Burhan
2 months ago
committed by
GitHub
commit
9a9b644f99
9 changed files with 88 additions and 144 deletions
@ -1,116 +1,76 @@ |
||||
# 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.solutions.solutions import BaseSolution, LineString |
||||
from ultralytics.utils.plotting import Annotator, colors |
||||
|
||||
|
||||
class SpeedEstimator: |
||||
class SpeedEstimator(BaseSolution): |
||||
"""A class to estimate the speed of objects in a real-time video stream based on their tracks.""" |
||||
|
||||
def __init__(self, names, reg_pts=None, view_img=False, line_thickness=2, spdl_dist_thresh=10): |
||||
""" |
||||
Initializes the SpeedEstimator with the given parameters. |
||||
|
||||
Args: |
||||
names (dict): Dictionary of class names. |
||||
reg_pts (list, optional): List of region points for speed estimation. Defaults to [(20, 400), (1260, 400)]. |
||||
view_img (bool, optional): Whether to display the image with annotations. Defaults to False. |
||||
line_thickness (int, optional): Thickness of the lines for drawing boxes and tracks. Defaults to 2. |
||||
spdl_dist_thresh (int, optional): Distance threshold for speed calculation. Defaults to 10. |
||||
""" |
||||
# Region information |
||||
self.reg_pts = reg_pts if reg_pts is not None else [(20, 400), (1260, 400)] |
||||
def __init__(self, **kwargs): |
||||
"""Initializes the SpeedEstimator with the given parameters.""" |
||||
super().__init__(**kwargs) |
||||
|
||||
self.names = names # Classes names |
||||
self.initialize_region() # Initialize speed region |
||||
|
||||
# Tracking information |
||||
self.trk_history = defaultdict(list) |
||||
|
||||
self.view_img = view_img # bool for displaying inference |
||||
self.tf = line_thickness # line thickness for annotator |
||||
self.spd = {} # set for speed data |
||||
self.trkd_ids = [] # list for already speed_estimated and tracked ID's |
||||
self.spdl = spdl_dist_thresh # Speed line distance threshold |
||||
self.trk_pt = {} # set for tracks previous time |
||||
self.trk_pp = {} # set for tracks previous point |
||||
|
||||
# Check if the environment supports imshow |
||||
self.env_check = check_imshow(warn=True) |
||||
|
||||
def estimate_speed(self, im0, tracks): |
||||
def estimate_speed(self, im0): |
||||
""" |
||||
Estimates the speed of objects based on tracking data. |
||||
|
||||
Args: |
||||
im0 (ndarray): Image. |
||||
tracks (list): List of tracks obtained from the object tracking process. |
||||
|
||||
Returns: |
||||
(ndarray): The image with annotated boxes and tracks. |
||||
im0 (ndarray): The input image that will be used for processing |
||||
Returns |
||||
im0 (ndarray): The processed image for more usage |
||||
""" |
||||
if tracks[0].boxes.id is None: |
||||
return im0 |
||||
self.annotator = Annotator(im0, line_width=self.line_width) # Initialize annotator |
||||
self.extract_tracks(im0) # Extract tracks |
||||
|
||||
boxes = tracks[0].boxes.xyxy.cpu() |
||||
clss = tracks[0].boxes.cls.cpu().tolist() |
||||
t_ids = tracks[0].boxes.id.int().cpu().tolist() |
||||
annotator = Annotator(im0, line_width=self.tf) |
||||
annotator.draw_region(reg_pts=self.reg_pts, color=(255, 0, 255), thickness=self.tf * 2) |
||||
self.annotator.draw_region( |
||||
reg_pts=self.region, color=(104, 0, 123), thickness=self.line_width * 2 |
||||
) # Draw region |
||||
|
||||
for box, t_id, cls in zip(boxes, t_ids, clss): |
||||
track = self.trk_history[t_id] |
