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Update .pre-commit-config.yaml (#1026)

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Glenn Jocher 2023-02-17 22:26:40 +01:00 • committed by GitHub
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ultralytics/yolo/data/utils.py
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@ -18,32 +18,32 @@ from ultralytics.yolo.utils.checks import check_file, check_font, is_ascii
from ultralytics.yolo.utils.downloads import download, safe_download
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
VID_FORMATS = "asf", "avi", "gif", "m4v", "mkv", "mov", "mp4", "mpeg", "mpg", "ts", "wmv" # include video suffixes
LOCAL_RANK = int(os.getenv("LOCAL_RANK", -1)) # https://pytorch.org/docs/stable/elastic/run.html
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
VID_FORMATS = 'asf', 'avi', 'gif', 'm4v', 'mkv', 'mov', 'mp4', 'mpeg', 'mpg', 'ts', 'wmv' # include video suffixes
LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1)) # https://pytorch.org/docs/stable/elastic/run.html
RANK = int(os.getenv('RANK', -1))
PIN_MEMORY = str(os.getenv("PIN_MEMORY", True)).lower() == "true" # global pin_memory for dataloaders
PIN_MEMORY = str(os.getenv('PIN_MEMORY', True)).lower() == 'true' # global pin_memory for dataloaders
IMAGENET_MEAN = 0.485, 0.456, 0.406 # RGB mean
IMAGENET_STD = 0.229, 0.224, 0.225 # RGB standard deviation
# Get orientation exif tag
for orientation in ExifTags.TAGS.keys():
if ExifTags.TAGS[orientation] == "Orientation":
if ExifTags.TAGS[orientation] == 'Orientation':
break
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
@ -61,21 +61,21 @@ def verify_image_label(args):
# Verify one image-label pair
im_file, lb_file, prefix, keypoint, num_cls = 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):
@ -90,31 +90,31 @@ def verify_image_label(args):
nl = len(lb)
if nl:
if keypoint:
assert lb.shape[1] == 56, "labels require 56 columns each"
assert (lb[:, 5::3] <= 1).all(), "non-normalized or out of bounds coordinate labels"
assert (lb[:, 6::3] <= 1).all(), "non-normalized or out of bounds coordinate labels"
assert lb.shape[1] == 56, 'labels require 56 columns each'
assert (lb[:, 5::3] <= 1).all(), 'non-normalized or out of bounds coordinate labels'
assert (lb[:, 6::3] <= 1).all(), 'non-normalized or out of bounds coordinate labels'
kpts = np.zeros((lb.shape[0], 39))
for i in range(len(lb)):
kpt = np.delete(lb[i, 5:], np.arange(2, lb.shape[1] - 5, 3)) # remove occlusion param from GT
kpts[i] = np.hstack((lb[i, :5], kpt))
lb = kpts
assert lb.shape[1] == 39, "labels require 39 columns each after removing occlusion parameter"
assert lb.shape[1] == 39, 'labels require 39 columns each after removing occlusion parameter'
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'
assert (lb[:, 1:] <= 1).all(), \
f"non-normalized or out of bounds coordinates {lb[:, 1:][lb[:, 1:] > 1]}"
f'non-normalized or out of bounds coordinates {lb[:, 1:][lb[:, 1:] > 1]}'
# All labels
max_cls = int(lb[:, 0].max()) # max label count
assert max_cls <= num_cls, \
f'Label class {max_cls} exceeds dataset class count {num_cls}. ' \
f'Possible class labels are 0-{num_cls - 1}'
assert (lb >= 0).all(), f"negative label values {lb[lb < 0]}"
assert (lb >= 0).all(), f'negative label values {lb[lb < 0]}'
_, 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, 39), dtype=np.float32) if keypoint else np.zeros((0, 5), dtype=np.float32)
@ -127,7 +127,7 @@ 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]
@ -248,8 +248,8 @@ def check_det_dataset(dataset, autodownload=True):
else: # python script
r = exec(s, {'yaml': data}) # return None
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")
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
@ -284,9 +284,9 @@ def check_cls_dataset(dataset: str):
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"
train_set = data_dir / 'train'
test_set = data_dir / 'test' if (data_dir / 'test').exists() else data_dir / 'val' # data/test or data/val
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)))
return {"train": train_set, "val": test_set, "nc": nc, "names": names}
return {'train': train_set, 'val': test_set, 'nc': nc, 'names': names}