HongChuang/ultralytics-ascend
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions 3

ultralytics 8.2.63 refactor FastSAMPredictor (#14582)

Browse source 
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>
Co-authored-by: Laughing-q <1185102784@qq.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
This commit is contained in:
Nguyễn Anh Bình 2024-07-23 00:59:37 +07:00 • committed by GitHub
parent db82d1c6ae
commit 3637516412
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
5 changed files with 22 additions and 118 deletions
Download patch file Download diff file Expand all files Collapse all files

42
ultralytics/models/fastsam/utils.py
View file

@ -1,7 +1,5 @@
# Ultralytics YOLO 🚀, AGPL-3.0 license
import torch
def adjust_bboxes_to_image_border(boxes, image_shape, threshold=20):
"""
@ -25,43 +23,3 @@ def adjust_bboxes_to_image_border(boxes, image_shape, threshold=20):
boxes[boxes[:, 2] > w - threshold, 2] = w # x2
boxes[boxes[:, 3] > h - threshold, 3] = h # y2
return boxes
def bbox_iou(box1, boxes, iou_thres=0.9, image_shape=(640, 640), raw_output=False):
"""
Compute the Intersection-Over-Union of a bounding box with respect to an array of other bounding boxes.
Args:
box1 (torch.Tensor): (4, )
boxes (torch.Tensor): (n, 4)
iou_thres (float): IoU threshold
image_shape (tuple): (height, width)
raw_output (bool): If True, return the raw IoU values instead of the indices
Returns:
high_iou_indices (torch.Tensor): Indices of boxes with IoU > thres
"""
boxes = adjust_bboxes_to_image_border(boxes, image_shape)
# Obtain coordinates for intersections
x1 = torch.max(box1[0], boxes[:, 0])
y1 = torch.max(box1[1], boxes[:, 1])
x2 = torch.min(box1[2], boxes[:, 2])
y2 = torch.min(box1[3], boxes[:, 3])
# Compute the area of intersection
intersection = (x2 - x1).clamp(0) * (y2 - y1).clamp(0)
# Compute the area of both individual boxes
box1_area = (box1[2] - box1[0]) * (box1[3] - box1[1])
box2_area = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
# Compute the area of union
union = box1_area + box2_area - intersection
# Compute the IoU
iou = intersection / union # Should be shape (n, )
if raw_output:
return 0 if iou.numel() == 0 else iou
# return indices of boxes with IoU > thres
return torch.nonzero(iou > iou_thres).flatten()