def __init__(self, prms=edict()): dArgs = edict() dArgs.folderName = 'nicks-house' dArgs.subFolderName = 'Angle1Lighting1' prms = cu.get_defaults(prms, dArgs, True) GetDataDir.__init__(self, prms) pths = get_datadirs() self.prms_.dirName = pths.vatic % (self.prms_.folderName, self.prms_.subFolderName)
def __init__(self, prms=edict()):
dArgs = edict()
dArgs.folderName = 'nicks-house'
dArgs.subFolderName = 'Angle1Lighting1'
prms = cu.get_defaults(prms, dArgs, True)
GetDataDir.__init__(self, prms)
pths = get_datadirs()
self.prms_.dirName = pths.vatic % (self.prms_.folderName,
self.prms_.subFolderName)
def __init__(self, prms={}):
dArgs = edict()
dArgs.ax = None
dArgs.drawOpts = {'color': 'r', 'linewidth': 3}
prms = cu.get_defaults(prms, dArgs, True)
ch.ChainObject.__init__(self, prms)
plt.ion()
if prms.ax is None:
fig = plt.figure()
self.prms_.ax = fig.add_subplot(111)
def __init__(self, prms={}):
"""
:param prms: directory of parameters in this case containing
only a possible output dir e.g.,
{'op_dir': '~/Desktop/'}
if None, the input directory is used
:return:
"""
defPrms = edict({'op_dir': None, 'vidName': None})
prms = cu.get_defaults(prms, defPrms, True)
ch.ChainObjectIter.__init__(self, prms)
assert self.prms_.vidName is not None
ip = self.prms_.vidName
#Form the path is necessary
if self.prms_['op_dir'] is None:
op_dir = os.path.dirname(ip)
else:
op_dir = self.prms_['op_dir']
try:
os.makedirs(op_dir)
except:
pass
sequence = ffmpeg.extract(ip)
opNames = []
try:
for frame, image in enumerate(sequence):
if frame % 100 == 0:
print ("Decoding frames {0} to {1}"
.format(frame, frame + 100))
path = getframepath(frame, op_dir)
try:
image.save(path)
except IOError:
os.makedirs(os.path.dirname(path))
image.save(path)
opNames.append(path)
except:
print "ffmpeg may not be installed"
print "Aborted. Cleaning up..."
shutil.rmtree(op_dir)
raise
self.iter_ = opNames
def set_rcnn_prms(**kwargs):
dArgs = edict()
#Object class that needs to be detected
dArgs.targetClass = kwargs.get('targetClass', 'person')
#NMS
dArgs.nmsThresh = kwargs.get('nmsThresh', 0.3)
#Detection Confidence
dArgs.confThresh = kwargs.get('confThresh', 0.8)
dArgs.topK = kwargs.get('topK', 5)
#What classnames was the detector trained on.
dArgs.trainDataSet = kwargs.get('trainDataSet', 'coco')
#The net to be used
dArgs.netName = kwargs.get('netName', 'vgg16-coco-rcnn')
dArgs = cu.get_defaults(kwargs, dArgs, True)
#Save the parameters in a database
dbFile = DB % 'rcnn'
dArgs._mysqlid = msq.get_sql_id(dbFile, dArgs)
#verify that the target class is detectable by the model
allCls = dataset2classnames(dArgs.trainDataSet)
assert dArgs.targetClass in allCls
return dArgs
def get_rcnn_prms(**kwargs): dArgs = edict() #Object class that needs to be detected dArgs.targetClass = 'person' #NMS dArgs.nmsThresh = 0.3 #Detection Confidence dArgs.confThresh = 0.8 dArgs.topK = 5 #What classnames was the detector trained on. dArgs.trainDataSet = 'pascal' #The net to be used dArgs.netName = 'vgg16-pascal-rcnn' dArgs = cu.get_defaults(kwargs, dArgs, True) #Save the parameters in a database dbFile = DB % 'rcnn' dArgs.prmStr = msq.get_sql_id(dbFile, dArgs) #verify that the target class is detectable by the model allCls = dataset2classnames(dArgs.trainDataSet) assert dArgs.targetClass in allCls #assert set(dArgs.targetClass).issubset(set(allCls)),\ # '%s cannot be detected' % dArgs.targetClass return dArgs
