diff --git a/.buildinfo b/.buildinfo index bfe1e0e..40a0a27 100644 --- a/.buildinfo +++ b/.buildinfo @@ -1,4 +1,4 @@ # Sphinx build info version 1 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. -config: 76e008e4845e9ce31818045e3e2f72f8 +config: dac2eef83a8a45408328865fe5e6d562 tags: d77d1c0d9ca2f4c8421862c7c5a0d620 diff --git a/.doctrees/camera.doctree b/.doctrees/camera.doctree index ad9c4f9..ff07560 100644 Binary files a/.doctrees/camera.doctree and b/.doctrees/camera.doctree differ diff --git a/.doctrees/environment.pickle b/.doctrees/environment.pickle index 06ef481..6fad986 100644 Binary files a/.doctrees/environment.pickle and b/.doctrees/environment.pickle differ diff --git a/.doctrees/mesh.doctree b/.doctrees/mesh.doctree index 5ea9502..b99a881 100644 Binary files a/.doctrees/mesh.doctree and b/.doctrees/mesh.doctree differ diff --git a/.doctrees/misc_api.doctree b/.doctrees/misc_api.doctree index ca99d5d..2fe1f6f 100644 Binary files a/.doctrees/misc_api.doctree and b/.doctrees/misc_api.doctree differ diff --git a/.doctrees/ops.doctree b/.doctrees/ops.doctree index 27db2ba..13a10ba 100644 Binary files a/.doctrees/ops.doctree and b/.doctrees/ops.doctree differ diff --git a/.doctrees/utils.doctree b/.doctrees/utils.doctree index 825dabf..4dcf12c 100644 Binary files a/.doctrees/utils.doctree and b/.doctrees/utils.doctree differ diff --git a/.doctrees/vis.doctree b/.doctrees/vis.doctree index b0b74d9..a4593cd 100644 Binary files a/.doctrees/vis.doctree and b/.doctrees/vis.doctree differ diff --git a/_modules/index.html b/_modules/index.html deleted file mode 100644 index 8d8e0e2..0000000 --- a/_modules/index.html +++ /dev/null @@ -1,311 +0,0 @@ - - - - - - - - Overview: module code - kiui 0.2.10 documentation - - - - - - - - - - - - - - - - - - Contents - - - - - - Menu - - - - - - - - Expand - - - - - - Light mode - - - - - - - - - - - - - - Dark mode - - - - - - - Auto light/dark, in light mode - - - - - - - - - - - - - - - Auto light/dark, in dark mode - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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- - - - - - - - \ No newline at end of file diff --git a/_modules/kiui/cam/index.html b/_modules/kiui/cam/index.html deleted file mode 100644 index 14e7309..0000000 --- a/_modules/kiui/cam/index.html +++ /dev/null @@ -1,717 +0,0 @@ - - - - - - - - kiui.cam - kiui 0.2.10 documentation - - - - - - - - - - - - - - - - - - Contents - - - - - - Menu - - - - - - - - Expand - - - - - - Light mode - - - - - - - - - - - - - - Dark mode - - - - - - - Auto light/dark, in light mode - - - - - - - - - - - - - - - Auto light/dark, in dark mode - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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kiui 0.2.10 documentation
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Source code for kiui.cam

-import numpy as np
-from scipy.spatial.transform import Rotation
-
-from kiui.op import safe_normalize
-from kiui.typing import *
-
-# convert between different world coordinate systems
-

-[docs]
-def convert(
-    pose, 
-    target: Literal['unity', 'blender', 'opencv', 'colmap', 'opengl'] = 'unity', 
-    original: Literal['unity', 'blender', 'opencv', 'colmap', 'opengl'] = 'opengl',
-):
-    """A method to convert between different world coordinate systems.
-
-    Args:
-        pose (np.ndarray): camera pose, float [4, 4].
-        target (Literal['unity', 'blender', 'opencv', 'colmap', 'opengl'], optional): from convention. Defaults to 'unity'.
-        original (Literal['unity', 'blender', 'opencv', 'colmap', 'opengl'], optional): to convention. Defaults to 'opengl'.
-
-    Returns:
-        np.ndarray: converted camera pose, float [4, 4].
-    """
-    
-    if original == 'opengl':
-        if target == 'unity':
-            pose[2] *= -1
-        elif target == 'blender':
-            pose[2] *= -1
-            pose[[1, 2]] = pose[[2, 1]]
-        elif target in ['opencv', 'colmap']:
-            pose[1:3] *= -1
-    elif original == 'unity':
-        if target == 'opengl':
-            pose[2] *= -1
-        elif target == 'blender':
-            pose[[1, 2]] = pose[[2, 1]]
-        elif target in ['opencv', 'colmap']:
-            pose[1] *= -1
-    elif original == 'blender':
-        if target == 'opengl':
-            pose[1] *= -1
-            pose[[1, 2]] = pose[[2, 1]]
-        elif target == 'unity':
-            pose[[1, 2]] = pose[[2, 1]]
-        elif target in ['opencv', 'colmap']:
-            pose[2] *= -1
-            pose[[1, 2]] = pose[[2, 1]]
-    elif original in ['opencv', 'colmap']:
-        if target == 'opengl':
-            pose[1:3] *= -1
-        elif target == 'unity':
-            pose[1] *= -1
-        elif target == 'blender':
-            pose[1] *= -1
-            pose[[1, 2]] = pose[[2, 1]]
-    return pose

-
-
-
-

-[docs]
-def look_at(campos, target, opengl=True):
-    """construct pose rotation matrix by look-at.
-
-    Args:
-        campos (np.ndarray): camera position, float [3]
-        target (np.ndarray): look at target, float [3]
-        opengl (bool, optional): whether use opengl camera convention (forward direction is target --> camera). Defaults to True.
-
-    Returns:
-        np.ndarray: the camera pose rotation matrix, float [3, 3], normalized.
-    """
-   
-    if not opengl:
-        # forward is camera --> target
-        forward_vector = safe_normalize(target - campos)
-        up_vector = np.array([0, 1, 0], dtype=np.float32)
-        right_vector = safe_normalize(np.cross(forward_vector, up_vector))
-        up_vector = safe_normalize(np.cross(right_vector, forward_vector))
-    else:
-        # forward is target --> camera
-        forward_vector = safe_normalize(campos - target)
-        up_vector = np.array([0, 1, 0], dtype=np.float32)
-        right_vector = safe_normalize(np.cross(up_vector, forward_vector))
-        up_vector = safe_normalize(np.cross(forward_vector, right_vector))
-    R = np.stack([right_vector, up_vector, forward_vector], axis=1)
-    return R

-
-
-
-

-[docs]
-def orbit_camera(elevation, azimuth, radius=1, is_degree=True, target=None, opengl=True):
-    """construct a camera pose matrix orbiting a target with elevation & azimuth angle.
-
-    Args:
-        elevation (float): elevation in (-90, 90), from +y to -y is (-90, 90)
-        azimuth (float): azimuth in (-180, 180), from +z to +x is (0, 90)
-        radius (int, optional): camera radius. Defaults to 1.
-        is_degree (bool, optional): if the angles are in degree. Defaults to True.
-        target (np.ndarray, optional): look at target position. Defaults to None.
-        opengl (bool, optional): whether to use OpenGL camera convention. Defaults to True.
-
-    Returns:
-        np.ndarray: the camera pose matrix, float [4, 4]
-    """
-    
-    if is_degree:
-        elevation = np.deg2rad(elevation)
-        azimuth = np.deg2rad(azimuth)
-    x = radius * np.cos(elevation) * np.sin(azimuth)
-    y = - radius * np.sin(elevation)
-    z = radius * np.cos(elevation) * np.cos(azimuth)
-    if target is None:
-        target = np.zeros([3], dtype=np.float32)
-    campos = np.array([x, y, z]) + target  # [3]
-    T = np.eye(4, dtype=np.float32)
-    T[:3, :3] = look_at(campos, target, opengl)
-    T[:3, 3] = campos
-    return T

-
-
-
-

-[docs]
-def undo_orbit_camera(T, is_degree=True):
-    """ undo an orbital camera pose matrix to elevation & azimuth
-
-    Args:
-        T (np.ndarray): camera pose matrix, float [4, 4], must be an orbital camera targeting at (0, 0, 0)!
-        is_degree (bool, optional): whether to return angles in degree. Defaults to True.
-
-    Returns:
-        Tuple[float]: elevation, azimuth, and radius.
-    """
-    
-    campos = T[:3, 3]
-    radius = np.linalg.norm(campos)
-    elevation = np.arcsin(-campos[1] / radius)
-    azimuth = np.arctan2(campos[0], campos[2])
-    if is_degree:
-        elevation = np.rad2deg(elevation)
-        azimuth = np.rad2deg(azimuth)
-    return elevation, azimuth, radius

-
-
-# perspective matrix
-

-[docs]
-def get_perspective(fovy, aspect=1, near=0.01, far=1000):
-    """construct a perspective matrix from fovy.
-
-    Args:
-        fovy (float): field of view in degree along y-axis.
-        aspect (int, optional): aspect ratio. Defaults to 1.
-        near (float, optional): near clip plane. Defaults to 0.01.
-        far (int, optional): far clip plane. Defaults to 1000.
-
-    Returns:
-        np.ndarray: perspective matrix, float [4, 4]
-    """
-    # fovy: field of view in degree.
-    
-    y = np.tan(np.deg2rad(fovy) / 2)
-    return np.array(
-        [
-            [1 / (y * aspect), 0, 0, 0],
-            [0, -1 / y, 0, 0],
-            [
-                0,
-                0,
-                -(far + near) / (far - near),
-                -(2 * far * near) / (far - near),
-            ],
-            [0, 0, -1, 0],
-        ],
-        dtype=np.float32,
-    )

-
-
-
-

-[docs]
-def get_rays(pose, h, w, fovy, opengl=True, normalize_dir=True):
-    """ construct rays origin and direction from a camera pose.
-
-    Args:
-        pose (np.ndarray): camera pose, float [4, 4]
-        h (int): image height
-        w (int): image width
-        fovy (float): field of view in degree along y-axis.
-        opengl (bool, optional): whether to use the OpenGL camera convention. Defaults to True.
-        normalize_dir (bool, optional): whether to normalize the ray directions. Defaults to True.
-
-    Returns:
-        Tuple[np.ndarray]: rays_o and rays_d, both are float [h, w, 3]
-    """
-    # pose: [4, 4]
-    # fov: in degree
-    # opengl: camera front view convention
-
-    x, y = np.meshgrid(np.arange(w), np.arange(h), indexing="xy")
-    x = x.reshape(-1)
-    y = y.reshape(-1)
-
-    cx = w * 0.5
-    cy = h * 0.5
-
-    # objaverse rendering has fixed focal of 560 at resolution 512 --> fov = 49.1 degree
-    focal = h * 0.5 / np.tan(0.5 * np.deg2rad(fovy))
-
-    camera_dirs = np.stack([
-        (x - cx + 0.5) / focal,
-        (y - cy + 0.5) / focal * (-1.0 if opengl else 1.0),
-        np.ones_like(x) * (-1.0 if opengl else 1.0),
-    ], axis=-1) # [hw, 3]
-
-    rays_d = camera_dirs @ pose[:3, :3].transpose(0, 1)  # [hw, 3]
-    rays_o = np.expand_dims(pose[:3, 3], 0).repeat(rays_d.shape[0], 0)  # [hw, 3]
-
-    if normalize_dir:
-        rays_d = safe_normalize(rays_d)
-
-    rays_o = rays_o.reshape(h, w, 3)
-    rays_d = rays_d.reshape(h, w, 3)
-
-    return rays_o, rays_d

-
-
-

-[docs]
-class OrbitCamera:
-    """ An orbital camera class.
-    """
-

-[docs]
-    def __init__(self, W, H, r=2, fovy=60, near=0.01, far=100):
-        """init function
-
-        Args:
-            W (int): image width
-            H (int): image height
-            r (int, optional): camera radius. Defaults to 2.
-            fovy (int, optional): camera field of view in degree along y-axis. Defaults to 60.
-            near (float, optional): near clip plane. Defaults to 0.01.
-            far (int, optional): far clip plane. Defaults to 100.
-        """
-        self.W = W
-        self.H = H
-        self.radius = r  # camera distance from center
-        self.fovy = np.deg2rad(fovy)  # deg 2 rad
-        self.near = near
-        self.far = far
-        self.center = np.array([0, 0, 0], dtype=np.float32)  # look at this point
-        self.rot = Rotation.from_matrix(np.eye(3))
-        self.up = np.array([0, 1, 0], dtype=np.float32)  # need to be normalized!

-
-
-    @property
-    def fovx(self):
-        """get the field of view in radians along x-axis
-
-        Returns:
-            float: field of view in radians along x-axis
-        """
-        return 2 * np.arctan(np.tan(self.fovy / 2) * self.W / self.H)
-
-    @property
-    def campos(self):
-        """get the camera position
-
-        Returns:
-            np.ndarray: camera position, float [3]
-        """
-        return self.pose[:3, 3]
-
-
-    @property
-    def pose(self):
-        """get the camera pose matrix (cam2world)
-
-        Returns:
-            np.ndarray: camera pose, float [4, 4]
-        """
-        # first move camera to radius
-        res = np.eye(4, dtype=np.float32)
-        res[2, 3] = self.radius  # opengl convention...
-        # rotate
-        rot = np.eye(4, dtype=np.float32)
-        rot[:3, :3] = self.rot.as_matrix()
-        res = rot @ res
-        # translate
-        res[:3, 3] -= self.center
-        return res
-
-    
-    @property
-    def view(self):
-        """get the camera view matrix (world2cam, inverse of cam2world)
-
-        Returns:
-            np.ndarray: camera view, float [4, 4]
-        """
-        return np.linalg.inv(self.pose)
-
-    
-    @property
-    def perspective(self):
-        """get the perspective matrix
-
-        Returns:
-            np.ndarray: camera perspective, float [4, 4]
-        """
-        y = np.tan(self.fovy / 2)
-        aspect = self.W / self.H
-        return np.array(
-            [
-                [1 / (y * aspect), 0, 0, 0],
-                [0, -1 / y, 0, 0],
-                [
-                    0,
-                    0,
-                    -(self.far + self.near) / (self.far - self.near),
-                    -(2 * self.far * self.near) / (self.far - self.near),
-                ],
-                [0, 0, -1, 0],
-            ],
-            dtype=np.float32,
-        )
-
-    # intrinsics
-    @property
-    def intrinsics(self):
-        """get the camera intrinsics
-
-        Returns:
-            np.ndarray: intrinsics (fx, fy, cx, cy), float [4]
-        """
-        focal = self.H / (2 * np.tan(self.fovy / 2))
-        return np.array([focal, focal, self.W // 2, self.H // 2], dtype=np.float32)
-
-    
-    @property
-    def mvp(self):
-        """get the MVP (model-view-perspective) matrix.
-
-        Returns:
-            np.ndarray: camera MVP, float [4, 4]
-        """
-        return self.perspective @ np.linalg.inv(self.pose)  # [4, 4]
-
-

-[docs]
-    def orbit(self, dx, dy):
-        """ rotate along camera up/side axis!
-
-        Args:
-            dx (float): delta step along x (up).
-            dy (float): delta step along y (side).
-        """
-        side = self.rot.as_matrix()[:3, 0]
-        rotvec_x = self.up * np.radians(-0.05 * dx)
-        rotvec_y = side * np.radians(-0.05 * dy)
-        self.rot = Rotation.from_rotvec(rotvec_x) * Rotation.from_rotvec(rotvec_y) * self.rot

-
-
-

-[docs]
-    def scale(self, delta):
-        """scale the camera.
-
-        Args:
-            delta (float): delta step.
-        """
-        self.radius *= 1.1 ** (-delta)

-
-
-

-[docs]
-    def pan(self, dx, dy, dz=0):
-        """pan the camera.
-
-        Args:
-            dx (float): delta step along x.
-            dy (float): delta step along y.
-            dz (float, optional): delta step along x. Defaults to 0.
-        """
-        # pan in camera coordinate system (careful on the sensitivity!)
-        self.center += 0.0005 * self.rot.as_matrix()[:3, :3] @ np.array([dx, -dy, dz])

-
-
-

-[docs]
-    def from_angle(self, elevation, azimuth, is_degree=True):
-        """set the camera pose from elevation & azimuth angle.
-
-        Args:
-            elevation (float): elevation in (-90, 90), from +y to -y is (-90, 90)
-            azimuth (float): azimuth in (-180, 180), from +z to +x is (0, 90)
-            is_degree (bool, optional): whether the angles are in degree. Defaults to True.
-        """
-        if is_degree:
-            elevation = np.deg2rad(elevation)
-            azimuth = np.deg2rad(azimuth)
-        x = self.radius * np.cos(elevation) * np.sin(azimuth)
-        y = - self.radius * np.sin(elevation)
-        z = self.radius * np.cos(elevation) * np.cos(azimuth)
-        campos = np.array([x, y, z])  # [N, 3]
-        rot_mat = look_at(campos, np.zeros([3], dtype=np.float32))
-        self.rot = Rotation.from_matrix(rot_mat)

-

-
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- Copyright © 2024, kiui -
- Made with Sphinx and @pradyunsg's - - Furo - -
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kiui 0.2.10 documentation
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Source code for kiui.grid_put

-import torch
-import torch.nn.functional as F
-
-from kiui.typing import *
-
-def stride_from_shape(shape):
-    stride = [1]
-    for x in reversed(shape[1:]):
-        stride.append(stride[-1] * x) 
-    return list(reversed(stride))
-
-
-def scatter_add_nd(input, indices, values):
-    # input: [..., C], D dimension + C channel
-    # indices: [N, D], long
-    # values: [N, C]
-
-    D = indices.shape[-1]
-    C = input.shape[-1]
-    size = input.shape[:-1]
-    stride = stride_from_shape(size)
-
-    assert len(size) == D
-
-    input = input.view(-1, C)  # [HW, C]
-    flatten_indices = (indices * torch.tensor(stride, dtype=torch.long, device=indices.device)).sum(-1)  # [N]
-
-    input.scatter_add_(0, flatten_indices.unsqueeze(1).repeat(1, C), values)
-
-    return input.view(*size, C)
-
-
-def scatter_add_nd_with_count(input, count, indices, values, weights=None):
-    # input: [..., C], D dimension + C channel
-    # count: [..., 1], D dimension
-    # indices: [N, D], long
-    # values: [N, C]
-
-    D = indices.shape[-1]
-    C = input.shape[-1]
-    size = input.shape[:-1]
-    stride = stride_from_shape(size)
-
-    assert len(size) == D
-
-    input = input.view(-1, C)  # [HW, C]
-    count = count.view(-1, 1)
-
-    flatten_indices = (indices * torch.tensor(stride, dtype=torch.long, device=indices.device)).sum(-1)  # [N]
-
-    if weights is None:
-        weights = torch.ones_like(values[..., :1]) 
-
-    input.scatter_add_(0, flatten_indices.unsqueeze(1).repeat(1, C), values)
-    count.scatter_add_(0, flatten_indices.unsqueeze(1), weights)
-
-    return input.view(*size, C), count.view(*size, 1)
-
-def nearest_grid_put_2d(H, W, coords, values, return_count=False):
-    # coords: [N, 2], float in [-1, 1]
-    # values: [N, C]
-
-    C = values.shape[-1]
-
-    indices = (coords * 0.5 + 0.5) * torch.tensor(
-        [H - 1, W - 1], dtype=torch.float32, device=coords.device
-    )
-    indices = indices.round().long()  # [N, 2]
-
-    result = torch.zeros(H, W, C, device=values.device, dtype=values.dtype)  # [H, W, C]
-    count = torch.zeros(H, W, 1, device=values.device, dtype=values.dtype)  # [H, W, 1]
-    weights = torch.ones_like(values[..., :1])  # [N, 1]
-    
-    result, count = scatter_add_nd_with_count(result, count, indices, values, weights)
-
-    if return_count:
-        return result, count
-
-    mask = (count.squeeze(-1) > 0)
-    result[mask] = result[mask] / count[mask].repeat(1, C)
-
-    return result
-
-
-def linear_grid_put_2d(H, W, coords, values, return_count=False):
-    # coords: [N, 2], float in [-1, 1]
-    # values: [N, C]
-
-    C = values.shape[-1]
-
-    indices = (coords * 0.5 + 0.5) * torch.tensor(
-        [H - 1, W - 1], dtype=torch.float32, device=coords.device
-    )
-    indices_00 = indices.floor().long()  # [N, 2]
-    indices_00[:, 0].clamp_(0, H - 2)
-    indices_00[:, 1].clamp_(0, W - 2)
-    indices_01 = indices_00 + torch.tensor(
-        [0, 1], dtype=torch.long, device=indices.device
-    )
-    indices_10 = indices_00 + torch.tensor(
-        [1, 0], dtype=torch.long, device=indices.device
-    )
-    indices_11 = indices_00 + torch.tensor(
-        [1, 1], dtype=torch.long, device=indices.device
-    )
-
-    h = indices[..., 0] - indices_00[..., 0].float()
-    w = indices[..., 1] - indices_00[..., 1].float()
-    w_00 = (1 - h) * (1 - w)
-    w_01 = (1 - h) * w
-    w_10 = h * (1 - w)
-    w_11 = h * w
-
-    result = torch.zeros(H, W, C, device=values.device, dtype=values.dtype)  # [H, W, C]
-    count = torch.zeros(H, W, 1, device=values.device, dtype=values.dtype)  # [H, W, 1]
-    weights = torch.ones_like(values[..., :1])  # [N, 1]
-    
-    result, count = scatter_add_nd_with_count(result, count, indices_00, values * w_00.unsqueeze(1), weights* w_00.unsqueeze(1))
-    result, count = scatter_add_nd_with_count(result, count, indices_01, values * w_01.unsqueeze(1), weights* w_01.unsqueeze(1))
-    result, count = scatter_add_nd_with_count(result, count, indices_10, values * w_10.unsqueeze(1), weights* w_10.unsqueeze(1))
-    result, count = scatter_add_nd_with_count(result, count, indices_11, values * w_11.unsqueeze(1), weights* w_11.unsqueeze(1))
-
-    if return_count:
-        return result, count
-
-    mask = (count.squeeze(-1) > 0)
-    result[mask] = result[mask] / count[mask].repeat(1, C)
-
-    return result
-
-def mipmap_linear_grid_put_2d(H, W, coords, values, min_resolution=32, return_count=False):
-    # coords: [N, 2], float in [-1, 1]
-    # values: [N, C]
-
-    C = values.shape[-1]
-
-    result = torch.zeros(H, W, C, device=values.device, dtype=values.dtype)  # [H, W, C]
-    count = torch.zeros(H, W, 1, device=values.device, dtype=values.dtype)  # [H, W, 1]
-
-    cur_H, cur_W = H, W
-    
-    while min(cur_H, cur_W) > min_resolution:
-
-        # try to fill the holes
-        mask = (count.squeeze(-1) == 0)
-        if not mask.any():
-            break
-
-        cur_result, cur_count = linear_grid_put_2d(cur_H, cur_W, coords, values, return_count=True)
-        result[mask] = result[mask] + F.interpolate(cur_result.permute(2,0,1).unsqueeze(0).contiguous(), (H, W), mode='bilinear', align_corners=False).squeeze(0).permute(1,2,0).contiguous()[mask]
-        count[mask] = count[mask] + F.interpolate(cur_count.view(1, 1, cur_H, cur_W), (H, W), mode='bilinear', align_corners=False).view(H, W, 1)[mask]
-        cur_H //= 2
-        cur_W //= 2
-    
-    if return_count:
-        return result, count
-
-    mask = (count.squeeze(-1) > 0)
-    result[mask] = result[mask] / count[mask].repeat(1, C)
-
-    return result
-
-def nearest_grid_put_3d(H, W, D, coords, values, return_count=False):
-    # coords: [N, 3], float in [-1, 1]
-    # values: [N, C]
-
-    C = values.shape[-1]
-
-    indices = (coords * 0.5 + 0.5) * torch.tensor(
-        [H - 1, W - 1, D - 1], dtype=torch.float32, device=coords.device
-    )
-    indices = indices.round().long()  # [N, 2]
-
-    result = torch.zeros(H, W, D, C, device=values.device, dtype=values.dtype)  # [H, W, C]
-    count = torch.zeros(H, W, D, 1, device=values.device, dtype=values.dtype)  # [H, W, 1]
-    weights = torch.ones_like(values[..., :1])  # [N, 1]
-
-    result, count = scatter_add_nd_with_count(result, count, indices, values, weights)
-    
-    if return_count:
-        return result, count
-
-    mask = (count.squeeze(-1) > 0)
-    result[mask] = result[mask] / count[mask].repeat(1, C)
-
-    return result
-
-
-def linear_grid_put_3d(H, W, D, coords, values, return_count=False):
-    # coords: [N, 3], float in [-1, 1]
-    # values: [N, C]
-
-    C = values.shape[-1]
-
-    indices = (coords * 0.5 + 0.5) * torch.tensor(
-        [H - 1, W - 1, D - 1], dtype=torch.float32, device=coords.device
-    )
-    indices_000 = indices.floor().long()  # [N, 3]
-    indices_000[:, 0].clamp_(0, H - 2)
-    indices_000[:, 1].clamp_(0, W - 2)
-    indices_000[:, 2].clamp_(0, D - 2)
-
-    indices_001 = indices_000 + torch.tensor([0, 0, 1], dtype=torch.long, device=indices.device)
-    indices_010 = indices_000 + torch.tensor([0, 1, 0], dtype=torch.long, device=indices.device)
-    indices_011 = indices_000 + torch.tensor([0, 1, 1], dtype=torch.long, device=indices.device)
-    indices_100 = indices_000 + torch.tensor([1, 0, 0], dtype=torch.long, device=indices.device)
-    indices_101 = indices_000 + torch.tensor([1, 0, 1], dtype=torch.long, device=indices.device)
-    indices_110 = indices_000 + torch.tensor([1, 1, 0], dtype=torch.long, device=indices.device)
-    indices_111 = indices_000 + torch.tensor([1, 1, 1], dtype=torch.long, device=indices.device)
-
-    h = indices[..., 0] - indices_000[..., 0].float()
-    w = indices[..., 1] - indices_000[..., 1].float()
-    d = indices[..., 2] - indices_000[..., 2].float()
-    
-    w_000 = (1 - h) * (1 - w) * (1 - d)
-    w_001 = (1 - h) * w * (1 - d)
-    w_010 = h * (1 - w) * (1 - d)
-    w_011 = h * w * (1 - d)
-    w_100 = (1 - h) * (1 - w) * d
-    w_101 = (1 - h) * w * d
-    w_110 = h * (1 - w) * d
-    w_111 = h * w * d
-
-    result = torch.zeros(H, W, D, C, device=values.device, dtype=values.dtype)  # [H, W, D, C]
-    count = torch.zeros(H, W, D, 1, device=values.device, dtype=values.dtype)  # [H, W, D, 1]
-    weights = torch.ones_like(values[..., :1])  # [N, 1]
-    
-    result, count = scatter_add_nd_with_count(result, count, indices_000, values * w_000.unsqueeze(1), weights * w_000.unsqueeze(1))
-    result, count = scatter_add_nd_with_count(result, count, indices_001, values * w_001.unsqueeze(1), weights * w_001.unsqueeze(1))
-    result, count = scatter_add_nd_with_count(result, count, indices_010, values * w_010.unsqueeze(1), weights * w_010.unsqueeze(1))
-    result, count = scatter_add_nd_with_count(result, count, indices_011, values * w_011.unsqueeze(1), weights * w_011.unsqueeze(1))
-    result, count = scatter_add_nd_with_count(result, count, indices_100, values * w_100.unsqueeze(1), weights * w_100.unsqueeze(1))
-    result, count = scatter_add_nd_with_count(result, count, indices_101, values * w_101.unsqueeze(1), weights * w_101.unsqueeze(1))
-    result, count = scatter_add_nd_with_count(result, count, indices_110, values * w_110.unsqueeze(1), weights * w_110.unsqueeze(1))
-    result, count = scatter_add_nd_with_count(result, count, indices_111, values * w_111.unsqueeze(1), weights * w_111.unsqueeze(1))
-
-    if return_count:
-        return result, count
-
-    mask = (count.squeeze(-1) > 0)
-    result[mask] = result[mask] / count[mask].repeat(1, C)
-
-    return result
-
-def mipmap_linear_grid_put_3d(H, W, D, coords, values, min_resolution=32, return_count=False):
-    # coords: [N, 3], float in [-1, 1]
-    # values: [N, C]
-
-    C = values.shape[-1]
-
-    result = torch.zeros(H, W, D, C, device=values.device, dtype=values.dtype)  # [H, W, D, C]
-    count = torch.zeros(H, W, D, 1, device=values.device, dtype=values.dtype)  # [H, W, D, 1]
-    cur_H, cur_W, cur_D = H, W, D
-    
-    while min(min(cur_H, cur_W), cur_D) > min_resolution:
-
-        # try to fill the holes
-        mask = (count.squeeze(-1) == 0)
-        if not mask.any():
-            break
-
-        cur_result, cur_count = linear_grid_put_3d(cur_H, cur_W, cur_D, coords, values, return_count=True)
-        result[mask] = result[mask] + F.interpolate(cur_result.permute(3,0,1,2).unsqueeze(0).contiguous(), (H, W, D), mode='trilinear', align_corners=False).squeeze(0).permute(1,2,3,0).contiguous()[mask]
-        count[mask] = count[mask] + F.interpolate(cur_count.view(1, 1, cur_H, cur_W, cur_D), (H, W, D), mode='trilinear', align_corners=False).view(H, W, D, 1)[mask]
-        cur_H //= 2
-        cur_W //= 2
-        cur_D //= 2
-    
-    if return_count:
-        return result, count
-
-    mask = (count.squeeze(-1) > 0)
-    result[mask] = result[mask] / count[mask].repeat(1, C)
-
-    return result
-
-
-

