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301 lines
15 KiB
301 lines
15 KiB
--- |
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comments: true |
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description: Advanced Data Visualization with Ultralytics YOLOv8 Heatmaps |
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keywords: Ultralytics, YOLOv8, Advanced Data Visualization, Heatmap Technology, Object Detection and Tracking, Jupyter Notebook, Python SDK, Command Line Interface |
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--- |
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# Advanced Data Visualization: Heatmaps using Ultralytics YOLOv8 🚀 |
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## Introduction to Heatmaps |
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A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics/) 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. |
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<p align="center"> |
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<br> |
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<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/4ezde5-nZZw" |
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title="YouTube video player" frameborder="0" |
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allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" |
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allowfullscreen> |
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</iframe> |
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<br> |
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<strong>Watch:</strong> Heatmaps using Ultralytics YOLOv8 |
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</p> |
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## Why Choose Heatmaps for Data Analysis? |
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- **Intuitive Data Distribution Visualization:** Heatmaps simplify the comprehension of data concentration and distribution, converting complex datasets into easy-to-understand visual formats. |
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- **Efficient Pattern Detection:** By visualizing data in heatmap format, it becomes easier to spot trends, clusters, and outliers, facilitating quicker analysis and insights. |
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- **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. |
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## Real World Applications |
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| Transportation | Retail | |
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|:-----------------------------------------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------------------------------:| |
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|  |  | |
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| Ultralytics YOLOv8 Transportation Heatmap | Ultralytics YOLOv8 Retail Heatmap | |
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!!! tip "Heatmap Configuration" |
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- `heatmap_alpha`: Ensure this value is within the range (0.0 - 1.0). |
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- `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). |
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!!! Example "Heatmaps using Ultralytics YOLOv8 Example" |
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=== "Heatmap" |
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```python |
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from ultralytics import YOLO |
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from ultralytics.solutions import heatmap |
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import cv2 |
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model = YOLO("yolov8n.pt") |
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cap = cv2.VideoCapture("path/to/video/file.mp4") |
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assert cap.isOpened(), "Error reading video file" |
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w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS)) |
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# Video writer |
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video_writer = cv2.VideoWriter("heatmap_output.avi", |
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cv2.VideoWriter_fourcc(*'mp4v'), |
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fps, |
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(w, h)) |
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# Init heatmap |
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heatmap_obj = heatmap.Heatmap() |
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heatmap_obj.set_args(colormap=cv2.COLORMAP_PARULA, |
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imw=w, |
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imh=h, |
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view_img=True, |
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shape="circle") |
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while cap.isOpened(): |
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success, im0 = cap.read() |
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if not success: |
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print("Video frame is empty or video processing has been successfully completed.") |
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break |
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tracks = model.track(im0, persist=True, show=False) |
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im0 = heatmap_obj.generate_heatmap(im0, tracks) |
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video_writer.write(im0) |
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cap.release() |
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video_writer.release() |
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cv2.destroyAllWindows() |
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``` |
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=== "Line Counting" |
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```python |
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from ultralytics import YOLO |
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from ultralytics.solutions import heatmap |
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import cv2 |
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model = YOLO("yolov8n.pt") |
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cap = cv2.VideoCapture("path/to/video/file.mp4") |
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assert cap.isOpened(), "Error reading video file" |
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w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS)) |
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# Video writer |
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video_writer = cv2.VideoWriter("heatmap_output.avi", |
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cv2.VideoWriter_fourcc(*'mp4v'), |
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fps, |
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(w, h)) |
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line_points = [(20, 400), (1080, 404)] # line for object counting |
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# Init heatmap |
