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Repository: menpo/itwmm
Branch: master
Commit: fa9575104565
Files: 31
Total size: 364.1 KB
Directory structure:
gitextract_75lg_kcj/
├── .gitignore
├── LICENSE.txt
├── README.md
├── evaluation/
│ ├── __init__.py
│ ├── benchmark.py
│ ├── demo.ipynb
│ ├── eos.py
│ ├── eos_landmark_settings.pkl
│ ├── fw_on_eos_low_res_settings.pkl
│ ├── kfeval.py
│ ├── lsfm_lms_indexes.pkl
│ └── trilist.pkl
├── itwmm/
│ ├── __init__.py
│ ├── base.py
│ ├── fitting/
│ │ ├── __init__.py
│ │ ├── algorithm.py
│ │ ├── base.py
│ │ ├── derivatives.py
│ │ ├── hessian.py
│ │ ├── initialization.py
│ │ ├── jacobian.py
│ │ └── projectout.py
│ ├── model/
│ │ ├── __init__.py
│ │ ├── extractimage.py
│ │ └── math.py
│ └── visualize.py
├── notebooks/
│ ├── 1. Building an "in-the-wild" texture model.ipynb
│ ├── 2. Creating an expressive 3DMM.ipynb
│ ├── 3. Fitting "in-the-wild" images.ipynb
│ └── 4. Fitting "in-the-wild" videos.ipynb
└── setup.py
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FILE CONTENTS
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FILE: .gitignore
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.idea
*.egg-info
*.ipynb_checkpoints
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FILE: LICENSE.txt
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Copyright (c) 2017, Imperial College London. All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in the
documentation and/or other materials provided with the distribution.
* The name of Imperial College London or that of other
contributors may not be used to endorse or promote products
derived from this software without specific prior written
permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTERS
''AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
HOLDERS OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS
OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR
TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE
USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH
DAMAGE.
================================================
FILE: README.md
================================================
# In The Wild 3D Morphable Models
The repository provides the source code of the algorithm of 3D reconstruction of "In-the-Wild" faces in **images** and **videos**, as outlined in the following papers:
> [**3D Reconstruction of "In-the-Wild" Faces in Images and Videos** ---
> J. Booth, A. Roussos, E. Ververas, E. Antonakos, S. Ploumpis, Y.
Panagagakis, S. Zafeiriou.
> Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), accepted for publication (2018).](https://doi.org/10.1109/TPAMI.2018.2832138)
> [**3D Face Morphable Models “In-the-Wild”** --- J. Booth, E. Antonakos, S.
Ploumpis, G. Trigeorgis, Y. Panagagakis, S. Zafeiriou.
CVPR 2017.](https://ibug.doc.ic.ac.uk/media/uploads/documents/booth2017itw3dmm.pdf)
If you use this code, **please cite the above papers**.
The following topics are covered, each one with its own dedicated notebook:
1. Building an "in-the-wild" texture model
2. Creating an expressive 3DMM
3. Fitting "in-the-wild" images
4. Fitting "in-the-wild" videos
### Release Notes (28/06/2018)
- There has been a major update in the code and the problems of the previous preliminary version have been addressed. The current version is fully functional.
### Prerequisites
To leverage this codebase users need to independently source the
following items to construct an "in-the-wild" 3DMM:
- A collection of "in-the-wild" images coupled with 3D fits
- A parametric facial shape model of identity and expression
And then to use this model, users will need to provide data to fit on:
- "in-the-wild" images or videos with iBUG 68 annotations
Examples are given for working with some common facial models (e.g. LSFM) and
it shouldn't be too challenging to adapt these examples for alternative inputs.
Just bear in mind that fitting parameters will need to be tuned when working
with different models.
### Notes
Please note that this public version of our code has some differences compared to what is described in our aforementioned papers (TPAMI and CVPR):
- The default parameters provided here work in generally quite well, but in our papers these had been fine-tuned in different sets of experiments.
- The "in-the-wild" texture model that can be extracted following Notebook 1 is a simplified version of the texture model used in our papers. This is because Notebook 1 uses a smaller set of images coming only from (Zhu, Xiangyu, et al. CVPR 2016). Also, the video fitting in Notebook 4 is still using this texture model, instead of the video-specific texture model described in our TPAMI paper.
- The video fitting in Notebook 4 uses a simpler initialisation than the one described in our TPAMI paper and corresponding supplementary material: this initialisation comes from a per-frame 3D pose estimation using the mean shape of the adopted parametric facial shape model.
