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Zero
| #!/usr/bin/env python3 | |
| # Copyright 2004-present Facebook. All Rights Reserved. | |
| import copy | |
| import numpy as np | |
| from typing import Dict | |
| import torch | |
| from scipy.optimize import linear_sum_assignment | |
| from detectron2.config import configurable | |
| from detectron2.structures import Boxes, Instances | |
| from ..config.config import CfgNode as CfgNode_ | |
| from .base_tracker import BaseTracker | |
| class BaseHungarianTracker(BaseTracker): | |
| """ | |
| A base class for all Hungarian trackers | |
| """ | |
| def __init__( | |
| self, | |
| video_height: int, | |
| video_width: int, | |
| max_num_instances: int = 200, | |
| max_lost_frame_count: int = 0, | |
| min_box_rel_dim: float = 0.02, | |
| min_instance_period: int = 1, | |
| **kwargs | |
| ): | |
| """ | |
| Args: | |
| video_height: height the video frame | |
| video_width: width of the video frame | |
| max_num_instances: maximum number of id allowed to be tracked | |
| max_lost_frame_count: maximum number of frame an id can lost tracking | |
| exceed this number, an id is considered as lost | |
| forever | |
| min_box_rel_dim: a percentage, smaller than this dimension, a bbox is | |
| removed from tracking | |
| min_instance_period: an instance will be shown after this number of period | |
| since its first showing up in the video | |
| """ | |
| super().__init__(**kwargs) | |
| self._video_height = video_height | |
| self._video_width = video_width | |
| self._max_num_instances = max_num_instances | |
| self._max_lost_frame_count = max_lost_frame_count | |
| self._min_box_rel_dim = min_box_rel_dim | |
| self._min_instance_period = min_instance_period | |
| def from_config(cls, cfg: CfgNode_) -> Dict: | |
| raise NotImplementedError("Calling HungarianTracker::from_config") | |
| def build_cost_matrix(self, instances: Instances, prev_instances: Instances) -> np.ndarray: | |
| raise NotImplementedError("Calling HungarianTracker::build_matrix") | |
| def update(self, instances: Instances) -> Instances: | |
| if instances.has("pred_keypoints"): | |
| raise NotImplementedError("Need to add support for keypoints") | |
| instances = self._initialize_extra_fields(instances) | |
| if self._prev_instances is not None: | |
| self._untracked_prev_idx = set(range(len(self._prev_instances))) | |
| cost_matrix = self.build_cost_matrix(instances, self._prev_instances) | |
| matched_idx, matched_prev_idx = linear_sum_assignment(cost_matrix) | |
| instances = self._process_matched_idx(instances, matched_idx, matched_prev_idx) | |
| instances = self._process_unmatched_idx(instances, matched_idx) | |
| instances = self._process_unmatched_prev_idx(instances, matched_prev_idx) | |
| self._prev_instances = copy.deepcopy(instances) | |
| return instances | |
| def _initialize_extra_fields(self, instances: Instances) -> Instances: | |
| """ | |
| If input instances don't have ID, ID_period, lost_frame_count fields, | |
| this method is used to initialize these fields. | |
| Args: | |
| instances: D2 Instances, for predictions of the current frame | |
| Return: | |
| D2 Instances with extra fields added | |
| """ | |
| if not instances.has("ID"): | |
| instances.set("ID", [None] * len(instances)) | |
| if not instances.has("ID_period"): | |
| instances.set("ID_period", [None] * len(instances)) | |
| if not instances.has("lost_frame_count"): | |
| instances.set("lost_frame_count", [None] * len(instances)) | |
| if self._prev_instances is None: | |
| instances.ID = list(range(len(instances))) | |
| self._id_count += len(instances) | |
| instances.ID_period = [1] * len(instances) | |
| instances.lost_frame_count = [0] * len(instances) | |
| return instances | |
| def _process_matched_idx( | |
| self, instances: Instances, matched_idx: np.ndarray, matched_prev_idx: np.ndarray | |
| ) -> Instances: | |
| assert matched_idx.size == matched_prev_idx.size | |
| for i in range(matched_idx.size): | |
| instances.ID[matched_idx[i]] = self._prev_instances.ID[matched_prev_idx[i]] | |
| instances.ID_period[matched_idx[i]] = ( | |
| self._prev_instances.ID_period[matched_prev_idx[i]] + 1 | |
| ) | |
| instances.lost_frame_count[matched_idx[i]] = 0 | |
| return instances | |
| def _process_unmatched_idx(self, instances: Instances, matched_idx: np.ndarray) -> Instances: | |
| untracked_idx = set(range(len(instances))).difference(set(matched_idx)) | |
| for idx in untracked_idx: | |
| instances.ID[idx] = self._id_count | |
| self._id_count += 1 | |
| instances.ID_period[idx] = 1 | |
| instances.lost_frame_count[idx] = 0 | |
| return instances | |
| def _process_unmatched_prev_idx( | |
| self, instances: Instances, matched_prev_idx: np.ndarray | |
| ) -> Instances: | |
| untracked_instances = Instances( | |
| image_size=instances.image_size, | |
| pred_boxes=[], | |
| pred_masks=[], | |
| pred_classes=[], | |
| scores=[], | |
| ID=[], | |
| ID_period=[], | |
| lost_frame_count=[], | |
| ) | |
| prev_bboxes = list(self._prev_instances.pred_boxes) | |
| prev_classes = list(self._prev_instances.pred_classes) | |
| prev_scores = list(self._prev_instances.scores) | |
| prev_ID_period = self._prev_instances.ID_period | |
| if instances.has("pred_masks"): | |
| prev_masks = list(self._prev_instances.pred_masks) | |
| untracked_prev_idx = set(range(len(self._prev_instances))).difference(set(matched_prev_idx)) | |
| for idx in untracked_prev_idx: | |
| x_left, y_top, x_right, y_bot = prev_bboxes[idx] | |
| if ( | |
| (1.0 * (x_right - x_left) / self._video_width < self._min_box_rel_dim) | |
| or (1.0 * (y_bot - y_top) / self._video_height < self._min_box_rel_dim) | |
| or self._prev_instances.lost_frame_count[idx] >= self._max_lost_frame_count | |
| or prev_ID_period[idx] <= self._min_instance_period | |
| ): | |
| continue | |
| untracked_instances.pred_boxes.append(list(prev_bboxes[idx].numpy())) | |
| untracked_instances.pred_classes.append(int(prev_classes[idx])) | |
| untracked_instances.scores.append(float(prev_scores[idx])) | |
| untracked_instances.ID.append(self._prev_instances.ID[idx]) | |
| untracked_instances.ID_period.append(self._prev_instances.ID_period[idx]) | |
| untracked_instances.lost_frame_count.append( | |
| self._prev_instances.lost_frame_count[idx] + 1 | |
| ) | |
| if instances.has("pred_masks"): | |
| untracked_instances.pred_masks.append(prev_masks[idx].numpy().astype(np.uint8)) | |
| untracked_instances.pred_boxes = Boxes(torch.FloatTensor(untracked_instances.pred_boxes)) | |
| untracked_instances.pred_classes = torch.IntTensor(untracked_instances.pred_classes) | |
| untracked_instances.scores = torch.FloatTensor(untracked_instances.scores) | |
| if instances.has("pred_masks"): | |
| untracked_instances.pred_masks = torch.IntTensor(untracked_instances.pred_masks) | |
| else: | |
| untracked_instances.remove("pred_masks") | |
| return Instances.cat( | |
| [ | |
| instances, | |
| untracked_instances, | |
| ] | |
| ) | |