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

ultralytics 8.0.239 Ultralytics Actions and hub-sdk adoption (#7431)

Browse source 
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Burhan <62214284+Burhan-Q@users.noreply.github.com>
Co-authored-by: Kayzwer <68285002+Kayzwer@users.noreply.github.com>
This commit is contained in:
Glenn Jocher 2024-01-10 03:16:08 +01:00 • committed by GitHub
parent e795277391
commit fe27db2f6e
No known key found for this signature in database
GPG key ID: 4AEE18F83AFDEB23
139 changed files with 6870 additions and 5125 deletions
Download patch file Download diff file Expand all files Collapse all files

302
ultralytics/data/utils.py
View file

@ -17,36 +17,47 @@ import numpy as np
from PIL import Image, ImageOps
from ultralytics.nn.autobackend import check_class_names
from ultralytics.utils import (DATASETS_DIR, LOGGER, NUM_THREADS, ROOT, SETTINGS_YAML, TQDM, clean_url, colorstr,
emojis, yaml_load, yaml_save)
from ultralytics.utils import (
DATASETS_DIR,
LOGGER,
NUM_THREADS,
ROOT,
SETTINGS_YAML,
TQDM,
clean_url,
colorstr,
emojis,
yaml_load,
yaml_save,
)
from ultralytics.utils.checks import check_file, check_font, is_ascii
from ultralytics.utils.downloads import download, safe_download, unzip_file
from ultralytics.utils.ops import segments2boxes
HELP_URL = 'See https://docs.ultralytics.com/datasets/detect for dataset formatting guidance.'
IMG_FORMATS = 'bmp', 'dng', 'jpeg', 'jpg', 'mpo', 'png', 'tif', 'tiff', 'webp', 'pfm' # image suffixes
VID_FORMATS = 'asf', 'avi', 'gif', 'm4v', 'mkv', 'mov', 'mp4', 'mpeg', 'mpg', 'ts', 'wmv', 'webm' # video suffixes
PIN_MEMORY = str(os.getenv('PIN_MEMORY', True)).lower() == 'true' # global pin_memory for dataloaders
HELP_URL = "See https://docs.ultralytics.com/datasets/detect for dataset formatting guidance."
IMG_FORMATS = "bmp", "dng", "jpeg", "jpg", "mpo", "png", "tif", "tiff", "webp", "pfm" # image suffixes
VID_FORMATS = "asf", "avi", "gif", "m4v", "mkv", "mov", "mp4", "mpeg", "mpg", "ts", "wmv", "webm" # video suffixes
PIN_MEMORY = str(os.getenv("PIN_MEMORY", True)).lower() == "true" # global pin_memory for dataloaders
def img2label_paths(img_paths):
"""Define label paths as a function of image paths."""
sa, sb = f'{os.sep}images{os.sep}', f'{os.sep}labels{os.sep}' # /images/, /labels/ substrings
return [sb.join(x.rsplit(sa, 1)).rsplit('.', 1)[0] + '.txt' for x in img_paths]
sa, sb = f"{os.sep}images{os.sep}", f"{os.sep}labels{os.sep}" # /images/, /labels/ substrings
return [sb.join(x.rsplit(sa, 1)).rsplit(".", 1)[0] + ".txt" for x in img_paths]
def get_hash(paths):
"""Returns a single hash value of a list of paths (files or dirs)."""
size = sum(os.path.getsize(p) for p in paths if os.path.exists(p)) # sizes
h = hashlib.sha256(str(size).encode()) # hash sizes
h.update(''.join(paths).encode()) # hash paths
h.update("".join(paths).encode()) # hash paths
return h.hexdigest() # return hash
def exif_size(img: Image.Image):
"""Returns exif-corrected PIL size."""
s = img.size # (width, height)
if img.format == 'JPEG': # only support JPEG images
if img.format == "JPEG": # only support JPEG images
with contextlib.suppress(Exception):
exif = img.getexif()
if exif:
@ -60,24 +71,24 @@ def verify_image(args):
"""Verify one image."""
