def getHandModelPoseCoeffsMultiFrame(numComp, numFrames, isOpenGLCoord):
chGlobalPoseCoeff = ch.zeros((numComp, ))
chGlobalBeta = ch.zeros((10, ))
chRotList = []
chTransList = []
mList = []
for i in range(numFrames):
chRot = ch.zeros((3, ))
if isOpenGLCoord:
chTrans = ch.array([0., 0., -0.5])
else:
chTrans = ch.array([0., 0., 0.5])
chRotList.append(chRot)
chTransList.append(chTrans)
m = load_model_withInputs_poseCoeffs(os.path.join(
os.path.dirname(os.path.realpath(__file__)),
'../mano/models/MANO_RIGHT.pkl'),
chRot=chRot,
chTrans=chTrans,
chPoseCoeff=chGlobalPoseCoeff,
chBetas=chGlobalBeta,
ncomps=numComp)
mList.append(m)
return mList, chRotList, chGlobalPoseCoeff, chTransList, chGlobalBeta
def chumpy_get_H(p1, p2):
""" Compute differentiable homography from p1 to p2.
Parameters
----------
p1,p2 : array_like, shape (4,2)
Containing points to match.
"""
xmin = 0
ymin = 0
xmax = 1024
ymax = 576
p1 = 2 * p1 / ch.array([[xmax, ymax]])
p1 = p1 - 1.0
p2 = 2 * p2 / ch.array([[xmax, ymax]])
p2 = p2 - 1.0
N = p1.shape[0]
A1 = ch.vstack((ch.zeros((3, N)), -p1.T, -ch.ones(
(1, N)), p2[:, 1] * p1[:, 0], p2[:, 1] * p1[:, 1], p2[:, 1])).T
A2 = ch.vstack((p1.T, ch.ones((1, N)), ch.zeros(
(3, N)), -p2[:, 0] * p1[:, 0], -p2[:, 0] * p1[:, 1], -p2[:, 0])).T
A = ch.vstack((A1, A2))
U, S, V = ch.linalg.svd(A.T.dot(A))
H_new = V[-1, :].reshape((3, 3))
# Re-normalize
ML = ch.array([[xmax / 2.0, 0.0, xmax / 2.0], [0, ymax / 2.0, ymax / 2.0],
[0, 0, 1.0]])
MR = ch.array([[2.0 / xmax, 0.0, -1.0], [0, 2.0 / ymax, -1.0], [0, 0,
1.0]])
H_new = ML.dot(H_new).dot(MR)
return H_new / H_new[2, 2]
def test_depth_image(self):
# Create renderer
import chumpy as ch
from .renderer import DepthRenderer
rn = DepthRenderer()
# Assign attributes to renderer
from .util_tests import get_earthmesh
m = get_earthmesh(trans=ch.array([0, 0, 0]), rotation=ch.zeros(3))
m.v = m.v * .01
m.v[:, 2] += 4
w, h = (320, 240)
from .camera import ProjectPoints
rn.camera = ProjectPoints(v=m.v,
rt=ch.zeros(3),
t=ch.zeros(3),
f=ch.array([w, w]) / 2.,
c=ch.array([w, h]) / 2.,
k=ch.zeros(5))
rn.frustum = {'near': 1., 'far': 10., 'width': w, 'height': h}
rn.set(v=m.v, f=m.f, vc=m.vc * 0 + 1, bgcolor=ch.zeros(3))
# import time
# tm = time.time()
# rn.r
# print 'took %es' % (time.time() - tm)
# print np.min(rn.r.ravel())
# print np.max(rn.r.ravel())
self.assertLess(np.abs(np.min(rn.r.ravel()) - 3.98), 1e-5)
self.assertLess(np.abs(np.min(m.v[:, 2]) - np.min(rn.r.ravel())), 1e-5)
self.assertLess(np.abs(rn.r[h / 2, w / 2] - 3.98), 1e-5)
def rigid_scan_2_mesh_alignment(scan, mesh, visualize=False):
options = {'sparse_solver': lambda A, x: cg(A, x, maxiter=2000)[0]}
options['disp'] = 1.0
options['delta_0'] = 0.1
options['e_3'] = 1e-4
s = ch.ones(1)
r = ch.zeros(3)
R = Rodrigues(r)
t = ch.zeros(3)
trafo_mesh = s*(R.dot(mesh.v.T)).T + t
sampler = sample_from_mesh(scan, sample_type='vertices')
s2m = ScanToMesh(scan, trafo_mesh, mesh.f, scan_sampler=sampler, signed=False, normalize=False)
if visualize:
#Visualization code
mv = MeshViewer()
mv.set_static_meshes([scan])
tmp_mesh = Mesh(trafo_mesh.r, mesh.f)
tmp_mesh.set_vertex_colors('light sky blue')
mv.set_dynamic_meshes([tmp_mesh])
def on_show(_):
tmp_mesh = Mesh(trafo_mesh.r, mesh.f)
tmp_mesh.set_vertex_colors('light sky blue')
mv.set_dynamic_meshes([tmp_mesh])
else:
def on_show(_):
pass
ch.minimize(fun={'dist': s2m, 's_reg': 100*(ch.abs(s)-s)}, x0=[s, r, t], callback=on_show, options=options)
return s,Rodrigues(r),t
def initialize_camera(iuv, j2d, model, fx=None, fy=None, cx=None, cy=None):
"""Initialize camera translation and body orientation
:param model: SMPL model
:param j2d: 14x2 array of CNN joints
:param img: h x w x 3 image
:param init_pose: 72D vector of pose parameters used for initialization
:param flength: camera focal length (kept fixed)
:param pix_thsh: threshold (in pixel), if the distance between shoulder joints in 2D
is lower than pix_thsh, the body orientation as ambiguous (so a fit is run on both
the estimated one and its flip)
:param viz: boolean, if True enables visualization during optimization
:returns: a tuple containing the estimated camera,
a boolean deciding if both the optimized body orientation and its flip should be considered,
3D vector for the body orientation
"""
rt = ch.zeros(3)
t = ch.zeros(3)
# check how close the shoulder joints are
# initialize the camera
cam = ProjectPoints(f=np.array([fx, fy]),
rt=rt,
t=t,
k=np.zeros(5),
c=[cx, cy])
return cam
def test_depth_image(self):
# Create renderer
import chumpy as ch
from renderer import DepthRenderer
rn = DepthRenderer()
# Assign attributes to renderer
from util_tests import get_earthmesh
m = get_earthmesh(trans=ch.array([0,0,0]), rotation=ch.zeros(3))
m.v = m.v * .01
m.v[:,2] += 4
w, h = (320, 240)
from camera import ProjectPoints
rn.camera = ProjectPoints(v=m.v, rt=ch.zeros(3), t=ch.zeros(3), f=ch.array([w,w])/2., c=ch.array([w,h])/2., k=ch.zeros(5))
rn.frustum = {'near': 1., 'far': 10., 'width': w, 'height': h}
rn.set(v=m.v, f=m.f, vc=m.vc*0+1, bgcolor=ch.zeros(3))
# import time
# tm = time.time()
# rn.r
# print 'took %es' % (time.time() - tm)
# print np.min(rn.r.ravel())
# print np.max(rn.r.ravel())
self.assertLess(np.abs(np.min(rn.r.ravel()) - 3.98), 1e-5)
self.assertLess(np.abs(np.min(m.v[:,2]) - np.min(rn.r.ravel())), 1e-5)
self.assertLess(np.abs(rn.r[h/2,w/2] - 3.98), 1e-5)
def get_renderer():
import chumpy as ch
from opendr.everything import *
# Load mesh
m = load_mesh('/Users/matt/geist/OpenDR/test_dr/nasa_earth.obj')
m.v += ch.array([0, 0, 3.])
