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

================================================
FILE CONTENTS
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================================================
FILE: .gitignore
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.idea
*.egg-info
*.ipynb_checkpoints


================================================
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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\nGGOMMcZU8P8By8gQ6E1wHbwAAAAASUVORK5CYII=\n",
      "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 CPU @ 3.40GHz. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[====================] 100% (440/440) - done.                                   \n"
     ]
    }
   ],
   "source": [
    "# Define the path of the reconstructed meshes\n",
    "path_reconstructions = DATA_PATH / '4DMaja_synthetic' / 'Reconstructions_dummy'\n",
    "\n",
    "# Define the path of the ground truth meshes\n",
    "path_gt = DATA_PATH / '4DMaja_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_4DMaja_synthetic(path_reconstructions, path_gt, model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IFvT6618/4rHH5rBt286Y65lB794nct99HTnsMH0nL/uXVatWsX79epYvX86Q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IIgCIIgCIKggAiYgyAIgiAIgiAIgqCACJiDIAiCIAiCIAiCoIAImIMgCIIgCIIgCIKggAiYgyAI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\nIAiCAiJgDoIgCIIgCIIgCIIC/h9sKP1lqJLnEgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f81f7680860>"
      ]
     },
     "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_synthetic({method_name: errors})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YvG8baftHsoj4I/3vshaPBB7TxnKtWNGjmMkP5T9Az6LpLxB38EcQJ6OmSqrWM+SaK6p/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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f81e83cdcf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_errors_per_frame_4DMaja_synthetic(errors, method_name)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}


================================================
FILE: evaluation/eos.py
================================================
from functools import lru_cache
from pathlib import Path

import menpo.io as mio
from menpo.shape import ColouredTriMesh

THIS_DIR = Path(__file__).parent



@lru_cache()
def load_eos_low_res_lm_index():
    return mio.import_pickle(
        THIS_DIR / 'eos_landmark_settings.pkl')['ibug_49_index']


@lru_cache()
def load_basel_kf_trilist():
    return mio.import_pickle(THIS_DIR / 'trilist.pkl')


@lru_cache()
def load_fw_on_eos_low_res_settings():
    d = mio.import_pickle(THIS_DIR / 'fw_on_eos_low_res_settings.pkl')
    bc_fw_on_eos_low_res = d['bc_fw_on_eos_low_res']
    tri_index_fw_on_eos_low_res = d['tri_index_fw_on_eos_low_res']
    return bc_fw_on_eos_low_res, tri_index_fw_on_eos_low_res


def upsample_eos_low_res_to_fw_no_texture(eos_mesh_low_res):
    bc_fw_on_eos_low_res, tri_index_fw_on_eos_low_res = load_fw_on_eos_low_res_settings()
    effective_fw_pc = eos_mesh_low_res.project_barycentric_coordinates(
        bc_fw_on_eos_low_res, tri_index_fw_on_eos_low_res)

    effective_fw = ColouredTriMesh(effective_fw_pc.points,
                                   trilist=load_basel_kf_trilist())
    return effective_fw


================================================
FILE: evaluation/kfeval.py
================================================
# Extract key landmarks and build a per-vertex inclusion mask
# (as we only want to evaluate on the inner region of the face)
import numpy as np

from menpo.landmark import face_ibug_49_to_face_ibug_49
from menpo.shape import PointCloud
from menpo.transform import AlignmentSimilarity
from menpo3d.vtkutils import VTKClosestPointLocator, trimesh_to_vtk


def landmark_and_mask_gt_mesh(gt_mesh, distance=1):
    gt_mesh.landmarks['ibug49'] = face_ibug_49_to_face_ibug_49(
        gt_mesh.landmarks['ibug49'])
    gt_mesh.landmarks['nosetip'] = PointCloud(
        gt_mesh.landmarks['ibug49'].get_label('nose').points[-5][None, :])
    gt_mesh.landmarks['eye_corners'] = PointCloud(
        gt_mesh.landmarks['ibug49'].points[[36 - 17, 45 - 17]])
    eval_mask = gt_mesh.distance_to(
        gt_mesh.landmarks['nosetip']).ravel() < distance
    return gt_mesh, eval_mask


# Dense procrustes align before we compute any error
def align_dense_fit_to_gt(fit_3d, gt_mesh):
    return AlignmentSimilarity(fit_3d, gt_mesh).apply(fit_3d)


def calculate_dense_error(fit_3d_aligned, gt_mesh):
    fit_vtk = trimesh_to_vtk(fit_3d_aligned)
    closest_points_on_fit = VTKClosestPointLocator(fit_vtk)
    # for each vertex on the ground truth, find the nearest distance
    # to the aligned fit.
    nearest_points, tri_indices = closest_points_on_fit(gt_mesh.points)
    err_per_vertex = np.sqrt(np.sum((nearest_points -
                                     gt_mesh.points) ** 2, axis=1))

    # normalize by inter-oc
    b, a = gt_mesh.landmarks['eye_corners'].points
    inter_occ_distance = np.sqrt(((a - b) ** 2).sum())
    # print('norm: {}'.format(inter_occ_distance))
    return err_per_vertex / inter_occ_distance


def mask_align_and_calculate_dense_error(fit_3d, gt_mesh, mask):
    fit_3d_masked = fit_3d.from_mask(mask)
    gt_mesh_masked = gt_mesh.from_mask(mask)

    fit_3d_masked_aligned = align_dense_fit_to_gt(
        fit_3d_masked,
        gt_mesh_masked
    )

    return (calculate_dense_error(fit_3d_masked_aligned, gt_mesh_masked),
            fit_3d_masked_aligned, gt_mesh_masked)


================================================
FILE: itwmm/__init__.py
================================================
from .base import instance_for_params
from .fitting import (fit_image, fit_video, initialize_camera_from_params,
                      initialize_camera)
from .model import generate_texture_model_from_image_3d_fits
from .visualize import (render_initialization, render_iteration,
                        render_overlay_of_mesh_in_img)


================================================
FILE: itwmm/base.py
================================================
import numpy as np

from menpo.base import copy_landmarks_and_path
from menpo.shape import ColouredTriMesh


def as_colouredtrimesh(self, colours=None, copy=True):
    """
    Converts this to a :map:`ColouredTriMesh`.

    Parameters
    ----------
    colours : ``(N, 3)`` `ndarray`, optional
        The floating point RGB colour per vertex. If not given, grey will be
        assigned to each vertex.
    copy : `bool`, optional
        If ``True``, the graph will be a copy.

