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import torch
import numpy as np
import scipy
from config import cfg
from torch.nn import functional as F
import torchgeometry as tgm
def cam2pixel(cam_coord, f, c):
x = cam_coord[:, 0] / cam_coord[:, 2] * f[0] + c[0]
y = cam_coord[:, 1] / cam_coord[:, 2] * f[1] + c[1]
z = cam_coord[:, 2]
return np.stack((x, y, z), 1)
def pixel2cam(pixel_coord, f, c):
x = (pixel_coord[:, 0] - c[0]) / f[0] * pixel_coord[:, 2]
y = (pixel_coord[:, 1] - c[1]) / f[1] * pixel_coord[:, 2]
z = pixel_coord[:, 2]
return np.stack((x, y, z), 1)
def world2cam(world_coord, R, t):
cam_coord = np.dot(R, world_coord.transpose(1, 0)).transpose(1, 0) + t.reshape(1, 3)
return cam_coord
def cam2world(cam_coord, R, t):
world_coord = np.dot(np.linalg.inv(R), (cam_coord - t.reshape(1, 3)).transpose(1, 0)).transpose(1, 0)
return world_coord
def rigid_transform_3D(A, B):
n, dim = A.shape
centroid_A = np.mean(A, axis=0)
centroid_B = np.mean(B, axis=0)
H = np.dot(np.transpose(A - centroid_A), B - centroid_B) / n
U, s, V = np.linalg.svd(H)
R = np.dot(np.transpose(V), np.transpose(U))
if np.linalg.det(R) < 0:
s[-1] = -s[-1]
V[2] = -V[2]
R = np.dot(np.transpose(V), np.transpose(U))
varP = np.var(A, axis=0).sum()
c = 1 / varP * np.sum(s)
t = -np.dot(c * R, np.transpose(centroid_A)) + np.transpose(centroid_B)
return c, R, t
def rigid_align(A, B):
c, R, t = rigid_transform_3D(A, B)
A2 = np.transpose(np.dot(c * R, np.transpose(A))) + t
return A2
def transform_joint_to_other_db(src_joint, src_name, dst_name):
src_joint_num = len(src_name)
dst_joint_num = len(dst_name)
new_joint = np.zeros(((dst_joint_num,) + src_joint.shape[1:]), dtype=np.float32)
for src_idx in range(len(src_name)):
name = src_name[src_idx]
if name in dst_name:
dst_idx = dst_name.index(name)
new_joint[dst_idx] = src_joint[src_idx]
return new_joint
def rot6d_to_axis_angle(x):
batch_size = x.shape[0]
x = x.view(-1, 3, 2)
a1 = x[:, :, 0]
a2 = x[:, :, 1]
b1 = F.normalize(a1)
b2 = F.normalize(a2 - torch.einsum('bi,bi->b', b1, a2).unsqueeze(-1) * b1)
b3 = torch.cross(b1, b2)
rot_mat = torch.stack((b1, b2, b3), dim=-1) # 3x3 rotation matrix
rot_mat = torch.cat([rot_mat, torch.zeros((batch_size, 3, 1)).to(cfg.device).float()], 2) # 3x4 rotation matrix
axis_angle = tgm.rotation_matrix_to_angle_axis(rot_mat).reshape(-1, 3) # axis-angle
axis_angle[torch.isnan(axis_angle)] = 0.0
return axis_angle
def sample_joint_features(img_feat, joint_xy):
height, width = img_feat.shape[2:]
x = joint_xy[:, :, 0] / (width - 1) * 2 - 1
y = joint_xy[:, :, 1] / (height - 1) * 2 - 1
grid = torch.stack((x, y), 2)[:, :, None, :]
img_feat = F.grid_sample(img_feat, grid, align_corners=True)[:, :, :, 0] # batch_size, channel_dim, joint_num
img_feat = img_feat.permute(0, 2, 1).contiguous() # batch_size, joint_num, channel_dim
return img_feat
def soft_argmax_2d(heatmap2d):
batch_size = heatmap2d.shape[0]
height, width = heatmap2d.shape[2:]
heatmap2d = heatmap2d.reshape((batch_size, -1, height * width))
heatmap2d = F.softmax(heatmap2d, 2)
heatmap2d = heatmap2d.reshape((batch_size, -1, height, width))
accu_x = heatmap2d.sum(dim=(2))
accu_y = heatmap2d.sum(dim=(3))
accu_x = accu_x * torch.arange(width).float().to(cfg.device)[None, None, :]
accu_y = accu_y * torch.arange(height).float().to(cfg.device)[None, None, :]
accu_x = accu_x.sum(dim=2, keepdim=True)
accu_y = accu_y.sum(dim=2, keepdim=True)
coord_out = torch.cat((accu_x, accu_y), dim=2)
return coord_out
def soft_argmax_3d(heatmap3d):
batch_size = heatmap3d.shape[0]
depth, height, width = heatmap3d.shape[2:]
heatmap3d = heatmap3d.reshape((batch_size, -1, depth * height * width))
heatmap3d = F.softmax(heatmap3d, 2)
heatmap3d = heatmap3d.reshape((batch_size, -1, depth, height, width))
accu_x = heatmap3d.sum(dim=(2, 3))
accu_y = heatmap3d.sum(dim=(2, 4))
accu_z = heatmap3d.sum(dim=(3, 4))
accu_x = accu_x * torch.arange(width).float().to(cfg.device)[None, None, :]
accu_y = accu_y * torch.arange(height).float().to(cfg.device)[None, None, :]
accu_z = accu_z * torch.arange(depth).float().to(cfg.device)[None, None, :]
accu_x = accu_x.sum(dim=2, keepdim=True)
accu_y = accu_y.sum(dim=2, keepdim=True)
accu_z = accu_z.sum(dim=2, keepdim=True)
coord_out = torch.cat((accu_x, accu_y, accu_z), dim=2)
return coord_out
def restore_bbox(bbox_center, bbox_size, aspect_ratio, extension_ratio):
bbox = bbox_center.view(-1, 1, 2) + torch.cat((-bbox_size.view(-1, 1, 2) / 2., bbox_size.view(-1, 1, 2) / 2.),
1) # xyxy in (cfg.output_hm_shape[2], cfg.output_hm_shape[1]) space
bbox[:, :, 0] = bbox[:, :, 0] / cfg.output_hm_shape[2] * cfg.input_body_shape[1]
bbox[:, :, 1] = bbox[:, :, 1] / cfg.output_hm_shape[1] * cfg.input_body_shape[0]
bbox = bbox.view(-1, 4)
# xyxy -> xywh
bbox[:, 2] = bbox[:, 2] - bbox[:, 0]
bbox[:, 3] = bbox[:, 3] - bbox[:, 1]
# aspect ratio preserving bbox
w = bbox[:, 2]
h = bbox[:, 3]
c_x = bbox[:, 0] + w / 2.
c_y = bbox[:, 1] + h / 2.
mask1 = w > (aspect_ratio * h)
mask2 = w < (aspect_ratio * h)
h[mask1] = w[mask1] / aspect_ratio
w[mask2] = h[mask2] * aspect_ratio
bbox[:, 2] = w * extension_ratio
bbox[:, 3] = h * extension_ratio
bbox[:, 0] = c_x - bbox[:, 2] / 2.
bbox[:, 1] = c_y - bbox[:, 3] / 2.
# xywh -> xyxy
bbox[:, 2] = bbox[:, 2] + bbox[:, 0]
bbox[:, 3] = bbox[:, 3] + bbox[:, 1]
return bbox
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