These differences result to a simplified version of our image and video fitting algorithms. In practice, we have observed that these simplifications do not have a drastic effect on the accuracy of the results and result to acceptable results.
### Installation
1. Follow the instructions to [install the Menpo Project with conda](http://www.menpo.org/installation/conda.html).
2. Whilst in the conda environment containing menpo, run `pip install git+https://github.com/menpo/itwmm`.
3. Download a [copy of the code](https://github.com/menpo/itwmm/archive/master.zip) into your Downloads folder.
4. Run `jupyter notebook` and navigate to the `notebooks` directory in the
downloaded folder.
5. Explore the notebooks in order to understand how to use this codebase.
# Evaluation Code
We are also providing evaluation code under the folder "evaluation". This will help you evaluate and compare 3D face reconstruction methods using our 3dMDLab & 4DMaja benchmarks (available in our [iBug website](https://ibug.doc.ic.ac.uk/resources/itwmm/)) or other similar benchmarks.
Please see the demo Notebook evaluation/demo.ipynb ("Reconstruction Evaluation Demo"), where you can also find detailed comments.
================================================
FILE: evaluation/__init__.py
================================================
================================================
FILE: evaluation/benchmark.py
================================================
import numpy as np
from pathlib import Path
import matplotlib.pyplot as plt
import menpo.io as mio
from menpo.landmark import face_ibug_49_to_face_ibug_49
from menpo.shape import TriMesh, PointCloud
import menpo3d.io as m3io
from menpofit.visualize import (plot_cumulative_error_distribution,
statistics_table,
print_progress)
from eos import load_eos_low_res_lm_index, upsample_eos_low_res_to_fw_no_texture
from kfeval import (landmark_and_mask_gt_mesh,
mask_align_and_calculate_dense_error)
THIS_DIR = Path(__file__).parent
# landmarks indexes of the LSFM model
lms_indexes = mio.import_pickle(THIS_DIR / 'lsfm_lms_indexes.pkl')
def calculate_errors_3dMDLab(path_fits, path_gt, model):
r"""
Parameters
----------
path_fits: 'string' of the directory that contains your reconstructed
meshes. Meshes' filenames should be the same with the
corresponding images/ground truth meshes filenames + the suffix
of the mesh type (e.g .obj, .ply)
path_gt: 'string' of the directory that contains the ground truth meshes.
model: 'string' of the model template you are using. Should be one of the
following: 'LSFM', 'Basel', 'Surrey'
Returns
-------
errors: python list that contains the error per vertex for all meshes
"""
path_fits = Path(path_fits)
path_gt = Path(path_gt)
# load meshes' filenames
filenames = [p.name for p in path_fits.glob('*')]
filenames.sort()
errors = [0]*len(filenames)
for i, filename in enumerate(print_progress(filenames)):
# load gt_mesh, fit_3d
gt_mesh = m3io.import_mesh(path_gt / filename, texture_resolver=None)
gt_mesh.landmarks['ibug49'] = PointCloud(
gt_mesh.points[lms_indexes][19:])
fit_3d = m3io.import_mesh(path_fits / filename, texture_resolver=None)
if model == 'Surrey':
lms = face_ibug_49_to_face_ibug_49(PointCloud(
fit_3d.points[load_eos_low_res_lm_index()]))
fit_3d = upsample_eos_low_res_to_fw_no_texture(fit_3d)
fit_3d.landmarks['ibug49'] = lms
elif model == 'LSFM' or model == 'Basel':
fit_3d.landmarks['ibug49'] = PointCloud(
fit_3d.points[lms_indexes][19:])
else:
print('Error: Not supported model template')
return
# calculate per vertex errors
gt_mesh, eval_mask = landmark_and_mask_gt_mesh(gt_mesh, distance=1)
errors[i], _, _ = mask_align_and_calculate_dense_error(fit_3d, gt_mesh,
eval_mask)
return errors
def calculate_errors_4DMaja_synthetic(path_fits, path_gt, model):
r"""
Parameters
----------
path_fits: 'string' of the directory that contains your reconstructed meshes.
