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camera
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dchen/__pycache__/camera_adapter.cpython-310.pyc
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dchen/__pycache__/camera_compute.cpython-310.pyc
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dchen/camera_adapter.py
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dchen/camera_adapter.py
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import torch
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import torch.nn as nn
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class SimpleAdapter(nn.Module):
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def __init__(self, in_dim, out_dim, kernel_size, stride, num_residual_blocks=1):
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super(SimpleAdapter, self).__init__()
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# Pixel Unshuffle: reduce spatial dimensions by a factor of 8
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self.pixel_unshuffle = nn.PixelUnshuffle(downscale_factor=8)
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# Convolution: reduce spatial dimensions by a factor
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# of 2 (without overlap)
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self.conv = nn.Conv2d(in_dim * 64, out_dim, kernel_size=kernel_size, stride=stride, padding=0)
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# Residual blocks for feature extraction
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self.residual_blocks = nn.Sequential(
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*[ResidualBlock(out_dim) for _ in range(num_residual_blocks)]
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)
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def forward(self, x):
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# Reshape to merge the frame dimension into batch
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bs, c, f, h, w = x.size()
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x = x.permute(0, 2, 1, 3, 4).contiguous().view(bs * f, c, h, w)
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# Pixel Unshuffle operation
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x_unshuffled = self.pixel_unshuffle(x)
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# Convolution operation
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x_conv = self.conv(x_unshuffled)
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# Feature extraction with residual blocks
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out = self.residual_blocks(x_conv)
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# Reshape to restore original bf dimension
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out = out.view(bs, f, out.size(1), out.size(2), out.size(3))
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# Permute dimensions to reorder (if needed), e.g., swap channels and feature frames
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out = out.permute(0, 2, 1, 3, 4)
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return out
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class ResidualBlock(nn.Module):
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def __init__(self, dim):
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super(ResidualBlock, self).__init__()
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self.conv1 = nn.Conv2d(dim, dim, kernel_size=3, padding=1)
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self.relu = nn.ReLU(inplace=True)
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self.conv2 = nn.Conv2d(dim, dim, kernel_size=3, padding=1)
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def forward(self, x):
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residual = x
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out = self.relu(self.conv1(x))
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out = self.conv2(out)
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out += residual
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return out
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# Example usage
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# in_dim = 3
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# out_dim = 64
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# adapter = SimpleAdapterWithReshape(in_dim, out_dim)
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# x = torch.randn(1, in_dim, 4, 64, 64) # e.g., batch size = 1, channels = 3, frames/features = 4
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# output = adapter(x)
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# print(output.shape) # Should reflect transformed dimensions
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174
dchen/camera_compute.py
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dchen/camera_compute.py
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import csv
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import gc
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import io
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import json
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import math
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import os
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import random
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from random import shuffle
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import albumentations
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import cv2
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import numpy as np
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import torch
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import torch.nn.functional as F
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import torchvision.transforms as transforms
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from decord import VideoReader
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from einops import rearrange
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from packaging import version as pver
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from PIL import Image
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from torch.utils.data import BatchSampler, Sampler
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from torch.utils.data.dataset import Dataset
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class Camera(object):
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"""Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py
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"""
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def __init__(self, entry):
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fx, fy, cx, cy = entry[1:5]
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self.fx = fx
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self.fy = fy
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self.cx = cx
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self.cy = cy
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w2c_mat = np.array(entry[7:]).reshape(3, 4)
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w2c_mat_4x4 = np.eye(4)
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w2c_mat_4x4[:3, :] = w2c_mat
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self.w2c_mat = w2c_mat_4x4
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self.c2w_mat = np.linalg.inv(w2c_mat_4x4)
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def get_relative_pose(cam_params):
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"""Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py
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"""
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abs_w2cs = [cam_param.w2c_mat for cam_param in cam_params]
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abs_c2ws = [cam_param.c2w_mat for cam_param in cam_params]
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cam_to_origin = 0
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target_cam_c2w = np.array([
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[1, 0, 0, 0],
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[0, 1, 0, -cam_to_origin],
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[0, 0, 1, 0],
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[0, 0, 0, 1]
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])
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abs2rel = target_cam_c2w @ abs_w2cs[0]
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ret_poses = [target_cam_c2w, ] + [abs2rel @ abs_c2w for abs_c2w in abs_c2ws[1:]]
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ret_poses = np.array(ret_poses, dtype=np.float32)
