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

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This commit is contained in:
Glenn Jocher
2023-02-17 22:26:40 +01:00
committed by GitHub
parent 9047d737f4
commit edd3ff1669
76 changed files with 928 additions and 935 deletions
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30
ultralytics/yolo/data/dataloaders/stream_loaders.py
View File

@ -47,7 +47,7 @@ class LoadStreams:
# YouTube format i.e. 'https://www.youtube.com/watch?v=Zgi9g1ksQHc' or 'https://youtu.be/Zgi9g1ksQHc'
check_requirements(('pafy', 'youtube_dl==2020.12.2'))
import pafy # noqa
s = pafy.new(s).getbest(preftype="mp4").url # YouTube URL
s = pafy.new(s).getbest(preftype='mp4').url # YouTube URL
s = eval(s) if s.isnumeric() else s # i.e. s = '0' local webcam
if s == 0 and (is_colab() or is_kaggle()):
raise NotImplementedError("'source=0' webcam not supported in Colab and Kaggle notebooks. "
@ -65,7 +65,7 @@ class LoadStreams:
if not success or self.imgs[i] is None:
raise ConnectionError(f'{st}Failed to read images from {s}')
self.threads[i] = Thread(target=self.update, args=([i, cap, s]), daemon=True)
LOGGER.info(f"{st}Success ✅ ({self.frames[i]} frames of shape {w}x{h} at {self.fps[i]:.2f} FPS)")
LOGGER.info(f'{st}Success ✅ ({self.frames[i]} frames of shape {w}x{h} at {self.fps[i]:.2f} FPS)')
self.threads[i].start()
LOGGER.info('') # newline
@ -145,11 +145,11 @@ class LoadScreenshots:
# Parse monitor shape
monitor = self.sct.monitors[self.screen]
self.top = monitor["top"] if top is None else (monitor["top"] + top)
self.left = monitor["left"] if left is None else (monitor["left"] + left)
self.width = width or monitor["width"]
self.height = height or monitor["height"]
self.monitor = {"left": self.left, "top": self.top, "width": self.width, "height": self.height}
self.top = monitor['top'] if top is None else (monitor['top'] + top)
self.left = monitor['left'] if left is None else (monitor['left'] + left)
self.width = width or monitor['width']
self.height = height or monitor['height']
self.monitor = {'left': self.left, 'top': self.top, 'width': self.width, 'height': self.height}
def __iter__(self):
return self
@ -157,7 +157,7 @@ class LoadScreenshots:
def __next__(self):
# mss screen capture: get raw pixels from the screen as np array
im0 = np.array(self.sct.grab(self.monitor))[:, :, :3] # [:, :, :3] BGRA to BGR
s = f"screen {self.screen} (LTWH): {self.left},{self.top},{self.width},{self.height}: "
s = f'screen {self.screen} (LTWH): {self.left},{self.top},{self.width},{self.height}: '
if self.transforms:
im = self.transforms(im0) # transforms
@ -172,7 +172,7 @@ class LoadScreenshots:
class LoadImages:
# YOLOv8 image/video dataloader, i.e. `yolo predict source=image.jpg/vid.mp4`
def __init__(self, path, imgsz=640, stride=32, auto=True, transforms=None, vid_stride=1):
if isinstance(path, str) and Path(path).suffix == ".txt": # *.txt file with img/vid/dir on each line
if isinstance(path, str) and Path(path).suffix == '.txt': # *.txt file with img/vid/dir on each line
path = Path(path).read_text().rsplit()
files = []
for p in sorted(path) if isinstance(path, (list, tuple)) else [path]:
@ -290,12 +290,12 @@ class LoadPilAndNumpy:
self.transforms = transforms
self.mode = 'image'
# generate fake paths
self.paths = [f"image{i}.jpg" for i in range(len(self.im0))]
self.paths = [f'image{i}.jpg' for i in range(len(self.im0))]
self.bs = len(self.im0)
@staticmethod
def _single_check(im):
assert isinstance(im, (Image.Image, np.ndarray)), f"Expected PIL/np.ndarray image type, but got {type(im)}"
assert isinstance(im, (Image.Image, np.ndarray)), f'Expected PIL/np.ndarray image type, but got {type(im)}'
if isinstance(im, Image.Image):
im = np.asarray(im)[:, :, ::-1]
im = np.ascontiguousarray(im) # contiguous
@ -338,16 +338,16 @@ def autocast_list(source):
elif isinstance(im, (Image.Image, np.ndarray)): # PIL or np Image
files.append(im)
else:
raise TypeError(f"type {type(im).__name__} is not a supported Ultralytics prediction source type. \n"
f"See https://docs.ultralytics.com/predict for supported source types.")
