__author__ = "Jie Lei"
# Ref: https://github.com/facebookresearch/maskrcnn-benchmark/blob/master/maskrcnn_benchmark/structures/bounding_box.py
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# import torch
import numpy as np
# transpose
FLIP_LEFT_RIGHT = 0
FLIP_TOP_BOTTOM = 1
class BoxList(object):
"""
This class represents a set of bounding boxes.
The bounding boxes are represented as a Nx4 Tensor.
In order to uniquely determine the bounding boxes with respect
to an image, we also store the corresponding image dimensions.
They can contain extra information that is specific to each bounding box, such as
labels.
"""
def __init__(self, bbox, image_size, mode="xyxy"):
# us np instead of torch
# device = bbox.device if isinstance(bbox, torch.Tensor) else torch.device("cpu")
# bbox = torch.as_tensor(bbox, dtype=torch.float32, device=device)
# if bbox.ndimension() != 2:
bbox = np.array(bbox, dtype=np.float32)
if bbox.ndim != 2:
print(bbox)
raise ValueError(
"bbox should have 2 dimensions, got {}".format(bbox.ndim)
)
# if bbox.size(-1) != 4:
if bbox.shape[-1] != 4:
raise ValueError(
"last dimenion of bbox should have a "
"size of 4, got {}".format(bbox.shape[-1])
)
if mode not in ("xyxy", "xywh"):
raise ValueError("mode should be 'xyxy' or 'xywh'")
self.bbox = bbox
self.size = image_size # (image_width, image_height)
self.mode = mode
self.extra_fields = {}
def add_field(self, field, field_data):
self.extra_fields[field] = field_data
def get_field(self, field):
return self.extra_fields[field]
def has_field(self, field):
return field in self.extra_fields
def fields(self):
return list(self.extra_fields.keys())
def _copy_extra_fields(self, bbox):
for k, v in bbox.extra_fields.items():
self.extra_fields[k] = v
def convert(self, mode):
if mode not in ("xyxy", "xywh"):
raise ValueError("mode should be 'xyxy' or 'xywh'")
if mode == self.mode:
return self
# we only have two modes, so don't need to check
# self.mode
xmin, ymin, xmax, ymax = self._split_into_xyxy()
if mode == "xyxy":
# bbox = torch.cat((xmin, ymin, xmax, ymax), dim=-1)
bbox = np.concatenate((xmin, ymin, xmax, ymax), axis=-1)
bbox = BoxList(bbox, self.size, mode=mode)
else:
TO_REMOVE = 1
# bbox = torch.cat(
# (xmin, ymin, xmax - xmin + TO_REMOVE, ymax - ymin + TO_REMOVE), dim=-1
# )
bbox = np.concatenate(
(xmin, ymin, xmax - xmin + TO_REMOVE, ymax - ymin + TO_REMOVE), axis=-1
)
bbox = BoxList(bbox, self.size, mode=mode)
bbox._copy_extra_fields(self)
return bbox
def _split_into_xyxy(self):
if self.mode == "xyxy":
# xmin, ymin, xmax, ymax = self.bbox.split(1, dim=-1)
xmin, ymin, xmax, ymax = np.split(self.bbox, 4, axis=1)
return xmin, ymin, xmax, ymax
elif self.mode == "xywh":
TO_REMOVE = 1
xmin, ymin, w, h = np.split(self.bbox, 4, axis=1)
return (
xmin,
ymin,
# xmin + (w - TO_REMOVE).clamp(min=0),
# ymin + (h - TO_REMOVE).clamp(min=0),
xmin + np.clip(w - TO_REMOVE, 0, None),
ymin + np.clip(h - TO_REMOVE, 0, None),
)
else:
raise RuntimeError("Should not be here")
# def resize(self, size, *args, **kwargs):
def resize(self, size):
"""
Returns a resized copy of this bounding box
:param size: The requested size in pixels, as a 2-tuple:
(width, height).
"""
ratios = tuple(float(s) / float(s_orig) for s, s_orig in zip(size, self.size))
if ratios[0] == ratios[1]:
ratio = ratios[0]
scaled_box = self.bbox * ratio
bbox = BoxList(scaled_box, size, mode=self.mode)
# bbox._copy_extra_fields(self)
for k, v in self.extra_fields.items():
# if not isinstance(v, torch.Tensor):
if not isinstance(v, np.ndarray):
# v = v.resize(size, *args, **kwargs)
v = v.reshape(size)
bbox.add_field(k, v)
return bbox
ratio_width, ratio_height = ratios
xmin, ymin, xmax, ymax = self._split_into_xyxy()
scaled_xmin = xmin * ratio_width
scaled_xmax = xmax * ratio_width
scaled_ymin = ymin * ratio_height
scaled_ymax = ymax * ratio_height
# scaled_box = torch.cat(
# (scaled_xmin, scaled_ymin, scaled_xmax, scaled_ymax), dim=-1
# )
scaled_box = np.concatenate(
(scaled_xmin, scaled_ymin, scaled_xmax, scaled_ymax), axis=-1
)
bbox = BoxList(scaled_box, size, mode="xyxy")
# bbox._copy_extra_fields(self)
for k, v in self.extra_fields.items():
# if not isinstance(v, torch.Tensor):
if not isinstance(v, np.ndarray):
# v = v.resize(size, *args, **kwargs)
v = v.resize(size)
bbox.add_field(k, v)
return bbox.convert(self.mode)
def transpose(self, method):
"""
Transpose bounding box (flip or rotate in 90 degree steps)
:param method: One of :py:attr:`PIL.Image.FLIP_LEFT_RIGHT`,
:py:attr:`PIL.Image.FLIP_TOP_BOTTOM`, :py:attr:`PIL.Image.ROTATE_90`,
:py:attr:`PIL.Image.ROTATE_180`, :py:attr:`PIL.Image.ROTATE_270`,
:py:attr:`PIL.Image.TRANSPOSE` or :py:attr:`PIL.Image.TRANSVERSE`.
"""
if method not in (FLIP_LEFT_RIGHT, FLIP_TOP_BOTTOM):
raise NotImplementedError(
"Only FLIP_LEFT_RIGHT and FLIP_TOP_BOTTOM implemented"
)
image_width, image_height = self.size
xmin, ymin, xmax, ymax = self._split_into_xyxy()
if method == FLIP_LEFT_RIGHT:
TO_REMOVE = 1
transposed_xmin = image_width - xmax - TO_REMOVE
transposed_xmax = image_width - xmin - TO_REMOVE
transposed_ymin = ymin
transposed_ymax = ymax
elif method == FLIP_TOP_BOTTOM:
transposed_xmin = xmin
transposed_xmax = xmax
transposed_ymin = image_height - ymax
transposed_ymax = image_height - ymin
# transposed_boxes = torch.cat(
# (transposed_xmin, transposed_ymin, transposed_xmax, transposed_ymax), dim=-1
# )
transposed_boxes = np.concatenate(
(transposed_xmin, transposed_ymin, transposed_xmax, transposed_ymax), axis=-1
)
bbox = BoxList(transposed_boxes, self.size, mode="xyxy")
# bbox._copy_extra_fields(self)
for k, v in self.extra_fields.items():
# if not isinstance(v, torch.Tensor):
if not isinstance(v, np.ndarray):
v = v.transpose(method)
bbox.add_field(k, v)
return bbox.convert(self.mode)
def crop(self, box):
"""
Cropss a rectangular region from this bounding box. The box is a
4-tuple defining the left, upper, right, and lower pixel
coordinate.
