HongChuang/ultralytics-ascend
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions 3

Docs Prettier reformat (#13483)

Browse source 
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
This commit is contained in:
Glenn Jocher 2024-06-10 12:59:01 +02:00 • committed by GitHub
parent 2f2e81614f
commit e5185ccf63
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
90 changed files with 763 additions and 742 deletions
Download patch file Download diff file Expand all files Collapse all files

12
docs/en/guides/isolating-segmentation-objects.md
View file

@ -14,7 +14,7 @@ After performing the [Segment Task](../tasks/segment.md), it's sometimes desirab
## Recipe Walk Through
1. Begin with the necessary imports
1. Begin with the necessary imports
```python
from pathlib import Path
@ -31,7 +31,7 @@ After performing the [Segment Task](../tasks/segment.md), it's sometimes desirab
***
2. Load a model and run `predict()` method on a source.
2. Load a model and run `predict()` method on a source.
```python
from ultralytics import YOLO
@ -58,7 +58,7 @@ After performing the [Segment Task](../tasks/segment.md), it's sometimes desirab
***
3. Now iterate over the results and the contours. For workflows that want to save an image to file, the source image `base-name` and the detection `class-label` are retrieved for later use (optional).
3. Now iterate over the results and the contours. For workflows that want to save an image to file, the source image `base-name` and the detection `class-label` are retrieved for later use (optional).
```{ .py .annotate }
# (2) Iterate detection results (helpful for multiple images)
@ -81,7 +81,7 @@ After performing the [Segment Task](../tasks/segment.md), it's sometimes desirab
***
4. Start with generating a binary mask from the source image and then draw a filled contour onto the mask. This will allow the object to be isolated from the other parts of the image. An example from `bus.jpg` for one of the detected `person` class objects is shown on the right.
4. Start with generating a binary mask from the source image and then draw a filled contour onto the mask. This will allow the object to be isolated from the other parts of the image. An example from `bus.jpg` for one of the detected `person` class objects is shown on the right.
![Binary Mask Image](https://github.com/ultralytics/ultralytics/assets/62214284/59bce684-fdda-4b17-8104-0b4b51149aca){ width="240", align="right" }
@ -140,7 +140,7 @@ After performing the [Segment Task](../tasks/segment.md), it's sometimes desirab
***
5. Next there are 2 options for how to move forward with the image from this point and a subsequent option for each.
5. Next there are 2 options for how to move forward with the image from this point and a subsequent option for each.
### Object Isolation Options
@ -251,7 +251,7 @@ After performing the [Segment Task](../tasks/segment.md), it's sometimes desirab
***
6. <u>What to do next is entirely left to you as the developer.</u> A basic example of one possible next step (saving the image to file for future use) is shown.
6. <u>What to do next is entirely left to you as the developer.</u> A basic example of one possible next step (saving the image to file for future use) is shown.
- **NOTE:** this step is optional and can be skipped if not required for your specific use case.