ProposalNetwork/proposals/proposals.py

from ProposalNetwork.utils import utils
from ProposalNetwork.utils.spaces import Cubes
from ProposalNetwork.utils.utils import gt_in_norm_range, sample_normal_in_range, vectorized_linspace
from ProposalNetwork.utils.conversions import pixel_to_normalised_space
import torch
import numpy as np
from cubercnn import util

# 0.0x meters is the minimum edge length
MIN_PROP_S = 0.05

def rescale_interval(x, min, max):
    '''operation  (min - max) * x + max'''
    return (min - max) * x + max

def lin_fun(x,coef):
    '''used for finishing the center of the cube proposal. The center is calculated as a linear function (typically of the depth image).'''
    return coef[0] * x + coef[1]

def propose_random(reference_box, depth_image, priors, im_shape, K, number_of_proposals=1, gt_cubes=None, ground_normal:torch.Tensor=None):
    number_of_instances = len(reference_box)
    # Center
    x = torch.rand(number_of_instances,number_of_proposals, device=reference_box.device) * 4 - 2
    y = torch.rand(number_of_instances,number_of_proposals, device=reference_box.device) * 2 - 1
    z = torch.rand(number_of_instances,number_of_proposals, device=reference_box.device) * 4 + 1

    # Dimensions
    w = rescale_interval(torch.rand(number_of_instances,number_of_proposals, device=reference_box.device), MIN_PROP_S, 2)
    h = rescale_interval(torch.rand(number_of_instances,number_of_proposals, device=reference_box.device), MIN_PROP_S, 2)
    l = rescale_interval(torch.rand(number_of_instances,number_of_proposals, device=reference_box.device), MIN_PROP_S, 2)

    xyzwhl = torch.stack([x, y, z, w, h, l], 2)
    
    # Pose
    rotation_matrices = utils.randn_orthobasis_torch(number_of_proposals, number_of_instances).to(reference_box.device)

    cubes = Cubes(torch.cat((xyzwhl, rotation_matrices.flatten(start_dim=2)), dim=2))

    # Statistics
    if gt_cubes is None:
        return cubes, None, None
    
    stats = statistics(gt_cubes,x,y,z,w,h,l)

    return cubes, stats, torch.ones(cubes.num_instances,9)

def propose_xy_patch(reference_box, depth_image, priors, im_shape, K, number_of_proposals=1, gt_cubes=None, ground_normal:torch.Tensor=None):
    '''
    only propose x and y values that are within the reference box'''
    number_of_instances = len(reference_box)
    # Center
    m = 4
    widths = reference_box.tensor[:,2] - reference_box.tensor[:,0]
    heights = reference_box.tensor[:,3] - reference_box.tensor[:,1]
    x_min, x_max = reference_box.tensor[:,0]+widths/m, reference_box.tensor[:,2]-widths/m
    y_min, y_max = reference_box.tensor[:,1]+heights/m, reference_box.tensor[:,3]-heights/m

    xt = pixel_to_normalised_space([x_min, x_max],[im_shape[0],im_shape[0]],[3,3])
    yt = pixel_to_normalised_space([y_min, y_max],[im_shape[1],im_shape[1]],[2,2])

    x = vectorized_linspace(xt[:,0],xt[:,1],number_of_proposals)
    y = vectorized_linspace(yt[:,0],yt[:,1],number_of_proposals)

    z = torch.rand(number_of_instances,number_of_proposals, device=reference_box.device) * 4 + 1

    # NOTE Finding the center like below might be more correct, this isnt though how we did it when we developed this method. Also, this works best when depth correct which it most likely isnt
    #x_0 = torch.linspace(x_min[0],x_max[0],number_of_proposals).to(K.device)
    #cube_x3d = z * (x_0 - K.unsqueeze(0).repeat(number_of_proposals,1,1)[:, 0, 2])/K.unsqueeze(0).repeat(number_of_proposals,1,1)[:, 0, 0]
    #print(cube_x3d)
    #cube_y3d = cube_z * (cube_y - Ks_scaled_per_box[:, 1, 2])/Ks_scaled_per_box[:, 1, 1]

    # Dimensions
    # constrain to interval [MIN_PROP_S, 2] meters 
    w = rescale_interval(torch.rand(number_of_instances,number_of_proposals, device=K.device), MIN_PROP_S, 2)
    h = rescale_interval(torch.rand(number_of_instances,number_of_proposals, device=K.device), MIN_PROP_S, 2)
    l = rescale_interval(torch.rand(number_of_instances,number_of_proposals, device=K.device), MIN_PROP_S, 2)

    xyzwhl = torch.stack([x, y, z, w, h, l], 2)
    