-[docs]
-def grid_put(shape: Sequence[int], coords: Tensor, values: Tensor, mode: Literal['nearest', 'linear', 'linear-mipmap']='linear-mipmap', min_resolution: int=32, return_count: bool=False) -> Tensor:
-    """ put back values to an image according to the coords. inverse operation of ``F.grid_sample``.
-
-    Args:
-        shape (Sequence[int]): shape of the image, support 2D image and 3D volume, sequence of [D]
-        coords (Tensor): coordinates, float [N, D] in [-1, 1].
-        values (Tensor): values, float [N, C].
-        mode (str, Literal['nearest', 'linear', 'linear-mipmap']): interpolation mode, see https://github.com/ashawkey/grid_put for examples. Defaults to 'linear-mipmap'.
-        min_resolution (int, optional): minimal resolution for mipmap. Defaults to 32.
-        return_count (bool, optional): whether to return the summed value and weights, instead of the divided results. Defaults to False.
-
-    Returns:
-        Tensor: the restored image/volume, float [H, W, C]/[H, W, D, C].
-    """
-    
-    D = len(shape)
-    assert D in [2, 3], f'only support D == 2 or 3, but got D == {D}'
-
-    if mode == 'nearest':
-        if D == 2:
-            return nearest_grid_put_2d(*shape, coords, values, return_count)
-        else:
-            return nearest_grid_put_3d(*shape, coords, values, return_count)
-    elif mode == 'linear':
-        if D == 2:
-            return linear_grid_put_2d(*shape, coords, values, return_count)
-        else:
-            return linear_grid_put_3d(*shape, coords, values, return_count)
-    elif mode == 'linear-mipmap':
-        if D == 2:
-            return mipmap_linear_grid_put_2d(*shape, coords, values, min_resolution, return_count)
-        else:
-            return mipmap_linear_grid_put_3d(*shape, coords, values, min_resolution, return_count)
-    else:
-        raise NotImplementedError(f"got mode {mode}")    

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Source code for kiui.mesh

-import os
-import cv2
-import torch
-import trimesh
-import numpy as np
-from packaging import version
-
-from kiui.op import safe_normalize, dot
-from kiui.typing import *
-
-

-[docs]
-class Mesh:
-    """
-    A torch-native trimesh class, with support for ``ply/obj/glb`` formats.
-
-    Note:
-        This class only supports one mesh with a single texture image (an albedo texture and a metallic-roughness texture).
-    """
-

-[docs]
-    def __init__(
-        self,
-        v: Optional[Tensor] = None,
-        f: Optional[Tensor] = None,
-        vn: Optional[Tensor] = None,
-        fn: Optional[Tensor] = None,
-        vt: Optional[Tensor] = None,
-        ft: Optional[Tensor] = None,
-        vc: Optional[Tensor] = None, # vertex color
-        albedo: Optional[Tensor] = None,
-        metallicRoughness: Optional[Tensor] = None,
-        device: Optional[torch.device] = None,
-    ):
-        """Init a mesh directly using all attributes.
-
-        Args:
-            v (Optional[Tensor]): vertices, float [N, 3]. Defaults to None.
-            f (Optional[Tensor]): faces, int [M, 3]. Defaults to None.
-            vn (Optional[Tensor]): vertex normals, float [N, 3]. Defaults to None.
-            fn (Optional[Tensor]): faces for normals, int [M, 3]. Defaults to None.
-            vt (Optional[Tensor]): vertex uv coordinates, float [N, 2]. Defaults to None.
-            ft (Optional[Tensor]): faces for uvs, int [M, 3]. Defaults to None.
-            vc (Optional[Tensor]): vertex colors, float [N, 3]. Defaults to None.
-            albedo (Optional[Tensor]): albedo texture, float [H, W, 3], RGB format. Defaults to None.
-            metallicRoughness (Optional[Tensor]): metallic-roughness texture, float [H, W, 3], metallic(Blue) = metallicRoughness[..., 2], roughness(Green) = metallicRoughness[..., 1]. Defaults to None.
-            device (Optional[torch.device]): torch device. Defaults to None.
-        """
-        self.device = device
-        self.v = v
-        self.vn = vn
-        self.vt = vt
-        self.f = f
-        self.fn = fn
-        self.ft = ft
-        # will first see if there is vertex color to use
-        self.vc = vc
-        # only support a single albedo image
-        self.albedo = albedo
-        # pbr extension, metallic(Blue) = metallicRoughness[..., 2], roughness(Green) = metallicRoughness[..., 1]
-        # ref: https://registry.khronos.org/glTF/specs/2.0/glTF-2.0.html
-        self.metallicRoughness = metallicRoughness
-
-        self.ori_center = 0
-        self.ori_scale = 1

-
-    
-    def __repr__(self):
-        out = f'<kiui.mesh.Mesh>'
-        if self.v is not None: out += f' v={self.v.shape}'
-        if self.f is not None: out += f' f={self.f.shape}'
-        if self.vc is not None: out += f' vc={self.vc.shape}'
-        if self.albedo is not None: out += f' albedo={self.albedo.shape}'
-        if self.metallicRoughness is not None: out += f' metallicRoughness={self.metallicRoughness.shape}'
-        return out
-
-

-[docs]
-    @classmethod
-    def load(cls, path, resize=True, clean=False, renormal=True, retex=False, bound=0.9, front_dir='+z', **kwargs):
-        """load mesh from path.
-
-        Args:
-            path (str): path to mesh file, supports ply, obj, glb.
-            clean (bool, optional): perform mesh cleaning at load (e.g., merge close vertices). Defaults to False.
-            resize (bool, optional): auto resize the mesh using ``bound`` into [-bound, bound]^3. Defaults to True.
-            renormal (bool, optional): re-calc the vertex normals. Defaults to True.
-            retex (bool, optional): re-calc the uv coordinates, will overwrite the existing uv coordinates. Defaults to False.
-            wotex (bool, optional): do not try to load any texture. Defaults to False.
-            bound (float, optional): bound to resize. Defaults to 0.9.
-            front_dir (str, optional): front-view direction of the mesh, should be [+-][xyz][ 123]. Defaults to '+z'.
-            device (torch.device, optional): torch device. Defaults to None.
-        
-        Note:
-            a ``device`` keyword argument can be provided to specify the torch device. 
-            If it's not provided, we will try to use ``'cuda'`` as the device if it's available.
-
-        Returns:
-            Mesh: the loaded Mesh object.
-        """
-        # obj supports face uv
-        if path.endswith(".obj"):
-            mesh = cls.load_obj(path, **kwargs)
-        # trimesh only supports vertex uv, but can load more formats
-        else:
-            mesh = cls.load_trimesh(path, **kwargs)
-        
-        # clean
-        if clean:
-            from kiui.mesh_utils import clean_mesh
-            vertices = mesh.v.detach().cpu().numpy()
-            triangles = mesh.f.detach().cpu().numpy()
-            vertices, triangles = clean_mesh(vertices, triangles, remesh=False)
-            mesh.v = torch.from_numpy(vertices).contiguous().float().to(mesh.device)
-            mesh.f = torch.from_numpy(triangles).contiguous().int().to(mesh.device)
-
-        # auto-normalize
-        if resize:
-            mesh.auto_size(bound=bound)
-        print(f"[INFO] load mesh, v: {mesh.v.shape}, f: {mesh.f.shape}")
-        
-        # auto-fix normal
-        if renormal or mesh.vn is None:
-            mesh.auto_normal()
-        print(f"[INFO] load mesh, vn: {mesh.vn.shape}, fn: {mesh.fn.shape}")
-
-        # auto-fix texcoords
-        if retex:
-            mesh.auto_uv(cache_path=path)
-        if mesh.vt is not None:
-            print(f"[INFO] load mesh, vt: {mesh.vt.shape}, ft: {mesh.ft.shape}")
-
-        # rotate front dir to +z
-        if front_dir != "+z":
-            # axis switch
-            if "-z" in front_dir:
-                T = torch.tensor([[1, 0, 0], [0, 1, 0], [0, 0, -1]], device=mesh.device, dtype=torch.float32)
-            elif "+x" in front_dir:
-                T = torch.tensor([[0, 0, 1], [0, 1, 0], [1, 0, 0]], device=mesh.device, dtype=torch.float32)
-            elif "-x" in front_dir:
-                T = torch.tensor([[0, 0, -1], [0, 1, 0], [1, 0, 0]], device=mesh.device, dtype=torch.float32)
-            elif "+y" in front_dir:
-                T = torch.tensor([[1, 0, 0], [0, 0, 1], [0, 1, 0]], device=mesh.device, dtype=torch.float32)
-            elif "-y" in front_dir:
-                T = torch.tensor([[1, 0, 0], [0, 0, -1], [0, 1, 0]], device=mesh.device, dtype=torch.float32)
-            else:
-                T = torch.tensor([[1, 0, 0], [0, 1, 0], [0, 0, 1]], device=mesh.device, dtype=torch.float32)
-            # rotation (how many 90 degrees)
-            if '1' in front_dir:
-                T @= torch.tensor([[0, -1, 0], [1, 0, 0], [0, 0, 1]], device=mesh.device, dtype=torch.float32) 
-            elif '2' in front_dir:
-                T @= torch.tensor([[1, 0, 0], [0, -1, 0], [0, 0, 1]], device=mesh.device, dtype=torch.float32) 
-            elif '3' in front_dir:
-                T @= torch.tensor([[0, 1, 0], [-1, 0, 0], [0, 0, 1]], device=mesh.device, dtype=torch.float32) 
-            mesh.v @= T
-            mesh.vn @= T
-
-        return mesh

-
-
-    # load from obj file
-

-[docs]
-    @classmethod
-    def load_obj(cls, path, wotex=False, albedo_path=None, device=None):
-        """load an ``obj`` mesh.
-
-        Args:
-            path (str): path to mesh.
-            wotex (bool, optional): do not try to load any texture. Defaults to False.
-            albedo_path (str, optional): path to the albedo texture image, will overwrite the existing texture path if specified in mtl. Defaults to None.
-            device (torch.device, optional): torch device. Defaults to None.
-        
-        Note: 
-            We will try to read `mtl` path from `obj`, else we assume the file name is the same as `obj` but with `mtl` extension.
-            The `usemtl` statement is ignored, and we only use the last material path in `mtl` file.
-
-        Returns:
-            Mesh: the loaded Mesh object.
-        """
-        assert os.path.splitext(path)[-1] == ".obj"
-
-        mesh = cls()
-
-        # device
-        if device is None:
-            device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-        mesh.device = device
-
-        # load obj
-        with open(path, "r") as f:
-            lines = f.readlines()
-
-        def parse_f_v(fv):
-            # pass in a vertex term of a face, return {v, vt, vn} (-1 if not provided)
-            # supported forms:
-            # f v1 v2 v3
-            # f v1/vt1 v2/vt2 v3/vt3
-            # f v1/vt1/vn1 v2/vt2/vn2 v3/vt3/vn3
-            # f v1//vn1 v2//vn2 v3//vn3
-            xs = [int(x) - 1 if x != "" else -1 for x in fv.split("/")]
-            xs.extend([-1] * (3 - len(xs)))
-            return xs[0], xs[1], xs[2]
-
-        vertices, texcoords, normals = [], [], []
-        faces, tfaces, nfaces = [], [], []
-        mtl_path = None
-
-        for line in lines:
-            split_line = line.split()
-            # empty line
-            if len(split_line) == 0:
-                continue
-            prefix = split_line[0].lower()
-            # mtllib
-            if prefix == "mtllib":
-                mtl_path = split_line[1]
-            # usemtl
-            elif prefix == "usemtl":
-                pass # ignored
-            # v/vn/vt
-            elif prefix == "v":
-                vertices.append([float(v) for v in split_line[1:]])
-            elif prefix == "vn":
-                normals.append([float(v) for v in split_line[1:]])
-            elif prefix == "vt":
-                val = [float(v) for v in split_line[1:]]
-                texcoords.append([val[0], 1.0 - val[1]])
-            elif prefix == "f":
-                vs = split_line[1:]
-                nv = len(vs)
-                v0, t0, n0 = parse_f_v(vs[0])
-                for i in range(nv - 2):  # triangulate (assume vertices are ordered)
-                    v1, t1, n1 = parse_f_v(vs[i + 1])
-                    v2, t2, n2 = parse_f_v(vs[i + 2])
-                    faces.append([v0, v1, v2])
-                    tfaces.append([t0, t1, t2])
-                    nfaces.append([n0, n1, n2])
-
-        mesh.v = torch.tensor(vertices, dtype=torch.float32, device=device)
-        mesh.vt = (
-            torch.tensor(texcoords, dtype=torch.float32, device=device)
-            if len(texcoords) > 0
-            else None
-        )
-        mesh.vn = (
-            torch.tensor(normals, dtype=torch.float32, device=device)
-            if len(normals) > 0
-            else None
-        )
-
-        mesh.f = torch.tensor(faces, dtype=torch.int32, device=device)
-        mesh.ft = (
-            torch.tensor(tfaces, dtype=torch.int32, device=device)
-            if len(texcoords) > 0
-            else None
-        )
-        mesh.fn = (
-            torch.tensor(nfaces, dtype=torch.int32, device=device)
-            if len(normals) > 0
-            else None
-        )
-
-        # if not loading texture
-        if wotex:
-            return mesh
-
-        # see if there is vertex color
-        use_vertex_color = False
-        if mesh.v.shape[1] == 6:
-            use_vertex_color = True
-            mesh.vc = mesh.v[:, 3:]
-            mesh.v = mesh.v[:, :3]
-            print(f"[INFO] load obj mesh: use vertex color: {mesh.vc.shape}")
-
-        # try to load texture image
-        if not use_vertex_color:
-            # try to retrieve mtl file
-            mtl_path_candidates = []
-            if mtl_path is not None:
-                mtl_path_candidates.append(mtl_path)
-                mtl_path_candidates.append(os.path.join(os.path.dirname(path), mtl_path))
-            mtl_path_candidates.append(path.replace(".obj", ".mtl"))
-
-            mtl_path = None
-            for candidate in mtl_path_candidates:
-                if os.path.exists(candidate):
-                    mtl_path = candidate
-                    break
-            
-            # if albedo_path is not provided, try retrieve it from mtl
-            metallic_path = None
-            roughness_path = None
-            if mtl_path is not None and albedo_path is None:
-                with open(mtl_path, "r") as f:
-                    lines = f.readlines()
-
-                for line in lines:
-                    split_line = line.split()
-                    # empty line
-                    if len(split_line) == 0:
-                        continue
-                    prefix = split_line[0]
-                    
-                    if "map_Kd" in prefix:
-                        # assume relative path!
-                        albedo_path = os.path.join(os.path.dirname(path), split_line[1])
-                        print(f"[INFO] load obj mesh: use texture from: {albedo_path}")
-                    elif "map_Pm" in prefix:
-                        metallic_path = os.path.join(os.path.dirname(path), split_line[1])
-                    elif "map_Pr" in prefix:
-                        roughness_path = os.path.join(os.path.dirname(path), split_line[1])
-                    
-            # still not found albedo_path, or the path doesn't exist
-            if albedo_path is None or not os.path.exists(albedo_path):
-                print(f"[INFO] load obj mesh: failed to load texture!")
-                mesh.albedo = None
-            else:
-                albedo = cv2.imread(albedo_path, cv2.IMREAD_UNCHANGED)
-                albedo = cv2.cvtColor(albedo, cv2.COLOR_BGR2RGB)
-                albedo = albedo.astype(np.float32) / 255
-                print(f"[INFO] load obj mesh: load texture: {albedo.shape}")
-                mesh.albedo = torch.tensor(albedo, dtype=torch.float32, device=device)
-            
-            # try to load metallic and roughness
-            if metallic_path is not None and roughness_path is not None:
-                print(f"[INFO] load obj mesh: load metallicRoughness from: {metallic_path}, {roughness_path}")
-                metallic = cv2.imread(metallic_path, cv2.IMREAD_UNCHANGED)
-                metallic = metallic.astype(np.float32) / 255
-                roughness = cv2.imread(roughness_path, cv2.IMREAD_UNCHANGED)
-                roughness = roughness.astype(np.float32) / 255
-                metallicRoughness = np.stack([np.zeros_like(metallic), roughness, metallic], axis=-1)
-
-                mesh.metallicRoughness = torch.tensor(metallicRoughness, dtype=torch.float32, device=device).contiguous()
-
-        return mesh

-
-
-

-[docs]
-    @classmethod
-    def load_trimesh(cls, path, wotex=False, device=None):
-        """load a mesh using ``trimesh.load()``.
-
-        Can load various formats like ``glb`` and serves as a fallback.
-
-        Note:
-            We will try to merge all meshes if the glb contains more than one, 
-            but **this may cause the texture to lose**, since we only support one texture image!
-
-        Args:
-            path (str): path to the mesh file.
-            wotex (bool, optional): do not try to load any texture. Defaults to False.
-            device (torch.device, optional): torch device. Defaults to None.
-
-        Returns:
-            Mesh: the loaded Mesh object.
-        """
-        mesh = cls()
-
-        # device
-        if device is None:
-            device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-        mesh.device = device
-
-        # use trimesh to load ply/glb
-        _data = trimesh.load(path)
-        # always convert scene to mesh, and apply all transforms...
-        if isinstance(_data, trimesh.Scene):
-            print(f"[INFO] load trimesh: concatenating {len(_data.geometry)} meshes.")
-            _concat = []
-            # loop the scene graph and apply transform to each mesh
-            scene_graph = _data.graph.to_flattened() # dict {name: {transform: 4x4 mat, geometry: str}}
-            for k, v in scene_graph.items():
-                name = v['geometry']
-                if name in _data.geometry and isinstance(_data.geometry[name], trimesh.Trimesh):
-                    transform = v['transform']
-                    _concat.append(_data.geometry[name].apply_transform(transform))
-            _mesh = trimesh.util.concatenate(_concat)
-        else:
-            _mesh = _data
-        
-        if not wotex:
-            if _mesh.visual.kind == 'vertex':
-                vertex_colors = _mesh.visual.vertex_colors
-                vertex_colors = np.array(vertex_colors[..., :3]).astype(np.float32) / 255
-                mesh.vc = torch.tensor(vertex_colors, dtype=torch.float32, device=device)
-                print(f"[INFO] load trimesh: use vertex color: {mesh.vc.shape}")
-            elif _mesh.visual.kind == 'texture':
-                try:
-                    _material = _mesh.visual.material
-                    if isinstance(_material, trimesh.visual.material.PBRMaterial):
-                        texture = np.array(_material.baseColorTexture).astype(np.float32) / 255
-                        # load metallicRoughness if present
-                        if _material.metallicRoughnessTexture is not None:
-                            metallicRoughness = np.array(_material.metallicRoughnessTexture).astype(np.float32) / 255
-                            # NOTE: fix a bug in trimesh that loads metallicRoughness in wrong channels: https://github.com/mikedh/trimesh/issues/2195
-                            if version.parse(trimesh.__version__) < version.parse('4.2.2'):
-                                metallicRoughness = metallicRoughness[..., [2, 1, 0]]
-                            mesh.metallicRoughness = torch.tensor(metallicRoughness, dtype=torch.float32, device=device).contiguous()
-                    elif isinstance(_material, trimesh.visual.material.SimpleMaterial):
-                        texture = np.array(_material.to_pbr().baseColorTexture).astype(np.float32) / 255
-                    else:
-                        raise NotImplementedError(f"material type {type(_material)} not supported!")
-                    if len(texture.shape) == 2:
-                        texture = texture[..., None].repeat(3, axis=-1)
-                    mesh.albedo = torch.tensor(texture[..., :3], dtype=torch.float32, device=device).contiguous()
-                    print(f"[INFO] load trimesh: load texture: {texture.shape}")
-                # there really can be lots of mysterious errors...
-                except Exception as e:
-                    mesh.albedo = None
-                    print(f"[INFO] load trimesh: failed to load texture.")
-            else:
-                mesh.albedo = None
-                print(f"[INFO] load trimesh: failed to load texture.")
-
-        vertices = _mesh.vertices
-
-        try:
-            texcoords = _mesh.visual.uv
-            texcoords[:, 1] = 1 - texcoords[:, 1]
-        except Exception as e:
-            texcoords = None
-
-        try:
-            normals = _mesh.vertex_normals
-        except Exception as e:
-            normals = None
-
-        # trimesh only support vertex uv...
-        faces = tfaces = nfaces = _mesh.faces
-
-        mesh.v = torch.tensor(vertices, dtype=torch.float32, device=device)
-        mesh.vt = (
-            torch.tensor(texcoords, dtype=torch.float32, device=device)
-            if texcoords is not None
-            else None
-        )
-        mesh.vn = (
-            torch.tensor(normals, dtype=torch.float32, device=device)
-            if normals is not None
-            else None
-        )
-
-        mesh.f = torch.tensor(faces, dtype=torch.int32, device=device)
-        mesh.ft = (
-            torch.tensor(tfaces, dtype=torch.int32, device=device)
-            if texcoords is not None
-            else None
-        )
-        mesh.fn = (
-            torch.tensor(nfaces, dtype=torch.int32, device=device)
-            if normals is not None
-            else None
-        )
-
-        return mesh

-
-
-    # sample surface (using trimesh)
-

-[docs]
-    def sample_surface(self, count: int):
-        """sample points on the surface of the mesh.
-
-        Args:
-            count (int): number of points to sample.
-
-        Returns:
-            torch.Tensor: the sampled points, float [count, 3].
-        """
-        _mesh = trimesh.Trimesh(vertices=self.v.detach().cpu().numpy(), faces=self.f.detach().cpu().numpy())
-        points, face_idx = trimesh.sample.sample_surface(_mesh, count)
-        points = torch.from_numpy(points).float().to(self.device)
-        return points

-
-
-    # aabb
-

-[docs]
-    def aabb(self):
-        """get the axis-aligned bounding box of the mesh.
-
-        Returns:
-            Tuple[torch.Tensor]: the min xyz and max xyz of the mesh.
-        """
-        return torch.min(self.v, dim=0).values, torch.max(self.v, dim=0).values

-
-
-    # unit size
-

-[docs]
-    @torch.no_grad()
-    def auto_size(self, bound=0.9):
-        """auto resize the mesh.
-
-        Args:
-            bound (float, optional): resizing into ``[-bound, bound]^3``. Defaults to 0.9.
-        """
-        vmin, vmax = self.aabb()
-        self.ori_center = (vmax + vmin) / 2
-        self.ori_scale = 2 * bound / torch.max(vmax - vmin).item()
-        self.v = (self.v - self.ori_center) * self.ori_scale

-
-
-

-[docs]
-    def auto_normal(self):
-        """auto calculate the vertex normals.
-        """
-        i0, i1, i2 = self.f[:, 0].long(), self.f[:, 1].long(), self.f[:, 2].long()
-        v0, v1, v2 = self.v[i0, :], self.v[i1, :], self.v[i2, :]
-
-        face_normals = torch.cross(v1 - v0, v2 - v0, dim=-1)
-
-        # Splat face normals to vertices
-        vn = torch.zeros_like(self.v)
-        vn.scatter_add_(0, i0[:, None].repeat(1, 3), face_normals)
-        vn.scatter_add_(0, i1[:, None].repeat(1, 3), face_normals)
-        vn.scatter_add_(0, i2[:, None].repeat(1, 3), face_normals)
-
-        # Normalize, replace zero (degenerated) normals with some default value
-        vn = torch.where(
-            dot(vn, vn) > 1e-20,
-            vn,
-            torch.tensor([0.0, 0.0, 1.0], dtype=torch.float32, device=vn.device),
-        )
-        vn = safe_normalize(vn)
-
-        self.vn = vn
-        self.fn = self.f

-
-
-

-[docs]
-    def auto_uv(self, cache_path=None, vmap=True):
-        """auto calculate the uv coordinates.
-
-        Args:
-            cache_path (str, optional): path to save/load the uv cache as a npz file, this can avoid calculating uv every time when loading the same mesh, which is time-consuming. Defaults to None.
-            vmap (bool, optional): remap vertices based on uv coordinates, so each v correspond to a unique vt (necessary for formats like gltf). 
-                Usually this will duplicate the vertices on the edge of uv atlas. Defaults to True.
-        """
-        # try to load cache
-        if cache_path is not None:
-            cache_path = os.path.splitext(cache_path)[0] + "_uv.npz"
-        if cache_path is not None and os.path.exists(cache_path):
-            data = np.load(cache_path)
-            vt_np, ft_np, vmapping = data["vt"], data["ft"], data["vmapping"]
-        else:
-            import xatlas
-
-            v_np = self.v.detach().cpu().numpy()
-            f_np = self.f.detach().int().cpu().numpy()
-            atlas = xatlas.Atlas()
-            atlas.add_mesh(v_np, f_np)
-            chart_options = xatlas.ChartOptions()
-            # chart_options.max_iterations = 4
-            atlas.generate(chart_options=chart_options)
-            vmapping, ft_np, vt_np = atlas[0]  # [N], [M, 3], [N, 2]
-
-            # save to cache
-            if cache_path is not None:
-                np.savez(cache_path, vt=vt_np, ft=ft_np, vmapping=vmapping)
-        
-        vt = torch.from_numpy(vt_np.astype(np.float32)).to(self.device)
-        ft = torch.from_numpy(ft_np.astype(np.int32)).to(self.device)
-        self.vt = vt
-        self.ft = ft
-
-        if vmap:
-            vmapping = torch.from_numpy(vmapping.astype(np.int64)).long().to(self.device)
-            self.align_v_to_vt(vmapping)

-
-    
-

-[docs]
-    def remap_uv(self, v):
-        """ remap uv texture (vt) to other surface.
-
-        Args:
-            v (torch.Tensor): the target mesh vertices, float [N, 3].
-        """
-
-        assert self.vt is not None
-
-        if self.v.shape[0] != self.vt.shape[0]:
-            self.align_v_to_vt()
-
-        # find the closest face for each vertex
-        import cubvh 
-        BVH = cubvh.cuBVH(self.v, self.f)
-        dist, face_id, uvw = BVH.unsigned_distance(v, return_uvw=True)
-
-        # get original uv
-        faces = self.f[face_id].long()
-        vt0 = self.vt[faces[:, 0]]
-        vt1 = self.vt[faces[:, 1]]
-        vt2 = self.vt[faces[:, 2]]
-
-        # calc new uv
-        vt = vt0 * uvw[:, 0:1] + vt1 * uvw[:, 1:2] + vt2 * uvw[:, 2:3]
-
-        return vt

-
-
-    
-

-[docs]
-    def align_v_to_vt(self, vmapping=None):
-        """ remap v/f and vn/fn to vt/ft.
-
-        Args:
-            vmapping (np.ndarray, optional): the mapping relationship from f to ft. Defaults to None.
-        """
-        if vmapping is None:
-            ft = self.ft.view(-1).long()
-            f = self.f.view(-1).long()
-            vmapping = torch.zeros(self.vt.shape[0], dtype=torch.long, device=self.device)
-            vmapping[ft] = f # scatter, randomly choose one if index is not unique
-
-        self.v = self.v[vmapping]
-        self.f = self.ft
-        
-        if self.vn is not None:
-            self.vn = self.vn[vmapping]
-            self.fn = self.ft

-
-
-

-[docs]
-    def to(self, device):
-        """move all tensor attributes to device.
-
-        Args:
-            device (torch.device): target device.
-
-        Returns:
-            Mesh: self.
-        """
-        self.device = device
-        for name in ["v", "f", "vn", "fn", "vt", "ft", "albedo", "vc", "metallicRoughness"]:
-            tensor = getattr(self, name)
-            if tensor is not None:
-                setattr(self, name, tensor.to(device))
-        return self

-
-    
-

-[docs]
-    def write(self, path):
-        """write the mesh to a path.
-
-        Args:
-            path (str): path to write, supports ply, obj and glb.
-        """
-        if path.endswith(".ply"):
-            self.write_ply(path)
-        elif path.endswith(".obj"):
-            self.write_obj(path)
-        elif path.endswith(".glb") or path.endswith(".gltf"):
-            self.write_glb(path)
-        else:
-            raise NotImplementedError(f"format {path} not supported!")

