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Update docs building code (#7601)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Muhammad Rizwan Munawar <chr043416@gmail.com>
Co-authored-by: Muhammad Rizwan Munawar <muhammadrizwanmunawar123@gmail.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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Glenn Jocher 2024-01-16 20:48:28 +01:00 committed by GitHub
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commit d021524e85
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9 changed files with 86 additions and 40 deletions
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3
.github/workflows/publish.yml vendored
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@ -28,7 +28,7 @@ jobs:
- name: Set up Python environment - name: Set up Python environment
uses: actions/setup-python@v5 uses: actions/setup-python@v5
with: with:
python-version: '3.10' python-version: '3.11'
cache: 'pip' # caching pip dependencies cache: 'pip' # caching pip dependencies
- name: Install dependencies - name: Install dependencies
run: | run: |
@ -66,6 +66,7 @@ jobs:
env: env:
PERSONAL_ACCESS_TOKEN: ${{ secrets.PERSONAL_ACCESS_TOKEN }} PERSONAL_ACCESS_TOKEN: ${{ secrets.PERSONAL_ACCESS_TOKEN }}
INDEXNOW_KEY: ${{ secrets.INDEXNOW_KEY_DOCS }} INDEXNOW_KEY: ${{ secrets.INDEXNOW_KEY_DOCS }}
WEGLOT_KEY: ${{ secrets.WEGLOT_KEY_DOCS }}
run: | run: |
python docs/build_docs.py python docs/build_docs.py
git config --global user.name "Glenn Jocher" git config --global user.name "Glenn Jocher"

52
docs/build_docs.py
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@ -23,14 +23,17 @@ Usage:
Note: Note:
- This script is built to be run in an environment where Python and MkDocs are installed and properly configured. - This script is built to be run in an environment where Python and MkDocs are installed and properly configured.
""" """
import os
import re import re
import shutil import shutil
import subprocess import subprocess
from pathlib import Path from pathlib import Path
from tqdm import tqdm
DOCS = Path(__file__).parent.resolve() DOCS = Path(__file__).parent.resolve()
SITE = DOCS.parent / "site" SITE = DOCS.parent / "site"
LANGUAGES = True
def build_docs(): def build_docs():
@ -44,9 +47,10 @@ def build_docs():
subprocess.run(f"mkdocs build -f {DOCS}/mkdocs.yml", check=True, shell=True) subprocess.run(f"mkdocs build -f {DOCS}/mkdocs.yml", check=True, shell=True)
# Build other localized documentations # Build other localized documentations
for file in DOCS.glob("mkdocs_*.yml"): if LANGUAGES:
print(f"Building MkDocs site with configuration file: {file}") for file in DOCS.glob("mkdocs_*.yml"):
subprocess.run(f"mkdocs build -f {file}", check=True, shell=True) print(f"Building MkDocs site with configuration file: {file}")
subprocess.run(f"mkdocs build -f {file}", check=True, shell=True)
print(f"Site built at {SITE}") print(f"Site built at {SITE}")
@ -100,19 +104,51 @@ def update_page_title(file_path: Path, new_title: str):
file.write(updated_content) file.write(updated_content)
def update_html_head(key=""):
"""Update the HTML head section of each file."""
html_files = Path(SITE).rglob("*.html")
for html_file in tqdm(html_files, desc="Processing HTML files"):
with html_file.open("r", encoding="utf-8") as file:
html_content = file.read()
script = f"""
<script type="text/javascript" src="https://cdn.weglot.com/weglot.min.js"></script>
<script>
Weglot.initialize({{
api_key: '{key}'
}});
</script>
"""
if script in html_content: # script already in HTML file
return
head_end_index = html_content.lower().rfind("</head>")
if head_end_index != -1:
# Add the specified JavaScript to the HTML file just before the end of the head tag.
new_html_content = html_content[:head_end_index] + script + html_content[head_end_index:]
with html_file.open("w", encoding="utf-8") as file:
file.write(new_html_content)
def main(): def main():
# Build the docs # Build the docs
build_docs() build_docs()
# Update titles
update_page_title(SITE / "404.html", new_title="Ultralytics Docs - Not Found")
# Update .md in href links # Update .md in href links
update_html_links() if LANGUAGES:
update_html_links()
# Update HTML file head section
key = os.environ.get("WEGLOT_KEY")
if not LANGUAGES and key:
update_html_head(key)
# Show command to serve built website # Show command to serve built website
print('Serve site at http://localhost:8000 with "python -m http.server --directory site"') print('Serve site at http://localhost:8000 with "python -m http.server --directory site"')
# Update titles
update_page_title(SITE / "404.html", new_title="Ultralytics Docs - Not Found")
if __name__ == "__main__": if __name__ == "__main__":
main() main()