    Returns
    -------
    coloured : :map:`ColouredTriMesh`
        A version of this mesh with per-vertex colour assigned.
    """
    ctm = ColouredTriMesh(self.points, trilist=self.trilist,
                          colours=colours, copy=copy)
    return copy_landmarks_and_path(self, ctm)


def instance_for_params(mm, id_ind, exp_ind, template_camera, p, q,
                        c):
    shape_params = np.zeros(mm.shape_model.n_active_components)
    shape_params[id_ind] = p
    shape_params[exp_ind] = q
    instance = mm.shape_model.instance(shape_params, normalized_weights=True)
    camera = template_camera.from_vector(c)
    instance_in_img = camera.apply(instance)
    return {
        'instance': instance,
        'instance_in_img': instance_in_img,
        'camera': camera
    }


================================================
FILE: itwmm/fitting/__init__.py
================================================
from .initialization import initialize_camera_from_params, initialize_camera
from .algorithm import (
    fit_image, fit_video
)


================================================
FILE: itwmm/fitting/algorithm.py
================================================
from collections import defaultdict
from datetime import timedelta
from time import time

import numpy as np
from scipy import sparse as sp

from menpo.visualize import bytes_str, print_progress

from .base import gradient_xy, camera_parameters_update
from .hessian import (initialize_hessian_and_JTe, insert_frame_to_H,
                      insert_frame_to_JTe)
from .jacobian import jacobians


def increment_parameters(images, mm, id_indices, exp_indices, template_camera,
                         p, qs, cs,
                         c_f=1, c_l=1, c_id=1, c_exp=1, c_sm=1,
                         lm_group=None, n_samples=1000,
                         compute_costs=True):

    n_frames = len(images)
    n_points = mm.shape_model.template_instance.n_points
    n_p = len(id_indices)
    n_q = len(exp_indices)
    n_c = cs.shape[1] - 2  # sub one for quaternion, one for focal length

    print('Precomputing....')
    # Rescale shape components to have size:
    # n_points x (n_components * n_dims)
    # and to be scaled by the relevant standard deviation.
    shape_pc = (
            mm.shape_model.components.T *
            np.sqrt(mm.shape_model.eigenvalues)
    ).reshape([n_points, -1])
    # include std.dev in principal components
    shape_pc_lms = shape_pc.reshape([n_points, 3, -1])[mm.model_landmarks_index]

    print('Initializing Hessian/JTe for frame...')
    H, JTe = initialize_hessian_and_JTe(c_id, c_exp, c_sm, n_p, n_q, n_c, p, qs,
                                        n_frames)
    print('H: {} ({})'.format(H.shape, bytes_str(H.nbytes)))

    if compute_costs:
        costs = defaultdict(list)

    for (f, image), c, q in zip(enumerate(print_progress(
            images, prefix='Incrementing H/JTe')), cs, qs):

        # Form the overall shape parameter: [p, q]
        s = np.zeros(mm.shape_model.n_active_components)
        s[id_indices] = p
        s[exp_indices] = q

        # In our error we consider landmarks stored [x, y] - so flip here.
        lms_points_xy = image.landmarks[lm_group].points[:, [1, 0]]

        # Compute input image gradient
        grad_x, grad_y = gradient_xy(image)

        j = jacobians(s, c, image, lms_points_xy, mm, id_indices, exp_indices,
                      template_camera, grad_x, grad_y, shape_pc, shape_pc_lms,
                      n_samples, compute_costs=compute_costs)
        insert_frame_to_H(H, j, f, n_p, n_q, n_c, c_f, c_l, n_frames)
        insert_frame_to_JTe(JTe, j, f, n_p, n_q, n_c, c_f, c_l, n_frames)

        if compute_costs:
            for cost, val in j['costs'].items():
                costs[cost].append(val)

    print('Converting Hessian to sparse format')
    H = sp.csr_matrix(H)
    print("Sparsity (prop. 0's) of H: {:.2%}".format(
        1 - (H.count_nonzero() / np.prod(np.array(H.shape)))))
    print('Solving for parameter update')
    d = sp.linalg.spsolve(H, JTe)
    dp = d[:n_p]
    dqs = d[n_p:(n_p + (n_frames * n_q))].reshape([n_frames, n_q])
    dcs = d[-(n_frames * n_c):].reshape([n_frames, n_c])
    # Add the focal length and degenerate quaternion parameters back on as
    # null delta updates
    dcs = np.hstack([np.tile(np.array([0, 1]), (n_frames, 1)), dcs])

    new_p = p + dp
    new_qs = qs + dqs
    new_cs = np.array([camera_parameters_update(c, dc)
                       for c, dc in zip(cs, dcs)])

    params = {
        'p': new_p,
        'qs': new_qs,
        'cs': new_cs,
        'dp': dp,
        'dqs': dqs,
        'dcs': dcs,
    }

    if compute_costs:
        c = {k: np.array(v) for k, v in costs.items()}

        err_s_id = (p ** 2).sum()
        err_s_exp = (qs ** 2).sum()
        err_sm = ((qs[:-2] - 2 * qs[1:-1] + qs[2:]) ** 2).sum()

        err_f_tot = c['err_f'].sum() * c_f / (n_c * n_samples)
        err_l_tot = c['err_l'].sum()

        total_energy = (err_f_tot +
                        c_l * err_l_tot +
                        c_id * err_s_id +
                        c_exp * err_s_exp +
                        c_sm * err_sm)

        c['total_energy'] = total_energy
        c['err_s_id'] = (c_id, err_s_id)
        c['err_s_exp'] = (c_exp, err_s_exp)
        c['err_sm'] = (c_sm, err_sm)
        c['err_f_tot'] = err_f_tot
        c['err_l_tot'] = (c_l, err_l_tot)

        print_cost_dict(c)

        params['costs'] = c
    return params


def fit_video(images, mm, id_indices, exp_indices, template_camera,
              p, qs, cs, c_f=1, c_l=1, c_id=1, c_exp=1, c_sm=1, lm_group=None,
              n_samples=1000, n_iters=10, compute_costs=True):
    params = [
        {
            "p": p,
            "qs": qs,
            "cs": cs
        }]

    for i in range(1, n_iters + 1):
        print('{} / {}'.format(i, n_iters))
        # retrieve the last used parameters and pass them into the increment
        l = params[-1]
        t1 = time()
        incs = increment_parameters(images, mm, id_indices, exp_indices,
                                    template_camera, l['p'], l['qs'], l['cs'],
                                    c_f=c_f, c_l=c_l, c_id=c_id, c_exp=c_exp,
                                    c_sm=c_sm,
                                    lm_group=lm_group, n_samples=n_samples,
                                    compute_costs=compute_costs)
        # update the parameter list
        params.append(incs)
        # And report the time taken for the iteration.
        dt = int(time() - t1)
        print('Iteration {} complete in {}\n'.format(i, timedelta(seconds=dt)))
    return params


def fit_image(image, mm, id_indices, exp_indices, template_camera,
              p, q, c, c_f=1, c_l=1, c_id=1, c_exp=1, lm_group=None,
              n_samples=1000, n_iters=10, compute_costs=True):
    # fit image is the same as fit_video, just for a single length video.
    return fit_video(
        [image], mm, id_indices, exp_indices, template_camera,
        p, q[None, :], c[None, :], c_f=c_f, c_l=c_l,
        c_id=c_id, c_exp=c_exp, c_sm=0, lm_group=lm_group,
        n_samples=n_samples, n_iters=n_iters, compute_costs=compute_costs
    )


def print_single_cost(k, c, tot):
    if isinstance(c, tuple):
        key = '{:03.0%} | {:>12}'.format((c[0] * c[1]) / tot, k)
        val = '{:>12.2f} x {:>12.2f} = {:.2f}'.format(c[0], c[1], c[0] * c[1])
    else:
        key = '{:03.0%} | {:>12}'.format(c / tot, k)
        val = '{:.2f}'.format(c)
    print('{:>20}: {}'.format(key, val))


def print_cost_dict(d):
    print('------------------------------------------------------------------')
    print_single_cost('total_energy', d['total_energy'], d['total_energy'])
    print('------------------------------------------------------------------')
    for k in ['err_f_tot', 'err_l_tot', 'err_s_id',
              'err_s_exp', 'err_sm']:
        print_single_cost(k, d[k], d['total_energy'])
    print('------------------------------------------------------------------')
    for k in ['err_f', 'err_l']:
        print('{} (median over frames): {:.2f}'.format(k, np.median(d[k])))
    print('------------------------------------------------------------------')