w, h = (320, 240)
trans = ch.array([[0, 0, 0]])
# Construct renderer
rn = TexturedRenderer()
rn.camera = ProjectPoints(v=m.v,
rt=ch.zeros(3),
t=ch.zeros(3),
f=ch.array([w, w]) / 2.,
c=ch.array([w, h]) / 2.,
k=ch.zeros(5))
rn.frustum = {'near': 1., 'far': 10., 'width': w, 'height': h}
rn.set(v=trans + m.v,
f=m.f,
texture_image=m.texture_image[:, :, ::-1],
ft=m.ft,
vt=m.vt,
bgcolor=ch.zeros(3))
rn.vc = SphericalHarmonics(vn=VertNormals(v=rn.v, f=rn.f),
components=ch.array([4., 0., 0., 0.]),
light_color=ch.ones(3))
return rn
def setup_silhouette_obj(silhs, rends, f):
n_model = [ch.sum(rend[:, :, 0] > 0) for rend in rends]
dist_tsf = [cv2.distanceTransform(np.uint8(1 - silh), cv2.DIST_L2, cv2.DIST_MASK_PRECISE) for silh in silhs]
# Make sigma proportional to image area.
# This gives radius 400 for 960 x 1920 image.
sigma_ratio = 0.2727
sigma = [np.sqrt(sigma_ratio * (silh.shape[0] * silh.shape[1]) / np.pi) for silh in silhs]
# Model2silhouette ==> Consistency (contraction)
def obj_m2s(w, i):
return w * (GMOf(rends[i][:, :, 0] * dist_tsf[i], sigma[i]) / np.sqrt(n_model[i]))
# silhouette error term (scan-to-model) ==> Coverage (expansion)
coords = [np.fliplr(np.array(np.where(silh > 0)).T) + 0.5 for silh in silhs]# is this offset needed?
scan_flat_v = [np.hstack((coord, ch.zeros(len(coord)).reshape((-1, 1)))) for coord in coords]
scan_flat = [Mesh(v=sflat_v, f=[]) for sflat_v in scan_flat_v]
# 2d + all 0.
sv_flat = [ch.hstack((rend.camera, ch.zeros(len(rend.v)).reshape((-1, 1)))) for rend in rends]
for i in range(len(rends)):
sv_flat[i].f = f
def obj_s2m(w, i):
from sbody.mesh_distance import ScanToMesh
return w * ch.sqrt(GMOf(ScanToMesh(scan_flat[i], sv_flat[i], f), sigma[i]))
# For vis
for i in range(len(rends)):
scan_flat[i].vc = np.tile(np.array([0, 0, 1]), (len(scan_flat[i].v), 1))
return obj_m2s, obj_s2m, dist_tsf
def test_earth():
m = get_earthmesh(trans=ch.array([0, 0, 0]), rotation=ch.zeros(3))
# Create V, A, U, f: geometry, brightness, camera, renderer
V = ch.array(m.v)
A = SphericalHarmonics(vn=VertNormals(v=V, f=m.f),
components=[3., 2., 0., 0., 0., 0., 0., 0., 0.],
light_color=ch.ones(3))
# camera
U = ProjectPoints(v=V,
f=[w, w],
c=[w / 2., h / 2.],
k=ch.zeros(5),
t=ch.zeros(3),
rt=ch.zeros(3))
f = TexturedRenderer(vc=A,
camera=U,
f=m.f,
bgcolor=[0., 0., 0.],
texture_image=m.texture_image,
vt=m.vt,
ft=m.ft,
frustum={
'width': w,
'height': h,
'near': 1,
'far': 20
})
# Parameterize the vertices
translation, rotation = ch.array([0, 0, 8]), ch.zeros(3)
f.v = translation + V.dot(Rodrigues(rotation))
observed = f.r
np.random.seed(1)
# this is reactive
# in the sense that changes to values will affect function which depend on them.
translation[:] = translation.r + np.random.rand(3)
rotation[:] = rotation.r + np.random.rand(3) * .2
# Create the energy
E_raw = f - observed
E_pyr = gaussian_pyramid(E_raw, n_levels=6, normalization='size')
Image.fromarray((observed * 255).astype(np.uint8)).save(
os.path.join(save_dir, "reference.png"))
step = 0
Image.fromarray((f.r * 255).astype(np.uint8)).save(
os.path.join(save_dir, "step_{:05d}.png".format(step)))
print('OPTIMIZING TRANSLATION, ROTATION, AND LIGHT PARMS')
free_variables = [translation, rotation]
ch.minimize({'pyr': E_pyr}, x0=free_variables, callback=create_callback(f))
ch.minimize({'raw': E_raw}, x0=free_variables, callback=create_callback(f))
def test_derivatives2(self):
import chumpy as ch
import numpy as np
from .renderer import DepthRenderer
rn = DepthRenderer()
# Assign attributes to renderer
from .util_tests import get_earthmesh
m = get_earthmesh(trans=ch.array([0, 0, 4]), rotation=ch.zeros(3))
w, h = (320, 240)
from .camera import ProjectPoints
rn.camera = ProjectPoints(v=m.v,
rt=ch.zeros(3),
t=ch.zeros(3),
f=ch.array([w, w]) / 2.,
c=ch.array([w, h]) / 2.,
k=ch.zeros(5))
rn.frustum = {'near': 1., 'far': 10., 'width': w, 'height': h}
rn.set(v=m.v, f=m.f, bgcolor=ch.zeros(3))
if visualize:
import matplotlib.pyplot as plt
plt.ion()
plt.figure()
for which in range(3):
r1 = rn.r
adder = np.random.rand(rn.v.r.size).reshape(rn.v.r.shape) * .01
change = rn.v.r * 0 + adder
dr_pred = rn.dr_wrt(rn.v).dot(change.ravel()).reshape(rn.shape)
rn.v = rn.v.r + change
r2 = rn.r
dr_emp = r2 - r1
#print np.mean(np.abs(dr_pred-dr_emp))
self.assertLess(np.mean(np.abs(dr_pred - dr_emp)), .024)
if visualize:
plt.subplot(2, 3, which + 1)
plt.imshow(dr_pred)
plt.clim(-.01, .01)
plt.title('emp')
plt.subplot(2, 3, which + 4)
plt.imshow(dr_emp)
plt.clim(-.01, .01)
plt.title('pred')
plt.draw()
plt.show()
def chumpy_get_H(p1, p2):
""" Compute differentiable homography from p1 to p2.
"""
N = p1.shape[0]
A1 = ch.vstack((ch.zeros((3, N)), -p1.T, -ch.ones(
(1, N)), p2[:, 1] * p1[:, 0], p2[:, 1] * p1[:, 1], p2[:, 1])).T
A2 = ch.vstack((p1.T, ch.ones((1, N)), ch.zeros(
(3, N)), -p2[:, 0] * p1[:, 0], -p2[:, 0] * p1[:, 1], -p2[:, 0])).T
A = ch.vstack((A1, A2))
U, S, V = ch.linalg.svd(A.T.dot(A))
H_new = V[-1, :].reshape((3, 3))
return H_new
def get_cam_params(self):
v_raw = np.sin(np.arange(900)).reshape((-1,3))
v_raw[:, 2] -= 2
rt = ch.zeros(3)
t = ch.zeros(3)
f = ch.array([500,500])
c = ch.array([320,240])
k = ch.zeros(5)
cam_params = {'v': ch.Ch(v_raw), 'rt': rt, 't': t, 'f': f, 'c': c, 'k': k}
return cam_params
def get_cam_params(self):
v_raw = np.sin(np.arange(900)).reshape((-1,3))
v_raw[:, 2] -= 2
rt = ch.zeros(3)
t = ch.zeros(3)
f = ch.array([500,500])
c = ch.array([320,240])
k = ch.zeros(5)
cam_params = {'v': ch.Ch(v_raw), 'rt': rt, 't': t, 'f': f, 'c': c, 'k': k}
return cam_params
def render(self, thetas, texture_bgr, rotate=np.array([0, 0, 0]), background_img=None):
"""
get the rendered image and rendered silhouette
:param thetas: model parameters, 3 * camera parameter + 72 * body pose + 10 * body shape
:param texture_bgr: texture image in bgr format
:return: the rendered image and deviation of rendered image to texture image
(rendered image, deviation of rendered image, silhouette)
"""
self.set_texture(texture_bgr)
thetas = thetas.reshape(-1)
cams = thetas[:self.num_cam]
theta = thetas[self.num_cam: (self.num_cam + self.num_theta)]
beta = thetas[(self.num_cam + self.num_theta):]
self.body.pose[:] = theta
self.body.betas[:] = beta
#
# size = cams[0] * min(self.w, self.h)
# position = cams[1:3] * min(self.w, self.h) / 2 + min(self.w, self.h) / 2
"""
####################################################################
ATTENTION!
