ultralytics 8.0.151 add DOTAv2.yaml for OBB training (#4258)
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Kayzwer <68285002+Kayzwer@users.noreply.github.com>
This commit is contained in:
Glenn Jocher
GitHub
@ -357,14 +357,15 @@ def scale_image(masks, im0_shape, ratio_pad=None):
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def xyxy2xywh(x):
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"""
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Convert bounding box coordinates from (x1, y1, x2, y2) format to (x, y, width, height) format.
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Convert bounding box coordinates from (x1, y1, x2, y2) format to (x, y, width, height) format where (x1, y1) is the
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top-left corner and (x2, y2) is the bottom-right corner.
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Args:
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x (np.ndarray | torch.Tensor): The input bounding box coordinates in (x1, y1, x2, y2) format.
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Returns:
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y (np.ndarray | torch.Tensor): The bounding box coordinates in (x, y, width, height) format.
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y (np.ndarray | torch.Tensor): The bounding box coordinates in (x, y, width, height) format.
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"""
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y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
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y = torch.empty_like(x) if isinstance(x, torch.Tensor) else np.empty_like(x)
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y[..., 0] = (x[..., 0] + x[..., 2]) / 2 # x center
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y[..., 1] = (x[..., 1] + x[..., 3]) / 2 # y center
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y[..., 2] = x[..., 2] - x[..., 0] # width
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@ -382,11 +383,13 @@ def xywh2xyxy(x):
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Returns:
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y (np.ndarray | torch.Tensor): The bounding box coordinates in (x1, y1, x2, y2) format.
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"""
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y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
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y[..., 0] = x[..., 0] - x[..., 2] / 2 # top left x
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y[..., 1] = x[..., 1] - x[..., 3] / 2 # top left y
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y[..., 2] = x[..., 0] + x[..., 2] / 2 # bottom right x
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y[..., 3] = x[..., 1] + x[..., 3] / 2 # bottom right y
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y = torch.empty_like(x) if isinstance(x, torch.Tensor) else np.empty_like(x)
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dw = x[..., 2] / 2 # half-width
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dh = x[..., 3] / 2 # half-height
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y[..., 0] = x[..., 0] - dw # top left x
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y[..., 1] = x[..., 1] - dh # top left y
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y[..., 2] = x[..., 0] + dw # bottom right x
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y[..., 3] = x[..., 1] + dh # bottom right y
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return y
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@ -404,7 +407,7 @@ def xywhn2xyxy(x, w=640, h=640, padw=0, padh=0):
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y (np.ndarray | torch.Tensor): The coordinates of the bounding box in the format [x1, y1, x2, y2] where
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x1,y1 is the top-left corner, x2,y2 is the bottom-right corner of the bounding box.
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"""
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y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
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y = torch.empty_like(x) if isinstance(x, torch.Tensor) else np.empty_like(x)
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y[..., 0] = w * (x[..., 0] - x[..., 2] / 2) + padw # top left x
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y[..., 1] = h * (x[..., 1] - x[..., 3] / 2) + padh # top left y
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y[..., 2] = w * (x[..., 0] + x[..., 2] / 2) + padw # bottom right x
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@ -428,7 +431,7 @@ def xyxy2xywhn(x, w=640, h=640, clip=False, eps=0.0):
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"""
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if clip:
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clip_boxes(x, (h - eps, w - eps)) # warning: inplace clip
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y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
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y = torch.empty_like(x) if isinstance(x, torch.Tensor) else np.empty_like(x)
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y[..., 0] = ((x[..., 0] + x[..., 2]) / 2) / w # x center
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y[..., 1] = ((x[..., 1] + x[..., 3]) / 2) / h # y center
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y[..., 2] = (x[..., 2] - x[..., 0]) / w # width
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@ -449,7 +452,7 @@ def xyn2xy(x, w=640, h=640, padw=0, padh=0):
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Returns:
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y (np.ndarray | torch.Tensor): The x and y coordinates of the top left corner of the bounding box
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"""
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y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
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y = torch.empty_like(x) if isinstance(x, torch.Tensor) else np.empty_like(x)
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y[..., 0] = w * x[..., 0] + padw # top left x
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y[..., 1] = h * x[..., 1] + padh # top left y
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return y
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@ -464,7 +467,7 @@ def xywh2ltwh(x):
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Returns:
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y (np.ndarray | torch.Tensor): The bounding box coordinates in the xyltwh format
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"""
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y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
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y = torch.empty_like(x) if isinstance(x, torch.Tensor) else np.empty_like(x)
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y[:, 0] = x[:, 0] - x[:, 2] / 2 # top left x
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y[:, 1] = x[:, 1] - x[:, 3] / 2 # top left y
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return y
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@ -479,7 +482,7 @@ def xyxy2ltwh(x):
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Returns:
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y (np.ndarray | torch.Tensor): The bounding box coordinates in the xyltwh format.