Meshes' filenames should be the same with the
corresponding ground truth meshes filenames + the suffix of
the mesh type (e.g <frame_number>.obj)
path_gt: 'string' of the directory that contains the ground truth meshes.
model: 'string' of the model template you are using. Should be one of the
following: 'LSFM', 'Basel', 'Surrey'
Returns
-------
errors: python list that contains the error per vertex for all meshes
"""
return calculate_errors_3dMDLab(path_fits, path_gt, model)
def calculate_errors_4DMaja_real(path_fits, path_gt, model):
r"""
Parameters
----------
path_fits: 'string' of the directory that contains your reconstructed meshes
Meshes' filenames should be the same with the
corresponding ground truth meshes filenames + the suffix of
the mesh type (e.g <frame_number>.obj, <frame_number>.ply)
path_gt: 'string' of the full path of the ground truth mesh.
model: 'string' of the model template you are using. Should be one of the
following: 'LSFM', 'Basel', 'Surrey'
Returns
-------
errors: python list that contains the error per vertex between the ground
truth mesh and a mean mesh calculated from your reconstructed meshes
"""
path_fits = Path(path_fits)
path_gt = Path(path_gt)
# load meshes' filenames
filenames = sorted([p.name for p in path_fits.glob('*')])
# load gt_mesh (it is only one, Maja's neutral face)
gt_mesh = m3io.import_mesh(path_gt, texture_resolver=None)
gt_mesh.landmarks['ibug49'] = PointCloud(gt_mesh.points[lms_indexes][19:])
errors = [0]
# accumulate fits
acc_points = np.zeros((gt_mesh.n_points, 3))
for i, filename in enumerate(print_progress(filenames)):
fit_3d = m3io.import_mesh(path_fits / filename, texture_resolver=None)
if model == 'Surrey':
lms = face_ibug_49_to_face_ibug_49(PointCloud(
fit_3d.points[load_eos_low_res_lm_index()]))
fit_3d = upsample_eos_low_res_to_fw_no_texture(fit_3d)
fit_3d.landmarks['ibug49'] = lms
elif model == 'LSFM' or model == 'Basel':
fit_3d.landmarks['ibug49'] = PointCloud(
fit_3d.points[lms_indexes][19:])
else:
print('Error: Not supported model template')
return
acc_points += fit_3d.points
# create mean_fit_3d
mean_fit_3d = TriMesh(acc_points / len(filenames), gt_mesh.trilist)
mean_fit_3d.landmarks['ibug49'] = PointCloud(
mean_fit_3d.points[lms_indexes][19:])
# calculate per vertex errors between the neutral gt_mesh and the mean_fit_3d
gt_mesh, eval_mask = landmark_and_mask_gt_mesh(gt_mesh, distance=1)
errors[0], _, _ = mask_align_and_calculate_dense_error(mean_fit_3d,
gt_mesh, eval_mask)
return errors
def plot_ced_3dMDLab(errors):
errs = [[error_vtx for error_mesh in errors[key] for error_vtx in error_mesh] for key in errors.keys()]
legs = [method for method in errors.keys()]
tab = statistics_table(errs, legs, 0.105, 0.01, precision=4, sort_by='auc')
r = plot_cumulative_error_distribution(
errs, legend_font_size=20, line_width=5, marker_size=10,
axes_font_size=20, error_range=[0, 0.105, 0.01], marker_edge_width=4,
y_label='Vertexes proportion', x_label='Normalized dense vertex error',
title='', legend_entries=legs,
axes_y_ticks=list(np.arange(0., 1.05, 0.1)), figure_size=(7, 5))
plt.gca().set_axisbelow(True)
plt.gca().yaxis.grid(color='gray', linestyle='dashed')
return tab, r
def plot_ced_4DMaja_synthetic(errors):
return plot_ced_3dMDLab(errors)
def plot_errors_per_frame_4DMaja_synthetic(errors, method_name):
errs = [error.mean() for error in errors]
axes = plt.gca()
axes.set_ylim([0, 0.150])
axes.set_xlim([1, 440])
plt.plot(errs, linewidth=2)
plt.title('4DMaja_synthetic - Mean errors per frame - ' + method_name)
def plot_ced_4DMaja_real(errors):
return plot_ced_3dMDLab(errors)
================================================
FILE: evaluation/demo.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Reconstruction Evaluation Demo \n",
"\n",
"Demo on how to evaluate your 3D reconstruction method by comparing your reconstructed meshes against the ground truth meshes of the benchmark datasets: \n",
" - 3dMDLab-real\n",
" - 3dMDLab-synthetic\n",
" - 4DMaya-real\n",
" - 4DMaya-synthetic\n",
"\n",
"#### Prerequisites\n",
"\n",
" 1. Download the datasets from [iBug website](https://ibug.doc.ic.ac.uk/resources/itwmm/).\n",
"\n",
