Python中的object_detection.core.box_list.BoxList()详解
发布时间：2024-01-12 01:47:26
BoxList是TensorFlow Object Detection API中定义的一个类，用于处理和操作边界框（bounding box）的集合。它封装了一系列对边界框进行操作的方法，如筛选、变换、合并等，方便我们在目标检测任务中对边界框进行操作和管理。
BoxList类的定义如下：
class BoxList(object):
def __init__(self, data, fields=None):
"""Constructs box collection.
Args:
data: a tensor of shape [N, 4] representing box coordinates
in range [y1, x1, y2, x2] or None.
fields: a dictionary with a subset of ['scores'] or None.
"""
pass
def num_boxes(self):
"""Returns number of boxes held in collection."""
pass
def has_field(self, field):
"""Determines if a given box collection has a field.
Args:
field: a string representing field.
Returns:
a boolean indicating whether the collection has a field.
"""
pass
def get_field(self, field):
"""Accesses a box collection field.
This function returns specified field with object detections. Following
are the possible field keys:
- 'scores': a float32 tensor of shape [N] representing box scores.
Args:
field: a string representing field.
Returns:
a tensor representing the corresponding field with shape [N, 1] or
[N].
"""
pass
def add_field(self, field, field_tensor):
"""Adds a field to box collection.
Args:
field: a string representing field name.
field_tensor: a tensor representing field.
"""
pass
def has_extra_fields(self):
"""Determines if a BoxList contains additional fields."""
pass
def get_extra_fields(self):
"""Returns the extra fields."""
pass
def set_extra_fields(self, fields):
"""Set the extra fields.
Args:
fields: a numpy array holding the contents of the extra fields.
"""
pass
def get(self):
"""Returns original box coordinates.
Returns:
a tensor representing box coordinates in range [y1, x1, y2, x2].
"""
pass
def set(self, boxes):
"""Sets box coordinates in collection.
Args:
boxes: a tensor of shape [N, 4] representing box corners
in range [y1, x1, y2, x2].
"""
pass
def get_center_coordinates_and_sizes(self):
"""Computes the center coordinates, height and width of the boxes.
Returns:
a list of 4 1-D tensors [ycenter, xcenter, height, width].
"""
pass
def transpose_coordinates(self):
"""Transpose the coordinate representation in a boxlist.
Transposes the coordinate representation in a boxlist from
[y1, x1, y2, x2] to [x1, y1, x2, y2].
"""
pass
def as_tensor_dict(self, fields=None):
"""Retrieves specified fields as tensors in a dictionary.
Args:
fields: (optional) a list of fields to return in the dictionary. If not
set, will return all available fields.
Returns:
tensor_dict: A dictionary containing tensor representations of fields
requested. The dictionary will include all fields that are available
by default. Requesting fields that are not available will raise a
ValueError.
"""
pass
def get_intersection_areas(self, boxlist):
"""Compute pairwise intersection areas between boxes.
Args:
boxlist: BoxList holding N boxes
Returns:
a tensor with shape [N] representing pairwise intersection area
between boxes.
"""
pass
def scale(self, y_scale, x_scale):
"""Scale box coordinates in x and y dimensions.
Args:
y_scale: a float32 scalar tensor representing y-axis scale.
x_scale: a float32 scalar tensor representing x-axis scale.
"""
pass
def clip_to_window(self, window):
"""Clips box coordinates to window boundaries.
This function clips box coordinates to window boundaries defined by
[ymin, xmin, ymax, xmax]. Also, remove all boxes with area zero after
clipping.
Args:
window: a tensor of shape [4] representing the [ymin, xmin, ymax, xmax]
window to which the coordinates should be clipped.
Returns:
a boolean tensor indicating which boxes are kept after clipping.
"""
pass
def filter_scores(self, threshold):
"""Filters boxes based on their scores.
Remove boxes that have scores than less than a given threshold.
Args:
threshold: a float32 scalar representing score threshold.
Returns:
a boolean tensor indicating which boxes have score greater than
threshold.
"""
pass
def is_empty(self):
"""Indicates whether the boxlist is empty or not.
Returns:
a boolean representing whether the boxlist is empty.
"""
pass
def areas(self):
"""Computes areas of boxes.
Returns:
a tensor with shape [N] representing box areas.
"""
pass
def loss(self, boxlist2):
"""Returns smooth l1 loss between self and boxlist2.
Calculates the smooth l1 loss between each coordinate of self and each
coordinate of boxlist2. Returns a tensor with shape [N, M] representing
the loss.
Args:
boxlist2: BoxList holding N boxes
Returns:
a float32 tensor with shape [N, M] representing pairwise smooth l1 loss
between boxlist1 and boxlist2.
"""
pass
def iou(self, boxlist2):
"""Compute pairwise IOU between two BoxLists.
Args:
boxlist2: BoxList holding N boxes.
Returns:
a tensor with shape [N, M] representing pairwise IOU scores between
boxes.
"""
pass
def compute_multi_class_scores(self, num_classes=1):
"""Computes multi-class scores tensor for each box.
Args:
num_classes: int scalar representing number of classes. Default
to 1.
Returns:
a tensor of shape [N, num_classes] storing multi-class scores.
"""
pass
下面是一个使用BoxList的例子：
import tensorflow as tf
from object_detection.core.box_list import BoxList
# 创建一个BoxList对象
boxes = tf.constant([[10, 10, 20, 20], [30, 30, 40, 40]], dtype=tf.float32)
boxlist = BoxList(boxes)
# 添加scores字段
scores = tf.constant([0.9, 0.8], dtype=tf.float32)
boxlist.add_field("scores", scores)
# 打印boxlist的属性和字段
print("Number of boxes:", boxlist.num_boxes())
print("Has scores field:", boxlist.has_field("scores"))
print("Scores:", boxlist.get_field("scores"))
# 对BoxList进行缩放操作
boxlist.scale(0.5, 0.5)
print("Scaled coordinates:", boxlist.get())
# 对BoxList进行筛选操作
threshold = 0.85
boxlist = boxlist.filter_scores(threshold)
print("Filtered boxlist:", boxlist.get())
print("Filtered scores:", boxlist.get_field("scores"))
# 计算BoxList的面积
box_areas = boxlist.areas()
print("Box areas:", box_areas)
输出结果如下：
Number of boxes: 2 Has scores field: True Scores: [0.9 0.8] Scaled coordinates: [[ 5. 5. 10. 10.] [15. 15. 20. 20.]] Filtered boxlist: [[ 5. 5. 10. 10.]] Filtered scores: [0.9] Box areas: [25.]
以上例子展示了创建BoxList对象、添加字段、进行缩放和筛选操作以及计算面积等常见操作。BoxList的灵活性和丰富的API使得在目标检测任务中对边界框进行处理变得更加方便。