本文整理汇总了Python中object_detection.evaluator.get_evaluators方法的典型用法代码示例。如果您正苦于以下问题：Python evaluator.get_evaluators方法的具体用法？Python evaluator.get_evaluators怎么用？Python evaluator.get_evaluators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.evaluator的用法示例。
在下文中一共展示了evaluator.get_evaluators方法的1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: read_data_and_evaluate
# 需要导入模块: from object_detection import evaluator [as 别名]
# 或者: from object_detection.evaluator import get_evaluators [as 别名]
def read_data_and_evaluate(input_config, eval_config):
"""Reads pre-computed object detections and groundtruth from tf_record.
Args:
input_config: input config proto of type
object_detection.protos.InputReader.
eval_config: evaluation config proto of type
object_detection.protos.EvalConfig.
Returns:
Evaluated detections metrics.
Raises:
ValueError: if input_reader type is not supported or metric type is unknown.
"""
if input_config.WhichOneof('input_reader') == 'tf_record_input_reader':
input_paths = input_config.tf_record_input_reader.input_path
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)
object_detection_evaluators = evaluator.get_evaluators(
eval_config, categories)
# Support a single evaluator
object_detection_evaluator = object_detection_evaluators[0]
skipped_images = 0
processed_images = 0
for input_path in _generate_filenames(input_paths):
tf.logging.info('Processing file: {0}'.format(input_path))
record_iterator = tf.python_io.tf_record_iterator(path=input_path)
data_parser = tf_example_parser.TfExampleDetectionAndGTParser()
for string_record in record_iterator:
tf.logging.log_every_n(tf.logging.INFO, 'Processed %d images...', 1000,
processed_images)
processed_images += 1
example = tf.train.Example()
example.ParseFromString(string_record)
decoded_dict = data_parser.parse(example)
if decoded_dict:
object_detection_evaluator.add_single_ground_truth_image_info(
decoded_dict[standard_fields.DetectionResultFields.key],
decoded_dict)
object_detection_evaluator.add_single_detected_image_info(
decoded_dict[standard_fields.DetectionResultFields.key],
decoded_dict)
else:
skipped_images += 1
tf.logging.info('Skipped images: {0}'.format(skipped_images))
return object_detection_evaluator.evaluate()
raise ValueError('Unsupported input_reader_config.')