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()