raise TypeError(f'type {type(im).__name__} is not a supported Ultralytics prediction source type. \n'
f'See https://docs.ultralytics.com/predict for supported source types.')
return files
LOADERS = [LoadStreams, LoadPilAndNumpy, LoadImages, LoadScreenshots]
if __name__ == "__main__":
img = cv2.imread(str(ROOT / "assets/bus.jpg"))
if __name__ == '__main__':
img = cv2.imread(str(ROOT / 'assets/bus.jpg'))
dataset = LoadPilAndNumpy(im0=img)
for d in dataset:
print(d[0])

34
ultralytics/yolo/data/dataloaders/v5loader.py
View File

@ -92,7 +92,7 @@ def exif_transpose(image):
if method is not None:
image = image.transpose(method)
del exif[0x0112]
image.info["exif"] = exif.tobytes()
image.info['exif'] = exif.tobytes()
return image
@ -217,11 +217,11 @@ class LoadScreenshots:
# Parse monitor shape
monitor = self.sct.monitors[self.screen]
self.top = monitor["top"] if top is None else (monitor["top"] + top)
self.left = monitor["left"] if left is None else (monitor["left"] + left)
self.width = width or monitor["width"]
self.height = height or monitor["height"]
self.monitor = {"left": self.left, "top": self.top, "width": self.width, "height": self.height}
self.top = monitor['top'] if top is None else (monitor['top'] + top)
self.left = monitor['left'] if left is None else (monitor['left'] + left)
self.width = width or monitor['width']
self.height = height or monitor['height']
self.monitor = {'left': self.left, 'top': self.top, 'width': self.width, 'height': self.height}
def __iter__(self):
return self
@ -229,7 +229,7 @@ class LoadScreenshots:
def __next__(self):
# mss screen capture: get raw pixels from the screen as np array
im0 = np.array(self.sct.grab(self.monitor))[:, :, :3] # [:, :, :3] BGRA to BGR
s = f"screen {self.screen} (LTWH): {self.left},{self.top},{self.width},{self.height}: "
s = f'screen {self.screen} (LTWH): {self.left},{self.top},{self.width},{self.height}: '
if self.transforms:
im = self.transforms(im0) # transforms
@ -244,7 +244,7 @@ class LoadScreenshots:
class LoadImages:
# YOLOv5 image/video dataloader, i.e. `python detect.py --source image.jpg/vid.mp4`
def __init__(self, path, img_size=640, stride=32, auto=True, transforms=None, vid_stride=1):
if isinstance(path, str) and Path(path).suffix == ".txt": # *.txt file with img/vid/dir on each line
if isinstance(path, str) and Path(path).suffix == '.txt': # *.txt file with img/vid/dir on each line
path = Path(path).read_text().rsplit()
files = []
for p in sorted(path) if isinstance(path, (list, tuple)) else [path]:
@ -363,7 +363,7 @@ class LoadStreams:
# YouTube format i.e. 'https://www.youtube.com/watch?v=Zgi9g1ksQHc' or 'https://youtu.be/Zgi9g1ksQHc'
check_requirements(('pafy', 'youtube_dl==2020.12.2'))
import pafy
s = pafy.new(s).getbest(preftype="mp4").url # YouTube URL
s = pafy.new(s).getbest(preftype='mp4').url # YouTube URL
s = eval(s) if s.isnumeric() else s # i.e. s = '0' local webcam
if s == 0:
assert not is_colab(), '--source 0 webcam unsupported on Colab. Rerun command in a local environment.'
