Advanced Data Visualization: Heatmaps using Ultralytics YOLO11 🚀

Introduction to Heatmaps

A heatmap generated with Ultralytics YOLO11 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.

Watch: Heatmaps using Ultralytics YOLO11

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 YOLO11 Transportation Heatmap	Ultralytics YOLO11 Retail Heatmap

Heatmaps using Ultralytics YOLO11 Example

CLIPython

# Run a heatmap example
yolo solutions heatmap show=True

# Pass a source video
yolo solutions heatmap source="path/to/video/file.mp4"

# Pass a custom colormap
yolo solutions heatmap colormap=cv2.COLORMAP_INFERNO

# Heatmaps + object counting
yolo solutions heatmap region=[(20, 400), (1080, 400), (1080, 360), (20, 360)]

import cv2

from ultralytics import solutions

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("heatmap_output.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))

# In case you want to apply object counting + heatmaps, you can pass region points.
# region_points = [(20, 400), (1080, 400)]  # Define line points
# region_points = [(20, 400), (1080, 400), (1080, 360), (20, 360)]  # Define region points
# region_points = [(20, 400), (1080, 400), (1080, 360), (20, 360), (20, 400)]  # Define polygon points

# Init heatmap
heatmap = solutions.Heatmap(
    show=True,  # Display the output
    model="yolo11n.pt",  # Path to the YOLO11 model file
    colormap=cv2.COLORMAP_PARULA,  # Colormap of heatmap
    # region=region_points,  # If you want to do object counting with heatmaps, you can pass region_points
    # classes=[0, 2],  # If you want to generate heatmap for specific classes i.e person and car.
    # show_in=True,  # Display in counts
    # show_out=True,  # Display out counts
    # line_width=2,  # Adjust the line width for bounding boxes and text display
)

# Process video
while cap.isOpened():
    success, im0 = cap.read()
    if not success:
        print("Video frame is empty or video processing has been successfully completed.")
        break
    im0 = heatmap.generate_heatmap(im0)
    video_writer.write(im0)

cap.release()
video_writer.release()
cv2.destroyAllWindows()

Arguments `Heatmap()`

Name	Type	Default	Description
`model`	`str`	`None`	Path to Ultralytics YOLO Model File
`colormap`	`int`	`cv2.COLORMAP_JET`	Colormap to use for the heatmap.
`show`	`bool`	`False`	Whether to display the image with the heatmap overlay.
`show_in`	`bool`	`True`	Whether to display the count of objects entering the region.
`show_out`	`bool`	`True`	Whether to display the count of objects exiting the region.
`region`	`list`	`None`	Points defining the counting region (either a line or a polygon).
`line_width`	`int`	`2`	Thickness of the lines used in drawing.

Arguments `model.track`

Argument	Type	Default	Description
`source`	`str`	`None`	Specifies the source directory for images or videos. Supports file paths and URLs.
`persist`	`bool`	`False`	Enables persistent tracking of objects between frames, maintaining IDs across video sequences.
`tracker`	`str`	`botsort.yaml`	Specifies the tracking algorithm to use, e.g., `bytetrack.yaml` or `botsort.yaml`.
`conf`	`float`	`0.3`	Sets the confidence threshold for detections; lower values allow more objects to be tracked but may include false positives.
`iou`	`float`	`0.5`	Sets the Intersection over Union (IoU) threshold for filtering overlapping detections.
`classes`	`list`	`None`	Filters results by class index. For example, `classes=[0, 2, 3]` only tracks the specified classes.
`verbose`	`bool`	`True`	Controls the display of tracking results, providing a visual output of tracked objects.

