models/ISOMER/scripts/utils.py

import torch
import numpy as np
from PIL import Image
import pymeshlab
import pymeshlab as ml
from pymeshlab import PercentageValue
from pytorch3d.renderer import TexturesVertex
from pytorch3d.structures import Meshes
import torch
import torch.nn.functional as F
from typing import List, Tuple
from PIL import Image
import trimesh

EPSILON = 1e-8

def load_mesh_with_trimesh(file_name, file_type=None):
    import trimesh
    mesh: trimesh.Trimesh = trimesh.load(file_name, file_type=file_type)
    if isinstance(mesh, trimesh.Scene):
        assert len(mesh.geometry) > 0
        # save to obj first and load again to avoid offset issue
        from io import BytesIO
        with BytesIO() as f:
            mesh.export(f, file_type="obj")
            f.seek(0)
            mesh = trimesh.load(f, file_type="obj")
        if isinstance(mesh, trimesh.Scene):
            # we lose texture information here
            mesh = trimesh.util.concatenate(
                tuple(trimesh.Trimesh(vertices=g.vertices, faces=g.faces)
                    for g in mesh.geometry.values()))
    assert isinstance(mesh, trimesh.Trimesh)

    vertices = torch.from_numpy(mesh.vertices).T
    faces = torch.from_numpy(mesh.faces).T
    colors = None
    if mesh.visual is not None:
        if hasattr(mesh.visual, 'vertex_colors'):
            colors = torch.from_numpy(mesh.visual.vertex_colors)[..., :3].T / 255.
    if colors is None:
        colors = torch.ones_like(vertices) * 0.5
    return vertices, faces, colors

def meshlab_mesh_to_py3dmesh(mesh: pymeshlab.Mesh) -> Meshes:
    verts = torch.from_numpy(mesh.vertex_matrix()).float()
    faces = torch.from_numpy(mesh.face_matrix()).long()
    colors = torch.from_numpy(mesh.vertex_color_matrix()[..., :3]).float()
    textures = TexturesVertex(verts_features=[colors])
    return Meshes(verts=[verts], faces=[faces], textures=textures)


def py3dmesh_to_meshlab_mesh(meshes: Meshes) -> pymeshlab.Mesh:
    colors_in = F.pad(meshes.textures.verts_features_packed().cpu().float(), [0,1], value=1).numpy().astype(np.float64)
    m1 = pymeshlab.Mesh(
        vertex_matrix=meshes.verts_packed().cpu().float().numpy().astype(np.float64),
        face_matrix=meshes.faces_packed().cpu().long().numpy().astype(np.int32),
        v_normals_matrix=meshes.verts_normals_packed().cpu().float().numpy().astype(np.float64),
        v_color_matrix=colors_in)
    return m1


def to_pyml_mesh(vertices,faces):
    m1 = pymeshlab.Mesh(
        vertex_matrix=vertices.cpu().float().numpy().astype(np.float64),
        face_matrix=faces.cpu().long().numpy().astype(np.int32),
    )
    return m1


def to_py3d_mesh(vertices, faces, normals=None):
    from pytorch3d.structures import Meshes
    from pytorch3d.renderer.mesh.textures import TexturesVertex
    mesh = Meshes(verts=[vertices], faces=[faces], textures=None)
    if normals is None:
        normals = mesh.verts_normals_packed()
    # set normals as vertext colors
    mesh.textures = TexturesVertex(verts_features=[normals / 2 + 0.5])
    return mesh


def from_py3d_mesh(mesh):
    return mesh.verts_list()[0], mesh.faces_list()[0], mesh.textures.verts_features_packed()

def rotate_normalmap_by_angle(normal_map: np.ndarray, angle: float):
    """
    rotate along y-axis
    normal_map: np.array, shape=(H, W, 3) in [-1, 1]
    angle: float, in degree
    """
    angle = angle / 180 * np.pi
    R = np.array([[np.cos(angle), 0, np.sin(angle)], [0, 1, 0], [-np.sin(angle), 0, np.cos(angle)]])
    return np.dot(normal_map.reshape(-1, 3), R.T).reshape(normal_map.shape)

