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ultralytics 8.0.134 add MobileSAM support (#3474)

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Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Chaoning Zhang 2023-07-13 20:25:56 +08:00 • committed by GitHub
parent c55a98ab8e
commit 201e69e4e4
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32 changed files with 1472 additions and 841 deletions
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10
ultralytics/yolo/engine/results.py
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@ -170,7 +170,7 @@ class Results(SimpleClass):
font='Arial.ttf',
pil=False,
img=None,
img_gpu=None,
im_gpu=None,
kpt_line=True,
labels=True,
boxes=True,
@ -188,7 +188,7 @@ class Results(SimpleClass):
font (str): The font to use for the text.
pil (bool): Whether to return the image as a PIL Image.
img (numpy.ndarray): Plot to another image. if not, plot to original image.
img_gpu (torch.Tensor): Normalized image in gpu with shape (1, 3, 640, 640), for faster mask plotting.
im_gpu (torch.Tensor): Normalized image in gpu with shape (1, 3, 640, 640), for faster mask plotting.
kpt_line (bool): Whether to draw lines connecting keypoints.
labels (bool): Whether to plot the label of bounding boxes.
boxes (bool): Whether to plot the bounding boxes.
@ -226,12 +226,12 @@ class Results(SimpleClass):
# Plot Segment results
if pred_masks and show_masks:
if img_gpu is None:
if im_gpu is None:
img = LetterBox(pred_masks.shape[1:])(image=annotator.result())
img_gpu = torch.as_tensor(img, dtype=torch.float16, device=pred_masks.data.device).permute(
im_gpu = torch.as_tensor(img, dtype=torch.float16, device=pred_masks.data.device).permute(
2, 0, 1).flip(0).contiguous() / 255
idx = pred_boxes.cls if pred_boxes else range(len(pred_masks))
annotator.masks(pred_masks.data, colors=[colors(x, True) for x in idx], im_gpu=img_gpu)
annotator.masks(pred_masks.data, colors=[colors(x, True) for x in idx], im_gpu=im_gpu)
# Plot Detect results
if pred_boxes and show_boxes: