ultralytics/docs/en/guides/view-results-in-terminal.md

comments	description	keywords
true	Learn how to visualize YOLO inference results directly in a VSCode terminal using sixel on Linux and MacOS.	YOLO, inference results, VSCode terminal, sixel, display images, Linux, MacOS

Viewing Inference Results in a Terminal

Image from the libsixel website.

Motivation

When connecting to a remote machine, normally visualizing image results is not possible or requires moving data to a local device with a GUI. The VSCode integrated terminal allows for directly rendering images. This is a short demonstration on how to use this in conjunction with ultralytics with prediction results.

!!! warning

Only compatible with Linux and MacOS. Check the [VSCode repository](https://github.com/microsoft/vscode), check [Issue status](https://github.com/microsoft/vscode/issues/198622), or [documentation](https://code.visualstudio.com/docs) for updates about Windows support to view images in terminal with `sixel`.

The VSCode compatible protocols for viewing images using the integrated terminal are sixel and iTerm. This guide will demonstrate use of the sixel protocol.

Process

1. First, you must enable settings terminal.integrated.enableImages and terminal.integrated.gpuAcceleration in VSCode.

   "terminal.integrated.gpuAcceleration": "auto" # "auto" is default, can also use "on"
   "terminal.integrated.enableImages": false
   

   

2. Install the python-sixel library in your virtual environment. This is a fork of the PySixel library, which is no longer maintained.

   pip install sixel
   

3. Load a model and execute inference, then plot the results and store in a variable. See more about inference arguments and working with results on the predict mode page.

   from ultralytics import YOLO
   
   # Load a model
   model = YOLO("yolov8n.pt")
   
   # Run inference on an image
   results = model.predict(source="ultralytics/assets/bus.jpg")
   
   # Plot inference results
   plot = results[0].plot()  # (1)!
   

   1. See plot method parameters to see possible arguments to use.

4. Now, use OpenCV to convert the numpy.ndarray to bytes data. Then use io.BytesIO to make a "file-like" object.

   import io
   
   import cv2
   
   # Results image as bytes
   im_bytes = cv2.imencode(
       ".png",  # (1)!
       plot,
   )[1].tobytes()  # (2)!
   
   # Image bytes as a file-like object
   mem_file = io.BytesIO(im_bytes)
   

   1. It's possible to use other image extensions as well.
   2. Only the object at index 1 that is returned is needed.

5. Create a SixelWriter instance, and then use the .draw() method to draw the image in the terminal.

   from sixel import SixelWriter
   
   # Create sixel writer object
   w = SixelWriter()
   
   # Draw the sixel image in the terminal
   w.draw(mem_file)
   

Example Inference Results

!!! danger

Using this example with videos or animated GIF frames has **not** been tested. Attempt at your own risk.

Full Code Example

import io

import cv2
from sixel import SixelWriter

from ultralytics import YOLO

# Load a model
model = YOLO("yolov8n.pt")

# Run inference on an image
results = model.predict(source="ultralytics/assets/bus.jpg")

# Plot inference results
plot = results[0].plot()  # (3)!

# Results image as bytes
im_bytes = cv2.imencode(
    ".png",  # (1)!
    plot,
)[1].tobytes()  # (2)!

mem_file = io.BytesIO(im_bytes)
w = SixelWriter()
w.draw(mem_file)

1. It's possible to use other image extensions as well.
2. Only the object at index 1 that is returned is needed.
3. See plot method parameters to see possible arguments to use.

---

!!! tip

You may need to use `clear` to "erase" the view of the image in the terminal.