================================================
FILE: itwmm/fitting/base.py
================================================
import numpy as np

from menpo.feature import gradient as fast_gradient
from menpo.image import Image
from menpo3d.rasterize import rasterize_barycentric_coordinates


def camera_parameters_update(c, dc):
    # Add for focal length and translation parameters, but multiply for
    # quaternions
    # supress focal length optimisation
    dc[0] = 0
    # Always lock the first quaternion value to 1
    dc[1] = 1
    new = c + dc
    new[1:5] = quaternion_multiply(c[1:5], dc[1:5])
    return new


def quaternion_multiply(current_q, increment_q):
    # Make sure that the q increment has unit norm
    increment_q /= np.linalg.norm(increment_q)
    # Update
    w0, x0, y0, z0 = current_q
    w1, x1, y1, z1 = increment_q
    return np.array([-x1*x0 - y1*y0 - z1*z0 + w1*w0,
                      x1*w0 + y1*z0 - z1*y0 + w1*x0,
                     -x1*z0 + y1*w0 + z1*x0 + w1*y0,
                      x1*y0 - y1*x0 + z1*w0 + w1*z0], dtype=np.float64)


def gradient_xy(image):
    # Compute the gradient of the image
    grad = fast_gradient(image)

    # Slice off the gradient for X and Y separately
    grad_y = Image(grad.pixels[:image.n_channels])
    grad_x = Image(grad.pixels[image.n_channels:])

    return grad_x, grad_y


def sample_at_bc_vi(x, bcoords, vertex_indices):
    per_vertex_per_pixel = x[vertex_indices]
    return np.sum(per_vertex_per_pixel * bcoords[..., None], axis=1)


def visible_sample_points(instance_in_img, image_shape, n_samples):
    # Inverse rendering
    yx, bcoords, tri_indices = rasterize_barycentric_coordinates(
        instance_in_img, image_shape)

    # Select triangles randomly
    rand = np.random.permutation(bcoords.shape[0])
    bcoords = bcoords[rand[:n_samples]]
    yx = yx[rand[:n_samples]]
    tri_indices = tri_indices[rand[:n_samples]]

    # Build the vertex indices (3 per pixel) for the visible triangles
    vertex_indices = instance_in_img.trilist[tri_indices]

    return vertex_indices, bcoords, tri_indices, yx


================================================
FILE: itwmm/fitting/derivatives.py
================================================
import numpy as np

from menpo3d.camera import PerspectiveCamera, OrthographicCamera


def d_perspective_camera_d_shape_parameters(shape_pc_uv, warped_uv, camera):
    """
    Calculates the derivative of the perspective projection with respect to the
    shape parameters.

    Parameters
    ----------
    shape_pc_uv : ``(n_points, 3, n_parameters)`` `ndarray`
        The (sampled) basis of the shape model.
    warped_uv : ``(n_points, 3)`` `ndarray`
        The shape instance with the view transform (rotation and translation)
        applied on it.
    camera : `menpo3d.camera.PerspectiveCamera`
        The camera object that is responsible of projecting the model to the
        image plane.

    Returns
    -------
    dw_da : ``(2, n_shape_parameters, n_points)`` `ndarray`
        The derivative of the perspective camera transform with respect to
        the shape parameters.
    """
    n_points, n_dims, n_parameters = shape_pc_uv.shape
    assert n_dims == 3

    # Compute constant
    # (focal length divided by squared Z dimension of warped shape)
    z_inv = 1 / warped_uv[:, 2]

    # 3 x n_c x n_samples
    dw_da = camera.rotation_transform.apply(shape_pc_uv.transpose(0, 2, 1)).T

    dw_da[:2] -= warped_uv[:, :2].T[:, None] * dw_da[2] * z_inv

    return camera.projection_transform.focal_length * dw_da[:2] * z_inv


def d_orthographic_camera_d_shape_parameters(shape_pc_uv, camera):
    """
    Calculates the derivative of the orthographic projection with respect to
    the shape parameters.

    Parameters
    ----------
    shape_pc_uv : ``(n_points, 3, n_parameters)`` `ndarray`
        The (sampled) basis of the shape model.
    camera : `menpo3d.camera.OrthographicCamera`
        The camera object that is responsible of projecting the model to the
        image plane.

    Returns
    -------
    dw_da : ``(2, n_shape_parameters, n_points)`` `ndarray`
        The derivative of the perspective camera transform with respect to
        the shape parameters.
    """
    n_points, n_dims, n_parameters = shape_pc_uv.shape
    assert n_dims == 3

    # n_dims, n_parameters, n_points
    dp_da_uv = camera.rotation_transform.apply(shape_pc_uv.transpose(0, 2, 1)).T

    return camera.projection_transform.focal_length * dp_da_uv[:2]


def d_camera_d_shape_parameters(camera, warped_uv, shape_pc_uv):
    if camera.__class__ == PerspectiveCamera:
        dp_da = d_perspective_camera_d_shape_parameters(shape_pc_uv, warped_uv,
                                                        camera)
    elif camera.__class__ == OrthographicCamera:
        dp_da = d_orthographic_camera_d_shape_parameters(shape_pc_uv, camera)
    else:
        raise ValueError("Camera must be either Perspective or "
                         "Orthographic.")
    return dp_da


def d_perspective_camera_d_camera_parameters(warped_uv, camera,
                                             with_focal_length=True,
                                             with_first_quaternion=False):
    """
    Calculates the derivative of the perspective projection with respect to the
    camera parameters.

    Parameters
    ----------
    warped_uv : ``(n_points, 3)`` `ndarray`
        The shape instance with the view transform (rotation and translation)
        applied on it.
    camera : `menpo3d.camera.PerspectiveCamera`
        The camera object that is responsible of projecting the model to the
        image plane.
    with_focal_length : `bool`, optional
        If ``False``, then the derivative with respect to the focal length
        parameter won't be returned.
    with_first_quaternion : `bool`, optional
        If ``False``, then the derivative with respect to the first
        quaternion parameter won't be returned.