I do not know why the flength is 500.
But it worked
####################################################################
"""
texture_rn = TexturedRenderer()
texture_rn.camera = ProjectPoints(v=self.body, rt=rotate, t=ch.array([0, 0, 2]),
f=np.ones(2) * self.img_size * 0.62,
c=np.array([self.w / 2, self.h / 2]),
k=ch.zeros(5))
texture_rn.frustum = {'near': 1., 'far': 10., 'width': self.w, 'height': self.h}
texture_rn.set(v=self.body, f=self.m.f, vc=self.m.vc, texture_image=self.m.texture_image, ft=self.m.ft,
vt=self.m.vt)
if background_img is not None:
texture_rn.background_image = background_img / 255. if background_img.max() > 1 else background_img
silhouette_rn = ColoredRenderer()
silhouette_rn.camera = ProjectPoints(v=self.body, rt=rotate, t=ch.array([0, 0, 2]),
f=np.ones(2) * self.img_size * 0.62,
c=np.array([self.w / 2, self.h / 2]),
k=ch.zeros(5))
silhouette_rn.frustum = {'near': 1., 'far': 10., 'width': self.w, 'height': self.h}
silhouette_rn.set(v=self.body, f=self.m.f, vc=np.ones_like(self.body), bgcolor=np.zeros(3))
return texture_rn.r, texture_dr_wrt(texture_rn, silhouette_rn.r), silhouette_rn.r
def render_mask(w, h, v, f, u):
"""renders silhouette"""
V = ch.array(v)
A = np.ones(v.shape)
rn = ColoredRenderer(camera=u, v=V, f=f, vc=A, bgcolor=ch.zeros(3),
frustum={'width': w, 'height': h, 'near': 0.1, 'far': 20})
return rn.r
def setupCamera(v, chAz, chEl, chDist, objCenter, width, height):
chCamModelWorld = computeHemisphereTransformation(chAz, chEl, chDist,
objCenter)
chMVMat = ch.dot(chCamModelWorld,
np.array(mathutils.Matrix.Rotation(radians(270), 4, 'X')))
chInvCam = ch.inv(chMVMat)
modelRotation = chInvCam[0:3, 0:3]
chRod = opendr.geometry.Rodrigues(rt=modelRotation).reshape(3)
chTranslation = chInvCam[0:3, 3]
translation, rotation = (chTranslation, chRod)
camera = ProjectPoints(v=v,
rt=rotation,
t=translation,
f=1.12 * ch.array([width, width]),
c=ch.array([width, height]) / 2.0,
k=ch.zeros(5))
camera.openglMat = np.array(mathutils.Matrix.Rotation(
radians(180), 4, 'X'))
return camera, modelRotation, chMVMat
def main(mesh_list, out_list, scale=1.0, move_scale=True):
assert len(mesh_list) == len(out_list)
for mesh_file, out_file in zip(mesh_list, out_list):
mesh = load_obj_data_binary(mesh_file)
if move_scale: # move to center and scale to unit bounding box
mesh['v'] = (mesh['v'] - np.array([128, -192, 128]) +
0.5) * voxel_size
if not ('vn' in mesh and mesh['vn'] is not None):
mesh['vn'] = np.array(VertNormals(f=mesh['f'], v=mesh['v']))
V = ch.array(mesh['v']) * scale
V -= trans
C = np.ones_like(mesh['v'])
C *= np.array([186, 212, 255], dtype=np.float32) / 255.0
# C *= np.array([158, 180, 216], dtype=np.float32) / 250.0
C = np.minimum(C, 1.0)
A = np.zeros_like(mesh['v'])
A += LambertianPointLight(f=mesh['f'],
v=V,
vn=-mesh['vn'],
num_verts=len(V),
light_pos=np.array([0, -50, -50]),
light_color=np.array([1.0, 1.0, 1.0]),
vc=C)
cam_t, cam_r = ch.array((0, 0, 0)), ch.array((3.14, 0, 0))
U = ProjectPoints(v=V,
f=[flength, flength],
c=[w / 2., h / 2.],
k=ch.zeros(5),
t=cam_t,
rt=cam_r)
rn = ColoredRenderer(camera=U,
v=V,
f=mesh['f'],
vc=A,
bgcolor=np.array([1.0, 0.0, 0.0]),
frustum={
'width': w,
'height': h,
'near': 0.1,
'far': 20
})
img = np.asarray(rn)[:, :, (2, 1, 0)]
msk = np.sum(np.abs(img - np.array([[[0, 0, 1.0]]], dtype=np.float32)),
axis=-1,
keepdims=True)
msk[msk > 0] = 1
img = cv.resize(img, (img.shape[1] // 2, img.shape[0] // 2))
msk = cv.resize(msk, (msk.shape[1] // 2, msk.shape[0] // 2),
interpolation=cv.INTER_AREA)
msk[msk < 1] = 0
msk = msk[:, :, np.newaxis]
img = np.concatenate([img, msk], axis=-1)
cv.imshow('render3', img)
cv.waitKey(3)
cv.imwrite(out_file, np.uint8(img * 255))
def setupCamera_centauro(v, cameraParams, tf_world_2_camera):
'''
Simplified setup for the centauro toy example or
most of cases where we don't need to regress for camera parameters
in : tf_world_2_camera (Preferably use Matrix44.look_at() to create this)
'''
modelRotation = tf_world_2_camera[0:3, 0:3]
chRod = opendr.geometry.Rodrigues(rt=modelRotation).reshape(3)
chTranslation = tf_world_2_camera[0:3, 3]
camera = ProjectPoints(v=v,
rt=rotation,
t=translation,
f=1000 * cameraParams['chCamFocalLength'] *
cameraParams['a'],
c=cameraParams['c'],
k=ch.zeros(5))
#print ('camera shape', camera.shape)
#print ('camera ', camera)
flipXRotation = np.array([[1.0, 0.0, 0.0, 0.0], [0.0, -1.0, 0., 0.0],
[0.0, 0., -1.0, 0.0], [0.0, 0.0, 0.0, 1.0]])
camera.openglMat = flipXRotation #Needed to match OpenGL flipped axis.
