegmented object. (Used only if the %(TM_LAP)s tracking method is applied and the second phase is run)Ĭare must be taken to adjust this setting appropriate to the data. Group.append( "wants_custom_names", cps.Binary( The module will create one more bin than there are thresholds.""")) Thresholds with commas (for example, 0.3, 1.5, 2.1 for four bins). You should enter one threshold between each bin, separating This setting establishes the threshold values for theīins. "Enter the custom thresholds separating the values between bins", Group.append( "custom_thresholds", cps.Text( Whose values are above the high threshold. Group.append( "wants_high_bin", cps.Binary( "Use a bin for objects above the threshold?", False, Note: If you would like two bins, choose Custom-defined bins.""")) Minval = cps.NumberConnector(min_upper_threshold),ĭoc= """This is the threshold that separates the last bin from Group.append( "high_threshold", cps.Float( If you do not want a bin for these objects."""))ĭef min_upper_threshold(): return group.low_threshold.value + np.finfo( float).eps Whose values fall below the low threshold. "Use a bin for objects below the threshold?", False,ĭoc= """Check this box if you want to create a bin for objects (Used only if saving the color-coded image)Įnter a name to give the color-coded image of tracked labels.''') "Name the output image", "TrackedCells", doc = ''' Will only show the penultimate result and not the final product."""% globals()) That means that saving the color-coded image The final labels are not assigned until after the pipeline hasĬompleted the analysis run. Please note that if you are using the second phase of the %(TM_LAP)s method, Saving the image with the SaveImages module. For example, a common use is for quality control purposes Select %(YES)s to retain the image showing the tracked objectsįor later use in the pipeline. Self.wants_image = cps.Binary( "Save color-coded image?", False, doc= """ %(DT_COLOR_AND_NUMBER)s: Same as above but with the tracked object

%(DT_COLOR_ONLY)s: A color-labeled image, with each tracked 'Select display option', DT_ALL, doc= """

Which last this number of frames or more are filtered out.""") Enter the maximum number of frames an object is permitted to persist.