(im_file, cls), prefix = args
# Number (found, corrupt), message
nf, nc, msg = 0, 0, ''
nf, nc, msg = 0, 0, ""
try:
im = Image.open(im_file)
im.verify() # PIL verify
shape = exif_size(im) # image size
shape = (shape[1], shape[0]) # hw
assert (shape[0] > 9) & (shape[1] > 9), f'image size {shape} <10 pixels'
assert im.format.lower() in IMG_FORMATS, f'invalid image format {im.format}'
if im.format.lower() in ('jpg', 'jpeg'):
with open(im_file, 'rb') as f:
assert (shape[0] > 9) & (shape[1] > 9), f"image size {shape} <10 pixels"
assert im.format.lower() in IMG_FORMATS, f"invalid image format {im.format}"
if im.format.lower() in ("jpg", "jpeg"):
with open(im_file, "rb") as f:
f.seek(-2, 2)
if f.read() != b'\xff\xd9': # corrupt JPEG
ImageOps.exif_transpose(Image.open(im_file)).save(im_file, 'JPEG', subsampling=0, quality=100)
msg = f'{prefix}WARNING ⚠️ {im_file}: corrupt JPEG restored and saved'
if f.read() != b"\xff\xd9": # corrupt JPEG
ImageOps.exif_transpose(Image.open(im_file)).save(im_file, "JPEG", subsampling=0, quality=100)
msg = f"{prefix}WARNING ⚠️ {im_file}: corrupt JPEG restored and saved"
nf = 1
except Exception as e:
nc = 1
msg = f'{prefix}WARNING ⚠️ {im_file}: ignoring corrupt image/label: {e}'
msg = f"{prefix}WARNING ⚠️ {im_file}: ignoring corrupt image/label: {e}"
return (im_file, cls), nf, nc, msg
@ -85,21 +96,21 @@ def verify_image_label(args):
"""Verify one image-label pair."""
im_file, lb_file, prefix, keypoint, num_cls, nkpt, ndim = args
# Number (missing, found, empty, corrupt), message, segments, keypoints
nm, nf, ne, nc, msg, segments, keypoints = 0, 0, 0, 0, '', [], None
nm, nf, ne, nc, msg, segments, keypoints = 0, 0, 0, 0, "", [], None
try:
# Verify images
im = Image.open(im_file)
im.verify() # PIL verify
shape = exif_size(im) # image size
shape = (shape[1], shape[0]) # hw
assert (shape[0] > 9) & (shape[1] > 9), f'image size {shape} <10 pixels'
assert im.format.lower() in IMG_FORMATS, f'invalid image format {im.format}'
if im.format.lower() in ('jpg', 'jpeg'):
with open(im_file, 'rb') as f:
assert (shape[0] > 9) & (shape[1] > 9), f"image size {shape} <10 pixels"
assert im.format.lower() in IMG_FORMATS, f"invalid image format {im.format}"
if im.format.lower() in ("jpg", "jpeg"):
with open(im_file, "rb") as f:
f.seek(-2, 2)
if f.read() != b'\xff\xd9': # corrupt JPEG
ImageOps.exif_transpose(Image.open(im_file)).save(im_file, 'JPEG', subsampling=0, quality=100)
msg = f'{prefix}WARNING ⚠️ {im_file}: corrupt JPEG restored and saved'
if f.read() != b"\xff\xd9": # corrupt JPEG
ImageOps.exif_transpose(Image.open(im_file)).save(im_file, "JPEG", subsampling=0, quality=100)
msg = f"{prefix}WARNING ⚠️ {im_file}: corrupt JPEG restored and saved"
# Verify labels
if os.path.isfile(lb_file):
@ -114,25 +125,26 @@ def verify_image_label(args):
nl = len(lb)
if nl:
if keypoint:
assert lb.shape[1] == (5 + nkpt * ndim), f'labels require {(5 + nkpt * ndim)} columns each'
assert lb.shape[1] == (5 + nkpt * ndim), f"labels require {(5 + nkpt * ndim)} columns each"
points = lb[:, 5:].reshape(-1, ndim)[:, :2]
else:
assert lb.shape[1] == 5, f'labels require 5 columns, {lb.shape[1]} columns detected'
assert lb.shape[1] == 5, f"labels require 5 columns, {lb.shape[1]} columns detected"
points = lb[:, 1:]
assert points.max() <= 1, f'non-normalized or out of bounds coordinates {points[points > 1]}'
assert lb.min() >= 0, f'negative label values {lb[lb < 0]}'
assert points.max() <= 1, f"non-normalized or out of bounds coordinates {points[points > 1]}"
assert lb.min() >= 0, f"negative label values {lb[lb < 0]}"
# All labels
max_cls = lb[:, 0].max() # max label count