# from view coord to front view world coord
def rotate_normals(normal_pils, return_types='np', rotate_direction=1) -> np.ndarray:  # [0, 255]
    n_views = len(normal_pils)
    ret = []
    for idx, rgba_normal in enumerate(normal_pils):
        # rotate normal
        normal_np = np.array(rgba_normal)[:, :, :3] / 255           # in [-1, 1]
        alpha_np = np.array(rgba_normal)[:, :, 3] / 255             # in [0, 1]
        normal_np = normal_np * 2 - 1
        normal_np = rotate_normalmap_by_angle(normal_np, rotate_direction * idx * (360 / n_views))
        normal_np = (normal_np + 1) / 2
        normal_np = normal_np * alpha_np[..., None]                 # make bg black
        rgba_normal_np = np.concatenate([normal_np * 255, alpha_np[:, :, None] * 255] , axis=-1)
        if return_types == 'np':
            ret.append(rgba_normal_np)
        elif return_types == 'pil':
            ret.append(Image.fromarray(rgba_normal_np.astype(np.uint8)))
        else:
            raise ValueError(f"return_types should be 'np' or 'pil', but got {return_types}")
    return ret


def rotate_normalmap_by_angle_torch(normal_map, angle):
    """
    rotate along y-axis
    normal_map: torch.Tensor, shape=(H, W, 3) in [-1, 1], device='cuda'
    angle: float, in degree
    """
    angle = torch.tensor(angle / 180 * np.pi).to(normal_map)
    R = torch.tensor([[torch.cos(angle), 0, torch.sin(angle)], 
                      [0, 1, 0], 
                      [-torch.sin(angle), 0, torch.cos(angle)]]).to(normal_map)
    return torch.matmul(normal_map.view(-1, 3), R.T).view(normal_map.shape)

def do_rotate(rgba_normal, angle):
    rgba_normal = torch.from_numpy(rgba_normal).float().cuda() / 255
    rotated_normal_tensor = rotate_normalmap_by_angle_torch(rgba_normal[..., :3] * 2 - 1, angle)
    rotated_normal_tensor = (rotated_normal_tensor + 1) / 2
    rotated_normal_tensor = rotated_normal_tensor * rgba_normal[:, :, [3]]    # make bg black
    rgba_normal_np = torch.cat([rotated_normal_tensor * 255, rgba_normal[:, :, [3]] * 255], dim=-1).cpu().numpy()
    return rgba_normal_np

def rotate_normals_torch(normal_pils, return_types='np', rotate_direction=1):
    n_views = len(normal_pils)
    ret = []
    for idx, rgba_normal in enumerate(normal_pils):
        # rotate normal
        angle = rotate_direction * idx * (360 / n_views)
        rgba_normal_np = do_rotate(np.array(rgba_normal), angle)
        if return_types == 'np':
            ret.append(rgba_normal_np)
        elif return_types == 'pil':
            ret.append(Image.fromarray(rgba_normal_np.astype(np.uint8)))
        else:
            raise ValueError(f"return_types should be 'np' or 'pil', but got {return_types}")
    return ret

def change_bkgd(img_pils, new_bkgd=(0., 0., 0.)):
    ret = []
    new_bkgd = np.array(new_bkgd).reshape(1, 1, 3)
    for rgba_img in img_pils:
        img_np = np.array(rgba_img)[:, :, :3] / 255
        alpha_np = np.array(rgba_img)[:, :, 3] / 255
        ori_bkgd = img_np[:1, :1]
        # color = ori_color * alpha + bkgd * (1-alpha)
        # ori_color = (color - bkgd * (1-alpha)) / alpha
        alpha_np_clamp = np.clip(alpha_np, 1e-6, 1) # avoid divide by zero
        ori_img_np = (img_np - ori_bkgd * (1 - alpha_np[..., None])) / alpha_np_clamp[..., None]
        img_np = np.where(alpha_np[..., None] > 0.05, ori_img_np * alpha_np[..., None] + new_bkgd * (1 - alpha_np[..., None]), new_bkgd)
        rgba_img_np = np.concatenate([img_np * 255, alpha_np[..., None] * 255], axis=-1)
        ret.append(Image.fromarray(rgba_img_np.astype(np.uint8)))
    return ret

