Spaces:
Build error
Build error
File size: 5,792 Bytes
2de1f98 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 |
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
|