-
-    
-

-[docs]
-    def write_ply(self, path):
-        """write the mesh in ply format. Only for geometry!
-
-        Args:
-            path (str): path to write.
-        """
-
-        if self.albedo is not None:
-            print(f'[WARN] ply format does not support exporting texture, will ignore!')
-
-        v_np = self.v.detach().cpu().numpy()
-        f_np = self.f.detach().cpu().numpy()
-
-        _mesh = trimesh.Trimesh(vertices=v_np, faces=f_np)
-        _mesh.export(path)

-
-
-
-

-[docs]
-    def write_glb(self, path):
-        """write the mesh in glb/gltf format.
-          This will create a scene with a single mesh.
-
-        Args:
-            path (str): path to write.
-        """
-
-        # assert self.v.shape[0] == self.vn.shape[0] and self.v.shape[0] == self.vt.shape[0]
-        if self.vt is not None and self.v.shape[0] != self.vt.shape[0]:
-            self.align_v_to_vt()
-
-        import pygltflib
-
-        f_np = self.f.detach().cpu().numpy().astype(np.uint32)
-        f_np_blob = f_np.flatten().tobytes()
-
-        v_np = self.v.detach().cpu().numpy().astype(np.float32)
-        v_np_blob = v_np.tobytes()
-
-        blob = f_np_blob + v_np_blob
-        byteOffset = len(blob)
-
-        # base mesh
-        gltf = pygltflib.GLTF2(
-            scene=0,
-            scenes=[pygltflib.Scene(nodes=[0])],
-            nodes=[pygltflib.Node(mesh=0)],
-            meshes=[pygltflib.Mesh(primitives=[pygltflib.Primitive(
-                # indices to accessors (0 is triangles)
-                attributes=pygltflib.Attributes(
-                    POSITION=1,
-                ),
-                indices=0,
-            )])],
-            buffers=[
-                pygltflib.Buffer(byteLength=len(f_np_blob) + len(v_np_blob))
-            ],
-            # buffer view (based on dtype)
-            bufferViews=[
-                # triangles; as flatten (element) array
-                pygltflib.BufferView(
-                    buffer=0,
-                    byteLength=len(f_np_blob),
-                    target=pygltflib.ELEMENT_ARRAY_BUFFER, # GL_ELEMENT_ARRAY_BUFFER (34963)
-                ),
-                # positions; as vec3 array
-                pygltflib.BufferView(
-                    buffer=0,
-                    byteOffset=len(f_np_blob),
-                    byteLength=len(v_np_blob),
-                    byteStride=12, # vec3
-                    target=pygltflib.ARRAY_BUFFER, # GL_ARRAY_BUFFER (34962)
-                ),
-            ],
-            accessors=[
-                # 0 = triangles
-                pygltflib.Accessor(
-                    bufferView=0,
-                    componentType=pygltflib.UNSIGNED_INT, # GL_UNSIGNED_INT (5125)
-                    count=f_np.size,
-                    type=pygltflib.SCALAR,
-                    max=[int(f_np.max())],
-                    min=[int(f_np.min())],
-                ),
-                # 1 = positions
-                pygltflib.Accessor(
-                    bufferView=1,
-                    componentType=pygltflib.FLOAT, # GL_FLOAT (5126)
-                    count=len(v_np),
-                    type=pygltflib.VEC3,
-                    max=v_np.max(axis=0).tolist(),
-                    min=v_np.min(axis=0).tolist(),
-                ),
-            ],
-        )
-
-        # append texture info
-        if self.vt is not None:
-
-            vt_np = self.vt.detach().cpu().numpy().astype(np.float32)
-            vt_np_blob = vt_np.tobytes()
-
-            albedo = self.albedo.detach().cpu().numpy()
-            albedo = (albedo * 255).astype(np.uint8)
-            albedo = cv2.cvtColor(albedo, cv2.COLOR_RGB2BGR)
-            albedo_blob = cv2.imencode('.png', albedo)[1].tobytes()
-
-            # update primitive
-            gltf.meshes[0].primitives[0].attributes.TEXCOORD_0 = 2
-            gltf.meshes[0].primitives[0].material = 0
-
-            # update materials
-            gltf.materials.append(pygltflib.Material(
-                pbrMetallicRoughness=pygltflib.PbrMetallicRoughness(
-                    baseColorTexture=pygltflib.TextureInfo(index=0, texCoord=0),
-                    metallicFactor=0.0,
-                    roughnessFactor=1.0,
-                ),
-                alphaMode=pygltflib.OPAQUE,
-                alphaCutoff=None,
-                doubleSided=True,
-            ))
-
-            gltf.textures.append(pygltflib.Texture(sampler=0, source=0))
-            gltf.samplers.append(pygltflib.Sampler(magFilter=pygltflib.LINEAR, minFilter=pygltflib.LINEAR_MIPMAP_LINEAR, wrapS=pygltflib.REPEAT, wrapT=pygltflib.REPEAT))
-            gltf.images.append(pygltflib.Image(bufferView=3, mimeType="image/png"))
-
-            # update buffers
-            gltf.bufferViews.append(
-                # index = 2, texcoords; as vec2 array
-                pygltflib.BufferView(
-                    buffer=0,
-                    byteOffset=byteOffset,
-                    byteLength=len(vt_np_blob),
-                    byteStride=8, # vec2
-                    target=pygltflib.ARRAY_BUFFER,
-                )
-            )
-
-            gltf.accessors.append(
-                # 2 = texcoords
-                pygltflib.Accessor(
-                    bufferView=2,
-                    componentType=pygltflib.FLOAT,
-                    count=len(vt_np),
-                    type=pygltflib.VEC2,
-                    max=vt_np.max(axis=0).tolist(),
-                    min=vt_np.min(axis=0).tolist(),
-                )
-            )
-
-            blob += vt_np_blob 
-            byteOffset += len(vt_np_blob)
-
-            gltf.bufferViews.append(
-                # index = 3, albedo texture; as none target
-                pygltflib.BufferView(
-                    buffer=0,
-                    byteOffset=byteOffset,
-                    byteLength=len(albedo_blob),
-                )
-            )
-
-            blob += albedo_blob
-            byteOffset += len(albedo_blob)
-
-            gltf.buffers[0].byteLength = byteOffset
-
-            # append metllic roughness
-            if self.metallicRoughness is not None:
-                metallicRoughness = self.metallicRoughness.detach().cpu().numpy()
-                metallicRoughness = (metallicRoughness * 255).astype(np.uint8)
-                metallicRoughness = cv2.cvtColor(metallicRoughness, cv2.COLOR_RGB2BGR)
-                metallicRoughness_blob = cv2.imencode('.png', metallicRoughness)[1].tobytes()
-
-                # update texture definition
-                gltf.materials[0].pbrMetallicRoughness.metallicFactor = 1.0
-                gltf.materials[0].pbrMetallicRoughness.roughnessFactor = 1.0
-                gltf.materials[0].pbrMetallicRoughness.metallicRoughnessTexture = pygltflib.TextureInfo(index=1, texCoord=0)
-
-                gltf.textures.append(pygltflib.Texture(sampler=1, source=1))
-                gltf.samplers.append(pygltflib.Sampler(magFilter=pygltflib.LINEAR, minFilter=pygltflib.LINEAR_MIPMAP_LINEAR, wrapS=pygltflib.REPEAT, wrapT=pygltflib.REPEAT))
-                gltf.images.append(pygltflib.Image(bufferView=4, mimeType="image/png"))
-
-                # update buffers
-                gltf.bufferViews.append(
-                    # index = 4, metallicRoughness texture; as none target
-                    pygltflib.BufferView(
-                        buffer=0,
-                        byteOffset=byteOffset,
-                        byteLength=len(metallicRoughness_blob),
-                    )
-                )
-
-                blob += metallicRoughness_blob
-                byteOffset += len(metallicRoughness_blob)
-
-                gltf.buffers[0].byteLength = byteOffset
-
-            
-        # set actual data
-        gltf.set_binary_blob(blob)
-
-        # glb = b"".join(gltf.save_to_bytes())
-        gltf.save(path)

-
-
-
-

-[docs]
-    def write_obj(self, path):
-        """write the mesh in obj format. Will also write the texture and mtl files.
-
-        Args:
-            path (str): path to write.
-        """
-
-        mtl_path = path.replace(".obj", ".mtl")
-        albedo_path = path.replace(".obj", "_albedo.png")
-        metallic_path = path.replace(".obj", "_metallic.png")
-        roughness_path = path.replace(".obj", "_roughness.png")
-
-        v_np = self.v.detach().cpu().numpy()
-        vt_np = self.vt.detach().cpu().numpy() if self.vt is not None else None
-        vn_np = self.vn.detach().cpu().numpy() if self.vn is not None else None
-        f_np = self.f.detach().cpu().numpy()
-        ft_np = self.ft.detach().cpu().numpy() if self.ft is not None else None
-        fn_np = self.fn.detach().cpu().numpy() if self.fn is not None else None
-
-        with open(path, "w") as fp:
-            fp.write(f"mtllib {os.path.basename(mtl_path)} \n")
-
-            for v in v_np:
-                fp.write(f"v {v[0]} {v[1]} {v[2]} \n")
-
-            if vt_np is not None:
-                for v in vt_np:
-                    fp.write(f"vt {v[0]} {1 - v[1]} \n")
-
-            if vn_np is not None:
-                for v in vn_np:
-                    fp.write(f"vn {v[0]} {v[1]} {v[2]} \n")
-
-            fp.write(f"usemtl defaultMat \n")
-            for i in range(len(f_np)):
-                fp.write(
-                    f'f {f_np[i, 0] + 1}/{ft_np[i, 0] + 1 if ft_np is not None else ""}/{fn_np[i, 0] + 1 if fn_np is not None else ""} \
-                             {f_np[i, 1] + 1}/{ft_np[i, 1] + 1 if ft_np is not None else ""}/{fn_np[i, 1] + 1 if fn_np is not None else ""} \
-                             {f_np[i, 2] + 1}/{ft_np[i, 2] + 1 if ft_np is not None else ""}/{fn_np[i, 2] + 1 if fn_np is not None else ""} \n'
-                )
-
-        with open(mtl_path, "w") as fp:
-            fp.write(f"newmtl defaultMat \n")
-            fp.write(f"Ka 1 1 1 \n")
-            fp.write(f"Kd 1 1 1 \n")
-            fp.write(f"Ks 0 0 0 \n")
-            fp.write(f"Tr 1 \n")
-            fp.write(f"illum 1 \n")
-            fp.write(f"Ns 0 \n")
-            if self.albedo is not None:
-                fp.write(f"map_Kd {os.path.basename(albedo_path)} \n")
-            if self.metallicRoughness is not None:
-                # ref: https://en.wikipedia.org/wiki/Wavefront_.obj_file#Physically-based_Rendering
-                fp.write(f"map_Pm {os.path.basename(metallic_path)} \n")
-                fp.write(f"map_Pr {os.path.basename(roughness_path)} \n")
-
-        if self.albedo is not None:
-            albedo = self.albedo.detach().cpu().numpy()
-            albedo = (albedo * 255).astype(np.uint8)
-            cv2.imwrite(albedo_path, cv2.cvtColor(albedo, cv2.COLOR_RGB2BGR))
-        
-        if self.metallicRoughness is not None:
-            metallicRoughness = self.metallicRoughness.detach().cpu().numpy()
-            metallicRoughness = (metallicRoughness * 255).astype(np.uint8)
-            cv2.imwrite(metallic_path, metallicRoughness[..., 2])
-            cv2.imwrite(roughness_path, metallicRoughness[..., 1])

-

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kiui 0.2.10 documentation
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Source code for kiui.mesh_utils

-import torch
-import numpy as np
-
-from kiui.op import safe_normalize
-
-import pymeshlab as pml
-from importlib.metadata import version
-
-PML_VER = version('pymeshlab') 
-
-# the code assumes the latest 2023.12 version, but we can patch older versions
-if PML_VER.startswith('0.2'):
-    # monkey patch for 0.2 (only the used functions in this file!)
-    pml.MeshSet.meshing_decimation_quadric_edge_collapse = pml.MeshSet.simplification_quadric_edge_collapse_decimation
-    pml.MeshSet.meshing_isotropic_explicit_remeshing = pml.MeshSet.remeshing_isotropic_explicit_remeshing
-    pml.MeshSet.meshing_remove_unreferenced_vertices = pml.MeshSet.remove_unreferenced_vertices
-    pml.MeshSet.meshing_merge_close_vertices = pml.MeshSet.merge_close_vertices
-    pml.MeshSet.meshing_remove_duplicate_faces = pml.MeshSet.remove_duplicate_faces
-    pml.MeshSet.meshing_remove_null_faces = pml.MeshSet.remove_zero_area_faces
-    pml.MeshSet.meshing_remove_connected_component_by_diameter = pml.MeshSet.remove_isolated_pieces_wrt_diameter
-    pml.MeshSet.meshing_remove_connected_component_by_face_number = pml.MeshSet.remove_isolated_pieces_wrt_face_num
-    pml.MeshSet.meshing_repair_non_manifold_edges = pml.MeshSet.repair_non_manifold_edges_by_removing_faces
-    pml.MeshSet.meshing_repair_non_manifold_vertices = pml.MeshSet.repair_non_manifold_vertices_by_splitting
-    pml.PercentageValue = pml.Percentage
-    pml.PureValue = float
-elif PML_VER.startswith('2022.2'):
-    # monkey patch for 2022.2
-    pml.PercentageValue = pml.Percentage
-    pml.PureValue = pml.AbsoluteValue
-
-
-

-[docs]
-def decimate_mesh(
-    verts, faces, target=5e4, backend="pymeshlab", remesh=False, optimalplacement=True, verbose=True
-):
-    """ perform mesh decimation.
-
-    Args:
-        verts (np.ndarray): mesh vertices, float [N, 3]
-        faces (np.ndarray): mesh faces, int [M, 3]
-        target (int): targeted number of faces
-        backend (str, optional): algorithm backend, can be "pymeshlab" or "pyfqmr". Defaults to "pymeshlab".
-        remesh (bool, optional): whether to remesh after decimation. Defaults to False.
-        optimalplacement (bool, optional): For flat mesh, use False to prevent spikes. Defaults to True.
-        verbose (bool, optional): whether to print the decimation process. Defaults to True.
-
-    Returns:
-        Tuple[np.ndarray]: vertices and faces after decimation.
-    """
-
-    _ori_vert_shape = verts.shape
-    _ori_face_shape = faces.shape
-
-    if backend == "pyfqmr":
-        import pyfqmr
-
-        solver = pyfqmr.Simplify()
-        solver.setMesh(verts, faces)
-        solver.simplify_mesh(target_count=target, preserve_border=False, verbose=False)
-        verts, faces, normals = solver.getMesh()
-    else:
-        m = pml.Mesh(verts, faces)
-        ms = pml.MeshSet()
-        ms.add_mesh(m, "mesh")  # will copy!
-
-        # filters
-        # ms.meshing_decimation_clustering(threshold=pml.PercentageValue(1))
-        ms.meshing_decimation_quadric_edge_collapse(
-            targetfacenum=int(target), optimalplacement=optimalplacement
-        )
-
-        if remesh:
-            # ms.apply_coord_taubin_smoothing()
-            ms.meshing_isotropic_explicit_remeshing(
-                iterations=3, targetlen=pml.PercentageValue(1)
-            )
-
-        # extract mesh
-        m = ms.current_mesh()
-        m.compact()
-        verts = m.vertex_matrix()
-        faces = m.face_matrix()
-
-    if verbose:
-        print(f"[INFO] mesh decimation: {_ori_vert_shape} --> {verts.shape}, {_ori_face_shape} --> {faces.shape}")
-
-    return verts, faces

-
-
-
-

-[docs]
-def clean_mesh(
-    verts,
-    faces,
-    v_pct=1,
-    min_f=0,
-    min_d=0,
-    repair=True,
-    remesh=False,
-    remesh_size=0.01,
-    remesh_iters=3,
-    verbose=True,
-):
-    """ perform mesh cleaning, including floater removal, non manifold repair, and remeshing.
-
-    Args:
-        verts (np.ndarray): mesh vertices, float [N, 3]
-        faces (np.ndarray): mesh faces, int [M, 3]
-        v_pct (int, optional): percentage threshold to merge close vertices. Defaults to 1.
-        min_f (int, optional): maximal number of faces for isolated component to remove. Defaults to 0.
-        min_d (int, optional): maximal diameter percentage of isolated component to remove. Defaults to 0.
-        repair (bool, optional): whether to repair non-manifold faces (cannot gurantee). Defaults to True.
-        remesh (bool, optional): whether to perform a remeshing after all cleaning. Defaults to True.
-        remesh_size (float, optional): the targeted edge length for remeshing. Defaults to 0.01.
-        remesh_iters (int, optional): the iterations of remeshing. Defaults to 3.
-        verbose (bool, optional): whether to print the cleaning process. Defaults to True.
-
-    Returns:
-        Tuple[np.ndarray]: vertices and faces after decimation.
-    """
-    # verts: [N, 3]
-    # faces: [N, 3]
-
-    _ori_vert_shape = verts.shape
-    _ori_face_shape = faces.shape
-
-    m = pml.Mesh(verts, faces)
-    ms = pml.MeshSet()
-    ms.add_mesh(m, "mesh")  # will copy!
-
-    # filters
-    ms.meshing_remove_unreferenced_vertices()  # verts not refed by any faces
-
-    if v_pct > 0:
-        ms.meshing_merge_close_vertices(
-            threshold=pml.PercentageValue(v_pct)
-        )  # 1/10000 of bounding box diagonal
-
-    ms.meshing_remove_duplicate_faces()  # faces defined by the same verts
-    ms.meshing_remove_null_faces()  # faces with area == 0
-
-    if min_d > 0:
-        ms.meshing_remove_connected_component_by_diameter(
-            mincomponentdiag=pml.PercentageValue(min_d)
-        )
-
-    if min_f > 0:
-        ms.meshing_remove_connected_component_by_face_number(mincomponentsize=min_f)
-
-    if repair:
-        # ms.meshing_remove_t_vertices(method=0, threshold=40, repeat=True)
-        ms.meshing_repair_non_manifold_edges(method=0)
-        ms.meshing_repair_non_manifold_vertices(vertdispratio=0)
-
-    if remesh:
-        # ms.apply_coord_taubin_smoothing()
-        ms.meshing_isotropic_explicit_remeshing(
-            iterations=remesh_iters, targetlen=pml.PureValue(remesh_size)
-        )
-
-    # extract mesh
-    m = ms.current_mesh()
-    m.compact()
-    verts = m.vertex_matrix()
-    faces = m.face_matrix()
-
-    if verbose:
-        print(f"[INFO] mesh cleaning: {_ori_vert_shape} --> {verts.shape}, {_ori_face_shape} --> {faces.shape}")
-
-    return verts, faces

-
-
-
-
-### mesh related losses
-
-

-[docs]
-def laplacian_uniform(verts, faces):
-    """ calculate laplacian uniform matrix
-
-    Args:
-        verts (torch.Tensor): mesh vertices, float [N, 3]
-        faces (torch.Tensor): mesh faces, long [M, 3]
-
-    Returns:
-        torch.Tensor: sparse laplacian matrix.
-    """
-
-    V = verts.shape[0]
-    F = faces.shape[0]
-
-    # Neighbor indices
-    ii = faces[:, [1, 2, 0]].flatten()
-    jj = faces[:, [2, 0, 1]].flatten()
-    adj = torch.stack([torch.cat([ii, jj]), torch.cat([jj, ii])], dim=0).unique(dim=1)
-    adj_values = torch.ones(adj.shape[1], device=verts.device, dtype=torch.float)
-
-    # Diagonal indices
-    diag_idx = adj[0]
-
-    # Build the sparse matrix
-    idx = torch.cat((adj, torch.stack((diag_idx, diag_idx), dim=0)), dim=1)
-    values = torch.cat((-adj_values, adj_values))
-
-    # The coalesce operation sums the duplicate indices, resulting in the
-    # correct diagonal
-    return torch.sparse_coo_tensor(idx, values, (V,V)).coalesce()

-
-
-
-

-[docs]
-def laplacian_smooth_loss(verts, faces):
-    """ calculate laplacian smooth loss.
-
-    Args:
-        verts (torch.Tensor): mesh vertices, float [N, 3]
-        faces (torch.Tensor): mesh faces, int [M, 3]
-
-    Returns:
-        torch.Tensor: loss value.
-    """
-    with torch.no_grad():
-        L = laplacian_uniform(verts, faces.long())
-    loss = L.mm(verts)
-    loss = loss.norm(dim=1)
-    loss = loss.mean()
-    return loss

-
-
-

-[docs]
-@torch.no_grad()
-def compute_edge_to_face_mapping(faces):
-    """ compute edge to face mapping.
-
-    Args:
-        faces (torch.Tensor): mesh faces, int [M, 3]
-
-    Returns:
-        torch.Tensor: indices to faces for each edge, long, [N, 2]
-    """
-    # Get unique edges
-    # Create all edges, packed by triangle
-    all_edges = torch.cat((
-        torch.stack((faces[:, 0], faces[:, 1]), dim=-1),
-        torch.stack((faces[:, 1], faces[:, 2]), dim=-1),
-        torch.stack((faces[:, 2], faces[:, 0]), dim=-1),
-    ), dim=-1).view(-1, 2)
-
-    # Swap edge order so min index is always first
-    order = (all_edges[:, 0] > all_edges[:, 1]).long().unsqueeze(dim=1)
-    sorted_edges = torch.cat((
-        torch.gather(all_edges, 1, order),
-        torch.gather(all_edges, 1, 1 - order)
-    ), dim=-1)
-
-    # Elliminate duplicates and return inverse mapping
-    unique_edges, idx_map = torch.unique(sorted_edges, dim=0, return_inverse=True)
-
-    tris = torch.arange(faces.shape[0]).repeat_interleave(3).cuda()
-
-    tris_per_edge = torch.zeros((unique_edges.shape[0], 2), dtype=torch.int64).cuda()
-
-    # Compute edge to face table
-    mask0 = order[:,0] == 0
-    mask1 = order[:,0] == 1
-    tris_per_edge[idx_map[mask0], 0] = tris[mask0]
-    tris_per_edge[idx_map[mask1], 1] = tris[mask1]
-
-    return tris_per_edge

-
-
-
-

-[docs]
-def normal_consistency(verts, faces, face_normals=None):
-    """ calculate normal consistency loss.
-
-    Args:
-        verts (torch.Tensor): mesh vertices, float [N, 3]
-        faces (torch.Tensor): mesh faces, int [M, 3]
-        face_normals (Optional[torch.Tensor]): the normal vector for each face, will be calculated if not provided, float [M, 3]
-
-    Returns:
-        torch.Tensor: loss value.
-    """
-
-    if face_normals is None:
-        
-        i0, i1, i2 = faces[:, 0].long(), faces[:, 1].long(), faces[:, 2].long()
-        v0, v1, v2 = verts[i0, :], verts[i1, :], verts[i2, :]
-
-        face_normals = torch.cross(v1 - v0, v2 - v0)
-        face_normals = safe_normalize(face_normals)
-
-    tris_per_edge = compute_edge_to_face_mapping(faces)
-
-    # Fetch normals for both faces sharind an edge
-    n0 = face_normals[tris_per_edge[:, 0], :]
-    n1 = face_normals[tris_per_edge[:, 1], :]
-
-    # Compute error metric based on normal difference
-    term = torch.clamp(torch.sum(n0 * n1, -1, keepdim=True), min=-1.0, max=1.0)
-    term = (1.0 - term)
-
-    return torch.mean(torch.abs(term))

-
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- Copyright © 2024, kiui -
- Made with Sphinx and @pradyunsg's - - Furo - -
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- - - - - - - - \ No newline at end of file diff --git a/_modules/kiui/op/index.html b/_modules/kiui/op/index.html deleted file mode 100644 index 0d0005f..0000000 --- a/_modules/kiui/op/index.html +++ /dev/null @@ -1,619 +0,0 @@ - - - - - - - - kiui.op - kiui 0.2.10 documentation - - - - - - - - - - - - - - - - - - Contents - - - - - - Menu - - - - - - - - Expand - - - - - - Light mode - - - - - - - - - - - - - - Dark mode - - - - - - - Auto light/dark, in light mode - - - - - - - - - - - - - - - Auto light/dark, in dark mode - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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kiui 0.2.10 documentation
 -
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- - - - - Back to top - -
-
- -
- -
-
-

Source code for kiui.op

-import cv2
-import torch
-import numpy as np
-
-from kiui.typing import *
-from kiui.grid_put import grid_put
-
-# torch / numpy math utils
-

-[docs]
-def dot(x: Union[Tensor, ndarray], y: Union[Tensor, ndarray]) -> Union[Tensor, ndarray]:
-    """dot product (along the last dim).
-
-    Args:
-        x (Union[Tensor, ndarray]): x, [..., C]
-        y (Union[Tensor, ndarray]): y, [..., C]
-
-    Returns:
-        Union[Tensor, ndarray]: x dot y, [..., 1]
-    """
-    if isinstance(x, np.ndarray):
-        return np.sum(x * y, -1, keepdims=True)
-    else:
-        return torch.sum(x * y, -1, keepdim=True)

-
-
-

-[docs]
-def length(x: Union[Tensor, ndarray], eps=1e-20) -> Union[Tensor, ndarray]:
-    """length of an array (along the last dim).
-
-    Args:
-        x (Union[Tensor, ndarray]): x, [..., C]
-        eps (float, optional): eps. Defaults to 1e-20.
-
-    Returns:
-        Union[Tensor, ndarray]: length, [..., 1]
-    """
-    if isinstance(x, np.ndarray):
-        return np.sqrt(np.maximum(np.sum(x * x, axis=-1, keepdims=True), eps))
-    else:
-        return torch.sqrt(torch.clamp(dot(x, x), min=eps))

-
-
-

-[docs]
-def safe_normalize(x: Union[Tensor, ndarray], eps=1e-20) -> Union[Tensor, ndarray]:
-    """normalize an array (along the last dim).
-
-    Args:
-        x (Union[Tensor, ndarray]): x, [..., C]
-        eps (float, optional): eps. Defaults to 1e-20.
-
-    Returns:
-        Union[Tensor, ndarray]: normalized x, [..., C]
-    """
-
-    return x / length(x, eps)

-
-
-

-[docs]
-def normalize(x: Union[Tensor, ndarray], eps=1e-20) -> Union[Tensor, ndarray]:
-    """normalize an array (along the last dim). alias of safe_normalize.
-
-    Args:
-        x (Union[Tensor, ndarray]): x, [..., C]
-        eps (float, optional): eps. Defaults to 1e-20.
-
-    Returns:
-        Union[Tensor, ndarray]: normalized x, [..., C]
-    """
-
-    return x / length(x, eps)

-
-
-

-[docs]
-def make_divisible(x: int, m: int = 8):
-    """make an int x divisible by m.
-
-    Args:
-        x (int): x
-        m (int, optional): m. Defaults to 8.
-
-    Returns:
-        int: x + (m - x % m)
-    """
-    return int(x + (m - x % m))

-
-
-

-[docs]
-def inverse_sigmoid(x: Tensor, eps=1e-6) -> Tensor:
-    """inversion of sigmoid function.
-
-    Args:
-        x (Tensor): x
-        eps (float, optional): eps. Defaults to 1e-6.
-
-    Returns:
-        Tensor: log(x / (1 - x))
-    """
-    x = x.clamp(eps, 1 - eps)
-    return torch.log(x / (1 - x))

-
-
-

-[docs]
-def inverse_softplus(x: Tensor) -> Tensor:
-    """inversion of softplus function.
-    
-    Args:
-        x (Tensor): x
-    
-    Returns:
-        Tensor: log(exp(x) - 1)
-    """
-    # a numerically stable equation (ref: https://github.com/pytorch/pytorch/issues/72759)
-    return x + torch.log(-torch.expm1(-x))

-
-
-# torch image scaling
-

-[docs]
-def scale_img_nhwc(x: Tensor, size: Sequence[int], mag='bilinear', min='bilinear') -> Tensor:
-    """image scaling helper.
-
-    Args:
-        x (Tensor): input image, float [N, H, W, C]
-        size (Sequence[int]): target size, tuple of [H', W']
-        mag (str, optional): upscale interpolation mode. Defaults to 'bilinear'.
-        min (str, optional): downscale interpolation mode. Defaults to 'bilinear'.
-
-    Returns:
-        Tensor: rescaled image, float [N, H', W', C]
-    """
-    assert (x.shape[1] >= size[0] and x.shape[2] >= size[1]) or (x.shape[1] < size[0] and x.shape[2] < size[1]), "Trying to magnify image in one dimension and minify in the other"
-    y = x.permute(0, 3, 1, 2) # NHWC -> NCHW
-    if x.shape[1] > size[0] and x.shape[2] > size[1]: # Minification, previous size was bigger
-        y = torch.nn.functional.interpolate(y, size, mode=min)
-    else: # Magnification
-        if mag == 'bilinear' or mag == 'bicubic':
-            y = torch.nn.functional.interpolate(y, size, mode=mag, align_corners=True)
-        else:
-            y = torch.nn.functional.interpolate(y, size, mode=mag)
-    return y.permute(0, 2, 3, 1).contiguous() # NCHW -> NHWC

