Spaces:
Running
on
Zero
Running
on
Zero
File size: 2,716 Bytes
7f51798 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 |
#
# Copyright (C) 2023, Inria
# GRAPHDECO research group, https://team.inria.fr/graphdeco
# All rights reserved.
#
# This software is free for non-commercial, research and evaluation use
# under the terms of the LICENSE.md file.
#
# For inquiries contact george.drettakis@inria.fr
#
import torch
import traceback
import socket
import json
from scene.cameras import MiniCam
host = "127.0.0.1"
port = 6009
conn = None
addr = None
listener = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
def init(wish_host, wish_port):
global host, port, listener
host = wish_host
port = wish_port
listener.bind((host, port))
listener.listen()
listener.settimeout(0)
def try_connect():
global conn, addr, listener
try:
conn, addr = listener.accept()
print(f"\nConnected by {addr}")
conn.settimeout(None)
except Exception as inst:
pass
def read():
global conn
messageLength = conn.recv(4)
messageLength = int.from_bytes(messageLength, 'little')
message = conn.recv(messageLength)
return json.loads(message.decode("utf-8"))
def send(message_bytes, verify):
global conn
if message_bytes != None:
conn.sendall(message_bytes)
conn.sendall(len(verify).to_bytes(4, 'little'))
conn.sendall(bytes(verify, 'ascii'))
def receive():
message = read()
width = message["resolution_x"]
height = message["resolution_y"]
if width != 0 and height != 0:
try:
do_training = bool(message["train"])
fovy = message["fov_y"]
fovx = message["fov_x"]
znear = message["z_near"]
zfar = message["z_far"]
do_shs_python = bool(message["shs_python"])
do_rot_scale_python = bool(message["rot_scale_python"])
keep_alive = bool(message["keep_alive"])
scaling_modifier = message["scaling_modifier"]
world_view_transform = torch.reshape(torch.tensor(message["view_matrix"]), (4, 4)).cuda()
world_view_transform[:,1] = -world_view_transform[:,1]
world_view_transform[:,2] = -world_view_transform[:,2]
full_proj_transform = torch.reshape(torch.tensor(message["view_projection_matrix"]), (4, 4)).cuda()
full_proj_transform[:,1] = -full_proj_transform[:,1]
custom_cam = MiniCam(width, height, fovy, fovx, znear, zfar, world_view_transform, full_proj_transform)
except Exception as e:
print("")
traceback.print_exc()
raise e
return custom_cam, do_training, do_shs_python, do_rot_scale_python, keep_alive, scaling_modifier
else:
return None, None, None, None, None, None |