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py-3d-face-reconstruction
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Richard Torenvliet
py-3d-face-reconstruction
Commits
426b9854
Commit
426b9854
authored
Oct 27, 2017
by
Richard Torenvliet
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Testing with eos reconstruction
parent
482b13b3
Changes
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11 changed files
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321 additions
and
178 deletions
+321
-178
.gitignore
.gitignore
+1
-0
Dockerfile
Dockerfile
+26
-5
actions.mk
actions.mk
+1
-1
makefile
makefile
+1
-1
scripts/PublicMM1/python/bfm_to_obj.py
scripts/PublicMM1/python/bfm_to_obj.py
+1
-1
scripts/PublicMM1/python/random_face.py
scripts/PublicMM1/python/random_face.py
+4
-4
src/datasets/ibug.py
src/datasets/ibug.py
+1
-0
src/datasets/ibug_to_bfm.py
src/datasets/ibug_to_bfm.py
+133
-0
src/fitting/camera.py
src/fitting/camera.py
+8
-0
src/reconstruction/fit-model.cpp
src/reconstruction/fit-model.cpp
+110
-163
src/test/reconstruction_test.py
src/test/reconstruction_test.py
+35
-3
No files found.
.gitignore
View file @
426b9854
...
...
@@ -2,6 +2,7 @@ data/*
src/.cache/*
src/*/.cache
src/reconstruction/*.c
scripts/PublicMM1/
*.o
*.so
src/reconstruction/build/
...
...
Dockerfile
View file @
426b9854
...
...
@@ -4,32 +4,51 @@ FROM smvanveen/computer-vision:20161109143812
COPY
requirements.txt /tmp
RUN
pip
install
-r
/tmp/requirements.txt
RUN
apt-get
install
software-properties-common
-y
RUN
add-apt-repository ppa:bzindovic/suitesparse-bugfix-1319687
-y
RUN
apt-get update
-y
RUN
apt-get
install
libsuitesparse-dev
-y
# extra packages:
# graphviz: for cProfiling using pycallgraph.
# libeigen3-dev: for eos: 3D morphable face model fitting library.
RUN
apt-get
install
-y
\
graphviz
\
libeigen3-dev
\
libgoogle-glog-dev
\
libatlas-base-dev
\
libeigen3-dev
WORKDIR
/libs
# install dlib
RUN
git clone https://github.com/davisking/dlib.git
RUN
(
cd
dlib
;
python setup.py
install
--yes
USE_AVX_INSTRUCTIONS
)
RUN
git clone https://ceres-solver.googlesource.com/ceres-solver
RUN
(
cd
ceres-solver
;
make
-j3
)
RUN
(
cd
ceres-solver
;
make
install
)
# install eos (face-recosntruction, (3D Morphable Face Model fitting library)
RUN
git clone
--recursive
https://github.com/patrikhuber/eos.git
RUN
git clone
--recursive
\
https://github.com/patrikhuber/eos.git
# remove dependency on opencv 2.4.3, opencv 3.0 works fine
WORKDIR
/libs/eos
RUN
sed
-i
's/2.4.3//g'
CMakeLists.txt
RUN
git checkout devel
# needed for master branch
#RUN sed -i 's/2.4.3//g' CMakeLists.txt
RUN
mkdir
build
WORKDIR
/libs/eos/build
RUN
cmake ../
\
-DCMAKE_INSTALL_PREFIX
=
/usr/local/eos
\
-DGENERATE_PYTHON_BINDINGS
=
on
\
-DBUILD_UTILS
=
on
\
-DPYTHON_EXECUTABLE
=
/usr/bin/python
-DEOS_GENERATE_PYTHON_BINDINGS
=
on
\
-DEOS_BUILD_CERES_EXAMPLE
=
on
\
-DEOS_BUILD_UTILS
=
on
\
-DEOS_BUILD_EXAMPLES
=
on
RUN
make
&&
make
install
...
...