" 2. Generate and save your reconstructions in the same format as the reconstructions provided in the datasets. The provided reconstructions in the folder `Reconstructions_dummy` of each dataset are just dummy .obj files which all contain the same mesh and exist just to make clear what is the desired format to be used.\n",
" \n",
"#### Note\n",
"\n",
"In case you use the Surrey model for your reconstructions, you might need to define a tighter area of the meshes for error calculation.This will produce better compatibility between your reconstructions and the ground trouth data. To do that you must change the distance parameter of the method `landmark_and_mask_gt_mesh()` in the file `benchmark.py`, from 1 to a smaller number (e.g. 0.6)."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from benchmark import *\n",
"%matplotlib inline\n",
"\n",
"# Replace DATA_PATH with the path to your data. It should have subdirectories:\n",
"# 3dMDLab_real/\n",
"# 3dMDLab_synthetic/\n",
"# 4DMaya_real/\n",
"# 4DMaya_synthetic/\n",
"DATA_PATH = Path('~/Dropbox/itwmm_src_data/datasets').expanduser()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 1. Evaluate on 3dMDLab-real"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[====================] 100% (8/8) - done. \n"
]
}
],
"source": [
"# Define the path of the reconstructed meshes\n",
"path_reconstructions = DATA_PATH / '3dMDLab_real' / 'Reconstructions_dummy'\n",
"\n",
"# Define the path of the ground truth meshes\n",
"path_gt = DATA_PATH / '3dMDLab_real' / 'Ground_Truth'\n",
"\n",
"# Define the model template you are using.\n",
"# You can choose on the three supported models according to the model\n",
"# you have used for your reconstructions:\n",
"# - 'LSFM'\n",
"# - 'Basel'\n",
"# - 'Surrey'\n",
"model = 'LSFM'\n",
"\n",
"errors = calculate_errors_3dMDLab(path_reconstructions, path_gt, model)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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psomIiETViROZfPzxd4wf/yUzZ64Pey9VfagVESk6WYt6/eUvf2H69Onccsst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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f820828ce48>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Define the name of your method. This is just used for visualization\n",
"method_name = 'my_method'\n",
"\n",
"table, renderer = plot_ced_3dMDLab({method_name: errors})"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>median</th>\n",
" <th>mad</th>\n",
" <th>max</th>\n",
" <th>auc</th>\n",
" <th>fr</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>my_method</th>\n",
" <td>0.0347</td>\n",
" <td>0.0329</td>\n",
" <td>0.0261</td>\n",
" <td>0.0203</td>\n",
" <td>0.1957</td>\n",
" <td>0.7216</td>\n",
" <td>0.0367</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" mean std median mad max auc fr\n",
"my_method 0.0347 0.0329 0.0261 0.0203 0.1957 0.7216 0.0367"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# statistics of the distribution of the per vertex errors\n",
"table"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 2. Evaluate on 3dMDLab-synthetic"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[====================] 100% (6/6) - done. \n"
]
}
],
"source": [
"# Define the path of the reconstructed meshes\n",
"path_reconstructions = DATA_PATH / '3dMDLab_synthetic' / 'Reconstructions_dummy'\n",
"\n",
"# Define the path of the ground truth meshes\n",
"path_gt = DATA_PATH / '3dMDLab_synthetic' / 'Ground_Truth'\n",
"\n",
"# Define the model template you are using.\n",
"# You can choose on the three supported models according to the model\n",
"# you have used for your reconstructions:\n",
"# - 'LSFM'\n",
"# - 'Basel'\n",
"# - 'Surrey'\n",
"model = 'LSFM'\n",
"\n",
"errors = calculate_errors_3dMDLab(path_reconstructions, path_gt, model)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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mZsnxwoULIybMCxcurGyocYn7GWYzOw/4HHgIGAb0BroCnaP4EhERqVLfflvI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\nwukyAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f823ae34630>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Define the name of your method. This is just used for visualization\n",