"""
xmin, ymin, xmax, ymax = self._split_into_xyxy()
w, h = box[2] - box[0], box[3] - box[1]
# cropped_xmin = (xmin - box[0]).clamp(min=0, max=w)
# cropped_ymin = (ymin - box[1]).clamp(min=0, max=h)
# cropped_xmax = (xmax - box[0]).clamp(min=0, max=w)
# cropped_ymax = (ymax - box[1]).clamp(min=0, max=h)
cropped_xmin = np.clip((xmin - box[0]), 0, w)
cropped_ymin = np.clip((ymin - box[1]), 0, h)
cropped_xmax = np.clip((xmax - box[0]), 0, w)
cropped_ymax = np.clip((ymax - box[1]), 0, h)
# TODO should I filter empty boxes here?
if False:
is_empty = (cropped_xmin == cropped_xmax) | (cropped_ymin == cropped_ymax)
# cropped_box = torch.cat(
# (cropped_xmin, cropped_ymin, cropped_xmax, cropped_ymax), dim=-1
# )
cropped_box = np.concatenate(
(cropped_xmin, cropped_ymin, cropped_xmax, cropped_ymax), axis=-1
)
bbox = BoxList(cropped_box, (w, h), mode="xyxy")
# bbox._copy_extra_fields(self)
for k, v in self.extra_fields.items():
# if not isinstance(v, torch.Tensor):
if not isinstance(v, np.ndarray):
v = v.crop(box)
bbox.add_field(k, v)
return bbox.convert(self.mode)
# Tensor-like methods
def to(self, device):
# bbox = BoxList(self.bbox.to(device), self.size, self.mode)
bbox = BoxList(self.bbox, self.size, self.mode)
for k, v in self.extra_fields.items():
# if hasattr(v, "to"):
# v = v.to(device)
bbox.add_field(k, v)
return bbox
def __getitem__(self, item):
bbox = BoxList(self.bbox[item], self.size, self.mode)
for k, v in self.extra_fields.items():
bbox.add_field(k, v[item])
return bbox
def __len__(self):
return self.bbox.shape[0]
def clip_to_image(self, remove_empty=True):
TO_REMOVE = 1
# self.bbox[:, 0].clamp_(min=0, max=self.size[0] - TO_REMOVE)
# self.bbox[:, 1].clamp_(min=0, max=self.size[1] - TO_REMOVE)
# self.bbox[:, 2].clamp_(min=0, max=self.size[0] - TO_REMOVE)
# self.bbox[:, 3].clamp_(min=0, max=self.size[1] - TO_REMOVE)
self.bbox[:, 0] = np.clip(self.bbox[:, 0], 0, self.size[0] - TO_REMOVE)
self.bbox[:, 1] = np.clip(self.bbox[:, 1], 0, self.size[1] - TO_REMOVE)
self.bbox[:, 2] = np.clip(self.bbox[:, 2], 0, self.size[0] - TO_REMOVE)
self.bbox[:, 3] = np.clip(self.bbox[:, 3], 0, self.size[1] - TO_REMOVE)
if remove_empty:
box = self.bbox
keep = (box[:, 3] > box[:, 1]) & (box[:, 2] > box[:, 0])
return self[keep]
return self
def area(self):
box = self.bbox
if self.mode == "xyxy":
TO_REMOVE = 1
area = (box[:, 2] - box[:, 0] + TO_REMOVE) * (box[:, 3] - box[:, 1] + TO_REMOVE)
elif self.mode == "xywh":
area = box[:, 2] * box[:, 3]
else:
raise RuntimeError("Should not be here")
return area
def copy_with_fields(self, fields):
bbox = BoxList(self.bbox, self.size, self.mode)
if not isinstance(fields, (list, tuple)):
fields = [fields]
for field in fields:
bbox.add_field(field, self.get_field(field))
return bbox
def __repr__(self):
s = self.__class__.__name__ + "("
s += "num_boxes={}, ".format(len(self))
s += "image_width={}, ".format(self.size[0])
s += "image_height={}, ".format(self.size[1])
s += "mode={})".format(self.mode)
return s
if __name__ == "__main__":
bbox = BoxList([[0, 0, 10, 10], [0, 0, 5, 5]], (10, 10))
s_bbox = bbox.resize((5, 5))
print(s_bbox)
print(s_bbox.bbox)
t_bbox = bbox.transpose(0)
print(t_bbox)
print(t_bbox.bbox)