    # Pose
    rotation_matrices = utils.randn_orthobasis_torch(number_of_proposals, number_of_instances).to(device=K.device)

    cubes = Cubes(torch.cat((xyzwhl, rotation_matrices.flatten(start_dim=2)), dim=2))

    # Statistics
    if gt_cubes is None:
        return cubes, None, None
    
    stats = statistics(gt_cubes,x,y,z,w,h,l)

    return cubes, stats, torch.ones(cubes.num_instances,9)

def propose_z(reference_box, depth_image, priors, im_shape, K, number_of_proposals=1, gt_cubes=None, ground_normal:torch.Tensor=None):
    '''
    picke a random x and y spot anywhere on the image and grab the z-value from that spot'''
    number_of_instances = len(reference_box)

    # Center
    m = 4
    widths = reference_box.tensor[:,2] - reference_box.tensor[:,0]
    heights = reference_box.tensor[:,3] - reference_box.tensor[:,1]
    x_min, x_max = reference_box.tensor[:,0]+widths/m, reference_box.tensor[:,2]-widths/m
    y_min, y_max = reference_box.tensor[:,1]+heights/m, reference_box.tensor[:,3]-heights/m

    xt = pixel_to_normalised_space([x_min, x_max],[im_shape[0],im_shape[0]],[3,3])
    yt = pixel_to_normalised_space([y_min, y_max],[im_shape[1],im_shape[1]],[2,2])

    x = vectorized_linspace(xt[:,0],xt[:,1],number_of_proposals)
    y = vectorized_linspace(yt[:,0],yt[:,1],number_of_proposals)

    z = torch.zeros_like(x)
    for i in range(number_of_instances):
        z_depth_patch = depth_image[int(reference_box.tensor[i,1]):int(reference_box.tensor[i,3]), int(reference_box.tensor[i,0]):int(reference_box.tensor[i,2])]
        quantiles = torch.quantile(z_depth_patch, torch.tensor([0.1, 0.9],device=z_depth_patch.device), dim=None)
        z[i] = torch.linspace(quantiles[0],quantiles[1],number_of_proposals)

    # Dimensions
    w = rescale_interval(torch.rand(number_of_instances,number_of_proposals, device=x.device), MIN_PROP_S, 2)
    h = rescale_interval(torch.rand(number_of_instances,number_of_proposals, device=x.device), MIN_PROP_S, 2)
    l = rescale_interval(torch.rand(number_of_instances,number_of_proposals, device=x.device), MIN_PROP_S, 2)

    xyzwhl = torch.stack([x, y, z, w, h, l], 2)
    
    # Pose
    rotation_matrices = utils.randn_orthobasis_torch(number_of_proposals, number_of_instances).to(device=x.device)

    cubes = Cubes(torch.cat((xyzwhl, rotation_matrices.flatten(start_dim=2)), dim=2))

    # Statistics
    if gt_cubes is None:
        return cubes, None, None
    
    stats = statistics(gt_cubes,x,y,z,w,h,l)

    return cubes, stats, torch.ones(cubes.num_instances,9)

def propose_random_dim(reference_box, depth_image, priors, im_shape, K, number_of_proposals=1, gt_cubes=None, ground_normal:torch.Tensor=None):
    number_of_instances = len(reference_box)

    ####### Center
    # Removing the outer % on each side of range for center point
    m = 4
    widths = reference_box.tensor[:,2] - reference_box.tensor[:,0]
    heights = reference_box.tensor[:,3] - reference_box.tensor[:,1]
    x_range_px = torch.stack((reference_box.tensor[:,0]+widths/m,reference_box.tensor[:,2]-widths/m),dim=1)
    y_range_px = torch.stack((reference_box.tensor[:,1]+heights/m,reference_box.tensor[:,3]-heights/m),dim=1)
    # Find depths
    x_grid_px = vectorized_linspace(x_range_px[:,0],x_range_px[:,1],number_of_proposals).long()
    y_grid_px = vectorized_linspace(y_range_px[:,0],y_range_px[:,1],number_of_proposals).long()
    x_indices = x_grid_px.round()
    y_indices = y_grid_px.round()
    d = depth_image[y_indices, x_indices]
    # Calculate x and y and temporary z
    opposite_side_x = x_grid_px-K[0,2].repeat(number_of_proposals) # x-directional distance in px between image center and object center
    opposite_side_y = y_grid_px-K[1,2].repeat(number_of_proposals) # y-directional distance in px between image center and object center
    adjacent_side = K[0,0].repeat(number_of_proposals) # depth in px to image plane
    angle_x = torch.atan2(opposite_side_x,adjacent_side)
    dx_inside_camera = torch.sqrt(opposite_side_x**2 + adjacent_side**2)
    angle_d = torch.atan2(opposite_side_y,dx_inside_camera)
    y = d * torch.sin(angle_d)
    dx = torch.sqrt(d**2 - y**2)
    x = dx * torch.sin(angle_x)
    z_tmp = torch.sqrt(dx**2 - x**2)

