def _compute_targets(ex_rois, gt_rois): assert ex_rois.shape[0] == gt_rois.shape[0] assert ex_rois.shape[1] == 4 assert gt_rois.shape[1] == 5 return bbox_transform(ex_rois, gt_rois[:, :4]).astype(np.float32, copy=False)
def _compute_targets(ex_rois, gt_rois): """Compute bounding-box regression targets for an image.""" assert ex_rois.shape[0] == gt_rois.shape[0] assert ex_rois.shape[1] == 4 assert gt_rois.shape[1] == 5 return bbox_transform(ex_rois, gt_rois[:, :4]).astype(np.float32, copy=False)
def _compute_targets(ex_rois, gt_rois): """Compute bounding-box regression targets for an image. gt_rois(shape: [:, 8]): 是label box:[:4] indx:[4] dis:[5:] """ assert ex_rois.shape[0] == gt_rois.shape[0] assert ex_rois.shape[1] == 4 assert gt_rois.shape[1] == 8 return bbox_transform(ex_rois, gt_rois[:, :4]).astype(np.float32, copy=False)
def _compute_targets(anchor_rois, gt_rois): """ Compute bounding box regression targets for an image :param anchor_rois <torch.Tensor>: :param gt_rois: :return: """ assert anchor_rois.shape[0] == gt_rois.shape[0] assert anchor_rois.shape[1] == 6 assert gt_rois.shape[1] == 7 return bbox_transform(anchor_rois, gt_rois[:, :6])
def _compute_targets(anchor_rois, gt_rois): """ Compute bounding box regression targets for an image :param anchor_rois <torch.Tensor>: :param gt_rois: :return: """ assert anchor_rois.shape[0] == gt_rois.shape[0] assert anchor_rois.shape[1] == 6 assert gt_rois.shape[1] == 7 return bbox_transform(anchor_rois, gt_rois[:,:6])
def _compute_targets(ex_rois, gt_rois, labels): """Compute bounding-box regression targets for an image.""" assert ex_rois.shape[0] == gt_rois.shape[0] assert ex_rois.shape[1] == 4 assert gt_rois.shape[1] == 4 targets = bbox_transform(ex_rois, gt_rois) if cfg.FLAGS.bbox_normalize_targets_precomputed: # Optionally normalize targets by a precomputed mean and stdev targets = ((targets - np.array(cfg.FLAGS2["bbox_normalize_means"])) / np.array(cfg.FLAGS2["bbox_normalize_stds"])) return np.hstack( (labels[:, np.newaxis], targets)).astype(np.float32, copy=False)
def _compute_targets(ex_rois, gt_rois, labels): """ Compute bounding box regression targets for an image Inputs are tensor :param ex_rois: :param gt_rois: :param labels: :return: """ assert ex_rois.shape[0] == gt_rois.shape[0] assert ex_rois.shape[1] == 6 assert gt_rois.shape[1] == 6 or gt_rois.shape[1] == 12 targets = bbox_transform(ex_rois, gt_rois) return torch.cat([targets, labels.unsqueeze(1)], 1)
def _compute_targets(ex_rois, gt_rois, labels): """Compute bounding-box regression targets for an image.""" assert ex_rois.shape[0] == gt_rois.shape[0] assert ex_rois.shape[1] == 4 assert gt_rois.shape[1] == 4 targets = bbox_transform(ex_rois, gt_rois) # 对bbox偏移量进行归一化操作,扩大了10倍 if cfg.FLAGS.bbox_normalize_targets_precomputed: # True targets = ((targets - np.array(cfg.FLAGS2["bbox_normalize_means"]) ) # (0.0, 0.0, 0.0, 0.0) / np.array(cfg.FLAGS2["bbox_normalize_stds"]) ) # (0.1, 0.1, 0.1, 0.1) # 将lable拼接到bbox的第一列 return np.hstack((labels[:, np.newaxis], targets)).astype(np.float32, copy=False)
def _compute_targets(ex_rois, gt_rois, labels): """ Compute bounding box regression targets for an image Inputs are tensor :param ex_rois: :param gt_rois: :param labels: :return: """ assert ex_rois.shape[0] == gt_rois.shape[0] assert ex_rois.shape[1] == 6 assert gt_rois.shape[1] == 6 or gt_rois.shape[1] == 12 targets = bbox_transform(ex_rois, gt_rois) return torch.cat([targets, labels.unsqueeze(1)], 1)