4D-Compositional-Representation-master/lib/common.py

# import multiprocessing
import torch
from lib.utils.libkdtree import KDTree
import numpy as np
import trimesh



def random_crop_occ(points, pcl, n_kernel=3, radius=0.2):
    ''' Randomly crop point cloud.
        Args:
            points (numpy array): Query points in the occupancy volume, (n, 3)
            pcl (numpy array): Point cloud corresponding to the occupancy volume, (n, 3)
            n_kernel (int): number of the removing centers
            radius (float): radius of the sphere mask
    '''
    bbox = [pcl.min(0), pcl.max(0)]
    x = np.random.rand(n_kernel) * bbox[1][0] * 2 - bbox[1][0]
    y = np.random.rand(n_kernel) * bbox[1][1] * 2 - bbox[1][1]
    z = np.random.rand(n_kernel) * bbox[1][2] * 2 - bbox[1][2]
    centers = np.vstack([x, y, z]).transpose(1, 0)

    n_pts = points.shape[0]
    pcl_k = np.tile(points[:, None, :], [1, n_kernel, 1])
    center = np.tile(centers[None, ...], [n_pts, 1, 1])
    dist = np.linalg.norm(pcl_k - center, 2, axis=-1)
    mask = (dist > radius).sum(-1) >= n_kernel
    return mask, centers, radius


def compute_iou(occ1, occ2):
    ''' Computes the Intersection over Union (IoU) value for two sets of
    occupancy values.

    Args:
        occ1 (tensor): first set of occupancy values
        occ2 (tensor): second set of occupancy values
    '''
    occ1 = np.asarray(occ1)
    occ2 = np.asarray(occ2)

    # Put all data in second dimension
    # Also works for 1-dimensional data
    if occ1.ndim >= 2:
        occ1 = occ1.reshape(occ1.shape[0], -1)
    if occ2.ndim >= 2:
        occ2 = occ2.reshape(occ2.shape[0], -1)

    # Convert to boolean values
    occ1 = (occ1 >= 0.5)
    occ2 = (occ2 >= 0.5)

    # Compute IOU
    area_union = (occ1 | occ2).astype(np.float32).sum(axis=-1)
    area_intersect = (occ1 & occ2).astype(np.float32).sum(axis=-1)

    iou = (area_intersect / area_union)

    return iou


def chamfer_distance(points1, points2, use_kdtree=True, give_id=False):
    ''' Returns the chamfer distance for the sets of points.

    Args:
        points1 (numpy array): first point set
        points2 (numpy array): second point set
        use_kdtree (bool): whether to use a kdtree
        give_id (bool): whether to return the IDs of nearest points
    '''
    if use_kdtree:
        return chamfer_distance_kdtree(points1, points2, give_id=give_id)
    else:
        return chamfer_distance_naive(points1, points2)


def chamfer_distance_naive(points1, points2):
    ''' Naive implementation of the Chamfer distance.

    Args:
        points1 (numpy array): first point set
        points2 (numpy array): second point set    
    '''
    assert(points1.size() == points2.size())
    batch_size, T, _ = points1.size()

    points1 = points1.view(batch_size, T, 1, 3)
    points2 = points2.view(batch_size, 1, T, 3)

    distances = (points1 - points2).pow(2).sum(-1)

    chamfer1 = distances.min(dim=1)[0].mean(dim=1)
    chamfer2 = distances.min(dim=2)[0].mean(dim=1)

    chamfer = chamfer1 + chamfer2
    return chamfer


def chamfer_distance_kdtree(points1, points2, give_id=False):
    ''' KD-tree based implementation of the Chamfer distance.

