Fix mkdocs.yml raw image URLs (#14213)
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: Burhan <62214284+Burhan-Q@users.noreply.github.com>
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Glenn Jocher
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@ -4,7 +4,7 @@ description: Learn to accurately identify and count objects in real-time using U
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keywords: object counting, YOLOv8, Ultralytics, real-time object detection, AI, deep learning, object tracking, crowd analysis, surveillance, resource optimization
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---
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# Object Counting using Ultralytics YOLOv8 🚀
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# Object Counting using Ultralytics YOLOv8
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## What is Object Counting?
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@ -253,3 +253,125 @@ Here's a table with the `ObjectCounter` arguments:
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| `iou` | `float` | `0.5` | IOU Threshold |
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| `classes` | `list` | `None` | filter results by class, i.e. classes=0, or classes=[0,2,3] |
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| `verbose` | `bool` | `True` | Display the object tracking results |
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## FAQ
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### How do I count objects in a video using Ultralytics YOLOv8?
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To count objects in a video using Ultralytics YOLOv8, you can follow these steps:
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1. Import the necessary libraries (`cv2`, `ultralytics`).
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2. Load a pretrained YOLOv8 model.
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3. Define the counting region (e.g., a polygon, line, etc.).
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4. Set up the video capture and initialize the object counter.
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5. Process each frame to track objects and count them within the defined region.
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Here's a simple example for counting in a region:
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```python
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import cv2
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from ultralytics import YOLO, solutions
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def count_objects_in_region(video_path, output_video_path, model_path):
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"""Count objects in a specific region within a video."""
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model = YOLO(model_path)
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cap = cv2.VideoCapture(video_path)
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assert cap.isOpened(), "Error reading video file"
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w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
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region_points = [(20, 400), (1080, 404), (1080, 360), (20, 360)]
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video_writer = cv2.VideoWriter(output_video_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))
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counter = solutions.ObjectCounter(
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view_img=True, reg_pts=region_points, classes_names=model.names, draw_tracks=True, line_thickness=2
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)
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while cap.isOpened():
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success, im0 = cap.read()
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if not success:
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print("Video frame is empty or video processing has been successfully completed.")
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break
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tracks = model.track(im0, persist=True, show=False)
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im0 = counter.start_counting(im0, tracks)
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video_writer.write(im0)
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cap.release()
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video_writer.release()
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cv2.destroyAllWindows()
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count_objects_in_region("path/to/video.mp4", "output_video.avi", "yolov8n.pt")
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```
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Explore more configurations and options in the [Object Counting](#object-counting-using-ultralytics-yolov8) section.
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### What are the advantages of using Ultralytics YOLOv8 for object counting?
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Using Ultralytics YOLOv8 for object counting offers several advantages:
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1. **Resource Optimization:** It facilitates efficient resource management by providing accurate counts, helping optimize resource allocation in industries like inventory management.
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2. **Enhanced Security:** It enhances security and surveillance by accurately tracking and counting entities, aiding in proactive threat detection.
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3. **Informed Decision-Making:** It offers valuable insights for decision-making, optimizing processes in domains like retail, traffic management, and more.
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For real-world applications and code examples, visit the [Advantages of Object Counting](#advantages-of-object-counting) section.
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### How can I count specific classes of objects using Ultralytics YOLOv8?
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To count specific classes of objects using Ultralytics YOLOv8, you need to specify the classes you are interested in during the tracking phase. Below is a Python example:
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```python
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import cv2
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from ultralytics import YOLO, solutions
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def count_specific_classes(video_path, output_video_path, model_path, classes_to_count):
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"""Count specific classes of objects in a video."""
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model = YOLO(model_path)
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cap = cv2.VideoCapture(video_path)
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assert cap.isOpened(), "Error reading video file"
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w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
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line_points = [(20, 400), (1080, 400)]
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video_writer = cv2.VideoWriter(output_video_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))
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counter = solutions.ObjectCounter(
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view_img=True, reg_pts=line_points, classes_names=model.names, draw_tracks=True, line_thickness=2
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)
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while cap.isOpened():
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success, im0 = cap.read()
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if not success:
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print("Video frame is empty or video processing has been successfully completed.")
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break
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tracks = model.track(im0, persist=True, show=False, classes=classes_to_count)
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im0 = counter.start_counting(im0, tracks)
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video_writer.write(im0)
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cap.release()
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video_writer.release()
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cv2.destroyAllWindows()
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count_specific_classes("path/to/video.mp4", "output_specific_classes.avi", "yolov8n.pt", [0, 2])
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```
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In this example, `classes_to_count=[0, 2]`, which means it counts objects of class `0` and `2` (e.g., person and car).
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### Why should I use YOLOv8 over other object detection models for real-time applications?
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Ultralytics YOLOv8 provides several advantages over other object detection models like Faster R-CNN, SSD, and previous YOLO versions:
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1. **Speed and Efficiency:** YOLOv8 offers real-time processing capabilities, making it ideal for applications requiring high-speed inference, such as surveillance and autonomous driving.
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2. **Accuracy:** It provides state-of-the-art accuracy for object detection and tracking tasks, reducing the number of false positives and improving overall system reliability.
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3. **Ease of Integration:** YOLOv8 offers seamless integration with various platforms and devices, including mobile and edge devices, which is crucial for modern AI applications.
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4. **Flexibility:** Supports various tasks like object detection, segmentation, and tracking with configurable models to meet specific use-case requirements.
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Check out Ultralytics [YOLOv8 Documentation](https://docs.ultralytics.com/models/yolov8) for a deeper dive into its features and performance comparisons.
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### Can I use YOLOv8 for advanced applications like crowd analysis and traffic management?
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Yes, Ultralytics YOLOv8 is perfectly suited for advanced applications like crowd analysis and traffic management due to its real-time detection capabilities, scalability, and integration flexibility. Its advanced features allow for high-accuracy object tracking, counting, and classification in dynamic environments. Example use cases include:
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- **Crowd Analysis:** Monitor and manage large gatherings, ensuring safety and optimizing crowd flow.
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- **Traffic Management:** Track and count vehicles, analyze traffic patterns, and manage congestion in real-time.
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For more information and implementation details, refer to the guide on [Real World Applications](#real-world-applications) of object counting with YOLOv8.
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