@@ -40,4 +59,6 @@ RUN git clone https://github.com/pybind/pybind11.git
RUN
(
cd
pybind11
;
mkdir
build
;
cd
build
;
cmake
-DPYBIND11_PYTHON_VERSION
=
2.7 ..
)
;
RUN
(
cd
pybind11/build
;
make
-j4
&&
make
install
)
;
#TODO, remove the tmp libs folder in production?
WORKDIR
/src
actions.mk
View file @
426b9854
.PHONY
:=
train_model show_pca test_model show_reconstruction
DEBUG_LEVEL
=
*
data/imm_face_db
:
data/imm_face_db.tar.gz
data/imm_face_db
:
#
data/imm_face_db.tar.gz
(
cd
data
;
mkdir
-p
imm_face_db
;
\
tar
-xvzf
imm_face_db.tar.gz
-C
imm_face_db
\
)
...
...
makefile
View file @
426b9854
...
...
@@ -13,7 +13,7 @@ BASE_DOCKER_CMD:= docker run $(DOCKER_RUN_FLAGS) $(IMAGE_TAG)
$(info
$(TARGETS))
DEPENDENCIES
:=
data/imm_face_db
TARGETS
:=
data/shape_predictor_68_face_landmarks.dat
\
TARGETS
:=
data/shape_predictor_68_face_landmarks.dat
\
src/reconstruction/texture.so
\
src/reconstruction/fit.so
\
data/pca_ibug_shape_model.npy
\
...
...
scripts/PublicMM1/python/bfm_to_obj.py
View file @
426b9854
...
...
@@ -44,7 +44,7 @@ def output_shape_and_texture(shape, texture, f):
for
i
in
range
(
0
,
len
(
shape
),
3
):
f
.
write
(
'v {} {} {} {} {} {}
\
n
'
.
format
(
shape
[
i
],
shape
[
i
+
1
],
shape
[
i
+
2
],
int
(
texture
[
i
]),
int
(
texture
[
i
+
1
]),
int
(
texture
[
i
+
2
])
texture
[
i
],
texture
[
i
+
1
],
texture
[
i
+
2
]
)
)
...
...
scripts/PublicMM1/python/random_face.py
View file @
426b9854
...
...
@@ -131,8 +131,8 @@ def weight_face_shape(attr, alpha, n=30):
def
gen_random_shape_coef
(
attr
,
dim
):
alpha
=
np
.
random
.
randn
(
dim
,
1
)
alpha
=
age_face_shape
(
attr
,
alpha
,
1
)
alpha
=
gender_face_shape
(
attr
,
alpha
,
-
5
)
alpha
=
age_face_shape
(
attr
,
alpha
,
1
0
)
alpha
=
gender_face_shape
(
attr
,
alpha
,
5
)
alpha
=
weight_face_shape
(
attr
,
alpha
,
-
10
)
return
alpha
...
...
@@ -140,8 +140,8 @@ def gen_random_shape_coef(attr, dim):
def
gen_random_tex_coef
(
attr
,
dim
):
betha
=
np
.
random
.
randn
(
dim
,
1
)
betha
=
age_face_tex
(
attr
,
betha
,
1
)
betha
=
gender_face_tex
(
attr
,
betha
,
-
5
)
betha
=
age_face_tex
(
attr
,
betha
,
1
0
)
betha
=
gender_face_tex
(
attr
,
betha
,
5
)
betha
=
weight_face_tex
(
attr
,
betha
,
-
10
)
return
betha
...
...
src/datasets/ibug.py
View file @
426b9854
...
...
@@ -12,6 +12,7 @@ import numpy as np
import
aam
import
landmarks
from
settings
import
logger
from
.ibug_to_bfm
import
ibug_mapping
# load detector (this loads the datafile from disk, so needs to be done once).
detector
=
landmarks
.
Detector
()
...
