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utils_3DMM.py
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utils_3DMM.py
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#############################################################################
#Copyright 2016-2017, Anh Tuan Tran, Tal Hassner, Iacopo Masi, and Gerard Medioni
#The SOFTWARE provided in this page is provided "as is", without any guarantee
#made as to its suitability or fitness for any particular use. It may contain
#bugs, so use of this tool is at your own risk. We take no responsibility for
#any damage of any sort that may unintentionally be caused through its use.
# Please, cite the paper:
# @article{tran16_3dmm_cnn,
# title={Regressing Robust and Discriminative {3D} Morphable Models with a very Deep Neural Network},
# author={Anh Tran
# and Tal Hassner
# and Iacopo Masi
# and G\'{e}rard Medioni}
# journal={arXiv preprint},
# year={2016}
# }
# if you find our code useful.
##############################################################################
import numpy as np
import cv2
rescaleCASIA = [1.9255, 2.2591, 1.9423, 1.6087]
rescaleBB = [1.785974, 1.951171, 1.835600, 1.670403]
def get_mean_shape(model):
S = model.shapeMU
numVert = S.shape[0]/3
## Final Saving for visualization
S = np.reshape(S,(numVert,3))
return S
def projectBackBFM(model,features):
alpha = model.shapeEV * 0
for it in range(0, 99):
alpha[it] = model.shapeEV[it] * features[it]
S = np.matmul(model.shapePC, alpha)
## Adding back average shape
S = model.shapeMU + S
numVert = S.shape[0]/3
# (Texture)
beta = model.texEV * 0
for it in range(0, 99):
beta[it] = model.texEV[it] * features[it+99]
T = np.matmul(model.texPC, beta)
## Adding back average texture
T = model.texMU + T
## Some filtering
T = [truncateUint8(value) for value in T]
## Final Saving for visualization
S = np.reshape(S,(numVert,3))
T = np.reshape(T,(numVert, 3))
return S,T
def projectBackBFM_withExpr(model, features, expr_paras):
alpha = model.shapeEV * 0
for it in range(0, 99):
alpha[it] = model.shapeEV[it] * features[it]
S = np.matmul(model.shapePC, alpha)
expr = model.expEV * 0
for it in range(0, 29):
expr[it] = model.expEV[it] * expr_paras[it]
E = np.matmul(model.expPC, expr)
## Adding back average shape
S = model.shapeMU + S + model.expMU + E
numVert = S.shape[0]/3
# (Texture)
beta = model.texEV * 0
for it in range(0, 99):
beta[it] = model.texEV[it] * features[it+99]
T = np.matmul(model.texPC, beta)
## Adding back average texture
T = model.texMU + T
## Some filtering
T = [truncateUint8(value) for value in T]
## Final Saving for visualization
S = np.reshape(S,(numVert,3))
T = np.reshape(T,(numVert, 3))
return S,T
def projectBackBFM_withEP(model, features, expr_paras, pose_paras):
alpha = model.shapeEV * 0
for it in range(0, 99):
alpha[it] = model.shapeEV[it] * features[it]
S = np.matmul(model.shapePC, alpha)
# Expression
expr = model.expEV * 0
for it in range(0, 29):
expr[it] = model.expEV[it] * expr_paras[it]
E = np.matmul(model.expPC, expr)
## Adding back average shape
S = model.shapeMU + S + model.expMU + E
numVert = S.shape[0]// 3
# Pose
#PI = np.array([[ 2.88000000e+03, 0.00000000e+00, 1.12000000e+02], [0.00000000e+00, 2.88000000e+03, 1.12000000e+02], [0, 0, 1]]);
r = pose_paras[0:3]
r[1] = -r[1]
r[2] = -r[2]
t = pose_paras[3:6]
t[0] = -t[0]
#print r.shape, t.shape
