-
Notifications
You must be signed in to change notification settings - Fork 92
/
nerfreal.py
271 lines (235 loc) · 10.3 KB
/
nerfreal.py
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
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
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 ttsreal import EdgeTTS,VoitsTTS,XTTS
import asyncio
from av import AudioFrame, VideoFrame
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.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.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]
self.customimg_index = 0
# build asr
self.asr = ASR(opt)
self.asr.warm_up()
if opt.tts == "edgetts":
self.tts = EdgeTTS(opt,self)
elif opt.tts == "gpt-sovits":
self.tts = VoitsTTS(opt,self)
elif opt.tts == "xtts":
self.tts = XTTS(opt,self)
'''
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 put_msg_txt(self,msg):
self.tts.put_msg_txt(msg)
def put_audio_frame(self,audio_chunk): #16khz 20ms pcm
self.asr.put_audio_frame(audio_chunk)
def pause_talk(self):
self.tts.pause_talk()
self.asr.pause_talk()
def mirror_index(self, index):
size = self.opt.customvideo_imgnum
turn = index // size
res = index % size
if turn % 2 == 0:
return res
else:
return size - res - 1
def test_step(self,loop=None,audio_track=None,video_track=None):
#starter, ender = torch.cuda.Event(enable_timing=True), torch.cuda.Event(enable_timing=True)
#starter.record()
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()
audiotype = 0
if self.opt.transport=='rtmp':
for _ in range(2):
frame,type = self.asr.get_audio_out()
audiotype += type
#print(f'[INFO] get_audio_out shape ',frame.shape)
self.streamer.stream_frame_audio(frame)
else:
for _ in range(2):
frame,type = self.asr.get_audio_out()
audiotype += type
frame = (frame * 32767).astype(np.int16)
new_frame = AudioFrame(format='s16', layout='mono', samples=frame.shape[0])
new_frame.planes[0].update(frame.tobytes())
new_frame.sample_rate=16000
# if audio_track._queue.qsize()>10:
# time.sleep(0.1)
asyncio.run_coroutine_threadsafe(audio_track._queue.put(new_frame), loop)
#t = time.time()
if self.opt.customvideo and audiotype!=0:
self.loader = iter(self.data_loader) #init
imgindex = self.mirror_index(self.customimg_index)
#print('custom img index:',imgindex)
image = cv2.imread(os.path.join(self.opt.customvideo_img, str(int(imgindex))+'.png'))
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
if self.opt.transport=='rtmp':
self.streamer.stream_frame(image)
else:
new_frame = VideoFrame.from_ndarray(image, format="rgb24")
asyncio.run_coroutine_threadsafe(video_track._queue.put(new_frame), loop)
self.customimg_index += 1
else:
self.customimg_index = 0
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:
if self.opt.transport=='rtmp':
self.streamer.stream_frame(image)
else:
new_frame = VideoFrame.from_ndarray(image, format="rgb24")
asyncio.run_coroutine_threadsafe(video_track._queue.put(new_frame), loop)
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
if self.opt.transport=='rtmp':
self.streamer.stream_frame(image_fullbody)
else:
new_frame = VideoFrame.from_ndarray(image_fullbody, format="rgb24")
asyncio.run_coroutine_threadsafe(video_track._queue.put(new_frame), loop)
#self.pipe.stdin.write(image.tostring())
#ender.record()
#torch.cuda.synchronize()
#t = starter.elapsed_time(ender)
def render(self,quit_event,loop=None,audio_track=None,video_track=None):
#if self.opt.asr:
# self.asr.warm_up()
if self.opt.transport=='rtmp':
from rtmp_streaming import StreamerConfig, Streamer
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()
count=0
totaltime=0
_starttime=time.perf_counter()
_totalframe=0
self.tts.render(quit_event)
while not quit_event.is_set(): #todo
# update texture every frame
# audio stream thread...
t = time.perf_counter()
# run 2 ASR steps (audio is at 50FPS, video is at 25FPS)
for _ in range(2):
self.asr.run_step()
self.test_step(loop,audio_track,video_track)
totaltime += (time.perf_counter() - t)
count += 1
_totalframe += 1
if count==100:
print(f"------actual avg infer fps:{count/totaltime:.4f}")
count=0
totaltime=0
if self.opt.transport=='rtmp':
delay = _starttime+_totalframe*0.04-time.perf_counter() #40ms
if delay > 0:
time.sleep(delay)
else:
if video_track._queue.qsize()>=5:
#print('sleep qsize=',video_track._queue.qsize())
time.sleep(0.04*video_track._queue.qsize()*0.8)
print('nerfreal thread stop')