import torch # 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") # only do as_tensor if isn't a "no-op", because it hurts JIT tracing if (not isinstance(bbox, torch.Tensor) or bbox.dtype != torch.float32 or bbox.device != device): bbox = torch.as_tensor(bbox, dtype=torch.float32, device=device) if bbox.ndimension() == 1 and bbox.size(-1) ==4: bbox = bbox.unsqueeze(0) if bbox.ndimension() != 2: raise ValueError( "bbox should have 2 dimensions, got {}".format(bbox.ndimension()) ) if bbox.size(-1) != 4: raise ValueError( "last dimenion 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 = {} # note: _jit_wrap/_jit_unwrap only work if the keys and the sizes don't change in between def _jit_unwrap(self): return (self.bbox,) + tuple(f for f in (self.get_field(field) for field in sorted(self.fields())) if isinstance(f, torch.Tensor)) def _jit_wrap(self, input_stream): self.bbox = input_stream[0] num_consumed = 1 for f in sorted(self.fields()): if isinstance(self.extra_fields[f], torch.Tensor): self.extra_fields[f] = input_stream[num_consumed] num_consumed += 1 return self, input_stream[num_consumed:] 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 # NOTE: explicitly specify dim to avoid tracing error in GPU 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? 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) # 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 x1s = self.bbox[:, 0].clamp(min=0, max=self.size[0] - TO_REMOVE) y1s = self.bbox[:, 1].clamp(min=0, max=self.size[1] - TO_REMOVE) x2s = self.bbox[:, 2].clamp(min=0, max=self.size[0] - TO_REMOVE) y2s = self.bbox[:, 3].clamp(min=0, max=self.size[1] - TO_REMOVE) self.bbox = torch.stack((x1s, y1s, x2s, y2s), dim=-1) if remove_empty: box = self.bbox keep = (box[:, 3] > box[:, 1]) & (box[:, 2] > box[:, 0]) return self[keep] return self def area(self): if self.mode == 'xyxy': TO_REMOVE = 1 box = self.bbox area = (box[:, 2] - box[:, 0] + TO_REMOVE) * (box[:, 3] - box[:, 1] + TO_REMOVE) elif self.mode == 'xywh': box = self.bbox 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 @staticmethod def concate_box_list(list_of_boxes): boxes = torch.cat([i.bbox for i in list_of_boxes], dim=0) extra_fields_keys = list(list_of_boxes[0].extra_fields.keys()) extra_fields = {} for key in extra_fields_keys: extra_fields[key] = torch.cat([i.extra_fields[key] for i in list_of_boxes], dim=0) final = list_of_boxes[0].copy_with_fields(extra_fields_keys) final.bbox = boxes final.extra_fields = extra_fields return final @torch.jit.unused def _onnx_clip_boxes_to_image(boxes, size): # type: (Tensor, Tuple[int, int]) """ Clip boxes so that they lie inside an image of size `size`. Clip's min max are traced as constants. Use torch.min/max to WAR this issue Arguments: boxes (Tensor[N, 4]): boxes in (x1, y1, x2, y2) format size (Tuple[height, width]): size of the image Returns: clipped_boxes (Tensor[N, 4]) """ TO_REMOVE = 1 device = boxes.device dim = boxes.dim() boxes_x = boxes[..., 0::2] boxes_y = boxes[..., 1::2] boxes_x = torch.max(boxes_x, torch.tensor(0., dtype=torch.float).to(device)) boxes_x = torch.min(boxes_x, torch.tensor(size[1] - TO_REMOVE, dtype=torch.float).to(device)) boxes_y = torch.max(boxes_y, torch.tensor(0., dtype=torch.float).to(device)) boxes_y = torch.min(boxes_y, torch.tensor(size[0] - TO_REMOVE, dtype=torch.float).to(device)) clipped_boxes = torch.stack((boxes_x, boxes_y), dim=dim) return clipped_boxes.reshape(boxes.shape) 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)