9
docs/en/datasets/detect/open-images-v7.md
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@ -8,6 +8,15 @@ keywords: Open Images V7, object detection, segmentation masks, visual relations
[Open Images V7](https://storage.googleapis.com/openimages/web/index.html) is a versatile and expansive dataset championed by Google. Aimed at propelling research in the realm of computer vision, it boasts a vast collection of images annotated with a plethora of data, including image-level labels, object bounding boxes, object segmentation masks, visual relationships, and localized narratives. [Open Images V7](https://storage.googleapis.com/openimages/web/index.html) is a versatile and expansive dataset championed by Google. Aimed at propelling research in the realm of computer vision, it boasts a vast collection of images annotated with a plethora of data, including image-level labels, object bounding boxes, object segmentation masks, visual relationships, and localized narratives.
## Open Images V7 Pretrained Models
| Model | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
|-------------------------------------------------------------------------------------------|-----------------------|----------------------|--------------------------------|-------------------------------------|--------------------|-------------------|
| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8n-oiv7.pt) | 640 | 18.4 | 142.4 | 1.21 | 3.5 | 10.5 |
| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8s-oiv7.pt) | 640 | 27.7 | 183.1 | 1.40 | 11.4 | 29.7 |
| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8m-oiv7.pt) | 640 | 33.6 | 408.5 | 2.26 | 26.2 | 80.6 |
| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8l-oiv7.pt) | 640 | 34.9 | 596.9 | 2.43 | 44.1 | 167.4 |
| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8x-oiv7.pt) | 640 | 36.3 | 860.6 | 3.56 | 68.7 | 260.6 |
![Open Images V7 classes visual](https://user-images.githubusercontent.com/26833433/258660358-2dc07771-ec08-4d11-b24a-f66e07550050.png) ![Open Images V7 classes visual](https://user-images.githubusercontent.com/26833433/258660358-2dc07771-ec08-4d11-b24a-f66e07550050.png)
## Key Features ## Key Features

30
docs/en/guides/object-counting.md
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@ -174,21 +174,21 @@ Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
| Name | Type | Default | Description | | Name | Type | Default | Description |
|---------------------|-------------|----------------------------|-----------------------------------------------| |---------------------|-------------|----------------------------|-----------------------------------------------|
| view_img | `bool` | `False` | Display frames with counts | | `view_img` | `bool` | `False` | Display frames with counts |
| view_in_counts | `bool` | `True` | Display incounts only on video frame | | `view_in_counts` | `bool` | `True` | Display incounts only on video frame |
| view_out_counts | `bool` | `True` | Display outcounts only on video frame | | `view_out_counts` | `bool` | `True` | Display outcounts only on video frame |
| line_thickness | `int` | `2` | Increase bounding boxes thickness | | `line_thickness` | `int` | `2` | Increase bounding boxes thickness |
| reg_pts | `list` | `[(20, 400), (1260, 400)]` | Points defining the Region Area | | `reg_pts` | `list` | `[(20, 400), (1260, 400)]` | Points defining the Region Area |
| classes_names | `dict` | `model.model.names` | Dictionary of Class Names | | `classes_names` | `dict` | `model.model.names` | Dictionary of Class Names |
| region_color | `RGB Color` | `(255, 0, 255)` | Color of the Object counting Region or Line | | `region_color` | `RGB Color` | `(255, 0, 255)` | Color of the Object counting Region or Line |
| track_thickness | `int` | `2` | Thickness of Tracking Lines | | `track_thickness` | `int` | `2` | Thickness of Tracking Lines |
| draw_tracks | `bool` | `False` | Enable drawing Track lines | | `draw_tracks` | `bool` | `False` | Enable drawing Track lines |
| track_color | `RGB Color` | `(0, 255, 0)` | Color for each track line | | `track_color` | `RGB Color` | `(0, 255, 0)` | Color for each track line |
| line_dist_thresh | `int` | `15` | Euclidean Distance threshold for line counter | | `line_dist_thresh` | `int` | `15` | Euclidean Distance threshold for line counter |
| count_txt_thickness | `int` | `2` | Thickness of Object counts text | | `count_txt_thickness` | `int` | `2` | Thickness of Object counts text |
| count_txt_color | `RGB Color` | `(0, 0, 0)` | Foreground color for Object counts text | | `count_txt_color` | `RGB Color` | `(0, 0, 0)` | Foreground color for Object counts text |
| count_color | `RGB Color` | `(255, 255, 255)` | Background color for Object counts text | | `count_color` | `RGB Color` | `(255, 255, 255)` | Background color for Object counts text |
| region_thickness | `int` | `5` | Thickness for object counter region or line | | `region_thickness` | `int` | `5` | Thickness for object counter region or line |
### Arguments `model.track` ### Arguments `model.track`