-
-
-

-[docs]
-def scale_img_hwc(x: Tensor, size: Sequence[int], mag='bilinear', min='bilinear') -> Tensor:
-    """image scaling helper.
-
-    Args:
-        x (Tensor): input image, float [H, W, C]
-        size (Sequence[int]): target size, tuple of [H', W']
-        mag (str, optional): upscale interpolation mode. Defaults to 'bilinear'.
-        min (str, optional): downscale interpolation mode. Defaults to 'bilinear'.
-
-    Returns:
-        Tensor: rescaled image, float [H', W', C]
-    """
-    return scale_img_nhwc(x[None, ...], size, mag, min)[0]

-
-
-

-[docs]
-def scale_img_nhw(x: Tensor, size: Sequence[int], mag='bilinear', min='bilinear') -> Tensor:
-    """image scaling helper.
-
-    Args:
-        x (Tensor): input image, float [N, H, W]
-        size (Sequence[int]): target size, tuple of [H', W']
-        mag (str, optional): upscale interpolation mode. Defaults to 'bilinear'.
-        min (str, optional): downscale interpolation mode. Defaults to 'bilinear'.
-
-    Returns:
-        Tensor: rescaled image, float [N, H', W']
-    """
-    return scale_img_nhwc(x[..., None], size, mag, min)[..., 0]

-
-
-

-[docs]
-def scale_img_hw(x: Tensor, size: Sequence[int], mag='bilinear', min='bilinear') -> Tensor:
-    """image scaling helper.
-
-    Args:
-        x (Tensor): input image, float [H, W]
-        size (Sequence[int]): target size, tuple of [H', W']
-        mag (str, optional): upscale interpolation mode. Defaults to 'bilinear'.
-        min (str, optional): downscale interpolation mode. Defaults to 'bilinear'.
-
-    Returns:
-        Tensor: rescaled image, float [H', W']
-    """
-    return scale_img_nhwc(x[None, ..., None], size, mag, min)[0, ..., 0]

-
-
-
-

-[docs]
-def uv_padding(image: Union[Tensor, ndarray], mask: Union[Tensor, ndarray], padding: Optional[int] = None, backend: Literal['knn', 'cv2'] = 'knn'):
-    """padding the uv-space texture image to avoid seam artifacts in mipmaps.
-
-    Args:
-        image (Union[Tensor, ndarray]): texture image, float, [H, W, C] in [0, 1].
-        mask (Union[Tensor, ndarray]): valid uv region, bool, [H, W].
-        padding (int, optional): padding size into the unmasked region. Defaults to 0.1 * max(H, W).
-        backend (Literal[&#39;knn&#39;, &#39;cv2&#39;], optional): algorithm backend, knn is faster. Defaults to 'knn'.
-
-    Returns:
-        Union[Tensor, ndarray]: padded texture image. float, [H, W, C].
-    """
-    
-    if torch.is_tensor(image):
-        image_input = image.detach().cpu().numpy()
-    else:
-        image_input = image
-
-    if torch.is_tensor(mask):
-        mask_input = mask.detach().cpu().numpy()
-    else:
-        mask_input = mask
-    
-    if padding is None:
-        H, W = image_input.shape[:2]
-        padding = int(0.1 * max(H, W))
-    
-    # padding backend
-    if backend == 'knn':
-
-        from sklearn.neighbors import NearestNeighbors
-        from scipy.ndimage import binary_dilation, binary_erosion
-
-        inpaint_region = binary_dilation(mask_input, iterations=padding)
-        inpaint_region[mask_input] = 0
-
-        search_region = mask_input.copy()
-        not_search_region = binary_erosion(search_region, iterations=2)
-        search_region[not_search_region] = 0
-
-        search_coords = np.stack(np.nonzero(search_region), axis=-1)
-        inpaint_coords = np.stack(np.nonzero(inpaint_region), axis=-1)
-
-        knn = NearestNeighbors(n_neighbors=1, algorithm='kd_tree').fit(search_coords)
-        _, indices = knn.kneighbors(inpaint_coords)
-
-        inpaint_image = image_input.copy()
-        inpaint_image[tuple(inpaint_coords.T)] = inpaint_image[tuple(search_coords[indices[:, 0]].T)]
-
-    elif backend == 'cv2':
-        # kind of slow
-        inpaint_image = cv2.inpaint(
-            (image_input * 255).astype(np.uint8),
-            (~mask_input * 255).astype(np.uint8),
-            padding,
-            cv2.INPAINT_TELEA,
-        ).astype(np.float32) / 255
-
-    if torch.is_tensor(image):
-        inpaint_image = torch.from_numpy(inpaint_image).to(image)
-    
-    return inpaint_image

-
-
-
-

-[docs]
-def recenter(image: ndarray, mask: ndarray, border_ratio: float = 0.2) -> ndarray:
-    """ recenter an image to leave some empty space at the image border.
-
-    Args:
-        image (ndarray): input image, float/uint8 [H, W, 3/4]
-        mask (ndarray): alpha mask, bool [H, W]
-        border_ratio (float, optional): border ratio, image will be resized to (1 - border_ratio). Defaults to 0.2.
-
-    Returns:
-        ndarray: output image, float/uint8 [H, W, 3/4]
-    """
-    
-    return_int = False
-    if image.dtype == np.uint8:
-        image = image.astype(np.float32) / 255
-        return_int = True
-    
-    H, W, C = image.shape
-    size = max(H, W)
-
-    # default to white bg if rgb, but use 0 if rgba
-    if C == 3:
-        result = np.ones((size, size, C), dtype=np.float32)
-    else:
-        result = np.zeros((size, size, C), dtype=np.float32)
-            
-    coords = np.nonzero(mask)
-    x_min, x_max = coords[0].min(), coords[0].max()
-    y_min, y_max = coords[1].min(), coords[1].max()
-    h = x_max - x_min
-    w = y_max - y_min
-    desired_size = int(size * (1 - border_ratio))
-    scale = desired_size / max(h, w)
-    h2 = int(h * scale)
-    w2 = int(w * scale)
-    x2_min = (size - h2) // 2
-    x2_max = x2_min + h2
-    y2_min = (size - w2) // 2
-    y2_max = y2_min + w2
-    result[x2_min:x2_max, y2_min:y2_max] = cv2.resize(image[x_min:x_max, y_min:y_max], (w2, h2), interpolation=cv2.INTER_AREA)
-
-    if return_int:
-        result = (result * 255).astype(np.uint8)
-
-    return result

-
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- Copyright © 2024, kiui -
- Made with Sphinx and @pradyunsg's - - Furo - -
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kiui 0.2.10 documentation
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Source code for kiui.sr

-import os
-import cv2
-import math
-
-import torch
-import torch.nn as nn
-import torch.nn.init as init
-from torch.nn import functional as F
-from torch.nn.modules.batchnorm import _BatchNorm
-
-import numpy as np
-from PIL import Image
-
-from huggingface_hub import hf_hub_download
-
-from kiui.typing import *
-
-HF_MODELS = {
-    2: dict(
-        repo_id='ai-forever/Real-ESRGAN',
-        filename='RealESRGAN_x2.pth',
-    ),
-    4: dict(
-        repo_id='ai-forever/Real-ESRGAN',
-        filename='RealESRGAN_x4.pth',
-    ),
-    8: dict(
-        repo_id='ai-forever/Real-ESRGAN',
-        filename='RealESRGAN_x8.pth',
-    ),
-}
-
-@torch.no_grad()
-def default_init_weights(module_list, scale=1, bias_fill=0, **kwargs):
-    """Initialize network weights.
-
-    Args:
-        module_list (list[nn.Module] | nn.Module): Modules to be initialized.
-        scale (float): Scale initialized weights, especially for residual
-            blocks. Default: 1.
-        bias_fill (float): The value to fill bias. Default: 0
-        kwargs (dict): Other arguments for initialization function.
-    """
-    if not isinstance(module_list, list):
-        module_list = [module_list]
-    for module in module_list:
-        for m in module.modules():
-            if isinstance(m, nn.Conv2d):
-                init.kaiming_normal_(m.weight, **kwargs)
-                m.weight.data *= scale
-                if m.bias is not None:
-                    m.bias.data.fill_(bias_fill)
-            elif isinstance(m, nn.Linear):
-                init.kaiming_normal_(m.weight, **kwargs)
-                m.weight.data *= scale
-                if m.bias is not None:
-                    m.bias.data.fill_(bias_fill)
-            elif isinstance(m, _BatchNorm):
-                init.constant_(m.weight, 1)
-                if m.bias is not None:
-                    m.bias.data.fill_(bias_fill)
-
-
-def make_layer(basic_block, num_basic_block, **kwarg):
-    """Make layers by stacking the same blocks.
-
-    Args:
-        basic_block (nn.module): nn.module class for basic block.
-        num_basic_block (int): number of blocks.
-
-    Returns:
-        nn.Sequential: Stacked blocks in nn.Sequential.
-    """
-    layers = []
-    for _ in range(num_basic_block):
-        layers.append(basic_block(**kwarg))
-    return nn.Sequential(*layers)
-
-
-class ResidualBlockNoBN(nn.Module):
-    """Residual block without BN.
-
-    It has a style of:
-        ---Conv-ReLU-Conv-+-
-         |________________|
-
-    Args:
-        num_feat (int): Channel number of intermediate features.
-            Default: 64.
-        res_scale (float): Residual scale. Default: 1.
-        pytorch_init (bool): If set to True, use pytorch default init,
-            otherwise, use default_init_weights. Default: False.
-    """
-
-    def __init__(self, num_feat=64, res_scale=1, pytorch_init=False):
-        super(ResidualBlockNoBN, self).__init__()
-        self.res_scale = res_scale
-        self.conv1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=True)
-        self.conv2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=True)
-        self.relu = nn.ReLU(inplace=True)
-
-        if not pytorch_init:
-            default_init_weights([self.conv1, self.conv2], 0.1)
-
-    def forward(self, x):
-        identity = x
-        out = self.conv2(self.relu(self.conv1(x)))
-        return identity + out * self.res_scale
-
-
-class Upsample(nn.Sequential):
-    """Upsample module.
-
-    Args:
-        scale (int): Scale factor. Supported scales: 2^n and 3.
-        num_feat (int): Channel number of intermediate features.
-    """
-
-    def __init__(self, scale, num_feat):
-        m = []
-        if (scale & (scale - 1)) == 0:  # scale = 2^n
-            for _ in range(int(math.log(scale, 2))):
-                m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
-                m.append(nn.PixelShuffle(2))
-        elif scale == 3:
-            m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
-            m.append(nn.PixelShuffle(3))
-        else:
-            raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')
-        super(Upsample, self).__init__(*m)
-
-
-def flow_warp(x, flow, interp_mode='bilinear', padding_mode='zeros', align_corners=True):
-    """Warp an image or feature map with optical flow.
-
-    Args:
-        x (Tensor): Tensor with size (n, c, h, w).
-        flow (Tensor): Tensor with size (n, h, w, 2), normal value.
-        interp_mode (str): 'nearest' or 'bilinear'. Default: 'bilinear'.
-        padding_mode (str): 'zeros' or 'border' or 'reflection'.
-            Default: 'zeros'.
-        align_corners (bool): Before pytorch 1.3, the default value is
-            align_corners=True. After pytorch 1.3, the default value is
-            align_corners=False. Here, we use the True as default.
-
-    Returns:
-        Tensor: Warped image or feature map.
-    """
-    assert x.size()[-2:] == flow.size()[1:3]
-    _, _, h, w = x.size()
-    # create mesh grid
-    grid_y, grid_x = torch.meshgrid(torch.arange(0, h).type_as(x), torch.arange(0, w).type_as(x))
-    grid = torch.stack((grid_x, grid_y), 2).float()  # W(x), H(y), 2
-    grid.requires_grad = False
-
-    vgrid = grid + flow
-    # scale grid to [-1,1]
-    vgrid_x = 2.0 * vgrid[:, :, :, 0] / max(w - 1, 1) - 1.0
-    vgrid_y = 2.0 * vgrid[:, :, :, 1] / max(h - 1, 1) - 1.0
-    vgrid_scaled = torch.stack((vgrid_x, vgrid_y), dim=3)
-    output = F.grid_sample(x, vgrid_scaled, mode=interp_mode, padding_mode=padding_mode, align_corners=align_corners)
-
-    # TODO, what if align_corners=False
-    return output
-
-
-def resize_flow(flow, size_type, sizes, interp_mode='bilinear', align_corners=False):
-    """Resize a flow according to ratio or shape.
-
-    Args:
-        flow (Tensor): Precomputed flow. shape [N, 2, H, W].
-        size_type (str): 'ratio' or 'shape'.
-        sizes (list[int | float]): the ratio for resizing or the final output
-            shape.
-            1) The order of ratio should be [ratio_h, ratio_w]. For
-            downsampling, the ratio should be smaller than 1.0 (i.e., ratio
-            < 1.0). For upsampling, the ratio should be larger than 1.0 (i.e.,
-            ratio > 1.0).
-            2) The order of output_size should be [out_h, out_w].
-        interp_mode (str): The mode of interpolation for resizing.
-            Default: 'bilinear'.
-        align_corners (bool): Whether align corners. Default: False.
-
-    Returns:
-        Tensor: Resized flow.
-    """
-    _, _, flow_h, flow_w = flow.size()
-    if size_type == 'ratio':
-        output_h, output_w = int(flow_h * sizes[0]), int(flow_w * sizes[1])
-    elif size_type == 'shape':
-        output_h, output_w = sizes[0], sizes[1]
-    else:
-        raise ValueError(f'Size type should be ratio or shape, but got type {size_type}.')
-
-    input_flow = flow.clone()
-    ratio_h = output_h / flow_h
-    ratio_w = output_w / flow_w
-    input_flow[:, 0, :, :] *= ratio_w
-    input_flow[:, 1, :, :] *= ratio_h
-    resized_flow = F.interpolate(
-        input=input_flow, size=(output_h, output_w), mode=interp_mode, align_corners=align_corners)
-    return resized_flow
-
-
-def pixel_unshuffle(x, scale):
-    """ Pixel unshuffle.
-
-    Args:
-        x (Tensor): Input feature with shape (b, c, hh, hw).
-        scale (int): Downsample ratio.
-
-    Returns:
-        Tensor: the pixel unshuffled feature.
-    """
-    b, c, hh, hw = x.size()
-    out_channel = c * (scale**2)
-    assert hh % scale == 0 and hw % scale == 0
-    h = hh // scale
-    w = hw // scale
-    x_view = x.view(b, c, h, scale, w, scale)
-    return x_view.permute(0, 1, 3, 5, 2, 4).reshape(b, out_channel, h, w)
-
-def pad_reflect(image, pad_size):
-    imsize = image.shape
-    height, width = imsize[:2]
-    new_img = np.zeros([height+pad_size*2, width+pad_size*2, imsize[2]]).astype(np.uint8)
-    new_img[pad_size:-pad_size, pad_size:-pad_size, :] = image
-    
-    new_img[0:pad_size, pad_size:-pad_size, :] = np.flip(image[0:pad_size, :, :], axis=0) #top
-    new_img[-pad_size:, pad_size:-pad_size, :] = np.flip(image[-pad_size:, :, :], axis=0) #bottom
-    new_img[:, 0:pad_size, :] = np.flip(new_img[:, pad_size:pad_size*2, :], axis=1) #left
-    new_img[:, -pad_size:, :] = np.flip(new_img[:, -pad_size*2:-pad_size, :], axis=1) #right
-    
-    return new_img
-
-def unpad_image(image, pad_size):
-    return image[pad_size:-pad_size, pad_size:-pad_size, :]
-
-
-def process_array(image_array, expand=True):
-    """ Process a 3-dimensional array into a scaled, 4 dimensional batch of size 1. """
-    
-    image_batch = image_array / 255.0
-    if expand:
-        image_batch = np.expand_dims(image_batch, axis=0)
-    return image_batch
-
-
-def process_output(output_tensor):
-    """ Transforms the 4-dimensional output tensor into a suitable image format. """
-    
-    sr_img = output_tensor.clip(0, 1) * 255
-    sr_img = np.uint8(sr_img)
-    return sr_img
-
-
-def pad_patch(image_patch, padding_size, channel_last=True):
-    """ Pads image_patch with with padding_size edge values. """
-    
-    if channel_last:
-        return np.pad(
-            image_patch,
-            ((padding_size, padding_size), (padding_size, padding_size), (0, 0)),
-            'edge',
-        )
-    else:
-        return np.pad(
-            image_patch,
-            ((0, 0), (padding_size, padding_size), (padding_size, padding_size)),
-            'edge',
-        )
-
-
-def unpad_patches(image_patches, padding_size):
-    return image_patches[:, padding_size:-padding_size, padding_size:-padding_size, :]
-
-
-def split_image_into_overlapping_patches(image_array, patch_size, padding_size=2):
-    """ Splits the image into partially overlapping patches.
-    The patches overlap by padding_size pixels.
-    Pads the image twice:
-        - first to have a size multiple of the patch size,
-        - then to have equal padding at the borders.
-    Args:
-        image_array: numpy array of the input image.
-        patch_size: size of the patches from the original image (without padding).
-        padding_size: size of the overlapping area.
-    """
-    
-    xmax, ymax, _ = image_array.shape
-    x_remainder = xmax % patch_size
-    y_remainder = ymax % patch_size
-    
-    # modulo here is to avoid extending of patch_size instead of 0
-    x_extend = (patch_size - x_remainder) % patch_size
-    y_extend = (patch_size - y_remainder) % patch_size
-    
-    # make sure the image is divisible into regular patches
-    extended_image = np.pad(image_array, ((0, x_extend), (0, y_extend), (0, 0)), 'edge')
-    
-    # add padding around the image to simplify computations
-    padded_image = pad_patch(extended_image, padding_size, channel_last=True)
-    
-    xmax, ymax, _ = padded_image.shape
-    patches = []
-    
-    x_lefts = range(padding_size, xmax - padding_size, patch_size)
-    y_tops = range(padding_size, ymax - padding_size, patch_size)
-    
-    for x in x_lefts:
-        for y in y_tops:
-            x_left = x - padding_size
-            y_top = y - padding_size
-            x_right = x + patch_size + padding_size
-            y_bottom = y + patch_size + padding_size
-            patch = padded_image[x_left:x_right, y_top:y_bottom, :]
-            patches.append(patch)
-    
-    return np.array(patches), padded_image.shape
-
-
-def stich_together(patches, padded_image_shape, target_shape, padding_size=4):
-    """ Reconstruct the image from overlapping patches.
-    After scaling, shapes and padding should be scaled too.
-    Args:
-        patches: patches obtained with split_image_into_overlapping_patches
-        padded_image_shape: shape of the padded image contructed in split_image_into_overlapping_patches
-        target_shape: shape of the final image
-        padding_size: size of the overlapping area.
-    """
-    
-    xmax, ymax, _ = padded_image_shape
-    patches = unpad_patches(patches, padding_size)
-    patch_size = patches.shape[1]
-    n_patches_per_row = ymax // patch_size
-    
-    complete_image = np.zeros((xmax, ymax, 3))
-    
-    row = -1
-    col = 0
-    for i in range(len(patches)):
-        if i % n_patches_per_row == 0:
-            row += 1
-            col = 0
-        complete_image[
-        row * patch_size: (row + 1) * patch_size, col * patch_size: (col + 1) * patch_size,:
-        ] = patches[i]
-        col += 1
-    return complete_image[0: target_shape[0], 0: target_shape[1], :]
-
-
-class ResidualDenseBlock(nn.Module):
-    """Residual Dense Block.
-
-    Used in RRDB block in ESRGAN.
-
-    Args:
-        num_feat (int): Channel number of intermediate features.
-        num_grow_ch (int): Channels for each growth.
-    """
-
-    def __init__(self, num_feat=64, num_grow_ch=32):
-        super(ResidualDenseBlock, self).__init__()
-        self.conv1 = nn.Conv2d(num_feat, num_grow_ch, 3, 1, 1)
-        self.conv2 = nn.Conv2d(num_feat + num_grow_ch, num_grow_ch, 3, 1, 1)
-        self.conv3 = nn.Conv2d(num_feat + 2 * num_grow_ch, num_grow_ch, 3, 1, 1)
-        self.conv4 = nn.Conv2d(num_feat + 3 * num_grow_ch, num_grow_ch, 3, 1, 1)
-        self.conv5 = nn.Conv2d(num_feat + 4 * num_grow_ch, num_feat, 3, 1, 1)
-
-        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
-
-        # initialization
-        default_init_weights([self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1)
-
-    def forward(self, x):
-        x1 = self.lrelu(self.conv1(x))
-        x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1)))
-        x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1)))
-        x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1)))
-        x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
-        # Emperically, we use 0.2 to scale the residual for better performance
-        return x5 * 0.2 + x
-
-
-class RRDB(nn.Module):
-    """Residual in Residual Dense Block.
-
-    Used in RRDB-Net in ESRGAN.
-
-    Args:
-        num_feat (int): Channel number of intermediate features.
-        num_grow_ch (int): Channels for each growth.
-    """
-
-    def __init__(self, num_feat, num_grow_ch=32):
-        super(RRDB, self).__init__()
-        self.rdb1 = ResidualDenseBlock(num_feat, num_grow_ch)
-        self.rdb2 = ResidualDenseBlock(num_feat, num_grow_ch)
-        self.rdb3 = ResidualDenseBlock(num_feat, num_grow_ch)
-
-    def forward(self, x):
-        out = self.rdb1(x)
-        out = self.rdb2(out)
-        out = self.rdb3(out)
-        # Emperically, we use 0.2 to scale the residual for better performance
-        return out * 0.2 + x
-
-
-class RRDBNet(nn.Module):
-    """Networks consisting of Residual in Residual Dense Block, which is used
-    in ESRGAN.
-
-    ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks.
-
-    We extend ESRGAN for scale x2 and scale x1.
-    Note: This is one option for scale 1, scale 2 in RRDBNet.
-    We first employ the pixel-unshuffle (an inverse operation of pixelshuffle to reduce the spatial size
-    and enlarge the channel size before feeding inputs into the main ESRGAN architecture.
-
-    Args:
-        num_in_ch (int): Channel number of inputs.
-        num_out_ch (int): Channel number of outputs.
-        num_feat (int): Channel number of intermediate features.
-            Default: 64
-        num_block (int): Block number in the trunk network. Defaults: 23
-        num_grow_ch (int): Channels for each growth. Default: 32.
-    """
-
-    def __init__(self, num_in_ch, num_out_ch, scale=4, num_feat=64, num_block=23, num_grow_ch=32):
-        super(RRDBNet, self).__init__()
-        self.scale = scale
-        if scale == 2:
-            num_in_ch = num_in_ch * 4
-        elif scale == 1:
-            num_in_ch = num_in_ch * 16
-        self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)
-        self.body = make_layer(RRDB, num_block, num_feat=num_feat, num_grow_ch=num_grow_ch)
-        self.conv_body = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-        # upsample
-        self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-        self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-        if scale == 8:
-            self.conv_up3 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-        self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-        self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-
-        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
-
-    def forward(self, x):
-        if self.scale == 2:
-            feat = pixel_unshuffle(x, scale=2)
-        elif self.scale == 1:
-            feat = pixel_unshuffle(x, scale=4)
-        else:
-            feat = x
-        feat = self.conv_first(feat)
-        body_feat = self.conv_body(self.body(feat))
-        feat = feat + body_feat
-        # upsample
-        feat = self.lrelu(self.conv_up1(F.interpolate(feat, scale_factor=2, mode='nearest')))
-        feat = self.lrelu(self.conv_up2(F.interpolate(feat, scale_factor=2, mode='nearest')))
-        if self.scale == 8:
-            feat = self.lrelu(self.conv_up3(F.interpolate(feat, scale_factor=2, mode='nearest')))
-        out = self.conv_last(self.lrelu(self.conv_hr(feat)))
-        return out
-
-
-class RealESRGAN:
-    def __init__(self, device, scale=4):
-        print(f'[INFO] init RealESRGAN_{scale}x: {device}')
-        self.device = device
-        self.scale = scale
-        self.model = RRDBNet(
-            num_in_ch=3, num_out_ch=3, num_feat=64, 
-            num_block=23, num_grow_ch=32, scale=scale
-        )
-        self.load_weights()
-        
-    def load_weights(self):
-        model_path = hf_hub_download(repo_id=HF_MODELS[self.scale]['repo_id'], filename=HF_MODELS[self.scale]['filename'])
-        checkpoint = torch.load(model_path)
-        if 'params' in checkpoint:
-            self.model.load_state_dict(checkpoint['params'], strict=True)
-        elif 'params_ema' in checkpoint:
-            self.model.load_state_dict(checkpoint['params_ema'], strict=True)
-        else:
-            self.model.load_state_dict(checkpoint, strict=True)
-        self.model.eval()
-        self.model.to(self.device)
-        
-    @torch.cuda.amp.autocast()
-    def predict(self, lr_image, batch_size=4, patches_size=192, padding=24, pad_size=15):
-        # lr_image: np.ndarray, [h, w, 3], RGB uint8
-        # return: np.ndarray, [H, W, 3], RGB uint8
-
-        return_tensor = False
-        if torch.is_tensor(lr_image):
-            # or Tensor, [1, 3, H, W], RGB float32
-            lr_image = (lr_image.detach().permute(0,2,3,1)[0].cpu().numpy() * 255).astype(np.uint8)
-            return_tensor = True
-        
-        lr_image = pad_reflect(lr_image, pad_size)
-
-        patches, p_shape = split_image_into_overlapping_patches(lr_image, patch_size=patches_size, padding_size=padding)
-        img = torch.from_numpy(patches.astype(np.float32) / 255).permute((0,3,1,2)).to(self.device).detach()
-
-        with torch.no_grad():
-            res = self.model(img[0:batch_size])
-            for i in range(batch_size, img.shape[0], batch_size):
-                res = torch.cat((res, self.model(img[i:i+batch_size])), 0)
-
-        sr_image = res.permute((0,2,3,1)).clamp_(0, 1).cpu()
-        np_sr_image = sr_image.numpy()
-
-        padded_size_scaled = tuple(np.multiply(p_shape[0:2], self.scale)) + (3,)
-        scaled_image_shape = tuple(np.multiply(lr_image.shape[0:2], self.scale)) + (3,)
-
-        np_sr_image = stich_together(
-            np_sr_image, padded_image_shape=padded_size_scaled, 
-            target_shape=scaled_image_shape, padding_size=padding * self.scale
-        )
-
-        sr_img = (np_sr_image * 255).astype(np.uint8)
-        sr_img = unpad_image(sr_img, pad_size * self.scale)
-
-        if return_tensor:
-            sr_img = torch.from_numpy(sr_img.astype(np.float32) / 255).permute((2,0,1)).unsqueeze(0).to(self.device)
-        
-        return sr_img
-
-
-MODELS = {}
-

-[docs]
-def sr(image: ndarray, scale: Literal[2, 4, 8] = 2, device=None):
-    """ lazy load functional super-resolution API for convenience.
-
-    Args:
-        image (ndarray): input image, uint8/float32 [H, W, 3]
-        scale (Literal[2, 4, 8], optional): upscale factor. Defaults to 2.
-        device (torch.device, optional): device to put SR models, if not provided, will try to use 'cuda'. Defaults to None.
-
-    Returns:
-        ndarray: super-resolutioned image, uint8/float32 [H * scale, W * scale, 3]
-    """
-    global MODELS
-    if scale not in MODELS:
-        if device is None:
-            device = 'cuda' if torch.cuda.is_available() else 'cpu'
-        MODELS[scale] = RealESRGAN(device, scale=scale)
-    
-    return_float = False
-    if image.dtype == np.float32:
-        return_float = True
-        image = (image * 255).astype(np.uint8)
-
-    sr_image = MODELS[scale].predict(image)
-
-    if return_float:
-        sr_image = sr_image.astype(np.float32) / 255.0
-
-    return sr_image

-
-
-def main():
-    import argparse
-    parser = argparse.ArgumentParser()
-    parser.add_argument('input', type=str)
-    parser.add_argument('--output', type=str, default=None)
-    parser.add_argument('--scale', type=int, default=4)
-    args = parser.parse_args()
-
-    model = RealESRGAN('cuda', scale=4)
-
-    if args.output is None:
-        args.output = os.path.splitext(args.input)[0] + f'_{args.scale}x.jpg'
-
-    image = cv2.imread(args.input)
-    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-    sr_image = model.predict(image)
-    sr_image = cv2.cvtColor(sr_image, cv2.COLOR_RGB2BGR)
-
-    cv2.imwrite(args.output, sr_image)
-
-
-if __name__ == '__main__':
-    main()
-
-
-
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Source code for kiui.utils

-import os
-import sys
-import glob
-import tqdm
-import json
-import pickle
-import varname
-from objprint import objstr
-from rich.console import Console
-
-import cv2
-from PIL import Image
-
-import numpy as np
-import torch
-
-from kiui.typing import *
-from kiui.env import is_imported
-
-