assert max_cls <= num_cls, \
f'Label class {int(max_cls)} exceeds dataset class count {num_cls}. ' \
f'Possible class labels are 0-{num_cls - 1}'
assert max_cls <= num_cls, (
f"Label class {int(max_cls)} exceeds dataset class count {num_cls}. "
f"Possible class labels are 0-{num_cls - 1}"
)
_, i = np.unique(lb, axis=0, return_index=True)
if len(i) < nl: # duplicate row check
lb = lb[i] # remove duplicates
if segments:
segments = [segments[x] for x in i]
msg = f'{prefix}WARNING ⚠️ {im_file}: {nl - len(i)} duplicate labels removed'
msg = f"{prefix}WARNING ⚠️ {im_file}: {nl - len(i)} duplicate labels removed"
else:
ne = 1 # label empty
lb = np.zeros((0, (5 + nkpt * ndim) if keypoint else 5), dtype=np.float32)
@ -148,7 +160,7 @@ def verify_image_label(args):
return im_file, lb, shape, segments, keypoints, nm, nf, ne, nc, msg
except Exception as e:
nc = 1
msg = f'{prefix}WARNING ⚠️ {im_file}: ignoring corrupt image/label: {e}'
msg = f"{prefix}WARNING ⚠️ {im_file}: ignoring corrupt image/label: {e}"
return [None, None, None, None, None, nm, nf, ne, nc, msg]
@ -194,8 +206,10 @@ def polygons2masks(imgsz, polygons, color, downsample_ratio=1):
def polygons2masks_overlap(imgsz, segments, downsample_ratio=1):
"""Return a (640, 640) overlap mask."""
masks = np.zeros((imgsz[0] // downsample_ratio, imgsz[1] // downsample_ratio),
dtype=np.int32 if len(segments) > 255 else np.uint8)
masks = np.zeros(
(imgsz[0] // downsample_ratio, imgsz[1] // downsample_ratio),
dtype=np.int32 if len(segments) > 255 else np.uint8,
)
areas = []
ms = []
for si in range(len(segments)):
@ -226,7 +240,7 @@ def find_dataset_yaml(path: Path) -> Path:
Returns:
(Path): The path of the found YAML file.
"""
files = list(path.glob('*.yaml')) or list(path.rglob('*.yaml')) # try root level first and then recursive
files = list(path.glob("*.yaml")) or list(path.rglob("*.yaml")) # try root level first and then recursive
assert files, f"No YAML file found in '{path.resolve()}'"
if len(files) > 1:
files = [f for f in files if f.stem == path.stem] # prefer *.yaml files that match
@ -253,7 +267,7 @@ def check_det_dataset(dataset, autodownload=True):
file = check_file(dataset)
# Download (optional)
extract_dir = ''
extract_dir = ""
if zipfile.is_zipfile(file) or is_tarfile(file):
new_dir = safe_download(file, dir=DATASETS_DIR, unzip=True, delete=False)
file = find_dataset_yaml(DATASETS_DIR / new_dir)
@ -263,43 +277,44 @@ def check_det_dataset(dataset, autodownload=True):
data = yaml_load(file, append_filename=True) # dictionary
# Checks
for k in 'train', 'val':
for k in "train", "val":
if k not in data:
if k != 'val' or 'validation' not in data:
if k != "val" or "validation" not in data:
raise SyntaxError(
emojis(f"{dataset} '{k}:' key missing ❌.\n'train' and 'val' are required in all data YAMLs."))
emojis(f"{dataset} '{k}:' key missing ❌.\n'train' and 'val' are required in all data YAMLs.")
)
LOGGER.info("WARNING ⚠️ renaming data YAML 'validation' key to 'val' to match YOLO format.")
data['val'] = data.pop('validation') # replace 'validation' key with 'val' key
if 'names' not in data and 'nc' not in data:
data["val"] = data.pop("validation") # replace 'validation' key with 'val' key
if "names" not in data and "nc" not in data:
raise SyntaxError(emojis(f"{dataset} key missing ❌.\n either 'names' or 'nc' are required in all data YAMLs."))
if 'names' in data and 'nc' in data and len(data['names']) != data['nc']:
if "names" in data and "nc" in data and len(data["names"]) != data["nc"]:
raise SyntaxError(emojis(f"{dataset} 'names' length {len(data['names'])} and 'nc: {data['nc']}' must match."))