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return ret_poses
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def ray_condition(K, c2w, H, W, device):
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"""Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py
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"""
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# c2w: B, V, 4, 4
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# K: B, V, 4
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B = K.shape[0]
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j, i = custom_meshgrid(
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torch.linspace(0, H - 1, H, device=device, dtype=c2w.dtype),
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torch.linspace(0, W - 1, W, device=device, dtype=c2w.dtype),
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)
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i = i.reshape([1, 1, H * W]).expand([B, 1, H * W]) + 0.5 # [B, HxW]
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j = j.reshape([1, 1, H * W]).expand([B, 1, H * W]) + 0.5 # [B, HxW]
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fx, fy, cx, cy = K.chunk(4, dim=-1) # B,V, 1
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zs = torch.ones_like(i) # [B, HxW]
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xs = (i - cx) / fx * zs
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ys = (j - cy) / fy * zs
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zs = zs.expand_as(ys)
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directions = torch.stack((xs, ys, zs), dim=-1) # B, V, HW, 3
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directions = directions / directions.norm(dim=-1, keepdim=True) # B, V, HW, 3
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rays_d = directions @ c2w[..., :3, :3].transpose(-1, -2) # B, V, 3, HW
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rays_o = c2w[..., :3, 3] # B, V, 3
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rays_o = rays_o[:, :, None].expand_as(rays_d) # B, V, 3, HW
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# c2w @ dirctions
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rays_dxo = torch.cross(rays_o, rays_d)
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plucker = torch.cat([rays_dxo, rays_d], dim=-1)
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plucker = plucker.reshape(B, c2w.shape[1], H, W, 6) # B, V, H, W, 6
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# plucker = plucker.permute(0, 1, 4, 2, 3)
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return plucker
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def custom_meshgrid(*args):
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"""Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py
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"""
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# ref: https://pytorch.org/docs/stable/generated/torch.meshgrid.html?highlight=meshgrid#torch.meshgrid
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if pver.parse(torch.__version__) < pver.parse('1.10'):
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return torch.meshgrid(*args)
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else:
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return torch.meshgrid(*args, indexing='ij')
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def process_pose_file(pose_file_path, width=672, height=384, original_pose_width=1280, original_pose_height=720, device='cpu', return_poses=False):
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"""Modified from https://github.com/hehao13/CameraCtrl/blob/main/inference.py
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"""
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with open(pose_file_path, 'r') as f:
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poses = f.readlines()
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poses = [pose.strip().split(' ') for pose in poses[1:]]
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cam_params = [[float(x) for x in pose] for pose in poses]
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if return_poses:
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return cam_params
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else:
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cam_params = [Camera(cam_param) for cam_param in cam_params]
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sample_wh_ratio = width / height
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pose_wh_ratio = original_pose_width / original_pose_height # Assuming placeholder ratios, change as needed
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if pose_wh_ratio > sample_wh_ratio:
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resized_ori_w = height * pose_wh_ratio
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for cam_param in cam_params:
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cam_param.fx = resized_ori_w * cam_param.fx / width
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else:
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resized_ori_h = width / pose_wh_ratio
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for cam_param in cam_params:
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cam_param.fy = resized_ori_h * cam_param.fy / height
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intrinsic = np.asarray([[cam_param.fx * width,
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cam_param.fy * height,
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cam_param.cx * width,
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cam_param.cy * height]
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for cam_param in cam_params], dtype=np.float32)
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K = torch.as_tensor(intrinsic)[None] # [1, 1, 4]
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c2ws = get_relative_pose(cam_params) # Assuming this function is defined elsewhere
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c2ws = torch.as_tensor(c2ws)[None] # [1, n_frame, 4, 4]
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plucker_embedding = ray_condition(K, c2ws, height, width, device=device)[0].permute(0, 3, 1, 2).contiguous() # V, 6, H, W
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plucker_embedding = plucker_embedding[None]
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plucker_embedding = rearrange(plucker_embedding, "b f c h w -> b f h w c")[0]
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return plucker_embedding
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def process_pose_params(cam_params, width=672, height=384, original_pose_width=1280, original_pose_height=720, device='cpu'):
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"""Modified from https://github.com/hehao13/CameraCtrl/blob/main/inference.py
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"""
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cam_params = [Camera(cam_param) for cam_param in cam_params]
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sample_wh_ratio = width / height
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pose_wh_ratio = original_pose_width / original_pose_height # Assuming placeholder ratios, change as needed
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if pose_wh_ratio > sample_wh_ratio:
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resized_ori_w = height * pose_wh_ratio
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for cam_param in cam_params:
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cam_param.fx = resized_ori_w * cam_param.fx / width
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else:
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resized_ori_h = width / pose_wh_ratio
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for cam_param in cam_params:
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cam_param.fy = resized_ori_h * cam_param.fy / height
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intrinsic = np.asarray([[cam_param.fx * width,
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cam_param.fy * height,
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cam_param.cx * width,
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cam_param.cy * height]
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for cam_param in cam_params], dtype=np.float32)
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K = torch.as_tensor(intrinsic)[None] # [1, 1, 4]
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c2ws = get_relative_pose(cam_params) # Assuming this function is defined elsewhere
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c2ws = torch.as_tensor(c2ws)[None] # [1, n_frame, 4, 4]
