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ultralytics 8.0.90 actions and docs improvements (#2326)

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This commit is contained in:
Glenn Jocher
2023-04-29 20:16:56 +02:00
committed by GitHub
parent 243fc4b1fe
commit 44c7c3514d
39 changed files with 783 additions and 143 deletions
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9
ultralytics/vit/sam/modules/mask_generator.py
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@ -45,7 +45,7 @@ class SamAutomaticMaskGenerator:
Arguments:
model (Sam): The SAM model to use for mask prediction.
points_per_side (int or None): The number of points to be sampled
points_per_side (int, None): The number of points to be sampled
along one side of the image. The total number of points is
points_per_side**2. If None, 'point_grids' must provide explicit
point sampling.
@ -70,7 +70,7 @@ class SamAutomaticMaskGenerator:
the image length. Later layers with more crops scale down this overlap.
crop_n_points_downscale_factor (int): The number of points-per-side
sampled in layer n is scaled down by crop_n_points_downscale_factor**n.
point_grids (list(np.ndarray) or None): A list over explicit grids
point_grids (list(np.ndarray), None): A list over explicit grids
of points used for sampling, normalized to [0,1]. The nth grid in the
list is used in the nth crop layer. Exclusive with points_per_side.
min_mask_region_area (int): If >0, postprocessing will be applied
@ -128,9 +128,8 @@ class SamAutomaticMaskGenerator:
image (np.ndarray): The image to generate masks for, in HWC uint8 format.
Returns:
list(dict(str, any)): A list over records for masks. Each record is
a dict containing the following keys:
segmentation (dict(str, any) or np.ndarray): The mask. If
list(dict(str, any)): A list over records for masks. Each record is a dict containing the following keys:
segmentation (dict(str, any), np.ndarray): The mask. If
output_mode='binary_mask', is an array of shape HW. Otherwise,
is a dictionary containing the RLE.
bbox (list(float)): The box around the mask, in XYWH format.