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

Docs Ruff codeblocks reformat and fix (#12847)

Browse source 
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
This commit is contained in:
Glenn Jocher 2024-05-19 19:13:04 +02:00 • committed by GitHub
parent be5cf7a033
commit 68031133fd
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
9 changed files with 167 additions and 178 deletions
Download patch file Download diff file Expand all files Collapse all files

202
docs/en/usage/simple-utilities.md
View file

@ -36,10 +36,10 @@ Dataset annotation is a very resource intensive and time-consuming process. If y
```{ .py .annotate }
from ultralytics.data.annotator import auto_annotate
auto_annotate(#(1)!
data='path/to/new/data',
det_model='yolov8n.pt',
sam_model='mobile_sam.pt',
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",
)
@ -58,9 +58,9 @@ Use to convert COCO JSON annotations into proper YOLO format. For object detecti
```{ .py .annotate }
from ultralytics.data.converter import convert_coco
convert_coco(#(1)!
'../datasets/coco/annotations/',
use_segments=False,
convert_coco( # (1)!
"../datasets/coco/annotations/",
use_segments=False,
use_keypoints=False,
cls91to80=True,
)
@ -113,10 +113,10 @@ data
```{ .py .annotate }
from ultralytics.data.converter import yolo_bbox2segment
yolo_bbox2segment(#(1)!
yolo_bbox2segment( # (1)!
im_dir="path/to/images",
save_dir=None, # saved to "labels-segment" in images directory
sam_model="sam_b.pt"
save_dir=None, # saved to "labels-segment" in images directory
sam_model="sam_b.pt",
)
```
@ -129,20 +129,22 @@ yolo_bbox2segment(#(1)!
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
import numpy as np
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],
[
[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
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)
@ -155,10 +157,11 @@ Compresses a single image file to reduced size while preserving its aspect ratio
```{ .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)!
for f in Path("path/to/dataset").rglob("*.jpg"):
compress_one_image(f) # (1)!
```
1. Nothing returns from this function
@ -170,10 +173,10 @@ Automatically split a dataset into `train`/`val`/`test` splits and save the resu
```{ .py .annotate }
from ultralytics.data.utils import autosplit
autosplit( #(1)!
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
weights=(0.9, 0.1, 0.0), # (train, validation, test) fractional splits
annotated_only=False, # split only images with annotation file when True
)
```
@ -194,9 +197,7 @@ 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)
)
polygon = np.array([805, 392, 797, 400, ..., 808, 714, 808, 392]) # (238, 2)
mask = polygon2mask(
imgsz, # tuple
@ -213,32 +214,36 @@ mask = polygon2mask(
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!
```python
import numpy as np
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,]]
[
[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])
# >>> 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]]
)
print(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](../reference/utils/instance.md#ultralytics.utils.instance.Bboxes) for more attributes and methods available.
@ -257,37 +262,39 @@ import numpy as np
from ultralytics.utils.ops import scale_boxes
image = cv.imread("ultralytics/assets/bus.jpg")
*(h, w), c = image.shape
h, w, c = image.shape
resized = cv.resize(image, None, (), fx=1.2, fy=1.2)
*(new_h, new_w), _ = resized.shape
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,]]
[
[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
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]]
)
print(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]]
# )
```
1. Bounding boxes scaled for the new image size
@ -303,24 +310,26 @@ 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,]]
[
[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]]
)
print(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
@ -352,9 +361,9 @@ import cv2 as cv
import numpy as np
from ultralytics.utils.plotting import Annotator, colors
names { #(1)!
0: "person",
5: "bus",
names = { # (1)!
0: "person",
5: "bus",
11: "stop sign",
}
@ -362,18 +371,20 @@ 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
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,]]
[
[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):
@ -412,7 +423,7 @@ ann = Annotator(
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))}"
label = f"{obb_names.get(int(c_idx))}"
ann.box_label(
obb,
label,
@ -434,11 +445,11 @@ 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:
with Profile(device="cuda:0") as dt:
pass # operation to measure
print(dt)
>>> "Elapsed time is 9.5367431640625e-07 s"
# >>> "Elapsed time is 9.5367431640625e-07 s"
```
### Ultralytics Supported Formats
@ -446,11 +457,10 @@ print(dt)
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.
```python
from ultralytics.data.utils import IMG_FORMATS
from ultralytics.data.utils import VID_FORMATS
from ultralytics.data.utils import IMG_FORMATS, VID_FORMATS
print(IMG_FORMATS)
>>> ('bmp', 'dng', 'jpeg', 'jpg', 'mpo', 'png', 'tif', 'tiff', 'webp', 'pfm')
# >>> ('bmp', 'dng', 'jpeg', 'jpg', 'mpo', 'png', 'tif', 'tiff', 'webp', 'pfm')
```
### Make Divisible
@ -461,7 +471,7 @@ Calculates the nearest whole number to `x` to make evenly divisible when divided
from ultralytics.utils.ops import make_divisible
make_divisible(7, 3)
>>> 9
# >>> 9
make_divisible(7, 2)
>>> 8
# >>> 8
```