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plucker_embedding = ray_condition(K, c2ws, height, width, device=device)[0].permute(0, 3, 1, 2).contiguous() # V, 6, H, W
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plucker_embedding = plucker_embedding[None]
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plucker_embedding = rearrange(plucker_embedding, "b f c h w -> b f h w c")[0]
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return plucker_embedding
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82
dchen/camera_information.txt
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dchen/camera_information.txt
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
|
||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.018518518518518517 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
|
||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.037037037037037035 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
|
||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.05555555555555555 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
|
||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.07407407407407407 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
|
||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.09259259259259259 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
|
||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.1111111111111111 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
|
||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.12962962962962962 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
|
||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.14814814814814814 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
|
||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.16666666666666666 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
|
||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.18518518518518517 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
|
||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.2037037037037037 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
|
||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.2222222222222222 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
|
||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.24074074074074073 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
|
||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.25925925925925924 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
|
||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.2777777777777778 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
|
||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.2962962962962963 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
|
||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.31481481481481477 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
|
||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.3333333333333333 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
|
||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.35185185185185186 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
|
||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.37037037037037035 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
|
||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.38888888888888884 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
|
||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.4074074074074074 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
|
||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.42592592592592593 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
|
||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.4444444444444444 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
|
||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.4629629629629629 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
|
||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.48148148148148145 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
|
||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.5 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
|
||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.5185185185185185 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
|
||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.537037037037037 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
|
||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.5555555555555556 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
|
||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.5740740740740741 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
|
||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.5925925925925926 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
|
||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.611111111111111 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
|
||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.6296296296296295 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
|
||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.6481481481481481 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
|
||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.6666666666666666 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
|
||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.6851851851851851 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
|
||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.7037037037037037 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
|
||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.7222222222222222 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
|
||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.7407407407407407 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
|
||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.7592592592592593 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
|
||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.7777777777777777 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.7962962962962963 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.8148148148148148 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.8333333333333334 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.8518518518518519 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.8703703703703705 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.8888888888888888 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.9074074074074074 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.9259259259259258 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.9444444444444444 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.9629629629629629 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.9814814814814815 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 1.0185185185185186 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 1.037037037037037 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 1.0555555555555556 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 1.074074074074074 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 1.0925925925925926 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 1.1111111111111112 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 1.1296296296296298 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 1.1481481481481481 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 1.1666666666666667 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 1.1851851851851851 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 1.2037037037037037 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 1.222222222222222 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 1.2407407407407407 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 1.259259259259259 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 1.2777777777777777 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 1.2962962962962963 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 1.3148148148148149 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 1.3333333333333333 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 1.3518518518518519 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 1.3703703703703702 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 1.3888888888888888 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 1.4074074074074074 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 1.425925925925926 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 1.4444444444444444 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 1.462962962962963 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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||||
0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 1.4814814814814814 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
|
||||
Reference in New Issue
Block a user