    Returns
    -------
    dw_dr : ``(2, n_parameters, n_points)`` `ndarray`
        The derivative of the perspective camera transform with respect to
        the camera parameters.
    """
    n_points, n_dims = warped_uv.shape
    assert n_dims == 3

    # Find total number of parameters
    n_parameters = camera.n_parameters
    if not with_focal_length:
        n_parameters -= 1
    if not with_first_quaternion:
        n_parameters -= 1

    # Initialize derivative
    dw_dr = np.zeros((2, n_parameters, n_points))

    # Initialize parameter counter
    r = 0

    # Get z-component of warped
    z = warped_uv[:, 2]

    # Focal length, if requested
    if with_focal_length:
        dw_dr[:, r] = (warped_uv[:, :2] / z[..., None]).T
        r += 1

    # Quaternions
    centered_warped_uv = camera.translation_transform.pseudoinverse().apply(
        warped_uv).T
    # q_1, if requested
    if with_first_quaternion:
        r0 = 2 * np.array([[1, 0, 0],
                           [0, 1, 0],
                           [0, 0, 1]]).dot(centered_warped_uv).T
        dw_dr[:, r] = r0[:, :2].T - r0[:, 2] * warped_uv[:, :2].T / z
        r += 1

    # q_2
    r1 = 2 * np.array([[0, 0,  0],
                       [0, 0, -1],
                       [0, 1,  0]]).dot(centered_warped_uv).T
    dw_dr[:, r] = r1[:, :2].T - r1[:, 2] * warped_uv[:, :2].T / z
    r += 1

    # q_3
    r2 = 2 * np.array([[ 0, 0, 1],
                       [ 0, 0, 0],
                       [-1, 0, 0]]).dot(centered_warped_uv).T
    dw_dr[:, r] = r2[:, :2].T - r2[:, 2] * warped_uv[:, :2].T / z
    r += 1

    # q_4
    r3 = 2 * np.array([[0, -1, 0],
                       [1,  0, 0],
                       [0,  0, 0]]).dot(centered_warped_uv).T
    dw_dr[:, r] = r3[:, :2].T - r3[:, 2] * warped_uv[:, :2].T / z
    r += 1

    # constant multiplication
    i1 = 1 if with_focal_length else 0
    i2 = i1 + 4 if with_first_quaternion else i1 + 3
    dw_dr[:, i1:i2] *= camera.projection_transform.focal_length / z

    # Translations
    # t_x
    dw_dr[0, r] = camera.projection_transform.focal_length / z
    r += 1
    # t_y
    dw_dr[1, r] = camera.projection_transform.focal_length / z
    r += 1
    # t_z
    dw_dr[:, r] = (- camera.projection_transform.focal_length *
                   warped_uv[:, :2] / z[..., None] ** 2).T

    return dw_dr


def d_orthographic_camera_d_camera_parameters(warped_uv, camera,
                                              with_focal_length=True,
                                              with_first_quaternion=False):
    """
    Calculates the derivative of the orthographic projection with respect to the
    camera parameters.

    Parameters
    ----------
    warped_uv : ``(n_points, 3)`` `ndarray`
        The shape instance with the view transform (rotation and translation)
        applied on it.
    camera : `menpo3d.camera.PerspectiveCamera`
        The camera object that is responsible of projecting the model to the
        image plane.
    with_focal_length : `bool`, optional
        If ``False``, then the derivative with respect to the focal length
        parameter won't be returned.
    with_first_quaternion : `bool`, optional
        If ``False``, then the derivative with respect to the first
        quaternion parameter won't be returned.

    Returns
    -------
    dw_dr : ``(2, n_parameters, n_points)`` `ndarray`
        The derivative of the orthographic camera transform with respect to
        the camera parameters.
    """
    n_points, n_dims = warped_uv.shape
    assert n_dims == 3

    # Find total number of parameters
    n_parameters = camera.n_parameters
    if not with_focal_length:
        n_parameters -= 1
    if not with_first_quaternion:
        n_parameters -= 1

    # Initialize derivative
    dw_dr = np.zeros((2, n_parameters, n_points))

    # Initialize parameter counter
    r = 0

    # Focal length, if requested
    if with_focal_length:
        dw_dr[:, r] = warped_uv[:, :2].T
        r += 1

    # Quaternions
    centered_warped_uv = camera.translation_transform.pseudoinverse().apply(
        warped_uv).T

    # q_1, if requested
    if with_first_quaternion:
        r0 = 2 * np.array([[1, 0, 0],
                           [0, 1, 0],
                           [0, 0, 1]]).dot(centered_warped_uv).T
        dw_dr[:, r] = camera.projection_transform.focal_length * r0[:, :2].T
        r += 1

    # q_2
    r1 = 2 * np.array([[0, 0,  0],
                       [0, 0, -1],
                       [0, 1,  0]]).dot(centered_warped_uv).T
    dw_dr[:, r] = camera.projection_transform.focal_length * r1[:, :2].T
    r += 1

    # q_3
    r2 = 2 * np.array([[ 0, 0, 1],
                       [ 0, 0, 0],
                       [-1, 0, 0]]).dot(centered_warped_uv).T
    dw_dr[:, r] = camera.projection_transform.focal_length * r2[:, :2].T
    r += 1

    # q_4
    r3 = 2 * np.array([[0, -1, 0],
                       [1,  0, 0],
                       [0,  0, 0]]).dot(centered_warped_uv).T
    dw_dr[:, r] = camera.projection_transform.focal_length * r3[:, :2].T
    r += 1

    # Translations
    # t_x
    dw_dr[0, r] = camera.projection_transform.focal_length
    r += 1

    # t_y
    dw_dr[1, r] = camera.projection_transform.focal_length
    r += 1

    return dw_dr


def d_camera_d_camera_parameters(camera, warped_uv, with_focal_length):
    if camera.__class__ == PerspectiveCamera:
        dp_dr = d_perspective_camera_d_camera_parameters(
            warped_uv, camera, with_focal_length=with_focal_length,
            with_first_quaternion=False)
    elif camera.__class__ == OrthographicCamera:
        dp_dr = d_orthographic_camera_d_camera_parameters(
            warped_uv, camera, with_focal_length=with_focal_length,
            with_first_quaternion=False)
    else:
        raise ValueError("Camera must be either Perspective or "
                         "Orthographic.")
    return dp_dr


================================================
FILE: itwmm/fitting/hessian.py
================================================
import numpy as np


def smoothing_kernel(n_frames):
    A = np.eye(n_frames - 2, n_frames)
    i, j = np.diag_indices(n_frames - 2)

    A[i, j + 1] = -2
    A[i, j + 2] = 1
    return A


# -------------------------- HESSIAN INITIALIZATION ------------------------- #
# Functions needed to construct the per-iteration ITW-V video Hessian object.
def insert_id_constraint(H, c_id, n_p):
    H_id = H[:n_p, :n_p]
    np.fill_diagonal(H_id, np.diag(H_id) + c_id)


def insert_exp_constraint(H, c_exp, n_p, n_q, n_frames):
    i_offset = n_p
    j_offset = n_p

    size = n_q * n_frames
    exp_const = np.diag(np.repeat(c_exp, size))
    H[i_offset:i_offset + size, j_offset:j_offset + size] = exp_const


def insert_smoothness_constraint(H, c_sm, n_p, n_q, n_frames):
    s = smoothing_kernel(n_frames)
    s_h = s.T.dot(s)

