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</p>
The `ultralytics` package comes with a myriad of utilities that can support, enhance, and speed up your workflows. There are many more available, but here are some that will be useful for most developers. They're also a great reference point to use when learning to program.
## Data
### YOLO Data Explorer
[YOLO Explorer](../datasets/explorer/index.md) was added in the `8.1.0` anniversary update and is a powerful tool you can use to better understand your dataset. One of the key functions that YOLO Explorer provides, is the ability to use text queries to find object instances in your dataset.
Dataset annotation is a very resource intensive and time-consuming process. If you have a YOLO object detection model trained on a reasonable amount of data, you can use it and [SAM](../models/sam.md) to auto-annotate additional data (segmentation format).
from ultralytics.data.annotator import auto_annotate
auto_annotate(#(1)!
data='path/to/new/data',
det_model='yolov8n.pt',
sam_model='mobile_sam.pt',
device="cuda",
output_dir="path/to/save_labels",
)
```
1. Nothing returns from this function
- [See the reference section for `annotator.auto_annotate`](../reference/data/annotator.md#ultralytics.data.annotator.auto_annotate) for more insight on how the function operates.
- Use in combination with the [function `segments2boxes`](#convert-segments-to-bounding-boxes) to generate object detection bounding boxes as well
### Convert COCO into YOLO Format
Use to convert COCO JSON annotations into proper YOLO format. For object detection (bounding box) datasets, `use_segments` and `use_keypoints` should both be `False`
```{ .py .annotate }
from ultralytics.data.converter import convert_coco
convert_coco(#(1)!
'../datasets/coco/annotations/',
use_segments=False,
use_keypoints=False,
cls91to80=True,
)
```
1. Nothing returns from this function
For additional information about the `convert_coco` function, [visit the reference page](../reference/data/converter.md#ultralytics.data.converter.convert_coco)
With existing `x y w h` bounding box data, convert to segments using the `yolo_bbox2segment` function. The files for images and annotations need to be organized like this:
```
data
|__ images
├─ 001.jpg
├─ 002.jpg
├─ ..
└─ NNN.jpg
|__ labels
├─ 001.txt
├─ 002.txt
├─ ..
└─ NNN.txt
```
```{ .py .annotate }
from ultralytics.data.converter import yolo_bbox2segment
yolo_bbox2segment(#(1)!
im_dir="path/to/images",
save_dir=None, # saved to "labels-segment" in images directory
sam_model="sam_b.pt"
)
```
1. Nothing returns from this function
[Visit the `yolo_bbox2segment` reference page](../reference/data/converter.md#ultralytics.data.converter.yolo_bbox2segment) for more information regarding the function.
### Convert Segments to Bounding Boxes
If you have a dataset that uses the [segmentation dataset format](../datasets/segment/index.md) you can easily convert these into up-right (or horizontal) bounding boxes (`x y w h` format) with this function.
```python
from ultralytics.utils.ops import segments2boxes
segments = np.array(
[[805, 392, 797, 400, ..., 808, 714, 808, 392],
[115, 398, 113, 400, ..., 150, 400, 149, 298],
[267, 412, 265, 413, ..., 300, 413, 299, 412],
]
)
segments2boxes([s.reshape(-1,2) for s in segments])
>>> array([[ 741.66, 631.12, 133.31, 479.25],
[ 146.81, 649.69, 185.62, 502.88],
[ 281.81, 636.19, 118.12, 448.88]],
dtype=float32) # xywh bounding boxes
```
To understand how this function works, visit the [reference page](../reference/utils/ops.md#ultralytics.utils.ops.segments2boxes)
## Utilities
### Image Compression
Compresses a single image file to reduced size while preserving its aspect ratio and quality. If the input image is smaller than the maximum dimension, it will not be resized.
```{ .py .annotate }
from pathlib import Path
from ultralytics.data.utils import compress_one_image
for f in Path('path/to/dataset').rglob('*.jpg'):
compress_one_image(f)#(1)!