    Args:
        points1 (numpy array): first point set
        points2 (numpy array): second point set
        give_id (bool): whether to return the IDs of the nearest points
    '''
    # Points have size batch_size x T x 3
    batch_size = points1.size(0)

    # First convert points to numpy
    points1_np = points1.detach().cpu().numpy()
    points2_np = points2.detach().cpu().numpy()

    # Get list of nearest neighbors indices
    idx_nn_12, _ = get_nearest_neighbors_indices_batch(points1_np, points2_np)
    idx_nn_12 = torch.LongTensor(idx_nn_12).to(points1.device)
    # Expands it as batch_size x 1 x 3
    idx_nn_12_expand = idx_nn_12.view(batch_size, -1, 1).expand_as(points1)

    # Get list of nearest neighbors indices
    idx_nn_21, _ = get_nearest_neighbors_indices_batch(points2_np, points1_np)
    idx_nn_21 = torch.LongTensor(idx_nn_21).to(points1.device)
    # Expands it as batch_size x T x 3
    idx_nn_21_expand = idx_nn_21.view(batch_size, -1, 1).expand_as(points2)

    # Compute nearest neighbors in points2 to points in points1
    # points_12[i, j, k] = points2[i, idx_nn_12_expand[i, j, k], k]
    points_12 = torch.gather(points2, dim=1, index=idx_nn_12_expand)

    # Compute nearest neighbors in points1 to points in points2
    # points_21[i, j, k] = points2[i, idx_nn_21_expand[i, j, k], k]
    points_21 = torch.gather(points1, dim=1, index=idx_nn_21_expand)

    # Compute chamfer distance
    chamfer1 = (points1 - points_12).pow(2).sum(2).mean(1)
    chamfer2 = (points2 - points_21).pow(2).sum(2).mean(1)

    # Take sum
    chamfer = chamfer1 + chamfer2

    # If required, also return nearest neighbors
    if give_id:
        return chamfer1, chamfer2, idx_nn_12, idx_nn_21

    return chamfer


def get_nearest_neighbors_indices_batch(points_src, points_tgt, k=1):
    ''' Returns the nearest neighbors for point sets batchwise.

    Args:
        points_src (numpy array): source points
        points_tgt (numpy array): target points
        k (int): number of nearest neighbors to return
    '''
    indices = []
    distances = []

    for (p1, p2) in zip(points_src, points_tgt):
        kdtree = KDTree(p2)
        dist, idx = kdtree.query(p1, k=k)
        indices.append(idx)
        distances.append(dist)

    return indices, distances


def normalize_imagenet(x):
    ''' Normalize input images according to ImageNet standards.

    Args:
        x (tensor): input images
    '''
    x = x.clone()
    x[:, 0] = (x[:, 0] - 0.485) / 0.229
    x[:, 1] = (x[:, 1] - 0.456) / 0.224
    x[:, 2] = (x[:, 2] - 0.406) / 0.225
    return x


def make_3d_grid(bb_min, bb_max, shape):
    ''' Makes a 3D grid.

    Args:
        bb_min (tuple): bounding box minimum
        bb_max (tuple): bounding box maximum
        shape (tuple): output shape
    '''
    size = shape[0] * shape[1] * shape[2]

    pxs = torch.linspace(bb_min[0], bb_max[0], shape[0])
    pys = torch.linspace(bb_min[1], bb_max[1], shape[1])
    pzs = torch.linspace(bb_min[2], bb_max[2], shape[2])

    pxs = pxs.view(-1, 1, 1).expand(*shape).contiguous().view(size)
    pys = pys.view(1, -1, 1).expand(*shape).contiguous().view(size)
    pzs = pzs.view(1, 1, -1).expand(*shape).contiguous().view(size)
    p = torch.stack([pxs, pys, pzs], dim=1)

    return p


def make_2d_grid(bb_min, bb_max, shape):
    size = shape[0] * shape[1]

    pxs = torch.linspace(bb_min[0], bb_max[0], shape[0])
    pys = torch.linspace(bb_min[1], bb_max[1], shape[1])
    pxs = pxs.view(-1, 1).expand(*shape).contiguous().view(size)
    pys = pys.view(1, -1).expand(*shape).contiguous().view(size)
    p = torch.stack([pxs, pys], dim=1)

    return p


def transform_points(points, transform):
    ''' Transforms points with regard to passed camera information.

    Args:
        points (tensor): points tensor
        transform (tensor): transformation matrices
    '''
    assert(points.size(2) == 3)
    assert(transform.size(1) == 3)
    assert(points.size(0) == transform.size(0))

    if transform.size(2) == 4:
        R = transform[:, :, :3]
        t = transform[:, :, 3:]
        points_out = points @ R.transpose(1, 2) + t.transpose(1, 2)
    elif transform.size(2) == 3:
        K = transform
        points_out = points @ K.transpose(1, 2)

    return points_out


def b_inv(b_mat):
    ''' Performs batch matrix inversion.