@ -378,7 +378,7 @@ class LoadStreams:
_, self.imgs[i] = cap.read() # guarantee first frame
self.threads[i] = Thread(target=self.update, args=([i, cap, s]), daemon=True)
LOGGER.info(f"{st} Success ({self.frames[i]} frames {w}x{h} at {self.fps[i]:.2f} FPS)")
LOGGER.info(f'{st} Success ({self.frames[i]} frames {w}x{h} at {self.fps[i]:.2f} FPS)')
self.threads[i].start()
LOGGER.info('') # newline
@ -500,7 +500,7 @@ class LoadImagesAndLabels(Dataset):
# Display cache
nf, nm, ne, nc, n = cache.pop('results') # found, missing, empty, corrupt, total
if exists and LOCAL_RANK in {-1, 0}:
d = f"Scanning {cache_path}... {nf} images, {nm + ne} backgrounds, {nc} corrupt"
d = f'Scanning {cache_path}... {nf} images, {nm + ne} backgrounds, {nc} corrupt'
tqdm(None, desc=prefix + d, total=n, initial=n, bar_format=TQDM_BAR_FORMAT) # display cache results
if cache['msgs']:
LOGGER.info('\n'.join(cache['msgs'])) # display warnings
@ -604,8 +604,8 @@ class LoadImagesAndLabels(Dataset):
mem = psutil.virtual_memory()
cache = mem_required * (1 + safety_margin) < mem.available # to cache or not to cache, that is the question
if not cache:
LOGGER.info(f"{prefix}{mem_required / gb:.1f}GB RAM required, "
f"{mem.available / gb:.1f}/{mem.total / gb:.1f}GB available, "
LOGGER.info(f'{prefix}{mem_required / gb:.1f}GB RAM required, '
f'{mem.available / gb:.1f}/{mem.total / gb:.1f}GB available, '
f"{'caching images ✅' if cache else 'not caching images ⚠️'}")
return cache
@ -615,7 +615,7 @@ class LoadImagesAndLabels(Dataset):
path.unlink() # remove *.cache file if exists
x = {} # dict
nm, nf, ne, nc, msgs = 0, 0, 0, 0, [] # number missing, found, empty, corrupt, messages
desc = f"{prefix}Scanning {path.parent / path.stem}..."
desc = f'{prefix}Scanning {path.parent / path.stem}...'
total = len(self.im_files)
with ThreadPool(NUM_THREADS) as pool:
results = pool.imap(verify_image_label, zip(self.im_files, self.label_files, repeat(prefix)))
@ -629,7 +629,7 @@ class LoadImagesAndLabels(Dataset):
x[im_file] = [lb, shape, segments]
if msg:
msgs.append(msg)
pbar.desc = f"{desc} {nf} images, {nm + ne} backgrounds, {nc} corrupt"
pbar.desc = f'{desc} {nf} images, {nm + ne} backgrounds, {nc} corrupt'
pbar.close()
if msgs:
@ -1060,7 +1060,7 @@ class HUBDatasetStats():
if zipped:
data['path'] = data_dir
except Exception as e:
raise Exception("error/HUB/dataset_stats/yaml_load") from e
raise Exception('error/HUB/dataset_stats/yaml_load') from e
check_det_dataset(data, autodownload) # download dataset if missing
self.hub_dir = Path(data['path'] + '-hub')
@ -1187,7 +1187,7 @@ class ClassificationDataset(torchvision.datasets.ImageFolder):
else: # read image
im = cv2.imread(f) # BGR
if self.album_transforms:
sample = self.album_transforms(image=cv2.cvtColor(im, cv2.COLOR_BGR2RGB))["image"]
sample = self.album_transforms(image=cv2.cvtColor(im, cv2.COLOR_BGR2RGB))['image']
else:
sample = self.torch_transforms(im)
return sample, j