# chMVMat construct might be wrong
# From line 308 chInvCam = ch.inv(chMVMat)
# It seems, chMVMat is tf_camera_2_world
# In any case, the create_renderer function ignores this returned variable
chMVMat = ch.inv(tf_world_2_camera)
return camera, modelRotation, chMVMat
def initialize_camera(fx, fy, cx, cy):
"""
@param: fx,fy,cx,cy as pinhole camera model parameter
@return: a differentiable camera instance
"""
rt = ch.zeros(3)
t = ch.zeros(3)
cam = ProjectPoints(f=np.array([fx, fy]),
rt=rt,
t=t,
k=np.zeros(5),
c=[cx, cy])
return cam
def _global_rigid_transformation(self):
results = {}
pose = self.pose.reshape((-1, 3))
parent = {
i: self.kintree_table[0, i]
for i in range(1, self.kintree_table.shape[1])
}
with_zeros = lambda x: ch.vstack((x, ch.array([[0.0, 0.0, 0.0, 1.0]])))
pack = lambda x: ch.hstack([ch.zeros((4, 3)), x.reshape((4, 1))])
results[0] = with_zeros(
ch.hstack((Rodrigues(pose[0, :]), self.J[0, :].reshape((3, 1)))))
for i in range(1, self.kintree_table.shape[1]):
results[i] = results[parent[i]].dot(
with_zeros(
ch.hstack((
Rodrigues(pose[i, :]), # rotation around bone endpoint
(self.J[i, :] - self.J[parent[i], :]).reshape(
(3, 1)) # bone
))))
results = [results[i] for i in sorted(results.keys())]
results_global = results
# subtract rotated J position
results2 = [
results[i] -
(pack(results[i].dot(ch.concatenate((self.J[i, :], [0])))))
for i in range(len(results))
]
result = ch.dstack(results2)
return result, results_global
def test_derivatives2(self):
import chumpy as ch
import numpy as np
from renderer import DepthRenderer
rn = DepthRenderer()
# Assign attributes to renderer
from util_tests import get_earthmesh
m = get_earthmesh(trans=ch.array([0,0,4]), rotation=ch.zeros(3))
w, h = (320, 240)
from camera import ProjectPoints
rn.camera = ProjectPoints(v=m.v, rt=ch.zeros(3), t=ch.zeros(3), f=ch.array([w,w])/2., c=ch.array([w,h])/2., k=ch.zeros(5))
rn.frustum = {'near': 1., 'far': 10., 'width': w, 'height': h}
rn.set(v=m.v, f=m.f, bgcolor=ch.zeros(3))
if visualize:
import matplotlib.pyplot as plt
plt.ion()
plt.figure()
for which in range(3):
r1 = rn.r
adder = np.random.rand(rn.v.r.size).reshape(rn.v.r.shape)*.01
change = rn.v.r * 0 + adder
dr_pred = rn.dr_wrt(rn.v).dot(change.ravel()).reshape(rn.shape)
rn.v = rn.v.r + change
r2 = rn.r
dr_emp = r2 - r1
#print np.mean(np.abs(dr_pred-dr_emp))
self.assertLess(np.mean(np.abs(dr_pred-dr_emp)), .024)
if visualize:
plt.subplot(2,3,which+1)
plt.imshow(dr_pred)
plt.clim(-.01,.01)
plt.title('emp')
plt.subplot(2,3,which+4)
plt.imshow(dr_emp)
plt.clim(-.01,.01)
plt.title('pred')
plt.draw()
plt.show()
def verts_decorated(trans, pose,
v_template, J, weights, kintree_table, bs_style, f,
bs_type=None, posedirs=None, betas=None, shapedirs=None, want_Jtr=False):
for which in [trans, pose, v_template, weights, posedirs, betas, shapedirs]:
if which is not None:
assert ischumpy(which)
v = v_template
if shapedirs is not None:
if betas is None:
betas = chumpy.zeros(shapedirs.shape[-1])
v_shaped = v + shapedirs.dot(betas)
else:
v_shaped = v
if posedirs is not None:
v_posed = v_shaped + posedirs.dot(posemap(bs_type)(pose))
else:
v_posed = v_shaped
v = v_posed
if sp.issparse(J):
regressor = J
J_tmpx = MatVecMult(regressor, v_shaped[:,0])
J_tmpy = MatVecMult(regressor, v_shaped[:,1])
J_tmpz = MatVecMult(regressor, v_shaped[:,2])
J = chumpy.vstack((J_tmpx, J_tmpy, J_tmpz)).T
else:
assert(ischumpy(J))
assert(bs_style=='lbs')
result, Jtr = verts_core(pose, v, J, weights, kintree_table, want_Jtr=True, xp=chumpy)
tr = trans.reshape((1,3))
result = result + tr
Jtr = Jtr + tr
result.trans = trans
result.f = f
result.pose = pose
result.v_template = v_template
result.J = J
result.weights = weights
result.kintree_table = kintree_table
result.bs_style = bs_style
result.bs_type =bs_type
if posedirs is not None:
result.posedirs = posedirs
result.v_posed = v_posed
if shapedirs is not None:
result.shapedirs = shapedirs
result.betas = betas
result.v_shaped = v_shaped
if want_Jtr:
result.J_transformed = Jtr
return result
def verts_decorated(trans, pose,
v_template, J, weights, kintree_table, bs_style, f,
bs_type=None, posedirs=None, betas=None, shapedirs=None, want_Jtr=False):
for which in [trans, pose, v_template, weights, posedirs, betas, shapedirs]:
if which is not None:
assert ischumpy(which)
v = v_template
if shapedirs is not None:
if betas is None:
betas = chumpy.zeros(shapedirs.shape[-1])
v_shaped = v + shapedirs.dot(betas)
else:
v_shaped = v
if posedirs is not None:
v_posed = v_shaped + posedirs.dot(posemap(bs_type)(pose))
else:
v_posed = v_shaped
v = v_posed
if sp.issparse(J):
regressor = J
J_tmpx = MatVecMult(regressor, v_shaped[:,0])
J_tmpy = MatVecMult(regressor, v_shaped[:,1])
J_tmpz = MatVecMult(regressor, v_shaped[:,2])
J = chumpy.vstack((J_tmpx, J_tmpy, J_tmpz)).T
else:
assert(ischumpy(J))
assert(bs_style=='lbs')
result, Jtr = lbs.verts_core(pose, v, J, weights, kintree_table, want_Jtr=True, xp=chumpy)
tr = trans.reshape((1,3))
result = result + tr
Jtr = Jtr + tr
result.trans = trans
result.f = f
result.pose = pose
result.v_template = v_template
result.J = J
result.weights = weights
result.kintree_table = kintree_table
result.bs_style = bs_style
result.bs_type =bs_type
if posedirs is not None:
result.posedirs = posedirs
result.v_posed = v_posed
if shapedirs is not None:
result.shapedirs = shapedirs
result.betas = betas
result.v_shaped = v_shaped
if want_Jtr:
result.J_transformed = Jtr
return result
def getHandModel():
globalJoints = ch.zeros((45, ))
globalBeta = ch.zeros((10, ))
chRot = ch.zeros((3, ))
chTrans = ch.array([0., 0., 0.5])
fullpose = ch.concatenate([chRot, globalJoints], axis=0)
m = load_model_withInputs(os.path.join(
os.path.dirname(os.path.realpath(__file__)),
'../mano/models/MANO_RIGHT.pkl'),
fullpose,
chTrans,
globalBeta,
ncomps=15,
flat_hand_mean=True)
return m, chRot, globalJoints, chTrans, globalBeta
def on_changed(self, which):
if 'model' in which:
if not isinstance(self.model, dict):
dd = pkl.load(open(self.model, "rb"), encoding="latin1")
else:
dd = self.model
backwards_compatibility_replacements(dd)
# for s in ['v_template', 'weights', 'posedirs', 'pose', 'trans', 'shapedirs', 'betas', 'J']:
for s in ['posedirs', 'shapedirs']:
if (s in dd) and not hasattr(dd[s], 'dterms'):
dd[s] = ch.array(dd[s])
self.f = dd['f']
self.shapedirs = dd['shapedirs']
self.J_regressor = dd['J_regressor']
if 'J_regressor_prior' in dd:
self.J_regressor_prior = dd['J_regressor_prior']
self.bs_type = dd['bs_type']
self.bs_style = dd['bs_style']
self.weights = ch.array(dd['weights'])
if 'vert_sym_idxs' in dd:
self.vert_sym_idxs = dd['vert_sym_idxs']
if 'weights_prior' in dd:
self.weights_prior = dd['weights_prior']
self.kintree_table = dd['kintree_table']
self.posedirs = dd['posedirs']
if not hasattr(self, 'betas'):
self.betas = ch.zeros(self.shapedirs.shape[-1])
if not hasattr(self, 'trans'):
self.trans = ch.zeros(3)
if not hasattr(self, 'pose'):
self.pose = ch.zeros(72)
if not hasattr(self, 'v_template'):
self.v_template = ch.array(dd['v_template'])
if not hasattr(self, 'v_personal'):
self.v_personal = ch.zeros_like(self.v_template)
self._set_up()
def get_renderer():
import chumpy as ch
from opendr.everything import *
# Load mesh
m = load_mesh('/Users/matt/geist/OpenDR/test_dr/nasa_earth.obj')
m.v += ch.array([0,0,3.])
