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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
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

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 2024-06-10
Authors: glenn-jocher (11), IvorZhu331 (1), RizwanMunawar (8), AyushExel (1), 1579093407@qq.com (1)

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