```
1. Nothing returns from this function
### Auto-split Dataset
Automatically split a dataset into `train`/`val`/`test` splits and save the resulting splits into `autosplit_*.txt` files. This function will use random sampling, which is not included when using [`fraction` argument for training](../modes/train.md#arguments).
annotated_only=False # split only images with annotation file when True
)
```
1. Nothing returns from this function
See the [Reference page](../reference/data/utils.md#ultralytics.data.utils.autosplit) for additional details on this function.
### Segment-polygon to Binary Mask
Convert a single polygon (as list) to a binary mask of the specified image size. Polygon in the form of `[N, 2]` with `N` as the number of `(x, y)` points defining the polygon contour.
To manage bounding box data, the `Bboxes` class will help to convert between box coordinate formatting, scale box dimensions, calculate areas, include offsets, and more!
Many of the following functions (and more) can be accessed using the [`Bboxes` class](#bounding-box-horizontal-instances) but if you prefer to work with the functions directly, see the next subsections on how to import these independently.
When scaling and image up or down, corresponding bounding box coordinates can be appropriately scaled to match using `ultralytics.utils.ops.scale_boxes`.
Convert bounding box coordinates from (x1, y1, x2, y2) format to (x, y, width, height) format where (x1, y1) is the top-left corner and (x2, y2) is the bottom-right corner.
```python
import numpy as np
from ultralytics.utils.ops import xyxy2xywh
xyxy_boxes = np.array(
[[ 22.878, 231.27, 804.98, 756.83,],
[ 48.552, 398.56, 245.35, 902.71,],
[ 669.47, 392.19, 809.72, 877.04,],
[ 221.52, 405.8, 344.98, 857.54,],
[ 0, 550.53, 63.01, 873.44,],
[ 0.0584, 254.46, 32.561, 324.87,]]
)
xywh = xyxy2xywh(xyxy_boxes)
xywh
>>> array(
[[ 413.93, 494.05, 782.1, 525.56],
[ 146.95, 650.63, 196.8, 504.15],
[ 739.6, 634.62, 140.25, 484.85],
[ 283.25, 631.67, 123.46, 451.74],
[ 31.505, 711.99, 63.01, 322.91],
[ 16.31, 289.67, 32.503, 70.41]]
)
```
### All Bounding Box Conversions
```python
from ultralytics.utils.ops import xywh2xyxy
from ultralytics.utils.ops import xywhn2xyxy # normalized → pixel
from ultralytics.utils.ops import xyxy2xywhn # pixel → normalized
from ultralytics.utils.ops import xywh2ltwh # xywh → top-left corner, w, h
from ultralytics.utils.ops import xyxy2ltwh # xyxy → top-left corner, w, h
from ultralytics.utils.ops import ltwh2xywh
from ultralytics.utils.ops import ltwh2xyxy
```
See docstring for each function or visit the `ultralytics.utils.ops` [reference page](../reference/utils/ops.md) to read more about each function.
## Plotting
### Drawing Annotations
Ultralytics includes an Annotator class that can be used to annotate any kind of data. It's easiest to use with [object detection bounding boxes](../modes/predict.md#boxes), [pose key points](../modes/predict.md#keypoints), and [oriented bounding boxes](../modes/predict.md#obb).
#### Horizontal Bounding Boxes
```{ .py .annotate }
import cv2 as cv
import numpy as np
from ultralytics.utils.plotting import Annotator, colors
Check duration for code to run/process either using `with` or as a decorator.
```python
from ultralytics.utils.ops import Profile
with Profile(device=device) as dt:
pass # operation to measure
print(dt)
>>> "Elapsed time is 9.5367431640625e-07 s"
```
### Ultralytics Supported Formats
Want or need to use the formats of [images or videos types supported](../modes/predict.md#image-and-video-formats) by Ultralytics programmatically? Use these constants if you need.
