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import os
from skimage import io,img_as_float32
import cv2
import torch
import numpy as np
import subprocess
import pandas
from models.audio2pose import audio2poseLSTM
from scipy.io import wavfile
import python_speech_features
import pyworld
import config
import json
from scipy.interpolate import interp1d
def inter_pitch(y,y_flag):
frame_num = y.shape[0]
i = 0
last = -1
while(i<frame_num):
if y_flag[i] == 0:
while True:
if y_flag[i]==0:
if i == frame_num-1:
if last !=-1:
y[last+1:] = y[last]
i+=1
break
i+=1
else:
break
if i >= frame_num:
break
elif last == -1:
y[:i] = y[i]
else:
inter_num = i-last+1
fy = np.array([y[last],y[i]])
fx = np.linspace(0, 1, num=2)
f = interp1d(fx,fy)
fx_new = np.linspace(0,1,inter_num)
fy_new = f(fx_new)
y[last+1:i] = fy_new[1:-1]
last = i
i+=1
else:
last = i
i+=1
return y
def load_ckpt(checkpoint_path, generator = None, kp_detector = None, ph2kp = None):
checkpoint = torch.load(checkpoint_path)
if ph2kp is not None:
ph2kp.load_state_dict(checkpoint['ph2kp'])
if generator is not None:
generator.load_state_dict(checkpoint['generator'])
if kp_detector is not None:
kp_detector.load_state_dict(checkpoint['kp_detector'])
def get_img_pose(img_path):
processor = config.OPENFACE_POSE_EXTRACTOR_PATH
tmp_dir = "samples/tmp_dir"
os.makedirs((tmp_dir),exist_ok=True)
subprocess.call([processor, "-f", img_path, "-out_dir", tmp_dir, "-pose"])
img_file = os.path.basename(img_path)[:-4]+".csv"
csv_file = os.path.join(tmp_dir,img_file)
pos_data = pandas.read_csv(csv_file)
i = 0
pose = [pos_data["pose_Rx"][i], pos_data["pose_Ry"][i], pos_data["pose_Rz"][i],pos_data["pose_Tx"][i], pos_data["pose_Ty"][i], pos_data["pose_Tz"][i]]
# pose = [pose]
pose = np.array(pose,dtype=np.float32)
return pose
def read_img(path):
img = io.imread(path)[:,:,:3]
img = cv2.resize(img, (256, 256))
# img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
img = np.array(img_as_float32(img))
img = img.transpose((2, 0, 1))
img = torch.from_numpy(img).unsqueeze(0)
return img
def parse_phoneme_file(phoneme_path,use_index = True):
with open(phoneme_path,'r') as f:
result_text = json.load(f)
frame_num = int(result_text[-1]['phones'][-1]['ed']/100*25)
phoneset_list = []
index = 0
word_len = len(result_text)
word_index = 0
phone_index = 0
cur_phone_list = result_text[0]["phones"]
phone_len = len(cur_phone_list)
cur_end = cur_phone_list[0]["ed"]
phone_list = []
phoneset_list.append(cur_phone_list[0]["ph"])
i = 0
while i < frame_num:
if i * 4 < cur_end:
phone_list.append(cur_phone_list[phone_index]["ph"])
i += 1
else:
phone_index += 1
if phone_index >= phone_len:
word_index += 1
if word_index >= word_len:
phone_list.append(cur_phone_list[-1]["ph"])
i += 1
else:
phone_index = 0
cur_phone_list = result_text[word_index]["phones"]
phone_len = len(cur_phone_list)
cur_end = cur_phone_list[phone_index]["ed"]
phoneset_list.append(cur_phone_list[phone_index]["ph"])
index += 1
else:
# print(word_index,phone_index)
cur_end = cur_phone_list[phone_index]["ed"]
phoneset_list.append(cur_phone_list[phone_index]["ph"])
index += 1
with open("phindex.json") as f:
ph2index = json.load(f)
if use_index:
phone_list = [ph2index[p] for p in phone_list]
saves = {"phone_list": phone_list}
return saves
def get_audio_feature_from_audio(audio_path):
sample_rate, audio = wavfile.read(audio_path)
if len(audio.shape) == 2:
if np.min(audio[:, 0]) <= 0:
audio = audio[:, 1]
else:
audio = audio[:, 0]
audio = audio - np.mean(audio)
audio = audio / np.max(np.abs(audio))
a = python_speech_features.mfcc(audio, sample_rate)
b = python_speech_features.logfbank(audio, sample_rate)
c, _ = pyworld.harvest(audio, sample_rate, frame_period=10)
c_flag = (c == 0.0) ^ 1
c = inter_pitch(c, c_flag)
c = np.expand_dims(c, axis=1)
c_flag = np.expand_dims(c_flag, axis=1)
frame_num = np.min([a.shape[0], b.shape[0], c.shape[0]])
cat = np.concatenate([a[:frame_num], b[:frame_num], c[:frame_num], c_flag[:frame_num]], axis=1)
return cat
def get_pose_from_audio(img,audio,audio2pose):
num_frame = len(audio) // 4
minv = np.array([-0.6, -0.6, -0.6, -128.0, -128.0, 128.0], dtype=np.float32)
maxv = np.array([0.6, 0.6, 0.6, 128.0, 128.0, 384.0], dtype=np.float32)
generator = audio2poseLSTM().cuda().eval()
ckpt_para = torch.load(audio2pose)
generator.load_state_dict(ckpt_para["generator"])
generator.eval()
audio_seq = []
for i in range(num_frame):
audio_seq.append(audio[i*4:i*4+4])
audio = torch.from_numpy(np.array(audio_seq,dtype=np.float32)).unsqueeze(0).cuda()
x = {}
x ["img"] = img
x["audio"] = audio
poses = generator(x)
poses = poses.cpu().data.numpy()[0]
poses = (poses+1)/2*(maxv-minv)+minv
return poses
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