def change_bkgd_to_normal(normal_pils) -> List[Image.Image]:
    n_views = len(normal_pils)
    ret = []
    for idx, rgba_normal in enumerate(normal_pils):
        # calcuate background normal
        target_bkgd = rotate_normalmap_by_angle(np.array([[[0., 0., 1.]]]), idx * (360 / n_views))
        normal_np = np.array(rgba_normal)[:, :, :3] / 255           # in [-1, 1]
        alpha_np = np.array(rgba_normal)[:, :, 3] / 255             # in [0, 1]
        normal_np = normal_np * 2 - 1
        old_bkgd = normal_np[:1,:1]
        normal_np[alpha_np > 0.05] = (normal_np[alpha_np > 0.05] - old_bkgd * (1 - alpha_np[alpha_np > 0.05][..., None])) / alpha_np[alpha_np > 0.05][..., None]
        normal_np = normal_np * alpha_np[..., None] + target_bkgd * (1 - alpha_np[..., None])
        normal_np = (normal_np + 1) / 2
        rgba_normal_np = np.concatenate([normal_np * 255, alpha_np[..., None] * 255] , axis=-1)
        ret.append(Image.fromarray(rgba_normal_np.astype(np.uint8)))
    return ret


def fix_vert_color_glb(mesh_path):
    from pygltflib import GLTF2, Material, PbrMetallicRoughness
    obj1 = GLTF2().load(mesh_path)
    obj1.meshes[0].primitives[0].material = 0
    obj1.materials.append(Material(
        pbrMetallicRoughness = PbrMetallicRoughness(
            baseColorFactor = [1.0, 1.0, 1.0, 1.0],
            metallicFactor = 0.,
            roughnessFactor = 1.0,
        ),
        emissiveFactor = [0.0, 0.0, 0.0],
        doubleSided = True,
    ))
    obj1.save(mesh_path)


def srgb_to_linear(c_srgb):
    c_linear = np.where(c_srgb <= 0.04045, c_srgb / 12.92, ((c_srgb + 0.055) / 1.055) ** 2.4)
    return c_linear.clip(0, 1.)


def save_py3dmesh_with_trimesh_fast(meshes: Meshes, save_glb_path, apply_sRGB_to_LinearRGB=True):
    # convert from pytorch3d meshes to trimesh mesh
    vertices = meshes.verts_packed().cpu().float().numpy()
    triangles = meshes.faces_packed().cpu().long().numpy()
    np_color = meshes.textures.verts_features_packed().cpu().float().numpy()
    if save_glb_path.endswith(".glb"):
        # rotate 180 along +Y
        vertices[:, [0, 2]] = -vertices[:, [0, 2]]

    if apply_sRGB_to_LinearRGB:
        np_color = srgb_to_linear(np_color)
    assert vertices.shape[0] == np_color.shape[0]
    assert np_color.shape[1] == 3
    assert 0 <= np_color.min() and np_color.max() <= 1, f"min={np_color.min()}, max={np_color.max()}"
    mesh = trimesh.Trimesh(vertices=vertices, faces=triangles, vertex_colors=np_color)
    mesh.remove_unreferenced_vertices()
    # save mesh
    mesh.export(save_glb_path)
    if save_glb_path.endswith(".glb"):
        fix_vert_color_glb(save_glb_path)


def save_glb_and_video(save_mesh_prefix: str, meshes: Meshes, with_timestamp=True, dist=3.5, azim_offset=180, resolution=512, fov_in_degrees=1 / 1.15, cam_type="ortho", view_padding=60, export_video=True) -> Tuple[str, str]:
    import time
    if '.' in save_mesh_prefix:
        save_mesh_prefix = ".".join(save_mesh_prefix.split('.')[:-1])
    if with_timestamp:
        save_mesh_prefix = save_mesh_prefix + f"_{int(time.time())}"
    ret_mesh = save_mesh_prefix + ".glb"
    # optimizied version
    save_py3dmesh_with_trimesh_fast(meshes, ret_mesh)
    return ret_mesh, None


def simple_clean_mesh(pyml_mesh: ml.Mesh, apply_smooth=True, stepsmoothnum=1, apply_sub_divide=False, sub_divide_threshold=0.25):
    ms = ml.MeshSet()
    ms.add_mesh(pyml_mesh, "cube_mesh")
    
    if apply_smooth:
        ms.apply_filter("apply_coord_laplacian_smoothing", stepsmoothnum=stepsmoothnum, cotangentweight=False)
    if apply_sub_divide:    # 5s, slow
        ms.apply_filter("meshing_repair_non_manifold_vertices")
        ms.apply_filter("meshing_repair_non_manifold_edges", method='Remove Faces')
        ms.apply_filter("meshing_surface_subdivision_loop", iterations=2, threshold=PercentageValue(sub_divide_threshold))
    return meshlab_mesh_to_py3dmesh(ms.current_mesh())