    B = np.eye(n_q) * c_sm
    smoothing = np.kron(s_h, B)
    H[n_p:(n_p + n_q * n_frames), n_p:(n_p + n_q * n_frames)] += smoothing


# ----------------------- J.T.dot(e) INITIALIZATION  ------------------------- #
def insert_id_constraint_to_JTe(JTe, p, c_id, n_p):
    JTe[:n_p] += -c_id * p


def insert_exp_constraint_to_JTe(JTe, qs, c_exp, n_p, n_q, n_frames):
    size = n_q * n_frames
    JTe[n_p:n_p + size] += - c_exp * qs.ravel()


def insert_smoothness_constraint_to_JTe(JTe, qs, c_sm, n_p, n_q, n_frames):
    s = smoothing_kernel(n_frames)
    # Analytically we can observe that the pattern of selection of terms in
    # JTe for the smoothing is the 2nd order central difference scheme formed
    # in the Hessian (s.T.dot(s)) - form this matrix here to select the qs as
    #  we want
    s_h = s.T.dot(s)
    # n_frames x n_frames @ n_frames x n_q = n_frames x n_q
    # ravelling this back preserves per-frame ordering of q in JTe.
    JTe_smoothing = s_h.dot(qs).ravel()
    JTe[n_p:n_p + n_frames * n_q] += - c_sm * JTe_smoothing


# -------------------------- TOTAL INITIALIZATION ---------------------------- #
def initialize_hessian_and_JTe(c_id, c_exp, c_sm, n_p, n_q, n_c, p, qs,
                               n_frames):
    n = n_p + n_frames * n_q + n_frames * n_c
    H = np.zeros((n, n))
    insert_id_constraint(H, c_id, n_p)
    insert_exp_constraint(H, c_exp, n_p, n_q, n_frames)
    if n_frames >= 3:
        insert_smoothness_constraint(H, c_sm, n_p, n_q, n_frames)
    else:
        print('This video has only {} frames, cannot include smoothing term '
              'in H/JTe.'
              .format(n_frames))

    # The J.T.dot(e) term is always the size of the Hessian w/h (n total params)
    JTe = np.zeros(n)
    insert_id_constraint_to_JTe(JTe, p, c_id, n_p)
    insert_exp_constraint_to_JTe(JTe, qs, c_exp, n_p, n_q, n_frames)


    if n_frames >= 3:
        insert_smoothness_constraint_to_JTe(JTe, qs, c_sm, n_p, n_q, n_frames)
    return H, JTe


# -------------------------- PER-FRAME UPDATES  ------------------------------ #

def insert_frame_to_H(H, j, f, n_p, n_q, n_c, c_f, c_l, n_frames):

    # normalize data term contributions by n_channels x n_pixels
    # otherwise scales of all other constants need to be n_channels x n_pixels
    # to compensate
    c_f = c_f / j['J_f_p'].shape[0]

    pp = c_f * j['J_f_p'].T @ j['J_f_p'] + c_l * j['J_l_p'].T @ j['J_l_p']
    qq = c_f * j['J_f_q'].T @ j['J_f_q'] + c_l * j['J_l_q'].T @ j['J_l_q']
    cc = c_f * j['J_f_c'].T @ j['J_f_c'] + c_l * j['J_l_c'].T @ j['J_l_c']

    pq = c_f * j['J_f_p'].T @ j['J_f_q'] + c_l * j['J_l_p'].T @ j['J_l_q']
    pc = c_f * j['J_f_p'].T @ j['J_f_c'] + c_l * j['J_l_p'].T @ j['J_l_c']

    qc = c_f * j['J_f_q'].T @ j['J_f_c'] + c_l * j['J_l_q'].T @ j['J_l_c']

    # Find the right offset into the Hessian for Q/C (which are per-frame
    # terms). Note that p terms always sum, so no per-frame offset needed.
    offset_q = n_p + f * n_q
    offset_c = n_p + n_frames * n_q + f * n_c

    # 1. Update the terms along the block diagonal of the Hessian
    H[:n_p, :n_p] += pp
    H[offset_q:offset_q + n_q, offset_q:offset_q + n_q] += qq
    H[offset_c:offset_c + n_c, offset_c:offset_c + n_c] += cc

    # 2. Update terms at the off diagonal. Each time we can immediately fill
    # in the symmetric part of the term (as we know the Hessian is symmetric).
    H[:n_p, offset_q:offset_q + n_q] += pq
    H[offset_q:offset_q + n_q, :n_p] += pq.T

    H[:n_p, offset_c:offset_c + n_c] += pc
    H[offset_c:offset_c + n_c, :n_p] += pc.T

    H[offset_q:offset_q + n_q, offset_c:offset_c + n_c] += qc
    H[offset_c:offset_c + n_c, offset_q:offset_q + n_q] += qc.T


def insert_frame_to_JTe(JTe, j, f, n_p, n_q, n_c, c_f, c_l, n_frames):

    # normalize data term contributions by n_channels x n_pixels
    # otherwise scales of all other constants need to be n_channels x n_pixels
    # to compensate
    c_f = c_f / j['J_f_p'].shape[0]

    JTe_p = c_f * j['J_f_p'].T @ j['e_f'] + c_l * j['J_l_p'].T @ j['e_l']
    JTe_q = c_f * j['J_f_q'].T @ j['e_f'] + c_l * j['J_l_q'].T @ j['e_l']
    JTe_c = c_f * j['J_f_c'].T @ j['e_f'] + c_l * j['J_l_c'].T @ j['e_l']

    JTe[:n_p] += JTe_p

    offset_q = n_p + f * n_q
    JTe[offset_q:offset_q + n_q] += JTe_q

    offset_c = n_p + n_frames * n_q + f * n_c
    JTe[offset_c:offset_c + n_c] += JTe_c


================================================
FILE: itwmm/fitting/initialization.py
================================================
import numpy as np
from menpo.shape import PointCloud
from menpo3d.camera import PerspectiveCamera


def initialize_camera(image, lms_3d, focal_length=None):
    return PerspectiveCamera.init_from_2d_projected_shape(
        lms_3d, image.landmarks[None], image.shape, focal_length=focal_length)


def initialize_camera_from_params(image, mm, id_ind, exp_ind,
                                  p, q, focal_length=None):
    shape_params = np.zeros(mm.shape_model.n_active_components)
    shape_params[id_ind] = p
    shape_params[exp_ind] = q
    mesh = mm.shape_model.instance(shape_params, normalized_weights=True)
    lms = PointCloud(mesh.points[mm.model_landmarks_index])
    return initialize_camera(image, lms, focal_length=focal_length)


