def vis(gan_id): """Visualize output from the given gan.""" builder = GanBuilder(gan_id) skeleton = get_skeleton( builder.params['dataset']['normalize_kwargs']['skeleton_id']) print('Building graph...') graph = tf.Graph() with graph.as_default(): gen_input = builder.get_random_generator_input() with tf.variable_scope('Generator'): sample = builder.get_generator_sample(gen_input) generator_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES) print('Starting session...') with tf.Session(graph=graph) as sess: print('Restoring variables...') saver = tf.train.Saver(var_list=generator_vars) saver.restore(sess, builder.latest_checkpoint) print('Generating...') sample_data = sess.run(sample) print('Visualizing...') for s in sample_data: vis3d(skeleton, s) plt.show()
def vis_data_plt(skeleton, ground_truth, inferred): """Visualizes ground_truth/inferred data using matplotlib.pyplot.""" from human_pose_util.skeleton import vis3d import matplotlib.pyplot as plt for i in range(0, len(ground_truth), 10): ax = vis3d(skeleton, ground_truth[i], linewidth=4.0) vis3d(skeleton, inferred[i], ax=ax) plt.show()
"""Get the root node for all other data.""" return _root_node('r') if __name__ == '__main__': if not os.path.exists(_hdf5_path): try: convert() except Exception: if os.path.isfile(_hdf5_path): os.remove(_hdf5_path) raise else: import imageio from skeleton import s24 from human_pose_util.skeleton import vis3d, vis2d, plt with root_node() as rn: for sequence in rn.sequences(): vis3d(s24, sequence.p3[0]) plt.show() for view in sequence.views: with imageio.get_reader(view.video_path) as reader: # frame = reader.get_data(0) frame = reader.get_next_data() p2 = view.p2[0] p3 = view.p3[0] ax = plt.gca() ax.imshow(frame) vis2d(s24, p2, ax=ax, change_ax=False) plt.show()
# raise Exception('Unrecognized skeleton, %s' % skeleton) # else: # return dataset if __name__ == '__main__': import numpy as np from human_pose_util.skeleton import vis3d, vis2d from human_pose_util.transforms.np_impl import transform_frame from human_pose_util.transforms.np_impl import project from skeleton import s14 # from skeleton import s16 from skeleton import s20 import matplotlib.pyplot as plt # dataset = human_eva_dataset(skeleton=s16) example = get_example(list(train_ids())[0]).map( {k: s20.to_s14 for k in ['p2', 'p3c', 'p3w']}) p3w, p3c, p2, r, t, f, c = example['p3w', 'p3c', 'p2', 'r', 't', 'f', 'c'] p3w = p3w[0] p3c = p3c[0] p2 = p2[0] p3c2 = transform_frame(p3w, r, t) print(np.max(np.abs(p3c - p3c2)) / np.max(np.abs(p3c))) p22 = project(p3c, f, c) print(np.max(np.abs(p22 - p2)) / np.max(np.abs(p2))) skeleton = s14 vis3d(skeleton, p3w) vis3d(skeleton, p3c) vis2d(skeleton, p2) plt.show()