||||
bbox_center = (float((box[0] + box[2]) / 2), float((box[1] + box[3]) / 2)) |
||||
track.append(bbox_center) |
||||
for box, track_id, cls in zip(self.boxes, self.track_ids, self.clss): |
||||
self.store_tracking_history(track_id, box) # Store track history |
||||
|
||||
if len(track) > 30: |
||||
track.pop(0) |
||||
# Check if track_id is already in self.trk_pp or trk_pt initialize if not |
||||
if track_id not in self.trk_pt: |
||||
self.trk_pt[track_id] = 0 |
||||
if track_id not in self.trk_pp: |
||||
self.trk_pp[track_id] = self.track_line[-1] |
||||
|
||||
trk_pts = np.hstack(track).astype(np.int32).reshape((-1, 1, 2)) |
||||
speed_label = f"{int(self.spd[track_id])} km/h" if track_id in self.spd else self.names[int(cls)] |
||||
self.annotator.box_label(box, label=speed_label, color=colors(track_id, True)) # Draw bounding box |
||||
|
||||
if t_id not in self.trk_pt: |
||||
self.trk_pt[t_id] = 0 |
||||
# Draw tracks of objects |
||||
self.annotator.draw_centroid_and_tracks( |
||||
self.track_line, color=colors(int(track_id), True), track_thickness=self.line_width |
||||
) |
||||
|
||||
speed_label = f"{int(self.spd[t_id])} km/h" if t_id in self.spd else self.names[int(cls)] |
||||
bbox_color = colors(int(t_id), True) |
||||
|
||||
annotator.box_label(box, speed_label, bbox_color) |
||||
cv2.polylines(im0, [trk_pts], isClosed=False, color=bbox_color, thickness=self.tf) |
||||
cv2.circle(im0, (int(track[-1][0]), int(track[-1][1])), self.tf * 2, bbox_color, -1) |
||||
|
||||
# Calculation of object speed |
||||
if not self.reg_pts[0][0] < track[-1][0] < self.reg_pts[1][0]: |
||||
return |
||||
if self.reg_pts[1][1] - self.spdl < track[-1][1] < self.reg_pts[1][1] + self.spdl: |
||||
direction = "known" |
||||
elif self.reg_pts[0][1] - self.spdl < track[-1][1] < self.reg_pts[0][1] + self.spdl: |
||||
# Calculate object speed and direction based on region intersection |
||||
if LineString([self.trk_pp[track_id], self.track_line[-1]]).intersects(self.l_s): |
||||
direction = "known" |
||||
else: |
||||
direction = "unknown" |
||||
|
||||
if self.trk_pt.get(t_id) != 0 and direction != "unknown" and t_id not in self.trkd_ids: |
||||
self.trkd_ids.append(t_id) |
||||
|
||||
time_difference = time() - self.trk_pt[t_id] |
||||
# Perform speed calculation and tracking updates if direction is valid |
||||
if direction == "known" and track_id not in self.trkd_ids: |
||||
self.trkd_ids.append(track_id) |
||||
time_difference = time() - self.trk_pt[track_id] |
||||
if time_difference > 0: |
||||
self.spd[t_id] = np.abs(track[-1][1] - self.trk_pp[t_id][1]) / time_difference |
||||
|
||||
self.trk_pt[t_id] = time() |
||||
self.trk_pp[t_id] = track[-1] |
||||
|
||||
if self.view_img and self.env_check: |
||||
cv2.imshow("Ultralytics Speed Estimation", im0) |
||||
if cv2.waitKey(1) & 0xFF == ord("q"): |
||||
return |
||||
self.spd[track_id] = np.abs(self.track_line[-1][1] - self.trk_pp[track_id][1]) / time_difference |
||||
|
||||
return im0 |
||||
self.trk_pt[track_id] = time() |
||||
self.trk_pp[track_id] = self.track_line[-1] |
||||
|
||||
self.display_output(im0) # display output with base class function |
||||
|
||||
if __name__ == "__main__": |
||||
names = {0: "person", 1: "car"} # example class names |
||||
speed_estimator = SpeedEstimator(names) |
||||
return im0 # return output image for more usage |
||||
|
||||
Loading…
Reference in new issue