================================================
FILE: itwmm/fitting/jacobian.py
================================================
import numpy as np

from .derivatives import (d_camera_d_shape_parameters,
                          d_camera_d_camera_parameters)
from .projectout import project_out, sample_uv_terms


def J_data(camera, warped_uv, shape_pc_uv, U_tex_pc, grad_x_uv,
           grad_y_uv, focal_length_update=False):
    # Compute derivative of camera wrt shape and camera parameters
    dp_da_dr = d_camera_d_shape_parameters(camera, warped_uv, shape_pc_uv)

    dp_dr = d_camera_d_camera_parameters(
        camera, warped_uv, with_focal_length=focal_length_update)

    # stack the shape_parameters/camera_parameters updates
    dp_da_dr = np.hstack((dp_da_dr, dp_dr))

    # Multiply image gradient with camera derivative
    permuted_grad_x = np.transpose(grad_x_uv[..., None], (0, 2, 1))
    permuted_grad_y = np.transpose(grad_y_uv[..., None], (0, 2, 1))
    J = permuted_grad_x * dp_da_dr[0] + permuted_grad_y * dp_da_dr[1]

    # Project-out
    n_params = J.shape[1]
    J = np.transpose(J, (1, 0, 2)).reshape(n_params, -1)
    return project_out(J, U_tex_pc)


def J_lms(camera, warped_uv, shape_pc_uv, focal_length_update=False):
    # Compute derivative of camera wrt shape and camera parameters
    J = d_camera_d_shape_parameters(camera, warped_uv, shape_pc_uv)
    dp_dr = d_camera_d_camera_parameters(
        camera, warped_uv, with_focal_length=focal_length_update)
    J = np.hstack((J, dp_dr))

    # Reshape to : n_params x (2 * N)
    n_params = J.shape[1]
    J = np.transpose(J, (1, 0, 2)).reshape(n_params, -1)
    return J


def jacobians(s, c, image, lms_points_xy, mm, id_ind, exp_ind, template_camera,
              grad_x, grad_y, shape_pc, shape_pc_lms, n_samples,
              compute_costs=False):

    instance = mm.shape_model.instance(s, normalized_weights=True)
    camera = template_camera.from_vector(c)

    (instance_w, instance_in_image, warped_uv, img_error_uv,
     shape_pc_uv, U_tex_pc, grad_x_uv, grad_y_uv
     ) = sample_uv_terms(instance, image, camera, mm, shape_pc, grad_x, grad_y,
                         n_samples)

    # Data term Jacobian
    JT = J_data(camera, warped_uv, shape_pc_uv, U_tex_pc, grad_x_uv, grad_y_uv)

    # Landmarks term Jacobian
    # Get projected instance on landmarks and error term
    warped_lms = instance_in_image.points[mm.model_landmarks_index]
    lms_error_xy = (lms_points_xy - warped_lms[:, [1, 0]]).T.ravel()
    warped_view_lms = instance_w[mm.model_landmarks_index]
    J_lT = J_lms(camera, warped_view_lms, shape_pc_lms)

    # form the main two Jacobians...
    J_f = JT.T
    J_l = J_lT.T

    # ...and then slice at the appropriate indices to break down by param type.
    c_offset = id_ind.shape[0] + exp_ind.shape[0]
    jacs = {
        'J_f_p': J_f[:, id_ind],
        'J_f_q': J_f[:, exp_ind],
        'J_f_c': J_f[:, c_offset:],

        'J_l_p': J_l[:, id_ind],
        'J_l_q': J_l[:, exp_ind],
        'J_l_c': J_l[:, c_offset:],

        'e_f': img_error_uv,
        'e_l': lms_error_xy
    }

    if compute_costs:
        resid_f = project_out(img_error_uv, U_tex_pc)
        err_f = (resid_f ** 2).sum()

        resid_l = lms_error_xy
        err_l = (resid_l ** 2).sum()

        jacs['costs'] = {
            'err_f': err_f,
            'err_l': err_l
        }

    return jacs


================================================
FILE: itwmm/fitting/projectout.py
================================================
import numpy as np
from .base import sample_at_bc_vi, visible_sample_points


def project_out(J, U, fast_approx=True):
    tmp = J.dot(U.T if fast_approx else np.linalg.pinv(U))
    return J - tmp.dot(U)


def sample_uv_terms(instance, image, camera, mm, shape_pc, grad_x, grad_y,
                    n_samples):
    # subsample all the terms we need to compute a project out update.

    # Apply camera projection on current instance
    instance_in_image = camera.apply(instance)

    # Compute indices locations for sampling
    (vert_indices, bcoords, tri_indices,
     yx) = visible_sample_points(instance_in_image, image.shape, n_samples)

    # Warp the mesh with the view matrix (rotation + translation)
    instance_w = camera.view_transform.apply(instance.points)

    # Sample all the terms from the model part at the sample locations
    warped_uv = sample_at_bc_vi(instance_w, bcoords, vert_indices)

    # n_samples x n_channels x n_texture_comps
    U_tex_uv = mm.sample_texture_model(bcoords, tri_indices)

    # n_texture_comps x (n_samples * n_channels)
    U_tex_uv = U_tex_uv.reshape((-1, U_tex_uv.shape[-1])).T

    # n_channels x n_samples
    m_texture_uv = mm.instance().sample_texture_with_barycentric_coordinates(
        bcoords, tri_indices).T

    # n_samples x 3 x n_shape_components
    shape_pc_uv = (sample_at_bc_vi(shape_pc, bcoords, vert_indices)
                   .reshape([n_samples, 3, -1]))

    # Sample all the terms from the image part at the sample locations
    # img_uv: (channels, samples)
    img_uv = image.sample(yx)
    grad_x_uv = grad_x.sample(yx)
    grad_y_uv = grad_y.sample(yx)

    # Compute error
    # img_error_uv: (channels x samples,)
    img_error_uv = (m_texture_uv - img_uv).ravel()

    return (instance_w, instance_in_image, warped_uv, img_error_uv,
            shape_pc_uv, U_tex_uv, grad_x_uv, grad_y_uv)


