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Add instance segmentation and vision-eye mapping in Docs + Fix minor code bug in other real-world-projects (#6972)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Muhammad Rizwan Munawar 2023-12-18 20:41:33 +05:00 • committed by GitHub
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54
docs/en/guides/object-counting.md
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@ -10,6 +10,17 @@ keywords: Ultralytics, YOLOv8, Object Detection, Object Counting, Object Trackin
Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics/) involves accurate identification and counting of specific objects in videos and camera streams. YOLOv8 excels in real-time applications, providing efficient and precise object counting for various scenarios like crowd analysis and surveillance, thanks to its state-of-the-art algorithms and deep learning capabilities.
<p align="center">
<br>
<iframe width="720" height="405" src="https://www.youtube.com/embed/Ag2e-5_NpS0"
title="YouTube video player" frameborder="0"
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
allowfullscreen>
</iframe>
<br>
<strong>Watch:</strong> Object Counting using Ultralytics YOLOv8
</p>
## Advantages of Object Counting?
- **Resource Optimization:** Object counting facilitates efficient resource management by providing accurate counts, and optimizing resource allocation in applications like inventory management.
@ -38,16 +49,19 @@ Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
counter = object_counter.ObjectCounter() # Init Object Counter
region_points = [(20, 400), (1080, 404), (1080, 360), (20, 360)]
counter.set_args(view_img=True,
reg_pts=region_points,
classes_names=model.names,
draw_tracks=True)
reg_pts=region_points,
classes_names=model.names,
draw_tracks=True)
while cap.isOpened():
success, im0 = cap.read()
if not success:
exit(0)
print("Video frame is empty or video processing has been successfully completed.")
break
tracks = model.track(im0, persist=True, show=False)
im0 = counter.start_counting(im0, tracks)
cv2.destroyAllWindows()
```
=== "Object Counting with Specific Classes"
@ -64,18 +78,20 @@ Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
counter = object_counter.ObjectCounter() # Init Object Counter
region_points = [(20, 400), (1080, 404), (1080, 360), (20, 360)]
counter.set_args(view_img=True,
reg_pts=region_points,
classes_names=model.names,
draw_tracks=True)
reg_pts=region_points,
classes_names=model.names,
draw_tracks=True)
while cap.isOpened():
success, im0 = cap.read()
if not success:
exit(0)
tracks = model.track(im0, persist=True,
show=False,
classes=classes_to_count)
print("Video frame is empty or video processing has been successfully completed.")
break
tracks = model.track(im0, persist=True, show=False,
classes=classes_to_count)
im0 = counter.start_counting(im0, tracks)
cv2.destroyAllWindows()
```
=== "Object Counting with Save Output"
@ -89,26 +105,28 @@ Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
assert cap.isOpened(), "Error reading video file"
video_writer = cv2.VideoWriter("object_counting.avi",
cv2.VideoWriter_fourcc(*'mp4v'),
int(cap.get(5)),
(int(cap.get(3)), int(cap.get(4))))
cv2.VideoWriter_fourcc(*'mp4v'),
int(cap.get(5)),
(int(cap.get(3)), int(cap.get(4))))
counter = object_counter.ObjectCounter() # Init Object Counter
region_points = [(20, 400), (1080, 404), (1080, 360), (20, 360)]
counter.set_args(view_img=True,
reg_pts=region_points,
classes_names=model.names,
draw_tracks=True)
reg_pts=region_points,
classes_names=model.names,
draw_tracks=True)
while cap.isOpened():
success, im0 = cap.read()
if not success:
exit(0)
print("Video frame is empty or video processing has been successfully completed.")
break
tracks = model.track(im0, persist=True, show=False)
im0 = counter.start_counting(im0, tracks)
video_writer.write(im0)
video_writer.release()
cv2.destroyAllWindows()
```
???+ tip "Region is Movable"