courtvision.vis

load_image(file_name)

Loads the image with OpenCV.

Source code in courtvision/vis.py

def load_image(file_name):
    """Loads the image with OpenCV."""
    assert os.path.isfile(file_name), f"Invalid file {file_name}"  # nosec
    # load image with OpenCV
    if isinstance(file_name, Path):
        file_name = file_name.as_posix()
    return cv2.cvtColor(cv2.imread(file_name, cv2.IMREAD_COLOR), cv2.COLOR_BGR2RGB)

load_timg(file_name)

Loads the image with OpenCV and converts to torch.Tensor.

Source code in courtvision/vis.py

def load_timg(file_name):
    """Loads the image with OpenCV and converts to torch.Tensor."""
    img = load_image(file_name)
    # convert image to torch tensor
    return K.image_to_tensor(img, None).float() / 255.0

log_court_layout(camera_matrix, image_width, image_height, translation_vector, rotation_vector, court_mesh_path)

summary

Parameters:

Name	Type	Description	Default
camera_matrix	np.array	description	required
image_width	int	description	required
image_height	int	description	required
translation_vector	np.array	description	required
rotation_vector	np.array	description	required
court_mesh_path	Path	description	required

Source code in courtvision/vis.py

def log_court_layout(
    camera_matrix: np.array,
    image_width: int,
    image_height: int,
    translation_vector: np.array,
    rotation_vector: np.array,
    court_mesh_path: Path,
):
    """_summary_

    Args:
        camera_matrix (np.array): _description_
        image_width (int): _description_
        image_height (int): _description_
        translation_vector (np.array): _description_
        rotation_vector (np.array): _description_
        court_mesh_path (Path): _description_
    """
    rr.log_pinhole(
        "world/camera/image",
        child_from_parent=camera_matrix,
        width=image_width,
        height=image_height,
        timeless=True,
    )
    rotation_matrix, _ = cv2.Rodrigues(rotation_vector)
    rr.log_transform3d(
        "world/camera",
        transform=rr.TranslationAndMat3(
            translation=translation_vector.squeeze(),
            matrix=rotation_matrix,
        ),
        from_parent=True,
    )
    rr.log_point("world/front_left", np.array([0, 0, 0]))
    # TODO: this should be refectored to use the court_size
    rr.log_point("world/front_right", np.array([100, 0, 0]))
    rr.log_mesh_file(
        "world",
        mesh_format=rr.MeshFormat("GLB"),
        mesh_path=court_mesh_path,
    )

plot_3d_lines(xs, ys, zs, plt_axis=None, view_init=(90.0, 0.0, 0.0))

plots lines on a Axes3D

Parameters:

Name	Type	Description	Default
xs	np.array	array of x start and stop coordinates	required
ys	np.array	array of y start and stop coordinates	required
zs	np.array	array of z start and stop coordinates	required
plt_axis	None | Axes3D	Axes3D to draw on. If not given one will be created Defaults to None.	None
view_init	tuple[float, float, float]	Position of the camera to view. Defaults to (90., 0., 0.).	(90.0, 0.0, 0.0)

Returns:

Name	Type	Description
Axes3D	Axes3D	matplotlib ax that can be added to or shown.

Source code in courtvision/vis.py

def plot_3d_lines(
    xs: np.array,
    ys: np.array,
    zs: np.array,
    plt_axis: None | Axes3D = None,
    view_init: tuple[float, float, float] = (90.0, 0.0, 0.0),
) -> Axes3D:
    """plots lines on a Axes3D

    Args:
        xs (np.array): array of x start and stop coordinates
        ys (np.array): array of y start and stop coordinates
        zs (np.array): array of z start and stop coordinates
        plt_axis (None | Axes3D, optional): Axes3D to draw on. If not given one will be created Defaults to None.
        view_init (tuple[float, float, float], optional): Position of the camera to view. Defaults to (90., 0., 0.).

    Returns:
        Axes3D: matplotlib ax that can be added to or shown.
    """

    fig = None
    if plt_axis is None:
        fig = plt.figure()
        plt_axis = fig.add_subplot(111, projection="3d")
    for i in range(len(xs)):
        plt_axis.plot(xs[i], ys[i], zs[i], c="b")
    # set the axis labels
    plt_axis.set_xlabel("X")
    plt_axis.set_ylabel("Y")
    plt_axis.set_zlabel("Z")
    plt_axis.set_aspect("equal")
    plt_axis.margins(x=0)
    plt_axis.view_init(*view_init)
    return plt_axis, fig

plot_coords(img, src_coords, show=True, thickness=2)

Draws lines on 'img'.

Parameters:

Name	Type	Description	Default
img	np.array	description	required
src_coords	np.array	description	required
show	bool	description. Defaults to True.	True

Source code in courtvision/vis.py

def plot_coords(img: np.array, src_coords: np.array, show: bool = True, thickness=2):
    """Draws lines on 'img'.

    Args:
        img (np.array): _description_
        src_coords (np.array): _description_
        show (bool, optional): _description_. Defaults to True.
    """
    src_coords = src_coords.astype(int)
    cv2.polylines(img, [src_coords], True, (0, 255, 0), thickness=2)
    if show:
        from matplotlib import pyplot as plt

        plt.imshow(img)

plot_n_images_in_a_grid(images, n_cols=3)

draws a grid of images

Parameters:

Name	Type	Description	Default
images	list[np.array]	images to draw on - this is inplace	required
n_cols	int	number of cols. Defaults to 3.	3

Returns:

Name	Type	Description
tuple	fig, ax	matplotlib fig and ax

Source code in courtvision/vis.py

def plot_n_images_in_a_grid(images: list[np.array], n_cols: int = 3):
    """draws a grid of images

    Args:
        images (list[np.array]): images to draw on - this is inplace
        n_cols (int, optional): number of cols. Defaults to 3.

    Returns:
        tuple(fig, ax): matplotlib fig and ax
    """
    n_cols = min(n_cols, len(images))
    n_rows = int(np.ceil(len(images) / n_cols))
    fig, ax = plt.subplots(n_rows, n_cols, figsize=(15, 15))
    if n_rows == 1:
        ax = ax.reshape(1, n_cols)
    for i, img in enumerate(images):
        ax[i // n_cols, i % n_cols].imshow(img)
    return fig, ax