if 'names' not in data:
data['names'] = [f'class_{i}' for i in range(data['nc'])]
if "names" not in data:
data["names"] = [f"class_{i}" for i in range(data["nc"])]
else:
data['nc'] = len(data['names'])
data["nc"] = len(data["names"])
data['names'] = check_class_names(data['names'])
data["names"] = check_class_names(data["names"])
# Resolve paths
path = Path(extract_dir or data.get('path') or Path(data.get('yaml_file', '')).parent) # dataset root
path = Path(extract_dir or data.get("path") or Path(data.get("yaml_file", "")).parent) # dataset root
if not path.is_absolute():
path = (DATASETS_DIR / path).resolve()
# Set paths
data['path'] = path # download scripts
for k in 'train', 'val', 'test':
data["path"] = path # download scripts
for k in "train", "val", "test":
if data.get(k): # prepend path
if isinstance(data[k], str):
x = (path / data[k]).resolve()
if not x.exists() and data[k].startswith('../'):
if not x.exists() and data[k].startswith("../"):
x = (path / data[k][3:]).resolve()
data[k] = str(x)
else:
data[k] = [str((path / x).resolve()) for x in data[k]]
# Parse YAML
val, s = (data.get(x) for x in ('val', 'download'))
val, s = (data.get(x) for x in ("val", "download"))
if val:
val = [Path(x).resolve() for x in (val if isinstance(val, list) else [val])] # val path
if not all(x.exists() for x in val):
@ -312,22 +327,22 @@ def check_det_dataset(dataset, autodownload=True):
raise FileNotFoundError(m)
t = time.time()
r = None # success
if s.startswith('http') and s.endswith('.zip'): # URL
if s.startswith("http") and s.endswith(".zip"): # URL
safe_download(url=s, dir=DATASETS_DIR, delete=True)
elif s.startswith('bash '): # bash script
LOGGER.info(f'Running {s} ...')
elif s.startswith("bash "): # bash script
LOGGER.info(f"Running {s} ...")
r = os.system(s)
else: # python script
exec(s, {'yaml': data})
dt = f'({round(time.time() - t, 1)}s)'
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}\n')
check_font('Arial.ttf' if is_ascii(data['names']) else 'Arial.Unicode.ttf') # download fonts
exec(s, {"yaml": data})
dt = f"({round(time.time() - t, 1)}s)"
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}\n")
check_font("Arial.ttf" if is_ascii(data["names"]) else "Arial.Unicode.ttf") # download fonts
return data # dictionary
def check_cls_dataset(dataset, split=''):
def check_cls_dataset(dataset, split=""):
"""
Checks a classification dataset such as Imagenet.
@ -348,54 +363,59 @@ def check_cls_dataset(dataset, split=''):
"""
# Download (optional if dataset=https://file.zip is passed directly)
if str(dataset).startswith(('http:/', 'https:/')):
if str(dataset).startswith(("http:/", "https:/")):
dataset = safe_download(dataset, dir=DATASETS_DIR, unzip=True, delete=False)
dataset = Path(dataset)
data_dir = (dataset if dataset.is_dir() else (DATASETS_DIR / dataset)).resolve()
if not data_dir.is_dir():
LOGGER.warning(f'\nDataset not found ⚠️, missing path {data_dir}, attempting download...')
LOGGER.warning(f"\nDataset not found ⚠️, missing path {data_dir}, attempting download...")
t = time.time()
if str(dataset) == 'imagenet':
if str(dataset) == "imagenet":
subprocess.run(f"bash {ROOT / 'data/scripts/get_imagenet.sh'}", shell=True, check=True)
else:
url = f'https://github.com/ultralytics/yolov5/releases/download/v1.0/{dataset}.zip'
url = f"https://github.com/ultralytics/yolov5/releases/download/v1.0/{dataset}.zip"
download(url, dir=data_dir.parent)
s = f"Dataset download success ✅ ({time.time() - t:.1f}s), saved to {colorstr('bold', data_dir)}\n"
LOGGER.info(s)
train_set = data_dir / 'train'
val_set = data_dir / 'val' if (data_dir / 'val').exists() else data_dir / 'validation' if \
(data_dir / 'validation').exists() else None # data/test or data/val
test_set = data_dir / 'test' if (data_dir / 'test').exists() else None # data/val or data/test
if split == 'val' and not val_set:
train_set = data_dir / "train"
val_set = (
data_dir / "val"
if (data_dir / "val").exists()
else data_dir / "validation"
if (data_dir / "validation").exists()
else None
) # data/test or data/val
test_set = data_dir / "test" if (data_dir / "test").exists() else None # data/val or data/test
if split == "val" and not val_set:
LOGGER.warning("WARNING ⚠️ Dataset 'split=val' not found, using 'split=test' instead.")
