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Optimize `speed estimation` solution (#16254)

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Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
pull/8642/head^2
Muhammad Rizwan Munawar 2 months ago committed by GitHub
parent 684519fe52
commit bf2b221d11
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2 changed files with 55 additions and 120 deletions
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  1. 3
      docs/en/guides/speed-estimation.md
  2. 172
      ultralytics/solutions/speed_estimation.py

3
docs/en/guides/speed-estimation.md
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@ -72,7 +72,7 @@ keywords: Ultralytics YOLOv8, speed estimation, object tracking, computer vision
print("Video frame is empty or video processing has been successfully completed.")
break
tracks = model.track(im0, persist=True, show=False)
tracks = model.track(im0, persist=True)
im0 = speed_obj.estimate_speed(im0, tracks)
video_writer.write(im0)
@ -94,7 +94,6 @@ keywords: Ultralytics YOLOv8, speed estimation, object tracking, computer vision
| `reg_pts` | `list` | `[(20, 400), (1260, 400)]` | List of region points for speed estimation. |
| `view_img` | `bool` | `False` | Whether to display the image with annotations. |
| `line_thickness` | `int` | `2` | Thickness of the lines for drawing boxes and tracks. |
| `region_thickness` | `int` | `5` | Thickness of the region lines. |
| `spdl_dist_thresh` | `int` | `10` | Distance threshold for speed calculation. |
### Arguments `model.track`

172
ultralytics/solutions/speed_estimation.py
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@ -13,7 +13,7 @@ from ultralytics.utils.plotting import Annotator, colors
class SpeedEstimator:
"""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, region_thickness=5, spdl_dist_thresh=10):
def __init__(self, names, reg_pts=None, view_img=False, line_thickness=2, spdl_dist_thresh=10):
"""
Initializes the SpeedEstimator with the given parameters.
@ -22,158 +22,94 @@ class SpeedEstimator:
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.
region_thickness (int, optional): Thickness of the region lines. Defaults to 5.
spdl_dist_thresh (int, optional): Distance threshold for speed calculation. Defaults to 10.
"""
# Visual & image information
self.im0 = None
self.annotator = None
self.view_img = view_img
# Region information
self.reg_pts = reg_pts if reg_pts is not None else [(20, 400), (1260, 400)]
self.region_thickness = region_thickness
self.names = names # Classes names
# Tracking information
self.clss = None
self.names = names
self.boxes = None
self.trk_ids = None
self.trk_pts = None
self.line_thickness = line_thickness
self.trk_history = defaultdict(list)
# Speed estimation information
self.current_time = 0
self.dist_data = {}
self.trk_idslist = []
self.spdl_dist_thresh = spdl_dist_thresh
self.trk_previous_times = {}
self.trk_previous_points = {}
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 extract_tracks(self, tracks):
def estimate_speed(self, im0, tracks):
"""
Extracts results from the provided tracking data.
Estimates the speed of objects based on tracking data.
Args:
im0 (ndarray): Image.
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):
"""
Stores track data.
Args:
track_id (int): Object track id.
box (list): Object bounding box data.
Returns:
(list): Updated tracking history for the given track_id.
(ndarray): The image with annotated boxes and tracks.
"""
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 tracks[0].boxes.id is None:
return im0
if len(track) > 30:
track.pop(0)
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.trk_pts = np.hstack(track).astype(np.int32).reshape((-1, 1, 2))
return track
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)
def plot_box_and_track(self, track_id, box, cls, track):
"""
Plots track and bounding box.
if len(track) > 30:
track.pop(0)
Args:
track_id (int): Object track id.
box (list): Object bounding box data.
cls (str): Object class name.
track (list): Tracking history for drawing tracks path.
"""
speed_label = f"{int(self.dist_data[track_id])} km/h" 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)
trk_pts = np.hstack(track).astype(np.int32).reshape((-1, 1, 2))
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)
if t_id not in self.trk_pt:
self.trk_pt[t_id] = 0
def calculate_speed(self, trk_id, track):
"""
Calculates the speed of an object.
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)
Args:
trk_id (int): Object track id.
track (list): Tracking history for drawing tracks path.
"""
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.get(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)):
"""
Estimates the speed of objects based on tracking data.
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)
Args:
im0 (ndarray): Image.
tracks (list): List of tracks obtained from the object tracking process.
region_color (tuple, optional): Color to use when drawing regions. Defaults to (255, 0, 0).
# 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:
direction = "known"
else:
direction = "unknown"
Returns:
(ndarray): The image with annotated boxes and tracks.
"""
self.im0 = im0
if tracks[0].boxes.id is None:
if self.view_img and self.env_check:
self.display_frames()
return im0
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)
self.extract_tracks(tracks)
self.annotator = Annotator(self.im0, line_width=self.line_thickness)
self.annotator.draw_region(reg_pts=self.reg_pts, color=region_color, thickness=self.region_thickness)
time_difference = time() - self.trk_pt[t_id]
if time_difference > 0:
self.spd[t_id] = np.abs(track[-1][1] - self.trk_pp[t_id][1]) / time_difference
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)
self.trk_pt[t_id] = time()
self.trk_pp[t_id] = track[-1]
if self.view_img and self.env_check:
self.display_frames()
cv2.imshow("Ultralytics Speed Estimation", im0)
if cv2.waitKey(1) & 0xFF == ord("q"):
return
return im0
def display_frames(self):
"""Displays the current frame."""
cv2.imshow("Ultralytics Speed Estimation", self.im0)
if cv2.waitKey(1) & 0xFF == ord("q"):
return
if __name__ == "__main__":
names = {0: "person", 1: "car"} # example class names

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