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| # | |
| # Toyota Motor Europe NV/SA and its affiliated companies retain all intellectual | |
| # property and proprietary rights in and to this software and related documentation. | |
| # Any commercial use, reproduction, disclosure or distribution of this software and | |
| # related documentation without an express license agreement from Toyota Motor Europe NV/SA | |
| # is strictly prohibited. | |
| # | |
| import torch | |
| def get_mtl_content(tex_fname): | |
| return f'newmtl Material\nmap_Kd {tex_fname}\n' | |
| def get_obj_content(vertices, faces, uv_coordinates=None, uv_indices=None, mtl_fname=None): | |
| obj = ('# Generated with multi-view-head-tracker\n') | |
| if mtl_fname is not None: | |
| obj += f'mtllib {mtl_fname}\n' | |
| obj += 'usemtl Material\n' | |
| # Write the vertices | |
| for vertex in vertices: | |
| obj += f"v {vertex[0]} {vertex[1]} {vertex[2]}\n" | |
| # Write the UV coordinates | |
| if uv_coordinates is not None: | |
| for uv in uv_coordinates: | |
| obj += f"vt {uv[0]} {uv[1]}\n" | |
| # Write the faces with UV indices | |
| if uv_indices is not None: | |
| for face, uv_indices in zip(faces, uv_indices): | |
| obj += f"f {face[0]+1}/{uv_indices[0]+1} {face[1]+1}/{uv_indices[1]+1} {face[2]+1}/{uv_indices[2]+1}\n" | |
| else: | |
| for face in faces: | |
| obj += f"f {face[0]+1} {face[1]+1} {face[2]+1}\n" | |
| return obj | |
| def normalize_image_points(u, v, resolution): | |
| """ | |
| normalizes u, v coordinates from [0 ,image_size] to [-1, 1] | |
| :param u: | |
| :param v: | |
| :param resolution: | |
| :return: | |
| """ | |
| u = 2 * (u - resolution[1] / 2.0) / resolution[1] | |
| v = 2 * (v - resolution[0] / 2.0) / resolution[0] | |
| return u, v | |
| def face_vertices(vertices, faces): | |
| """ | |
| :param vertices: [batch size, number of vertices, 3] | |
| :param faces: [batch size, number of faces, 3] | |
| :return: [batch size, number of faces, 3, 3] | |
| """ | |
| assert vertices.ndimension() == 3 | |
| assert faces.ndimension() == 3 | |
| assert vertices.shape[0] == faces.shape[0] | |
| assert vertices.shape[2] == 3 | |
| assert faces.shape[2] == 3 | |
| bs, nv = vertices.shape[:2] | |
| bs, nf = faces.shape[:2] | |
| device = vertices.device | |
| faces = faces + (torch.arange(bs, dtype=torch.int32).to(device) * nv)[:, None, None] | |
| vertices = vertices.reshape((bs * nv, 3)) | |
| # pytorch only supports long and byte tensors for indexing | |
| return vertices[faces.long()] | |