elif split == 'test' and not test_set:
elif split == "test" and not test_set:
LOGGER.warning("WARNING ⚠️ Dataset 'split=test' not found, using 'split=val' instead.")
nc = len([x for x in (data_dir / 'train').glob('*') if x.is_dir()]) # number of classes
names = [x.name for x in (data_dir / 'train').iterdir() if x.is_dir()] # class names list
nc = len([x for x in (data_dir / "train").glob("*") if x.is_dir()]) # number of classes
names = [x.name for x in (data_dir / "train").iterdir() if x.is_dir()] # class names list
names = dict(enumerate(sorted(names)))
# Print to console
for k, v in {'train': train_set, 'val': val_set, 'test': test_set}.items():
for k, v in {"train": train_set, "val": val_set, "test": test_set}.items():
prefix = f'{colorstr(f"{k}:")} {v}...'
if v is None:
LOGGER.info(prefix)
else:
files = [path for path in v.rglob('*.*') if path.suffix[1:].lower() in IMG_FORMATS]
files = [path for path in v.rglob("*.*") if path.suffix[1:].lower() in IMG_FORMATS]
nf = len(files) # number of files
nd = len({file.parent for file in files}) # number of directories
if nf == 0:
if k == 'train':
if k == "train":
raise FileNotFoundError(emojis(f"{dataset} '{k}:' no training images found ❌ "))
else:
LOGGER.warning(f'{prefix} found {nf} images in {nd} classes: WARNING ⚠️ no images found')
LOGGER.warning(f"{prefix} found {nf} images in {nd} classes: WARNING ⚠️ no images found")
elif nd != nc:
LOGGER.warning(f'{prefix} found {nf} images in {nd} classes: ERROR ❌️ requires {nc} classes, not {nd}')
LOGGER.warning(f"{prefix} found {nf} images in {nd} classes: ERROR ❌️ requires {nc} classes, not {nd}")
else:
LOGGER.info(f'{prefix} found {nf} images in {nd} classes ✅ ')
LOGGER.info(f"{prefix} found {nf} images in {nd} classes ✅ ")
return {'train': train_set, 'val': val_set, 'test': test_set, 'nc': nc, 'names': names}
return {"train": train_set, "val": val_set, "test": test_set, "nc": nc, "names": names}
class HUBDatasetStats:
@ -423,42 +443,43 @@ class HUBDatasetStats:
```
"""
def __init__(self, path='coco8.yaml', task='detect', autodownload=False):
def __init__(self, path="coco8.yaml", task="detect", autodownload=False):
"""Initialize class."""
path = Path(path).resolve()
LOGGER.info(f'Starting HUB dataset checks for {path}....')
LOGGER.info(f"Starting HUB dataset checks for {path}....")
self.task = task # detect, segment, pose, classify
if self.task == 'classify':
if self.task == "classify":
unzip_dir = unzip_file(path)
data = check_cls_dataset(unzip_dir)
data['path'] = unzip_dir
data["path"] = unzip_dir
else: # detect, segment, pose
_, data_dir, yaml_path = self._unzip(Path(path))
try:
# Load YAML with checks
data = yaml_load(yaml_path)
data['path'] = '' # strip path since YAML should be in dataset root for all HUB datasets
data["path"] = "" # strip path since YAML should be in dataset root for all HUB datasets
yaml_save(yaml_path, data)
data = check_det_dataset(yaml_path, autodownload) # dict
data['path'] = data_dir # YAML path should be set to '' (relative) or parent (absolute)
data["path"] = data_dir # YAML path should be set to '' (relative) or parent (absolute)
except Exception as e:
raise Exception('error/HUB/dataset_stats/init') from e
raise Exception("error/HUB/dataset_stats/init") from e
self.hub_dir = Path(f'{data["path"]}-hub')
self.im_dir = self.hub_dir / 'images'
self.im_dir = self.hub_dir / "images"
self.im_dir.mkdir(parents=True, exist_ok=True) # makes /images
self.stats = {'nc': len(data['names']), 'names': list(data['names'].values())} # statistics dictionary
self.stats = {"nc": len(data["names"]), "names": list(data["names"].values())} # statistics dictionary
self.data = data
@staticmethod
def _unzip(path):
"""Unzip data.zip."""
