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comments	description	keywords
true	Discover how to extend the utility of the Ultralytics package to support your development process.	Ultralytics, YOLO, custom, function, workflow, utility, support,

Simple Utilities

code with perspective

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 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.

Auto Labeling / Annotations

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 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",
)

Nothing returns from this function

See the reference section for annotator.auto_annotate for more insight on how the function operates.
Use in combination with the function segments2boxes 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

from ultralytics.data.converter import convert_coco

convert_coco(#(1)!
    '../datasets/coco/annotations/',
    use_segments=False, 
    use_keypoints=False,
    cls91to80=True,
)

Nothing returns from this function

For additional information about the convert_coco function, visit the reference page

Get Bounding Box Dimensions

from ultralytics.utils.plotting import Annotator
from ultralytics import YOLO
import cv2

model = YOLO('yolov8n.pt')  # Load pretrain or fine-tune model

# Process the image
source = cv2.imread('path/to/image.jpg')
results = model(source)

# Extract results
annotator = Annotator(source, example=model.names)

for box in results[0].boxes.xyxy.cpu():
    width, height, area = annotator.get_bbox_dimension(box)
    print("Bounding Box Width {}, Height {}, Area {}".format(
        width.item(), height.item(), area.item()))

Convert Bounding Boxes to Segments

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

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"
)

Nothing returns from this function

Visit the yolo_bbox2segment reference page for more information regarding the function.

Convert Segments to Bounding Boxes

If you have a dataset that uses the segmentation dataset format you can easily convert these into up-right (or horizontal) bounding boxes (x y w h format) with this function.

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

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.

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)!

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.

from ultralytics.data.utils import autosplit

autosplit( #(1)!
    path="path/to/images",
    weights=(0.9, 0.1, 0.0), # (train, validation, test) fractional splits
    annotated_only=False     # split only images with annotation file when True
)

Nothing returns from this function

See the Reference page 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.

!!! warning

`N` <b><u>must always</b></u> be even.

import numpy as np
from ultralytics.data.utils import polygon2mask

imgsz = (1080, 810)
polygon = np.array(
    [805, 392, 797, 400, ..., 808, 714, 808, 392], # (238, 2)
)

mask = polygon2mask(
    imgsz,     # tuple
    [polygon], # input as list
    color=255, # 8-bit binary
    downsample_ratio=1
)

Bounding Boxes

Bounding Box (horizontal) Instances

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!

from ultralytics.utils.instance import Bboxes

boxes = Bboxes(
    bboxes=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,]]
    ),
    format="xyxy",
)

boxes.areas()
>>> array([ 4.1104e+05,       99216,       68000,       55772,       20347,      2288.5])
boxes.convert("xywh")
boxes.bboxes
>>> 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]]
)

See the Bboxes reference section for more attributes and methods available.

!!! tip

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.

Scaling Boxes

When scaling and image up or down, corresponding bounding box coordinates can be appropriately scaled to match using ultralytics.utils.ops.scale_boxes.

import cv2 as cv
import numpy as np
from ultralytics.utils.ops import scale_boxes

image = cv.imread("ultralytics/assets/bus.jpg")
*(h, w), c = image.shape
resized = cv.resize(image, None, (), fx=1.2, fy=1.2)
*(new_h, new_w), _ = resized.shape

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,]]
)

new_boxes = scale_boxes(
    img1_shape=(h, w),          # original image dimensions
    boxes=xyxy_boxes,           # boxes from original image
    img0_shape=(new_h, new_w),  # resized image dimensions (scale to)
    ratio_pad=None,
    padding=False,
    xywh=False,
)

new_boxes#(1)!
>>> array(
    [[  27.454,  277.52,  965.98,   908.2],
    [   58.262,  478.27,  294.42,  1083.3],
    [   803.36,  470.63,  971.66,  1052.4],
    [   265.82,  486.96,  413.98,    1029],
    [        0,  660.64,  75.612,  1048.1],
    [   0.0701,  305.35,  39.073,  389.84]]
)

Bounding boxes scaled for the new image size

Bounding Box Format Conversions

XYXY → XYWH

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.

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

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 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, pose key points, and oriented bounding boxes.

Horizontal Bounding Boxes

import cv2 as cv
import numpy as np
from ultralytics.utils.plotting import Annotator, colors

names { #(1)!
     0: "person",
     5: "bus",
    11: "stop sign",
}

image = cv.imread("ultralytics/assets/bus.jpg")
ann = Annotator(
    image,
    line_width=None,  # default auto-size
    font_size=None,   # default auto-size
    font="Arial.ttf", # must be ImageFont compatible
    pil=False,        # use PIL, otherwise uses OpenCV
)

xyxy_boxes = np.array(
    [[ 5,   22.878,  231.27,  804.98,  756.83,], # class-idx x1 y1 x2 y2
     [ 0,   48.552,  398.56,  245.35,  902.71,],
     [ 0,   669.47,  392.19,  809.72,  877.04,],
     [ 0,   221.52,   405.8,  344.98,  857.54,],
     [ 0,        0,  550.53,   63.01,  873.44,],
     [11,   0.0584,  254.46,  32.561,  324.87,]]
)

for nb, box in enumerate(xyxy_boxes):
    c_idx, *box = box
    label = f"{str(nb).zfill(2)}:{names.get(int(c_idx))}"
    ann.box_label(box, label, color=colors(c_idx, bgr=True))

image_with_bboxes = ann.result()

Names can be used from model.names when working with detection results

Oriented Bounding Boxes (OBB)

import cv2 as cv
import numpy as np
from ultralytics.utils.plotting import Annotator, colors

obb_names = {10: "small vehicle"}
obb_image = cv.imread("datasets/dota8/images/train/P1142__1024__0___824.jpg")
obb_boxes = np.array(
    [[ 0, 635, 560, 919, 719, 1087, 420, 803,  261,], # class-idx x1 y1 x2 y2 x3 y2 x4 y4
     [ 0, 331,  19, 493, 260, 776,   70, 613, -171,],
     [ 9, 869, 161, 886, 147, 851,  101, 833,  115,]
    ]
)
ann = Annotator(
    obb_image,
    line_width=None,  # default auto-size
    font_size=None,   # default auto-size
    font="Arial.ttf", # must be ImageFont compatible
    pil=False,        # use PIL, otherwise uses OpenCV
)
for obb in obb_boxes:
    c_idx, *obb = obb
    obb = np.array(obb).reshape(-1, 4, 2).squeeze()
    label = f"{names.get(int(c_idx))}"
    ann.box_label(
        obb,
        label,
        color=colors(c_idx, True),
        rotated=True,
    )

image_with_obb = ann.result()

See the Annotator Reference Page for additional insight.

Miscellaneous

Code Profiling

Check duration for code to run/process either using with or as a decorator.

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 by Ultralytics programmatically? Use these constants if you need.

from ultralytics.data.utils import IMG_FORMATS
from ultralytics.data.utils import VID_FORMATS

print(IMG_FORMATS)
>>> ('bmp', 'dng', 'jpeg', 'jpg', 'mpo', 'png', 'tif', 'tiff', 'webp', 'pfm')

Make Divisible

Calculates the nearest whole number to x to make evenly divisible when divided by y.

from ultralytics.utils.ops import make_divisible

make_divisible(7, 3)
>>> 9
make_divisible(7, 2)
>>> 8

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