"method_name = 'my_method'\n",
"\n",
"table, renderer = plot_ced_3dMDLab({method_name: errors})"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>median</th>\n",
" <th>mad</th>\n",
" <th>max</th>\n",
" <th>auc</th>\n",
" <th>fr</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>my_method</th>\n",
" <td>0.0328</td>\n",
" <td>0.0342</td>\n",
" <td>0.0228</td>\n",
" <td>0.0196</td>\n",
" <td>0.1957</td>\n",
" <td>0.7398</td>\n",
" <td>0.0441</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" mean std median mad max auc fr\n",
"my_method 0.0328 0.0342 0.0228 0.0196 0.1957 0.7398 0.0441"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# statistics of the distribution of the per vertex errors\n",
"table"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 3. Evaluate on 4DMaja-real"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[====================] 100% (387/387) - done. \n"
]
}
],
"source": [
"# Define the path of the reconstructed meshes\n",
"path_reconstructions = DATA_PATH / '4DMaja_real' / 'Reconstructions_dummy'\n",
"\n",
"# Define the full path of the ground truth mesh\n",
"path_gt = DATA_PATH / '4DMaja_real' / 'Ground_Truth' / 'neutral.obj'\n",
"\n",
"# Define the model template you are using.\n",
"# You can choose on the three supported models according to the model\n",
"# you have used for your reconstructions:\n",
"# - 'LSFM'\n",
"# - 'Basel'\n",
"# - 'Surrey'\n",
"model = 'LSFM'\n",
"\n",
"errors = calculate_errors_4DMaja_real(path_reconstructions, path_gt, model)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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OHMnWrVsxM37/+98XONeoUSMuv/xypk+fTlpaGm+99dYxfZmdnc0tt9yCmeGi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\nZ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"text/plain": [
"<matplotlib.figure.Figure at 0x7f81e8354d30>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Define the name of your method. This is just used for visualization\n",
"method_name = 'my_method'\n",
"\n",
"table, renderer = plot_ced_4DMaja_real({method_name: errors})"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>median</th>\n",
" <th>mad</th>\n",
" <th>max</th>\n",
" <th>auc</th>\n",
" <th>fr</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>my_method</th>\n",
" <td>0.0434</td>\n",
" <td>0.034</td>\n",
" <td>0.0344</td>\n",
" <td>0.0173</td>\n",
" <td>0.1786</td>\n",
" <td>0.6495</td>\n",
" <td>0.0548</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" mean std median mad max auc fr\n",
"my_method 0.0434 0.034 0.0344 0.0173 0.1786 0.6495 0.0548"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"table"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 4. Evaluate on 4DMaja-synthetic"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Note that\n",
"this calculation can take several minutes depending on your machine, because it measures the per vertex errors for each one of the 440 pairs \"ground truth mesh - reconstructred mesh\". \n",
"\n",
"For example, it takes about 11 minutes in an Intel(R) Core(TM) i7-6700 C
gitextract_75lg_kcj/ ├── .gitignore ├── LICENSE.txt ├── README.md ├── evaluation/ │ ├── __init__.py │ ├── benchmark.py │ ├── demo.ipynb │ ├── eos.py │ ├── eos_landmark_settings.pkl │ ├── fw_on_eos_low_res_settings.pkl │ ├── kfeval.py │ ├── lsfm_lms_indexes.pkl │ └── trilist.pkl ├── itwmm/ │ ├── __init__.py │ ├── base.py │ ├── fitting/ │ │ ├── __init__.py │ │ ├── algorithm.py │ │ ├── base.py │ │ ├── derivatives.py │ │ ├── hessian.py │ │ ├── initialization.py │ │ ├── jacobian.py │ │ └── projectout.py │ ├── model/ │ │ ├── __init__.py │ │ ├── extractimage.py │ │ └── math.py │ └── visualize.py ├── notebooks/ │ ├── 1. Building an "in-the-wild" texture model.ipynb │ ├── 2. Creating an expressive 3DMM.ipynb │ ├── 3. Fitting "in-the-wild" images.ipynb │ └── 4. Fitting "in-the-wild" videos.ipynb └── setup.py