    # Dimensions
    w = rescale_interval(torch.rand(number_of_instances,number_of_proposals, device=reference_box.device), MIN_PROP_S, 2)
    h = rescale_interval(torch.rand(number_of_instances,number_of_proposals, device=reference_box.device), MIN_PROP_S, 2)
    l = rescale_interval(torch.rand(number_of_instances,number_of_proposals, device=reference_box.device), MIN_PROP_S, 2)

    # Finish center
    # x
    x_coefficients = torch.tensor([1.15, 0])
    x = sample_normal_in_range(lin_fun(torch.median(x,dim=1).values,x_coefficients), torch.std(x,dim=1) * 1.2, number_of_proposals)
    
    # y
    y_coefficients  = torch.tensor([1.1, 0])
    y = sample_normal_in_range(lin_fun(torch.median(y,dim=1).values,y_coefficients), torch.std(y,dim=1) * 0.8, number_of_proposals)
    
    # z
    z = z_tmp+l/2
    z_coefficients = torch.tensor([0.85, 0.35])
    z = sample_normal_in_range(lin_fun(torch.median(z,dim=1).values,z_coefficients), torch.std(z,dim=1) * 1.2, number_of_proposals)

    xyzwhl = torch.stack([x, y, z, w, h, l], 2)
    
    # Pose
    rotation_matrices = utils.randn_orthobasis_torch(number_of_proposals, number_of_instances).to(device=reference_box.device)

    cubes = Cubes(torch.cat((xyzwhl, rotation_matrices.flatten(start_dim=2)), dim=2))

    # Statistics
    if gt_cubes is None:
        return cubes, None, None
    
    stats = statistics(gt_cubes,x,y,z,w,h,l)

    return cubes, stats, torch.ones(cubes.num_instances,9)

def propose_aspect_ratio(reference_box, depth_image, priors, im_shape, K, number_of_proposals=1, gt_cubes=None, ground_normal:torch.Tensor=None):
    '''    
    sample width from the prior and then apply a set of ratios on h. Then take a random shuffled version of the set and apply it to L.
    '''
    number_of_instances = len(reference_box)

    ####### Center
    # Removing the outer % on each side of range for center point
    m = 4
    widths = reference_box.tensor[:,2] - reference_box.tensor[:,0]
    heights = reference_box.tensor[:,3] - reference_box.tensor[:,1]
    x_range_px = torch.stack((reference_box.tensor[:,0]+widths/m,reference_box.tensor[:,2]-widths/m),dim=1)
    y_range_px = torch.stack((reference_box.tensor[:,1]+heights/m,reference_box.tensor[:,3]-heights/m),dim=1)
    # Find depths
    x_grid_px = vectorized_linspace(x_range_px[:,0],x_range_px[:,1],number_of_proposals).long()
    y_grid_px = vectorized_linspace(y_range_px[:,0],y_range_px[:,1],number_of_proposals).long()
    x_indices = x_grid_px.round()
    y_indices = y_grid_px.round()
    d = depth_image[y_indices, x_indices]
    # Calculate x and y and temporary z
    opposite_side_x = x_grid_px-K[0,2].repeat(number_of_proposals) # x-directional distance in px between image center and object center
    opposite_side_y = y_grid_px-K[1,2].repeat(number_of_proposals) # y-directional distance in px between image center and object center
    adjacent_side = K[0,0].repeat(number_of_proposals) # depth in px to image plane
    angle_x = torch.atan2(opposite_side_x,adjacent_side)
    dx_inside_camera = torch.sqrt(opposite_side_x**2 + adjacent_side**2)
    angle_d = torch.atan2(opposite_side_y,dx_inside_camera)
    y = d * torch.sin(angle_d)
    dx = torch.sqrt(d**2 - y**2)
    x = dx * torch.sin(angle_x)
    z_tmp = torch.sqrt(dx**2 - x**2)

