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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
parent 9047d737f4
commit edd3ff1669
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76 changed files with 928 additions and 935 deletions
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30
ultralytics/yolo/data/dataloaders/stream_loaders.py
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@ -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])