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| import numpy as np | |
| import torch | |
| import sys | |
| import cv2 | |
| import gdown | |
| from os.path import exists as file_exists, join | |
| import torchvision.transforms as transforms | |
| from sort.nn_matching import NearestNeighborDistanceMetric | |
| from sort.detection import Detection | |
| from sort.tracker import Tracker | |
| from reid_multibackend import ReIDDetectMultiBackend | |
| from ultralytics.yolo.utils.ops import xyxy2xywh | |
| class StrongSORT(object): | |
| def __init__(self, | |
| model_weights, | |
| device, | |
| fp16, | |
| max_dist=0.2, | |
| max_iou_dist=0.7, | |
| max_age=70, | |
| max_unmatched_preds=7, | |
| n_init=3, | |
| nn_budget=100, | |
| mc_lambda=0.995, | |
| ema_alpha=0.9 | |
| ): | |
| self.model = ReIDDetectMultiBackend(weights=model_weights, device=device, fp16=fp16) | |
| self.max_dist = max_dist | |
| metric = NearestNeighborDistanceMetric( | |
| "cosine", self.max_dist, nn_budget) | |
| self.tracker = Tracker( | |
| metric, max_iou_dist=max_iou_dist, max_age=max_age, n_init=n_init, max_unmatched_preds=max_unmatched_preds, mc_lambda=mc_lambda, ema_alpha=ema_alpha) | |
| def update(self, dets, ori_img): | |
| xyxys = dets[:, 0:4] | |
| confs = dets[:, 4] | |
| clss = dets[:, 5] | |
| classes = clss.numpy() | |
| xywhs = xyxy2xywh(xyxys.numpy()) | |
| confs = confs.numpy() | |
| self.height, self.width = ori_img.shape[:2] | |
| # generate detections | |
| features = self._get_features(xywhs, ori_img) | |
| bbox_tlwh = self._xywh_to_tlwh(xywhs) | |
| detections = [Detection(bbox_tlwh[i], conf, features[i]) for i, conf in enumerate( | |
| confs)] | |
| # run on non-maximum supression | |
| boxes = np.array([d.tlwh for d in detections]) | |
| scores = np.array([d.confidence for d in detections]) | |
| # update tracker | |
| self.tracker.predict() | |
| self.tracker.update(detections, clss, confs) | |
| # output bbox identities | |
| outputs = [] | |
| for track in self.tracker.tracks: | |
| if not track.is_confirmed() or track.time_since_update > 1: | |
| continue | |
| box = track.to_tlwh() | |
| x1, y1, x2, y2 = self._tlwh_to_xyxy(box) | |
| track_id = track.track_id | |
| class_id = track.class_id | |
| conf = track.conf | |
| queue = track.q | |
| outputs.append(np.array([x1, y1, x2, y2, track_id, class_id, conf, queue], dtype=object)) | |
| if len(outputs) > 0: | |
| outputs = np.stack(outputs, axis=0) | |
| return outputs | |
| """ | |
| TODO: | |
| Convert bbox from xc_yc_w_h to xtl_ytl_w_h | |
| Thanks JieChen91@github.com for reporting this bug! | |
| """ | |
| def _xywh_to_tlwh(bbox_xywh): | |
| if isinstance(bbox_xywh, np.ndarray): | |
| bbox_tlwh = bbox_xywh.copy() | |
| elif isinstance(bbox_xywh, torch.Tensor): | |
| bbox_tlwh = bbox_xywh.clone() | |
| bbox_tlwh[:, 0] = bbox_xywh[:, 0] - bbox_xywh[:, 2] / 2. | |
| bbox_tlwh[:, 1] = bbox_xywh[:, 1] - bbox_xywh[:, 3] / 2. | |
| return bbox_tlwh | |
| def _xywh_to_xyxy(self, bbox_xywh): | |
| x, y, w, h = bbox_xywh | |
| x1 = max(int(x - w / 2), 0) | |
| x2 = min(int(x + w / 2), self.width - 1) | |
| y1 = max(int(y - h / 2), 0) | |
| y2 = min(int(y + h / 2), self.height - 1) | |
| return x1, y1, x2, y2 | |
| def _tlwh_to_xyxy(self, bbox_tlwh): | |
| """ | |
| TODO: | |
| Convert bbox from xtl_ytl_w_h to xc_yc_w_h | |
| Thanks JieChen91@github.com for reporting this bug! | |
| """ | |
| x, y, w, h = bbox_tlwh | |
| x1 = max(int(x), 0) | |
| x2 = min(int(x+w), self.width - 1) | |
| y1 = max(int(y), 0) | |
| y2 = min(int(y+h), self.height - 1) | |
| return x1, y1, x2, y2 | |
| def increment_ages(self): | |
| self.tracker.increment_ages() | |
| def _xyxy_to_tlwh(self, bbox_xyxy): | |
| x1, y1, x2, y2 = bbox_xyxy | |
| t = x1 | |
| l = y1 | |
| w = int(x2 - x1) | |
| h = int(y2 - y1) | |
| return t, l, w, h | |
| def _get_features(self, bbox_xywh, ori_img): | |
| im_crops = [] | |
| for box in bbox_xywh: | |
| x1, y1, x2, y2 = self._xywh_to_xyxy(box) | |
| im = ori_img[y1:y2, x1:x2] | |
| im_crops.append(im) | |
| if im_crops: | |
| features = self.model(im_crops) | |
| else: | |
| features = np.array([]) | |
| return features | |
| def trajectory(self, im0, q, color): | |
| # Add rectangle to image (PIL-only) | |
| for i, p in enumerate(q): | |
| thickness = int(np.sqrt(float (i + 1)) * 1.5) | |
| if p[0] == 'observationupdate': | |
| cv2.circle(im0, p[1], 2, color=color, thickness=thickness) | |
| else: | |
| cv2.circle(im0, p[1], 2, color=(255,255,255), thickness=thickness) | |