def __init__(self, uriList, streamType, moreVars, expandPatterns=True):
DataProvider.__init__(self, streamType, moreVars)
self.expandPatterns = expandPatterns
if "expandPatterns" in moreVars:
if self.expandPatterns=="False" or self.expandPatterns=="false":
self.expandPatterns = False
del moreVars["expandPatterns"]
if self.expandPatterns:
fileNamePatterns = []
for f in uriList:
if f.startswith("file://"):
fileNamePatterns.append(f[7:])
else:
fileNamePatterns.append(f)
else:
fileNamePatterns = uriList
if len(fileNamePatterns) == 0:
raise NoFilesSpecified()
self.myFileNames = [name for name in expandFiles(fileNamePatterns, shouldOpen=False, checkPattern=self.expandPatterns) ]
if not self._streamType:
colonPos = self.myFileNames[0].rfind(":")
if colonPos==-1 or (colonPos==1 and len(self.myFileNames)>=3 and self.myFileNames[2]=='\\'):
name = self.myFileNames[0]
else:
name = self.myFileNames[0][0:colonPos]
ext = os.path.splitext(name)[1]
if ext == ".gz":
# try to get prev extension before gzip
ext = os.path.splitext(os.path.splitext(name)[0])[1]
self._streamType = io_targets.getTypeByExtension(ext)
if not self._streamType:
raise UnknownExtensionType(name)
def __init__(self, rss_url, max_news=2):
"""
.ctor
:return:
"""
DataProvider.__init__(self, max_news=max_news)
self.rss_url = rss_url
self.max_new_article = max_news
def __init__(self, cfg):
DataProvider.__init__(self, cfg)
# Load training images (path) and labels
train_path = os.path.join(Paths.data_path, 'cell/labels/train.csv')
test_path = os.path.join(Paths.data_path, 'cell/labels/test.csv')
data_type = {'image_name': np.str, 'label': np.int}
self._train_df = pd.read_csv(train_path, dtype=data_type)
self._test_df = pd.read_csv(test_path, dtype=data_type)
self._train_list = list(self._train_df.index)
# random.shuffle(self._train_list)
self._test_list = list(self._test_df.index)
self._test_size = len(self._test_list)
self._train_index = 0
self._test_index = 0
def __init__(self, cfg):
DataProvider.__init__(self, cfg)
csv_dir = os.path.join(Paths.data_path, 'thyroid nodule/annotations',
self._cfg.dir_name)
train_path = os.path.join(csv_dir, 'train.csv')
test_path = os.path.join(csv_dir, 'test.csv')
data_type = {
'image_name': np.str,
'x1': np.int,
'y1': np.int,
'x2': np.int,
'y2': np.int,
'label': np.int
}
self._train_df = pd.read_csv(train_path, dtype=data_type)
self._test_df = pd.read_csv(test_path, dtype=data_type)
self._train_list = list(self._train_df.index)
random.shuffle(self._train_list)
self._test_list = list(self._test_df.index)
self._test_size = len(self._test_list)
self._train_index = 0
self._test_index = 0
def __init__(self, data_split, batchsize=1):
DataProvider.__init__(self, data_split, batchsize)
