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| #!/usr/bin/env python3 | |
| # Copyright 2004-present Facebook. All Rights Reserved. | |
| import copy | |
| import numpy as np | |
| from typing import List | |
| import torch | |
| from detectron2.config import configurable | |
| from detectron2.structures import Boxes, Instances | |
| from detectron2.structures.boxes import pairwise_iou | |
| from ..config.config import CfgNode as CfgNode_ | |
| from .base_tracker import TRACKER_HEADS_REGISTRY, BaseTracker | |
| class BBoxIOUTracker(BaseTracker): | |
| """ | |
| A bounding box tracker to assign ID based on IoU between current and previous instances | |
| """ | |
| 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, | |
| track_iou_threshold: float = 0.5, | |
| **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 | |
| track_iou_threshold: iou threshold, below this number a bbox pair is removed | |
| from tracking | |
| """ | |
| 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 | |
| self._track_iou_threshold = track_iou_threshold | |
| def from_config(cls, cfg: CfgNode_): | |
| """ | |
| Old style initialization using CfgNode | |
| Args: | |
| cfg: D2 CfgNode, config file | |
| Return: | |
| dictionary storing arguments for __init__ method | |
| """ | |
| assert "VIDEO_HEIGHT" in cfg.TRACKER_HEADS | |
| assert "VIDEO_WIDTH" in cfg.TRACKER_HEADS | |
| video_height = cfg.TRACKER_HEADS.get("VIDEO_HEIGHT") | |
| video_width = cfg.TRACKER_HEADS.get("VIDEO_WIDTH") | |
| max_num_instances = cfg.TRACKER_HEADS.get("MAX_NUM_INSTANCES", 200) | |
| max_lost_frame_count = cfg.TRACKER_HEADS.get("MAX_LOST_FRAME_COUNT", 0) | |
| min_box_rel_dim = cfg.TRACKER_HEADS.get("MIN_BOX_REL_DIM", 0.02) | |
| min_instance_period = cfg.TRACKER_HEADS.get("MIN_INSTANCE_PERIOD", 1) | |
| track_iou_threshold = cfg.TRACKER_HEADS.get("TRACK_IOU_THRESHOLD", 0.5) | |
| return { | |
| "_target_": "detectron2.tracking.bbox_iou_tracker.BBoxIOUTracker", | |
| "video_height": video_height, | |
| "video_width": video_width, | |
| "max_num_instances": max_num_instances, | |
| "max_lost_frame_count": max_lost_frame_count, | |
| "min_box_rel_dim": min_box_rel_dim, | |
| "min_instance_period": min_instance_period, | |
| "track_iou_threshold": track_iou_threshold, | |
| } | |
| def update(self, instances: Instances) -> Instances: | |
| """ | |
| See BaseTracker description | |
| """ | |
| instances = self._initialize_extra_fields(instances) | |
| if self._prev_instances is not None: | |
| # calculate IoU of all bbox pairs | |
| iou_all = pairwise_iou( | |
| boxes1=instances.pred_boxes, | |
| boxes2=self._prev_instances.pred_boxes, | |
| ) | |
| # sort IoU in descending order | |
| bbox_pairs = self._create_prediction_pairs(instances, iou_all) | |
| # assign previous ID to current bbox if IoU > track_iou_threshold | |
| self._reset_fields() | |
| for bbox_pair in bbox_pairs: | |
| idx = bbox_pair["idx"] | |
| prev_id = bbox_pair["prev_id"] | |
| if ( | |
| idx in self._matched_idx | |
| or prev_id in self._matched_ID | |
| or bbox_pair["IoU"] < self._track_iou_threshold | |
| ): | |
| continue | |
| instances.ID[idx] = prev_id | |
| instances.ID_period[idx] = bbox_pair["prev_period"] + 1 | |
| instances.lost_frame_count[idx] = 0 | |
| self._matched_idx.add(idx) | |
| self._matched_ID.add(prev_id) | |
| self._untracked_prev_idx.remove(bbox_pair["prev_idx"]) | |
| instances = self._assign_new_id(instances) | |
| instances = self._merge_untracked_instances(instances) | |
| self._prev_instances = copy.deepcopy(instances) | |
