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| # Ultralytics YOLO 🚀, AGPL-3.0 license | |
| from collections import defaultdict | |
| from time import time | |
| import cv2 | |
| import numpy as np | |
| from ultralytics.utils.checks import check_imshow | |
| from ultralytics.utils.plotting import Annotator, colors | |
| class SpeedEstimator: | |
| """A class to estimation speed of objects in real-time video stream based on their tracks.""" | |
| def __init__(self): | |
| """Initializes the speed-estimator class with default values for Visual, Image, track and speed parameters.""" | |
| # Visual & im0 information | |
| self.im0 = None | |
| self.annotator = None | |
| self.view_img = False | |
| # Region information | |
| self.reg_pts = [(20, 400), (1260, 400)] | |
| self.region_thickness = 3 | |
| # Predict/track information | |
| self.clss = None | |
| self.names = None | |
| self.boxes = None | |
| self.trk_ids = None | |
| self.trk_pts = None | |
| self.line_thickness = 2 | |
| self.trk_history = defaultdict(list) | |
| # Speed estimator information | |
| self.current_time = 0 | |
| self.dist_data = {} | |
| self.trk_idslist = [] | |
| self.spdl_dist_thresh = 10 | |
| self.trk_previous_times = {} | |
| self.trk_previous_points = {} | |
| # Check if environment support imshow | |
| self.env_check = check_imshow(warn=True) | |
| def set_args( | |
| self, | |
| reg_pts, | |
| names, | |
| view_img=False, | |
| line_thickness=2, | |
| region_thickness=5, | |
| spdl_dist_thresh=10, | |
| ): | |
| """ | |
| Configures the speed estimation and display parameters. | |
| Args: | |
| reg_pts (list): Initial list of points defining the speed calculation region. | |
| names (dict): object detection classes names | |
| view_img (bool): Flag indicating frame display | |
| line_thickness (int): Line thickness for bounding boxes. | |
| region_thickness (int): Speed estimation region thickness | |
| spdl_dist_thresh (int): Euclidean distance threshold for speed line | |
| """ | |
| if reg_pts is None: | |
| print("Region points not provided, using default values") | |
| else: | |
| self.reg_pts = reg_pts | |
| self.names = names | |
| self.view_img = view_img | |
| self.line_thickness = line_thickness | |
| self.region_thickness = region_thickness | |
| self.spdl_dist_thresh = spdl_dist_thresh | |
| def extract_tracks(self, tracks): | |
| """ | |
| Extracts results from the provided data. | |
| Args: | |
| tracks (list): List of tracks obtained from the object tracking process. | |
| """ | |
| self.boxes = tracks[0].boxes.xyxy.cpu() | |
| self.clss = tracks[0].boxes.cls.cpu().tolist() | |
| self.trk_ids = tracks[0].boxes.id.int().cpu().tolist() | |
| def store_track_info(self, track_id, box): | |
| """ | |
| Store track data. | |
| Args: | |
| track_id (int): object track id. | |
| box (list): object bounding box data | |
| """ | |
| track = self.trk_history[track_id] | |
| bbox_center = (float((box[0] + box[2]) / 2), float((box[1] + box[3]) / 2)) | |
| track.append(bbox_center) | |
| if len(track) > 30: | |
| track.pop(0) | |
| self.trk_pts = np.hstack(track).astype(np.int32).reshape((-1, 1, 2)) | |
| return track | |
| def plot_box_and_track(self, track_id, box, cls, track): | |
| """ | |
| Plot track and bounding box. | |
| Args: | |
| track_id (int): object track id. | |
| box (list): object bounding box data | |
| cls (str): object class name | |
| track (list): tracking history for tracks path drawing | |
| """ | |
| speed_label = f"{int(self.dist_data[track_id])}km/ph" if track_id in self.dist_data else self.names[int(cls)] | |
| bbox_color = colors(int(track_id)) if track_id in self.dist_data else (255, 0, 255) | |
| self.annotator.box_label(box, speed_label, bbox_color) | |
| cv2.polylines(self.im0, [self.trk_pts], isClosed=False, color=(0, 255, 0), thickness=1) | |
| cv2.circle(self.im0, (int(track[-1][0]), int(track[-1][1])), 5, bbox_color, -1) | |
| def calculate_speed(self, trk_id, track): | |
| """ | |
| Calculation of object speed. | |
| Args: | |
| trk_id (int): object track id. | |
| track (list): tracking history for tracks path drawing | |
| """ | |
| if not self.reg_pts[0][0] < track[-1][0] < self.reg_pts[1][0]: | |
| return | |
| if self.reg_pts[1][1] - self.spdl_dist_thresh < track[-1][1] < self.reg_pts[1][1] + self.spdl_dist_thresh: | |
| direction = "known" | |
| elif self.reg_pts[0][1] - self.spdl_dist_thresh < track[-1][1] < self.reg_pts[0][1] + self.spdl_dist_thresh: | |
| direction = "known" | |
| else: | |
| direction = "unknown" | |
| if self.trk_previous_times[trk_id] != 0 and direction != "unknown" and trk_id not in self.trk_idslist: | |
| self.trk_idslist.append(trk_id) | |
| time_difference = time() - self.trk_previous_times[trk_id] | |
| if time_difference > 0: | |
| dist_difference = np.abs(track[-1][1] - self.trk_previous_points[trk_id][1]) | |
| speed = dist_difference / time_difference | |
| self.dist_data[trk_id] = speed | |
| self.trk_previous_times[trk_id] = time() | |
| self.trk_previous_points[trk_id] = track[-1] | |
| def estimate_speed(self, im0, tracks, region_color=(255, 0, 0)): | |
| """ | |
| Calculate object based on tracking data. | |
| Args: | |
| im0 (nd array): Image | |
| tracks (list): List of tracks obtained from the object tracking process. | |
| region_color (tuple): Color to use when drawing regions. | |
| """ | |
| self.im0 = im0 | |
| if tracks[0].boxes.id is None: | |
| if self.view_img and self.env_check: | |
| self.display_frames() | |
| return im0 | |
| self.extract_tracks(tracks) | |
| self.annotator = Annotator(self.im0, line_width=2) | |
| self.annotator.draw_region(reg_pts=self.reg_pts, color=region_color, thickness=self.region_thickness) | |
| for box, trk_id, cls in zip(self.boxes, self.trk_ids, self.clss): | |
| track = self.store_track_info(trk_id, box) | |
| if trk_id not in self.trk_previous_times: | |
| self.trk_previous_times[trk_id] = 0 | |
| self.plot_box_and_track(trk_id, box, cls, track) | |
| self.calculate_speed(trk_id, track) | |
| if self.view_img and self.env_check: | |
| self.display_frames() | |
| return im0 | |
| def display_frames(self): | |
| """Display frame.""" | |
| cv2.imshow("Ultralytics Speed Estimation", self.im0) | |
| if cv2.waitKey(1) & 0xFF == ord("q"): | |
| return | |
| if __name__ == "__main__": | |
| SpeedEstimator() | |