# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. import torch import copy # 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"): 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: raise ValueError( "bbox should have 2 dimensions, got {}".format(bbox.ndimension()) ) if bbox.size(-1) != 4: raise ValueError( "last dimension of bbox should have a " "size of 4, got {}".format(bbox.size(-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 = 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 = 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) return xmin, ymin, xmax, ymax elif self.mode == "xywh": TO_REMOVE = 1 xmin, ymin, w, h = self.bbox.split(1, dim=-1) return ( xmin, ymin, xmin + (w - TO_REMOVE).clamp(min=0), ymin + (h - TO_REMOVE).clamp(min=0), ) else: raise RuntimeError("Should not be here") def resize(self, size, *args, **kwargs): """ 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): v = v.resize(size, *args, **kwargs) 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 ) 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): v = v.resize(size, *args, **kwargs) 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 ) 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): 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) # 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 ) 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): v = v.crop(box) bbox.add_field(k, v) return bbox.convert(self.mode) # ##################################################3 add by hui ######################################3 def translate(self, delta_x, delta_y, clip=True): bbox = copy.deepcopy(self) #BoxList(self.bbox, self.size, mode=self.mode) bbox.bbox[:, 0] += delta_x bbox.bbox[:, 1] += delta_y if self.mode == 'xyxy': bbox.bbox[:, 2] += delta_x bbox.bbox[:, 3] += delta_y if clip: bbox = bbox.clip_to_image() return bbox ######################################3################################################################# # Tensor-like methods def to(self, device): bbox = BoxList(self.bbox.to(device), 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) 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, skip_missing=False): bbox = BoxList(self.bbox, self.size, self.mode) if not isinstance(fields, (list, tuple)): fields = [fields] for field in fields: if self.has_field(field): bbox.add_field(field, self.get_field(field)) elif not skip_missing: raise KeyError("Field '{}' not found in {}".format(field, self)) 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)