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Reference for ultralytics/models/fastsam/utils.py

Note

Full source code for this file is available at https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/fastsam/utils.py. Help us fix any issues you see by submitting a Pull Request 🛠️. Thank you 🙏!


ultralytics.models.fastsam.utils.adjust_bboxes_to_image_border(boxes, image_shape, threshold=20)

Adjust bounding boxes to stick to image border if they are within a certain threshold.

Parameters:

Name Type Description Default
boxes Tensor

(n, 4)

required
image_shape tuple

(height, width)

required
threshold int

pixel threshold

20

Returns:

Name Type Description
adjusted_boxes Tensor

adjusted bounding boxes

Source code in ultralytics/models/fastsam/utils.py
def adjust_bboxes_to_image_border(boxes, image_shape, threshold=20):
    """
    Adjust bounding boxes to stick to image border if they are within a certain threshold.

    Args:
        boxes (torch.Tensor): (n, 4)
        image_shape (tuple): (height, width)
        threshold (int): pixel threshold

    Returns:
        adjusted_boxes (torch.Tensor): adjusted bounding boxes
    """

    # Image dimensions
    h, w = image_shape

    # Adjust boxes
    boxes[boxes[:, 0] < threshold, 0] = 0  # x1
    boxes[boxes[:, 1] < threshold, 1] = 0  # y1
    boxes[boxes[:, 2] > w - threshold, 2] = w  # x2
    boxes[boxes[:, 3] > h - threshold, 3] = h  # y2
    return boxes




ultralytics.models.fastsam.utils.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.

Parameters:

Name Type Description Default
box1 Tensor

(4, )

required
boxes Tensor

(n, 4)

required
iou_thres float

IoU threshold

0.9
image_shape tuple

(height, width)

(640, 640)
raw_output bool

If True, return the raw IoU values instead of the indices

False

Returns:

Name Type Description
high_iou_indices Tensor

Indices of boxes with IoU > thres

Source code in ultralytics/models/fastsam/utils.py
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()




Created 2023-07-16, Updated 2023-08-07
Authors: glenn-jocher (5), Laughing-q (1)