yolov8_test/docs/en/guides/heatmaps.md

comments	description	keywords
true	Advanced Data Visualization with Ultralytics YOLOv8 Heatmaps	Ultralytics, YOLOv8, Advanced Data Visualization, Heatmap Technology, Object Detection and Tracking, Jupyter Notebook, Python SDK, Command Line Interface

Advanced Data Visualization: Heatmaps using Ultralytics YOLOv8 🚀

Introduction to Heatmaps

A heatmap generated with Ultralytics YOLOv8 transforms complex data into a vibrant, color-coded matrix. This visual tool employs a spectrum of colors to represent varying data values, where warmer hues indicate higher intensities and cooler tones signify lower values. Heatmaps excel in visualizing intricate data patterns, correlations, and anomalies, offering an accessible and engaging approach to data interpretation across diverse domains.

Why Choose Heatmaps for Data Analysis?

• Intuitive Data Distribution Visualization: Heatmaps simplify the comprehension of data concentration and distribution, converting complex datasets into easy-to-understand visual formats.
• Efficient Pattern Detection: By visualizing data in heatmap format, it becomes easier to spot trends, clusters, and outliers, facilitating quicker analysis and insights.
• Enhanced Spatial Analysis and Decision Making: Heatmaps are instrumental in illustrating spatial relationships, aiding in decision-making processes in sectors such as business intelligence, environmental studies, and urban planning.

Real World Applications

Transportation	Retail
Ultralytics YOLOv8 Transportation Heatmap	Ultralytics YOLOv8 Retail Heatmap

???+ tip "heatmap_alpha"

heatmap_alpha value should be in range (0.0 - 1.0)

!!! Example "Heatmap Example"

=== "Heatmap"
    ```python
    from ultralytics import YOLO
    from ultralytics.solutions import heatmap
    import cv2

    model = YOLO("yolov8s.pt")
    cap = cv2.VideoCapture("path/to/video/file.mp4")
    if not cap.isOpened():
        print("Error reading video file")
        exit(0)

    heatmap_obj = heatmap.Heatmap()
    heatmap_obj.set_args(colormap=cv2.COLORMAP_CIVIDIS,
                         imw=cap.get(4),  # should same as im0 width
                         imh=cap.get(3),  # should same as im0 height
                         view_img=True)

    while cap.isOpened():
        success, im0 = cap.read()
        if not success:
            exit(0)
        results = model.track(im0, persist=True)
        frame = heatmap_obj.generate_heatmap(im0, tracks=results)

    ```

=== "Heatmap with Specific Classes"
    ```python
    from ultralytics import YOLO
    from ultralytics.solutions import heatmap
    import cv2

    model = YOLO("yolov8s.pt")
    cap = cv2.VideoCapture("path/to/video/file.mp4")
    if not cap.isOpened():
        print("Error reading video file")
        exit(0)

    classes_for_heatmap = [0, 2]

    heatmap_obj = heatmap.Heatmap()
    heatmap_obj.set_args(colormap=cv2.COLORMAP_CIVIDIS,
                         imw=cap.get(4),  # should same as im0 width
                         imh=cap.get(3),  # should same as im0 height
                         view_img=True)

    while cap.isOpened():
        success, im0 = cap.read()
        if not success:
            exit(0)
        results = model.track(im0, persist=True,
                              classes=classes_for_heatmap)
        frame = heatmap_obj.generate_heatmap(im0, tracks=results)

    ```

=== "Heatmap with Save Output"
    ```python
    from ultralytics import YOLO
    import heatmap
    import cv2

    model = YOLO("yolov8n.pt")
    cap = cv2.VideoCapture("path/to/video/file.mp4")
    if not cap.isOpened():
        print("Error reading video file")
        exit(0)

    video_writer = cv2.VideoWriter("heatmap_output.avi",
                                   cv2.VideoWriter_fourcc(*'mp4v'),
                                   int(cap.get(5)),
                                   (int(cap.get(3)), int(cap.get(4))))

    heatmap_obj = heatmap.Heatmap()
    heatmap_obj.set_args(colormap=cv2.COLORMAP_CIVIDIS,
                         imw=cap.get(4),  # should same as im0 width
                         imh=cap.get(3),  # should same as im0 height
                         view_img=True)

    while cap.isOpened():
        success, im0 = cap.read()
        if not success:
            exit(0)
        results = model.track(im0, persist=True)
        frame = heatmap_obj.generate_heatmap(im0, tracks=results)
        video_writer.write(im0)

    video_writer.release()
    ```

Arguments set_args

Name	Type	Default	Description
view_img	bool	False	Display the frame with heatmap
colormap	cv2.COLORMAP	None	cv2.COLORMAP for heatmap
imw	int	None	Width of Heatmap
imh	int	None	Height of Heatmap
heatmap_alpha	float	0.5	Heatmap alpha value

Arguments model.track

Name	Type	Default	Description
source	im0	None	source directory for images or videos
persist	bool	False	persisting tracks between frames
tracker	str	botsort.yaml	Tracking method 'bytetrack' or 'botsort'
conf	float	0.3	Confidence Threshold
iou	float	0.5	IOU Threshold
classes	list	None	filter results by class, i.e. classes=0, or classes=[0,2,3]