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SyncNetInstance.py
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SyncNetInstance.py
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#!/usr/bin/python
#-*- coding: utf-8 -*-
# Video 25 FPS, Audio 16000HZ
import torch
import numpy
import time, pdb, argparse, subprocess, os
import cv2
import python_speech_features
from scipy import signal
from scipy.io import wavfile
from SyncNetModel import *
# ==================== Get OFFSET ====================
def calc_pdist(feat1, feat2, vshift=10):
win_size = vshift*2+1
feat2p = torch.nn.functional.pad(feat2,(0,0,vshift,vshift))
dists = []
for i in range(0,len(feat1)):
dists.append(torch.nn.functional.pairwise_distance(feat1[[i],:].repeat(win_size, 1), feat2p[i:i+win_size,:]))
return dists
# ==================== MAIN DEF ====================
class SyncNetInstance(torch.nn.Module):
def __init__(self, dropout = 0, num_layers_in_fc_layers = 1024):
super(SyncNetInstance, self).__init__();
self.__S__ = S(num_layers_in_fc_layers = num_layers_in_fc_layers).cuda();
def evaluate(self, opt, videofile):
self.__S__.eval();
# ========== ==========
# Load video
# ========== ==========
cap = cv2.VideoCapture(videofile)
frame_num = 1;
images = []
while frame_num:
frame_num += 1
ret, image = cap.read()
if ret == 0:
break
image_np = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
images.append(image_np)
im = numpy.stack(images,axis=3)
im = numpy.expand_dims(im,axis=0)
im = numpy.transpose(im,(0,3,4,1,2))
imtv = torch.autograd.Variable(torch.from_numpy(im.astype(float)).float())
# ========== ==========
# Load audio
# ========== ==========
audiotmp = os.path.join(opt.tmp_dir,'audio.wav')
command = ("ffmpeg -y -i %s -async 1 -ac 1 -vn -acodec pcm_s16le -ar 16000 %s" % (videofile,audiotmp))
output = subprocess.call(command, shell=True, stdout=None)
sample_rate, audio = wavfile.read(audiotmp)
mfcc = zip(*python_speech_features.mfcc(audio,sample_rate))
mfcc = numpy.stack([numpy.array(i) for i in mfcc])
cc = numpy.expand_dims(numpy.expand_dims(mfcc,axis=0),axis=0)
cct = torch.autograd.Variable(torch.from_numpy(cc.astype(float)).float())
# ========== ==========
# Check audio and video input length
# ========== ==========
if (float(len(audio))/16000) < (float(len(images))/25) :
print(" *** WARNING: The audio (%.4fs) is shorter than the video (%.4fs). Type 'cont' to continue. *** "%(float(len(audio))/16000,float(len(images))/25))
pdb.set_trace()
# ========== ==========
# Generate video and audio feats
# ========== ==========
lastframe = len(images)-6
im_feat = []
cc_feat = []
tS = time.time()
for i in range(0,lastframe,opt.batch_size):
im_batch = [ imtv[:,:,vframe:vframe+5,:,:] for vframe in range(i,min(lastframe,i+opt.batch_size)) ]
im_in = torch.cat(im_batch,0)
im_out = self.__S__.forward_lip(im_in.cuda());
im_feat.append(im_out.data.cpu())
cc_batch = [ cct[:,:,:,vframe*4:vframe*4+20] for vframe in range(i,min(lastframe,i+opt.batch_size)) ]
cc_in = torch.cat(cc_batch,0)
cc_out = self.__S__.forward_aud(cc_in.cuda())
cc_feat.append(cc_out.data.cpu())
im_feat = torch.cat(im_feat,0)
cc_feat = torch.cat(cc_feat,0)
# ========== ==========
# Compute offset
# ========== ==========
# print('Compute time %.3f sec.' % (time.time()-tS))
dists = calc_pdist(im_feat,cc_feat,vshift=opt.vshift)
mdist = torch.mean(torch.stack(dists,1),1)
minval, minidx = torch.min(mdist,0)
offset = opt.vshift-minidx
conf = torch.median(mdist) - minval
fdist = numpy.stack([dist[minidx].numpy() for dist in dists])
# fdist = numpy.pad(fdist, (3,3), 'constant', constant_values=15)
fconf = torch.median(mdist).numpy() - fdist
fconfm = signal.medfilt(fconf,kernel_size=9)
# numpy.set_printoptions(formatter={'float': '{: 0.3f}'.format})
# print('Framewise conf: ')
# print(fconfm)
# print('AV offset: \t%d \nMin dist: \t%.3f\nConfidence: \t%.3f' % (offset,minval,conf))
dists_npy = numpy.array([ dist.numpy() for dist in dists ])
torch.cuda.empty_cache()
return offset.numpy(), conf.numpy(), dists_npy
def extract_feature(self, opt, videofile):
self.__S__.eval();
# ========== ==========
# Load video
# ========== ==========
cap = cv2.VideoCapture(videofile)
frame_num = 1;
images = []
while frame_num:
frame_num += 1
ret, image = cap.read()
if ret == 0:
break
image_np = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
images.append(image_np)
im = numpy.stack(images,axis=3)
im = numpy.expand_dims(im,axis=0)
im = numpy.transpose(im,(0,3,4,1,2))
imtv = torch.autograd.Variable(torch.from_numpy(im.astype(float)).float())
# ========== ==========
# Generate video feats
# ========== ==========
lastframe = len(images)-4
im_feat = []
tS = time.time()
for i in range(0,lastframe,opt.batch_size):
im_batch = [ imtv[:,:,vframe:vframe+5,:,:] for vframe in range(i,min(lastframe,i+opt.batch_size)) ]
im_in = torch.cat(im_batch,0)
im_out = self.__S__.forward_lipfeat(im_in.cuda());
im_feat.append(im_out.data.cpu())
im_feat = torch.cat(im_feat,0)
# ========== ==========
# Compute offset
# ========== ==========
print('Compute time %.3f sec.' % (time.time()-tS))
return im_feat
def loadParameters(self, path):
loaded_state = torch.load(path, map_location=lambda storage, loc: storage);
self_state = self.__S__.state_dict();
for name, param in loaded_state.items():
self_state[name].copy_(param);