    # Dimensions
    w = rescale_interval(torch.rand(number_of_instances,number_of_proposals, device=reference_box.device), MIN_PROP_S, 2)
    #
    ratios = [0.33, 0.66, 1, 1.33, 1.67, 2, 3]
    h = torch.zeros_like(w)
    l = torch.zeros_like(w)
    for i in range(number_of_instances):
        # must
        ratio1, ratio2 = torch.randperm(len(ratios))[0], torch.randperm(len(ratios))[0]
        h[i] = w[i] * ratios[ratio1]
        l[i] = w[i] * ratios[ratio2]

    # Finish center
    # x
    x_coefficients = torch.tensor([1.15, 0])
    x = sample_normal_in_range(lin_fun(torch.median(x,dim=1).values,x_coefficients), torch.std(x,dim=1) * 1.2, number_of_proposals)
    
    # y
    y_coefficients  = torch.tensor([1.1, 0])
    y = sample_normal_in_range(lin_fun(torch.median(y,dim=1).values,y_coefficients), torch.std(y,dim=1) * 0.8, number_of_proposals)
    
    # z
    z = z_tmp+l/2
    z_coefficients = torch.tensor([0.85, 0.35])
    z = sample_normal_in_range(lin_fun(torch.median(z,dim=1).values,z_coefficients), torch.std(z,dim=1) * 1.2, number_of_proposals)

    xyzwhl = torch.stack([x, y, z, w, h, l], 2)
    
    # Pose
    rotation_matrices = utils.randn_orthobasis_torch(number_of_proposals, number_of_instances).to(device=reference_box.device)

    cubes = Cubes(torch.cat((xyzwhl, rotation_matrices.flatten(start_dim=2)), dim=2))

    # Statistics
    if gt_cubes is None:
        return cubes, None, None
    
    stats = statistics(gt_cubes,x,y,z,w,h,l)

    return cubes, stats, torch.ones(cubes.num_instances,9)


def propose_random_rotation(reference_box, depth_image, priors, im_shape, K, number_of_proposals=1, gt_cubes=None, ground_normal:torch.Tensor=None):
    number_of_instances = len(reference_box)

    ####### Center
    # Removing the outer % on each side of range for center point
    m = 4
    widths = reference_box.tensor[:,2] - reference_box.tensor[:,0]
    heights = reference_box.tensor[:,3] - reference_box.tensor[:,1]
    x_range_px = torch.stack((reference_box.tensor[:,0]+widths/m,reference_box.tensor[:,2]-widths/m),dim=1)
    y_range_px = torch.stack((reference_box.tensor[:,1]+heights/m,reference_box.tensor[:,3]-heights/m),dim=1)
    # Find depths
    x_grid_px = vectorized_linspace(x_range_px[:,0],x_range_px[:,1],number_of_proposals).long()
    y_grid_px = vectorized_linspace(y_range_px[:,0],y_range_px[:,1],number_of_proposals).long()
    x_indices = x_grid_px.round()
    y_indices = y_grid_px.round()
    d = depth_image[y_indices, x_indices]
    # Calculate x and y and temporary z
    opposite_side_x = x_grid_px-K[0,2].repeat(number_of_proposals) # x-directional distance in px between image center and object center
    opposite_side_y = y_grid_px-K[1,2].repeat(number_of_proposals) # y-directional distance in px between image center and object center
    adjacent_side = K[0,0].repeat(number_of_proposals) # depth in px to image plane
    angle_x = torch.atan2(opposite_side_x,adjacent_side)
    dx_inside_camera = torch.sqrt(opposite_side_x**2 + adjacent_side**2)
    angle_d = torch.atan2(opposite_side_y,dx_inside_camera)
    y = d * torch.sin(angle_d)
    dx = torch.sqrt(d**2 - y**2)
    x = dx * torch.sin(angle_x)
    z_tmp = torch.sqrt(dx**2 - x**2)

    # Dimensions
    w_prior = [priors[0][:,0], priors[1][:,0]]
    h_prior = [priors[0][:,1], priors[1][:,1]]
    l_prior = [priors[0][:,2], priors[1][:,2]]
    w = sample_normal_in_range(w_prior[0], w_prior[1], number_of_proposals, MIN_PROP_S, w_prior[0] + 2 * w_prior[1])
    h = sample_normal_in_range(h_prior[0], h_prior[1]*1.1, number_of_proposals, MIN_PROP_S, h_prior[0] + 2.2 * h_prior[1])
    l = sample_normal_in_range(l_prior[0], l_prior[1], number_of_proposals, MIN_PROP_S, l_prior[0] + 2 * l_prior[1])