-[docs]
-def lo(*xs, verbose=0):
-    """inspect array like objects and report statistics.
-
-    Args:
-        xs (Any): array like objects to inspect.
-        verbose (int, optional): level of verbosity, set to 1 to report mean and std, 2 to print the content. Defaults to 0.
-    """
-
-    console = Console()
-
-    def _lo(x, name):
-
-        if isinstance(x, np.ndarray):
-            # general stats
-            text = ""
-            text += f"[orange1]Array {name}[/orange1] {x.shape} {x.dtype}"
-            if x.size > 0:
-                text += f" ∈ [{x.min()}, {x.max()}]"
-            if verbose >= 1:
-                text += f" μ = {x.mean()} σ = {x.std()}"
-            # detect abnormal values
-            if np.isnan(x).any():
-                text += "[red] NaN![/red]"
-            if np.isinf(x).any():
-                text += "[red] Inf![/red]"
-            console.print(text)
-
-            # show values if shape is small or verbose is high
-            if x.size < 50 or verbose >= 2:
-                # np.set_printoptions(precision=4)
-                print(x)
-
-        elif torch.is_tensor(x):
-            # general stats
-            text = ""
-            text += f"[orange1]Tensor {name}[/orange1] {x.shape} {x.dtype} {x.device}"
-            if x.numel() > 0:
-                text += f" ∈ [{x.min().item()}, {x.max().item()}]"
-            if verbose >= 1:
-                text += f" μ = {x.mean().item()} σ = {x.std().item()}"
-            # detect abnormal values
-            if torch.isnan(x).any():
-                text += "[red] NaN![/red]"
-            if torch.isinf(x).any():
-                text += "[red] Inf![/red]"
-            console.print(text)
-
-            # show values if shape is small or verbose is high
-            if x.numel() < 50 or verbose >= 2:
-                # np.set_printoptions(precision=4)
-                print(x)
-
-        else:  # other type, just print them
-            console.print(f"[orange1]{type(x)} {name}[/orange1] {objstr(x)}")
-
-    # inspect names
-    for i, x in enumerate(xs):
-        try:
-            name = varname.argname(f"xs[{i}]", func=lo)
-        except:
-            name = f"UNKNOWN"
-        _lo(x, name)

-
-
-
-

-[docs]
-def seed_everything(seed=42, verbose=False, strict=False):
-    """auto set seed for random, numpy and torch.
-
-    Args:
-        seed (int, optional): random seed. Defaults to 42.
-        verbose (bool, optional): whether to report each seed setting. Defaults to False.
-        strict (bool, optional): whether to use strict deterministic mode for better torch reproduction. Defaults to False.
-    """
-
-    os.environ['PYTHONHASHSEED'] = str(seed)
-
-    if is_imported('random'):
-        import random # still need to import it here
-        random.seed(seed)
-        if verbose: print(f'[INFO] set random.seed = {seed}')
-    else:
-        if verbose: print(f'[INFO] random not imported, skip setting seed')
-
-    # assume numpy is imported as np
-    if is_imported('np'):
-        import numpy as np
-        np.random.seed(seed)
-        if verbose: print(f'[INFO] set np.random.seed = {seed}')
-    else:
-        if verbose: print(f'[INFO] numpy not imported, skip setting seed')
-        
-    if is_imported('torch'):
-        import torch
-        torch.manual_seed(seed)
-        torch.cuda.manual_seed(seed)
-        if verbose: print(f'[INFO] set torch.manual_seed = {seed}')
-
-        if strict:
-            torch.backends.cudnn.deterministic = True
-            torch.backends.cudnn.benchmark = False
-            torch.use_deterministic_algorithms(True)
-            if verbose: print(f'[INFO] set strict deterministic mode for torch.')
-    else:
-        if verbose: print(f'[INFO] torch not imported, skip setting seed')

-
-
-
-

-[docs]
-def read_json(path):
-    """load a json file.
-
-    Args:
-        path (str): path to json file.
-
-    Returns:
-        dict: json content.
-    """
-    with open(path, "r") as f:
-        return json.load(f)

-
-
-
-

-[docs]
-def write_json(path, x):
-    """write a json file.
-
-    Args:
-        path (str): path to write json file.
-        x (dict): dict to write.
-    """
-    with open(path, "w") as f:
-        json.dump(x, f, indent=2)

-
-
-
-

-[docs]
-def read_pickle(path):
-    """read a pickle file.
-
-    Args:
-        path (str): path to pickle file.
-
-    Returns:
-        Any: pickle content.
-    """
-    with open(path, "rb") as f:
-        return pickle.load(f)

-
-
-
-

-[docs]
-def write_pickle(path, x):
-    """write a pickle file.
-
-    Args:
-        path (str): path to write pickle file.
-        x (Any): content to write.
-    """
-    with open(path, "wb") as f:
-        pickle.dump(x, f)

-
-
-
-

-[docs]
-def read_image(
-    path: str, 
-    mode: Literal["float", "uint8", "pil", "torch", "tensor"] = "float", 
-    order: Literal["RGB", "RGBA", "BGR", "BGRA"] = "RGB",
-):
-    """read an image file into various formats and color mode.
-
-    Args:
-        path (str): path to the image file.
-        mode (Literal["float", "uint8", "pil", "torch", "tensor"], optional): returned image format. Defaults to "float".
-            float: float32 numpy array, range [0, 1];
-            uint8: uint8 numpy array, range [0, 255];
-            pil: PIL image;
-            torch/tensor: float32 torch tensor, range [0, 1];
-        order (Literal["RGB", "RGBA", "BGR", "BGRA"], optional): channel order. Defaults to "RGB".
-    
-    Note:
-        By default this function will convert RGBA image to white-background RGB image. Use ``order="RGBA"`` to keep the alpha channel.
-
-    Returns:
-        Union[np.ndarray, PIL.Image, torch.Tensor]: the image array.
-    """
-
-    if mode == "pil":
-        return Image.open(path).convert(order)
-
-    if path.endswith('.exr'):
-        os.environ['OPENCV_IO_ENABLE_OPENEXR'] = '1'
-        img = cv2.imread(path, cv2.IMREAD_ANYCOLOR | cv2.IMREAD_ANYDEPTH)
-    else:
-        img = cv2.imread(path, cv2.IMREAD_UNCHANGED)
-
-    # cvtColor
-    if len(img.shape) == 3: # ignore if gray scale
-        if order in ["RGB", "RGBA"]:
-            if img.shape[-1] == 4:
-                img = cv2.cvtColor(img, cv2.COLOR_BGRA2RGBA)
-            elif img.shape[-1] == 3:
-                img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-    
-        # mix background
-        if img.shape[-1] == 4 and 'A' not in order:
-            img = img.astype(np.float32) / 255
-            img = img[..., :3] * img[..., 3:] + (1 - img[..., 3:])
-
-    # mode
-    if mode == "uint8":
-        if img.dtype != np.uint8:
-            img = (img * 255).astype(np.uint8)
-        return img
-    elif mode == "float":
-        if img.dtype == np.uint8:
-            img = img.astype(np.float32) / 255
-        return img
-    elif mode in ["tensor", "torch"]:
-        if img.dtype == np.uint8:
-            img = img.astype(np.float32) / 255
-        return torch.from_numpy(img)
-    else:
-        raise ValueError(f"Unknown read_image mode {mode}")

-
-
-
-

-[docs]
-def write_image(
-        path: str, 
-        img: Union[Tensor, np.ndarray, Image.Image], 
-        order: Literal["RGB", "BGR"] = "RGB",
-    ):
-    """write an image to various formats.
-
-    Args:
-        path (str): path to write the image file.
-        img (Union[torch.Tensor, np.ndarray, PIL.Image.Image]): image to write.
-        order (str, optional): channel order. Defaults to "RGB".
-    """
-
-    if isinstance(img, Image.Image):
-        img.save(path)
-        return
-
-    if torch.is_tensor(img):
-        img = img.detach().cpu().numpy()
-
-    if img.dtype == np.float32 or img.dtype == np.float64:
-        img = (img * 255).astype(np.uint8)
-    
-    if len(img.shape) == 4:
-        if img.shape[0] > 1:
-            raise ValueError(f'only support saving a single image! current image: {img.shape}')
-        img = img[0]
-        
-    if len(img.shape) == 3:
-        # cvtColor
-        if order == "RGB":
-            if img.shape[-1] == 4:
-                img = cv2.cvtColor(img, cv2.COLOR_RGBA2BGRA)
-            elif img.shape[-1] == 3:
-                img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
-
-    dir_path = os.path.dirname(path)
-    if dir_path != '' and not os.path.exists(dir_path):
-        os.makedirs(os.path.dirname(path), exist_ok=True)
-    cv2.imwrite(path, img)

-
-
-
-

-[docs]
-def load_file_from_url(url, model_dir=None, progress=True, file_name=None):
-    """Load file form http url, will download models if necessary.
-
-    Args:
-        url (str): URL to be downloaded.
-        model_dir (str): The path to save the downloaded model. Should be a full path. If None, use pytorch hub_dir.
-            Default: None.
-        progress (bool): Whether to show the download progress. Default: True.
-        file_name (str): The downloaded file name. If None, use the file name in the url. Default: None.
-
-    Returns:
-        str: The path to the downloaded file.
-    """
-
-    from torch.hub import download_url_to_file, get_dir
-    from urllib.parse import urlparse
-
-    if model_dir is None:  # use the pytorch hub_dir
-        hub_dir = get_dir()
-        model_dir = os.path.join(hub_dir, "checkpoints")
-
-    os.makedirs(model_dir, exist_ok=True)
-
-    parts = urlparse(url)
-    filename = os.path.basename(parts.path)
-    if file_name is not None:
-        filename = file_name
-    cached_file = os.path.abspath(os.path.join(model_dir, filename))
-    if not os.path.exists(cached_file):
-        print(f'[INFO] Downloading: "{url}" to {cached_file}\n')
-        download_url_to_file(url, cached_file, hash_prefix=None, progress=progress)
-    return cached_file

-
-
-
-

-[docs]
-def is_format(f: str, format: Sequence[str]):
-    """if a file's extension is in a set of format
-
-    Args:
-        f (str): file name.
-        format (Sequence[str]): set of extensions (both '.jpg' or 'jpg' is ok).
-
-    Returns:
-        bool: if the file's extension is in the set.
-    """
-    ext = os.path.splitext(f)[1].lower() # include the dot
-    return ext in format or ext[1:] in format

-
-
-

-[docs]
-def batch_process_files(
-    process_fn, path, out_path, 
-    overwrite=False,
-    in_format=[".jpg", ".jpeg", ".png"],
-    out_format=None,
-    image_mode='uint8',
-    image_color_order="RGB",
-    **kwargs
-):
-    """simple function wrapper to batch processing files.
-
-    Args:
-        process_fn (Callable): process function.
-        path (str): path to a file or a directory containing the files to process.
-        out_path (str): output path of a file or a directory.
-        overwrite (bool, optional): whether to overwrite existing results. Defaults to False.
-        in_format (list, optional): input file formats. Defaults to [".jpg", ".jpeg", ".png"].
-        out_format (str, optional): output file format. Defaults to None.
-        image_mode (str, optional): for images, the mode to read. Defaults to 'uint8'.
-        image_color_order (str, optional): for images, the color order. Defaults to "RGB".
-    """
-   
-    if os.path.isdir(path):
-        file_paths = glob.glob(os.path.join(path, "*"))
-        file_paths = [f for f in file_paths if is_format(f, in_format)]
-    else:
-        file_paths = [path]
-
-    if os.path.dirname(out_path) != '':
-        os.makedirs(os.path.dirname(out_path), exist_ok=True)
-
-    for file_path in tqdm.tqdm(file_paths):
-        try:
-            
-            if len(file_paths) == 1:
-                file_out_path = out_path
-            else:
-                file_out_path = os.path.join(out_path, os.path.basename(file_path))
-            
-            if out_format is not None:
-                file_out_path = os.path.splitext(file_out_path)[0] + out_format
-
-            if os.path.exists(file_out_path) and not overwrite:
-                print(f"[INFO] ignoring {file_path} --> {file_out_path}")
-                continue
-            
-            # dispatch loader
-            if is_format(file_path, ['.jpg', '.jpeg', '.png']):
-                input = read_image(file_path, mode=image_mode, order=image_color_order)
-            elif is_format(file_path, ['.ply', '.obj', '.glb', '.gltf']):
-                from kiui.mesh import Mesh
-                input = Mesh.load(file_path)
-            else:
-                with open(file_path, "r") as f:
-                    input = f.read()
-            
-            # process
-            output = process_fn(input, **kwargs)
-
-            # dispatch writer
-            if is_format(file_out_path, ['.jpg', '.jpeg', '.png']):
-                write_image(file_out_path, output, order=image_color_order)
-            elif is_format(file_out_path, ['.ply', '.obj', '.glb', '.gltf']):
-                output.write(file_out_path)
-            elif is_format(file_out_path, ['.npy']):
-                np.save(file_out_path, output)
-            else:
-                with open(file_out_path, "w") as f:
-                    f.write(output)
-
-        except Exception as e:
-            print(f"[ERROR] when processing {file_path} --> {file_out_path}")
-            print(e)

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- Copyright © 2024, kiui -
- Made with Sphinx and @pradyunsg's - - Furo - -
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- - - - - - - - \ No newline at end of file diff --git a/_modules/kiui/vis/index.html b/_modules/kiui/vis/index.html deleted file mode 100644 index 4e20098..0000000 --- a/_modules/kiui/vis/index.html +++ /dev/null @@ -1,569 +0,0 @@ - - - - - - - - kiui.vis - kiui 0.2.10 documentation - - - - - - - - - - - - - - - - - - Contents - - - - - - Menu - - - - - - - - Expand - - - - - - Light mode - - - - - - - - - - - - - - Dark mode - - - - - - - Auto light/dark, in light mode - - - - - - - - - - - - - - - Auto light/dark, in dark mode - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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kiui 0.2.10 documentation
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- - - - - Back to top - -
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Source code for kiui.vis

-import time
-import torch
-import numpy as np
-from datetime import datetime
-
-import matplotlib.cm as cm
-import matplotlib.pyplot as plt
-
-from kiui.typing import *
-from kiui.utils import lo, write_image
-
-
-

-[docs]
-def map_color(value: ndarray, cmap_name: str="viridis", vmin: float=None, vmax: float=None):
-    """ map a 1D array to continuous color.
-
-    Args:
-        value (ndarray): array of float, [N]
-        cmap_name (str, optional): color map name, ref: https://matplotlib.org/stable/users/explain/colors/colormaps.html#classes-of-colormaps. Defaults to "viridis".
-        vmin (float, optional): min value. Defaults to None.
-        vmax (float, optional): max value. Defaults to None.
-
-    Returns:
-        ndarray: array of color, [N, 3] in [0, 1]
-    """
-    # value: [N], float
-    # return: RGB, [N, 3], float in [0, 1]
-
-    if vmin is None:
-        vmin = value.min()
-    if vmax is None:
-        vmax = value.max()
-    value = (value - vmin) / (vmax - vmin)  # range in [0, 1]
-    cmap = cm.get_cmap(cmap_name)
-    rgb = cmap(value)[:, :3]  # will return rgba, we take only first 3 so we get rgb
-    return rgb

-
-
-
-

-[docs]
-def plot_image(*xs, normalize=False, save=False, prefix='kiui_vis_plot_image'):
-    """ sequentially plot provided images, optionally save to current dir.
-    
-    Args:
-        xs (Sequence[Union[torch.Tensor, numpy.ndarray]]): can be uint8 or float32.
-            [B, 4/3/1, H, W], [B, H, W, 4/3/1], [4/3/1, H, W], [H, W, 4/3/1], [H, W] torch.Tensor or numpy.ndarray
-        normalize (bool, optional): whether to renormalize the image to [0, 1]. Defaults to False.
-        save (bool, optional): whether to save the image to current dir (in case the plot cannot be showed, like in vscode remote). Defaults to False.
-        prefix (str, optional): image save name prefix if save=True.
-    """
-
-    _cnt = 0
-    _signature = datetime.now().strftime('%Y_%m_%d_%H_%M_%S_%f')
-
-    def _plot_image(image):
-
-        nonlocal _cnt
-
-        lo(image)
-
-        if isinstance(image, torch.Tensor):
-            image = image.detach().cpu().numpy()
-
-        if image.dtype == np.uint8:
-            image = image.astype(np.float32) / 255.0
-        
-        # empirially to channel-last
-        if len(image.shape) == 3 and image.shape[0] < image.shape[-1]:
-            image = image.transpose(1, 2, 0)
-
-        # normalize
-        if normalize:
-            image = (image - image.min(axis=0, keepdims=True)) / (
-                image.max(axis=0, keepdims=True)
-                - image.min(axis=0, keepdims=True)
-                + 1e-8
-            )
-
-        if save:
-            _path = f'{prefix}_{_signature}_{_cnt}.png'
-            _cnt += 1
-            write_image(_path, image.astype(np.float32))
-            print(f'[INFO] write image to {_path}')
-        else:
-            plt.imshow(image.astype(np.float32))
-            plt.show()
-
-    for x in xs:
-        if len(x.shape) == 4:
-            for i in range(x.shape[0]):
-                _plot_image(x[i])
-        else: # 3 or 2
-            _plot_image(x)

-
-
-
-

-[docs]
-def plot_matrix(*xs):
-    """ visualize some 2D matrix, different from ``kiui.vis.plot_image``, this will keep the original range and plot channel-by-channel.
-    
-    Args:
-        xs (Sequence[Union[torch.Tensor, numpy.ndarray]]): [B, C, H, W], [C, H, W], or [H, W] torch.Tensor or numpy.ndarray
-    """
-    
-    def _plot_matrix(matrix):
-
-        lo(matrix)
-
-        if isinstance(matrix, torch.Tensor):
-            if len(matrix.shape) == 3:
-                matrix = matrix.permute(1, 2, 0).squeeze()
-            matrix = matrix.detach().cpu().numpy()
-
-        if len(matrix.shape) == 3:
-            # per channel
-            for i in range(matrix.shape[-1]):
-                plt.matshow(matrix[..., i])
-                plt.show()
-        else:
-            plt.matshow(matrix.astype(np.float32))
-            plt.show()
-
-    for x in xs:
-        if len(x.shape) == 4:
-            for i in range(x.shape[0]):
-                _plot_matrix(x[i])
-        else: # 3 or 2
-            _plot_matrix(x)

-
-
-
-

-[docs]
-def plot_pointcloud(pc, color=None):
-    """plot point cloud.
-
-    Args:
-        pc (ndarray): point cloud positions, float [N, 3].
-        color (ndarray, optional): point cloud colors, float/uint8 [N, 3/4]. Defaults to None.
-    
-    Note:
-        This function requires a desktop (cannot be forwarded by ssh)!
-    """
-    
-    lo(pc)
-
-    if color is not None:
-        lo(color)
-        if color.dtype == np.float32:
-            color = (color * 255).astype(np.uint8)
-
-    if color is None or color.shape[-1] == 3:
-        # use o3d as it's better to control
-        import open3d as o3d
-
-        pcd = o3d.geometry.PointCloud()
-        pcd.points = o3d.utility.Vector3dVector(pc)
-        if color is not None:
-            pcd.colors = o3d.utility.Vector3dVector(color)
-        o3d.visualization.draw_geometries([pcd])
-
-    else:
-        import trimesh
-
-        pc = trimesh.PointCloud(pc, color)
-        # axis
-        axes = trimesh.creation.axis(axis_length=4)
-        # sphere
-        box = trimesh.primitives.Box(extents=(2, 2, 2)).as_outline()
-        box.colors = np.array([[128, 128, 128]] * len(box.entities))
-
-        trimesh.Scene([pc, axes, box]).show()

-
-
-
-

-[docs]
-def plot_poses(poses, size=0.1, bound=1, points=None, mesh=None, opengl=True):
-    """plot camera poses.
-
-    Args:
-        poses (ndarray): camera poses, float [N, 4, 4].
-        size (float, optional): line width. Defaults to 0.1.
-        bound (int, optional): bounding box bound. Defaults to 1.
-        points (ndarray, optional): also draw point clouds, float [M, 3]. Defaults to None.
-        mesh (trimesh.Trimesh, optional): also draw mesh. Defaults to None.
-        opengl (bool, optional): use OpenGL camera convention. Defaults to True.
-    
-    Note:
-        This function requires a desktop (cannot be forwarded by ssh)!
-    """
-
-    lo(poses)
-
-    if torch.is_tensor(poses):
-        poses = poses.detach().cpu().numpy()
-
-    import trimesh
-
-    axes = trimesh.creation.axis(axis_length=4)
-    box = trimesh.primitives.Box(extents=[2 * bound] * 3).as_outline()
-    box.colors = np.array([[128, 128, 128]] * len(box.entities))
-    objects = [axes, box]
-
-    if bound > 1:
-        unit_box = trimesh.primitives.Box(extents=[2] * 3).as_outline()
-        unit_box.colors = np.array([[128, 128, 128]] * len(unit_box.entities))
-        objects.append(unit_box)
-
-    for pose in poses:
-        # a camera is visualized with 8 line segments.
-        pos = pose[:3, 3]
-        a = pos + size * pose[:3, 0] + size * pose[:3, 1] + size * pose[:3, 2] * (-1 if opengl else 1)
-        b = pos - size * pose[:3, 0] + size * pose[:3, 1] + size * pose[:3, 2] * (-1 if opengl else 1)
-        c = pos - size * pose[:3, 0] - size * pose[:3, 1] + size * pose[:3, 2] * (-1 if opengl else 1)
-        d = pos + size * pose[:3, 0] - size * pose[:3, 1] + size * pose[:3, 2] * (-1 if opengl else 1)
-
-        # construct 3D paths
-        frame = np.array([
-            [pos, a],
-            [pos, b],
-            [pos, c],
-            [pos, d],
-            [a, b],
-            [b, c],
-            [c, d],
-            [d, a],
-            [pos, pos + pose[:3, 2] * (-1 if opengl else 1) * 3], # point to target
-        ])
-        frame = trimesh.load_path(frame)
-        objects.append(frame)
-
-        right_line = np.array([[pos, pos + pose[:3, 0] * size]])
-        right_line = trimesh.load_path(right_line)
-        right_line.colors = np.array([[255, 0, 0, 255]]).repeat(len(right_line.entities), axis=0)
-        objects.append(right_line)
-
-        up_line = np.array([[pos, pos + pose[:3, 1] * size]])
-        up_line = trimesh.load_path(up_line)
-        up_line.colors = np.array([[0, 255, 0, 255]]).repeat(len(up_line.entities), axis=0)
-        objects.append(up_line)
-
-        forward_line = np.array([[pos, pos + pose[:3, 2] * size]])
-        forward_line = trimesh.load_path(forward_line)
-        forward_line.colors = np.array([[0, 0, 255, 255]]).repeat(len(forward_line.entities), axis=0)
-        objects.append(forward_line)
-
-    if points is not None:
-
-        lo(points)
-
-        colors = np.zeros((points.shape[0], 4), dtype=np.uint8)
-        colors[:, 2] = 255  # blue
-        colors[:, 3] = 30  # transparent
-        objects.append(trimesh.PointCloud(points, colors))
-
-    if mesh is not None:
-        objects.append(mesh)
-
-    scene = trimesh.Scene(objects)
-    scene.set_camera(distance=bound, center=[0, 0, 0])
-    scene.show()

