mirror of
https://github.com/graphdeco-inria/gaussian-splatting
synced 2024-11-25 21:28:17 +00:00
75 lines
2.4 KiB
Python
75 lines
2.4 KiB
Python
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import torch
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import traceback
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import socket
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import json
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from scene.cameras import MiniCam
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host = "127.0.0.1"
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port = 6009
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conn = None
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addr = None
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listener = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
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def init(wish_host, wish_port):
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global host, port, listener
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host = wish_host
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port = wish_port
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listener.bind((host, port))
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listener.listen()
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listener.settimeout(0)
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def try_connect():
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global conn, addr, listener
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try:
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conn, addr = listener.accept()
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print(f"\nConnected by {addr}")
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conn.settimeout(None)
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except Exception as inst:
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pass
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def read():
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global conn
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messageLength = conn.recv(4)
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messageLength = int.from_bytes(messageLength, 'little')
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message = conn.recv(messageLength)
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return json.loads(message.decode("utf-8"))
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def send(message_bytes, verify):
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global conn
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if message_bytes != None:
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conn.sendall(message_bytes)
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conn.sendall(len(verify).to_bytes(4, 'little'))
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conn.sendall(bytes(verify, 'ascii'))
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def receive():
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message = read()
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width = message["resolution_x"]
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height = message["resolution_y"]
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if width != 0 and height != 0:
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try:
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do_training = bool(message["train"])
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fovy = message["fov_y"]
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fovx = message["fov_x"]
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znear = message["z_near"]
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zfar = message["z_far"]
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do_shs_python = bool(message["shs_python"])
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do_rot_scale_python = bool(message["rot_scale_python"])
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keep_alive = bool(message["keep_alive"])
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scaling_modifier = message["scaling_modifier"]
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world_view_transform = torch.reshape(torch.tensor(message["view_matrix"]), (4, 4)).cuda()
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world_view_transform[:,1] = -world_view_transform[:,1]
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world_view_transform[:,2] = -world_view_transform[:,2]
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full_proj_transform = torch.reshape(torch.tensor(message["view_projection_matrix"]), (4, 4)).cuda()
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full_proj_transform[:,1] = -full_proj_transform[:,1]
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custom_cam = MiniCam(width, height, fovy, fovx, znear, zfar, world_view_transform, full_proj_transform)
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except Exception as e:
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print("")
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traceback.print_exc()
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raise e
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return custom_cam, do_training, do_shs_python, do_rot_scale_python, keep_alive, scaling_modifier
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else:
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return None, None, None, None, None, None
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