8
docs/en/models/fast-sam.md
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@ -34,10 +34,10 @@ FastSAM is designed to address the limitations of the [Segment Anything Model (S
This table presents the available models with their specific pre-trained weights, the tasks they support, and their compatibility with different operating modes like [Inference](../modes/predict.md), [Validation](../modes/val.md), [Training](../modes/train.md), and [Export](../modes/export.md), indicated by ✅ emojis for supported modes and ❌ emojis for unsupported modes. This table presents the available models with their specific pre-trained weights, the tasks they support, and their compatibility with different operating modes like [Inference](../modes/predict.md), [Validation](../modes/val.md), [Training](../modes/train.md), and [Export](../modes/export.md), indicated by ✅ emojis for supported modes and ❌ emojis for unsupported modes.
| Model Type | Pre-trained Weights | Tasks Supported | Inference | Validation | Training | Export | | Model Type | Pre-trained Weights | Tasks Supported | Inference | Validation | Training | Export |
|------------|---------------------|----------------------------------------------|-----------|------------|----------|--------| |------------|---------------------------------------------------------------------------------------------|----------------------------------------------|-----------|------------|----------|--------|
| FastSAM-s | `FastSAM-s.pt` | [Instance Segmentation](../tasks/segment.md) | ✅ | ❌ | ❌ | ✅ | | FastSAM-s | [FastSAM-s.pt](https://github.com/ultralytics/assets/releases/download/v8.1.0/FastSAM-s.pt) | [Instance Segmentation](../tasks/segment.md) | ✅ | ❌ | ❌ | ✅ |
| FastSAM-x | `FastSAM-x.pt` | [Instance Segmentation](../tasks/segment.md) | ✅ | ❌ | ❌ | ✅ | | FastSAM-x | [FastSAM-x.pt](https://github.com/ultralytics/assets/releases/download/v8.1.0/FastSAM-x.pt) | [Instance Segmentation](../tasks/segment.md) | ✅ | ❌ | ❌ | ✅ |
## Usage Examples ## Usage Examples

6
docs/en/models/mobile-sam.md
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@ -20,9 +20,9 @@ MobileSAM is trained on a single GPU with a 100k dataset (1% of the original ima
This table presents the available models with their specific pre-trained weights, the tasks they support, and their compatibility with different operating modes like [Inference](../modes/predict.md), [Validation](../modes/val.md), [Training](../modes/train.md), and [Export](../modes/export.md), indicated by ✅ emojis for supported modes and ❌ emojis for unsupported modes. This table presents the available models with their specific pre-trained weights, the tasks they support, and their compatibility with different operating modes like [Inference](../modes/predict.md), [Validation](../modes/val.md), [Training](../modes/train.md), and [Export](../modes/export.md), indicated by ✅ emojis for supported modes and ❌ emojis for unsupported modes.
| Model Type | Pre-trained Weights | Tasks Supported | Inference | Validation | Training | Export | | Model Type | Pre-trained Weights | Tasks Supported | Inference | Validation | Training | Export |
|------------|---------------------|----------------------------------------------|-----------|------------|----------|--------| |------------|-----------------------------------------------------------------------------------------------|----------------------------------------------|-----------|------------|----------|--------|
| MobileSAM | `mobile_sam.pt` | [Instance Segmentation](../tasks/segment.md) | ✅ | ❌ | ❌ | ❌ | | MobileSAM | [mobile_sam.pt](https://github.com/ultralytics/assets/releases/download/v8.1.0/mobile_sam.pt) | [Instance Segmentation](../tasks/segment.md) | ✅ | ❌ | ❌ | ❌ |
## Adapting from SAM to MobileSAM ## Adapting from SAM to MobileSAM