...
src/datasets/ibug_to_bfm.py
0 → 100644
View file @
426b9854
# Filename: MPEG4_FDP_face05.fp
# Format: (vertex_nr) (x y z) (x y) (name)
# mapping from ibug idx to bfm vertex
ibug_mapping
=
{
# nose
30
:
8309
,
# -72.8548 18986.1 121850 0 0 9.12
31
:
8319
,
# 13.2323 4430.29 131560 0 0 9.3
# 6611 # -12555.7 29870.6 96949.7 0 0 9.6 --
# 6244 # -15010.6 13787.1 106131 0 0 9.14 --
33
:
5879
,
# -15364.7 -7915.79 105577 0 0 9.4
32
:
7032
,
# -13710 -543.09 119384 0 0 9.2
34
:
8334
,
# -174.404 -12553.8 115099 0 0 9.15
# eye left
# 4410 # -31025.3 33492.2 94793.8 0 0 3.6 -- center eye
39
:
4404
,
# -31993.5 37990.5 94498 0 0 3.2 -- eye, not perfect
41
:
4416
,
# -30242.7 29444 93887.9 0 0 3.4
# 4402 -32257.8 38510.2 95919.1 0 0 3.14 --
# 4418 -30262.3 28855.5 94215.7 0 0 3.10 --
37
:
2088
,
# -43304.9 33261.1 85744.4 0 0 3.12
40
:
5959
,
# -18690.6 32105.7 91502.5 0 0 3.8
52
:
8344
,
# -192.025 -24960.5 116208 0 0 8.1
51
:
7570
,
# -5897.69 -23762.2 115642 0 0 8.9
50
:
5768
,
# -18543.4 -28311.2 107964 0 0 8.6
49
:
5006
,
# -29800 -34598.4 97775.3 0 0 8.4
58
:
8374
,
# -154.684 -40559.2 112944 0 0 8.2
60
:
6697
,
# -17838.4 -37488.3 106002 0 0 8.8
1
:
21628
,
# -77447 30661.5 12096.9 0 0 10.10
# 19963 -88262.2 36394.8 -4947.64 0 0 10.2 -- ears
# 19330 -85528.2 2072.17 -10931.6 0 0 10.4 -- ears
# 20203 -73194.6 -20281.3 11874.6 0 0 10.6 -- ears
# 20547 -67842.2 -17904 16423.6 0 0 10.8 -- ears
22
:
40514
,
# -14438.3 49609.7 107232 0 0 4.2 -- eyebrow
20
:
40087
,
# -37475.1 56446.7 100681 0 0 4.4 -- eyebrow
37
:
38792
,
# -57986.8 45943.2 78728.1 0 0 4.6
# 9965# 12130.9 29795.3 96702.2 0 0 9.7 -- nose
# 10372# 14681.1 13742.5 105748 0 0 9.13 -- nose
35
:
10781
,
# 14916.3 -7662.06 105539 0 0 9.5 -- nose
36
:
9612
,
# 13568 -523.124 119279 0 0 9.1 -- nose
# 12150# 30157.7 33516.8 94540 0 0 3.5 -- center eye
44
:
12144
,
# 31037.6 38025.8 94139.1 0 0 3.1 -- eye, not perfect
48
:
12156
,
# 29458.6 29444.7 93770.8 0 0 3.3 -- eye, not perfect
# 12142 31349.6 38584.3 95585.3 0 0 3.13 -- eye
# 12158 29537.5 28834.2 94046.5 0 0 3.9 -- eye
43
:
10603
,
# 17667.8 32012.7 91252.6 0 0 3.11 -- eye
55
:
11714
,
# 29517.6 -34275.5 97471.5 0 0 8.3
17
:
33496
,
# 76722.4 30669.8 12318.6 0 0 10.9
# 35151 87810.5 36294.1 -4628.25 0 0 10.1 -- ear
# 35807 85139.7 2150.69 -10665 0 0 10.3
# 34981 72909.8 -20461.1 12009.1 0 0 10.5
# 34656 67686.9 -18200 16564.5 0 0 10.7
63
:
8354
,
# -275.894 -31384.5 110683 0 0 2.2 -- mouth
67
:
8366
,
# -257.674 -31426.9 110736 0 0 2.3 -- mouth
# 40777 -802.905 96304.2 99265.5 0 0 11.1 -- upside
46