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heatmap_obj = heatmap.Heatmap() |
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heatmap_obj.set_args(colormap=cv2.COLORMAP_PARULA, |
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imw=w, |
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imh=h, |
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view_img=True, |
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shape="circle", |
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count_reg_pts=line_points) |
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while cap.isOpened(): |
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success, im0 = cap.read() |
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if not success: |
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print("Video frame is empty or video processing has been successfully completed.") |
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break |
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tracks = model.track(im0, persist=True, show=False) |
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im0 = heatmap_obj.generate_heatmap(im0, tracks) |
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video_writer.write(im0) |
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cap.release() |
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video_writer.release() |
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cv2.destroyAllWindows() |
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``` |
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=== "Region Counting" |
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```python |
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from ultralytics import YOLO |
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from ultralytics.solutions import heatmap |
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import cv2 |
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model = YOLO("yolov8n.pt") |
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cap = cv2.VideoCapture("path/to/video/file.mp4") |
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assert cap.isOpened(), "Error reading video file" |
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w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS)) |
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# Video writer |
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video_writer = cv2.VideoWriter("heatmap_output.avi", |
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cv2.VideoWriter_fourcc(*'mp4v'), |
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fps, |
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(w, h)) |
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# Define region points |
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region_points = [(20, 400), (1080, 404), (1080, 360), (20, 360)] |
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# Init heatmap |
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heatmap_obj = heatmap.Heatmap() |
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heatmap_obj.set_args(colormap=cv2.COLORMAP_PARULA, |
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imw=w, |
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imh=h, |
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view_img=True, |
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shape="circle", |
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count_reg_pts=region_points) |
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while cap.isOpened(): |
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success, im0 = cap.read() |
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if not success: |
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print("Video frame is empty or video processing has been successfully completed.") |
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break |
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tracks = model.track(im0, persist=True, show=False) |
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im0 = heatmap_obj.generate_heatmap(im0, tracks) |
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video_writer.write(im0) |
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cap.release() |
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video_writer.release() |
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cv2.destroyAllWindows() |
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``` |
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=== "Im0" |
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```python |
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from ultralytics import YOLO |
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from ultralytics.solutions import heatmap |
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import cv2 |
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model = YOLO("yolov8s.pt") # YOLOv8 custom/pretrained model |
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im0 = cv2.imread("path/to/image.png") # path to image file |
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h, w = im0.shape[:2] # image height and width |
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# Heatmap Init |
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heatmap_obj = heatmap.Heatmap() |
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heatmap_obj.set_args(colormap=cv2.COLORMAP_PARULA, |
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imw=w, |
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imh=h, |
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view_img=True, |
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shape="circle") |
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results = model.track(im0, persist=True) |
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im0 = heatmap_obj.generate_heatmap(im0, tracks=results) |
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cv2.imwrite("ultralytics_output.png", im0) |
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``` |
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=== "Specific Classes" |
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```python |
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from ultralytics import YOLO |
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from ultralytics.solutions import heatmap |
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import cv2 |
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model = YOLO("yolov8n.pt") |
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cap = cv2.VideoCapture("path/to/video/file.mp4") |
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assert cap.isOpened(), "Error reading video file" |
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w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS)) |
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# Video writer |
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video_writer = cv2.VideoWriter("heatmap_output.avi", |
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cv2.VideoWriter_fourcc(*'mp4v'), |
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fps, |
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(w, h)) |
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classes_for_heatmap = [0, 2] # classes for heatmap |
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# Init heatmap |
