def _load_namemap(labelmap_path):
label_map = string_int_label_map_pb2.StringIntLabelMap()
with open(labelmap_path, 'r') as fid:
label_map_string = fid.read()
text_format.Merge(label_map_string, label_map)
labelmap_dict = {}
categories = []
for item in label_map.item:
labelmap_dict[item.name] = item.id
categories.append(item.display_name)
return labelmap_dict, categories
def load_labelmap(path):
with tf.gfile.GFile(path, 'r') as fid:
label_map_string = fid.read()
label_map = string_int_label_map_pb2.StringIntLabelMap()
try:
text_format.Merge(label_map_string, label_map)
except text_format.ParseError:
label_map.ParseFromString(label_map_string)
_validate_label_map(label_map)
return label_map
def load_labelmap(path):
"""Loads label map proto.
Args:
path: path to StringIntLabelMap proto text file.
Returns:
a StringIntLabelMapProto
"""
with tf.compat.v1.gfile.GFile(path, 'r') as fid:
label_map_string = fid.read()
label_map = string_int_label_map_pb2.StringIntLabelMap()
try:
text_format.Merge(label_map_string, label_map)
except text_format.ParseError:
label_map.ParseFromString(label_map_string)
_validate_label_map(label_map)
return label_map
def load_labelmap(path):
# https://github.com/tensorflow/models/blob/67fd2bef6500c14b95e0b0de846960ed13802405/research/object_detection/utils/label_map_util.py#L159
"""Loads label map proto.
Args:
path: path to StringIntLabelMap proto text file.
Returns:
a StringIntLabelMapProto
"""
with tf.io.gfile.GFile(path, 'r') as fid:
label_map_string = fid.read()
label_map = string_int_label_map_pb2.StringIntLabelMap()
try:
text_format.Merge(label_map_string, label_map)
except text_format.ParseError:
label_map.ParseFromString(label_map_string)
_validate_label_map(label_map)
return label_map
def _load_labelmap(labelmap_path):
"""Loads labelmap from the labelmap path.
Args:
labelmap_path: Path to the labelmap.
Returns:
A dictionary mapping class name to class numerical id.
"""
label_map = string_int_label_map_pb2.StringIntLabelMap()
with open(labelmap_path, 'r') as fid:
label_map_string = fid.read()
text_format.Merge(label_map_string, label_map)
labelmap_dict = {}
for item in label_map.item:
labelmap_dict[item.name] = item.id
return labelmap_dict
def load_labelmap(path):
"""Loads label map proto.
Args:
path: path to StringIntLabelMap proto text file.
Returns:
a StringIntLabelMapProto
"""
with tf.compat.v2.io.gfile.GFile(path, 'rb') as fid:
#with tf.compat.v2.io.gfile.GFile(path, 'r') as fid: # use this line to run it with TensorFlow version 2.x
label_map_string = fid.read()
label_map = string_int_label_map_pb2.StringIntLabelMap()
try:
text_format.Merge(label_map_string, label_map)
except text_format.ParseError:
label_map.ParseFromString(label_map_string)
_validate_label_map(label_map)
return label_map
def _load_labelmap(labelmap_path):
"""Loads labelmap from the labelmap path.
Args:
labelmap_path: Path to the labelmap.
Returns:
A dictionary mapping class name to class numerical id
A list with dictionaries, one dictionary per category.
"""
label_map = string_int_label_map_pb2.StringIntLabelMap()
with open(labelmap_path, 'r') as fid:
label_map_string = fid.read()
text_format.Merge(label_map_string, label_map)
labelmap_dict = {}
categories = []
for item in label_map.item:
labelmap_dict[item.name] = item.id
categories.append({'id': item.id, 'name': item.name})
return labelmap_dict, categories
def load_labels(labels_name):
labels_url = 'https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/data/' + labels_name
labels_path = tf.keras.utils.get_file(
fname=labels_name,
origin=labels_url,
cache_dir=pathlib.Path('.tmp').absolute())
labels_file = open(labels_path, 'r')
labels_string = labels_file.read()
labels_map = string_int_label_map_pb2.StringIntLabelMap()
try:
text_format.Merge(labels_string, labels_map)
except text_format.ParseError:
labels_map.ParseFromString(labels_string)
labels_dict = {}
for item in labels_map.item:
labels_dict[item.id] = item.display_name
return labels_dict
def load_pbtxt_file(path):
"""Read .pbtxt file.
Args:
path: Path to StringIntLabelMap proto text file (.pbtxt file).
Returns:
A StringIntLabelMapProto.
Raises:
ValueError: If path is not exist.
"""
if not tf.gfile.Exists(path):
raise ValueError('`path` is not exist.')
with tf.gfile.GFile(path, 'r') as fid:
pbtxt_string = fid.read()
pbtxt = string_int_label_map_pb2.StringIntLabelMap()
try:
text_format.Merge(pbtxt_string, pbtxt)
except text_format.ParseError:
pbtxt.ParseFromString(pbtxt_string)
return pbtxt
print('Probablity = {0:.2f}% that this is a {1}'.format(
scores[0] * 100, LABELS[classes[0]]))
print('Probablity = {0:.2f}% that this is a {1}'.format(
scores[1] * 100, LABELS[classes[1]]))
print(boxes[0])
print(boxes[1])
return 'recognized successfully'
if PRE_TRAINED_MODEL is None:
PRE_TRAINED_MODEL = load_pre_trained_model()
if LABELS is None:
DESTINATION_FOLDER = UPLOAD_FOLDER = os.path.join(
os.path.dirname(__file__), 'datasets')
PATH_TO_LABELS = os.path.join(DESTINATION_FOLDER, 'data',
'mscoco_label_map.pbtxt')
NUM_CLASSES = 90
label_map = string_int_label_map_pb2.StringIntLabelMap()
with tf.gfile.GFile(PATH_TO_LABELS, 'r') as fid:
label_map_string = fid.read()
text_format.Merge(label_map_string, label_map)
LABELS = {item.id: item.display_name for item in label_map.item}
if __name__ == "__main__":
print('recognition __main__')
else:
print('recognition ' + __name__)