| return instances | |
| def _create_prediction_pairs(self, instances: Instances, iou_all: np.ndarray) -> List: | |
| """ | |
| For all instances in previous and current frames, create pairs. For each | |
| pair, store index of the instance in current frame predcitions, index in | |
| previous predictions, ID in previous predictions, IoU of the bboxes in this | |
| pair, period in previous predictions. | |
| Args: | |
| instances: D2 Instances, for predictions of the current frame | |
| iou_all: IoU for all bboxes pairs | |
| Return: | |
| A list of IoU for all pairs | |
| """ | |
| bbox_pairs = [] | |
| for i in range(len(instances)): | |
| for j in range(len(self._prev_instances)): | |
| bbox_pairs.append( | |
| { | |
| "idx": i, | |
| "prev_idx": j, | |
| "prev_id": self._prev_instances.ID[j], | |
| "IoU": iou_all[i, j], | |
| "prev_period": self._prev_instances.ID_period[j], | |
| } | |
| ) | |
| return bbox_pairs | |
| 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 _reset_fields(self): | |
| """ | |
| Before each uodate call, reset fields first | |
| """ | |
| self._matched_idx = set() | |
| self._matched_ID = set() | |
| self._untracked_prev_idx = set(range(len(self._prev_instances))) | |
| def _assign_new_id(self, instances: Instances) -> Instances: | |
| """ | |
| For each untracked instance, assign a new id | |
| Args: | |
| instances: D2 Instances, for predictions of the current frame | |
| Return: | |
| D2 Instances with new ID assigned | |
| """ | |
| untracked_idx = set(range(len(instances))).difference(self._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 _merge_untracked_instances(self, instances: Instances) -> Instances: | |
| """ | |
| For untracked previous instances, under certain condition, still keep them | |
| in tracking and merge with the current instances. | |
| Args: | |
| instances: D2 Instances, for predictions of the current frame | |
| Return: | |
| D2 Instances merging current instances and instances from previous | |
| frame decided to keep tracking | |
| """ | |
| untracked_instances = Instances( | |
| image_size=instances.image_size, | |
| pred_boxes=[], | |
| 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"): | |
| untracked_instances.set("pred_masks", []) | |
| prev_masks = list(self._prev_instances.pred_masks) | |
| if instances.has("pred_keypoints"): | |
| untracked_instances.set("pred_keypoints", []) | |
| prev_keypoints = list(self._prev_instances.pred_keypoints) | |
| if instances.has("pred_keypoint_heatmaps"): | |
| untracked_instances.set("pred_keypoint_heatmaps", []) | |
| prev_keypoint_heatmaps = list(self._prev_instances.pred_keypoint_heatmaps) | |
| for idx in self._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)) | |
| if instances.has("pred_keypoints"): | |
| untracked_instances.pred_keypoints.append( | |
| prev_keypoints[idx].numpy().astype(np.uint8) | |
| ) | |
| if instances.has("pred_keypoint_heatmaps"): | |
| untracked_instances.pred_keypoint_heatmaps.append( | |
| prev_keypoint_heatmaps[idx].numpy().astype(np.float32) | |
| ) | |
| 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) | |
| if instances.has("pred_keypoints"): | |
| untracked_instances.pred_keypoints = torch.IntTensor(untracked_instances.pred_keypoints) | |
| if instances.has("pred_keypoint_heatmaps"): | |
| untracked_instances.pred_keypoint_heatmaps = torch.FloatTensor( | |
| untracked_instances.pred_keypoint_heatmaps | |
| ) | |
| return Instances.cat( | |
| [ | |
| instances, | |
| untracked_instances, | |
| ] | |
| ) | |