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"""
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y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
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y = torch.empty_like(x) if isinstance(x, torch.Tensor) else np.empty_like(x)
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y[:, 2] = x[:, 2] - x[:, 0] # width
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y[:, 3] = x[:, 3] - x[:, 1] # height
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return y
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@ -492,12 +495,91 @@ def ltwh2xywh(x):
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Args:
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x (torch.Tensor): the input tensor
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"""
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y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
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y = torch.empty_like(x) if isinstance(x, torch.Tensor) else np.empty_like(x)
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y[:, 0] = x[:, 0] + x[:, 2] / 2 # center x
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y[:, 1] = x[:, 1] + x[:, 3] / 2 # center y
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return y
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def xyxyxyxy2xywhr(corners):
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"""
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Convert batched Oriented Bounding Boxes (OBB) from [xy1, xy2, xy3, xy4] to [xywh, rotation].
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Args:
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corners (numpy.ndarray | torch.Tensor): Input corners of shape (n, 8).
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Returns:
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(numpy.ndarray | torch.Tensor): Converted data in [cx, cy, w, h, rotation] format of shape (n, 5).
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"""
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if isinstance(corners, torch.Tensor):
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is_numpy = False
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atan2 = torch.atan2
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sqrt = torch.sqrt
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else:
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is_numpy = True
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atan2 = np.arctan2
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sqrt = np.sqrt
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x1, y1, x2, y2, x3, y3, x4, y4 = corners.T
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cx = (x1 + x3) / 2
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cy = (y1 + y3) / 2
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dx21 = x2 - x1
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dy21 = y2 - y1
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w = sqrt(dx21 ** 2 + dy21 ** 2)
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h = sqrt((x2 - x3) ** 2 + (y2 - y3) ** 2)
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rotation = atan2(-dy21, dx21)
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rotation *= 180.0 / math.pi # radians to degrees
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return np.vstack((cx, cy, w, h, rotation)).T if is_numpy else torch.stack((cx, cy, w, h, rotation), dim=1)
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def xywhr2xyxyxyxy(center):
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"""
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Convert batched Oriented Bounding Boxes (OBB) from [xywh, rotation] to [xy1, xy2, xy3, xy4].
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Args:
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center (numpy.ndarray | torch.Tensor): Input data in [cx, cy, w, h, rotation] format of shape (n, 5).
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Returns:
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(numpy.ndarray | torch.Tensor): Converted corner points of shape (n, 8).
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"""
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if isinstance(center, torch.Tensor):
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is_numpy = False
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cos = torch.cos
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sin = torch.sin
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else:
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is_numpy = True
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cos = np.cos
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sin = np.sin
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cx, cy, w, h, rotation = center.T
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rotation *= math.pi / 180.0 # degrees to radians
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dx = w / 2
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dy = h / 2
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cos_rot = cos(rotation)
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sin_rot = sin(rotation)
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dx_cos_rot = dx * cos_rot
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dx_sin_rot = dx * sin_rot
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dy_cos_rot = dy * cos_rot
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dy_sin_rot = dy * sin_rot
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x1 = cx - dx_cos_rot - dy_sin_rot
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y1 = cy + dx_sin_rot - dy_cos_rot
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x2 = cx + dx_cos_rot - dy_sin_rot
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y2 = cy - dx_sin_rot - dy_cos_rot
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x3 = cx + dx_cos_rot + dy_sin_rot
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y3 = cy - dx_sin_rot + dy_cos_rot
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x4 = cx - dx_cos_rot + dy_sin_rot
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y4 = cy + dx_sin_rot + dy_cos_rot
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return np.vstack((x1, y1, x2, y2, x3, y3, x4, y4)).T if is_numpy else torch.stack(
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(x1, y1, x2, y2, x3, y3, x4, y4), dim=1)
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def ltwh2xyxy(x):
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"""
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It converts the bounding box from [x1, y1, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
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@ -508,7 +590,7 @@ def ltwh2xyxy(x):
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Returns:
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y (np.ndarray | torch.Tensor): the xyxy coordinates of the bounding boxes.
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"""
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y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
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y = torch.empty_like(x) if isinstance(x, torch.Tensor) else np.empty_like(x)
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y[:, 2] = x[:, 2] + x[:, 0] # width
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y[:, 3] = x[:, 3] + x[:, 1] # height
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return y
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