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Update speed-estimation solution (#16798)

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Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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Muhammad Rizwan Munawar 2024-10-09 18:59:18 +05:00 • committed by GitHub
parent 28f31f14e8
commit 30f64bd35f
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4 changed files with 74 additions and 129 deletions
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60
docs/en/guides/speed-estimation.md
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@ -45,40 +45,33 @@ keywords: Ultralytics YOLO11, speed estimation, object tracking, computer vision
```python
import cv2
from ultralytics import YOLO, solutions
from ultralytics import solutions
model = YOLO("yolo11n.pt")
names = model.model.names
cap = cv2.VideoCapture("Path/to/video/file.mp4")
cap = cv2.VideoCapture("path/to/video/file.mp4")
assert cap.isOpened(), "Error reading video file"
w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
# Video writer
video_writer = cv2.VideoWriter("speed_estimation.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))
video_writer = cv2.VideoWriter("speed_management.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))
line_pts = [(0, 360), (1280, 360)]
speed_region = [(20, 400), (1080, 404), (1080, 360), (20, 360)]
# Init speed-estimation obj
speed_obj = solutions.SpeedEstimator(
reg_pts=line_pts,
names=names,
view_img=True,
)
speed = solutions.SpeedEstimator(model="yolo11n.pt", region=speed_region, show=True)
while cap.isOpened():
success, im0 = cap.read()
if not success:
print("Video frame is empty or video processing has been successfully completed.")
break
tracks = model.track(im0, persist=True)
if success:
out = speed.estimate_speed(im0)
video_writer.write(im0)
if cv2.waitKey(1) & 0xFF == ord("q"):
break
continue
im0 = speed_obj.estimate_speed(im0, tracks)
video_writer.write(im0)
print("Video frame is empty or video processing has been successfully completed.")
break
cap.release()
video_writer.release()
cv2.destroyAllWindows()
```
@ -88,13 +81,12 @@ keywords: Ultralytics YOLO11, speed estimation, object tracking, computer vision
### Arguments `SpeedEstimator`
| Name | Type | Default | Description |
| ------------------ | ------ | -------------------------- | ---------------------------------------------------- |
| `names` | `dict` | `None` | Dictionary of class names. |
| `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. |
| `spdl_dist_thresh` | `int` | `10` | Distance threshold for speed calculation. |
| Name | Type | Default | Description |
| ------------ | ------ | -------------------------- | ---------------------------------------------------- |
| `model` | `str` | `None` | Path to Ultralytics YOLO Model File |
| `region` | `list` | `[(20, 400), (1260, 400)]` | List of points defining the counting region. |
| `line_width` | `int` | `2` | Line thickness for bounding boxes. |
| `show` | `bool` | `False` | Flag to control whether to display the video stream. |
### Arguments `model.track`
@ -111,10 +103,7 @@ Estimating object speed with Ultralytics YOLO11 involves combining [object detec
```python
import cv2
from ultralytics import YOLO, solutions
model = YOLO("yolo11n.pt")
names = model.model.names
from ultralytics import solutions
cap = cv2.VideoCapture("path/to/video/file.mp4")
w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
@ -122,17 +111,16 @@ video_writer = cv2.VideoWriter("speed_estimation.avi", cv2.VideoWriter_fourcc(*"
# Initialize SpeedEstimator
speed_obj = solutions.SpeedEstimator(
reg_pts=[(0, 360), (1280, 360)],
names=names,
view_img=True,
region=[(0, 360), (1280, 360)],
model="yolo11n.pt",
show=True,
)
while cap.isOpened():
success, im0 = cap.read()
if not success:
break
tracks = model.track(im0, persist=True, show=False)
im0 = speed_obj.estimate_speed(im0, tracks)
im0 = speed_obj.estimate_speed(im0)
video_writer.write(im0)
cap.release()