def expand2square(pil_img, background_color):
    width, height = pil_img.size
    if width == height:
        return pil_img
    elif width > height:
        result = Image.new(pil_img.mode, (width, width), background_color)
        result.paste(pil_img, (0, (width - height) // 2))
        return result
    else:
        result = Image.new(pil_img.mode, (height, height), background_color)
        result.paste(pil_img, ((height - width) // 2, 0))
        return result



def init_target(img_pils, new_bkgd=(0., 0., 0.), device="cuda"):
    new_bkgd = torch.tensor(new_bkgd, dtype=torch.float32).view(1, 1, 3).to(device)
    
    imgs = torch.stack([torch.from_numpy(np.array(img, dtype=np.float32)) for img in img_pils]).to(device) / 255
    img_nps = imgs[..., :3]
    alpha_nps = imgs[..., 3]
    ori_bkgds = img_nps[:, :1, :1]
    
    alpha_nps_clamp = torch.clamp(alpha_nps, 1e-6, 1)
    ori_img_nps = (img_nps - ori_bkgds * (1 - alpha_nps.unsqueeze(-1))) / alpha_nps_clamp.unsqueeze(-1)
    ori_img_nps = torch.clamp(ori_img_nps, 0, 1)
    img_nps = torch.where(alpha_nps.unsqueeze(-1) > 0.05, ori_img_nps * alpha_nps.unsqueeze(-1) + new_bkgd * (1 - alpha_nps.unsqueeze(-1)), new_bkgd)

    rgba_img_np = torch.cat([img_nps, alpha_nps.unsqueeze(-1)], dim=-1)
    return rgba_img_np



def rotation_matrix_axis_angle(axis, angle, device='cuda'):
    """
    Return the rotation matrix associated with counterclockwise rotation about
    the given axis by angle degrees, using PyTorch.
    """
    if type(axis) != torch.tensor:
        axis = torch.tensor(axis, device=device)
    axis = axis.float().to(device)
    if type(angle) != torch.tensor:
        angle = torch.tensor(angle, device=device)
    angle = angle.float().to(device)

    theta = angle * torch.pi / 180.0
    axis = torch.tensor(axis, dtype=torch.float32)
    if torch.dot(axis, axis) > 0:
        denom = torch.sqrt(torch.dot(axis, axis))
        demon = torch.where(denom == 0, torch.tensor(EPSILON).to(denom.device), denom)
        axis = axis / torch.sqrt(demon)
        a = torch.cos(theta / 2.0)
        b, c, d = -axis[0] * torch.sin(theta / 2.0), -axis[1] * torch.sin(theta / 2.0), -axis[2] * torch.sin(theta / 2.0)

        aa, bb, cc, dd = a*a, b*b, c*c, d*d
        bc, ad, ac, ab, bd, cd = b*c, a*d, a*c, a*b, b*d, c*d
        return torch.stack([
            torch.stack([aa+bb-cc-dd, 2*(bc+ad), 2*(bd-ac)]),
            torch.stack([2*(bc-ad), aa+cc-bb-dd, 2*(cd+ab)]),
            torch.stack([2*(bd+ac), 2*(cd-ab), aa+dd-bb-cc])
        ])
    else:
        return torch.eye(3)



def normal_rotation_img2img_angle_axis(image, angle, axis=None, device='cuda'):
    """
    Rotate an image by a given angle around a given axis using PyTorch.
    
    Args:
        image: Input Image to rotate.
        angle: Rotation angle in degrees.
        axis: Rotation axis as a array of 3 floats.

    Returns:
        Image: Rotated Image.
    """
    if axis is None:
        axis = [0,1,0]
    axis = torch.tensor(axis, device=device)
    
    
    if type(image) == Image.Image:
        image_array = torch.tensor(np.array(image, dtype='float32'))
    else:
        image_array = image
    image_array = image_array.to(device)

    if type(angle) != torch.Tensor:
        angle = torch.tensor(angle)
    angle = angle.to(device)

    if image_array.shape[2] == 4:
        rgb_array, alpha_array = image_array[:, :, :3], image_array[:, :, 3]
    else:
        rgb_array = image_array
        alpha_array = None

    rgb_array =  rgb_array / 255.0 - 0.5

    rgb_array = rgb_array.permute(2, 0, 1)

    rotated_tensor = apply_rotation_angle_axis(rgb_array.unsqueeze(0), axis, torch.tensor([angle], device=rgb_array.device))


