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