sd
/
stable-diffusion-webui
/extensions
/sd-webui-controlnet
/annotator
/oneformer
/detectron2
/tracking
/hungarian_tracker.py
#!/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 annotator.oneformer.detectron2.config import configurable | |
from annotator.oneformer.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, | |
] | |
) | |