:
14472
,
# 42745.8 33135.4 85724.1 0 0 3.7 -- eye left
53
:
9118
,
# 5483.92 -23769.2 115655 0 0 8.10 -- mouth
54
:
10928
,
# 18155.1 -28245 107864 0 0 8.5 -- mouth
56
:
10051
,
# 17625.3 -37478 105917 0 0 8.7 -- not perfect
23
:
41091
,
# 13583.4 49767.2 107218 0 0 4.1
25
:
41511
,
# 36713.9 56793.8 100463 0 0 4.3
46
:
42825
,
# 57424.9 46059.1 78590.1 0 0 4.5
9
:
48187
# -48.2388 -76505.2 99442.1 0 0 gn
# 19963 -88262.2 36394.8 -4947.64 0 0 sa --
# 20205 -71257.4 -20598.4 13258.3 0 0 sba --
# 21629 -77516 30127.9 12058.9 0 0 pra --
# 19325 -88087.9 14538.9 -14157.6 0 0 pa --
# 20983 -79905 34425.4 8525.19 0 0 obs --
# 20786 -66866.5 -16819 17784.3 0 0 obi
# 21776 -72643.3 11413.3 11140.1 0 0 t
# 26140 -65298.1 23275.2 64256.6 0 0 zy
# 43070 -59848.7 -42669.3 41668.6 0 0 go
# 8374 -154.684 -40559.2 112944 0 0 li
# 8381 -140.066 -49939.9 106747 0 0 sl
# 48187 -48.2388 -76505.2 99442.1 0 0 gn
# 23188 -71385.1 15971.4 28332 0 0 cdl
# 8261 -369.986 47259.7 108662 0 0 g
# 26236 -60480.3 43221.1 72106.9 0 0 ft
# 7441 -6628.37 -23768.2 115463 0 0 cph
# 8344 -192.025 -24960.5 116208 0 0 ls
# 8366 -257.674 -31426.9 110736 0 0 sto
# 5392 -26244.2 -33187.9 98582 0 0 ch
# 8275 -190.815 39984 108192 0 0 n
# 8286 -144.161 35988.3 109453 0 0 se
# 8311 -47.9918 16757.4 123572 0 0 se
# 6389 -16981.4 -3046.09 111901 0 0 al
# 8320 20.1202 2232.26 131944 0 0 prn
# 8332 -175.672 -10527.6 116009 0 0 sn
# 6912 -9848.33 -8505.34 111144 0 0 sbal
# 5488 -17814.8 -3696.18 103442 0 0 ac
# 7809 -6093.17 -3557.61 124863 0 0 c'
# 7032 -13710 -543.09 119384 0 0 al'
# 7165 -10444.4 -2520.99 117527 0 0 al'
# 7814 -4855.38 -5635.67 114857 0 0 sn'
# 5959 -18690.6 32105.7 91502.5 0 0 en
# 2088 -43304.9 33261.1 85744.4 0 0 ex
# 4280 -32240.9 34083.1 94614 0 0 p
# 3393 -38037.4 23596.3 91323.3 0 0 or
# 4158 -32467.7 29315.4 93418 0 0 pi
# 40087 -37475.1 56446.7 100681 0 0 sci
# 4646 -37373.6 48977.9 99911.2 0 0 os
# 6796 -10468.9 -24402.7 113952 0 0 ls'
# 4404 -31993.5 37990.5 94498 0 0 ps
# 48180 -41.2938 -65127.6 108493 0 0 pg
# 35151 87810.5 36294.1 -4628.25 0 0 sa*
# 34983 71040 -20787.8 13418.5 0 0 sba*
# 33497 76793.9 30128.9 12280.4 0 0 pra*
# 35802 87874.7 14663.6 -13676.7 0 0 pa*
# 34132 79275.1 34409.8 8855 0 0 obs*
# 34425 66654.4 -17219.2 17867.2 0 0 obi*
# 33386 71894.4 11245.4 11118.2 0 0 t*
# 28978 64756.8 23136.4 64176 0 0 zy*
# 53307 59692.2 -42997 41678.6 0 0 go*
# 31960 70614.6 15771.9 28283.9 0 0 cdl*
# 28816 59843 43216.9 71989.8 