if not str(path).endswith('.zip'): # path is data.yaml
if not str(path).endswith(".zip"): # path is data.yaml
return False, None, path
unzip_dir = unzip_file(path, path=path.parent)
assert unzip_dir.is_dir(), f'Error unzipping {path}, {unzip_dir} not found. ' \
f'path/to/abc.zip MUST unzip to path/to/abc/'
assert unzip_dir.is_dir(), (
f"Error unzipping {path}, {unzip_dir} not found. " f"path/to/abc.zip MUST unzip to path/to/abc/"
)
return True, str(unzip_dir), find_dataset_yaml(unzip_dir) # zipped, data_dir, yaml_path
def _hub_ops(self, f):
@ -470,31 +491,31 @@ class HUBDatasetStats:
def _round(labels):
"""Update labels to integer class and 4 decimal place floats."""
if self.task == 'detect':
coordinates = labels['bboxes']
elif self.task == 'segment':
coordinates = [x.flatten() for x in labels['segments']]
elif self.task == 'pose':
n = labels['keypoints'].shape[0]
coordinates = np.concatenate((labels['bboxes'], labels['keypoints'].reshape(n, -1)), 1)
if self.task == "detect":
coordinates = labels["bboxes"]
elif self.task == "segment":
coordinates = [x.flatten() for x in labels["segments"]]
elif self.task == "pose":
n = labels["keypoints"].shape[0]
coordinates = np.concatenate((labels["bboxes"], labels["keypoints"].reshape(n, -1)), 1)
else:
raise ValueError('Undefined dataset task.')
zipped = zip(labels['cls'], coordinates)
raise ValueError("Undefined dataset task.")
zipped = zip(labels["cls"], coordinates)
return [[int(c[0]), *(round(float(x), 4) for x in points)] for c, points in zipped]
for split in 'train', 'val', 'test':
for split in "train", "val", "test":
self.stats[split] = None # predefine
path = self.data.get(split)
# Check split
if path is None: # no split
continue
files = [f for f in Path(path).rglob('*.*') if f.suffix[1:].lower() in IMG_FORMATS] # image files in split
files = [f for f in Path(path).rglob("*.*") if f.suffix[1:].lower() in IMG_FORMATS] # image files in split
if not files: # no images
continue
# Get dataset statistics
if self.task == 'classify':
if self.task == "classify":
from torchvision.datasets import ImageFolder
dataset = ImageFolder(self.data[split])
@ -504,38 +525,35 @@ class HUBDatasetStats:
x[im[1]] += 1
self.stats[split] = {
'instance_stats': {
'total': len(dataset),
'per_class': x.tolist()},
'image_stats': {
'total': len(dataset),
'unlabelled': 0,
'per_class': x.tolist()},
'labels': [{
Path(k).name: v} for k, v in dataset.imgs]}
"instance_stats": {"total": len(dataset), "per_class": x.tolist()},
"image_stats": {"total": len(dataset), "unlabelled": 0, "per_class": x.tolist()},
"labels": [{Path(k).name: v} for k, v in dataset.imgs],
}
else:
from ultralytics.data import YOLODataset
dataset = YOLODataset(img_path=self.data[split], data=self.data, task=self.task)
x = np.array([
np.bincount(label['cls'].astype(int).flatten(), minlength=self.data['nc'])
for label in TQDM(dataset.labels, total=len(dataset), desc='Statistics')]) # shape(128x80)
x = np.array(
[
np.bincount(label["cls"].astype(int).flatten(), minlength=self.data["nc"])
for label in TQDM(dataset.labels, total=len(dataset), desc="Statistics")
]
) # shape(128x80)
self.stats[split] = {
'instance_stats': {
'total': int(x.sum()),
'per_class': x.sum(0).tolist()},
'image_stats': {
'total': len(dataset),
'unlabelled': int(np.all(x == 0, 1).sum()),
'per_class': (x > 0).sum(0).tolist()},
'labels': [{
Path(k).name: _round(v)} for k, v in zip(dataset.im_files, dataset.labels)]}
"instance_stats": {"total": int(x.sum()), "per_class": x.sum(0).tolist()},
"image_stats": {
"total": len(dataset),
"unlabelled": int(np.all(x == 0, 1).sum()),
"per_class": (x > 0).sum(0).tolist(),
},
"labels": [{Path(k).name: _round(v)} for k, v in zip(dataset.im_files, dataset.labels)],
}
# Save, print and return
if save:
stats_path = self.hub_dir / 'stats.json'
LOGGER.info(f'Saving {stats_path.resolve()}...')