8
docs/en/models/rtdetr.md
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@ -63,10 +63,10 @@ This example provides simple RT-DETRR training and inference examples. For full
This table presents the model types, the specific pre-trained weights, the tasks supported by each model, and the various modes ([Train](../modes/train.md) , [Val](../modes/val.md), [Predict](../modes/predict.md), [Export](../modes/export.md)) that are supported, indicated by ✅ emojis. This table presents the model types, the specific pre-trained weights, the tasks supported by each model, and the various modes ([Train](../modes/train.md) , [Val](../modes/val.md), [Predict](../modes/predict.md), [Export](../modes/export.md)) that are supported, indicated by ✅ emojis.
| Model Type | Pre-trained Weights | Tasks Supported | Inference | Validation | Training | Export | | Model Type | Pre-trained Weights | Tasks Supported | Inference | Validation | Training | Export |
|---------------------|---------------------|----------------------------------------|-----------|------------|----------|--------| |---------------------|-------------------------------------------------------------------------------------------|----------------------------------------|-----------|------------|----------|--------|
| RT-DETR Large | `rtdetr-l.pt` | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ | | RT-DETR Large | [rtdetr-l.pt](https://github.com/ultralytics/assets/releases/download/v8.1.0/rtdetr-l.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
| RT-DETR Extra-Large | `rtdetr-x.pt` | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ | | RT-DETR Extra-Large | [rtdetr-x.pt](https://github.com/ultralytics/assets/releases/download/v8.1.0/rtdetr-x.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
## Citations and Acknowledgements ## Citations and Acknowledgements

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docs/en/models/sam.md
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@ -29,10 +29,10 @@ For an in-depth look at the Segment Anything Model and the SA-1B dataset, please
This table presents the available models with their specific pre-trained weights, the tasks they support, and their compatibility with different operating modes like [Inference](../modes/predict.md), [Validation](../modes/val.md), [Training](../modes/train.md), and [Export](../modes/export.md), indicated by ✅ emojis for supported modes and ❌ emojis for unsupported modes. This table presents the available models with their specific pre-trained weights, the tasks they support, and their compatibility with different operating modes like [Inference](../modes/predict.md), [Validation](../modes/val.md), [Training](../modes/train.md), and [Export](../modes/export.md), indicated by ✅ emojis for supported modes and ❌ emojis for unsupported modes.
| Model Type | Pre-trained Weights | Tasks Supported | Inference | Validation | Training | Export | | Model Type | Pre-trained Weights | Tasks Supported | Inference | Validation | Training | Export |
|------------|---------------------|----------------------------------------------|-----------|------------|----------|--------| |------------|-------------------------------------------------------------------------------------|----------------------------------------------|-----------|------------|----------|--------|
| SAM base | `sam_b.pt` | [Instance Segmentation](../tasks/segment.md) | ✅ | ❌ | ❌ | ❌ | | SAM base | [sam_b.pt](https://github.com/ultralytics/assets/releases/download/v8.1.0/sam_b.pt) | [Instance Segmentation](../tasks/segment.md) | ✅ | ❌ | ❌ | ❌ |
| SAM large | `sam_l.pt` | [Instance Segmentation](../tasks/segment.md) | ✅ | ❌ | ❌ | ❌ | | SAM large | [sam_l.pt](https://github.com/ultralytics/assets/releases/download/v8.1.0/sam_l.pt) | [Instance Segmentation](../tasks/segment.md) | ✅ | ❌ | ❌ | ❌ |
## How to Use SAM: Versatility and Power in Image Segmentation ## How to Use SAM: Versatility and Power in Image Segmentation

2
ultralytics/utils/ops.py
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@ -547,7 +547,7 @@ def xywhr2xyxyxyxy(rboxes):
be in degrees from 0 to 90. be in degrees from 0 to 90.
Args: Args:
center (numpy.ndarray | torch.Tensor): Input data in [cx, cy, w, h, rotation] format of shape (n, 5) or (b, n, 5). rboxes (numpy.ndarray | torch.Tensor): Input data in [cx, cy, w, h, rotation] format of shape (n, 5) or (b, n, 5).
Returns: Returns:
(numpy.ndarray | torch.Tensor): Converted corner points of shape (n, 4, 2) or (b, n, 4, 2). (numpy.ndarray | torch.Tensor): Converted corner points of shape (n, 4, 2) or (b, n, 4, 2).