w, h = (320, 240)
trans = ch.array([[0,0,0]])
# Construct renderer
rn = TexturedRenderer()
rn.camera = ProjectPoints(v=m.v, rt=ch.zeros(3), t=ch.zeros(3), f=ch.array([w,w])/2., c=ch.array([w,h])/2., k=ch.zeros(5))
rn.frustum = {'near': 1., 'far': 10., 'width': w, 'height': h}
rn.set(v=trans+m.v, f=m.f, texture_image=m.texture_image[:,:,::-1], ft=m.ft, vt=m.vt, bgcolor=ch.zeros(3))
rn.vc = SphericalHarmonics(vn=VertNormals(v=rn.v, f=rn.f), components=ch.array([4.,0.,0.,0.]), light_color=ch.ones(3))
return rn
def _project_vertices(v, w, h, cam_r, cam_t):
"""projects vertices onto image plane"""
V = ch.array(v)
U = ProjectPointsOrthogonal(v=V,
f=[w, w],
c=[w / 2., h / 2.],
k=ch.zeros(5),
t=cam_t,
rt=cam_r)
return U
def _project_vertices(v, w, h, cam_r, cam_t):
"""projects vertices onto image plane"""
V = ch.array(v)
U = ProjectPoints(
v=V,
f=[w * 2, w * 2],
c=[w / 2., h / 2.], # camera intrinsics
k=ch.zeros(5),
t=cam_t,
rt=cam_r)
return U
def initialize_camera(model,
j2d,
img,
init_pose,
flength=flength):
"""Initialize camera translation and body orientation
:param model: SMPL model
:param j2d: 14x2 array of CNN joints
:param img: h x w x 3 image
:param init_pose: 72D vector of pose parameters used for initialization
:param flength: camera focal length (kept fixed)
:param pix_thsh: threshold (in pixel), if the distance between shoulder joints in 2D
is lower than pix_thsh, the body orientation as ambiguous (so a fit is run on
both the estimated one and its flip)
:param viz: boolean, if True enables visualization during optimization
:returns: a tuple containing the estimated camera,
a boolean deciding if both the optimized body orientation and its flip should be
considered, 3D vector for the body orientation
"""
# optimize camera translation and body orientation based on torso joints
# LSP torso ids:
# 2=right hip, 3=left hip, 8=right shoulder, 9=left shoulder
torso_cids = [2, 3, 8, 9]
# corresponding SMPL torso ids
torso_smpl_ids = [2, 1, 17, 16]
center = np.array([img.shape[1] / 2, img.shape[0] / 2])
rt = ch.zeros(3) # initial camera rotation
init_t = guess_init(model, flength, j2d, init_pose)
t = ch.array(init_t) # initial camera translation
opt_pose = ch.array(init_pose)
_, A_global = global_rigid_transformation(opt_pose, model.J, model.kintree_table, xp=ch)
Jtr = ch.vstack([g[:3, 3] for g in A_global])
# initialize the camera and project SMPL joints
cam = ProjectPoints(f=np.array([flength, flength]), rt=rt, t=t, k=np.zeros(5), c=center)
cam.v = Jtr # project SMPL joints
# optimize for camera translation and body orientation
free_variables = [cam.t, opt_pose[:3]]
ch.minimize(
# data term defined over torso joints...
{'cam': j2d[torso_cids] - cam[torso_smpl_ids],
# ...plus a regularizer for the camera translation
'cam_t': 1e2 * (cam.t[2] - init_t[2])},
x0=free_variables, method='dogleg', callback=None,
options={'maxiter': 100, 'e_3': .0001, 'disp': 0})
return cam, opt_pose[:3].r
def load_BFM_2017(fname):
'''
Loads BFM 2017 in h5 file format and returns a chumpy object and all model parameters
'''
with h5py.File(fname, 'r') as f:
shape_mean = f['shape']['model']['mean'][:]
shape_pcaBasis = f['shape']['model']['pcaBasis'][:]
shape_pcaVariance = f['shape']['model']['pcaVariance'][:]
expression_mean = f['expression']['model']['mean'][:]
expression_pcaBasis = f['expression']['model']['pcaBasis'][:]
expression_pcaVariance = f['expression']['model']['pcaVariance'][:]
color_mean = f['color']['model']['mean'][:]
color_pcaBasis = f['color']['model']['pcaBasis'][:]
color_pcaVariance = f['color']['model']['pcaVariance'][:]
shape_coeffs = ch.zeros(shape_pcaBasis.shape[1])
exp_coeffs = ch.zeros(expression_pcaBasis.shape[1])
color_coeffs = ch.zeros(color_pcaBasis.shape[1])
sc = ch.diag(np.sqrt(shape_pcaVariance)).dot(shape_coeffs)
ec = ch.diag(np.sqrt(expression_pcaVariance)).dot(exp_coeffs)
cc = ch.diag(np.sqrt(color_pcaVariance)).dot(color_coeffs)
v_bfm = ch.array(shape_mean).reshape(-1,3) + ch.array(shape_pcaBasis).dot(sc).reshape(-1,3) + \
ch.array(expression_mean).reshape(-1, 3) + ch.array(expression_pcaBasis).dot(ec).reshape(-1, 3)
c_bfm = ch.array(color_mean).reshape(
-1, 3) + ch.array(color_pcaBasis).dot(cc).reshape(-1, 3)
return {
'verts': v_bfm,
'color': c_bfm,
'shape_coeffs': shape_coeffs,
'exp_coeffs': exp_coeffs,
'color_coeffs': color_coeffs
}
def proj_smpl_onto_img(img, smpl, pose, shape, cam_f, cam_t):
if isinstance(cam_f, float):
cam_f = np.array([cam_f, cam_f])
smpl.pose[:] = pose
smpl.betas[:] = shape
center = np.array([img.shape[1] / 2, img.shape[0] / 2])
cam = ProjectPoints(
f=cam_f, rt=ch.zeros(3), t=cam_t, k=np.zeros(5), c=center)
cam.v = smpl.r
for v in cam.r:
r = int(round(v[1]))
c = int(round(v[0]))
if 0 <= r < img.shape[0] and 0 <= c < img.shape[1]:
img[r, c, :] = np.asarray([255, 255, 255])
return img
def smooth_rings(gar_rings, verts):
"""
:param gar_rings:
:param verts:
:returns
"""
error = ch.zeros([0, 3])
for ring in gar_rings:
N = len(ring)
aring = np.array(ring)
ring_0 = aring[np.arange(0, N)]
ring_m1 = aring[np.array([N - 1] + range(0, N - 1))]
ring_p1 = aring[np.array(range(1, N) + [0])]
err_ring = verts[ring_m1] - 2 * verts[ring_0] + verts[ring_p1]
error = ch.vstack([error, err_ring])
error = ch.vstack([error, err_ring])
return error
def setupCamera(v, cameraParams):
chDistMat = geometry.Translate(x=0,
y=cameraParams['Zshift'],
z=cameraParams['chCamHeight'])
chRotElMat = geometry.RotateX(a=-cameraParams['chCamEl'])
chCamModelWorld = ch.dot(chDistMat, chRotElMat)
flipZYRotation = np.array([[1.0, 0.0, 0.0, 0.0], [0.0, 0, 1.0, 0.0],
[0.0, -1.0, 0, 0.0], [0.0, 0.0, 0.0, 1.0]])
chMVMat = ch.dot(chCamModelWorld, flipZYRotation)
chInvCam = ch.inv(chMVMat)
modelRotation = chInvCam[0:3, 0:3]
chRod = opendr.geometry.Rodrigues(rt=modelRotation).reshape(3)
chTranslation = chInvCam[0:3, 3]
translation, rotation = (chTranslation, chRod)
camera = ProjectPoints(v=v,
rt=rotation,
t=translation,
f=1000 * cameraParams['chCamFocalLength'] *
cameraParams['a'],
c=cameraParams['c'],
k=ch.zeros(5))
flipXRotation = np.array([[1.0, 0.0, 0.0, 0.0], [0.0, -1.0, 0., 0.0],
[0.0, 0., -1.0, 0.0], [0.0, 0.0, 0.0, 1.0]])
camera.openglMat = flipXRotation #Needed to match OpenGL flipped axis.