-
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- - -
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- Copyright © 2024, kiui -
- Made with Sphinx and @pradyunsg's - - Furo - -
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Remove the inheritance of text transform in Firefox.\n */\n\nbutton,\nselect { /* 1 */\n text-transform: none;\n}\n\n/**\n * Correct the inability to style clickable types in iOS and Safari.\n */\n\nbutton,\n[type=\"button\"],\n[type=\"reset\"],\n[type=\"submit\"] {\n -webkit-appearance: button;\n}\n\n/**\n * Remove the inner border and padding in Firefox.\n */\n\nbutton::-moz-focus-inner,\n[type=\"button\"]::-moz-focus-inner,\n[type=\"reset\"]::-moz-focus-inner,\n[type=\"submit\"]::-moz-focus-inner {\n border-style: none;\n padding: 0;\n}\n\n/**\n * Restore the focus styles unset by the previous rule.\n */\n\nbutton:-moz-focusring,\n[type=\"button\"]:-moz-focusring,\n[type=\"reset\"]:-moz-focusring,\n[type=\"submit\"]:-moz-focusring {\n outline: 1px dotted ButtonText;\n}\n\n/**\n * Correct the padding in Firefox.\n */\n\nfieldset {\n padding: 0.35em 0.75em 0.625em;\n}\n\n/**\n * 1. Correct the text wrapping in Edge and IE.\n * 2. Correct the color inheritance from `fieldset` elements in IE.\n * 3. Remove the padding so developers are not caught out when they zero out\n * `fieldset` elements in all browsers.\n */\n\nlegend {\n box-sizing: border-box; /* 1 */\n color: inherit; /* 2 */\n display: table; /* 1 */\n max-width: 100%; /* 1 */\n padding: 0; /* 3 */\n white-space: normal; /* 1 */\n}\n\n/**\n * Add the correct vertical alignment in Chrome, Firefox, and Opera.\n */\n\nprogress {\n vertical-align: baseline;\n}\n\n/**\n * Remove the default vertical scrollbar in IE 10+.\n */\n\ntextarea {\n overflow: auto;\n}\n\n/**\n * 1. Add the correct box sizing in IE 10.\n * 2. Remove the padding in IE 10.\n */\n\n[type=\"checkbox\"],\n[type=\"radio\"] {\n box-sizing: border-box; /* 1 */\n padding: 0; /* 2 */\n}\n\n/**\n * Correct the cursor style of increment and decrement buttons in Chrome.\n */\n\n[type=\"number\"]::-webkit-inner-spin-button,\n[type=\"number\"]::-webkit-outer-spin-button {\n height: auto;\n}\n\n/**\n * 1. Correct the odd appearance in Chrome and Safari.\n * 2. Correct the outline style in Safari.\n */\n\n[type=\"search\"] {\n -webkit-appearance: textfield; /* 1 */\n outline-offset: -2px; /* 2 */\n}\n\n/**\n * Remove the inner padding in Chrome and Safari on macOS.\n */\n\n[type=\"search\"]::-webkit-search-decoration {\n -webkit-appearance: none;\n}\n\n/**\n * 1. Correct the inability to style clickable types in iOS and Safari.\n * 2. Change font properties to `inherit` in Safari.\n */\n\n::-webkit-file-upload-button {\n -webkit-appearance: button; /* 1 */\n font: inherit; /* 2 */\n}\n\n/* Interactive\n ========================================================================== */\n\n/*\n * Add the correct display in Edge, IE 10+, and Firefox.\n */\n\ndetails {\n display: block;\n}\n\n/*\n * Add the correct display in all browsers.\n */\n\nsummary {\n display: list-item;\n}\n\n/* Misc\n ========================================================================== */\n\n/**\n * Add the correct display in IE 10+.\n */\n\ntemplate {\n display: none;\n}\n\n/**\n * Add the correct display in IE 10.\n */\n\n[hidden] {\n display: none;\n}\n","// This file contains styles for managing print media.\n\n////////////////////////////////////////////////////////////////////////////////\n// Hide elements not relevant to print media.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n // Hide icon container.\n .content-icon-container\n display: none !important\n\n // Hide showing header links if hovering over when printing.\n .headerlink\n display: none !important\n\n // Hide mobile header.\n .mobile-header\n display: none !important\n\n // Hide navigation links.\n .related-pages\n display: none !important\n\n////////////////////////////////////////////////////////////////////////////////\n// Tweaks related to decolorization.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n // Apply a border around code which no longer have a color background.\n .highlight\n border: 0.1pt solid var(--color-foreground-border)\n\n////////////////////////////////////////////////////////////////////////////////\n// Avoid page break in some relevant cases.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n ul, ol, dl, a, table, pre, blockquote\n page-break-inside: avoid\n\n h1, h2, h3, h4, h5, h6, img, figure, caption\n page-break-inside: avoid\n page-break-after: avoid\n\n ul, ol, dl\n page-break-before: avoid\n",".visually-hidden\n position: absolute !important\n width: 1px !important\n height: 1px !important\n padding: 0 !important\n margin: -1px !important\n overflow: hidden !important\n clip: rect(0,0,0,0) !important\n white-space: nowrap !important\n border: 0 !important\n color: var(--color-foreground-primary)\n background: var(--color-background-primary)\n\n:-moz-focusring\n outline: auto\n","// This file serves as the \"skeleton\" of the theming logic.\n//\n// This contains the bulk of the logic for handling dark mode, color scheme\n// toggling and the handling of color-scheme-specific hiding of elements.\n\nbody\n @include fonts\n @include spacing\n @include icons\n @include admonitions\n @include default-admonition(#651fff, \"abstract\")\n @include default-topic(#14B8A6, \"pencil\")\n\n @include colors\n\n.only-light\n display: block !important\nhtml body .only-dark\n display: none !important\n\n// Ignore dark-mode hints if print media.\n@media not print\n // Enable dark-mode, if requested.\n body[data-theme=\"dark\"]\n @include colors-dark\n\n html & .only-light\n display: none !important\n .only-dark\n display: block !important\n\n // Enable dark mode, unless explicitly told to avoid.\n @media (prefers-color-scheme: dark)\n body:not([data-theme=\"light\"])\n @include colors-dark\n\n html & .only-light\n display: none !important\n .only-dark\n display: block !important\n\n//\n// Theme toggle presentation\n//\nbody[data-theme=\"auto\"]\n .theme-toggle svg.theme-icon-when-auto-light\n display: block\n\n @media (prefers-color-scheme: dark)\n .theme-toggle svg.theme-icon-when-auto-dark\n display: block\n .theme-toggle svg.theme-icon-when-auto-light\n display: none\n\nbody[data-theme=\"dark\"]\n .theme-toggle svg.theme-icon-when-dark\n display: block\n\nbody[data-theme=\"light\"]\n .theme-toggle svg.theme-icon-when-light\n display: block\n","// Fonts used by this theme.\n//\n// There are basically two things here -- using the system font stack and\n// defining sizes for various elements in %ages. We could have also used `em`\n// but %age is easier to reason about for me.\n\n@mixin fonts {\n // These are adapted from https://systemfontstack.com/\n --font-stack: -apple-system, BlinkMacSystemFont, Segoe UI, Helvetica, Arial,\n sans-serif, Apple Color Emoji, Segoe UI Emoji;\n --font-stack--monospace: \"SFMono-Regular\", Menlo, Consolas, Monaco,\n Liberation Mono, Lucida Console, monospace;\n --font-stack--headings: var(--font-stack);\n\n --font-size--normal: 100%;\n --font-size--small: 87.5%;\n --font-size--small--2: 81.25%;\n --font-size--small--3: 75%;\n --font-size--small--4: 62.5%;\n\n // Sidebar\n --sidebar-caption-font-size: var(--font-size--small--2);\n --sidebar-item-font-size: var(--font-size--small);\n --sidebar-search-input-font-size: var(--font-size--small);\n\n // Table of Contents\n --toc-font-size: var(--font-size--small--3);\n --toc-font-size--mobile: var(--font-size--normal);\n --toc-title-font-size: var(--font-size--small--4);\n\n // Admonitions\n //\n // These aren't defined in terms of %ages, since nesting these is permitted.\n --admonition-font-size: 0.8125rem;\n --admonition-title-font-size: 0.8125rem;\n\n // Code\n --code-font-size: var(--font-size--small--2);\n\n // API\n --api-font-size: var(--font-size--small);\n}\n","// Spacing for various elements on the page\n//\n// If the user wants to tweak things in a certain way, they are permitted to.\n// They also have to deal with the consequences though!\n\n@mixin spacing {\n // Header!\n --header-height: calc(\n var(--sidebar-item-line-height) + 4 * #{var(--sidebar-item-spacing-vertical)}\n );\n --header-padding: 0.5rem;\n\n // Sidebar\n --sidebar-tree-space-above: 1.5rem;\n --sidebar-caption-space-above: 1rem;\n\n --sidebar-item-line-height: 1rem;\n --sidebar-item-spacing-vertical: 0.5rem;\n --sidebar-item-spacing-horizontal: 1rem;\n --sidebar-item-height: calc(\n var(--sidebar-item-line-height) + 2 *#{var(--sidebar-item-spacing-vertical)}\n );\n\n --sidebar-expander-width: var(--sidebar-item-height); // be square\n\n --sidebar-search-space-above: 0.5rem;\n --sidebar-search-input-spacing-vertical: 0.5rem;\n --sidebar-search-input-spacing-horizontal: 0.5rem;\n --sidebar-search-input-height: 1rem;\n --sidebar-search-icon-size: var(--sidebar-search-input-height);\n\n // Table of Contents\n --toc-title-padding: 0.25rem 0;\n --toc-spacing-vertical: 1.5rem;\n --toc-spacing-horizontal: 1.5rem;\n --toc-item-spacing-vertical: 0.4rem;\n --toc-item-spacing-horizontal: 1rem;\n}\n","// Expose theme icons as CSS variables.\n\n$icons: (\n // Adapted from tabler-icons\n // url: https://tablericons.com/\n \"search\":\n url('data:image/svg+xml;charset=utf-8,'),\n // Factored out from mkdocs-material on 24-Aug-2020.\n // url: https://squidfunk.github.io/mkdocs-material/reference/admonitions/\n \"pencil\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"abstract\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"info\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"flame\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"question\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"warning\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"failure\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"spark\":\n url('data:image/svg+xml;charset=utf-8,')\n);\n\n@mixin icons {\n @each $name, $glyph in $icons {\n --icon-#{$name}: #{$glyph};\n }\n}\n","// Admonitions\n\n// Structure of these is:\n// admonition-class: color \"icon-name\";\n//\n// The colors are translated into CSS variables below. The icons are\n// used directly in the main declarations to set the `mask-image` in\n// the title.\n\n// prettier-ignore\n$admonitions: (\n // Each of these has an reST directives for it.\n \"caution\": #ff9100 \"spark\",\n \"warning\": #ff9100 \"warning\",\n \"danger\": #ff5252 \"spark\",\n \"attention\": #ff5252 \"warning\",\n \"error\": #ff5252 \"failure\",\n \"hint\": #00c852 \"question\",\n \"tip\": #00c852 \"info\",\n \"important\": #00bfa5 \"flame\",\n \"note\": #00b0ff \"pencil\",\n \"seealso\": #448aff \"info\",\n \"admonition-todo\": #808080 \"pencil\"\n);\n\n@mixin default-admonition($color, $icon-name) {\n --color-admonition-title: #{$color};\n --color-admonition-title-background: #{rgba($color, 0.2)};\n\n --icon-admonition-default: var(--icon-#{$icon-name});\n}\n\n@mixin default-topic($color, $icon-name) {\n --color-topic-title: #{$color};\n --color-topic-title-background: #{rgba($color, 0.2)};\n\n --icon-topic-default: var(--icon-#{$icon-name});\n}\n\n@mixin admonitions {\n @each $name, $values in $admonitions {\n --color-admonition-title--#{$name}: #{nth($values, 1)};\n --color-admonition-title-background--#{$name}: #{rgba(\n nth($values, 1),\n 0.2\n )};\n }\n}\n","// Colors used throughout this theme.\n//\n// The aim is to give the user more control. Thus, instead of hard-coding colors\n// in various parts of the stylesheet, the approach taken is to define all\n// colors as CSS variables and reusing them in all the places.\n//\n// `colors-dark` depends on `colors` being included at a lower specificity.\n\n@mixin colors {\n --color-problematic: #b30000;\n\n // Base Colors\n --color-foreground-primary: black; // for main text and headings\n --color-foreground-secondary: #5a5c63; // for secondary text\n --color-foreground-muted: #6b6f76; // for muted text\n --color-foreground-border: #878787; // for content borders\n\n --color-background-primary: white; // for content\n --color-background-secondary: #f8f9fb; // for navigation + ToC\n --color-background-hover: #efeff4ff; // for navigation-item hover\n --color-background-hover--transparent: #efeff400;\n --color-background-border: #eeebee; // for UI borders\n --color-background-item: #ccc; // for \"background\" items (eg: copybutton)\n\n // Announcements\n --color-announcement-background: #000000dd;\n --color-announcement-text: #eeebee;\n\n // Brand colors\n --color-brand-primary: #0a4bff;\n --color-brand-content: #2757dd;\n --color-brand-visited: #872ee0;\n\n // API documentation\n --color-api-background: var(--color-background-hover--transparent);\n --color-api-background-hover: var(--color-background-hover);\n --color-api-overall: var(--color-foreground-secondary);\n --color-api-name: var(--color-problematic);\n --color-api-pre-name: var(--color-problematic);\n --color-api-paren: var(--color-foreground-secondary);\n --color-api-keyword: var(--color-foreground-primary);\n\n --color-api-added: #21632c;\n --color-api-added-border: #38a84d;\n --color-api-changed: #046172;\n --color-api-changed-border: #06a1bc;\n --color-api-deprecated: #605706;\n --color-api-deprecated-border: #f0d90f;\n --color-api-removed: #b30000;\n --color-api-removed-border: #ff5c5c;\n\n --color-highlight-on-target: #ffffcc;\n\n // Inline code background\n --color-inline-code-background: var(--color-background-secondary);\n\n // Highlighted text (search)\n --color-highlighted-background: #ddeeff;\n --color-highlighted-text: var(--color-foreground-primary);\n\n // GUI Labels\n --color-guilabel-background: #ddeeff80;\n --color-guilabel-border: #bedaf580;\n --color-guilabel-text: var(--color-foreground-primary);\n\n // Admonitions!\n --color-admonition-background: transparent;\n\n //////////////////////////////////////////////////////////////////////////////\n // Everything below this should be one of:\n // - var(...)\n // - *-gradient(...)\n // - special literal values (eg: transparent, none)\n //////////////////////////////////////////////////////////////////////////////\n\n // Tables\n --color-table-header-background: var(--color-background-secondary);\n --color-table-border: var(--color-background-border);\n\n // Cards\n --color-card-border: var(--color-background-secondary);\n --color-card-background: transparent;\n --color-card-marginals-background: var(--color-background-secondary);\n\n // Header\n --color-header-background: var(--color-background-primary);\n --color-header-border: var(--color-background-border);\n --color-header-text: var(--color-foreground-primary);\n\n // Sidebar (left)\n --color-sidebar-background: var(--color-background-secondary);\n --color-sidebar-background-border: var(--color-background-border);\n\n --color-sidebar-brand-text: var(--color-foreground-primary);\n --color-sidebar-caption-text: var(--color-foreground-muted);\n --color-sidebar-link-text: var(--color-foreground-secondary);\n --color-sidebar-link-text--top-level: var(--color-brand-primary);\n\n --color-sidebar-item-background: var(--color-sidebar-background);\n --color-sidebar-item-background--current: var(\n --color-sidebar-item-background\n );\n --color-sidebar-item-background--hover: linear-gradient(\n 90deg,\n var(--color-background-hover--transparent) 0%,\n var(--color-background-hover) var(--sidebar-item-spacing-horizontal),\n var(--color-background-hover) 100%\n );\n\n --color-sidebar-item-expander-background: transparent;\n --color-sidebar-item-expander-background--hover: var(\n --color-background-hover\n );\n\n --color-sidebar-search-text: var(--color-foreground-primary);\n --color-sidebar-search-background: var(--color-background-secondary);\n --color-sidebar-search-background--focus: var(--color-background-primary);\n --color-sidebar-search-border: var(--color-background-border);\n --color-sidebar-search-icon: var(--color-foreground-muted);\n\n // Table of Contents (right)\n --color-toc-background: var(--color-background-primary);\n --color-toc-title-text: var(--color-foreground-muted);\n --color-toc-item-text: var(--color-foreground-secondary);\n --color-toc-item-text--hover: var(--color-foreground-primary);\n --color-toc-item-text--active: var(--color-brand-primary);\n\n // Actual page contents\n --color-content-foreground: var(--color-foreground-primary);\n --color-content-background: transparent;\n\n // Links\n --color-link: var(--color-brand-content);\n --color-link-underline: var(--color-background-border);\n --color-link--hover: var(--color-brand-content);\n --color-link-underline--hover: var(--color-foreground-border);\n\n --color-link--visited: var(--color-brand-visited);\n --color-link-underline--visited: var(--color-background-border);\n --color-link--visited--hover: var(--color-brand-visited);\n --color-link-underline--visited--hover: var(--color-foreground-border);\n}\n\n@mixin colors-dark {\n --color-problematic: #ee5151;\n\n // Base Colors\n --color-foreground-primary: #cfd0d0; // for main text and headings\n --color-foreground-secondary: #9ca0a5; // for secondary text\n --color-foreground-muted: #81868d; // for muted text\n --color-foreground-border: #666666; // for content borders\n\n --color-background-primary: #131416; // for content\n --color-background-secondary: #1a1c1e; // for navigation + ToC\n --color-background-hover: #1e2124ff; // for navigation-item hover\n --color-background-hover--transparent: #1e212400;\n --color-background-border: #303335; // for UI borders\n --color-background-item: #444; // for \"background\" items (eg: copybutton)\n\n // Announcements\n --color-announcement-background: #000000dd;\n --color-announcement-text: #eeebee;\n\n // Brand colors\n --color-brand-primary: #3d94ff;\n --color-brand-content: #5ca5ff;\n --color-brand-visited: #b27aeb;\n\n // Highlighted text (search)\n --color-highlighted-background: #083563;\n\n // GUI Labels\n --color-guilabel-background: #08356380;\n --color-guilabel-border: #13395f80;\n\n // API documentation\n --color-api-keyword: var(--color-foreground-secondary);\n --color-highlight-on-target: #333300;\n\n --color-api-added: #3db854;\n --color-api-added-border: #267334;\n --color-api-changed: #09b0ce;\n --color-api-changed-border: #056d80;\n --color-api-deprecated: #b1a10b;\n --color-api-deprecated-border: #6e6407;\n --color-api-removed: #ff7575;\n --color-api-removed-border: #b03b3b;\n\n // Admonitions\n --color-admonition-background: #18181a;\n\n // Cards\n --color-card-border: var(--color-background-secondary);\n --color-card-background: #18181a;\n --color-card-marginals-background: var(--color-background-hover);\n}\n","// This file contains the styling for making the content throughout the page,\n// including fonts, paragraphs, headings and spacing among these elements.\n\nbody\n font-family: var(--font-stack)\npre,\ncode,\nkbd,\nsamp\n font-family: var(--font-stack--monospace)\n\n// Make fonts look slightly nicer.\nbody\n -webkit-font-smoothing: antialiased\n -moz-osx-font-smoothing: grayscale\n\n// Line height from Bootstrap 4.1\narticle\n line-height: 1.5\n\n//\n// Headings\n//\nh1,\nh2,\nh3,\nh4,\nh5,\nh6\n line-height: 1.25\n font-family: var(--font-stack--headings)\n font-weight: bold\n\n border-radius: 0.5rem\n margin-top: 0.5rem\n margin-bottom: 0.5rem\n margin-left: -0.5rem\n margin-right: -0.5rem\n padding-left: 0.5rem\n padding-right: 0.5rem\n\n + p\n margin-top: 0\n\nh1\n font-size: 2.5em\n margin-top: 1.75rem\n margin-bottom: 1rem\nh2\n font-size: 2em\n margin-top: 1.75rem\nh3\n font-size: 1.5em\nh4\n font-size: 1.25em\nh5\n font-size: 1.125em\nh6\n font-size: 1em\n\nsmall\n opacity: 75%\n font-size: 80%\n\n// Paragraph\np\n margin-top: 0.5rem\n margin-bottom: 0.75rem\n\n// Horizontal rules\nhr.docutils\n height: 1px\n padding: 0\n margin: 2rem 0\n background-color: var(--color-background-border)\n border: 0\n\n.centered\n text-align: center\n\n// Links\na\n text-decoration: underline\n\n color: var(--color-link)\n text-decoration-color: var(--color-link-underline)\n\n &:visited\n color: var(--color-link--visited)\n text-decoration-color: var(--color-link-underline--visited)\n &:hover\n color: var(--color-link--visited--hover)\n text-decoration-color: var(--color-link-underline--visited--hover)\n\n &:hover\n color: var(--color-link--hover)\n text-decoration-color: var(--color-link-underline--hover)\n &.muted-link\n color: inherit\n &:hover\n color: var(--color-link--hover)\n text-decoration-color: var(--color-link-underline--hover)\n &:visited\n color: var(--color-link--visited--hover)\n text-decoration-color: var(--color-link-underline--visited--hover)\n","// This file contains the styles for the overall layouting of the documentation\n// skeleton, including the responsive changes as well as sidebar toggles.\n//\n// This is implemented as a mobile-last design, which isn't ideal, but it is\n// reasonably good-enough and I got pretty tired by the time I'd finished this\n// to move the rules around to fix this. Shouldn't take more than 3-4 hours,\n// if you know what you're doing tho.\n\n// HACK: Not all browsers account for the scrollbar width in media queries.\n// This results in horizontal scrollbars in the breakpoint where we go\n// from displaying everything to hiding the ToC. We accomodate for this by\n// adding a bit of padding to the TOC drawer, disabling the horizontal\n// scrollbar and allowing the scrollbars to cover the padding.\n// https://www.456bereastreet.com/archive/201301/media_query_width_and_vertical_scrollbars/\n\n// HACK: Always having the scrollbar visible, prevents certain browsers from\n// causing the content to stutter horizontally between taller-than-viewport and\n// not-taller-than-viewport pages.\n\nhtml\n overflow-x: hidden\n overflow-y: scroll\n scroll-behavior: smooth\n\n.sidebar-scroll, .toc-scroll, article[role=main] *\n // Override Firefox scrollbar style\n scrollbar-width: thin\n scrollbar-color: var(--color-foreground-border) transparent\n\n // Override Chrome scrollbar styles\n &::-webkit-scrollbar\n width: 0.25rem\n height: 0.25rem\n &::-webkit-scrollbar-thumb\n background-color: var(--color-foreground-border)\n border-radius: 0.125rem\n\n//\n// Overalls\n//\nhtml,\nbody\n height: 100%\n color: var(--color-foreground-primary)\n background: var(--color-background-primary)\n\n.skip-to-content\n position: fixed\n padding: 1rem\n border-radius: 1rem\n left: 0.25rem\n top: 0.25rem\n z-index: 40\n background: var(--color-background-primary)\n color: var(--color-foreground-primary)\n\n transform: translateY(-200%)\n transition: transform 300ms ease-in-out\n\n &:focus-within\n transform: translateY(0%)\n\narticle\n color: var(--color-content-foreground)\n background: var(--color-content-background)\n overflow-wrap: break-word\n\n.page\n display: flex\n // fill the viewport for pages with little content.\n min-height: 100%\n\n.mobile-header\n width: 100%\n height: var(--header-height)\n background-color: var(--color-header-background)\n color: var(--color-header-text)\n border-bottom: 1px solid var(--color-header-border)\n\n // Looks like sub-script/super-script have this, and we need this to\n // be \"on top\" of those.\n z-index: 10\n\n // We don't show the header on large screens.\n display: none\n\n // Add shadow when scrolled\n &.scrolled\n border-bottom: none\n box-shadow: 0 0 0.2rem rgba(0, 0, 0, 0.1), 0 0.2rem 0.4rem rgba(0, 0, 0, 0.2)\n\n .header-center\n a\n color: var(--color-header-text)\n text-decoration: none\n\n.main\n display: flex\n flex: 1\n\n// Sidebar (left) also covers the entire left portion of screen.\n.sidebar-drawer\n box-sizing: border-box\n\n border-right: 1px solid var(--color-sidebar-background-border)\n background: var(--color-sidebar-background)\n\n display: flex\n justify-content: flex-end\n // These next two lines took me two days to figure out.\n width: calc((100% - #{$full-width}) / 2 + #{$sidebar-width})\n min-width: $sidebar-width\n\n// Scroll-along sidebars\n.sidebar-container,\n.toc-drawer\n box-sizing: border-box\n width: $sidebar-width\n\n.toc-drawer\n background: var(--color-toc-background)\n // See HACK described on top of this document\n padding-right: 1rem\n\n.sidebar-sticky,\n.toc-sticky\n position: sticky\n top: 0\n height: min(100%, 100vh)\n height: 100vh\n\n display: flex\n flex-direction: column\n\n.sidebar-scroll,\n.toc-scroll\n flex-grow: 1\n flex-shrink: 1\n\n overflow: auto\n scroll-behavior: smooth\n\n// Central items.\n.content\n padding: 0 $content-padding\n width: $content-width\n\n display: flex\n flex-direction: column\n justify-content: space-between\n\n.icon\n display: inline-block\n height: 1rem\n width: 1rem\n svg\n width: 100%\n height: 100%\n\n//\n// Accommodate announcement banner\n//\n.announcement\n background-color: var(--color-announcement-background)\n color: var(--color-announcement-text)\n\n height: var(--header-height)\n display: flex\n align-items: center\n overflow-x: auto\n & + .page\n min-height: calc(100% - var(--header-height))\n\n.announcement-content\n box-sizing: border-box\n padding: 0.5rem\n min-width: 100%\n white-space: nowrap\n text-align: center\n\n a\n color: var(--color-announcement-text)\n text-decoration-color: var(--color-announcement-text)\n\n &:hover\n color: var(--color-announcement-text)\n text-decoration-color: var(--color-link--hover)\n\n////////////////////////////////////////////////////////////////////////////////\n// Toggles for theme\n////////////////////////////////////////////////////////////////////////////////\n.no-js .theme-toggle-container // don't show theme toggle if there's no JS\n display: none\n\n.theme-toggle-container\n display: flex\n\n.theme-toggle\n display: flex\n cursor: pointer\n border: none\n padding: 0\n background: transparent\n\n.theme-toggle svg\n height: 1.25rem\n width: 1.25rem\n color: var(--color-foreground-primary)\n display: none\n\n.theme-toggle-header\n display: flex\n align-items: center\n justify-content: center\n\n////////////////////////////////////////////////////////////////////////////////\n// Toggles for elements\n////////////////////////////////////////////////////////////////////////////////\n.toc-overlay-icon, .nav-overlay-icon\n display: none\n cursor: pointer\n\n .icon\n color: var(--color-foreground-secondary)\n height: 1.5rem\n width: 1.5rem\n\n.toc-header-icon, .nav-overlay-icon\n // for when we set display: flex\n justify-content: center\n align-items: center\n\n.toc-content-icon\n height: 1.5rem\n width: 1.5rem\n\n.content-icon-container\n float: right\n display: flex\n margin-top: 1.5rem\n margin-left: 1rem\n margin-bottom: 1rem\n gap: 0.5rem\n\n .edit-this-page, .view-this-page\n svg\n color: inherit\n height: 1.25rem\n width: 1.25rem\n\n.sidebar-toggle\n position: absolute\n display: none\n// \n.sidebar-toggle[name=\"__toc\"]\n left: 20px\n.sidebar-toggle:checked\n left: 40px\n// \n\n.overlay\n position: fixed\n top: 0\n width: 0\n height: 0\n\n transition: width 0ms, height 0ms, opacity 250ms ease-out\n\n opacity: 0\n background-color: rgba(0, 0, 0, 0.54)\n.sidebar-overlay\n z-index: 20\n.toc-overlay\n z-index: 40\n\n// Keep things on top and smooth.\n.sidebar-drawer\n z-index: 30\n transition: left 250ms ease-in-out\n.toc-drawer\n z-index: 50\n transition: right 250ms ease-in-out\n\n// Show the Sidebar\n#__navigation:checked\n & ~ .sidebar-overlay\n width: 100%\n height: 100%\n opacity: 1\n & ~ .page\n .sidebar-drawer\n top: 0\n left: 0\n // Show the toc sidebar\n#__toc:checked\n & ~ .toc-overlay\n width: 100%\n height: 100%\n opacity: 1\n & ~ .page\n .toc-drawer\n top: 0\n right: 0\n\n////////////////////////////////////////////////////////////////////////////////\n// Back to top\n////////////////////////////////////////////////////////////////////////////////\n.back-to-top\n text-decoration: none\n\n display: none\n position: fixed\n left: 0\n top: 1rem\n padding: 0.5rem\n padding-right: 0.75rem\n border-radius: 1rem\n font-size: 0.8125rem\n\n background: var(--color-background-primary)\n box-shadow: 0 0.2rem 0.5rem rgba(0, 0, 0, 0.05), #6b728080 0px 0px 1px 0px\n\n z-index: 10\n\n margin-left: 50%\n transform: translateX(-50%)\n svg\n height: 1rem\n width: 1rem\n fill: currentColor\n display: inline-block\n\n span\n margin-left: 0.25rem\n\n .show-back-to-top &\n display: flex\n align-items: center\n\n////////////////////////////////////////////////////////////////////////////////\n// Responsive layouting\n////////////////////////////////////////////////////////////////////////////////\n// Make things a bit bigger on bigger screens.\n@media (min-width: $full-width + $sidebar-width)\n html\n font-size: 110%\n\n@media (max-width: $full-width)\n // Collapse \"toc\" into the icon.\n .toc-content-icon\n display: flex\n .toc-drawer\n position: fixed\n height: 100vh\n top: 0\n right: -$sidebar-width\n border-left: 1px solid var(--color-background-muted)\n .toc-tree\n border-left: none\n font-size: var(--toc-font-size--mobile)\n\n // Accomodate for a changed content width.\n .sidebar-drawer\n width: calc((100% - #{$full-width - $sidebar-width}) / 2 + #{$sidebar-width})\n\n@media (max-width: $full-width - $sidebar-width)\n // Collapse \"navigation\".\n .nav-overlay-icon\n display: flex\n .sidebar-drawer\n position: fixed\n height: 100vh\n width: $sidebar-width\n\n top: 0\n left: -$sidebar-width\n\n // Swap which icon is visible.\n .toc-header-icon, .theme-toggle-header\n display: flex\n .toc-content-icon, .theme-toggle-content\n display: none\n\n // Show the header.\n .mobile-header\n position: sticky\n top: 0\n display: flex\n justify-content: space-between\n align-items: center\n\n .header-left,\n .header-right\n display: flex\n height: var(--header-height)\n padding: 0 var(--header-padding)\n label\n height: 100%\n width: 100%\n user-select: none\n\n .nav-overlay-icon .icon,\n .theme-toggle svg\n height: 1.5rem\n width: 1.5rem\n\n // Add a scroll margin for the content\n :target\n scroll-margin-top: calc(var(--header-height) + 2.5rem)\n\n // Show back-to-top below the header\n .back-to-top\n top: calc(var(--header-height) + 0.5rem)\n\n // Center the page, and accommodate for the header.\n .page\n flex-direction: column\n justify-content: center\n .content\n margin-left: auto\n margin-right: auto\n\n@media (max-width: $content-width + 2* $content-padding)\n // Content should respect window limits.\n .content\n width: 100%\n overflow-x: auto\n\n@media (max-width: $content-width)\n .content\n padding: 0 $content-padding--small\n // Don't float sidebars to the right.\n article aside.sidebar\n float: none\n width: 100%\n margin: 1rem 0\n","// Overall Layout Variables\n//\n// Because CSS variables can't be used in media queries. The fact that this\n// makes the layout non-user-configurable is a good thing.\n$content-padding: 3em;\n$content-padding--small: 1em;\n$content-width: 46em;\n$sidebar-width: 15em;\n$full-width: $content-width + 2 * ($content-padding + $sidebar-width);\n","//\n// The design here is strongly inspired by mkdocs-material.\n.admonition, .topic\n margin: 1rem auto\n padding: 0 0.5rem 0.5rem 0.5rem\n\n background: var(--color-admonition-background)\n\n border-radius: 0.2rem\n box-shadow: 0 0.2rem 0.5rem rgba(0, 0, 0, 0.05), 0 0 0.0625rem rgba(0, 0, 0, 0.1)\n\n font-size: var(--admonition-font-size)\n\n overflow: hidden\n page-break-inside: avoid\n\n // First element should have no margin, since the title has it.\n > :nth-child(2)\n margin-top: 0\n\n // Last item should have no margin, since we'll control that w/ padding\n > :last-child\n margin-bottom: 0\n\n.admonition p.admonition-title,\np.topic-title\n position: relative\n margin: 0 -0.5rem 0.5rem\n padding-left: 2rem\n padding-right: .5rem\n padding-top: .4rem\n padding-bottom: .4rem\n\n font-weight: 500\n font-size: var(--admonition-title-font-size)\n line-height: 1.3\n\n // Our fancy icon\n &::before\n content: \"\"\n position: absolute\n left: 0.5rem\n width: 1rem\n height: 1rem\n\n// Default styles\np.admonition-title\n background-color: var(--color-admonition-title-background)\n &::before\n background-color: var(--color-admonition-title)\n mask-image: var(--icon-admonition-default)\n mask-repeat: no-repeat\n\np.topic-title\n background-color: var(--color-topic-title-background)\n &::before\n background-color: var(--color-topic-title)\n mask-image: var(--icon-topic-default)\n mask-repeat: no-repeat\n\n//\n// Variants\n//\n.admonition\n border-left: 0.2rem solid var(--color-admonition-title)\n\n @each $type, $value in $admonitions\n &.#{$type}\n border-left-color: var(--color-admonition-title--#{$type})\n > .admonition-title\n background-color: var(--color-admonition-title-background--#{$type})\n &::before\n background-color: var(--color-admonition-title--#{$type})\n mask-image: var(--icon-#{nth($value, 2)})\n\n.admonition-todo > .admonition-title\n text-transform: uppercase\n","// This file stylizes the API documentation (stuff generated by autodoc). It's\n// deeply nested due to how autodoc structures the HTML without enough classes\n// to select the relevant items.\n\n// API docs!\ndl[class]:not(.option-list):not(.field-list):not(.footnote):not(.glossary):not(.simple)\n // Tweak the spacing of all the things!\n dd\n margin-left: 2rem\n > :first-child\n margin-top: 0.125rem\n > :last-child\n margin-bottom: 0.75rem\n\n // This is used for the arguments\n .field-list\n margin-bottom: 0.75rem\n\n // \"Headings\" (like \"Parameters\" and \"Return\")\n > dt\n text-transform: uppercase\n font-size: var(--font-size--small)\n\n dd:empty\n margin-bottom: 0.5rem\n dd > ul\n margin-left: -1.2rem\n > li\n > p:nth-child(2)\n margin-top: 0\n // When the last-empty-paragraph follows a paragraph, it doesn't need\n // to augument the existing spacing.\n > p + p:last-child:empty\n margin-top: 0\n margin-bottom: 0\n\n // Colorize the elements\n > dt\n color: var(--color-api-overall)\n\n.sig:not(.sig-inline)\n font-weight: bold\n\n font-size: var(--api-font-size)\n font-family: var(--font-stack--monospace)\n\n margin-left: -0.25rem\n margin-right: -0.25rem\n padding-top: 0.25rem\n padding-bottom: 0.25rem\n padding-right: 0.5rem\n\n // These are intentionally em, to properly match the font size.\n padding-left: 3em\n text-indent: -2.5em\n\n border-radius: 0.25rem\n\n background: var(--color-api-background)\n transition: background 100ms ease-out\n\n &:hover\n background: var(--color-api-background-hover)\n\n // adjust the size of the [source] link on the right.\n a.reference\n .viewcode-link\n font-weight: normal\n width: 4.25rem\n\nem.property\n font-style: normal\n &:first-child\n color: var(--color-api-keyword)\n.sig-name\n color: var(--color-api-name)\n.sig-prename\n font-weight: normal\n color: var(--color-api-pre-name)\n.sig-paren\n color: var(--color-api-paren)\n.sig-param\n font-style: normal\n\ndiv.versionadded,\ndiv.versionchanged,\ndiv.deprecated,\ndiv.versionremoved\n border-left: 0.1875rem solid\n border-radius: 0.125rem\n\n padding-left: 0.75rem\n\n p\n margin-top: 0.125rem\n margin-bottom: 0.125rem\n\ndiv.versionadded\n border-color: var(--color-api-added-border)\n .versionmodified\n color: var(--color-api-added)\n\ndiv.versionchanged\n border-color: var(--color-api-changed-border)\n .versionmodified\n color: var(--color-api-changed)\n\ndiv.deprecated\n border-color: var(--color-api-deprecated-border)\n .versionmodified\n color: var(--color-api-deprecated)\n\ndiv.versionremoved\n border-color: var(--color-api-removed-border)\n .versionmodified\n color: var(--color-api-removed)\n\n// Align the [docs] and [source] to the right.\n.viewcode-link, .viewcode-back\n float: right\n text-align: right\n",".line-block\n margin-top: 0.5rem\n margin-bottom: 0.75rem\n .line-block\n margin-top: 0rem\n margin-bottom: 0rem\n padding-left: 1rem\n","// Captions\narticle p.caption,\ntable > caption,\n.code-block-caption\n font-size: var(--font-size--small)\n text-align: center\n\n// Caption above a TOCTree\n.toctree-wrapper.compound\n .caption, :not(.caption) > .caption-text\n font-size: var(--font-size--small)\n text-transform: uppercase\n\n text-align: initial\n margin-bottom: 0\n\n > ul\n margin-top: 0\n margin-bottom: 0\n","// Inline code\ncode.literal, .sig-inline\n background: var(--color-inline-code-background)\n border-radius: 0.2em\n // Make the font smaller, and use padding to recover.\n font-size: var(--font-size--small--2)\n padding: 0.1em 0.2em\n\n pre.literal-block &\n font-size: inherit\n padding: 0\n\n p &\n border: 1px solid var(--color-background-border)\n\n.sig-inline\n font-family: var(--font-stack--monospace)\n\n// Code and Literal Blocks\n$code-spacing-vertical: 0.625rem\n$code-spacing-horizontal: 0.875rem\n\n// Wraps every literal block + line numbers.\ndiv[class*=\" highlight-\"],\ndiv[class^=\"highlight-\"]\n margin: 1em 0\n display: flex\n\n .table-wrapper\n margin: 0\n padding: 0\n\npre\n margin: 0\n padding: 0\n overflow: auto\n\n // Needed to have more specificity than pygments' \"pre\" selector. :(\n article[role=\"main\"] .highlight &\n line-height: 1.5\n\n &.literal-block,\n .highlight &\n font-size: var(--code-font-size)\n padding: $code-spacing-vertical $code-spacing-horizontal\n\n // Make it look like all the other blocks.\n &.literal-block\n margin-top: 1rem\n margin-bottom: 1rem\n\n border-radius: 0.2rem\n background-color: var(--color-code-background)\n color: var(--color-code-foreground)\n\n// All code is always contained in this.\n.highlight\n width: 100%\n border-radius: 0.2rem\n\n // Make line numbers and prompts un-selectable.\n .gp, span.linenos\n user-select: none\n pointer-events: none\n\n // Expand the line-highlighting.\n .hll\n display: block\n margin-left: -$code-spacing-horizontal\n margin-right: -$code-spacing-horizontal\n padding-left: $code-spacing-horizontal\n padding-right: $code-spacing-horizontal\n\n/* Make code block captions be nicely integrated */\n.code-block-caption\n display: flex\n padding: $code-spacing-vertical $code-spacing-horizontal\n\n border-radius: 0.25rem\n border-bottom-left-radius: 0\n border-bottom-right-radius: 0\n font-weight: 300\n border-bottom: 1px solid\n\n background-color: var(--color-code-background)\n color: var(--color-code-foreground)\n border-color: var(--color-background-border)\n\n + div[class]\n margin-top: 0\n pre\n border-top-left-radius: 0\n border-top-right-radius: 0\n\n// When `html_codeblock_linenos_style` is table.\n.highlighttable\n width: 100%\n display: block\n tbody\n display: block\n\n tr\n display: flex\n\n // Line numbers\n td.linenos\n background-color: var(--color-code-background)\n color: var(--color-code-foreground)\n padding: $code-spacing-vertical $code-spacing-horizontal\n padding-right: 0\n border-top-left-radius: 0.2rem\n border-bottom-left-radius: 0.2rem\n\n .linenodiv\n padding-right: $code-spacing-horizontal\n font-size: var(--code-font-size)\n box-shadow: -0.0625rem 0 var(--color-foreground-border) inset\n\n // Actual code\n td.code\n padding: 0\n display: block\n flex: 1\n overflow: hidden\n\n .highlight\n border-top-left-radius: 0\n border-bottom-left-radius: 0\n\n// When `html_codeblock_linenos_style` is inline.\n.highlight\n span.linenos\n display: inline-block\n padding-left: 0\n padding-right: $code-spacing-horizontal\n margin-right: $code-spacing-horizontal\n box-shadow: -0.0625rem 0 var(--color-foreground-border) inset\n","// Inline Footnote Reference\n.footnote-reference\n font-size: var(--font-size--small--4)\n vertical-align: super\n\n// Definition list, listing the content of each note.\n// docutils <= 0.17\ndl.footnote.brackets\n font-size: var(--font-size--small)\n color: var(--color-foreground-secondary)\n\n display: grid\n grid-template-columns: max-content auto\n dt\n margin: 0\n > .fn-backref\n margin-left: 0.25rem\n\n &:after\n content: \":\"\n\n .brackets\n &:before\n content: \"[\"\n &:after\n content: \"]\"\n\n dd\n margin: 0\n padding: 0 1rem\n\n// docutils >= 0.18\naside.footnote\n font-size: var(--font-size--small)\n color: var(--color-foreground-secondary)\n\naside.footnote > span,\ndiv.citation > span\n float: left\n font-weight: 500\n padding-right: 0.25rem\n\naside.footnote > *:not(span),\ndiv.citation > p\n margin-left: 2rem\n","//\n// Figures\n//\nimg\n box-sizing: border-box\n max-width: 100%\n height: auto\n\narticle\n figure, .figure\n border-radius: 0.2rem\n\n margin: 0\n :last-child\n margin-bottom: 0\n\n .align-left\n float: left\n clear: left\n margin: 0 1rem 1rem\n\n .align-right\n float: right\n clear: right\n margin: 0 1rem 1rem\n\n .align-default,\n .align-center\n display: block\n text-align: center\n margin-left: auto\n margin-right: auto\n\n // WELL, table needs to be stylised like a table.\n table.align-default\n display: table\n text-align: initial\n",".genindex-jumpbox, .domainindex-jumpbox\n border-top: 1px solid var(--color-background-border)\n border-bottom: 1px solid var(--color-background-border)\n padding: 0.25rem\n\n.genindex-section, .domainindex-section\n h2\n margin-top: 0.75rem\n margin-bottom: 0.5rem\n ul\n margin-top: 0\n margin-bottom: 0\n","ul,\nol\n padding-left: 1.2rem\n\n // Space lists out like paragraphs\n margin-top: 1rem\n margin-bottom: 1rem\n // reduce margins within li.\n li\n > p:first-child\n margin-top: 0.25rem\n margin-bottom: 0.25rem\n\n > p:last-child\n margin-top: 0.25rem\n\n > ul,\n > ol\n margin-top: 0.5rem\n margin-bottom: 0.5rem\n\nol\n &.arabic\n list-style: decimal\n &.loweralpha\n list-style: lower-alpha\n &.upperalpha\n list-style: upper-alpha\n &.lowerroman\n list-style: lower-roman\n &.upperroman\n list-style: upper-roman\n\n// Don't space lists out when they're \"simple\" or in a `.. toctree::`\n.simple,\n.toctree-wrapper\n li\n > ul,\n > ol\n margin-top: 0\n margin-bottom: 0\n\n// Definition Lists\n.field-list,\n.option-list,\ndl:not([class]),\ndl.simple,\ndl.footnote,\ndl.glossary\n dt\n font-weight: 500\n margin-top: 0.25rem\n + dt\n margin-top: 0\n\n .classifier::before\n content: \":\"\n margin-left: 0.2rem\n margin-right: 0.2rem\n\n dd\n > p:first-child,\n ul\n margin-top: 0.125rem\n\n ul\n margin-bottom: 0.125rem\n",".math-wrapper\n width: 100%\n overflow-x: auto\n\ndiv.math\n position: relative\n text-align: center\n\n .headerlink,\n &:focus .headerlink\n display: none\n\n &:hover .headerlink\n display: inline-block\n\n span.eqno\n position: absolute\n right: 0.5rem\n top: 50%\n transform: translate(0, -50%)\n z-index: 1\n","// Abbreviations\nabbr[title]\n cursor: help\n\n// \"Problematic\" content, as identified by Sphinx\n.problematic\n color: var(--color-problematic)\n\n// Keyboard / Mouse \"instructions\"\nkbd:not(.compound)\n margin: 0 0.2rem\n padding: 0 0.2rem\n border-radius: 0.2rem\n border: 1px solid var(--color-foreground-border)\n color: var(--color-foreground-primary)\n vertical-align: text-bottom\n\n font-size: var(--font-size--small--3)\n display: inline-block\n\n box-shadow: 0 0.0625rem 0 rgba(0, 0, 0, 0.2), inset 0 0 0 0.125rem var(--color-background-primary)\n\n background-color: var(--color-background-secondary)\n\n// Blockquote\nblockquote\n border-left: 4px solid var(--color-background-border)\n background: var(--color-background-secondary)\n\n margin-left: 0\n margin-right: 0\n padding: 0.5rem 1rem\n\n .attribution\n font-weight: 600\n text-align: right\n\n &.pull-quote,\n &.highlights\n font-size: 1.25em\n\n &.epigraph,\n &.pull-quote\n border-left-width: 0\n border-radius: 0.5rem\n\n &.highlights\n border-left-width: 0\n background: transparent\n\n// Center align embedded-in-text images\np .reference img\n vertical-align: middle\n","p.rubric\n line-height: 1.25\n font-weight: bold\n font-size: 1.125em\n\n // For Numpy-style documentation that's got rubrics within it.\n // https://github.com/pradyunsg/furo/discussions/505\n dd &\n line-height: inherit\n font-weight: inherit\n\n font-size: var(--font-size--small)\n text-transform: uppercase\n","article .sidebar\n float: right\n clear: right\n width: 30%\n\n margin-left: 1rem\n margin-right: 0\n\n border-radius: 0.2rem\n background-color: var(--color-background-secondary)\n border: var(--color-background-border) 1px solid\n\n > *\n padding-left: 1rem\n padding-right: 1rem\n\n > ul, > ol // lists need additional padding, because bullets.\n padding-left: 2.2rem\n\n .sidebar-title\n margin: 0\n padding: 0.5rem 1rem\n border-bottom: var(--color-background-border) 1px solid\n\n font-weight: 500\n\n// TODO: subtitle\n// TODO: dedicated variables?\n",".table-wrapper\n width: 100%\n overflow-x: auto\n margin-top: 1rem\n margin-bottom: 0.5rem\n padding: 0.2rem 0.2rem 0.75rem\n\ntable.docutils\n border-radius: 0.2rem\n border-spacing: 0\n border-collapse: collapse\n\n box-shadow: 0 0.2rem 0.5rem rgba(0, 0, 0, 0.05), 0 0 0.0625rem rgba(0, 0, 0, 0.1)\n\n th\n background: var(--color-table-header-background)\n\n td,\n th\n // Space things out properly\n padding: 0 0.25rem\n\n // Get the borders looking just-right.\n border-left: 1px solid var(--color-table-border)\n border-right: 1px solid var(--color-table-border)\n border-bottom: 1px solid var(--color-table-border)\n\n p\n margin: 0.25rem\n\n &:first-child\n border-left: none\n &:last-child\n border-right: none\n\n // MyST-parser tables set these classes for control of column alignment\n &.text-left\n text-align: left\n &.text-right\n text-align: right\n &.text-center\n text-align: center\n",":target\n scroll-margin-top: 2.5rem\n\n@media (max-width: $full-width - $sidebar-width)\n :target\n scroll-margin-top: calc(2.5rem + var(--header-height))\n\n // When a heading is selected\n section > span:target\n scroll-margin-top: calc(2.8rem + var(--header-height))\n\n// Permalinks\n.headerlink\n font-weight: 100\n user-select: none\n\nh1,\nh2,\nh3,\nh4,\nh5,\nh6,\ndl dt,\np.caption,\nfigcaption p,\ntable > caption,\n.code-block-caption\n > .headerlink\n margin-left: 0.5rem\n visibility: hidden\n &:hover > .headerlink\n visibility: visible\n\n // Don't change to link-like, if someone adds the contents directive.\n > .toc-backref\n color: inherit\n text-decoration-line: none\n\n// Figure and table captions are special.\nfigure:hover > figcaption > p > .headerlink,\ntable:hover > caption > .headerlink\n visibility: visible\n\n:target >, // Regular section[id] style anchors\nspan:target ~ // Non-regular span[id] style \"extra\" anchors\n h1,\n h2,\n h3,\n h4,\n h5,\n h6\n &:nth-of-type(1)\n background-color: var(--color-highlight-on-target)\n // .headerlink\n // visibility: visible\n code.literal\n background-color: transparent\n\ntable:target > caption,\nfigure:target\n background-color: var(--color-highlight-on-target)\n\n// Inline page contents\n.this-will-duplicate-information-and-it-is-still-useful-here li :target\n background-color: var(--color-highlight-on-target)\n\n// Code block permalinks\n.literal-block-wrapper:target .code-block-caption\n background-color: var(--color-highlight-on-target)\n\n// When a definition list item is selected\n//\n// There isn't really an alternative to !important here, due to the\n// high-specificity of API documentation's selector.\ndt:target\n background-color: var(--color-highlight-on-target) !important\n\n// When a footnote reference is selected\n.footnote > dt:target + dd,\n.footnote-reference:target\n background-color: var(--color-highlight-on-target)\n",".guilabel\n background-color: var(--color-guilabel-background)\n border: 1px solid var(--color-guilabel-border)\n color: var(--color-guilabel-text)\n\n padding: 0 0.3em\n border-radius: 0.5em\n font-size: 0.9em\n","// This file contains the styles used for stylizing the footer that's shown\n// below the content.\n\nfooter\n font-size: var(--font-size--small)\n display: flex\n flex-direction: column\n\n margin-top: 2rem\n\n// Bottom of page information\n.bottom-of-page\n display: flex\n align-items: center\n justify-content: space-between\n\n margin-top: 1rem\n padding-top: 1rem\n padding-bottom: 1rem\n\n color: var(--color-foreground-secondary)\n border-top: 1px solid var(--color-background-border)\n\n line-height: 1.5\n\n @media (max-width: $content-width)\n text-align: center\n flex-direction: column-reverse\n gap: 0.25rem\n\n .left-details\n font-size: var(--font-size--small)\n\n .right-details\n display: flex\n flex-direction: column\n gap: 0.25rem\n text-align: right\n\n .icons\n display: flex\n justify-content: flex-end\n gap: 0.25rem\n font-size: 1rem\n\n a\n text-decoration: none\n\n svg,\n img\n font-size: 1.125rem\n height: 1em\n width: 1em\n\n// Next/Prev page information\n.related-pages\n a\n display: flex\n align-items: center\n\n text-decoration: none\n &:hover .page-info .title\n text-decoration: underline\n color: var(--color-link)\n text-decoration-color: var(--color-link-underline)\n\n svg.furo-related-icon,\n svg.furo-related-icon > use\n flex-shrink: 0\n\n color: var(--color-foreground-border)\n\n width: 0.75rem\n height: 0.75rem\n margin: 0 0.5rem\n\n &.next-page\n max-width: 50%\n\n float: right\n clear: right\n text-align: right\n\n &.prev-page\n max-width: 50%\n\n float: left\n clear: left\n\n svg\n transform: rotate(180deg)\n\n.page-info\n display: flex\n flex-direction: column\n overflow-wrap: anywhere\n\n .next-page &\n align-items: flex-end\n\n .context\n display: flex\n align-items: center\n\n padding-bottom: 0.1rem\n\n color: var(--color-foreground-muted)\n font-size: var(--font-size--small)\n text-decoration: none\n","// This file contains the styles for the contents of the left sidebar, which\n// contains the navigation tree, logo, search etc.\n\n////////////////////////////////////////////////////////////////////////////////\n// Brand on top of the scrollable tree.\n////////////////////////////////////////////////////////////////////////////////\n.sidebar-brand\n display: flex\n flex-direction: column\n flex-shrink: 0\n\n padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)\n text-decoration: none\n\n.sidebar-brand-text\n color: var(--color-sidebar-brand-text)\n overflow-wrap: break-word\n margin: var(--sidebar-item-spacing-vertical) 0\n font-size: 1.5rem\n\n.sidebar-logo-container\n margin: var(--sidebar-item-spacing-vertical) 0\n\n.sidebar-logo\n margin: 0 auto\n display: block\n max-width: 100%\n\n////////////////////////////////////////////////////////////////////////////////\n// Search\n////////////////////////////////////////////////////////////////////////////////\n.sidebar-search-container\n display: flex\n align-items: center\n margin-top: var(--sidebar-search-space-above)\n\n position: relative\n\n background: var(--color-sidebar-search-background)\n &:hover,\n &:focus-within\n background: var(--color-sidebar-search-background--focus)\n\n &::before\n content: \"\"\n position: absolute\n left: var(--sidebar-item-spacing-horizontal)\n width: var(--sidebar-search-icon-size)\n height: var(--sidebar-search-icon-size)\n\n background-color: var(--color-sidebar-search-icon)\n mask-image: var(--icon-search)\n\n.sidebar-search\n box-sizing: border-box\n\n border: none\n border-top: 1px solid var(--color-sidebar-search-border)\n border-bottom: 1px solid var(--color-sidebar-search-border)\n\n padding-top: var(--sidebar-search-input-spacing-vertical)\n padding-bottom: var(--sidebar-search-input-spacing-vertical)\n padding-right: var(--sidebar-search-input-spacing-horizontal)\n padding-left: calc(var(--sidebar-item-spacing-horizontal) + var(--sidebar-search-input-spacing-horizontal) + var(--sidebar-search-icon-size))\n\n width: 100%\n\n color: var(--color-sidebar-search-foreground)\n background: transparent\n z-index: 10\n\n &:focus\n outline: none\n\n &::placeholder\n font-size: var(--sidebar-search-input-font-size)\n\n//\n// Hide Search Matches link\n//\n#searchbox .highlight-link\n padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal) 0\n margin: 0\n text-align: center\n\n a\n color: var(--color-sidebar-search-icon)\n font-size: var(--font-size--small--2)\n\n////////////////////////////////////////////////////////////////////////////////\n// Structure/Skeleton of the navigation tree (left)\n////////////////////////////////////////////////////////////////////////////////\n.sidebar-tree\n font-size: var(--sidebar-item-font-size)\n margin-top: var(--sidebar-tree-space-above)\n margin-bottom: var(--sidebar-item-spacing-vertical)\n\n ul\n padding: 0\n margin-top: 0\n margin-bottom: 0\n\n display: flex\n flex-direction: column\n\n list-style: none\n\n li\n position: relative\n margin: 0\n\n > ul\n margin-left: var(--sidebar-item-spacing-horizontal)\n\n .icon\n color: var(--color-sidebar-link-text)\n\n .reference\n box-sizing: border-box\n color: var(--color-sidebar-link-text)\n\n // Fill the parent.\n display: inline-block\n line-height: var(--sidebar-item-line-height)\n text-decoration: none\n\n // Don't allow long words to cause wrapping.\n overflow-wrap: anywhere\n\n height: 100%\n width: 100%\n\n padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)\n\n &:hover\n color: var(--color-sidebar-link-text)\n background: var(--color-sidebar-item-background--hover)\n\n // Add a nice little \"external-link\" arrow here.\n &.external::after\n content: url('data:image/svg+xml,')\n margin: 0 0.25rem\n vertical-align: middle\n color: var(--color-sidebar-link-text)\n\n // Make the current page reference bold.\n .current-page > .reference\n font-weight: bold\n\n label\n position: absolute\n top: 0\n right: 0\n height: var(--sidebar-item-height)\n width: var(--sidebar-expander-width)\n\n cursor: pointer\n user-select: none\n\n display: flex\n justify-content: center\n align-items: center\n\n .caption, :not(.caption) > .caption-text\n font-size: var(--sidebar-caption-font-size)\n color: var(--color-sidebar-caption-text)\n\n font-weight: bold\n text-transform: uppercase\n\n margin: var(--sidebar-caption-space-above) 0 0 0\n padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)\n\n // If it has children, add a bit more padding to wrap the content to avoid\n // overlapping with the
-
kiui 0.2.10 documentation
 +
kiui 0.2.12 documentation
@@ -261,407 +261,8 @@

