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nerfreal.py
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nerfreal.py
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import math
import torch
import numpy as np
#from .utils import *
import subprocess
import os
import time
import torch.nn.functional as F
import cv2
from asrreal import ASR
from rtmp_streaming import StreamerConfig, Streamer
class NeRFReal:
def __init__(self, opt, trainer, data_loader, debug=True):
self.opt = opt # shared with the trainer's opt to support in-place modification of rendering parameters.
self.W = opt.W
self.H = opt.H
self.debug = debug
self.training = False
self.step = 0 # training step
self.trainer = trainer
self.data_loader = data_loader
# use dataloader's bg
bg_img = data_loader._data.bg_img #.view(1, -1, 3)
if self.H != bg_img.shape[0] or self.W != bg_img.shape[1]:
bg_img = F.interpolate(bg_img.permute(2, 0, 1).unsqueeze(0).contiguous(), (self.H, self.W), mode='bilinear').squeeze(0).permute(1, 2, 0).contiguous()
self.bg_color = bg_img.view(1, -1, 3)
# audio features (from dataloader, only used in non-playing mode)
self.audio_features = data_loader._data.auds # [N, 29, 16]
self.audio_idx = 0
#self.frame_total_num = data_loader._data.end_index
#print("frame_total_num:",self.frame_total_num)
# control eye
self.eye_area = None if not self.opt.exp_eye else data_loader._data.eye_area.mean().item()
# playing seq from dataloader, or pause.
self.playing = True #False todo
self.loader = iter(data_loader)
self.render_buffer = np.zeros((self.W, self.H, 3), dtype=np.float32)
self.need_update = True # camera moved, should reset accumulation
self.spp = 1 # sample per pixel
self.mode = 'image' # choose from ['image', 'depth']
self.dynamic_resolution = False # assert False!
self.downscale = 1
self.train_steps = 16
self.ind_index = 0
self.ind_num = trainer.model.individual_codes.shape[0]
# build asr
if self.opt.asr:
self.asr = ASR(opt)
'''
video_path = 'video_stream'
if not os.path.exists(video_path):
os.mkfifo(video_path, mode=0o777)
audio_path = 'audio_stream'
if not os.path.exists(audio_path):
os.mkfifo(audio_path, mode=0o777)
width=450
height=450
command = ['ffmpeg',
'-y', #'-an',
#'-re',
'-f', 'rawvideo',
'-vcodec','rawvideo',
'-pix_fmt', 'rgb24', #像素格式
'-s', "{}x{}".format(width, height),
'-r', str(fps),
'-i', video_path,
'-f', 's16le',
'-acodec','pcm_s16le',
'-ac', '1',
'-ar', '16000',
'-i', audio_path,
#'-fflags', '+genpts',
'-map', '0:v',
'-map', '1:a',
#'-copyts',
'-acodec', 'aac',
'-pix_fmt', 'yuv420p', #'-vcodec', "h264",
#"-rtmp_buffer", "100",
'-f' , 'flv',
push_url]
self.pipe = subprocess.Popen(command, shell=False) #, stdin=subprocess.PIPE)
self.fifo_video = open(video_path, 'wb')
self.fifo_audio = open(audio_path, 'wb')
#self.test_step()
'''
def __enter__(self):
return self
def __exit__(self, exc_type, exc_value, traceback):
if self.opt.asr:
self.asr.stop()
def push_audio(self,chunk):
self.asr.push_audio(chunk)
def prepare_buffer(self, outputs):
if self.mode == 'image':
return outputs['image']
else:
return np.expand_dims(outputs['depth'], -1).repeat(3, -1)
def test_step(self):
#starter, ender = torch.cuda.Event(enable_timing=True), torch.cuda.Event(enable_timing=True)
#starter.record()
if self.playing:
try:
data = next(self.loader)
except StopIteration:
self.loader = iter(self.data_loader)
data = next(self.loader)
if self.opt.asr:
# use the live audio stream
data['auds'] = self.asr.get_next_feat()
#t = time.time()
outputs = self.trainer.test_gui_with_data(data, self.W, self.H)
#print('-------ernerf time: ',time.time()-t)
#print(f'[INFO] outputs shape ',outputs['image'].shape)
image = (outputs['image'] * 255).astype(np.uint8)
if not self.opt.fullbody:
self.streamer.stream_frame(image)
else: #fullbody human
#print("frame index:",data['index'])
image_fullbody = cv2.imread(os.path.join(self.opt.fullbody_img, str(data['index'][0])+'.jpg'))
image_fullbody = cv2.cvtColor(image_fullbody, cv2.COLOR_BGR2RGB)
start_x = self.opt.fullbody_offset_x # 合并后小图片的起始x坐标
start_y = self.opt.fullbody_offset_y # 合并后小图片的起始y坐标
image_fullbody[start_y:start_y+image.shape[0], start_x:start_x+image.shape[1]] = image
self.streamer.stream_frame(image_fullbody)
#self.pipe.stdin.write(image.tostring())
for _ in range(2):
frame = self.asr.get_audio_out()
#print(f'[INFO] get_audio_out shape ',frame.shape)
self.streamer.stream_frame_audio(frame)
# frame = (frame * 32767).astype(np.int16).tobytes()
# self.fifo_audio.write(frame)
else:
if self.audio_features is not None:
auds = get_audio_features(self.audio_features, self.opt.att, self.audio_idx)
else:
auds = None
outputs = self.trainer.test_gui(self.cam.pose, self.cam.intrinsics, self.W, self.H, auds, self.eye_area, self.ind_index, self.bg_color, self.spp, self.downscale)
#ender.record()
#torch.cuda.synchronize()
#t = starter.elapsed_time(ender)
def render(self):
if self.opt.asr:
self.asr.warm_up()
count=0
totaltime=0
fps=25
#push_url='rtmp://localhost/live/livestream' #'data/video/output_0.mp4'
sc = StreamerConfig()
sc.source_width = self.W
sc.source_height = self.H
sc.stream_width = self.W
sc.stream_height = self.H
if self.opt.fullbody:
sc.source_width = self.opt.fullbody_width
sc.source_height = self.opt.fullbody_height
sc.stream_width = self.opt.fullbody_width
sc.stream_height = self.opt.fullbody_height
sc.stream_fps = fps
sc.stream_bitrate = 1000000
sc.stream_profile = 'baseline' #'high444' # 'main'
sc.audio_channel = 1
sc.sample_rate = 16000
sc.stream_server = self.opt.push_url
self.streamer = Streamer()
self.streamer.init(sc)
#self.streamer.enable_av_debug_log()
while True: #todo
# update texture every frame
# audio stream thread...
t = time.time()
if self.opt.asr and self.playing:
# run 2 ASR steps (audio is at 50FPS, video is at 25FPS)
for _ in range(2):
self.asr.run_step()
self.test_step()
totaltime += (time.time() - t)
count += 1
if count==100:
print(f"------actual avg fps:{count/totaltime:.4f}")
count=0
totaltime=0
# delay = 0.04 - (time.time() - t) #40ms
# if delay > 0:
# time.sleep(delay)