return camera, modelRotation, chMVMat
def get_capsules(model, wrt_betas=None, length_regs=None, rad_regs=None):
from opendr.geometry import Rodrigues
if length_regs is not None:
n_shape_dofs = length_regs.shape[0] - 1
else:
n_shape_dofs = model.betas.r.size
segm = np.argmax(model.weights_prior, axis=1)
J_off = ch.zeros((len(joint2name), 3))
rots = rots0.copy()
mujoco_t_mid = [0, 3, 6, 9]
if wrt_betas is not None:
# if we want to differentiate wrt betas (shape), we must have the
# regressors...
assert (length_regs is not None and rad_regs is not None)
# ... and betas must be a chumpy object
assert (hasattr(wrt_betas, 'dterms'))
pad = ch.concatenate(
(wrt_betas, ch.zeros(n_shape_dofs - len(wrt_betas)), ch.ones(1)))
lengths = pad.dot(length_regs)
rads = pad.dot(rad_regs)
else:
lengths = ch.ones(len(joint2name))
rads = ch.ones(len(joint2name))
betas = wrt_betas if wrt_betas is not None else model.betas
n_betas = len(betas)
# the joint regressors are the original, pre-optimized ones
# (middle of the part frontier)
myJ_regressor = model.J_regressor_prior
myJ0 = ch.vstack(
(ch.ch.MatVecMult(myJ_regressor, model.v_template[:, 0] +
model.shapedirs[:, :, :n_betas].dot(betas)[:, 0]),
ch.ch.MatVecMult(myJ_regressor, model.v_template[:, 1] +
model.shapedirs[:, :, :n_betas].dot(betas)[:, 1]),
ch.ch.MatVecMult(myJ_regressor, model.v_template[:, 2] +
model.shapedirs[:, :, :n_betas].dot(betas)[:, 2]))).T
# with small adjustments for hips, spine and feet
myJ = ch.vstack(
[ch.concatenate([myJ0[0, 0], (
.6 * myJ0[0, 1] + .2 * myJ0[1, 1] + .2 * myJ0[2, 1]), myJ0[9, 2]]),
ch.vstack([myJ0[i] for i in range(1, 7)]), ch.concatenate(
[myJ0[7, 0], (1.1 * myJ0[7, 1] - .1 * myJ0[4, 1]), myJ0[7, 2]]),
ch.concatenate(
[myJ0[8, 0], (1.1 * myJ0[8, 1] - .1 * myJ0[5, 1]), myJ0[8, 2]]),
ch.concatenate(
[myJ0[9, 0], myJ0[9, 1], (.2 * myJ0[9, 2] + .8 * myJ0[12, 2])]),
ch.vstack([myJ0[i] for i in range(10, 24)])])
capsules = []
# create one capsule per mujoco joint
for ijoint, segms in enumerate(mujoco2segm):
if wrt_betas is None:
vidxs = np.asarray([segm == k for k in segms]).any(axis=0)
verts = model.v_template[vidxs].r
dims = (verts.max(axis=0) - verts.min(axis=0))
rads[ijoint] = .5 * ((dims[(np.argmax(dims) + 1) % 3] + dims[(
np.argmax(dims) + 2) % 3]) / 4.)
lengths[ijoint] = max(dims) - 2. * rads[ijoint].r
# the core joints are different, since the capsule is not in the joint
# but in the middle
if ijoint in mujoco_t_mid:
len_offset = ch.vstack([ch.zeros(1), ch.abs(lengths[ijoint]) / 2.,
ch.zeros(1)]).reshape(3, 1)
caps = Capsule(
(J_off[ijoint] + myJ[mujoco2segm[ijoint][0]]).reshape(
3, 1) - Rodrigues(rots[ijoint]).dot(len_offset),
rots[ijoint], rads[ijoint], lengths[ijoint])
else:
caps = Capsule(
(J_off[ijoint] + myJ[mujoco2segm[ijoint][0]]).reshape(3, 1),
rots[ijoint], rads[ijoint], lengths[ijoint])
caps.id = ijoint
capsules.append(caps)
return capsules
def get_capsules(model, wrt_betas=None, length_regs=None, rad_regs=None):
from opendr.geometry import Rodrigues
if length_regs is not None:
n_shape_dofs = length_regs.shape[0] - 1
else:
n_shape_dofs = model.betas.r.size
segm = np.argmax(model.weights_prior, axis=1)
J_off = ch.zeros((len(joint2name), 3))
rots = rots0.copy()
mujoco_t_mid = [0, 3, 6, 9]
if wrt_betas is not None:
# if we want to differentiate wrt betas (shape), we must have the
# regressors...
assert (length_regs is not None and rad_regs is not None)
# ... and betas must be a chumpy object
assert (hasattr(wrt_betas, 'dterms'))
pad = ch.concatenate(
(wrt_betas, ch.zeros(n_shape_dofs - len(wrt_betas)), ch.ones(1)))
lengths = pad.dot(length_regs)
rads = pad.dot(rad_regs)
else:
lengths = ch.ones(len(joint2name))
rads = ch.ones(len(joint2name))
betas = wrt_betas if wrt_betas is not None else model.betas
n_betas = len(betas)
# the joint regressors are the original, pre-optimized ones
# (middle of the part frontier)
myJ_regressor = model.J_regressor_prior
myJ0 = ch.vstack((ch.ch.MatVecMult(
myJ_regressor, model.v_template[:, 0] +
model.shapedirs[:, :, :n_betas].dot(betas)[:, 0]),
ch.ch.MatVecMult(
myJ_regressor, model.v_template[:, 1] +
model.shapedirs[:, :, :n_betas].dot(betas)[:, 1]),
ch.ch.MatVecMult(
myJ_regressor, model.v_template[:, 2] +
model.shapedirs[:, :, :n_betas].dot(betas)[:, 2]))).T
# with small adjustments for hips, spine and feet
myJ = ch.vstack([
ch.concatenate([
myJ0[0, 0], (.6 * myJ0[0, 1] + .2 * myJ0[1, 1] + .2 * myJ0[2, 1]),
myJ0[9, 2]
]),
ch.vstack([myJ0[i] for i in range(1, 7)]),
ch.concatenate(
[myJ0[7, 0], (1.1 * myJ0[7, 1] - .1 * myJ0[4, 1]), myJ0[7, 2]]),
ch.concatenate(
[myJ0[8, 0], (1.1 * myJ0[8, 1] - .1 * myJ0[5, 1]), myJ0[8, 2]]),
ch.concatenate(
[myJ0[9, 0], myJ0[9, 1], (.2 * myJ0[9, 2] + .8 * myJ0[12, 2])]),
ch.vstack([myJ0[i] for i in range(10, 24)])
])
capsules = []
# create one capsule per mujoco joint
for ijoint, segms in enumerate(mujoco2segm):
if wrt_betas is None:
vidxs = np.asarray([segm == k for k in segms]).any(axis=0)
verts = model.v_template[vidxs].r
dims = (verts.max(axis=0) - verts.min(axis=0))
rads[ijoint] = .5 * ((dims[(np.argmax(dims) + 1) % 3] + dims[
(np.argmax(dims) + 2) % 3]) / 4.)
lengths[ijoint] = max(dims) - 2. * rads[ijoint].r
# the core joints are different, since the capsule is not in the joint
# but in the middle
if ijoint in mujoco_t_mid:
len_offset = ch.vstack(
[ch.zeros(1),
ch.abs(lengths[ijoint]) / 2.,
ch.zeros(1)]).reshape(3, 1)
caps = Capsule(
(J_off[ijoint] + myJ[mujoco2segm[ijoint][0]]).reshape(3, 1) -
Rodrigues(rots[ijoint]).dot(len_offset), rots[ijoint],
rads[ijoint], lengths[ijoint])
else:
caps = Capsule(
(J_off[ijoint] + myJ[mujoco2segm[ijoint][0]]).reshape(3, 1),
rots[ijoint], rads[ijoint], lengths[ijoint])
caps.id = ijoint
capsules.append(caps)
return capsules
def optimize_on_joints(j2d,
model,
cam,
img,
prior,
try_both_orient,
body_orient,
n_betas=10,
regs=None,
conf=None,
viz=False):
"""Fit the model to the given set of joints, given the estimated camera
:param j2d: 14x2 array of CNN joints
:param model: SMPL model
:param cam: estimated camera
:param img: h x w x 3 image
:param prior: mixture of gaussians pose prior
:param try_both_orient: boolean, if True both body_orient and its flip are considered for the fit
:param body_orient: 3D vector, initialization for the body orientation
:param n_betas: number of shape coefficients considered during optimization
:param regs: regressors for capsules' axis and radius, if not None enables the interpenetration error term
:param conf: 14D vector storing the confidence values from the CNN
:param viz: boolean, if True enables visualization during optimization
:returns: a tuple containing the optimized model, its joints projected on image space, the camera translation
"""
t0 = time()
# define the mapping LSP joints -> SMPL joints
# cids are joints ids for LSP:
cids = range(12) + [13]
