def __init__(self, staining_type, input_file, output_dir, training_type,
conf_threshold, annotation_dir, overlap_threshod):
'''Merge when common area ratio of two individual rectangles is this threshold or more.'''
self.OVERLAP_THRESHOLD = overlap_threshod
'''When the common area ratios for both rectangles are equal to or more than this value,
they are merged regardless of the size.'''
self.UNCONDITIONAL_MERGE_THRESHOLD = 0.6
'''If one side of rectangles is nearly the same, merge them regardless of their size.'''
self.SIDE_LENGTH_MERGE_THRESHOLD = 30 # unit is micrometer
'''The glomerulus diameter is assumed to be 350 micrometers at the maximum.
If the merge result exceeds the following value, do not merge them.'''
#self.MAX_GLOMUS_SIZE = 240.0
#self.MAX_GLOMUS_AREA = 220.0 * 220.0
self.MAX_GLOMUS_SIZE = 350.0
self.MAX_GLOMUS_AREA = 300.0 * 300.0
self.png = ['.png', '.PNG']
self.rect_list = [[]]
self.input_file = input_file
self.output_dir = output_dir
self.staining_type = staining_type
self.staining_dir = GlomusHandler.get_staining_type(staining_type)
self.training_type = training_type
self.CONF_THRESH = conf_threshold
self.annotation_dir = annotation_dir
'''variable for opening ndpi image file for mpp value check.'''
self.slide = None
'''variable for recording mpp value of PNG image recorded in the target list file.'''
self.target_list = {}
def __init__(self, annotation_dir, staining_type):
self.gt_list = []
self.gt_name_list = []
self.annotation_dir = annotation_dir
self.staining_type = staining_type
self.staining_dir = GlomusHandler.get_staining_type(self.staining_type)
if self.staining_dir is None:
raise AnnotationHandlerException('Unknown Argument is given.:' +
self.staining_type)
self.annotation_file_pattern = '(.*)_pw(\d{2})_ds(\d{1,2})'
self.repattern = re.compile(self.annotation_file_pattern,
re.IGNORECASE)
self.set_sheet_index()
def __init__(self, data_category, target_list, data_dir, output_dir, output_file_ext,
window_size, overlap_ratio, conf_threshold):
"""
Class Initializer
:param data_category: The symbol representing the observation and staining method.(write like "OPT_PAS")
:param target_list: The path of text file in which the procession target file list is written.
:param data_dir: The path of the file folder in which there are whole slide images.
:param output_dir: The path of the file folder in which this process write output files.
:param output_file_ext: This parameter is used to extend the result file name to distinguish execution results.
:param window_size: The size of a sliding window for detection.
:param overlap_ratio: The overlapping ration of the sliding windows.
:param conf_threshold: The minimum confidence value of adopt for candidates ot region of glomerulus.
"""
'''identifier of a image file type'''
self.ndpi_image_ext = ['.ndpi']
self.png_image_ext = ['.PNG', '.png']
self.image_type = None
'''The target file identifier is set according to the saining category.(set_type set in GlomusHander）'''
self.data_category = data_category
self.set_type(data_category)
'''Sliding window size(The unit is micrometre.)'''
if window_size is None or window_size == '':
self.STD_SIZE = 500 # original -> 200
self.OVERLAP_RATIO = 0.5
else:
self.STD_SIZE = window_size
self.OVERLAP_RATIO = overlap_ratio
self.COPY_EXEC = False
self.CONF_THRESH = conf_threshold
self.NMS_THRESH = 0.3
self.CLASSES = ('__background__', # always index 0
'glomerulus')
self.staining_dir = GlomusHandler.get_staining_type(self.data_category)
self.target_list = target_list
self.data_dir = data_dir
self.output_root_dir = output_dir
'''Preparing a directory to write the output file'''
head, _ = os.path.split(self.output_root_dir)
head2, _ = os.path.split(head)
if not os.path.isdir(head2):
os.mkdir(head2)
if not os.path.isdir(head):
os.mkdir(head)
if not os.path.isdir(self.output_root_dir):
os.mkdir(self.output_root_dir)
self.output_file_path = os.path.join(self.output_root_dir, self.TYPE + output_file_ext + '.csv')
self.log_file = os.path.join(self.output_root_dir, self.TYPE + output_file_ext + '_log.csv')
'''information of each slide'''
self.org_slide_width = 0
self.org_slide_height = 0
self.org_slide_objective_power = 0.0
self.slide_downsample = 0.0
self.mpp_x = 0.0
self.mpp_y = 0.0
parser.add_argument('--overlap_ratio', dest='overlap_ratio', help="Please set --overlap_ratio", type=float)
parser.add_argument('--conf_threshold', dest='conf_threshold', help="Please set --conf_threshold", type=float,
default=0.6)
return parser.parse_args()
if __name__ == '__main__':
print('Tensorflow version:{}'.format(tf.__version__))
args = parse_args()
# load network
body, ext = os.path.splitext(os.path.basename(args.target_list))
TEST_SET = body
TRAIN_SET = body.replace('test', 'train')
staining_dir = GlomusHandler.get_staining_type(args.data_category)
TRAIN_MODEL = args.model
PATH_TO_CKPT = os.path.join(args.model, 'frozen_inference_graph.pb')
'''Load Tensorflow Model into Memory'''
detection_graph = tf.Graph()
with detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
# init session
# sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
# The setting of visible_device_list does not work(?).
# If you want to limit the GPU to be used, set the environment variable CUDA_VISIBLE_DEVICES to limit it.