Index

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_ | A | B | C | D | F | G | I | K | L | M | N | O | P | R | S | T | U | V | W
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K

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    -
  • - kiui.cam - -
    • -
  • - kiui.mesh - -
    • -
  • - kiui.mesh_utils - -
    • -
    -
  • - kiui.op - -
    • -
  • - kiui.utils - -
    • -
  • - kiui.vis - -
    • -
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W

- + diff --git a/index.html b/index.html index d2ad4d7..110b9dd 100644 --- a/index.html +++ b/index.html @@ -5,12 +5,12 @@ - - kiui 0.2.10 documentation + + kiui 0.2.12 documentation - + - + @@ -168,7 +168,7 @@
-
kiui 0.2.10 documentation
 +
kiui 0.2.12 documentation
@@ -359,7 +359,7 @@

Installation +

diff --git a/mesh/index.html b/mesh/index.html index 61770ce..4d79aa7 100644 --- a/mesh/index.html +++ b/mesh/index.html @@ -5,12 +5,12 @@ - - Mesh - kiui 0.2.10 documentation + + Mesh - kiui 0.2.12 documentation - + - + @@ -168,7 +168,7 @@
-
kiui 0.2.10 documentation
 +
kiui 0.2.12 documentation
@@ -287,410 +287,8 @@

Examples

-
-

API

-
-
-class kiui.mesh.Mesh(v: Tensor | None = None, f: Tensor | None = None, vn: Tensor | None = None, fn: Tensor | None = None, vt: Tensor | None = None, ft: Tensor | None = None, vc: Tensor | None = None, albedo: Tensor | None = None, metallicRoughness: Tensor | None = None, device: device | None = None)[source]
-

A torch-native trimesh class, with support for ply/obj/glb formats.

-
-

Note

-

This class only supports one mesh with a single texture image (an albedo texture and a metallic-roughness texture).

-
-
-
-__init__(v: Tensor | None = None, f: Tensor | None = None, vn: Tensor | None = None, fn: Tensor | None = None, vt: Tensor | None = None, ft: Tensor | None = None, vc: Tensor | None = None, albedo: Tensor | None = None, metallicRoughness: Tensor | None = None, device: device | None = None)[source]
-

Init a mesh directly using all attributes.

-
-
Parameters:
-
    -

  • v (Optional[Tensor]) – vertices, float [N, 3]. Defaults to None.

    • -

  • f (Optional[Tensor]) – faces, int [M, 3]. Defaults to None.

    • -

  • vn (Optional[Tensor]) – vertex normals, float [N, 3]. Defaults to None.

    • -

  • fn (Optional[Tensor]) – faces for normals, int [M, 3]. Defaults to None.

    • -

  • vt (Optional[Tensor]) – vertex uv coordinates, float [N, 2]. Defaults to None.

    • -

  • ft (Optional[Tensor]) – faces for uvs, int [M, 3]. Defaults to None.

    • -

  • vc (Optional[Tensor]) – vertex colors, float [N, 3]. Defaults to None.

    • -

  • albedo (Optional[Tensor]) – albedo texture, float [H, W, 3], RGB format. Defaults to None.

    • -

  • metallicRoughness (Optional[Tensor]) – metallic-roughness texture, float [H, W, 3], metallic(Blue) = metallicRoughness[…, 2], roughness(Green) = metallicRoughness[…, 1]. Defaults to None.

    • -

  • device (Optional[torch.device]) – torch device. Defaults to None.

    • -
-
-
-
- -
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-classmethod load(path, resize=True, clean=False, renormal=True, retex=False, bound=0.9, front_dir='+z', **kwargs)[source]
-

load mesh from path.

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-
Parameters:
-
    -

  • path (str) – path to mesh file, supports ply, obj, glb.

    • -

  • clean (bool, optional) – perform mesh cleaning at load (e.g., merge close vertices). Defaults to False.

    • -

  • resize (bool, optional) – auto resize the mesh using bound into [-bound, bound]^3. Defaults to True.

    • -

  • renormal (bool, optional) – re-calc the vertex normals. Defaults to True.

    • -

  • retex (bool, optional) – re-calc the uv coordinates, will overwrite the existing uv coordinates. Defaults to False.

    • -

  • wotex (bool, optional) – do not try to load any texture. Defaults to False.

    • -

  • bound (float, optional) – bound to resize. Defaults to 0.9.

    • -

  • front_dir (str, optional) – front-view direction of the mesh, should be [+-][xyz][ 123]. Defaults to ‘+z’.

    • -

  • device (torch.device, optional) – torch device. Defaults to None.

    • -
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Note

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a device keyword argument can be provided to specify the torch device. -If it’s not provided, we will try to use 'cuda' as the device if it’s available.

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Returns:
-

the loaded Mesh object.

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-
Return type:
-

Mesh

-
-
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- -
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-classmethod load_obj(path, wotex=False, albedo_path=None, device=None)[source]
-

load an obj mesh.

-
-
Parameters:
-
    -

  • path (str) – path to mesh.

    • -

  • wotex (bool, optional) – do not try to load any texture. Defaults to False.

    • -

  • albedo_path (str, optional) – path to the albedo texture image, will overwrite the existing texture path if specified in mtl. Defaults to None.

    • -

  • device (torch.device, optional) – torch device. Defaults to None.

    • -
-
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Note

-

We will try to read mtl path from obj, else we assume the file name is the same as obj but with mtl extension. -The usemtl statement is ignored, and we only use the last material path in mtl file.

-
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Returns:
-

the loaded Mesh object.

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-
Return type:
-

Mesh

-
-
-
- -
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-classmethod load_trimesh(path, wotex=False, device=None)[source]
-

load a mesh using trimesh.load().

-

Can load various formats like glb and serves as a fallback.

-
-

Note

-

We will try to merge all meshes if the glb contains more than one, -but this may cause the texture to lose, since we only support one texture image!

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-
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Parameters:
-
    -

  • path (str) – path to the mesh file.

    • -

  • wotex (bool, optional) – do not try to load any texture. Defaults to False.

    • -

  • device (torch.device, optional) – torch device. Defaults to None.

    • -
-
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Returns:
-

the loaded Mesh object.

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-
Return type:
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Mesh

-
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- -
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-sample_surface(count: int)[source]
-

sample points on the surface of the mesh.

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Parameters:
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count (int) – number of points to sample.

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Returns:
-

the sampled points, float [count, 3].

-
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Return type:
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torch.Tensor

-
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- -
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-aabb()[source]
-

get the axis-aligned bounding box of the mesh.

-
-
Returns:
-

the min xyz and max xyz of the mesh.

-
-
Return type:
-

Tuple[torch.Tensor]

-
-
-
- -
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-auto_size(bound=0.9)[source]
-

auto resize the mesh.

-
-
Parameters:
-

bound (float, optional) – resizing into [-bound, bound]^3. Defaults to 0.9.