================================================
FILE: itwmm/model/__init__.py
================================================
import numpy as np

from menpo.model import PCAVectorModel
from menpo.visualize import print_progress

from itwmm.base import instance_for_params

from .extractimage import extract_per_vertex_colour_with_occlusion
from .math import rpca_missing


def generate_texture_model_from_itwmm(images, mm, id_ind, exp_ind,
                                      template_camera, p, qs, cs,
                                      lambda_=0.01,
                                      n_components=0.99):
    r"""Build a new texture model from an existing model and fitting
    information to a collection of images."""
    n_channels = images[0].n_channels
    n_features = n_channels * mm.n_vertices
    n_samples = len(images)
    X = np.empty((n_samples, n_features), dtype=mm.texture_model.mean().dtype)
    M = np.empty_like(X, dtype=np.bool)
    proportion_masked = []
    for i, (img, q, c) in enumerate(zip(print_progress(images, 'Extracting '
                                                               'masks and '
                                                               'features'), qs,
                                        cs)):
        i_in_img = instance_for_params(mm, id_ind, exp_ind,
                                       template_camera,
                                       p, q, c)['instance_in_img']
        features, mask = extract_per_vertex_colour_with_occlusion(i_in_img, img)
        mask_repeated = np.repeat(mask.ravel(), n_channels)
        X[i] = features.ravel()
        M[i] = mask_repeated.ravel()
        proportion_masked.append(mask.sum() / mask.shape[0])
    print('Extraction concluded. Self-occlusions on average masked {:.0%} of '
          'vertices.'.format(np.array(proportion_masked).mean()))
    print('Performing R-PCA to complete missing textures')
    A, E = rpca_missing(X, M, verbose=True, lambda_=lambda_)
    print('R-PCA completed. Building PCA model of features on completed '
          'samples.')
    model = PCAVectorModel(A, inplace=True)
    print('Trimming the components to retain only what was required.')
    model.trim_components(n_components=n_components)
    return model, X, M


def generate_texture_model_from_image_3d_fits(images_and_fits,
                                              lambda_=0.01, n_components=0.99):
    """Build an ITW texture model from a list of images with associated dense
    3D fits (one per image). Note that the input images should already have an
    image feature taken on them, and have all been resized to a consistent
    scale."""
    feat_img, fit_2d = images_and_fits[0]
    n_channels = feat_img.n_channels
    n_features = n_channels * fit_2d.n_points
    n_samples = len(images_and_fits)
    X = np.empty((n_samples, n_features), dtype=feat_img.pixels.dtype)
    M = np.empty_like(X, dtype=np.bool)
    proportion_masked = []
    for i, (img, fit_2d) in enumerate(print_progress(
            images_and_fits, prefix='Extracting masks & features')):
        features, mask = extract_per_vertex_colour_with_occlusion(fit_2d, img)
        mask_repeated = np.repeat(mask.ravel(), n_channels)
        X[i] = features.ravel()
        M[i] = mask_repeated.ravel()
        proportion_masked.append(mask.sum() / mask.shape[0])
    print('Performing R-PCA to complete missing textures')
    A, E = rpca_missing(X, M, verbose=True, lambda_=lambda_)
    print('R-PCA completed. Building PCA model of features on completed '
          'samples.')
    model = PCAVectorModel(A, inplace=True)
    print('Trimming the components to retain only what was required.')
    model.trim_components(n_components=n_components)

    return model, X, M


================================================
FILE: itwmm/model/extractimage.py
================================================
import numpy as np

from menpo.shape import PointCloud
from menpo.transform import Scale
from menpo3d.rasterize import (
    rasterize_shape_image_from_barycentric_coordinate_images,
    rasterize_barycentric_coordinate_images
)

from itwmm.base import as_colouredtrimesh


def per_vertex_occlusion(mesh_in_img, err_proportion=0.0001, render_diag=600):

    [x_r, y_r, z_r] = mesh_in_img.range()
    av_xy_r = (x_r + y_r) / 2.0

    rescale = render_diag / np.sqrt((mesh_in_img.range()[:2] ** 2).sum())
    rescale_z = av_xy_r / z_r

    mesh = Scale([rescale, rescale, rescale * rescale_z]).apply(mesh_in_img)
    mesh.points[...] = mesh.points - mesh.points.min(axis=0)
    mesh.points[:, :2] = mesh.points[:, :2] + 2
    shape = np.round(mesh.points.max(axis=0)[:2] + 2)

    bc, ti = rasterize_barycentric_coordinate_images(mesh, shape)
    si = rasterize_shape_image_from_barycentric_coordinate_images(
        as_colouredtrimesh(mesh), bc, ti)

    # err_proportion=0.01 is 1% deviation of total range of 3D shape
    threshold = render_diag * err_proportion
    xyz_found = si.as_unmasked().sample(mesh.with_dims([0, 1]), order=1).T
    err = np.sum((xyz_found - mesh.points) ** 2, axis=1)

    visible = err < threshold
    return visible


def render_hi_res_shape_image(mesh, render_width=3000):
    from menpo3d.rasterize import GLRasterizer
    h, w = mesh.range()[:2]
    aspect_ratio = w / h
    height = render_width * aspect_ratio

    r = GLRasterizer(
        projection_matrix=model_to_clip_transform(mesh).h_matrix,
        width=render_width, height=height)
    return r.model_to_image_transform, r.rasterize_mesh_with_shape_image(mesh)[1]


def per_vertex_occlusion_gl_rasterizer(mesh, err_proportion=0.0001,
                                       err_norm='z', render_width=3000):
    # Render a high-resolution shape image for visibility testing

    # z scale can be very large for high focal lengths.
    # ensure z is scaled to match x/y for the purposes of masking.
    print('scaling down ')
    [x_r, y_r, z_r] = mesh.range()
    av_xy_r = (x_r + y_r) / 2.0
    mesh = Scale([1, 1, av_xy_r / z_r]).apply(mesh)

    model_to_image_transform, shape_image = render_hi_res_shape_image(mesh, render_width=render_width)
    # err_proportion=0.01 is 1% deviation of total range of 3D shape
    err_scale = mesh.range()[2].sum() if err_norm == 'z' else np.sqrt(
        (mesh.range() ** 2).sum())
    threshold = err_scale * err_proportion

    sample_points_3d = mesh
    sample_points_2d = model_to_image_transform.apply(sample_points_3d)

    xyz_found = shape_image.as_unmasked().sample(sample_points_2d, order=1).T
    err = np.sum((xyz_found - sample_points_3d.points) ** 2, axis=1)
    return err < threshold


def per_vertex_occlusion_accurate(mesh):
    from menpo3d.vtkutils import trimesh_to_vtk
    import vtk
    tol = mesh.mean_edge_length() / 1000
    min_, max_ = mesh.bounds()
    z_min = min_[-1] - 10
    z_max = max_[-1] + 10

    ray_start = mesh.points.copy()
    ray_end = mesh.points.copy()
    points = mesh.points
    ray_start[:, 2] = z_min
    ray_end[:, 2] = z_max

    vtk_mesh = trimesh_to_vtk(mesh)

    obbTree = vtk.vtkOBBTree()
    obbTree.SetDataSet(vtk_mesh)
    obbTree.BuildLocator()

    vtk_points = vtk.vtkPoints()
    vtk_cellIds = vtk.vtkIdList()
    bad_val = tuple(ray_start[0])
    first_intersects = []
    for start, end, point in zip(ray_start, ray_end, points):
        start = tuple(start)
        end = tuple(end)
        obbTree.IntersectWithLine(start, end, vtk_points, vtk_cellIds)
        data = vtk_points.GetData()
        break
    for start, end, point in zip(ray_start, ray_end, points):
        start = tuple(start)
        end = tuple(end)
        obbTree.IntersectWithLine(start, end, vtk_points, vtk_cellIds)
        data = vtk_points.GetData()
        if data.GetNumberOfTuples() > 0:
            first_intersects.append(data.GetTuple3(0))
        else:
            first_intersects.append(bad_val)

    visible = np.linalg.norm(points - np.array(first_intersects), axis=1) < tol
    return visible


def extract_per_vertex_colour(mesh, image):
    return image.sample(PointCloud(mesh.points[:, :2])).T


def extract_per_vertex_colour_with_occlusion(mesh, image,
                                             err_proportion=0.0001,
                                             render_diag=600):
    colours = extract_per_vertex_colour(mesh, image)
    mask = per_vertex_occlusion(mesh,
                                err_proportion=err_proportion,
                                render_diag=render_diag)
    return colours, mask


def extract_per_vertex_features(mesh, image, feature_f, diagonal_range=None):
    image = image.copy()
    image.landmarks['mesh_2d'] = mesh.with_dims([0, 1])
    if diagonal_range is not None:
        image = image.rescale_landmarks_to_diagonal_range(diagonal_range,
                                                          group='mesh_2d')
    feature_image = feature_f(image)
    return extract_per_vertex_colour(feature_image.landmarks['mesh_2d'],
                                     feature_image)