SYMBOL INDEX (68 symbols across 15 files) FILE: evaluation/benchmark.py function calculate_errors_3dMDLab (line 24) | def calculate_errors_3dMDLab(path_fits, path_gt, model): function calculate_errors_4DMaja_synthetic (line 75) | def calculate_errors_4DMaja_synthetic(path_fits, path_gt, model): function calculate_errors_4DMaja_real (line 93) | def calculate_errors_4DMaja_real(path_fits, path_gt, model): function plot_ced_3dMDLab (line 153) | def plot_ced_3dMDLab(errors): function plot_ced_4DMaja_synthetic (line 171) | def plot_ced_4DMaja_synthetic(errors): function plot_errors_per_frame_4DMaja_synthetic (line 175) | def plot_errors_per_frame_4DMaja_synthetic(errors, method_name): function plot_ced_4DMaja_real (line 186) | def plot_ced_4DMaja_real(errors): FILE: evaluation/eos.py function load_eos_low_res_lm_index (line 12) | def load_eos_low_res_lm_index(): function load_basel_kf_trilist (line 18) | def load_basel_kf_trilist(): function load_fw_on_eos_low_res_settings (line 23) | def load_fw_on_eos_low_res_settings(): function upsample_eos_low_res_to_fw_no_texture (line 30) | def upsample_eos_low_res_to_fw_no_texture(eos_mesh_low_res): FILE: evaluation/kfeval.py function landmark_and_mask_gt_mesh (line 11) | def landmark_and_mask_gt_mesh(gt_mesh, distance=1): function align_dense_fit_to_gt (line 24) | def align_dense_fit_to_gt(fit_3d, gt_mesh): function calculate_dense_error (line 28) | def calculate_dense_error(fit_3d_aligned, gt_mesh): function mask_align_and_calculate_dense_error (line 44) | def mask_align_and_calculate_dense_error(fit_3d, gt_mesh, mask): FILE: itwmm/base.py function as_colouredtrimesh (line 7) | def as_colouredtrimesh(self, colours=None, copy=True): function instance_for_params (line 29) | def instance_for_params(mm, id_ind, exp_ind, template_camera, p, q, FILE: itwmm/fitting/algorithm.py function increment_parameters (line 16) | def increment_parameters(images, mm, id_indices, exp_indices, template_c... function fit_video (line 127) | def fit_video(images, mm, id_indices, exp_indices, template_camera, function fit_image (line 156) | def fit_image(image, mm, id_indices, exp_indices, template_camera, function print_single_cost (line 168) | def print_single_cost(k, c, tot): function print_cost_dict (line 178) | def print_cost_dict(d): FILE: itwmm/fitting/base.py function camera_parameters_update (line 8) | def camera_parameters_update(c, dc): function quaternion_multiply (line 20) | def quaternion_multiply(current_q, increment_q): function gradient_xy (line 32) | def gradient_xy(image): function sample_at_bc_vi (line 43) | def sample_at_bc_vi(x, bcoords, vertex_indices): function visible_sample_points (line 48) | def visible_sample_points(instance_in_img, image_shape, n_samples): FILE: itwmm/fitting/derivatives.py function d_perspective_camera_d_shape_parameters (line 6) | def d_perspective_camera_d_shape_parameters(shape_pc_uv, warped_uv, came... function d_orthographic_camera_d_shape_parameters (line 43) | def d_orthographic_camera_d_shape_parameters(shape_pc_uv, camera): function d_camera_d_shape_parameters (line 71) | def d_camera_d_shape_parameters(camera, warped_uv, shape_pc_uv): function d_perspective_camera_d_camera_parameters (line 83) | def d_perspective_camera_d_camera_parameters(warped_uv, camera, function d_orthographic_camera_d_camera_parameters (line 186) | def d_orthographic_camera_d_camera_parameters(warped_uv, camera, function d_camera_d_camera_parameters (line 280) | def d_camera_d_camera_parameters(camera, warped_uv, with_focal_length): FILE: itwmm/fitting/hessian.py function smoothing_kernel (line 4) | def smoothing_kernel(n_frames): function insert_id_constraint (line 15) | def insert_id_constraint(H, c_id, n_p): function insert_exp_constraint (line 20) | def insert_exp_constraint(H, c_exp, n_p, n_q, n_frames): function insert_smoothness_constraint (line 29) | def insert_smoothness_constraint(H, c_sm, n_p, n_q, n_frames): function insert_id_constraint_to_JTe (line 39) | def insert_id_constraint_to_JTe(JTe, p, c_id, n_p): function insert_exp_constraint_to_JTe (line 43) | def insert_exp_constraint_to_JTe(JTe, qs, c_exp, n_p, n_q, n_frames): function insert_smoothness_constraint_to_JTe (line 48) | def insert_smoothness_constraint_to_JTe(JTe, qs, c_sm, n_p, n_q, n_frames): function initialize_hessian_and_JTe (line 62) | def