    # x
    x_coefficients = torch.tensor([1.15, 0])
    x = sample_normal_in_range(lin_fun(torch.median(x,dim=1).values,x_coefficients), torch.std(x,dim=1) * 1.2, number_of_proposals)
    
    # y
    y_coefficients  = torch.tensor([1.1, 0])
    y = sample_normal_in_range(lin_fun(torch.median(y,dim=1).values,y_coefficients), torch.std(y,dim=1) * 0.8, number_of_proposals)
    
    # z
    z = z_tmp+l/2
    z_coefficients = torch.tensor([0.85, 0.35])
    z = sample_normal_in_range(lin_fun(torch.median(z,dim=1).values,z_coefficients), torch.std(z,dim=1) * 1.2, number_of_proposals)

    xyzwhl = torch.stack([x, y, z, w, h, l], 2)
    
    # Pose
    rotation_matrices = utils.randn_orthobasis_torch(number_of_proposals, number_of_instances).to(device=reference_box.device)
    cubes = Cubes(torch.cat((xyzwhl, rotation_matrices.flatten(start_dim=2)), dim=2))

    # Statistics
    if gt_cubes is None:
        return cubes, None, None
    
    stats = statistics(gt_cubes,x,y,z,w,h,l)

    n = gt_cubes.num_instances
    ranges = torch.stack([torch.std(x,dim=1)*1.2, torch.std(y,dim=1)*0.8, torch.std(z,dim=1)*1.2, w_prior[1], h_prior[1]*1.1, l_prior[1], torch.tensor(torch.pi,device=reference_box.device).repeat(n),torch.tensor(torch.pi,device=reference_box.device).repeat(n),torch.tensor(torch.pi,device=reference_box.device).repeat(n)],dim=1).cpu().numpy()

    return cubes, stats, ranges

def propose(reference_box, depth_image, priors, im_shape, K, number_of_proposals=1, gt_cubes=None, ground_normal:torch.Tensor=None):
    '''
    Proposes a cube. The ranges are largely random, except for that the center needs to be inside the reference box.
    Also, objects have a length, width and height according to priors.

    im_shape = [x,y]
    priors = [prior_mean, prior_std] 2x3

    Output:
    cubes : Cubes with (number of proposals) cubes
    stats         : tensor N x number_of_proposals
    '''
    number_of_instances = len(reference_box)

    ####### Center
    # Removing the outer % on each side of range for center point
    m = 4
    widths = reference_box.tensor[:,2] - reference_box.tensor[:,0]
    heights = reference_box.tensor[:,3] - reference_box.tensor[:,1]
    x_range_px = torch.stack((reference_box.tensor[:,0]+widths/m,reference_box.tensor[:,2]-widths/m),dim=1)
    y_range_px = torch.stack((reference_box.tensor[:,1]+heights/m,reference_box.tensor[:,3]-heights/m),dim=1)
    # Find depths
    x_grid_px = vectorized_linspace(x_range_px[:,0],x_range_px[:,1],number_of_proposals).long()
    y_grid_px = vectorized_linspace(y_range_px[:,0],y_range_px[:,1],number_of_proposals).long()
    x_indices = x_grid_px.round()
    y_indices = y_grid_px.round()
    d = depth_image[y_indices, x_indices]
    # Calculate x and y and temporary z
    opposite_side_x = x_grid_px-K[0,2].repeat(number_of_proposals) # x-directional distance in px between image center and object center
    opposite_side_y = y_grid_px-K[1,2].repeat(number_of_proposals) # y-directional distance in px between image center and object center
    adjacent_side = K[0,0].repeat(number_of_proposals) # depth in px to image plane
    angle_x = torch.atan2(opposite_side_x,adjacent_side)
    dx_inside_camera = torch.sqrt(opposite_side_x**2 + adjacent_side**2)
    angle_d = torch.atan2(opposite_side_y,dx_inside_camera)
    y = d * torch.sin(angle_d)
    dx = torch.sqrt(d**2 - y**2)
    x = dx * torch.sin(angle_x)
    z_tmp = torch.sqrt(dx**2 - x**2)

