yo-WASSUP/YOLOv8-Regions-Counter

YOLOv8 区域物体计数

Regions Counting Using YOLOv8 (Inference on Video)

• Region counting is a method employed to tally the objects within a specified area, allowing for more sophisticated analyses when multiple regions are considered. These regions can be adjusted interactively using a Left Mouse Click, and the counting process occurs in real time.
• Regions can be adjusted to suit the user's preferences and requirements.

Table of Contents

• Step 1: Install the Required Libraries
• Step 2: Run the Region Counting Using Ultralytics YOLOv8
• Usage Options
• FAQ

Step 1: Install the Required Libraries

Clone the repository, install dependencies and cd to this local directory for commands in Step 2.

# Clone ultralytics repo
git clone https://github.com/ultralytics/ultralytics

# cd to local directory
cd ultralytics/examples/YOLOv8-Region-Counter

Step 2: Run the Region Counting Using Ultralytics YOLOv8

Here are the basic commands for running the inference:

Note

After the video begins playing, you can freely move the region anywhere within the video by simply clicking and dragging using the left mouse button.

# If you want to save results
python yolov8_region_counter.py --source "path/to/video.mp4" --save-img --view-img

# If you want to run model on CPU
python yolov8_region_counter.py --source "path/to/video.mp4" --save-img --view-img --device cpu

# If you want to change model file
python yolov8_region_counter.py --source "path/to/video.mp4" --save-img --weights "path/to/model.pt"

# If you want to detect specific class (first class and third class)
python yolov8_region_counter.py --source "path/to/video.mp4" --classes 0 2 --weights "path/to/model.pt"

# If you dont want to save results
python yolov8_region_counter.py --source "path/to/video.mp4" --view-img

Usage Options

• --source: Specifies the path to the video file you want to run inference on.
• --device: Specifies the device cpu or 0
• --save-img: Flag to save the detection results as images.
• --weights: Specifies a different YOLOv8 model file (e.g., yolov8n.pt, yolov8s.pt, yolov8m.pt, yolov8l.pt, yolov8x.pt).
• --classes: Specifies the class to be detected
• --line-thickness: Specifies the bounding box thickness
• --region-thickness: Specifies the region boxes thickness
• --track-thickness: Specifies the track line thickness

FAQ

1. What Does Region Counting Involve?

Region counting is a computational method utilized to ascertain the quantity of objects within a specific area in recorded video or real-time streams. This technique finds frequent application in image processing, computer vision, and pattern recognition, facilitating the analysis and segmentation of objects or features based on their spatial relationships.

2. Is Friendly Region Plotting Supported by the Region Counter?

The Region Counter offers the capability to create regions in various formats, such as polygons and rectangles. You have the flexibility to modify region attributes, including coordinates, colors, and other details, as demonstrated in the following code:

from shapely.geometry import Polygon

counting_regions = [
    {
        "name": "YOLOv8 Polygon Region",
        "polygon": Polygon(
            [(50, 80), (250, 20), (450, 80), (400, 350), (100, 350)]
        ),  # Polygon with five points (Pentagon)
        "counts": 0,
        "dragging": False,
        "region_color": (255, 42, 4),  # BGR Value
        "text_color": (255, 255, 255),  # Region Text Color
    },
    {
        "name": "YOLOv8 Rectangle Region",
        "polygon": Polygon(
            [(200, 250), (440, 250), (440, 550), (200, 550)]
        ),  # Rectangle with four points
        "counts": 0,
        "dragging": False,
        "region_color": (37, 255, 225),  # BGR Value
        "text_color": (0, 0, 0),  # Region Text Color
    },
]

3. Why Combine Region Counting with YOLOv8?

YOLOv8 specializes in the detection and tracking of objects in video streams. Region counting complements this by enabling object counting within designated areas, making it a valuable application of YOLOv8.

4. How Can I Troubleshoot Issues?

To gain more insights during inference, you can include the --debug flag in your command:

python yolov8_region_counter.py --source "path to video file" --debug

5. Can I Employ Other YOLO Versions?

Certainly, you have the flexibility to specify different YOLO model weights using the --weights option.

6. Where Can I Access Additional Information?

For a comprehensive guide on using YOLOv8 with Object Tracking, please refer to Multi-Object Tracking with Ultralytics YOLO.