def display_results(in_image, data_2d, joint_visibility, data_3d):
"""Plot 2D and 3D poses for each of the people in the image."""
plt.figure()
draw_limbs(in_image, data_2d, joint_visibility)
plt.imshow(in_image)
plt.axis('off')
# Show 3D poses
for single_3D in data_3d:
# or plot_pose(Prob3dPose.centre_all(single_3D))
plot_pose(single_3D)
plt.show()
def display_results(in_image, data_2d, joint_visibility, data_3d, frame):
"""Plot 2D and 3D poses for each of the people in the image."""
plt.figure()
draw_limbs(in_image, data_2d, joint_visibility)
# plt.imshow(in_image)
plt.axis('off')
# Show 3D poses
for single_3D in data_3d:
# or plot_pose(Prob3dPose.centre_all(single_3D))
plot_pose(single_3D)
pngName = 'png/test_{0}.jpg'.format(str(frame).zfill(12))
plt.savefig(pngName)
def display_results(in_image, data_2d, joint_visibility, data_3d):
"""
FOR VISUALIZATION OF 'LIFTING FROM THE DEEP' OUTPUT FOR ONE FRAME
Plot 2D and 3D poses for each of the people in the image.
"""
plt.figure()
draw_limbs(in_image, data_2d, joint_visibility)
plt.imshow(in_image)
plt.axis('off')
# Show 3D poses
for single_3D in data_3d:
# or plot_pose(Prob3dPose.centre_all(single_3D))
plot_pose(single_3D)
plt.show()
def display_results(mode, in_image, data_2d, visibilities, data_3d):
"""Plot 2D and 3D poses for each of the people in the image."""
n_poses = len(data_3d)
if mode == 'openpose':
color_im = OpPoseEstimator.draw_humans(in_image,
data_2d,
imgcopy=False)
else:
draw_limbs(in_image, data_2d, visibilities)
plt.subplot(1, n_poses + 1, 1)
plt.imshow(in_image)
plt.axis('off')
# Show 3D poses
for idx, single_3D in enumerate(data_3d):
plot_pose(Prob3dPose.centre_all(single_3D),
visibility_to_3d(visibilities[idx]), n_poses + 1, idx + 2)
def display_results(in_image, data_2d, joint_visibility, data_3d, i):
"""
Plot 2D and 3D poses for each of the people in the image;
:param in_image:
:param data_2d:
:param joint_visibility:
:param data_3d:
:param i:
:return:
"""
plt.figure(i)
draw_limbs(in_image, data_2d, joint_visibility, i)
plt.imshow(in_image)
# plt.axis('off')
# Show 3D poses
for single_3D in data_3d:
# or plot_pose(Prob3dPose.centre_all(single_3D))
plot_pose(single_3D)
plt.show()
import scipy.io
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from lifting import utils
mats = scipy.io.loadmat('data/saved_sessions/prob_model/prob_model_params.mat')
e = mats['e']
mu = mats['mu']
sigma = mats['sigma']
mu = np.reshape(mu, (mu.shape[0], 3, -1))
e = np.reshape(e, (e.shape[0], e.shape[1], 3, -1))
sh = np.shape(e)
for i in range(sh[0]):
for j in range(sh[1]):
#pose0 = mu[i, :, :]
pose0 = e[i, j, :, :]
utils.plot_pose(pose0)
plt.show()
