Tensorflow Object Detection API 源码分析之 utils/label_map_util.py
protos/string_int_label_map.proto
syntax = "proto2";
package object_detection.protos;
message StringIntLabelMapItem {
// String name. The most common practice is to set this to a MID or synsets
// id.
optional string name = 1;
// Integer id that maps to the string name above. Label ids should start from
// 1.
optional int32 id = 2;
// Human readable string label.
optional string display_name = 3;
};
message StringIntLabelMap {
repeated StringIntLabelMapItem item = 1;
};utils/label_map_util.py
# 有关 label map 的辅助函数
"""Label map utility functions."""
import logging
import tensorflow as tf
from google.protobuf import text_format
from object_detection.protos import string_int_label_map_pb2
# background默认为0，所以其他的不能为0，否则报错
def _validate_label_map(label_map):
"""Checks if a label map is valid.
Args:
label_map: StringIntLabelMap to validate.
Raises:
ValueError: if label map is invalid.
"""
for item in label_map.item:
if item.id < 0:
raise ValueError('Label map ids should be >= 0.')
if (item.id == 0 and item.name != 'background' and
item.display_name != 'background'):
raise ValueError('Label map id 0 is reserved for the background label')
# 返回 类别 和 index 的字典 {key为index，value为类别id}
# 例如 category_index[1] = id ?
def create_category_index(categories):
"""Creates dictionary of COCO compatible categories keyed by category id.
Args:
categories: a list of dicts, each of which has the following keys:
'id': (required) an integer id uniquely identifying this category.
'name': (required) string representing category name
e.g., 'cat', 'dog', 'pizza'.
Returns:
category_index: a dict containing the same entries as categories, but keyed
by the 'id' field of each category.
"""
category_index = {}
for cat in categories:
category_index[cat['id']] = cat
return category_index
# 返回最大的index
def get_max_label_map_index(label_map):
"""Get maximum index in label map.
Args:
label_map: a StringIntLabelMapProto
Returns:
an integer
"""
return max([item.id for item in label_map.item])
# 将label_map 转化为 categories 列表 [{'id': item.id, 'name': name} , ...]
# 可选 name 或者 display_name
def convert_label_map_to_categories(label_map,
max_num_classes,
use_display_name=True):
"""Loads label map proto and returns categories list compatible with eval.
This function loads a label map and returns a list of dicts, each of which
has the following keys:
'id': (required) an integer id uniquely identifying this category.
'name': (required) string representing category name
e.g., 'cat', 'dog', 'pizza'.
We only allow class into the list if its id-label_id_offset is
between 0 (inclusive) and max_num_classes (exclusive).
If there are several items mapping to the same id in the label map,
we will only keep the first one in the categories list.
Args:
label_map: a StringIntLabelMapProto or None. If None, a default categories
list is created with max_num_classes categories.
max_num_classes: maximum number of (consecutive) label indices to include.
use_display_name: (boolean) choose whether to load 'display_name' field
as category name. If False or if the display_name field does not exist,
uses 'name' field as category names instead.
Returns:
categories: a list of dictionaries representing all possible categories.
"""
categories = []
list_of_ids_already_added = []
if not label_map:
label_id_offset = 1
for class_id in range(max_num_classes):
categories.append({
'id': class_id + label_id_offset,
'name': 'category_{}'.format(class_id + label_id_offset)
})
return categories
for item in label_map.item:
if not 0 < item.id <= max_num_classes:
logging.info('Ignore item %d since it falls outside of requested '
'label range.', item.id)
continue
if use_display_name and item.HasField('display_name'):
name = item.display_name
else:
name = item.name
if item.id not in list_of_ids_already_added:
list_of_ids_already_added.append(item.id)
categories.append({'id': item.id, 'name': name})
return categories
# 从文件载入 label map，下一个函数使用
def load_labelmap(path):
"""Loads label map proto.
Args:
path: path to StringIntLabelMap proto text file.
Returns:
a StringIntLabelMapProto
"""
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
# 获取 label_map 字典，返回形式 label_map_dict['background'] = 0
def get_label_map_dict(label_map_path,
use_display_name=False,
fill_in_gaps_and_background=False):
"""Reads a label map and returns a dictionary of label names to id.
Args:
label_map_path: path to StringIntLabelMap proto text file.
use_display_name: whether to use the label map items' display names as keys.
fill_in_gaps_and_background: whether to fill in gaps and background with
respect to the id field in the proto. The id: 0 is reserved for the
'background' class and will be added if it is missing. All other missing
ids in range(1, max(id)) will be added with a dummy class name
("class_<id>") if they are missing.
Returns:
A dictionary mapping label names to id.
Raises:
ValueError: if fill_in_gaps_and_background and label_map has non-integer or
negative values.
"""
label_map = load_labelmap(label_map_path)
label_map_dict = {}
for item in label_map.item:
if use_display_name:
label_map_dict[item.display_name] = item.id
else:
label_map_dict[item.name] = item.id
if fill_in_gaps_and_background:
values = set(label_map_dict.values())
if 0 not in values:
label_map_dict['background'] = 0
if not all(isinstance(value, int) for value in values):
raise ValueError('The values in label map must be integers in order to'
'fill_in_gaps_and_background.')
if not all(value >= 0 for value in values):
raise ValueError('The values in the label map must be positive.')
if len(values) != max(values) + 1:
# there are gaps in the labels, fill in gaps.
for value in range(1, max(values)):
if value not in values:
label_map_dict['class_' + str(value)] = value
return label_map_dict
# 建立从类别索引 到 label_map的字典
# {1: {'id': 1, 'name': 'dog'}, 2: {'id': 2, 'name': 'cat'}, ...}
# model_lib.py 中调用的函数，重要
def create_category_index_from_labelmap(label_map_path):
"""Reads a label map and returns a category index.
Args:
label_map_path: Path to `StringIntLabelMap` proto text file.
Returns:
A category index, which is a dictionary that maps integer ids to dicts
containing categories, e.g.
{1: {'id': 1, 'name': 'dog'}, 2: {'id': 2, 'name': 'cat'}, ...}
"""
label_map = load_labelmap(label_map_path)
max_num_classes = max(item.id for item in label_map.item)
categories = convert_label_map_to_categories(label_map, max_num_classes)
return create_category_index(categories)
# 建立类别无关的索引，只有一个object物体的类别
def create_class_agnostic_category_index():
"""Creates a category index with a single `object` class."""
return {1: {'id': 1, 'name': 'object'}}转自：
Tensorflow Object Detection API 源码分析之 utils/label_map_util.py：这个人的博客里还有关于其它目标检测API的代码