Heatmap COLORMAPs

Colormap Name	Description
`cv::COLORMAP_AUTUMN`	Autumn color map
`cv::COLORMAP_BONE`	Bone color map
`cv::COLORMAP_JET`	Jet color map
`cv::COLORMAP_WINTER`	Winter color map
`cv::COLORMAP_RAINBOW`	Rainbow color map
`cv::COLORMAP_OCEAN`	Ocean color map
`cv::COLORMAP_SUMMER`	Summer color map
`cv::COLORMAP_SPRING`	Spring color map
`cv::COLORMAP_COOL`	Cool color map
`cv::COLORMAP_HSV`	HSV (Hue, Saturation, Value) color map
`cv::COLORMAP_PINK`	Pink color map
`cv::COLORMAP_HOT`	Hot color map
`cv::COLORMAP_PARULA`	Parula color map
`cv::COLORMAP_MAGMA`	Magma color map
`cv::COLORMAP_INFERNO`	Inferno color map
`cv::COLORMAP_PLASMA`	Plasma color map
`cv::COLORMAP_VIRIDIS`	Viridis color map
`cv::COLORMAP_CIVIDIS`	Cividis color map
`cv::COLORMAP_TWILIGHT`	Twilight color map
`cv::COLORMAP_TWILIGHT_SHIFTED`	Shifted Twilight color map
`cv::COLORMAP_TURBO`	Turbo color map
`cv::COLORMAP_DEEPGREEN`	Deep Green color map

These colormaps are commonly used for visualizing data with different color representations.

FAQ

How does Ultralytics YOLO11 generate heatmaps and what are their benefits?

Ultralytics YOLO11 generates heatmaps by transforming complex data into a color-coded matrix where different hues represent data intensities. Heatmaps make it easier to visualize patterns, correlations, and anomalies in the data. Warmer hues indicate higher values, while cooler tones represent lower values. The primary benefits include intuitive visualization of data distribution, efficient pattern detection, and enhanced spatial analysis for decision-making. For more details and configuration options, refer to the Heatmap Configuration section.

Can I use Ultralytics YOLO11 to perform object tracking and generate a heatmap simultaneously?

Yes, Ultralytics YOLO11 supports object tracking and heatmap generation concurrently. This can be achieved through its Heatmap solution integrated with object tracking models. To do so, you need to initialize the heatmap object and use YOLO11's tracking capabilities. Here's a simple example:

import cv2

from ultralytics import solutions

cap = cv2.VideoCapture("path/to/video/file.mp4")
heatmap = solutions.Heatmap(colormap=cv2.COLORMAP_PARULA, show=True, model="yolo11n.pt")

while cap.isOpened():
    success, im0 = cap.read()
    if not success:
        break
    im0 = heatmap.generate_heatmap(im0)
    cv2.imshow("Heatmap", im0)
    if cv2.waitKey(1) & 0xFF == ord("q"):
        break

cap.release()
cv2.destroyAllWindows()

For further guidance, check the Tracking Mode page.

What makes Ultralytics YOLO11 heatmaps different from other data visualization tools like those from OpenCV or Matplotlib?

Ultralytics YOLO11 heatmaps are specifically designed for integration with its object detection and tracking models, providing an end-to-end solution for real-time data analysis. Unlike generic visualization tools like OpenCV or Matplotlib, YOLO11 heatmaps are optimized for performance and automated processing, supporting features like persistent tracking, decay factor adjustment, and real-time video overlay. For more information on YOLO11's unique features, visit the Ultralytics YOLO11 Introduction.

How can I visualize only specific object classes in heatmaps using Ultralytics YOLO11?

You can visualize specific object classes by specifying the desired classes in the track() method of the YOLO model. For instance, if you only want to visualize cars and persons (assuming their class indices are 0 and 2), you can set the classes parameter accordingly.

import cv2

from ultralytics import solutions

cap = cv2.VideoCapture("path/to/video/file.mp4")
heatmap = solutions.Heatmap(show=True, model="yolo11n.pt", classes=[0, 2])

while cap.isOpened():
    success, im0 = cap.read()
    if not success:
        break
    im0 = heatmap.generate_heatmap(im0)
    cv2.imshow("Heatmap", im0)
    if cv2.waitKey(1) & 0xFF == ord("q"):
        break

cap.release()
cv2.destroyAllWindows()

Why should businesses choose Ultralytics YOLO11 for heatmap generation in data analysis?

Ultralytics YOLO11 offers seamless integration of advanced object detection and real-time heatmap generation, making it an ideal choice for businesses looking to visualize data more effectively. The key advantages include intuitive data distribution visualization, efficient pattern detection, and enhanced spatial analysis for better decision-making. Additionally, YOLO11's cutting-edge features such as persistent tracking, customizable colormaps, and support for various export formats make it superior to other tools like TensorFlow and OpenCV for comprehensive data analysis. Learn more about business applications at Ultralytics Plans.

📅 Created 1 year ago ✏️ Updated 16 days ago