0 0 ft*
# 9247 6180.74 -23794.4 115481 0 0 cph*
# 11326 25887.4 -32810.2 98262 0 0 ch*
# 10259 16732.3 -2973.56 111813 0 0 al*
# 9750 9386.51 -8462.76 111119 0 0 sbal*
# 11164 17421.1 -3511.99 103224 0 0 ac*
# 8841 5890.83 -3559.51 124776 0 0 c'*
# 9612 13568 -523.124 119279 0 0 al'*
# 9487 10105.6 -2556.09 117492 0 0 al'*
# 8846 4183.15 -5680.2 114730 0 0 sn'*
# 10603 17667.8 32012.7 91252.6 0 0 en*
# 14472 42745.8 33135.4 85724.1 0 0 ex*
# 12278 31373.5 34100.5 94353.7 0 0 p*
# 13197 37566.8 23529.1 91177.5 0 0 or*
# 12414 31723.8 29338.3 93324.8 0 0 pi*
# 41511 36713.9 56793.8 100463 0 0 sci*
# 11870 36697.5 49438 99579.8 0 0 os*
# 9892 9980 -24508.4 113969 0 0 ls'*
# 12144 31037.6 38025.8 94139.1 0 0 ps*
}
src/fitting/camera.py
0 → 100644
View file @
426b9854
import
cv2
def
calc_affine_projection_matrix
(
landmarks
,
vertices
,
image
):
"""
returns the 3x4 camera matrix based on the given locations of the landmarks
and matching 3d vertices.
"""
cv2
.
initCameraMatrix2D
(
vertices
,
landmarks
,
image
.
shape
)
src/reconstruction/fit-model.cpp
View file @
426b9854
This diff is collapsed.
Click to expand it.
src/test/reconstruction_test.py
View file @
426b9854
...
...
@@ -50,13 +50,45 @@ def fit_model():
input_points
=
dataset_module
.
factory
(
filename
=
image_filename
)
input_image
=
input_points
.
get_image
()
input_points
.
get_points
()
print
dir
(
eos
)
# scale points to output image shape. We MUST do this.
points
=
input_points
.
get_scaled_points
(
input_image
.
shape
)
print
(
input_image
.
shape
)
print
dir
(
eos
)
fit
.
fit
(
input_image
,
points
)
# fit.add(input_image, points)
#fit.add(input_image, points)
if
__name__
==
'__main__'
:
fit_model
()
## Try seo python bindings
# model = eos.morphablemodel.load_model(
# '/usr/local/eos/share/sfm_shape_3448.bin')
# blend_shapes = eos.morphablemodel.load_blendshapes(
# '/usr/local/eos/share/expression_blendshapes_3448.bin'
# )
#
# s = model.get_shape_model().draw_sample([1.0, -0.5, 0.7, 0.1])
#
# sample = np.array(s)
# tri = model.get_shape_model().get_triangle_list()
# mean = model.get_shape_model().get_mean()
# dims = model.get_shape_model().get_data_dimension()
#
# mean = np.array(mean)
# with open('/data/test.obj', 'w') as f:
# for i in range(0, len(sample), 3):
# f.write('v {} {} {}\n'.format(sample[i], sample[i + 1], sample[i + 2])
# )
#
# for i in range(0, len(tri)):
# f.write('f {} {} {}\n'.format(
# tri[i][0], tri[i][1], tri[i][2],
# )
# )
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