    # Dimensions
    w_prior = [priors[0][:,0], priors[1][:,0]]
    h_prior = [priors[0][:,1], priors[1][:,1]]
    l_prior = [priors[0][:,2], priors[1][:,2]]
    w = sample_normal_in_range(w_prior[0], w_prior[1], number_of_proposals, MIN_PROP_S, w_prior[0] + 2 * w_prior[1])
    h = sample_normal_in_range(h_prior[0], h_prior[1]*1.1, number_of_proposals, MIN_PROP_S, h_prior[0] + 2.2 * h_prior[1])
    l = sample_normal_in_range(l_prior[0], l_prior[1], number_of_proposals, MIN_PROP_S, l_prior[0] + 2 * l_prior[1])

    # x
    x_coefficients = torch.tensor([1.15, 0])
    x = sample_normal_in_range(lin_fun(torch.median(x,dim=1).values,x_coefficients), torch.std(x,dim=1) * 1.2, number_of_proposals)
    
    # y
    y_coefficients  = torch.tensor([1.1, 0])
    y = sample_normal_in_range(lin_fun(torch.median(y,dim=1).values,y_coefficients), torch.std(y,dim=1) * 0.8, number_of_proposals)
    
    # z
    z = z_tmp+l/2
    z_coefficients = torch.tensor([0.85, 0.35])
    z = sample_normal_in_range(lin_fun(torch.median(z,dim=1).values,z_coefficients), torch.std(z,dim=1) * 1.2, number_of_proposals)
    #z = gt_cubes.tensor[:,0,2].view(-1,1).repeat(1,number_of_proposals)
    xyzwhl = torch.stack([x, y, z, w, h, l], 2)
    
    # Pose
    if ground_normal is None:
        rotation_matrices = utils.randn_orthobasis_torch(number_of_proposals, number_of_instances).to(device=reference_box.device)
    else:
        ground_normal = ground_normal.to(device=reference_box.device)
        angles = torch.linspace(0, np.pi, 36, device=ground_normal.device)
        rotation_matrices_inner = utils.orthobasis_from_normal_t(ground_normal, angles)
        rotation_matrices = rotation_matrices_inner[torch.randint(len(rotation_matrices_inner), (number_of_instances,number_of_proposals))]  
    
    # Check whether it is possible to find gt
    # if not (gt_cube == None) and not is_gt_included(gt_cube,x_range, y_range, z_range, w_prior, h_prior, l_prior):
    #    pass

    cubes = Cubes(torch.cat((xyzwhl, rotation_matrices.flatten(start_dim=2)), dim=2))

    # Statistics
    if gt_cubes is None:
        return cubes, None, None
    
    stats = statistics(gt_cubes,x,y,z,w,h,l)

    n = gt_cubes.num_instances
    ranges = torch.stack([torch.std(x,dim=1)*1.2, torch.std(y,dim=1)*0.8, torch.std(z,dim=1)*1.2, w_prior[1], h_prior[1]*1.1, l_prior[1], torch.tensor(torch.pi,device=reference_box.device).repeat(n),torch.tensor(torch.pi,device=reference_box.device).repeat(n),torch.tensor(torch.pi,device=reference_box.device).repeat(n)],dim=1).cpu().numpy()

    return cubes, stats, ranges


def statistics(gt_cubes,x,y,z,w,h,l):    
    n = gt_cubes.num_instances
    stats = torch.zeros((n,9))
    for i in range(n):
        gt_cube = gt_cubes[i].tensor[0,0]
        stat_x = gt_in_norm_range([torch.min(x[i]),torch.max(x[i])],gt_cube[0])
        stat_y = gt_in_norm_range([torch.min(y[i]),torch.max(y[i])],gt_cube[1])
        stat_z = gt_in_norm_range([torch.min(z[i]),torch.max(z[i])],gt_cube[2])
        stat_w = gt_in_norm_range([torch.min(w[i]),torch.max(w[i])],gt_cube[3])
        stat_h = gt_in_norm_range([torch.min(h[i]),torch.max(h[i])],gt_cube[4])
        stat_l = gt_in_norm_range([torch.min(l[i]),torch.max(l[i])],gt_cube[5])
        angles = util.mat2euler(gt_cube[-9:].reshape((3,3)))
        stat_rx = gt_in_norm_range(torch.tensor([0,np.pi]),torch.tensor(angles[0]))
        stat_ry = gt_in_norm_range(torch.tensor([0,np.pi/2]),torch.tensor(angles[1]))
        stat_rz = gt_in_norm_range(torch.tensor([0,np.pi]),torch.tensor(angles[2]))

        stats[i] = torch.tensor([stat_x,stat_y,stat_z,stat_w,stat_h,stat_l,stat_rx,stat_ry,stat_rz])

    return stats