in lib/dataset/JointsDataset.py [0:0]
def generate_target(self, joints, joints_vis):
'''
:param joints: [num_joints, 3]
:param joints_vis: [num_joints, 3]
:return: target, target_weight(1: visible, 0: invisible)
'''
target_weight = np.ones((self.num_joints, 1), dtype=np.float32)
target_weight[:, 0] = joints_vis[:, 0]
assert self.target_type == 'gaussian', \
'Only support gaussian map now!'
if self.target_type == 'gaussian':
target = np.zeros((self.num_joints,
self.heatmap_size[1],
self.heatmap_size[0]),
dtype=np.float32)
tmp_size = self.sigma * 3
for joint_id in range(self.num_joints):
feat_stride = self.image_size / self.heatmap_size
mu_x = int(joints[joint_id][0] / feat_stride[0] + 0.5)
mu_y = int(joints[joint_id][1] / feat_stride[1] + 0.5)
# Check that any part of the gaussian is in-bounds
ul = [int(mu_x - tmp_size), int(mu_y - tmp_size)]
br = [int(mu_x + tmp_size + 1), int(mu_y + tmp_size + 1)]
if ul[0] >= self.heatmap_size[0] or ul[1] >= self.heatmap_size[1] \
or br[0] < 0 or br[1] < 0:
# If not, just return the image as is
target_weight[joint_id] = 0
continue
# # Generate gaussian
size = 2 * tmp_size + 1
x = np.arange(0, size, 1, np.float32)
y = x[:, np.newaxis]
x0 = y0 = size // 2
# The gaussian is not normalized, we want the center value to equal 1
g = np.exp(- ((x - x0) ** 2 + (y - y0) ** 2) / (2 * self.sigma ** 2))
# Usable gaussian range
g_x = max(0, -ul[0]), min(br[0], self.heatmap_size[0]) - ul[0]
g_y = max(0, -ul[1]), min(br[1], self.heatmap_size[1]) - ul[1]
# Image range
img_x = max(0, ul[0]), min(br[0], self.heatmap_size[0])
img_y = max(0, ul[1]), min(br[1], self.heatmap_size[1])
v = target_weight[joint_id]
if v > 0.5:
target[joint_id][img_y[0]:img_y[1], img_x[0]:img_x[1]] = \
g[g_y[0]:g_y[1], g_x[0]:g_x[1]]
return target, target_weight