R, jacobian = cv2.Rodrigues(r, None)
#print R
S = np.reshape(S,(numVert,3))
#print S.shape
S_RT = np.matmul(R, np.transpose(S)) + np.reshape(t, [3,1])
#S_RT = np.matmul(PI, S_RT)
S_RT = np.transpose(S_RT)
# (Texture)
beta = model.texEV * 0
for it in range(0, 99):
beta[it] = model.texEV[it] * features[it+99]
T = np.matmul(model.texPC, beta)
## Adding back average texture
T = model.texMU + T
## Some filtering
T = [truncateUint8(value) for value in T]
## Final Saving for visualization
S = np.reshape(S_RT,(numVert,3))
T = np.reshape(T,(numVert, 3))
return S,T
def truncateUint8(val):
if val < 0:
return 0
elif val > 255:
return 255
else:
return val
def write_ply(fname, S, T, faces):
nV = S.shape[0]
nF = faces.shape[0]
f = open(fname,'w')
f.write('ply\n')
f.write('format ascii 1.0\n')
f.write('element vertex ' + str(nV) + '\n')
f.write('property float x\n')
f.write('property float y\n')
f.write('property float z\n')
f.write('property uchar red\n')
f.write('property uchar green\n')
f.write('property uchar blue\n')
f.write('element face ' + str(nF) + '\n')
f.write('property list uchar int vertex_indices\n')
f.write('end_header\n')
for i in range(0,nV):
f.write('%0.4f %0.4f %0.4f %d %d %d\n' % (S[i,0],S[i,1],S[i,2],T[i,0],T[i,1],T[i,2]))
for i in range(0,nF):
f.write('3 %d %d %d\n' % (faces[i,0],faces[i,1],faces[i,2]))
f.close()
def write_ply_textureless(fname, S, faces):
nV = S.shape[0]
nF = faces.shape[0]
f = open(fname,'w')
f.write('ply\n')
f.write('format ascii 1.0\n')
f.write('element vertex ' + str(nV) + '\n')
f.write('property float x\n')
f.write('property float y\n')
f.write('property float z\n')
f.write('element face ' + str(nF) + '\n')
f.write('property list uchar int vertex_indices\n')
f.write('end_header\n')
for i in range(0,nV):
f.write('%0.4f %0.4f %0.4f\n' % (S[i,0],S[i,1],S[i,2]))
for i in range(0,nF):
f.write('3 %d %d %d\n' % (faces[i,0],faces[i,1],faces[i,2]))
f.close()
def cropImg(img,tlx,tly,brx,bry, img2, rescale):
l = float( tlx )
t = float ( tly )
ww = float ( brx - l )
hh = float( bry - t )
# Approximate LM tight BB
h = img.shape[0]
w = img.shape[1]
cv2.rectangle(img2, (int(l),int(t)), (int(brx), int(bry)), (0,255,255),2)
cx = l + ww/2
cy = t + hh/2
tsize = max(ww,hh)/2
l = cx - tsize
t = cy - tsize
# Approximate expanded bounding box
bl = int(round(cx - rescale[0]*tsize))
bt = int(round(cy - rescale[1]*tsize))
br = int(round(cx + rescale[2]*tsize))
bb = int(round(cy + rescale[3]*tsize))
nw = int(br-bl)
nh = int(bb-bt)
imcrop = np.zeros((nh,nw,3), dtype = "uint8")
ll = 0
if bl < 0:
ll = -bl
bl = 0
rr = nw
if br > w:
rr = w+nw - br
br = w
tt = 0
if bt < 0:
tt = -bt
bt = 0
bbb = nh
if bb > h:
bbb = h+nh - bb
bb = h
imcrop[tt:bbb,ll:rr,:] = img[bt:bb,bl:br,:]
return imcrop
def cropByInputLM(img, lms, img2):
nLM = lms.shape[0]
lms_x = [lms[i,0] for i in range(0,nLM)];
lms_y = [lms[i,1] for i in range(0,nLM)];
return cropImg(img,min(lms_x),min(lms_y),max(lms_x),max(lms_y), img2, rescaleCASIA)
def cropByFaceDet(img, detected_face, img2):
return cropImg(img,detected_face.left(),detected_face.top(),\
detected_face.right(),detected_face.bottom(), img2, rescaleBB)
def cropByLM(img, shape, img2):
nLM = shape.num_parts
lms_x = [shape.part(i).x for i in range(0,nLM)]
lms_y = [shape.part(i).y for i in range(0,nLM)]
return cropImg(img,min(lms_x),min(lms_y),max(lms_x),max(lms_y), img2, rescaleCASIA)