-
-
-
- -
-
-auto_normal()[source]
-

auto calculate the vertex normals.

-
- -
-
-auto_uv(cache_path=None, vmap=True)[source]
-

auto calculate the uv coordinates.

-
-
Parameters:
-
    -

  • cache_path (str, optional) – path to save/load the uv cache as a npz file, this can avoid calculating uv every time when loading the same mesh, which is time-consuming. Defaults to None.

    • -

  • vmap (bool, optional) – remap vertices based on uv coordinates, so each v correspond to a unique vt (necessary for formats like gltf). -Usually this will duplicate the vertices on the edge of uv atlas. Defaults to True.

    • -
-
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- -
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-remap_uv(v)[source]
-

remap uv texture (vt) to other surface.

-
-
Parameters:
-

v (torch.Tensor) – the target mesh vertices, float [N, 3].

-
-
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- -
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-align_v_to_vt(vmapping=None)[source]
-

remap v/f and vn/fn to vt/ft.

-
-
Parameters:
-

vmapping (np.ndarray, optional) – the mapping relationship from f to ft. Defaults to None.

-
-
-
- -
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-to(device)[source]
-

move all tensor attributes to device.

-
-
Parameters:
-

device (torch.device) – target device.

-
-
Returns:
-

self.

-
-
Return type:
-

Mesh

-
-
-
- -
-
-write(path)[source]
-

write the mesh to a path.

-
-
Parameters:
-

path (str) – path to write, supports ply, obj and glb.

-
-
-
- -
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-write_ply(path)[source]
-

write the mesh in ply format. Only for geometry!

-
-
Parameters:
-

path (str) – path to write.

-
-
-
- -
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-write_glb(path)[source]
-
-
write the mesh in glb/gltf format.

This will create a scene with a single mesh.

-
-
-
-
Parameters:
-

path (str) – path to write.

-
-
-
- -
-
-write_obj(path)[source]
-

write the mesh in obj format. Will also write the texture and mtl files.

-
-
Parameters:
-

path (str) – path to write.

-
-
-
- -
- -
-
-kiui.mesh_utils.decimate_mesh(verts, faces, target=50000.0, backend='pymeshlab', remesh=False, optimalplacement=True, verbose=True)[source]
-

perform mesh decimation.

-
-
Parameters:
-
    -

  • verts (np.ndarray) – mesh vertices, float [N, 3]

    • -

  • faces (np.ndarray) – mesh faces, int [M, 3]

    • -

  • target (int) – targeted number of faces

    • -

  • backend (str, optional) – algorithm backend, can be “pymeshlab” or “pyfqmr”. Defaults to “pymeshlab”.

    • -

  • remesh (bool, optional) – whether to remesh after decimation. Defaults to False.

    • -

  • optimalplacement (bool, optional) – For flat mesh, use False to prevent spikes. Defaults to True.

    • -

  • verbose (bool, optional) – whether to print the decimation process. Defaults to True.

    • -
-
-
Returns:
-

vertices and faces after decimation.

-
-
Return type:
-

Tuple[np.ndarray]

-
-
-
- -
-
-kiui.mesh_utils.clean_mesh(verts, faces, v_pct=1, min_f=0, min_d=0, repair=True, remesh=False, remesh_size=0.01, remesh_iters=3, verbose=True)[source]
-

perform mesh cleaning, including floater removal, non manifold repair, and remeshing.

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Parameters:
-
    -

  • verts (np.ndarray) – mesh vertices, float [N, 3]

    • -

  • faces (np.ndarray) – mesh faces, int [M, 3]

    • -

  • v_pct (int, optional) – percentage threshold to merge close vertices. Defaults to 1.

    • -

  • min_f (int, optional) – maximal number of faces for isolated component to remove. Defaults to 0.

    • -

  • min_d (int, optional) – maximal diameter percentage of isolated component to remove. Defaults to 0.

    • -

  • repair (bool, optional) – whether to repair non-manifold faces (cannot gurantee). Defaults to True.

    • -

  • remesh (bool, optional) – whether to perform a remeshing after all cleaning. Defaults to True.

    • -

  • remesh_size (float, optional) – the targeted edge length for remeshing. Defaults to 0.01.

    • -

  • remesh_iters (int, optional) – the iterations of remeshing. Defaults to 3.

    • -

  • verbose (bool, optional) – whether to print the cleaning process. Defaults to True.

    • -
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Returns:
-

vertices and faces after decimation.

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Return type:
-

Tuple[np.ndarray]

-
-
-
- -
-
-kiui.mesh_utils.laplacian_uniform(verts, faces)[source]
-

calculate laplacian uniform matrix

-
-
Parameters:
-
    -

  • verts (torch.Tensor) – mesh vertices, float [N, 3]

    • -

  • faces (torch.Tensor) – mesh faces, long [M, 3]

    • -
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Returns:
-

sparse laplacian matrix.

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Return type:
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torch.Tensor

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-
- -
-
-kiui.mesh_utils.laplacian_smooth_loss(verts, faces)[source]
-

calculate laplacian smooth loss.

-
-
Parameters:
-
    -

  • verts (torch.Tensor) – mesh vertices, float [N, 3]

    • -

  • faces (torch.Tensor) – mesh faces, int [M, 3]

    • -
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Returns:
-

loss value.

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Return type:
-

torch.Tensor

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- -
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-kiui.mesh_utils.compute_edge_to_face_mapping(faces)[source]
-

compute edge to face mapping.

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Parameters:
-

faces (torch.Tensor) – mesh faces, int [M, 3]

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Returns:
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indices to faces for each edge, long, [N, 2]

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Return type:
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torch.Tensor

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- -
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-kiui.mesh_utils.normal_consistency(verts, faces, face_normals=None)[source]
-

calculate normal consistency loss.

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Parameters:
-
    -

  • verts (torch.Tensor) – mesh vertices, float [N, 3]

    • -

  • faces (torch.Tensor) – mesh faces, int [M, 3]

    • -

  • face_normals (Optional[torch.Tensor]) – the normal vector for each face, will be calculated if not provided, float [M, 3]

    • -
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Returns:
-

loss value.

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Return type:
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torch.Tensor

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+

API

@@ -751,34 +349,7 @@

Examples @@ -790,7 +361,7 @@

Examples - + diff --git a/misc_api/index.html b/misc_api/index.html index fffc445..300fd71 100644 --- a/misc_api/index.html +++ b/misc_api/index.html @@ -5,12 +5,12 @@ - - Misc API - kiui 0.2.10 documentation + + Misc API - kiui 0.2.12 documentation - + - + @@ -168,7 +168,7 @@
-
kiui 0.2.10 documentation
 +
kiui 0.2.12 documentation
@@ -271,54 +271,9 @@

Misc API

Miscellaneous API.

Super-resolution

-
-
-kiui.sr.sr(image: ndarray, scale: Literal[2, 4, 8] = 2, device=None)[source]
-

lazy load functional super-resolution API for convenience.

-
-
Parameters:
-
    -

  • image (ndarray) – input image, uint8/float32 [H, W, 3]

    • -

  • scale (Literal[2, 4, 8], optional) – upscale factor. Defaults to 2.

    • -

  • device (torch.device, optional) – device to put SR models, if not provided, will try to use ‘cuda’. Defaults to None.

    • -
-
-
Returns:
-

super-resolutioned image, uint8/float32 [H * scale, W * scale, 3]

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-
Return type:
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ndarray

-
-
-
-

Grid-put

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-
-kiui.grid_put.grid_put(shape: Sequence[int], coords: Tensor, values: Tensor, mode: Literal[‘nearest’, ‘linear’, ‘linear-mipmap’] = 'linear-mipmap', min_resolution: int = 32, return_count: bool = False) → Tensor[source]
-

put back values to an image according to the coords. inverse operation of F.grid_sample.

-
-
Parameters:
-
    -

  • shape (Sequence[int]) – shape of the image, support 2D image and 3D volume, sequence of [D]

    • -

  • coords (Tensor) – coordinates, float [N, D] in [-1, 1].

    • -

  • values (Tensor) – values, float [N, C].

    • -

  • mode (str, Literal[‘nearest’, ‘linear’, ‘linear-mipmap’]) – interpolation mode, see https://github.com/ashawkey/grid_put for examples. Defaults to ‘linear-mipmap’.

    • -

  • min_resolution (int, optional) – minimal resolution for mipmap. Defaults to 32.

    • -

  • return_count (bool, optional) – whether to return the summed value and weights, instead of the divided results. Defaults to False.

    • -
-
-
Returns:
-

the restored image/volume, float [H, W, C]/[H, W, D, C].

-
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Return type:
-

Tensor

-
-
-
-
@@ -378,14 +333,8 @@

Grid-put
@@ -397,7 +346,7 @@

Grid-put

-

+
diff --git a/misc_cli/index.html b/misc_cli/index.html index f0f55f3..1a9d053 100644 --- a/misc_cli/index.html +++ b/misc_cli/index.html @@ -5,12 +5,12 @@ - - Misc CLI - kiui 0.2.10 documentation + + Misc CLI - kiui 0.2.12 documentation - + - + @@ -168,7 +168,7 @@
-
kiui 0.2.10 documentation
 +
kiui 0.2.12 documentation
@@ -378,7 +378,7 @@

Usage

-
+ diff --git a/objects.inv b/objects.inv index e7bd1fa..04254e1 100644 Binary files a/objects.inv and b/objects.inv differ diff --git a/ops/index.html b/ops/index.html index 9ece94a..558588b 100644 --- a/ops/index.html +++ b/ops/index.html @@ -5,12 +5,12 @@ - - Operators - kiui 0.2.10 documentation + + Operators - kiui 0.2.12 documentation - + - + @@ -168,7 +168,7 @@
-
kiui 0.2.10 documentation
 +
kiui 0.2.12 documentation
@@ -269,279 +269,8 @@

Operators

A collection of operators for numpy and torch.

-
-

API

-
-
-kiui.op.dot(x: Tensor | ndarray, y: Tensor | ndarray) → Tensor | ndarray[source]
-

dot product (along the last dim).

-
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Parameters:
-
    -

  • x (Union[Tensor, ndarray]) – x, […, C]

    • -

  • y (Union[Tensor, ndarray]) – y, […, C]

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x dot y, […, 1]

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Union[Tensor, ndarray]

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-kiui.op.length(x: Tensor | ndarray, eps=1e-20) → Tensor | ndarray[source]
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length of an array (along the last dim).

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Parameters:
-
    -

  • x (Union[Tensor, ndarray]) – x, […, C]

    • -

  • eps (float, optional) – eps. Defaults to 1e-20.

    • -
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Returns:
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length, […, 1]

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Return type:
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Union[Tensor, ndarray]

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- -
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-kiui.op.safe_normalize(x: Tensor | ndarray, eps=1e-20) → Tensor | ndarray[source]
-

normalize an array (along the last dim).

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Parameters:
-
    -

  • x (Union[Tensor, ndarray]) – x, […, C]

    • -

  • eps (float, optional) – eps. Defaults to 1e-20.

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Returns:
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normalized x, […, C]

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Return type:
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Union[Tensor, ndarray]

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-
-
- -
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-kiui.op.normalize(x: Tensor | ndarray, eps=1e-20) → Tensor | ndarray[source]
-

normalize an array (along the last dim). alias of safe_normalize.

-
-
Parameters:
-
    -

  • x (Union[Tensor, ndarray]) – x, […, C]

    • -

  • eps (float, optional) – eps. Defaults to 1e-20.

    • -
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Returns:
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normalized x, […, C]

-
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Return type:
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Union[Tensor, ndarray]

-
-
-
- -
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-kiui.op.make_divisible(x: int, m: int = 8)[source]
-

make an int x divisible by m.

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Parameters:
-
    -

  • x (int) – x

    • -

  • m (int, optional) –

      -

    1. Defaults to 8.

      2. -
    -

    • -
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Returns:
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x + (m - x % m)

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Return type:
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int

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-kiui.op.inverse_sigmoid(x: Tensor, eps=1e-06) → Tensor[source]
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inversion of sigmoid function.

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Parameters:
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    -

  • x (Tensor) – x

    • -

  • eps (float, optional) – eps. Defaults to 1e-6.

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Returns:
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log(x / (1 - x))

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Return type:
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Tensor

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- -
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-kiui.op.inverse_softplus(x: Tensor) → Tensor[source]
-

inversion of softplus function.

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Parameters:
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x (Tensor) – x

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Returns:
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log(exp(x) - 1)

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Return type:
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Tensor

-
-
-
- -
-
-kiui.op.scale_img_nhwc(x: Tensor, size: Sequence[int], mag='bilinear', min='bilinear') → Tensor[source]
-

image scaling helper.

-
-
Parameters:
-
    -

  • x (Tensor) – input image, float [N, H, W, C]

    • -

  • size (Sequence[int]) – target size, tuple of [H’, W’]

    • -

  • mag (str, optional) – upscale interpolation mode. Defaults to ‘bilinear’.

    • -

  • min (str, optional) – downscale interpolation mode. Defaults to ‘bilinear’.

    • -
-
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Returns:
-

rescaled image, float [N, H’, W’, C]

-
-
Return type:
-

Tensor

-
-
-
- -
-
-kiui.op.scale_img_hwc(x: Tensor, size: Sequence[int], mag='bilinear', min='bilinear') → Tensor[source]
-

image scaling helper.

-
-
Parameters:
-
    -

  • x (Tensor) – input image, float [H, W, C]

    • -

  • size (Sequence[int]) – target size, tuple of [H’, W’]

    • -

  • mag (str, optional) – upscale interpolation mode. Defaults to ‘bilinear’.

    • -

  • min (str, optional) – downscale interpolation mode. Defaults to ‘bilinear’.

    • -
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Returns:
-

rescaled image, float [H’, W’, C]

-
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Return type:
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Tensor

-
-
-
- -
-
-kiui.op.scale_img_nhw(x: Tensor, size: Sequence[int], mag='bilinear', min='bilinear') → Tensor[source]
-

image scaling helper.

-
-
Parameters:
-
    -

  • x (Tensor) – input image, float [N, H, W]

    • -

  • size (Sequence[int]) – target size, tuple of [H’, W’]

    • -

  • mag (str, optional) – upscale interpolation mode. Defaults to ‘bilinear’.

    • -

  • min (str, optional) – downscale interpolation mode. Defaults to ‘bilinear’.

    • -
-
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Returns:
-

rescaled image, float [N, H’, W’]

-
-
Return type:
-

Tensor

-
-
-
- -
-
-kiui.op.scale_img_hw(x: Tensor, size: Sequence[int], mag='bilinear', min='bilinear') → Tensor[source]
-

image scaling helper.

-
-
Parameters:
-
    -

  • x (Tensor) – input image, float [H, W]

    • -

  • size (Sequence[int]) – target size, tuple of [H’, W’]

    • -

  • mag (str, optional) – upscale interpolation mode. Defaults to ‘bilinear’.

    • -

  • min (str, optional) – downscale interpolation mode. Defaults to ‘bilinear’.

    • -
-
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Returns:
-

rescaled image, float [H’, W’]

-
-
Return type:
-

Tensor

-
-
-
- -
-
-kiui.op.uv_padding(image: Tensor | ndarray, mask: Tensor | ndarray, padding: int | None = None, backend: Literal[‘knn’, ‘cv2’] = 'knn')[source]
-

padding the uv-space texture image to avoid seam artifacts in mipmaps.

-
-
Parameters:
-
    -

  • image (Union[Tensor, ndarray]) – texture image, float, [H, W, C] in [0, 1].

    • -

  • mask (Union[Tensor, ndarray]) – valid uv region, bool, [H, W].

    • -

  • padding (int, optional) – padding size into the unmasked region. Defaults to 0.1 * max(H, W).

    • -

  • backend (Literal['knn', 'cv2'], optional) – algorithm backend, knn is faster. Defaults to ‘knn’.

    • -
-
-
Returns:
-

padded texture image. float, [H, W, C].

-
-
Return type:
-

Union[Tensor, ndarray]

-
-
-
- -
-
-kiui.op.recenter(image: ndarray, mask: ndarray, border_ratio: float = 0.2) → ndarray[source]
-

recenter an image to leave some empty space at the image border.

-
-
Parameters:
-
    -

  • image (ndarray) – input image, float/uint8 [H, W, 3/4]

    • -

  • mask (ndarray) – alpha mask, bool [H, W]

    • -

  • border_ratio (float, optional) – border ratio, image will be resized to (1 - border_ratio). Defaults to 0.2.

    • -
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Returns:
-

output image, float/uint8 [H, W, 3/4]

-
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Return type:
-

ndarray

-
-
-
- +
+

API

@@ -601,22 +330,7 @@

Operators @@ -628,7 +342,7 @@

Operators +

diff --git a/py-modindex/index.html b/py-modindex/index.html deleted file mode 100644 index f6024e9..0000000 --- a/py-modindex/index.html +++ /dev/null @@ -1,357 +0,0 @@ - - - - - - - Python Module Index - kiui 0.2.10 documentation - - - - - - - - - - - - - - - - - - Contents - - - - - - Menu - - - - - - - - Expand - - - - - - Light mode - - - - - - - - - - - - - - Dark mode - - - - - - - Auto light/dark, in light mode - - - - - - - - - - - - - - - Auto light/dark, in dark mode - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-
- -
-
-
kiui 0.2.10 documentation
 -
-
-
- -
- -
-
- -
-
-
- - - - - Back to top - -
-
- -
- -
- -
-
- -
- - -
-
-
-
- Copyright © 2024, kiui -
- Made with Sphinx and @pradyunsg's - - Furo - -
-
- -
-
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-
- - - - - - - - \ No newline at end of file diff --git a/remesh/index.html b/remesh/index.html index dc57863..44378c8 100644 --- a/remesh/index.html +++ b/remesh/index.html @@ -5,12 +5,12 @@ - - Remesh - kiui 0.2.10 documentation + + Remesh - kiui 0.2.12 documentation - + - + @@ -168,7 +168,7 @@
-
kiui 0.2.10 documentation
 +
kiui 0.2.12 documentation
@@ -332,7 +332,7 @@

Remesh

-
+ diff --git a/render/index.html b/render/index.html index 93a3768..c597652 100644 --- a/render/index.html +++ b/render/index.html @@ -5,12 +5,12 @@ - - Mesh Renderer - kiui 0.2.10 documentation + + Mesh Renderer - kiui 0.2.12 documentation - + - + @@ -168,7 +168,7 @@
-
kiui 0.2.10 documentation
 +
kiui 0.2.12 documentation
@@ -383,7 +383,7 @@

Examples

-
+ diff --git a/search/index.html b/search/index.html index b12fee4..eb09c77 100644 --- a/search/index.html +++ b/search/index.html @@ -5,12 +5,12 @@ - + -Search - kiui 0.2.10 documentation - +Search - kiui 0.2.12 documentation + - + @@ -168,7 +168,7 @@
-
kiui 0.2.10 documentation
 +
kiui 0.2.12 documentation
@@ -303,7 +303,7 @@
-
+ diff --git a/searchindex.js b/searchindex.js index f83c0f1..350eaca 100644 --- a/searchindex.js +++ b/searchindex.js @@ -1 +1 @@ -Search.setIndex({"alltitles": {"API": [[1, "api"], [3, "module-kiui.mesh"], [6, "module-kiui.op"], [9, "module-kiui.utils"], [10, "module-kiui.vis"]], "Blender Scripts": [[0, null]], "Camera": [[1, null]], "Camera pose coordinate systems": [[1, "camera-pose-coordinate-systems"]], "Examples": [[3, "examples"], [8, "examples"], [9, "examples"]], "Features": [[2, "features"]], "Grid-put": [[4, "grid-put"]], "Installation": [[2, "installation"]], "Kiuikit": [[2, null]], "Mesh": [[3, null]], "Mesh Renderer": [[8, null]], "Misc API": [[4, null]], "Misc CLI": [[5, null]], "Operators": [[6, null]], "Our camera convention": [[1, "our-camera-convention"]], "Remesh": [[7, null]], "Super-resolution": [[4, "super-resolution"]], "Usage": [[5, "usage"], [8, "usage"]], "Utils": [[9, null]], "Visualization": [[10, null]], "World coordinate systems": [[1, 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"kiui.cam.OrbitCamera.from_angle", false]], "get_perspective() (in module kiui.cam)": [[1, "kiui.cam.get_perspective", false]], "get_rays() (in module kiui.cam)": [[1, "kiui.cam.get_rays", false]], "grid_put() (in module kiui.grid_put)": [[4, "kiui.grid_put.grid_put", false]], "intrinsics (kiui.cam.orbitcamera property)": [[1, "kiui.cam.OrbitCamera.intrinsics", false]], "inverse_sigmoid() (in module kiui.op)": [[6, "kiui.op.inverse_sigmoid", false]], "inverse_softplus() (in module kiui.op)": [[6, "kiui.op.inverse_softplus", false]], "is_format() (in module kiui.utils)": [[9, "kiui.utils.is_format", false]], "kiui.cam": [[1, "module-kiui.cam", false]], "kiui.mesh": [[3, "module-kiui.mesh", false]], "kiui.mesh_utils": [[3, "module-kiui.mesh_utils", false]], "kiui.op": [[6, "module-kiui.op", false]], "kiui.utils": [[9, "module-kiui.utils", false]], "kiui.vis": [[10, "module-kiui.vis", false]], "laplacian_smooth_loss() (in module kiui.mesh_utils)": [[3, 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"our": 1, "pose": 1, "put": 4, "remesh": 7, "render": 8, "resolut": 4, "script": 0, "super": 4, "system": 1, "usag": [5, 8], "util": 9, "visual": 10, "world": 1}}) \ No newline at end of file diff --git a/utils/index.html b/utils/index.html index cda0abf..c6b8e37 100644 --- a/utils/index.html +++ b/utils/index.html @@ -5,12 +5,12 @@ - - Utils - kiui 0.2.10 documentation + + Utils - kiui 0.2.12 documentation - + - + @@ -168,7 +168,7 @@
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kiui 0.2.10 documentation
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kiui 0.2.12 documentation
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Examples

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API

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-kiui.utils.lo(*xs, verbose=0)[source]
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inspect array like objects and report statistics.

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  • xs (Any) – array like objects to inspect.

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  • verbose (int, optional) – level of verbosity, set to 1 to report mean and std, 2 to print the content. Defaults to 0.

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-kiui.utils.seed_everything(seed=42, verbose=False, strict=False)[source]
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auto set seed for random, numpy and torch.

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  • seed (int, optional) – random seed. Defaults to 42.

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  • verbose (bool, optional) – whether to report each seed setting. Defaults to False.

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  • strict (bool, optional) – whether to use strict deterministic mode for better torch reproduction. Defaults to False.

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-kiui.utils.read_json(path)[source]
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load a json file.

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path (str) – path to json file.

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json content.

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dict

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-kiui.utils.write_json(path, x)[source]
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write a json file.

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  • path (str) – path to write json file.

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  • x (dict) – dict to write.

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-kiui.utils.read_pickle(path)[source]
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read a pickle file.

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path (str) – path to pickle file.

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pickle content.

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Any

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-kiui.utils.write_pickle(path, x)[source]
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write a pickle file.

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  • path (str) – path to write pickle file.

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  • x (Any) – content to write.

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-kiui.utils.read_image(path: str, mode: Literal[‘float’, ‘uint8’, ‘pil’, ‘torch’, ‘tensor’] = 'float', order: Literal[‘RGB’, ‘RGBA’, ‘BGR’, ‘BGRA’] = 'RGB')[source]
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read an image file into various formats and color mode.

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  • path (str) – path to the image file.

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  • mode (Literal[“float”, “uint8”, “pil”, “torch”, “tensor”], optional) – returned image format. Defaults to “float”. -float: float32 numpy array, range [0, 1]; -uint8: uint8 numpy array, range [0, 255]; -pil: PIL image; -torch/tensor: float32 torch tensor, range [0, 1];

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  • order (Literal[“RGB”, “RGBA”, “BGR”, “BGRA”], optional) – channel order. Defaults to “RGB”.

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Note

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By default this function will convert RGBA image to white-background RGB image. Use order="RGBA" to keep the alpha channel.

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Returns:
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the image array.

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Union[np.ndarray, PIL.Image, torch.Tensor]

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-kiui.utils.write_image(path: str, img: Tensor | ndarray | Image, order: Literal[‘RGB’, ‘BGR’] = 'RGB')[source]
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write an image to various formats.

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  • path (str) – path to write the image file.

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  • img (Union[torch.Tensor, np.ndarray, PIL.Image.Image]) – image to write.

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-kiui.utils.load_file_from_url(url, model_dir=None, progress=True, file_name=None)[source]
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Load file form http url, will download models if necessary.

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  • url (str) – URL to be downloaded.

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  • model_dir (str) – The path to save the downloaded model. Should be a full path. If None, use pytorch hub_dir. -Default: None.

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  • progress (bool) – Whether to show the download progress. Default: True.

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  • file_name (str) – The downloaded file name. If None, use the file name in the url. Default: None.

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Returns:
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The path to the downloaded file.

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str

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-kiui.utils.is_format(f: str, format: Sequence[str])[source]
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if a file’s extension is in a set of format

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  • f (str) – file name.

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  • format (Sequence[str]) – set of extensions (both ‘.jpg’ or ‘jpg’ is ok).

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if the file’s extension is in the set.

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bool

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-kiui.utils.batch_process_files(process_fn, path, out_path, overwrite=False, in_format=['.jpg', '.jpeg', '.png'], out_format=None, image_mode='uint8', image_color_order='RGB', **kwargs)[source]
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simple function wrapper to batch processing files.

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  • process_fn (Callable) – process function.

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  • path (str) – path to a file or a directory containing the files to process.

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  • out_path (str) – output path of a file or a directory.

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  • overwrite (bool, optional) – whether to overwrite existing results. Defaults to False.

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  • in_format (list, optional) – input file formats. Defaults to [“.jpg”, “.jpeg”, “.png”].

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  • out_format (str, optional) – output file format. Defaults to None.

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  • image_mode (str, optional) – for images, the mode to read. Defaults to ‘uint8’.

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  • image_color_order (str, optional) – for images, the color order. Defaults to “RGB”.

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API

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Examples @@ -566,7 +353,7 @@

Examples - + diff --git a/vis/index.html b/vis/index.html index c014708..fa5c836 100644 --- a/vis/index.html +++ b/vis/index.html @@ -5,12 +5,12 @@ - - Visualization - kiui 0.2.10 documentation + + Visualization - kiui 0.2.12 documentation - + - + @@ -168,7 +168,7 @@
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kiui 0.2.10 documentation
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kiui 0.2.12 documentation
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Visualization

Visualization tools.

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API

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-kiui.vis.map_color(value: ndarray, cmap_name: str = 'viridis', vmin: float | None = None, vmax: float | None = None)[source]
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map a 1D array to continuous color.

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Returns:
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array of color, [N, 3] in [0, 1]

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ndarray

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-kiui.vis.plot_image(*xs, normalize=False, save=False, prefix='kiui_vis_plot_image')[source]
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sequentially plot provided images, optionally save to current dir.

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Parameters:
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  • xs (Sequence[Union[torch.Tensor, numpy.ndarray]]) – can be uint8 or float32. -[B, 4/3/1, H, W], [B, H, W, 4/3/1], [4/3/1, H, W], [H, W, 4/3/1], [H, W] torch.Tensor or numpy.ndarray

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  • normalize (bool, optional) – whether to renormalize the image to [0, 1]. Defaults to False.

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  • save (bool, optional) – whether to save the image to current dir (in case the plot cannot be showed, like in vscode remote). Defaults to False.

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  • prefix (str, optional) – image save name prefix if save=True.

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-kiui.vis.plot_matrix(*xs)[source]
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visualize some 2D matrix, different from kiui.vis.plot_image, this will keep the original range and plot channel-by-channel.

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Parameters:
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xs (Sequence[Union[torch.Tensor, numpy.ndarray]]) – [B, C, H, W], [C, H, W], or [H, W] torch.Tensor or numpy.ndarray

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-kiui.vis.plot_pointcloud(pc, color=None)[source]
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plot point cloud.

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Parameters:
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  • pc (ndarray) – point cloud positions, float [N, 3].

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  • color (ndarray, optional) – point cloud colors, float/uint8 [N, 3/4]. Defaults to None.

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Note

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This function requires a desktop (cannot be forwarded by ssh)!

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-kiui.vis.plot_poses(poses, size=0.1, bound=1, points=None, mesh=None, opengl=True)[source]
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plot camera poses.

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Parameters:
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  • poses (ndarray) – camera poses, float [N, 4, 4].

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  • size (float, optional) – line width. Defaults to 0.1.

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  • bound (int, optional) – bounding box bound. Defaults to 1.

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  • points (ndarray, optional) – also draw point clouds, float [M, 3]. Defaults to None.

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  • mesh (trimesh.Trimesh, optional) – also draw mesh. Defaults to None.

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  • opengl (bool, optional) – use OpenGL camera convention. Defaults to True.

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Note

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This function requires a desktop (cannot be forwarded by ssh)!

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+

API

@@ -420,14 +330,7 @@

Visualization @@ -439,7 +342,7 @@

Visualization +