    rotated_array = rotated_tensor.squeeze().permute(1, 2, 0)

    rotated_array = (rotated_array/2 + 0.5) * 255

    if alpha_array is not None:
        rotated_array = torch.cat((rotated_array, alpha_array.unsqueeze(2)), dim=2)
            
    rotated_array_uint8 = np.array(rotated_array.detach().cpu()).astype('uint8')

    rotated_normal = Image.fromarray(rotated_array_uint8)
    
    return rotated_normal

def normal_rotation_img2img_c2w(image, c2w, device='cuda'):

    if type(image) != torch.Tensor:
        image_array = torch.tensor(np.array(image, dtype='float32'))
    else:
        image_array = image
    
    
    image_array = image_array.to(device)

    if image_array.shape[2] == 4:
        rgb_array, alpha_array = image_array[:, :, :3], image_array[:, :, 3]
    else:
        rgb_array = image_array
        alpha_array = None

    rgb_array =  rgb_array / 255.0 - 0.5

    rotation_matrix = c2w
    
    rotated_tensor = transform_normals_R(rgb_array, rotation_matrix)

    rotated_array = rotated_tensor.squeeze().permute(1, 2, 0)
    rotated_array = (rotated_array/2 + 0.5) * 255

    if alpha_array is not None:
        rotated_array = torch.cat((rotated_array, alpha_array.unsqueeze(2)), dim=2)
            
    rotated_array_uint8 = np.array(rotated_array.detach().cpu()).astype('uint8')

    rotated_normal = Image.fromarray(rotated_array_uint8)
    
    return rotated_normal

def normal_rotation_img2img_azi_ele(image, azi, ele, device='cuda'):
    """
    Rotate an image by a given angle around a given axis using PyTorch.
    
    Args:
        image: Input Image to rotate.

    Returns:
        Image: Rotated Image.
    """
    
    if type(image) == Image.Image:
        image_array = torch.tensor(np.array(image, dtype='float32'))
    else:
        image_array = image
    image_array = image_array.to(device)

    if type(azi) != torch.Tensor:
        azi = torch.tensor(azi)
    azi = azi.to(device)

    if type(ele) != torch.Tensor:
        ele = torch.tensor(ele)
    ele = ele.to(device)

    if image_array.shape[2] == 4:
        rgb_array, alpha_array = image_array[:, :, :3], image_array[:, :, 3]
    else:
        rgb_array = image_array
        alpha_array = None

    rgb_array =  rgb_array / 255.0 - 0.5

    rotation_matrix = get_rotation_matrix_azi_ele(azi, ele)
    rotated_tensor = transform_normals_R(rgb_array, rotation_matrix)

    rotated_array = rotated_tensor.squeeze().permute(1, 2, 0)

    rotated_array = (rotated_array/2 + 0.5) * 255

    if alpha_array is not None:
        rotated_array = torch.cat((rotated_array, alpha_array.unsqueeze(2)), dim=2)
            
    rotated_array_uint8 = np.array(rotated_array.detach().cpu()).astype('uint8')

    rotated_normal = Image.fromarray(rotated_array_uint8)
    
    return rotated_normal


def rotate_normal_R(image, rotation_matrix, save_addr="", device="cuda"):
    """
    Rotate a normal map by a given Rotation matrix using PyTorch.

    Args:
        image: Input Image to rotate.

    Returns:
        Image: Rotated Image.
    """

    if type(image) != torch.tensor:
        image_array = torch.tensor(np.array(image, dtype='float32'))
    else:
        image_array = image
    image_array = image_array.to(device)

    if image_array.shape[2] == 4:
        rgb_array, alpha_array = image_array[:, :, :3], image_array[:, :, 3]
    else:
        rgb_array = image_array
        alpha_array = None

    rgb_array =  rgb_array / 255.0 - 0.5

    rotated_tensor = transform_normals_R(rgb_array, rotation_matrix.to(device))

    rotated_array = rotated_tensor.squeeze().permute(1, 2, 0)

    rotated_array = (rotated_array/2 + 0.5) * 255

    if alpha_array is not None:
        rotated_array = torch.cat((rotated_array, alpha_array.unsqueeze(2)), dim=2)
            
    rotated_array_uint8 = np.array(rotated_array.detach().cpu()).astype('uint8')

    rotated_normal = Image.fromarray(rotated_array_uint8)

