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General ultralytics==8.0.6 updates (#351)

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Co-authored-by: Dzmitry Plashchynski <plashchynski@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
This commit is contained in:
Glenn Jocher
2023-01-14 17:39:50 +01:00
committed by GitHub
parent 70427579b8
commit f8e32c4c13
16 changed files with 79 additions and 80 deletions
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22
ultralytics/yolo/data/utils.py
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@ -14,12 +14,11 @@ import numpy as np
import torch
from PIL import ExifTags, Image, ImageOps
from ultralytics.yolo.utils import LOGGER, ROOT, colorstr, yaml_load
from ultralytics.yolo.utils import DATASETS_DIR, LOGGER, ROOT, colorstr, yaml_load
from ultralytics.yolo.utils.checks import check_file, check_font, is_ascii
from ultralytics.yolo.utils.downloads import download
from ultralytics.yolo.utils.files import unzip_file
from ..utils.ops import segments2boxes
from ultralytics.yolo.utils.ops import segments2boxes
HELP_URL = "See https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data"
IMG_FORMATS = "bmp", "dng", "jpeg", "jpg", "mpo", "png", "tif", "tiff", "webp", "pfm" # include image suffixes
@ -173,12 +172,7 @@ def polygons2masks_overlap(imgsz, segments, downsample_ratio=1):
areas = []
ms = []
for si in range(len(segments)):
mask = polygon2mask(
imgsz,
[segments[si].reshape(-1)],
downsample_ratio=downsample_ratio,
color=1,
)
mask = polygon2mask(imgsz, [segments[si].reshape(-1)], downsample_ratio=downsample_ratio, color=1)
ms.append(mask)
areas.append(mask.sum())
areas = np.asarray(areas)
@ -194,13 +188,14 @@ def polygons2masks_overlap(imgsz, segments, downsample_ratio=1):
def check_dataset_yaml(data, autodownload=True):
# Download, check and/or unzip dataset if not found locally
data = check_file(data)
DATASETS_DIR = (Path.cwd() / "../datasets").resolve() # TODO: handle global dataset dir
# Download (optional)
extract_dir = ''
if isinstance(data, (str, Path)) and (is_zipfile(data) or is_tarfile(data)):
download(data, dir=f'{DATASETS_DIR}/{Path(data).stem}', unzip=True, delete=False, curl=False, threads=1)
data = next((DATASETS_DIR / Path(data).stem).rglob('*.yaml'))
extract_dir, autodownload = data.parent, False
# Read yaml (optional)
if isinstance(data, (str, Path)):
data = yaml_load(data, append_filename=True) # dictionary
@ -215,7 +210,7 @@ def check_dataset_yaml(data, autodownload=True):
# Resolve paths
path = Path(extract_dir or data.get('path') or '') # optional 'path' default to '.'
if not path.is_absolute():
path = (Path.cwd() / path).resolve()
path = (DATASETS_DIR / path).resolve()
data['path'] = path # download scripts
for k in 'train', 'val', 'test':
if data.get(k): # prepend path
@ -253,6 +248,7 @@ def check_dataset_yaml(data, autodownload=True):
s = f"success ✅ {dt}, saved to {colorstr('bold', DATASETS_DIR)}" if r in (0, None) else f"failure {dt} ❌"
LOGGER.info(f"Dataset download {s}")
check_font('Arial.ttf' if is_ascii(data['names']) else 'Arial.Unicode.ttf', progress=True) # download fonts
return data # dictionary
@ -274,12 +270,12 @@ def check_dataset(dataset: str):
'nc': Number of classes in the dataset
'names': List of class names in the dataset
"""
data_dir = (Path.cwd() / "datasets" / dataset).resolve()
data_dir = (DATASETS_DIR / dataset).resolve()
if not data_dir.is_dir():
LOGGER.info(f'\nDataset not found ⚠️, missing path {data_dir}, attempting download...')
t = time.time()
if dataset == 'imagenet':
subprocess.run(f"bash {ROOT / 'data/scripts/get_imagenet.sh'}", shell=True, check=True)
subprocess.run(f"bash {ROOT / 'yolo/data/scripts/get_imagenet.sh'}", shell=True, check=True)
else:
url = f'https://github.com/ultralytics/yolov5/releases/download/v1.0/{dataset}.zip'
download(url, dir=data_dir.parent)