masks : tf.Tensor of shape [batch, c, n_people, 1]
"""
im_size = [512, 512]
paf_sigma = 20
keypoints = InputLayer(input_shape=[32, 24, 8, 2], name='keypoints')
masks = InputLayer(input_shape=[32, 24, 8, 1], name='keypoints')
paf_layer = PAFLayer(im_size=im_size,
sigma=paf_sigma,
skeleton=CONNECT_INDEXES_FOR_PAFF)
paf = paf_layer([keypoints, masks])
sess = tf.Session()
paf_shape = sess.run(tf.shape(paf.get_data_tensor()),
feed_dict={
keypoints.get_data_tensor():
np.random.randn(32, 24, 8, 2),
masks.get_data_tensor():
np.random.randn(32, 24, 8, 1)
})
print(paf)
print(paf_shape)
import matplotlib
# For some reason matplotlib doesn't want to show the plot when it is called from PyCharm
matplotlib.use('TkAgg')
import seaborn as sns
import matplotlib.pyplot as plt
import math
def put_paf_on_plane(vectormap, countmap, plane_idx, x1, y1, x2, y2,
from makiflow.layers import InputLayer
# RUN A SANITY CHECK FIRST
in_x = InputLayer(input_shape=[1, 3, 3, 100 * 2], name='offsets')
# Never pass in a numpy array to the `custom_embedding` argument. Always use list.
coords_ish = SkeletonEmbeddingLayer(embedding_dim=None, name='TestEmbedding', custom_embedding=points)(in_x)
print('Coords MakiTensor', coords_ish)
print('Coords TfTensor', coords_ish.get_data_tensor())
sess = tf.Session()
sess.run(tf.global_variables_initializer())
coords = sess.run(
coords_ish.get_data_tensor(),
feed_dict={
in_x.get_data_tensor(): np.zeros(shape=[1, 3, 3, 200], dtype='float32')
}
)
# Visualize the circles
import matplotlib
# For some reason matplotlib doesn't want to show the plot when it is called from PyCharm
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
coords = coords.reshape(-1, 2)
plt.scatter(coords[:, 0], coords[:, 1])
plt.show()
from makiflow.core.debug import DebugContext
from .head import Head
head = Head(coords, point_indicators, human_indicators)
model = SSPModel(heads=[head], in_x=in_x)
sess = tf.Session()
model.set_session(sess)
coords, _, _ = model._session.run(
[
model._regressed_points, model._point_indicators,
model._human_indicators
],
feed_dict={
model._in_x.get_data_tensor():
np.zeros(shape=[1, 3, 3, 200], dtype='float32'),
point_indicators.get_data_tensor():
np.ones(shape=[1, 3, 3, 100], dtype='float32'),
human_indicators.get_data_tensor():
np.ones(shape=[1, 3, 3, 1], dtype='float32')
})
# Visualize the circles
import matplotlib
# For some reason matplotlib doesn't want to show the plot when it is called from PyCharm
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
coords = coords.reshape(-1, 2)
plt.scatter(coords[:, 0], coords[:, 1])
plt.show()