    Arguments:
        b_mat: the batch of matrices that should be inverted
    '''

    eye = b_mat.new_ones(b_mat.size(-1)).diag().expand_as(b_mat)
    b_inv, _ = torch.gesv(eye, b_mat)
    return b_inv


def transform_points_back(points, transform):
    ''' Inverts the transformation.

    Args:
        points (tensor): points tensor
        transform (tensor): transformation matrices
    '''
    assert(points.size(2) == 3)
    assert(transform.size(1) == 3)
    assert(points.size(0) == transform.size(0))

    if transform.size(2) == 4:
        R = transform[:, :, :3]
        t = transform[:, :, 3:]
        points_out = points - t.transpose(1, 2)
        points_out = points_out @ b_inv(R.transpose(1, 2))
    elif transform.size(2) == 3:
        K = transform
        points_out = points @ b_inv(K.transpose(1, 2))

    return points_out


def project_to_camera(points, transform):
    ''' Projects points to the camera plane.

    Args:
        points (tensor): points tensor
        transform (tensor): transformation matrices
    '''
    p_camera = transform_points(points, transform)
    p_camera = p_camera[..., :2] / p_camera[..., 2:]
    return p_camera


def get_camera_args(data, loc_field=None, scale_field=None, device=None):
    ''' Returns dictionary of camera arguments.

    Args:
        data (dict): data dictionary
        loc_field (str): name of location field
        scale_field (str): name of scale field
        device (device): pytorch device
    '''
    Rt = data['inputs.world_mat'].to(device)
    K = data['inputs.camera_mat'].to(device)

    if loc_field is not None:
        loc = data[loc_field].to(device)
    else:
        loc = torch.zeros(K.size(0), 3, device=K.device, dtype=K.dtype)

    if scale_field is not None:
        scale = data[scale_field].to(device)
    else:
        scale = torch.zeros(K.size(0), device=K.device, dtype=K.dtype)

    Rt = fix_Rt_camera(Rt, loc, scale)
    K = fix_K_camera(K, img_size=137.)
    kwargs = {'Rt': Rt, 'K': K}
    return kwargs


def fix_Rt_camera(Rt, loc, scale):
    ''' Fixes Rt camera matrix.

    Args:
        Rt (tensor): Rt camera matrix
        loc (tensor): location
        scale (float): scale
    '''
    # Rt is B x 3 x 4
    # loc is B x 3 and scale is B
    batch_size = Rt.size(0)
    R = Rt[:, :, :3]
    t = Rt[:, :, 3:]

    scale = scale.view(batch_size, 1, 1)
    R_new = R * scale
    t_new = t + R @ loc.unsqueeze(2)

    Rt_new = torch.cat([R_new, t_new], dim=2)

    assert(Rt_new.size() == (batch_size, 3, 4))
    return Rt_new


def fix_K_camera(K, img_size=137):
    """Fix camera projection matrix.
    This changes a camera projection matrix that maps to
    [0, img_size] x [0, img_size] to one that maps to [-1, 1] x [-1, 1].

    Args:
        K (np.ndarray):     Camera projection matrix.
        img_size (float):   Size of image plane K projects to.
    """
    # Unscale and recenter
    scale_mat = torch.tensor([
        [2./img_size, 0, -1],
        [0, 2./img_size, -1],
        [0, 0, 1.],
    ], device=K.device, dtype=K.dtype)
    K_new = scale_mat.view(1, 3, 3) @ K
    return K_new


def load_and_scale_mesh(mesh_path, loc=None, scale=None):
    ''' Loads and scales a mesh.

    Args:
        mesh_path (str): mesh path
        loc (tuple): location
        scale (float): scaling factor
    '''
    mesh = trimesh.load(mesh_path, process=False)

    # Compute location and scale
    if loc is None or scale is None:
        bbox = mesh.bounding_box.bounds
        loc = (bbox[0] + bbox[1]) / 2
        scale = (bbox[1] - bbox[0]).max()

    mesh.apply_translation(-loc)
    mesh.apply_scale(1/scale)

    return loc, scale, mesh