File size: 4,980 Bytes
c254ac1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 |
---
comments: true
description: Learn how to view image results inside a compatible VSCode terminal.
keywords: YOLOv8, VSCode, Terminal, Remote Development, Ultralytics, SSH, Object Detection, Inference, Results, Remote Tunnel, Images, Helpful, Productivity Hack
---
# Viewing Inference Results in a Terminal
<p align="center">
<img width="800" src="https://raw.githubusercontent.com/saitoha/libsixel/data/data/sixel.gif" alt="Sixel example of image in Terminal">
</p>
Image from the [libsixel](https://saitoha.github.io/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](../modes/predict.md).
!!! 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`](https://en.wikipedia.org/wiki/Sixel) and [`iTerm`](https://iterm2.com/documentation-images.html). 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.
```yaml
"terminal.integrated.gpuAcceleration": "auto" # "auto" is default, can also use "on"
"terminal.integrated.enableImages": false
```
<p align="center">
<img width="800" src="https://github.com/ultralytics/ultralytics/assets/62214284/d158ab1c-893c-4397-a5de-2f9f74f81175" alt="VSCode enable terminal images setting">
</p>
1. Install the `python-sixel` library in your virtual environment. This is a [fork](https://github.com/lubosz/python-sixel?tab=readme-ov-file) of the `PySixel` library, which is no longer maintained.
```bash
pip install sixel
```
1. Import the relevant libraries
```py
import io
import cv2 as cv
from ultralytics import YOLO
from sixel import SixelWriter
```
1. 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](../modes/predict.md) page.
```{ .py .annotate }
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](../modes/predict.md#plot-method-parameters) to see possible arguments to use.
1. Now, use OpenCV to convert the `numpy.ndarray` to `bytes` data. Then use `io.BytesIO` to make a "file-like" object.
```{ .py .annotate }
# Results image as bytes
im_bytes = cv.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.
1. Create a `SixelWriter` instance, and then use the `.draw()` method to draw the image in the terminal.
```py
# Create sixel writer object
w = SixelWriter()
# Draw the sixel image in the terminal
w.draw(mem_file)
```
## Example Inference Results
<p align="center">
<img width="800" src="https://github.com/ultralytics/ultralytics/assets/62214284/6743ab64-300d-4429-bdce-e246455f7b68" alt="View Image in Terminal">
</p>
!!! danger
Using this example with videos or animated GIF frames has **not** been tested. Attempt at your own risk.
## Full Code Example
```{ .py .annotate }
import io
import cv2 as cv
from ultralytics import YOLO
from sixel import SixelWriter
# 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 = cv.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](../modes/predict.md#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.
|