initialize_hessian_and_JTe(c_id, c_exp, c_sm, n_p, n_q, n_c, p, qs, function insert_frame_to_H (line 88) | def insert_frame_to_H(H, j, f, n_p, n_q, n_c, c_f, c_l, n_frames): function insert_frame_to_JTe (line 126) | def insert_frame_to_JTe(JTe, j, f, n_p, n_q, n_c, c_f, c_l, n_frames): FILE: itwmm/fitting/initialization.py function initialize_camera (line 6) | def initialize_camera(image, lms_3d, focal_length=None): function initialize_camera_from_params (line 11) | def initialize_camera_from_params(image, mm, id_ind, exp_ind, FILE: itwmm/fitting/jacobian.py function J_data (line 8) | def J_data(camera, warped_uv, shape_pc_uv, U_tex_pc, grad_x_uv, function J_lms (line 30) | def J_lms(camera, warped_uv, shape_pc_uv, focal_length_update=False): function jacobians (line 43) | def jacobians(s, c, image, lms_points_xy, mm, id_ind, exp_ind, template_... FILE: itwmm/fitting/projectout.py function project_out (line 5) | def project_out(J, U, fast_approx=True): function sample_uv_terms (line 10) | def sample_uv_terms(instance, image, camera, mm, shape_pc, grad_x, grad_y, FILE: itwmm/model/__init__.py function generate_texture_model_from_itwmm (line 12) | def generate_texture_model_from_itwmm(images, mm, id_ind, exp_ind, function generate_texture_model_from_image_3d_fits (line 48) | def generate_texture_model_from_image_3d_fits(images_and_fits, FILE: itwmm/model/extractimage.py function per_vertex_occlusion (line 13) | def per_vertex_occlusion(mesh_in_img, err_proportion=0.0001, render_diag... function render_hi_res_shape_image (line 39) | def render_hi_res_shape_image(mesh, render_width=3000): function per_vertex_occlusion_gl_rasterizer (line 51) | def per_vertex_occlusion_gl_rasterizer(mesh, err_proportion=0.0001, function per_vertex_occlusion_accurate (line 76) | def per_vertex_occlusion_accurate(mesh): function extract_per_vertex_colour (line 120) | def extract_per_vertex_colour(mesh, image): function extract_per_vertex_colour_with_occlusion (line 124) | def extract_per_vertex_colour_with_occlusion(mesh, image, function extract_per_vertex_features (line 134) | def extract_per_vertex_features(mesh, image, feature_f, diagonal_range=N... FILE: itwmm/model/math.py function rpca_missing (line 8) | def rpca_missing(X, M, lambda_=None, tol=1e-6, max_iter=1000, verbose=Fa... FILE: itwmm/visualize.py function l2_normalize (line 9) | def l2_normalize(x, axis=0, epsilon=1e-12): function mesh_in_unit_sphere (line 28) | def mesh_in_unit_sphere(mesh): function lambertian_shading (line 34) | def lambertian_shading(mesh, diffuse_colour=0.4, function render_mesh_in_img (line 64) | def render_mesh_in_img(trimesh_3d_in_img, img_shape): function render_overlay_of_mesh_in_img (line 72) | def render_overlay_of_mesh_in_img(trimesh_3d_in_img, img): function render_initialization (line 77) | def render_initialization(images, mm, id_indices, exp_indices, template_... function render_iteration (line 91) | def render_iteration(mm, id_ind, exp_ind, img_shape, camera, params, function overlay_image (line 106) | def overlay_image(background, overlay, offset_vector):
Condensed preview — 31 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (385K chars).
[
{
"path": ".gitignore",
"chars": 37,
"preview": ".idea\n*.egg-info\n*.ipynb_checkpoints\n"
},
{
"path": "LICENSE.txt",
"chars": 1536,
"preview": "Copyright (c) 2017, Imperial College London. All rights reserved.\n\nRedistribution and use in source and binary forms, wi"
},
{
"path": "README.md",
"chars": 4047,
"preview": "# In The Wild 3D Morphable Models\n\nThe repository provides the source code of the algorithm of 3D reconstruction of \"In-"
},
{
"path": "evaluation/__init__.py",
"chars": 1,
"preview": "\n"
},
{
"path": "evaluation/benchmark.py",
"chars": 7215,
"preview": "import numpy as np\nfrom pathlib import Path\nimport matplotlib.pyplot as plt\n\nimport menpo.io as mio\nfrom menpo.landmark "
},
{
"path": "evaluation/demo.ipynb",
"chars": 271871,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Reconstruction Evaluation Demo \\n"
},
{
"path": "evaluation/eos.py",
"chars": 1140,
"preview": "from functools import lru_cache\nfrom pathlib import Path\n\nimport menpo.io as mio\nfrom menpo.shape import ColouredTriMesh"
