def jlabels_list_2_dict(labelsList):
# helper function to return which of a set of items exists in a list
def _which_in(toCheckList, itemsToCheck):
for item in itemsToCheck:
if item in toCheckList:
return item
else:
return None
# get each category item
obj = _which_in(labelsList, cfg.get_label_types('object'))
occ = _which_in(labelsList, cfg.get_label_types('occlusion'))
act = _which_in(labelsList, cfg.get_label_types('activity'))
return {'object': obj, 'occlusion': occ, 'activity': act}
def __init__(self, prms={}):
# define location for output file and image folder
try:
self.outFile = prms['outFile']
except KeyError:
outFolder = '/mnt/Ext/training_data/rcnn'
dateExt = cuu.get_datetime_ext()
self.outFile = osp.join(outFolder, dateExt, 'rcnn_' + dateExt + '.txt')
os.system('mkdir -p ' + osp.dirname(self.outFile))
prms['outFile'] = self.outFile
try:
self.imageFolder = prms['imageFolder']
except KeyError:
self.imageFolder = osp.join(osp.dirname(self.outFile), 'imageFolder')
os.system('mkdir -p ' + self.imageFolder)
prms['imageFolder'] = self.imageFolder
# define dataset used for pretraining
self.pretrainDataSet = cfg.get_rcnn_prms(**prms)['trainDataSet']
# match available labels with available classes from the pretraining dataset
clsNames = cfg.dataset2classnames(self.pretrainDataSet)
labeledObjs = cfg.get_label_types(category='object')
self.clsLookup = {}
for obj in labeledObjs:
try:
self.clsLookup[obj] = clsNames.index(cfg.label2dataset(obj, dataset=self.pretrainDataSet))
except ValueError as e:
print e
ch.ChainObject.__init__(self, prms)