# joint ids for SMPL
# SMPL does not have a joint for head, instead we use a vertex for the head
# and append it later.
smpl_ids = [8, 5, 2, 1, 4, 7, 21, 19, 17, 16, 18, 20]
# the vertex id for the joint corresponding to the head
head_id = 411
# weights assigned to each joint during optimization;
# the definition of hips in SMPL and LSP is significantly different so set
# their weights to zero
base_weights = np.array(
[1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=np.float64)
if try_both_orient:
flipped_orient = cv2.Rodrigues(body_orient)[0].dot(
cv2.Rodrigues(np.array([0., np.pi, 0]))[0])
flipped_orient = cv2.Rodrigues(flipped_orient)[0].ravel()
orientations = [body_orient, flipped_orient]
else:
orientations = [body_orient]
if try_both_orient:
# store here the final error for both orientations,
# and pick the orientation resulting in the lowest error
errors = []
svs = []
cams = []
for o_id, orient in enumerate(orientations):
# initialize the shape to the mean shape in the SMPL training set
betas = ch.zeros(n_betas)
# initialize the pose by using the optimized body orientation and the
# pose prior
init_pose = np.hstack((orient, prior.weights.dot(prior.means)))
# instantiate the model:
# verts_decorated allows us to define how many
# shape coefficients (directions) we want to consider (here, n_betas)
sv = verts_decorated(
trans=ch.zeros(3),
pose=ch.array(init_pose),
v_template=model.v_template,
J=model.J_regressor,
betas=betas,
shapedirs=model.shapedirs[:, :, :n_betas],
weights=model.weights,
kintree_table=model.kintree_table,
bs_style=model.bs_style,
f=model.f,
bs_type=model.bs_type,
posedirs=model.posedirs)
# make the SMPL joints depend on betas
Jdirs = np.dstack([model.J_regressor.dot(model.shapedirs[:, :, i])
for i in range(len(betas))])
J_onbetas = ch.array(Jdirs).dot(betas) + model.J_regressor.dot(
model.v_template.r)
# get joint positions as a function of model pose, betas and trans
(_, A_global) = global_rigid_transformation(
sv.pose, J_onbetas, model.kintree_table, xp=ch)
Jtr = ch.vstack([g[:3, 3] for g in A_global]) + sv.trans
# add the head joint, corresponding to a vertex...
Jtr = ch.vstack((Jtr, sv[head_id]))
# ... and add the joint id to the list
if o_id == 0:
smpl_ids.append(len(Jtr) - 1)
# update the weights using confidence values
weights = base_weights * conf[
cids] if conf is not None else base_weights
# project SMPL joints on the image plane using the estimated camera
cam.v = Jtr
# data term: distance between observed and estimated joints in 2D
obj_j2d = lambda w, sigma: (
w * weights.reshape((-1, 1)) * GMOf((j2d[cids] - cam[smpl_ids]), sigma))
# mixture of gaussians pose prior
pprior = lambda w: w * prior(sv.pose)
# joint angles pose prior, defined over a subset of pose parameters:
# 55: left elbow, 90deg bend at -np.pi/2
# 58: right elbow, 90deg bend at np.pi/2
# 12: left knee, 90deg bend at np.pi/2
# 15: right knee, 90deg bend at np.pi/2
alpha = 10
my_exp = lambda x: alpha * ch.exp(x)
obj_angle = lambda w: w * ch.concatenate([my_exp(sv.pose[55]), my_exp(-sv.pose[
58]), my_exp(-sv.pose[12]), my_exp(-sv.pose[15])])
if viz:
import matplotlib.pyplot as plt
plt.ion()
def on_step(_):
"""Create visualization."""
plt.figure(1, figsize=(10, 10))
plt.subplot(1, 2, 1)
# show optimized joints in 2D
tmp_img = img.copy()
for coord, target_coord in zip(
np.around(cam.r[smpl_ids]).astype(int),
np.around(j2d[cids]).astype(int)):
if (coord[0] < tmp_img.shape[1] and coord[0] >= 0 and
coord[1] < tmp_img.shape[0] and coord[1] >= 0):
cv2.circle(tmp_img, tuple(coord), 3, [0, 0, 255])
if (target_coord[0] < tmp_img.shape[1] and
target_coord[0] >= 0 and
target_coord[1] < tmp_img.shape[0] and
target_coord[1] >= 0):
cv2.circle(tmp_img, tuple(target_coord), 3,
[0, 255, 0])
plt.imshow(tmp_img[:, :, ::-1])
plt.draw()
plt.show()
plt.pause(1e-2)
on_step(_)
else:
on_step = None
if regs is not None:
# interpenetration term
sp = SphereCollisions(
pose=sv.pose, betas=sv.betas, model=model, regs=regs)
sp.no_hands = True
# weight configuration used in the paper, with joints + confidence values from the CNN
# (all the weights used in the code were obtained via grid search, see the paper for more details)
# the first list contains the weights for the pose priors,
# the second list contains the weights for the shape prior
opt_weights = zip([4.04 * 1e2, 4.04 * 1e2, 57.4, 4.78],
[1e2, 5 * 1e1, 1e1, .5 * 1e1])
# run the optimization in 4 stages, progressively decreasing the
# weights for the priors
for stage, (w, wbetas) in enumerate(opt_weights):
_LOGGER.info('stage %01d', stage)
objs = {}
objs['j2d'] = obj_j2d(1., 100)
objs['pose'] = pprior(w)
objs['pose_exp'] = obj_angle(0.317 * w)
objs['betas'] = wbetas * betas
if regs is not None:
objs['sph_coll'] = 1e3 * sp
ch.minimize(
objs,
x0=[sv.betas, sv.pose],
method='dogleg',
callback=on_step,
options={'maxiter': 100,
'e_3': .0001,
'disp': 0})
t1 = time()
_LOGGER.info('elapsed %.05f', (t1 - t0))
if try_both_orient:
errors.append((objs['j2d'].r**2).sum())
svs.append(sv)
cams.append(cam)
if try_both_orient and errors[0] > errors[1]:
choose_id = 1
else:
choose_id = 0
if viz:
plt.ioff()
return (svs[choose_id], cams[choose_id].r, cams[choose_id].t.r)
def inverseKinematicCh(pts3D, fullposeInit = np.zeros((48,), dtype=np.float32),
transInit = np.array([0., 0., -0.45]), betaInit = np.zeros((10,), dtype=np.float32)):
'''
Performs inverse kinematic optimization when given the 3d points
3D points --> MANO params
:param pts3D:
:param fullposeInit:
:param transInit:
:param betaInit:
:return: fullpose, trans and beta vector
'''
import chumpy as ch
from mano.chumpy.smpl_handpca_wrapper_HAND_only import load_model, load_model_withInputs
from ghope.constraints import Constraints
assert pts3D.shape == (21,3)
constraints = Constraints()
validTheta = ch.zeros((len(constraints.validThetaIDs,)))
fullposeCh = constraints.fullThetaMat.dot(ch.expand_dims(validTheta,1))[:,0]
transCh = ch.array(undo_chumpy(transInit))
betaCh = ch.array(undo_chumpy(betaInit))
pts3DGT = pts3D
m = load_model_withInputs(MANO_MODEL_PATH, fullposeCh, transCh, betaCh, ncomps=6, flat_hand_mean=True)
if fullposeInit.shape == (16,3,3):
fpList = []
for i in range(16):
fpList.append(cv2.Rodrigues(fullposeInit[i])[0][:, 0])
validTheta[:] = np.concatenate(fpList, 0)[constraints.validThetaIDs]
else:
validTheta[:] = undo_chumpy(fullposeInit[constraints.validThetaIDs])
loss = {}
projPts = m.J_transformed
projPtsGT = pts3DGT
loss['joints'] = (projPts - projPtsGT)
