本文整理汇总了Python中object_detection.utils.label_map_util.load_labelmap方法的典型用法代码示例。如果您正苦于以下问题：Python label_map_util.load_labelmap方法的具体用法？Python label_map_util.load_labelmap怎么用？Python label_map_util.load_labelmap使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类object_detection.utils.label_map_util的用法示例。
在下文中一共展示了label_map_util.load_labelmap方法的7个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: main
# 需要导入模块: from object_detection.utils import label_map_util [as 别名]
# 或者: from object_detection.utils.label_map_util import load_labelmap [as 别名]
def main(unused_argv):
assert FLAGS.checkpoint_dir, '`checkpoint_dir` is missing.'
assert FLAGS.eval_dir, '`eval_dir` is missing.'
if FLAGS.pipeline_config_path:
model_config, eval_config, input_config = get_configs_from_pipeline_file()
else:
model_config, eval_config, input_config = get_configs_from_multiple_files()
model_fn = functools.partial(
model_builder.build,
model_config=model_config,
is_training=False)
create_input_dict_fn = functools.partial(
input_reader_builder.build,
input_config)
label_map = label_map_util.load_labelmap(input_config.label_map_path)
max_num_classes = max([item.id for item in label_map.item])
categories = label_map_util.convert_label_map_to_categories(
label_map, max_num_classes)
evaluator.evaluate(create_input_dict_fn, model_fn, eval_config, categories,
FLAGS.checkpoint_dir, FLAGS.eval_dir) 示例2: test_load_bad_label_map
# 需要导入模块: from object_detection.utils import label_map_util [as 别名]
# 或者: from object_detection.utils.label_map_util import load_labelmap [as 别名]
def test_load_bad_label_map(self):
label_map_string = """
item {
id:0
name:'class that should not be indexed at zero'
}
item {
id:2
name:'cat'
}
item {
id:1
name:'dog'
}
"""
label_map_path = os.path.join(self.get_temp_dir(), 'label_map.pbtxt')
with tf.gfile.Open(label_map_path, 'wb') as f:
f.write(label_map_string)
with self.assertRaises(ValueError):
label_map_util.load_labelmap(label_map_path) 示例3: load_model
# 需要导入模块: from object_detection.utils import label_map_util [as 别名]
# 或者: from object_detection.utils.label_map_util import load_labelmap [as 别名]
def load_model(self):
"""
Loads the detection model
Args:
Returns:
"""
with self._detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(self._path_to_ckpt, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
label_map = label_map_util.load_labelmap(self._path_to_labels)
categories = label_map_util.convert_label_map_to_categories(\
label_map, max_num_classes=self._num_classes, use_display_name=True)
self.category_index = label_map_util.create_category_index(categories) 示例4: evaluate
# 需要导入模块: from object_detection.utils import label_map_util [as 别名]
# 或者: from object_detection.utils.label_map_util import load_labelmap [as 别名]
def evaluate(self, eval_pipeline_file, model_dir, eval_dir):
configs = self._get_configs_from_pipeline_file(eval_pipeline_file)
model_config = configs['model']
eval_config = configs['eval_config']
input_config = configs['eval_input_config']
model_fn = functools.partial(
model_builder.build,
model_config=model_config,
is_training=True)
create_input_dict_fn = functools.partial(self.get_next, input_config)
label_map = label_map_util.load_labelmap(input_config.label_map_path)
max_num_classes = max([item.id for item in label_map.item])
categories = label_map_util.convert_label_map_to_categories(
label_map, max_num_classes)
evaluator.evaluate(create_input_dict_fn, model_fn, eval_config, categories,
model_dir, eval_dir) 示例5: main
# 需要导入模块: from object_detection.utils import label_map_util [as 别名]
# 或者: from object_detection.utils.label_map_util import load_labelmap [as 别名]
def main(unused_argv):
assert FLAGS.checkpoint_dir, '`checkpoint_dir` is missing.'
assert FLAGS.eval_dir, '`eval_dir` is missing.'
model_config, train_config, input_config, eval_config = get_configs_from_pipeline_file()
model_fn = functools.partial(
build_man_model,
model_config=model_config,
is_training=False)
create_input_dict_fn = functools.partial(
input_reader_builder.build,
input_config)
label_map = label_map_util.load_labelmap(input_config.label_map_path)
max_num_classes = max([item.id for item in label_map.item])
categories = label_map_util.convert_label_map_to_categories(
label_map, max_num_classes)
evaluator.evaluate(create_input_dict_fn, model_fn, eval_config, categories,
FLAGS.checkpoint_dir, FLAGS.eval_dir) 示例6: main
# 需要导入模块: from object_detection.utils import label_map_util [as 别名]
# 或者: from object_detection.utils.label_map_util import load_labelmap [as 别名]
def main(unused_argv):
assert FLAGS.checkpoint_dir, '`checkpoint_dir` is missing.'
assert FLAGS.eval_dir, '`eval_dir` is missing.'
model_config, train_config, input_config, eval_config = get_configs_from_pipeline_file()
model_fn = functools.partial(
build_man_model,
model_config=model_config,
is_training=False)
create_input_dict_fn = functools.partial(
input_reader.read_seq, input_config)
label_map = label_map_util.load_labelmap(input_config.label_map_path)
max_num_classes = max([item.id for item in label_map.item])
categories = label_map_util.convert_label_map_to_categories(
label_map, max_num_classes)
visualizer_seq.evaluate(create_input_dict_fn, model_fn, eval_config, categories,
FLAGS.checkpoint_dir, FLAGS.eval_dir, FLAGS.image_root) 示例7: main
# 需要导入模块: from object_detection.utils import label_map_util [as 别名]
# 或者: from object_detection.utils.label_map_util import load_labelmap [as 别名]
def main(unused_argv):
assert FLAGS.checkpoint_dir, '`checkpoint_dir` is missing.'
assert FLAGS.eval_dir, '`eval_dir` is missing.'
model_config, train_config, input_config, eval_config = get_configs_from_pipeline_file()
model_fn = functools.partial(
build_man_model,
model_config=model_config,
is_training=False)
create_input_dict_fn = functools.partial(
input_reader.read,
input_config)
label_map = label_map_util.load_labelmap(input_config.label_map_path)
max_num_classes = max([item.id for item in label_map.item])
categories = label_map_util.convert_label_map_to_categories(
label_map, max_num_classes)
visualizer.evaluate(create_input_dict_fn, model_fn, eval_config, categories,
FLAGS.checkpoint_dir, FLAGS.eval_dir, FLAGS.image_root)