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