guides/analytics/

[giscus[bot]]

guides/analytics/

Comprehensive Guide to Understanding and Creating Line Graphs, Bar Plots, and Pie Charts

https://docs.ultralytics.com/guides/analytics/

[yeongnamtan]

I tried to use google colab but it is giving me error because of cv2.imshow...
How can I modify it to use cv2_imshow instead ...
from google.colab.patches import cv2_imshow # Import cv2_imshow from google.colab.patches

DisabledFunctionError: cv2.imshow() is disabled in Colab, because it causes Jupyter sessions
to crash; see jupyter/notebook#3935.
As a substitution, consider using
from google.colab.patches import cv2_imshow

[pderrenger]

Hello! To use cv2_imshow instead of cv2.imshow in Google Colab, you can make a simple modification to your code. First, ensure you import cv2_imshow from google.colab.patches as you've mentioned. Then, replace all instances of cv2.imshow with cv2_imshow. Here’s how you can do it:

1. Import the function:

   from google.colab.patches import cv2_imshow

2. Replace cv2.imshow with cv2_imshow:

   # Instead of cv2.imshow("Frame", frame)
   cv2_imshow(frame)

This change should resolve the issue with displaying images in Google Colab. Let me know if there's anything else you need help with! 😊

[yeongnamtan]

In the code for bar plots , I don't see any cv2.imshow. when I run the code, I get cv2.imshow error.

[pderrenger]

@yeongnamtan hello,

It seems like you're encountering an issue with cv2.imshow not being present in the bar plot code example. This function is indeed necessary to display the frames with the visualizations in real-time. To resolve the error, you can add the following line just after updating the bar plot in the loop:

cv2.imshow('Bar Plot', frame)

Make sure to include this inside the loop where frames are being processed, and it will display the updated frame with the bar plot. Additionally, ensure that you have the line if cv2.waitKey(1) & 0xFF == ord('q'): to handle the window closure properly.

If you need further assistance or have more questions, feel free to ask!

[yeongnamtan]

ani = FuncAnimation(fig, animate, frames=range(int(cap.get(cv2.CAP_PROP_FRAME_COUNT))),
blit=False, interval=1000/fps, repeat=False)

plt.show()

The code is still not working... I

error meesage ...
WARNING ⚠️ best.pt appears to require 'dill', which is not in ultralytics requirements.
AutoInstall will run now for 'dill' but this feature will be removed in the future.
Recommend fixes are to train a new model using the latest 'ultralytics' package or to run a command with an official YOLOv8 model, i.e. 'yolo predict model=yolov8n.pt'
requirements: Ultralytics requirement ['dill'] not found, attempting AutoUpdate...
Collecting dill
Downloading dill-0.3.8-py3-none-any.whl (116 kB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 116.3/116.3 kB 4.9 MB/s eta 0:00:00
Installing collected packages: dill
Successfully installed dill-0.3.8

requirements: AutoUpdate success ✅ 5.8s, installed 1 package: ['dill']
requirements: ⚠️ Restart runtime or rerun command for updates to take effect

requirements: Ultralytics requirement ['lapx>=0.5.2'] not found, attempting AutoUpdate...
Collecting lapx>=0.5.2
Downloading lapx-0.5.9-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.7 MB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.7/1.7 MB 11.8 MB/s eta 0:00:00
Requirement already satisfied: numpy>=1.21.6 in /usr/local/lib/python3.10/dist-packages (from lapx>=0.5.2) (1.25.2)
Installing collected packages: lapx
Successfully installed lapx-0.5.9

requirements: AutoUpdate success ✅ 7.4s, installed 1 package: ['lapx>=0.5.2']
requirements: ⚠️ Restart runtime or rerun command for updates to take effect

0: 384x640 2 fishs, 134.7ms
Speed: 13.4ms preprocess, 134.7ms inference, 747.7ms postprocess per image at shape (1, 3, 384, 640)

DisabledFunctionError Traceback (most recent call last)
in <cell line: 26>()
37 clswise_count[model.names[int(cls)]] = 1
38
---> 39 analytics.update_bar(clswise_count)
40 cv2_imshow('Bar Plot', frame)
41 clswise_count = {}

1 frames
/usr/local/lib/python3.10/dist-packages/google/colab/_import_hooks/_cv2.py in wrapped(*args, **kwargs)
46 def wrapped(*args, **kwargs):
47 if not os.environ.get(env_var, False):
---> 48 raise DisabledFunctionError(message, name or func.name)
49 return func(*args, **kwargs)
50

DisabledFunctionError: cv2.imshow() is disabled in Colab, because it causes Jupyter sessions
to crash; see jupyter/notebook#3935.
As a substitution, consider using
from google.colab.patches import cv2_imshow

[pderrenger]

Hello,

Thank you for sharing the details of the issue you're encountering. It seems like the dill and lapx packages were missing but have now been successfully installed. Please ensure you restart your runtime or rerun your command to allow the updates to take effect, as suggested by the AutoUpdate messages.

Regarding the DisabledFunctionError for cv2.imshow() in Colab, this function is indeed disabled because it can cause Jupyter sessions to crash. As an alternative, you can use cv2_imshow from the google.colab.patches module to display images in Colab notebooks. Here’s how you can modify your code:

from google.colab.patches import cv2_imshow

# Replace cv2.imshow with cv2_imshow
cv2_imshow(frame)

This should allow you to display images without causing any issues in your Colab environment.

If you have any more questions or need further assistance, feel free to ask. Happy coding! 🚀

[yeongnamtan]

thank you

[pderrenger]

@yeongnamtan you're welcome! If you have any more questions or need further assistance as you continue exploring the analytics capabilities of YOLOv8, feel free to reach out. Happy coding! 😊

[Peymankar]

Hi, how can i count number of left turns or right turns in an intersection?

[glenn-jocher]

@Peymankar hello!

To count the number of left or right turns at an intersection using Ultralytics YOLO, you can set up a tracking system that identifies and follows each vehicle's trajectory. Here’s a brief outline on how you might approach this:

1. Track Vehicles: Use the YOLO model to detect and track vehicles. You can use the model.track() function to keep track of each vehicle's movement frame by frame.

2. Define Zones: Define areas or lines within the video frame that represent the boundaries of different turn directions (left, right).

3. Analyze Trajectories: Analyze the paths of tracked vehicles to determine when they cross these defined zones. This can be done by checking the intersection of vehicle tracks with these zones and classifying the movement as a left or right turn based on the zone they intersect.

4. Count Turns: Maintain a count of how many vehicles make left or right turns based on these intersections.

For a more detailed guide on setting up vehicle tracking and analyzing trajectories, you might find the tracking and analytics features in the Ultralytics documentation helpful. Implementing this will require some custom coding to define zones and analyze paths relative to these zones.

If you need further assistance or more specific guidance, feel free to ask. Happy coding! 😊

[yeongnamtan]

How to integrate the code for line graph with line counter ?

[glenn-jocher]

@yeongnamtan to integrate the line graph with a line counter, use the Analytics class to update the line graph with the total object counts per frame. Ensure you initialize the Analytics with type="line" and update it within your video processing loop. For detailed guidance, please refer to the analytics section in the documentation.

[yeongnamtan]

thank you very much @glenn-jocher

[glenn-jocher]

@yeongnamtan you're welcome! If you have any further questions about using Ultralytics YOLOv8 for analytics or need additional assistance, feel free to ask. The community and documentation are great resources for support.