Python detection.Detection方法代码示例

# 需要导入模块: from deep_sort import detection [as 别名]
# 或者: from deep_sort.detection import Detection [as 别名]
def create_detections(detection_mat, frame_idx, min_height=0):
    """Create detections for given frame index from the raw detection matrix.

    Parameters
    ----------
    detection_mat : ndarray
        Matrix of detections. The first 10 columns of the detection matrix are
        in the standard MOTChallenge detection format. In the remaining columns
        store the feature vector associated with each detection.
    frame_idx : int
        The frame index.
    min_height : Optional[int]
        A minimum detection bounding box height. Detections that are smaller
        than this value are disregarded.

    Returns
    -------
    List[tracker.Detection]
        Returns detection responses at given frame index.

    """
    frame_indices = detection_mat[:, 0].astype(np.int)
    mask = frame_indices == frame_idx

    detection_list = []
    for row in detection_mat[mask]:
        bbox, confidence, feature = row[2:6], row[6], row[10:]
        if bbox[3] < min_height:
            continue
        detection_list.append(Detection(bbox, confidence, feature))
    return detection_list 

# 需要导入模块: from deep_sort import detection [as 别名]
# 或者: from deep_sort.detection import Detection [as 别名]
def parse_args():
    """ Parse command line arguments.
    """
    parser = argparse.ArgumentParser(description="Deep SORT")
    parser.add_argument(
        "--sequence_dir", help="Path to MOTChallenge sequence directory",
        default=None, required=True)
    parser.add_argument(
        "--detection_file", help="Path to custom detections.", default=None,
        required=True)
    parser.add_argument(
        "--output_file", help="Path to the tracking output file. This file will"
        " contain the tracking results on completion.",
        default="/tmp/hypotheses.txt")
    parser.add_argument(
        "--min_confidence", help="Detection confidence threshold. Disregard "
        "all detections that have a confidence lower than this value.",
        default=0.8, type=float)
    parser.add_argument(
        "--min_detection_height", help="Threshold on the detection bounding "
        "box height. Detections with height smaller than this value are "
        "disregarded", default=0, type=int)
    parser.add_argument(
        "--nms_max_overlap",  help="Non-maxima suppression threshold: Maximum "
        "detection overlap.", default=1.0, type=float)
    parser.add_argument(
        "--max_cosine_distance", help="Gating threshold for cosine distance "
        "metric (object appearance).", type=float, default=0.2)
    parser.add_argument(
        "--nn_budget", help="Maximum size of the appearance descriptors "
        "gallery. If None, no budget is enforced.", type=int, default=None)
    parser.add_argument(
        "--display", help="Show intermediate tracking results",
        default=True, type=bool_string)
    return parser.parse_args() 

# 需要导入模块: from deep_sort import detection [as 别名]
# 或者: from deep_sort.detection import Detection [as 别名]
def create_obj_infos(cur_frame, final_boxes, final_probs, final_labels,
                     box_feats, targetid2class, tracking_objs, min_confidence,
                     min_detection_height, scale, is_coco_model=False,
                     coco_to_actev_mapping=None):
  obj_infos = []
  tracking_boxes = final_boxes / scale
  for j, (box, prob, label) in enumerate(zip(tracking_boxes, final_probs, final_labels)):
    cat_name = targetid2class[label]
    if is_coco_model:
      if cat_name not in coco_to_actev_mapping:
        continue
      else:
        cat_name = coco_to_actev_mapping[cat_name]

    confidence_socre = float(round(prob, 7))
    if cat_name not in tracking_objs or confidence_socre < min_confidence:
      continue
    box[2] -= box[0]
    box[3] -= box[1]
    avg_feat = box_feats[j]
    if len(avg_feat.shape) > 2:
      avg_feat = np.mean(np.mean(box_feats[j], axis=1), axis=1)


    norm_feat = avg_feat / np.linalg.norm(avg_feat)

    list_feat = norm_feat.tolist()
    bbox_data = [cur_frame, box[0], box[1], box[2], box[3], confidence_socre] + list_feat
    obj_infos.append(bbox_data)

  detections = []
  for row in obj_infos:
    bbox, confidence, feature = row[1:5], row[5], row[6:]
    if bbox[3] < min_detection_height:
      continue
    detections.append(Detection(bbox, confidence, feature))
  return detections


# 1 

# 需要导入模块: from deep_sort import detection [as 别名]
# 或者: from deep_sort.detection import Detection [as 别名]
def create_detections(self, bboxes, feature):
        detections = []
        for box in np.array(bboxes):
            if box is None or len(box) == 0:
                continue
            box[2:4] -= box[:2]
            # too small to do reid
            if box[2] < self.conf.min_width and box[3] < self.conf.min_height:
                continue
            detections.append(Detection(tlwh=box[:4], confidence=box[4], feature=[]))
        return detections