zh/guides/heatmaps/

[giscus[bot]]

zh/guides/heatmaps/

利用Ultralytics YOLOv8 热图实现高级数据可视化

https://docs.ultralytics.com/zh/guides/heatmaps/

[nzbanana]

I'm having some problems with the script.
(yolo) D:\Vscode\workspace\ultralytics-240618>python D:\Vscode\workspace\ultralytics-240618\heat_map.py.
None

0: 384x640 1 person, 97.5ms
Speed: 3.6ms preprocess, 97.5ms inference, 55.3ms postprocess per image at shape (1, 3, 384, 640)
Traceback (most recent call last):
File "D:\Vscode\workspace\ultralytics-240618\heat_map.py", line 28, in
im0 = heatmap_obj.generate_heatmap(im0, tracks)
File "D:\Vscode\workspace\ultralytics-240618\ultralytics\solutions\heatmap.py", line 138, in generate_heatmap
self.extract_results(tracks)
File "D:\Vscode\workspace\ultralytics-240618\ultralytics\solutions\heatmap.py", line 119, in extract_results
self.track_ids = tracks[0].boxes.id.int().cpu().tolist()
AttributeError: 'NoneType' object has no attribute 'int'

[glenn-jocher]

@nzbanana hi there!

Thank you for reaching out and providing the error details. It looks like the issue arises because the tracks object is None, leading to the AttributeError.

To help us investigate further, could you please provide a minimum reproducible example of your script? This will allow us to replicate the issue on our end. You can refer to our guide on creating a minimum reproducible example here: Minimum Reproducible Example.

Additionally, please ensure that you are using the latest versions of torch and ultralytics. You can upgrade them using the following commands:

pip install --upgrade torch
pip install --upgrade ultralytics

Here's a quick checklist to help debug the issue:

1. Check if tracks is None: Ensure that the model.track() method is returning valid tracks.
2. Verify the video source: Make sure the video file is correctly read and processed.
3. Update packages: Ensure you are using the latest versions of torch and ultralytics.

Here's a modified snippet to help you debug:

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)
    if tracks is None:
        print("No tracks found.")
        continue

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

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

This snippet includes a check to ensure tracks is not None before proceeding with the heatmap generation.

Please try these steps and let us know if the issue persists. We're here to help! 😊

[fangyuZ1025]

That just track task , if i want do some detect, classify,segement task? how can i modify the code?

[glenn-jocher]

@fangyuZ1025 to perform detection, classification, or segmentation tasks, you can modify the code by loading the corresponding YOLO11 model (e.g., yolo11n.pt for detection, yolo11n-cls.pt for classification, or yolo11n-seg.pt for segmentation) and applying the desired mode. Refer to the Ultralytics Docs for detailed examples and configurations. Let me know if you need further clarification!

[fangyuZ1025]

Sorry, I think I didn't express what I meant. I am working on the heat map of yolov8 to explain the interpretability of the model. This code is doing the heat map of the video tracking task, and I want to implement my image heat map feature detection based on it. I wonder if there is any relevant code. Thanks again for your reply

[glenn-jocher]

Thank you for clarifying! To implement image-based heatmaps for feature detection, you can adapt the existing Heatmap class by modifying the input handling to process static images instead of video streams. You can leverage the generate_heatmap method to overlay heatmaps on single images. For further guidance, refer to the heatmap.py implementation. Let us know if you need further assistance!