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ultralytics 8.0.133 add torchvision compatibility check (#3703)

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Glenn Jocher 2023-07-13 01:40:50 +02:00 • committed by GitHub
parent 0821ccb618
commit c55a98ab8e
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17 changed files with 140 additions and 68 deletions
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17
ultralytics/yolo/utils/plotting.py
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@ -21,7 +21,8 @@ from .ops import clip_boxes, scale_image, xywh2xyxy, xyxy2xywh
class Colors:
# Ultralytics color palette https://ultralytics.com/
"""Ultralytics color palette https://ultralytics.com/."""
def __init__(self):
"""Initialize colors as hex = matplotlib.colors.TABLEAU_COLORS.values()."""
hexs = ('FF3838', 'FF9D97', 'FF701F', 'FFB21D', 'CFD231', '48F90A', '92CC17', '3DDB86', '1A9334', '00D4BB',
@ -48,7 +49,8 @@ colors = Colors() # create instance for 'from utils.plots import colors'
class Annotator:
# YOLOv8 Annotator for train/val mosaics and jpgs and detect/hub inference annotations
"""YOLOv8 Annotator for train/val mosaics and jpgs and detect/hub inference annotations."""
def __init__(self, im, line_width=None, font_size=None, font='Arial.ttf', pil=False, example='abc'):
"""Initialize the Annotator class with image and line width along with color palette for keypoints and limbs."""
assert im.data.contiguous, 'Image not contiguous. Apply np.ascontiguousarray(im) to Annotator() input images.'
@ -204,7 +206,14 @@ class Annotator:
self.draw.rectangle((xy[0], xy[1], xy[0] + w + 1, xy[1] + h + 1), fill=txt_color)
# Using `txt_color` for background and draw fg with white color
txt_color = (255, 255, 255)
self.draw.text(xy, text, fill=txt_color, font=self.font)
if '\n' in text:
lines = text.split('\n')
_, h = self.font.getsize(text)
for line in lines:
self.draw.text(xy, line, fill=txt_color, font=self.font)
xy[1] += h
else:
self.draw.text(xy, text, fill=txt_color, font=self.font)
else:
if box_style:
tf = max(self.lw - 1, 1) # font thickness
@ -310,7 +319,7 @@ def plot_images(images,
fname='images.jpg',
names=None,
on_plot=None):
# Plot image grid with labels
"""Plot image grid with labels."""
if isinstance(images, torch.Tensor):
images = images.cpu().float().numpy()
if isinstance(cls, torch.Tensor):