with open(stats_path, 'w') as f:
stats_path = self.hub_dir / "stats.json"
LOGGER.info(f"Saving {stats_path.resolve()}...")
with open(stats_path, "w") as f:
json.dump(self.stats, f) # save stats.json
if verbose:
LOGGER.info(json.dumps(self.stats, indent=2, sort_keys=False))
@ -545,14 +563,14 @@ class HUBDatasetStats:
"""Compress images for Ultralytics HUB."""
from ultralytics.data import YOLODataset # ClassificationDataset
for split in 'train', 'val', 'test':
for split in "train", "val", "test":
if self.data.get(split) is None:
continue
dataset = YOLODataset(img_path=self.data[split], data=self.data)
with ThreadPool(NUM_THREADS) as pool:
for _ in TQDM(pool.imap(self._hub_ops, dataset.im_files), total=len(dataset), desc=f'{split} images'):
for _ in TQDM(pool.imap(self._hub_ops, dataset.im_files), total=len(dataset), desc=f"{split} images"):
pass
LOGGER.info(f'Done. All images saved to {self.im_dir}')
LOGGER.info(f"Done. All images saved to {self.im_dir}")
return self.im_dir
@ -583,9 +601,9 @@ def compress_one_image(f, f_new=None, max_dim=1920, quality=50):
r = max_dim / max(im.height, im.width) # ratio
if r < 1.0: # image too large
im = im.resize((int(im.width * r), int(im.height * r)))
im.save(f_new or f, 'JPEG', quality=quality, optimize=True) # save
im.save(f_new or f, "JPEG", quality=quality, optimize=True) # save
except Exception as e: # use OpenCV
LOGGER.info(f'WARNING ⚠️ HUB ops PIL failure {f}: {e}')
LOGGER.info(f"WARNING ⚠️ HUB ops PIL failure {f}: {e}")
im = cv2.imread(f)
im_height, im_width = im.shape[:2]
r = max_dim / max(im_height, im_width) # ratio
@ -594,7 +612,7 @@ def compress_one_image(f, f_new=None, max_dim=1920, quality=50):
cv2.imwrite(str(f_new or f), im)
def autosplit(path=DATASETS_DIR / 'coco8/images', weights=(0.9, 0.1, 0.0), annotated_only=False):
def autosplit(path=DATASETS_DIR / "coco8/images", weights=(0.9, 0.1, 0.0), annotated_only=False):
"""
Automatically split a dataset into train/val/test splits and save the resulting splits into autosplit_*.txt files.
@ -612,18 +630,18 @@ def autosplit(path=DATASETS_DIR / 'coco8/images', weights=(0.9, 0.1, 0.0), annot
"""
path = Path(path) # images dir
files = sorted(x for x in path.rglob('*.*') if x.suffix[1:].lower() in IMG_FORMATS) # image files only
files = sorted(x for x in path.rglob("*.*") if x.suffix[1:].lower() in IMG_FORMATS) # image files only
n = len(files) # number of files
random.seed(0) # for reproducibility
indices = random.choices([0, 1, 2], weights=weights, k=n) # assign each image to a split
txt = ['autosplit_train.txt', 'autosplit_val.txt', 'autosplit_test.txt'] # 3 txt files
txt = ["autosplit_train.txt", "autosplit_val.txt", "autosplit_test.txt"] # 3 txt files
for x in txt:
if (path.parent / x).exists():
(path.parent / x).unlink() # remove existing
LOGGER.info(f'Autosplitting images from {path}' + ', using *.txt labeled images only' * annotated_only)
LOGGER.info(f"Autosplitting images from {path}" + ", using *.txt labeled images only" * annotated_only)
for i, img in TQDM(zip(indices, files), total=n):
if not annotated_only or Path(img2label_paths([str(img)])[0]).exists(): # check label
with open(path.parent / txt[i], 'a') as f:
f.write(f'./{img.relative_to(path.parent).as_posix()}' + '\n') # add image to txt file
with open(path.parent / txt[i], "a") as f:
f.write(f"./{img.relative_to(path.parent).as_posix()}" + "\n") # add image to txt file