    if save_addr:
        rotated_normal.save(save_addr)
    return rotated_normal



def transform_normals_R(local_normals, rotation_matrix):
    assert local_normals.shape[2] ==3 ,f'local_normals.shape[2]: {local_normals.shape[2]}. only support rgb image'

    h, w = local_normals.shape[:2]
    local_normals_flat = local_normals.view(-1, 3).permute(1, 0)

    images_flat = local_normals_flat.unsqueeze(0) 
    rotation_matrices = rotation_matrix.unsqueeze(0) 
    rotated_images_flat = torch.bmm(rotation_matrices, images_flat) 
    
    rotated_images = rotated_images_flat.view(1, 3, h, w)

    norms = torch.norm(rotated_images, p=2, dim=1, keepdim=True)
    norms = torch.where(norms == 0, torch.tensor(EPSILON).to(norms.device), norms)
    normalized_images = rotated_images / norms

    return normalized_images


def manage_elevation_azimuth(ele_list, azi_list):
    """deal with cases when elevation > 90"""

    for i in range(len(ele_list)):
        elevation = ele_list[i] % 360
        azimuth = azi_list[i] % 360
        if elevation > 90 and elevation<=270:
            # when elevation is too big，camera gets to the other side
            # print(f'!!! elevation({elevation}) > 90 and <=270, set to 180-elevation, and add 180 to azimuth')
            elevation = 180 - elevation
            azimuth = azimuth + 180
            # print(f'new elevation: {elevation}, new azimuth: {azimuth}')
        
        elif elevation>270:
            # print(f'!!! elevation({elevation}) > 270, set to elevation-360, and use original azimuth')
            elevation = elevation - 360
            azimuth = azimuth
            # print(f'new elevation: {elevation}, new azimuth: {azimuth}')

        ele_list[i] = elevation
        azi_list[i] = azimuth

    return ele_list, azi_list

def get_rotation_matrix_azi_ele(azimuth, elevation):
    
    ele = elevation/180 * torch.pi
    azi = azimuth/180 * torch.pi

    Rz = torch.tensor([
        [torch.cos(azi), 0, -torch.sin(azi)],
        [0, 1, 0],
        [torch.sin(azi), 0, torch.cos(azi)],
    ]).to(azimuth.device)
    
    Re = torch.tensor([
        [1, 0, 0],
        [0, torch.cos(ele), torch.sin(ele)],
        [0, -torch.sin(ele), torch.cos(ele)],
    ]).to(elevation.device)

    return torch.matmul(Rz,Re).to(azimuth.device)
    

def rotate_vector(vector, axis, angle, device='cuda'):
    rot_matrix = rotation_matrix_axis_angle(axis, angle)
    return torch.matmul(vector.to(device).float(), rot_matrix.to(device).float())

def apply_rotation_angle_axis(image, axis, angle, device='cuda'):
    """Apply rotation to a batch of images with shape [batch_size, 3(rgb), h, w] using PyTorch.
    
    Args:
        image (torch.Tensor): Input RGB image tensor of shape [batch_size, 3, h, w]. each pixel's rgb channels refer to direction of normal (can be negative)
        axis (torch.Tensor): Rotation axis of shape [3].
        angle (torch.Tensor): Rotation angles in degrees, of shape [batch_size].
    Returns:
        torch.Tensor: Rotated image tensor of shape [batch_size, 3, h, w]. values between [-1., 1.]
    
    """

    if not isinstance(image, torch.Tensor):
        image_tensor = torch.tensor(image).to(device)
    else:
        image_tensor = image.to(device)

    if not isinstance(axis, torch.Tensor):
        axis = torch.tensor(axis)
    axis = axis.to(device)
    
    if not isinstance(angle, torch.Tensor):
        angle = torch.tensor(angle)
    angle = angle.to(device)

    batch_size, channels, h, w = image_tensor.shape
    rot_matrix = rotation_matrix_axis_angle(axis, angle)

    rotation_matrices = rot_matrix.permute(2, 0, 1)

    batch_size, c, h, w = image_tensor.shape
    images_flat = image_tensor.view(batch_size, c, h * w)

    rotated_images_flat = torch.bmm(rotation_matrices, images_flat)

    rotated_images = rotated_images_flat.view(batch_size, c, h, w)

    norms = torch.norm(rotated_images, p=2, dim=1, keepdim=True)

    norms = torch.where(norms == 0, torch.tensor(EPSILON).to(norms.device), norms)

    normalized_images = rotated_images / norms

    return normalized_images