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()