44 lines
1.7 KiB
Python
44 lines
1.7 KiB
Python
import numpy as np
|
||
import torch
|
||
import torch.nn as nn
|
||
import torch.nn.functional as F
|
||
|
||
def smooth_array(array, weight = [0.1,0.8,0.1]):
|
||
'''
|
||
|
||
Args:
|
||
array: [n_frames, n_values]， 需要转换为[n_values, 1, n_frames]
|
||
weight: Conv1d.weight, 一维卷积核权重
|
||
Returns:
|
||
array: [n_frames, n_values]， 光滑后的array
|
||
'''
|
||
input = torch.Tensor(np.transpose(array[:,np.newaxis,:], (2, 1, 0)))
|
||
smooth_length = len(weight)
|
||
assert smooth_length%2 == 1, "卷积核权重个数必须使用奇数"
|
||
pad = (smooth_length//2, smooth_length//2) # 当pad只有两个参数时，仅改变最后一个维度, 左边扩充1列，右边扩充1列
|
||
input = F.pad(input, pad, "replicate")
|
||
|
||
with torch.no_grad():
|
||
conv1 = nn.Conv1d(in_channels=1, out_channels=1, kernel_size=smooth_length)
|
||
# 卷积核的元素值初始化
|
||
weight = torch.tensor(weight).view(1, 1, -1)
|
||
conv1.weight = torch.nn.Parameter(weight)
|
||
nn.init.constant_(conv1.bias, 0) # 偏置值为0
|
||
# print(conv1.weight)
|
||
out = conv1(input)
|
||
return out.permute(2,1,0).squeeze().numpy()
|
||
|
||
if __name__ == '__main__':
|
||
model_id = "new_case"
|
||
Path_output_pkl = "../preparation/{}/mouth_info.pkl".format(model_id + "/00001")
|
||
import pickle
|
||
with open(Path_output_pkl, "rb") as f:
|
||
images_info = pickle.load(f)
|
||
pts_array_normalized = np.array(images_info[2])
|
||
pts_array_normalized = pts_array_normalized.reshape(-1, 16)
|
||
smooth_array_ = smooth_array(pts_array_normalized)
|
||
print(smooth_array_, smooth_array_.shape)
|
||
smooth_array_ = smooth_array_.reshape(-1, 4, 4)
|
||
import pandas as pd
|
||
|
||
pd.DataFrame(smooth_array_[:, :, 0]).to_csv("mat2.csv") |