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Zero
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import time
import torch
import torch.nn.functional as F
import numpy as np
def get_gaussian_kernel_1d(kernel_size, sigma, device):
x = torch.arange(kernel_size).float() - (kernel_size // 2)
g = torch.exp(-((x ** 2) / (2 * sigma ** 2)))
g /= g.sum()
kernel_weight = g.view(1, 1, -1).to(device)
return kernel_weight
def gaussian_filter_1d(data, kernel_size=3, sigma=1.0, weight=None):
kernel_weight = get_gaussian_kernel_1d(kernel_size, sigma, data.device) if weight is None else weight
data = F.pad(data, (kernel_size // 2, kernel_size // 2), mode='replicate')
return F.conv1d(data, kernel_weight)
def exponential_smoothing(x, d_x, alpha=0.5):
return d_x + alpha * (x - d_x)
class OneEuroFilter:
# param setting:
# realtime v2m: min_cutoff=1.0, beta=1.5
# motionshop 2d keypoint: min_cutoff=1.7, beta=0.3
def __init__(self, min_cutoff=1.0, beta=0.0, sampling_rate=30, d_cutoff=1.0, device='cuda'):
self.min_cutoff = min_cutoff
self.beta = beta
self.sampling_rate = sampling_rate
self.x_prev = None
self.dx_prev = None
self.d_cutoff = d_cutoff
self.pi = torch.tensor(torch.pi, device=device)
def smoothing_factor(self, cutoff):
r = 2 * self.pi * cutoff / self.sampling_rate
return r/ (1 + r)
def filter(self, x):
if self.x_prev is None:
self.x_prev = x
self.dx_prev = torch.zeros_like(x)
return x
a_d = self.smoothing_factor(self.d_cutoff)
# 计算当前的速度
dx = (x - self.x_prev) * self.sampling_rate
dx_hat = exponential_smoothing(dx, self.dx_prev, a_d)
cutoff = self.min_cutoff + self.beta * torch.abs(dx_hat)
a = self.smoothing_factor(cutoff)
x_hat = exponential_smoothing(x, self.x_prev, a)
self.x_prev = x_hat
self.dx_prev = dx_hat
return x_hat
class Filter():
filter_factory = {
'gaussian': get_gaussian_kernel_1d,
}
def __init__(self, target_data, filter_type, filter_args):
self.target_data = target_data
self.filter = self.filter_factory[filter_type]
self.filter_args = filter_args
def process(self, network_outputs):
filter_data = []
for human in network_outputs:
filter_data.append(human[self.target_data])
filter_data = torch.stack(filter_data, dim=0)
filter_data = self.filter(filter_data, **self.filter_args)
for i, human in enumerate(network_outputs):
human[self.target_data] = filter_data[i]
if __name__ == '__main__':
import argparse
import matplotlib.pyplot as plt
import numpy as np
from rot6d import rotation_6d_to_axis_angle, axis_angle_to_rotation_6d
from humans import get_smplx_joint_names
parser = argparse.ArgumentParser()
parser.add_argument('--data_path', type=str)
parser.add_argument('--save_path', type=str)
parser.add_argument('--name', type=str)
args = parser.parse_args()
fig, axs = plt.subplots(nrows=2, ncols=2, figsize=(10, 8))
data_types = ['rotvec']#, 'j3d']
observe_keypoints = ['pelvis', 'head', 'left_wrist', 'left_knee']
joint_names = get_smplx_joint_names()
data = np.load(f'{args.data_path}/shape_{args.name}.npy')
fig, axs = plt.subplots(nrows=2, ncols=2, figsize=(10, 8))
for i in range(2):
for j in range(2):
x = data[:, i*4 + j*2]
print(x.shape)
axs[i, j].plot(x)
axs[i, j].set_title(f'{4 * i + 2 * j}')
axs[i, j].plot(np.load(f'{args.data_path}/dist_{args.name}.npy'))
plt.tight_layout()
plt.savefig(f'{args.save_path}/shape_{args.name}.jpg')
# for data_type in data_types:
# data = np.load(f'{args.data_path}/{data_type}_{args.name}.npy')
# fig, axs = plt.subplots(nrows=2, ncols=2, figsize=(10, 8))
# for i in range(2):
# for j in range(2):
# # todo: something wrong here
# filter = OneEuroFilter(min_cutoff=1, beta=0.01, sampling_rate=30, device='cuda:0')
# x = data[:, joint_names.index(observe_keypoints[i*2+j])] #(F, 3)
# print(x.shape)
# x = axis_angle_to_rotation_6d(torch.tensor(x, device='cuda:0'))
# x_filtered = x.clone()
# start = time.time()
# for k in range(x.shape[0]):
# x_filtered[k] = filter.filter(x[k])
# print(x_filtered.shape[0]/(time.time()-start))
# # x_filtered = x.clone()
# # a = 0.5
# # for k in range(1, x.shape[0]):
# # x_filtered[k] = (1 - a) * x_filtered[k-1] + a * x[k]
# #theta = np.linalg.norm(x, axis=-1)
# #x = x / theta[..., None]
# # f, n = x.shape
# # x_filtered = gaussian_filter_1d(x.permute(1, 0).view(n, 1, -1), 11, 11)
# # x_filtered = x_filtered.view(n, -1).permute(1, 0)
# x = rotation_6d_to_axis_angle(x).cpu().numpy()
# x_filtered = rotation_6d_to_axis_angle(x_filtered).cpu().numpy()
# axs[i, j].plot(x[..., 0])
# axs[i, j].plot(x[..., 1])
# axs[i, j].plot(x[..., 2])
# axs[i, j].plot(x_filtered[..., 0])
# axs[i, j].plot(x_filtered[..., 1])
# axs[i, j].plot(x_filtered[..., 2])
# #axs[i, j].plot(theta)
# axs[i, j].set_title(f'{observe_keypoints[i*2 + j]}')
# plt.tight_layout()
# plt.savefig(f'{args.save_path}/{data_type}_{args.name}.jpg')
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