Load result file and return a result api object. :param resFile (str) : file name of result file :return: res (obj)

Transform the set of class-agnostic boxes into class-specific boxes by applying the predicted offsets (box_deltas) :param boxes: !importa

compute bounding box regression targets from ex_rois to gt_rois :param ex_rois: [N, 4] :param gt_rois: [N, 4] :return: [N, 4]

get rpn roidb and ground truth roidb :param gt_roidb: ground truth roidb :param append_gt: append ground truth :retur

get rpn roidb and ground truth roidb :param gt_roidb: ground truth roidb :param append_gt: append ground truth :retur

generate random sample of ROIs comprising foreground and background examples :param rois: all_rois [n, 4]; e2e: [n, 5] with batch_index :

Display the specified annotations. :param anns (array of object): annotations to display :return: None

transform into mxnet tensor substract pixel size and transform to correct format :param im: [height, width, channel] in BGR :param pi

transform from mxnet im_tensor to ordinary RGB image im_tensor is limited to one image :param im_tensor: [batch, channel, height, width]

return indices of unique boxes