},
{
"path": "evaluation/kfeval.py",
"chars": 2096,
"preview": "# Extract key landmarks and build a per-vertex inclusion mask\n# (as we only want to evaluate on the inner region of the "
},
{
"path": "itwmm/__init__.py",
"chars": 335,
"preview": "from .base import instance_for_params\nfrom .fitting import (fit_image, fit_video, initialize_camera_from_params,\n "
},
{
"path": "itwmm/base.py",
"chars": 1282,
"preview": "import numpy as np\n\nfrom menpo.base import copy_landmarks_and_path\nfrom menpo.shape import ColouredTriMesh\n\n\ndef as_colo"
},
{
"path": "itwmm/fitting/__init__.py",
"chars": 129,
"preview": "from .initialization import initialize_camera_from_params, initialize_camera\nfrom .algorithm import (\n fit_image, fit"
},
{
"path": "itwmm/fitting/algorithm.py",
"chars": 7076,
"preview": "from collections import defaultdict\nfrom datetime import timedelta\nfrom time import time\n\nimport numpy as np\nfrom scipy "
},
{
"path": "itwmm/fitting/base.py",
"chars": 1982,
"preview": "import numpy as np\n\nfrom menpo.feature import gradient as fast_gradient\nfrom menpo.image import Image\nfrom menpo3d.raste"
},
{
"path": "itwmm/fitting/derivatives.py",
"chars": 9677,
"preview": "import numpy as np\n\nfrom menpo3d.camera import PerspectiveCamera, OrthographicCamera\n\n\ndef d_perspective_camera_d_shape_"
},
{
"path": "itwmm/fitting/hessian.py",
"chars": 5208,
"preview": "import numpy as np\n\n\ndef smoothing_kernel(n_frames):\n A = np.eye(n_frames - 2, n_frames)\n i, j = np.diag_indices(n"
},
{
"path": "itwmm/fitting/initialization.py",
"chars": 745,
"preview": "import numpy as np\nfrom menpo.shape import PointCloud\nfrom menpo3d.camera import PerspectiveCamera\n\n\ndef initialize_came"
},
{
"path": "itwmm/fitting/jacobian.py",
"chars": 3278,
"preview": "import numpy as np\n\nfrom .derivatives import (d_camera_d_shape_parameters,\n d_camera_d_camera_p"
},
{
"path": "itwmm/fitting/projectout.py",
"chars": 1875,
"preview": "import numpy as np\nfrom .base import sample_at_bc_vi, visible_sample_points\n\n\ndef project_out(J, U, fast_approx=True):\n "
},
{
"path": "itwmm/model/__init__.py",
"chars": 3637,
"preview": "import numpy as np\n\nfrom menpo.model import PCAVectorModel\nfrom menpo.visualize import print_progress\n\nfrom itwmm.base i"
},
{
"path": "itwmm/model/extractimage.py",
"chars": 5125,
"preview": "import numpy as np\n\nfrom menpo.shape import PointCloud\nfrom menpo.transform import Scale\nfrom menpo3d.rasterize import ("
},
{
"path": "itwmm/model/math.py",
"chars": 3388,
"preview": "from time import time\n\nimport numpy as np\n\nfrom menpo.visualize import bytes_str\n\n\ndef rpca_missing(X, M, lambda_=None, "
},
{
"path": "itwmm/visualize.py",
"chars": 4473,
"preview": "import numpy as np\n\nfrom menpo.transform import Scale, UniformScale, Translation\nfrom menpo3d.rasterize import rasterize"
},
{
"path": "notebooks/1. Building an \"in-the-wild\" texture model.ipynb",
"chars": 11346,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. Building an \\\"in-the-wild\\\" t"
},
{
"path": "notebooks/2. Creating an expressive 3DMM.ipynb",
"chars": 7644,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2. Creating an Expressive 3D Mor"
},
{
"path": "notebooks/3. Fitting \"in-the-wild\" images.ipynb",
"chars": 8368,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3. Fitting \\\"in-the-wild\\\" image"
},
{
"path": "notebooks/4. Fitting \"in-the-wild\" videos.ipynb",
"chars": 9012,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 4. Fitting \\\"in-the-wild\\\" video"
},
{
"path": "setup.py",
"chars": 324,
"preview": "from setuptools import setup, find_packages\n\ninstall_requires = ['menpo3d>=0.6.0']\n\nsetup(name='itwmm',\n version='1"
}
]
// ... and 4 more files (download for full content)
About this extraction
This page contains the full source code of the menpo/itwmm GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 31 files (364.1 KB), approximately 206.0k tokens, and a symbol index with 68 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
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