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import numpy as np
import torch
#from torch.utils.serialization import load_lua
import os
import scipy.io as sio
import cv2
import math
from math import cos, sin
def plot_pose_cube(img, yaw, pitch, roll, tdx=None, tdy=None, size=150.):
# Input is a cv2 image
# pose_params: (pitch, yaw, roll, tdx, tdy)
# Where (tdx, tdy) is the translation of the face.
# For pose we have [pitch yaw roll tdx tdy tdz scale_factor]
p = pitch * np.pi / 180
y = -(yaw * np.pi / 180)
r = roll * np.pi / 180
if tdx != None and tdy != None:
face_x = tdx - 0.50 * size
face_y = tdy - 0.50 * size
else:
height, width = img.shape[:2]
face_x = width / 2 - 0.5 * size
face_y = height / 2 - 0.5 * size
x1 = size * (cos(y) * cos(r)) + face_x
y1 = size * (cos(p) * sin(r) + cos(r) * sin(p) * sin(y)) + face_y
x2 = size * (-cos(y) * sin(r)) + face_x
y2 = size * (cos(p) * cos(r) - sin(p) * sin(y) * sin(r)) + face_y
x3 = size * (sin(y)) + face_x
y3 = size * (-cos(y) * sin(p)) + face_y
# Draw base in red
cv2.line(img, (int(face_x), int(face_y)), (int(x1),int(y1)),(0,0,255),3)
cv2.line(img, (int(face_x), int(face_y)), (int(x2),int(y2)),(0,0,255),3)
cv2.line(img, (int(x2), int(y2)), (int(x2+x1-face_x),int(y2+y1-face_y)),(0,0,255),3)
cv2.line(img, (int(x1), int(y1)), (int(x1+x2-face_x),int(y1+y2-face_y)),(0,0,255),3)
# Draw pillars in blue
cv2.line(img, (int(face_x), int(face_y)), (int(x3),int(y3)),(255,0,0),2)
cv2.line(img, (int(x1), int(y1)), (int(x1+x3-face_x),int(y1+y3-face_y)),(255,0,0),2)
cv2.line(img, (int(x2), int(y2)), (int(x2+x3-face_x),int(y2+y3-face_y)),(255,0,0),2)
cv2.line(img, (int(x2+x1-face_x),int(y2+y1-face_y)), (int(x3+x1+x2-2*face_x),int(y3+y2+y1-2*face_y)),(255,0,0),2)
# Draw top in green
cv2.line(img, (int(x3+x1-face_x),int(y3+y1-face_y)), (int(x3+x1+x2-2*face_x),int(y3+y2+y1-2*face_y)),(0,255,0),2)
cv2.line(img, (int(x2+x3-face_x),int(y2+y3-face_y)), (int(x3+x1+x2-2*face_x),int(y3+y2+y1-2*face_y)),(0,255,0),2)
cv2.line(img, (int(x3), int(y3)), (int(x3+x1-face_x),int(y3+y1-face_y)),(0,255,0),2)
cv2.line(img, (int(x3), int(y3)), (int(x3+x2-face_x),int(y3+y2-face_y)),(0,255,0),2)
return img
def draw_axis(img, yaw, pitch, roll, tdx=None, tdy=None, size = 100):
pitch = pitch * np.pi / 180
yaw = -(yaw * np.pi / 180)
roll = roll * np.pi / 180
if tdx != None and tdy != None:
tdx = tdx
tdy = tdy
else:
height, width = img.shape[:2]
tdx = width / 2
tdy = height / 2
# X-Axis pointing to right. drawn in red
x1 = size * (cos(yaw) * cos(roll)) + tdx
y1 = size * (cos(pitch) * sin(roll) + cos(roll) * sin(pitch) * sin(yaw)) + tdy
# Y-Axis | drawn in green
# v
x2 = size * (-cos(yaw) * sin(roll)) + tdx
y2 = size * (cos(pitch) * cos(roll) - sin(pitch) * sin(yaw) * sin(roll)) + tdy
# Z-Axis (out of the screen) drawn in blue
x3 = size * (sin(yaw)) + tdx
y3 = size * (-cos(yaw) * sin(pitch)) + tdy
cv2.line(img, (int(tdx), int(tdy)), (int(x1),int(y1)),(0,0,255),4)
cv2.line(img, (int(tdx), int(tdy)), (int(x2),int(y2)),(0,255,0),4)
cv2.line(img, (int(tdx), int(tdy)), (int(x3),int(y3)),(255,0,0),4)
return img
def get_pose_params_from_mat(mat_path):
# This functions gets the pose parameters from the .mat
# Annotations that come with the Pose_300W_LP dataset.
mat = sio.loadmat(mat_path)
# [pitch yaw roll tdx tdy tdz scale_factor]
pre_pose_params = mat['Pose_Para'][0]
# Get [pitch, yaw, roll, tdx, tdy]
pose_params = pre_pose_params[:5]
return pose_params
def get_ypr_from_mat(mat_path):
