Source code for deepfinder.protocols.protocol_cluster

# -*- coding: utf-8 -*-
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# *
# * Authors: Emmanuel Moebel (emmanuel.moebel@inria.fr)
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# * Inria - Centre de Rennes Bretagne Atlantique, France
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from pyworkflow import BETA
from pyworkflow.object import Integer, Set, String, Float
from pyworkflow.protocol import Protocol, params, IntParam, EnumParam, PointerParam
from pyworkflow.utils.properties import Message
from tomo.objects import Coordinate3D
from tomo.protocols import ProtTomoPicking
from deepfinder.objects import Coordinate3DWithScore

from deepfinder import Plugin
import deepfinder.convert as cv
from deepfinder.protocols import ProtDeepFinderBase

import os


[docs]class DeepFinderCluster(ProtTomoPicking, ProtDeepFinderBase): """This protocol analyses segmentation maps and outputs particle coordinates and class.""" _label = 'cluster' _devStatus = BETA def __init__(self, **args): ProtTomoPicking.__init__(self, **args) #ProtDeepFinderBase.__init__(self, **args) self.clusteringSummary = String() # --------------------------- DEFINE param functions ---------------------- def _defineParams(self, form): form.addSection(label=Message.LABEL_INPUT) form.addParam('inputSegmentations', PointerParam, pointerClass='SetOfTomoMasks', label="Segmentation maps", important=True, help='Please select the segmentation maps you would like to analyze.') form.addParam('cradius', params.IntParam, default=5, label='Clustering radius', important=True, help='Should correspond to average radius of target objects (in voxels)') # --------------------------- INSERT steps functions ---------------------- def _insertAllSteps(self): # Launch Boxing GUI self._insertFunctionStep('launchClusteringStep') self._insertFunctionStep('createOutputStep') # --------------------------- STEPS functions -----------------------------
[docs] def launchClusteringStep(self): for segm in self.inputSegmentations.get().iterItems(): fname_segm = os.path.splitext(segm.getFileName()) fname_segm = os.path.basename(fname_segm[0]) fname_objl = 'objl_' + fname_segm + '.xml' fname_objl = os.path.abspath(os.path.join(self._getExtraPath(), fname_objl)) # Launch DeepFinder executable: deepfinder_args = '-l ' + segm.getFileName() deepfinder_args += ' -r ' + str(self.cradius) deepfinder_args += ' -o ' + fname_objl Plugin.runDeepFinder(self, 'cluster', deepfinder_args)
[docs] def createOutputStep(self): # Convert DeepFinder annotation output to Scipion SetOfCoordinates3D setSegmentations = self.inputSegmentations.get() coord3DSet = self._createSetOfCoordinates3DWithScore(setSegmentations) coord3DSet.setName('Detected objects') coord3DSet.setPrecedents(setSegmentations) coord3DSet.setSamplingRate(setSegmentations.getSamplingRate()) coordCounter = 0 clusteringSummary = '' for segmInd, segm in enumerate(setSegmentations.iterItems()): # Get objl filename: fname_segm = os.path.splitext(segm.getFileName()) fname_segm = os.path.basename(fname_segm[0]) fname_objl = 'objl_' + fname_segm + '.xml' # Read objl: objl_tomo = cv.objl_read(os.path.abspath(os.path.join(self._getExtraPath(), fname_objl))) # Generate string for protocol summary: msg = 'Segmentation '+str(segmInd+1)+': a total of ' + str(len(objl_tomo)) + ' objects has been found.' clusteringSummary += msg lbl_list = cv.objl_get_labels(objl_tomo) for lbl in lbl_list: objl_class = cv.objl_get_class(objl_tomo, lbl) msg = '\nClass ' + str(lbl) + ': ' + str(len(objl_class)) + ' objects' clusteringSummary += msg clusteringSummary += '\n' # Get tomo corresponding to current tomomask: tomo = segm.getTomogram() for idx in range(len(objl_tomo)): x = objl_tomo[idx]['x'] y = objl_tomo[idx]['y'] z = objl_tomo[idx]['z'] lbl = objl_tomo[idx]['label'] score = objl_tomo[idx]['cluster_size'] coord = Coordinate3D() coord.setObjId(coordCounter) coord.setPosition(x, y, z) coord.setVolume(tomo) coord.setVolId(segmInd + 1) coord._dfLabel = String(str(lbl)) coord._dfScore = Float(score) coord3DSet.append(coord) coordCounter += 1 self._defineOutputs(outputCoordinates=coord3DSet) self._defineSourceRelation(setSegmentations, coord3DSet) self.clusteringSummary.set(clusteringSummary) self._store(self.clusteringSummary)
# --------------------------- DEFINE info functions ---------------------- # TODO def _summary(self): """ Summarize what the protocol has done""" summary = [] if self.isFinished(): if self.clusteringSummary.get(): summary.append(self.clusteringSummary.get()) # if self._noAnnotations.get(): # summary.append('NO OBJECTS WERE TAKEN.') return summary