# m.fullpose[Constraints().invalidThetaIDs] = 0.
thetaConstMin, thetaConstMax = Constraints().getHandJointConstraintsCh(fullposeCh[3:])
# loss['constMin'] = 1e4 * thetaConstMin
# loss['constMax'] = 1e4 * thetaConstMax
freeVars = [validTheta, transCh]
def cbPass(_):
print(np.max(np.abs(m.J_transformed - pts3DGT)))
pass
ch.minimize(loss, x0=freeVars, callback=cbPass, method='dogleg', options={'maxiter': 10})
if True:
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(m.J_transformed[:, 0], m.J_transformed[:, 1], m.J_transformed[:, 2], c='r')
ax.scatter(pts3DGT[:, 0], pts3DGT[:, 1], pts3DGT[:, 2], c='b')
plt.show()
return undo_chumpy(m.fullpose), undo_chumpy(m.trans), undo_chumpy(m.betas)
def initialize_camera(model,
j2d,
img,
init_pose,
flength=5000.,
pix_thsh=25.,
viz=False):
"""Initialize camera translation and body orientation
:param model: SMPL model
:param j2d: 14x2 array of CNN joints
:param img: h x w x 3 image
:param init_pose: 72D vector of pose parameters used for initialization
:param flength: camera focal length (kept fixed)
:param pix_thsh: threshold (in pixel), if the distance between shoulder joints in 2D
is lower than pix_thsh, the body orientation as ambiguous (so a fit is run on both
the estimated one and its flip)
:param viz: boolean, if True enables visualization during optimization
:returns: a tuple containing the estimated camera,
a boolean deciding if both the optimized body orientation and its flip should be considered,
3D vector for the body orientation
"""
# optimize camera translation and body orientation based on torso joints
# LSP torso ids:
# 2=right hip, 3=left hip, 8=right shoulder, 9=left shoulder
torso_cids = [2, 3, 8, 9]
# corresponding SMPL torso ids
torso_smpl_ids = [2, 1, 17, 16]
center = np.array([img.shape[1] / 2, img.shape[0] / 2])
# initialize camera rotation
rt = ch.zeros(3)
# initialize camera translation
_LOGGER.info('initializing translation via similar triangles')
init_t = guess_init(model, flength, j2d, init_pose)
t = ch.array(init_t)
# check how close the shoulder joints are
try_both_orient = np.linalg.norm(j2d[8] - j2d[9]) < pix_thsh
opt_pose = ch.array(init_pose)
(_, A_global) = global_rigid_transformation(
opt_pose, model.J, model.kintree_table, xp=ch)
Jtr = ch.vstack([g[:3, 3] for g in A_global])
# initialize the camera
cam = ProjectPoints(
f=np.array([flength, flength]), rt=rt, t=t, k=np.zeros(5), c=center)
# we are going to project the SMPL joints
cam.v = Jtr
if viz:
viz_img = img.copy()
# draw the target (CNN) joints
for coord in np.around(j2d).astype(int):
if (coord[0] < img.shape[1] and coord[0] >= 0 and
coord[1] < img.shape[0] and coord[1] >= 0):
cv2.circle(viz_img, tuple(coord), 3, [0, 255, 0])
import matplotlib.pyplot as plt
plt.ion()
# draw optimized joints at each iteration
def on_step(_):
"""Draw a visualization."""
plt.figure(1, figsize=(5, 5))
plt.subplot(1, 1, 1)
viz_img = img.copy()
for coord in np.around(cam.r[torso_smpl_ids]).astype(int):
if (coord[0] < viz_img.shape[1] and coord[0] >= 0 and
coord[1] < viz_img.shape[0] and coord[1] >= 0):
cv2.circle(viz_img, tuple(coord), 3, [0, 0, 255])
plt.imshow(viz_img[:, :, ::-1])
plt.draw()
plt.show()
plt.pause(1e-3)
else:
on_step = None
# optimize for camera translation and body orientation
free_variables = [cam.t, opt_pose[:3]]
ch.minimize(
# data term defined over torso joints...
{'cam': j2d[torso_cids] - cam[torso_smpl_ids],
# ...plus a regularizer for the camera translation
'cam_t': 1e2 * (cam.t[2] - init_t[2])},
x0=free_variables,
method='dogleg',
callback=on_step,
options={'maxiter': 100,
'e_3': .0001,
# disp set to 1 enables verbose output from the optimizer
'disp': 0})
if viz:
plt.ioff()
return (cam, try_both_orient, opt_pose[:3].r)
def test_occlusion(self):
if visualize:
import matplotlib.pyplot as plt
plt.ion()
# Create renderer
import chumpy as ch
import numpy as np
from opendr.renderer import TexturedRenderer, ColoredRenderer
#rn = TexturedRenderer()
rn = ColoredRenderer()
# Assign attributes to renderer
from util_tests import get_earthmesh
m = get_earthmesh(trans=ch.array([0,0,4]), rotation=ch.zeros(3))
rn.texture_image = m.texture_image
rn.ft = m.ft
rn.vt = m.vt
m.v[:,2] = np.mean(m.v[:,2])
# red is front and zero
# green is back and 1
t0 = ch.array([1,0,.1])
t1 = ch.array([-1,0,.1])
v0 = ch.array(m.v) + t0
if False:
v1 = ch.array(m.v*.4 + np.array([0,0,3.8])) + t1
else:
v1 = ch.array(m.v) + t1
vc0 = v0*0 + np.array([[.4,0,0]])
vc1 = v1*0 + np.array([[0,.4,0]])
vc = ch.vstack((vc0, vc1))
v = ch.vstack((v0, v1))
f = np.vstack((m.f, m.f+len(v0)))
w, h = (320, 240)
rn.camera = ProjectPoints(v=v, rt=ch.zeros(3), t=ch.zeros(3), f=ch.array([w,w])/2., c=ch.array([w,h])/2., k=ch.zeros(5))
rn.camera.t = ch.array([0,0,-2.5])
rn.frustum = {'near': 1., 'far': 10., 'width': w, 'height': h}
m.vc = v.r*0 + np.array([[1,0,0]])
rn.set(v=v, f=f, vc=vc)
t0[:] = np.array([1.4, 0, .1-.02])
t1[:] = np.array([-0.6, 0, .1+.02])
target = rn.r
if visualize:
plt.figure()
plt.imshow(target)
plt.title('target')
plt.figure()
plt.show()
im_orig = rn.r.copy()
from cvwrap import cv2
tr = t0
eps_emp = .02
eps_pred = .02
#blur = lambda x : cv2.blur(x, ksize=(5,5))
blur = lambda x : x
for tr in [t0, t1]:
if tr is t0:
sum_limits = np.array([2.1e+2, 6.9e+1, 1.6e+2])
else:
sum_limits = [1., 5., 4.]
if visualize:
plt.figure()
for i in range(3):
dr_pred = np.array(rn.dr_wrt(tr[i]).todense()).reshape(rn.shape) * eps_pred
dr_pred = blur(dr_pred)
# central differences
tr[i] = tr[i].r + eps_emp/2.
rn_greater = rn.r.copy()
tr[i] = tr[i].r - eps_emp/1.
rn_lesser = rn.r.copy()
tr[i] = tr[i].r + eps_emp/2.
dr_emp = blur((rn_greater - rn_lesser) * eps_pred / eps_emp)
dr_pred_shown = np.clip(dr_pred, -.5, .5) + .5
dr_emp_shown = np.clip(dr_emp, -.5, .5) + .5
if visualize:
plt.subplot(3,3,i+1)
plt.imshow(dr_pred_shown)
plt.title('pred')
plt.axis('off')
plt.subplot(3,3,3+i+1)
plt.imshow(dr_emp_shown)
plt.title('empirical')
plt.axis('off')
plt.subplot(3,3,6+i+1)
diff = np.abs(dr_emp - dr_pred)
if visualize:
plt.imshow(diff)
diff = diff.ravel()
if visualize:
plt.title('diff (sum: %.2e)' % (np.sum(diff)))
plt.axis('off')
# print 'dr pred sum: %.2e' % (np.sum(np.abs(dr_pred.ravel())),)
# print 'dr emp sum: %.2e' % (np.sum(np.abs(dr_emp.ravel())),)
#import pdb; pdb.set_trace()
self.assertTrue(np.sum(diff) < sum_limits[i])