# Get yaw, pitch, roll from .mat annotation.
# They are in radians
mat = sio.loadmat(mat_path)
# [pitch yaw roll tdx tdy tdz scale_factor]
pre_pose_params = mat['Pose_Para'][0]
# Get [pitch, yaw, roll]
pose_params = pre_pose_params[:3]
return pose_params
def get_pt2d_from_mat(mat_path):
# Get 2D landmarks
mat = sio.loadmat(mat_path)
pt2d = mat['pt2d']
return pt2d
# batch*n
def normalize_vector( v, use_gpu=True):
batch=v.shape[0]
v_mag = torch.sqrt(v.pow(2).sum(1))# batch
if use_gpu:
v_mag = torch.max(v_mag, torch.autograd.Variable(torch.FloatTensor([1e-8]).cuda()))
else:
v_mag = torch.max(v_mag, torch.autograd.Variable(torch.FloatTensor([1e-8])))
v_mag = v_mag.view(batch,1).expand(batch,v.shape[1])
v = v/v_mag
return v
# u, v batch*n
def cross_product( u, v):
batch = u.shape[0]
#print (u.shape)
#print (v.shape)
i = u[:,1]*v[:,2] - u[:,2]*v[:,1]
j = u[:,2]*v[:,0] - u[:,0]*v[:,2]
k = u[:,0]*v[:,1] - u[:,1]*v[:,0]
out = torch.cat((i.view(batch,1), j.view(batch,1), k.view(batch,1)),1)#batch*3
return out
#poses batch*6
#poses
def compute_rotation_matrix_from_ortho6d(poses, use_gpu=True):
x_raw = poses[:,0:3]#batch*3
y_raw = poses[:,3:6]#batch*3
x = normalize_vector(x_raw, use_gpu) #batch*3
z = cross_product(x,y_raw) #batch*3
z = normalize_vector(z, use_gpu)#batch*3
y = cross_product(z,x)#batch*3
x = x.view(-1,3,1)
y = y.view(-1,3,1)
z = z.view(-1,3,1)
matrix = torch.cat((x,y,z), 2) #batch*3*3
return matrix
#input batch*4*4 or batch*3*3
#output torch batch*3 x, y, z in radiant
#the rotation is in the sequence of x,y,z
def compute_euler_angles_from_rotation_matrices(rotation_matrices, use_gpu=True):
batch=rotation_matrices.shape[0]
R=rotation_matrices
sy = torch.sqrt(R[:,0,0]*R[:,0,0]+R[:,1,0]*R[:,1,0])
singular= sy<1e-6
singular=singular.float()
x=torch.atan2(R[:,2,1], R[:,2,2])
y=torch.atan2(-R[:,2,0], sy)
z=torch.atan2(R[:,1,0],R[:,0,0])
xs=torch.atan2(-R[:,1,2], R[:,1,1])
ys=torch.atan2(-R[:,2,0], sy)
zs=R[:,1,0]*0
if use_gpu:
out_euler=torch.autograd.Variable(torch.zeros(batch,3).cuda())
else:
out_euler=torch.autograd.Variable(torch.zeros(batch,3))
out_euler[:,0]=x*(1-singular)+xs*singular
out_euler[:,1]=y*(1-singular)+ys*singular
out_euler[:,2]=z*(1-singular)+zs*singular
return out_euler
def get_R(x,y,z):
''' Get rotation matrix from three rotation angles (radians). right-handed.
Args:
angles: [3,]. x, y, z angles
Returns:
R: [3, 3]. rotation matrix.
'''
# x
Rx = np.array([[1, 0, 0],
[0, np.cos(x), -np.sin(x)],
[0, np.sin(x), np.cos(x)]])
# y
Ry = np.array([[np.cos(y), 0, np.sin(y)],
[0, 1, 0],
[-np.sin(y), 0, np.cos(y)]])
# z
Rz = np.array([[np.cos(z), -np.sin(z), 0],
[np.sin(z), np.cos(z), 0],
[0, 0, 1]])
R = Rz.dot(Ry.dot(Rx))
return R |