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heatmap_obj = heatmap.Heatmap() |
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heatmap_obj.set_args(colormap=cv2.COLORMAP_PARULA, |
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imw=w, |
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imh=h, |
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view_img=True, |
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shape="circle") |
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while cap.isOpened(): |
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success, im0 = cap.read() |
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if not success: |
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print("Video frame is empty or video processing has been successfully completed.") |
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break |
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tracks = model.track(im0, persist=True, show=False, |
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classes=classes_for_heatmap) |
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im0 = heatmap_obj.generate_heatmap(im0, tracks) |
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video_writer.write(im0) |
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cap.release() |
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video_writer.release() |
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cv2.destroyAllWindows() |
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``` |
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### Arguments `set_args` |
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| Name | Type | Default | Description | |
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|-----------------------|----------------|-------------------|-----------------------------------------------------------| |
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| `view_img` | `bool` | `False` | Display the frame with heatmap | |
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| `colormap` | `cv2.COLORMAP` | `None` | cv2.COLORMAP for heatmap | |
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| `imw` | `int` | `None` | Width of Heatmap | |
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| `imh` | `int` | `None` | Height of Heatmap | |
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| `heatmap_alpha` | `float` | `0.5` | Heatmap alpha value | |
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| `count_reg_pts` | `list` | `None` | Object counting region points | |
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| `count_txt_thickness` | `int` | `2` | Count values text size | |
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| `count_txt_color` | `RGB Color` | `(0, 0, 0)` | Foreground color for Object counts text | |
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| `count_color` | `RGB Color` | `(255, 255, 255)` | Background color for Object counts text | |
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| `count_reg_color` | `RGB Color` | `(255, 0, 255)` | Counting region color | |
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| `region_thickness` | `int` | `5` | Counting region thickness value | |
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| `decay_factor` | `float` | `0.99` | Decay factor for heatmap area removal after specific time | |
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| `shape` | `str` | `circle` | Heatmap shape for display "rect" or "circle" supported | |
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| `line_dist_thresh` | `int` | `15` | Euclidean Distance threshold for line counter | |
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### Arguments `model.track` |
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| Name | Type | Default | Description | |
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|-----------|---------|----------------|-------------------------------------------------------------| |
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| `source` | `im0` | `None` | source directory for images or videos | |
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| `persist` | `bool` | `False` | persisting tracks between frames | |
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| `tracker` | `str` | `botsort.yaml` | Tracking method 'bytetrack' or 'botsort' | |
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| `conf` | `float` | `0.3` | Confidence Threshold | |
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| `iou` | `float` | `0.5` | IOU Threshold | |
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| `classes` | `list` | `None` | filter results by class, i.e. classes=0, or classes=[0,2,3] | |
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### Heatmap COLORMAPs |
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| Colormap Name | Description | |
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|---------------------------------|----------------------------------------| |
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| `cv::COLORMAP_AUTUMN` | Autumn color map | |
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| `cv::COLORMAP_BONE` | Bone color map | |
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| `cv::COLORMAP_JET` | Jet color map | |
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| `cv::COLORMAP_WINTER` | Winter color map | |
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| `cv::COLORMAP_RAINBOW` | Rainbow color map | |
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| `cv::COLORMAP_OCEAN` | Ocean color map | |
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| `cv::COLORMAP_SUMMER` | Summer color map | |
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| `cv::COLORMAP_SPRING` | Spring color map | |
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| `cv::COLORMAP_COOL` | Cool color map | |
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| `cv::COLORMAP_HSV` | HSV (Hue, Saturation, Value) color map | |
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| `cv::COLORMAP_PINK` | Pink color map | |
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| `cv::COLORMAP_HOT` | Hot color map | |
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| `cv::COLORMAP_PARULA` | Parula color map | |
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| `cv::COLORMAP_MAGMA` | Magma color map | |
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| `cv::COLORMAP_INFERNO` | Inferno color map | |
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| `cv::COLORMAP_PLASMA` | Plasma color map | |
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| `cv::COLORMAP_VIRIDIS` | Viridis color map | |
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| `cv::COLORMAP_CIVIDIS` | Cividis color map | |
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| `cv::COLORMAP_TWILIGHT` | Twilight color map | |
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| `cv::COLORMAP_TWILIGHT_SHIFTED` | Shifted Twilight color map | |
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| `cv::COLORMAP_TURBO` | Turbo color map | |
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| `cv::COLORMAP_DEEPGREEN` | Deep Green color map | |
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These colormaps are commonly used for visualizing data with different color representations.
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