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.

Watch: Heatmaps using Ultralytics YOLOv8

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

Heatmap Configuration

heatmap_alpha: Ensure this value is within the range (0.0 - 1.0).
decay_factor: Used for removing heatmap after an object is no longer in the frame, its value should also be in the range (0.0 - 1.0).

Heatmaps using Ultralytics YOLOv8 Example

HeatmapLine CountingPolygon CountingRegion CountingIm0Specific Classes

import cv2
from ultralytics import YOLO, solutions

model = YOLO("yolov8n.pt")
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))

# Init heatmap
heatmap_obj = solutions.Heatmap(
    colormap=cv2.COLORMAP_PARULA,
    view_img=True,
    shape="circle",
    classes_names=model.names,
)

while cap.isOpened():
    success, im0 = cap.read()
    if not success:
        print("Video frame is empty or video processing has been successfully completed.")
        break
    tracks = model.track(im0, persist=True, show=False)

    im0 = heatmap_obj.generate_heatmap(im0, tracks)
    video_writer.write(im0)

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

import cv2
from ultralytics import YOLO, solutions

model = YOLO("yolov8n.pt")
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))

line_points = [(20, 400), (1080, 404)]  # line for object counting

# Init heatmap
heatmap_obj = solutions.Heatmap(
    colormap=cv2.COLORMAP_PARULA,
    view_img=True,
    shape="circle",
    count_reg_pts=line_points,
    classes_names=model.names,
)

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

    tracks = model.track(im0, persist=True, show=False)
    im0 = heatmap_obj.generate_heatmap(im0, tracks)
    video_writer.write(im0)

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

import cv2
from ultralytics import YOLO, solutions

model = YOLO("yolov8n.pt")
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))

# Define polygon points
region_points = [(20, 400), (1080, 404), (1080, 360), (20, 360), (20, 400)]

# Init heatmap
heatmap_obj = solutions.Heatmap(
    colormap=cv2.COLORMAP_PARULA,
    view_img=True,
    shape="circle",
    count_reg_pts=region_points,
    classes_names=model.names,
)

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

    tracks = model.track(im0, persist=True, show=False)
    im0 = heatmap_obj.generate_heatmap(im0, tracks)
    video_writer.write(im0)

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

import cv2
from ultralytics import YOLO, solutions

model = YOLO("yolov8n.pt")
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))

# Define region points
region_points = [(20, 400), (1080, 404), (1080, 360), (20, 360)]

# Init heatmap
heatmap_obj = solutions.Heatmap(
    colormap=cv2.COLORMAP_PARULA,
    view_img=True,
    shape="circle",
    count_reg_pts=region_points,
    classes_names=model.names,
)

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

    tracks = model.track(im0, persist=True, show=False)
    im0 = heatmap_obj.generate_heatmap(im0, tracks)
    video_writer.write(im0)

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

import cv2
from ultralytics import YOLO, solutions

model = YOLO("yolov8s.pt")  # YOLOv8 custom/pretrained model

im0 = cv2.imread("path/to/image.png")  # path to image file
h, w = im0.shape[:2]  # image height and width

# Heatmap Init
heatmap_obj = solutions.Heatmap(
    colormap=cv2.COLORMAP_PARULA,
    view_img=True,
    shape="circle",
    classes_names=model.names,
)

results = model.track(im0, persist=True)
im0 = heatmap_obj.generate_heatmap(im0, tracks=results)
cv2.imwrite("ultralytics_output.png", im0)

import cv2
from ultralytics import YOLO, solutions

model = YOLO("yolov8n.pt")
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))

classes_for_heatmap = [0, 2]  # classes for heatmap

# Init heatmap
heatmap_obj = solutions.Heatmap(
    colormap=cv2.COLORMAP_PARULA,
    view_img=True,
    shape="circle",
    classes_names=model.names,
)

while cap.isOpened():
    success, im0 = cap.read()
    if not success:
        print("Video frame is empty or video processing has been successfully completed.")
        break
    tracks = model.track(im0, persist=True, show=False, classes=classes_for_heatmap)

    im0 = heatmap_obj.generate_heatmap(im0, tracks)
    video_writer.write(im0)

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

Arguments `Heatmap()`

Name	Type	Default	Description
`classes_names`	`dict`	`None`	Dictionary of class names.
`imw`	`int`	`0`	Image width.
`imh`	`int`	`0`	Image height.
`colormap`	`int`	`cv2.COLORMAP_JET`	Colormap to use for the heatmap.
`heatmap_alpha`	`float`	`0.5`	Alpha blending value for heatmap overlay.
`view_img`	`bool`	`False`	Whether to display the image with the heatmap overlay.
`view_in_counts`	`bool`	`True`	Whether to display the count of objects entering the region.
`view_out_counts`	`bool`	`True`	Whether to display the count of objects exiting the region.
`count_reg_pts`	`list` or `None`	`None`	Points defining the counting region (either a line or a polygon).
`count_txt_color`	`tuple`	`(0, 0, 0)`	Text color for displaying counts.
`count_bg_color`	`tuple`	`(255, 255, 255)`	Background color for displaying counts.
`count_reg_color`	`tuple`	`(255, 0, 255)`	Color for the counting region.
`region_thickness`	`int`	`5`	Thickness of the region line.
`line_dist_thresh`	`int`	`15`	Distance threshold for line-based counting.
`line_thickness`	`int`	`2`	Thickness of the lines used in drawing.
`decay_factor`	`float`	`0.99`	Decay factor for the heatmap to reduce intensity over time.
`shape`	`str`	`"circle"`	Shape of the heatmap blobs ('circle' or 'rect').

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]

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.

Created 2023-12-07, Updated 2024-05-18
Authors: glenn-jocher (9), RizwanMunawar (8), AyushExel (1), 1579093407@qq.com (1)