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# Ultralytics YOLO 🚀, AGPL-3.0 license
from collections import defaultdict
from time import time
import logging
import cv2
import numpy as np
from ultralytics.utils.checks import check_imshow
from ultralytics.utils.plotting import Annotator, colors
# create logger
logging.getLogger(__name__).addHandler(logging.NullHandler())
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 for the speed calc 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:
logging.warning("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 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)
# modified by steve.yin @ 3/1/2024 for traffic monitoring demo
# added for a combo label display with id, class name, speed
box_label = f"{track_id}:{self.names[int(cls)]}"
box_label += f":{(int)(self.dist_data[track_id]*0.621371)}mph" \
if track_id in self.dist_data else ''
bbox_color = colors(int(track_id)) \
if track_id in self.dist_data else (255, 0, 255)
self.annotator.box_label(box, box_label, bbox_color)
cv2.polylines(
self.im0, [self.trk_pts],
isClosed=False, color=(0, 255, 0), thickness=self.line_thickness
)
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 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=3)
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()
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