================================================
FILE: itwmm/model/math.py
================================================
from time import time

import numpy as np

from menpo.visualize import bytes_str


def rpca_missing(X, M, lambda_=None, tol=1e-6, max_iter=1000, verbose=False):
    r"""
    Robust PCA with Missing Values using the inexact augmented Lagrange
    multiplier method.
    Parameters
    ----------
    X : ``(n_samples, n_features)`` `ndarray`
        Data matrix.
    M : ``(n_components, n_features)`` `ndarray` of type `np.bool`
        Mask matrix. For each element, if ``True`` indicates that the
        corresponding element in ``X`` is meaningful data. If ``False``
        the corresponding element in ``X`` is not considered in the
        calculation.
    lambda_ : float, optional
        The weight on sparse error term in the cost function. If ``None``,
        the heuristic value of ``1 / np.sqrt(n_samples)`` is used.
    tol : `float`, optional
        The tolerance for the stopping criterion.
    max_iter : `float`, optional
        The maximum allowed number of iterations.
    verbose : `boolean`, optional
        If ``True``, details of the progress of the algorithm will be printed
        every 10 iterations.
    Returns
    -------
    A : ``(n_samples, n_features)`` `ndarray`
        Low rank reconstruction
    E : ``(n_samples, n_features)`` `ndarray`
        Sparse reconstruction
    """
    m, n = X.shape

    if verbose:
        print('X {} of type {}: {}'.format(
            X.shape, X.dtype, bytes_str(X.nbytes)))
        # Have to allocate 7 arrays (X, Y, A, E, T, Z, V) all of this size
        # + 4 in temp computations
        print('Estimated total memory required: {}'.format(bytes_str(X.nbytes
                                                                     * 11)))
        t = time()

    if lambda_ is None:
        lambda_ = 1. / np.sqrt(m)

    norm_fro = np.linalg.norm(X, ord='fro')
    norm_two = np.linalg.norm(X, ord=2)
    norm_inf = np.linalg.norm(X.ravel(), ord=np.inf) / lambda_
    dual_norm_inv = 1.0 / max(norm_two, norm_inf)
    Y = X * dual_norm_inv

    A = np.zeros_like(X)
    notM = ~M
    mu = 1.25 / norm_two
    mu_bar = mu * 1e7
    rho = 1.5
    sv = 10
    for i in range(1, max_iter + 1):
        T = X - A + (1 / mu) * Y
        E = (np.maximum(T - (lambda_ / mu), 0) +
             np.minimum(T + (lambda_ / mu), 0))
        E = E * M + T * notM
        U, s, V = np.linalg.svd(X - E + (1 / mu) * Y, full_matrices=False)

        svp = (s > 1 / mu).sum()
        sv = min(svp + 1 if svp < sv else svp + round(0.05 * n), n)

        S_svp = np.diag(s[:svp] - 1 / mu)
        A = np.dot(U[:, :svp], np.dot(S_svp, V[:svp]))

        Z = X - A - E
        Y += mu * Z
        mu = min(mu * rho, mu_bar)

        stopping_criterion = np.linalg.norm(Z, ord='fro') / norm_fro

        if verbose and (time() - t > 1):
            print('{i:02d} ({time:.1f} sec/iter) r(A): {r_A} |E|_0: {E_0} '
                  'criterion/tol: {sc:.0f} '.format(
                i=i, r_A=np.linalg.matrix_rank(A), time=time() - t,
                E_0=(np.abs(E) > 0).sum(), sc=stopping_criterion / tol))
            t = time()
        if stopping_criterion < tol:
            if verbose:
                print('Converged after {} iterations'.format(i))
            break
    else:
        if verbose:
            print('Maximum iterations ({}) reached without '
                  'convergence'.format(max_iter))

    return A, E


================================================
FILE: itwmm/visualize.py
================================================
import numpy as np

from menpo.transform import Scale, UniformScale, Translation
from menpo3d.rasterize import rasterize_mesh

from itwmm.base import as_colouredtrimesh, instance_for_params


def l2_normalize(x, axis=0, epsilon=1e-12):
    """
    Transforms an `ndarray` to have a unit l2 norm along
    a given direction.
    ----------
    x : `ndarray`
        The array to be transformed.
    axis : `int`
        The axis that will be l2 unit normed.
    epsilon: `float`
        A small value such as to avoid division by zero.
    Returns
    -------
    x : (D,) `ndarray`
        The transformed array.
    """
    return x / np.maximum(np.linalg.norm(x, axis=axis), epsilon)


def mesh_in_unit_sphere(mesh):
    scale = UniformScale(1 / mesh.norm(), mesh.n_dims)
    translation = Translation(-scale.apply(mesh).centre())
    return translation.compose_after(scale)


def lambertian_shading(mesh, diffuse_colour=0.4,
                       albedo_weighting=0.5, ambient_colour=0.3,
                       light_positions=((2, -2, 1), (-2, 2, 1))):

    diffuse_colour = np.asarray(diffuse_colour)
    light_positions = l2_normalize(np.asarray(light_positions).reshape(-1, 3),
                                   axis=0)

    unit_transform = mesh_in_unit_sphere(mesh)
    mesh = unit_transform.apply(mesh)

    light_directions = l2_normalize(light_positions.reshape(-1, 1, 3) -
                                    mesh.points[None, ...], axis=0)

    # Calculate the lambertian reflectance for each light source.
    # This will be an `ndarray` of shape(num_light_sources, num_vertices)
    lambertian = np.sum(light_directions *
                        mesh.vertex_normals()[None, ...], 2)[..., None]

    # Sum up the contribution of all the light sources and multiply by the
    # diffusion colour.
    lambertian = lambertian.sum(0) * diffuse_colour + ambient_colour

    mesh.colours[...] = np.clip(mesh.colours * albedo_weighting +
                                lambertian * (1 - albedo_weighting),
                                0, 1)

    return unit_transform.pseudoinverse().apply(mesh)


def render_mesh_in_img(trimesh_3d_in_img, img_shape):
    [x_r, y_r, z_r] = trimesh_3d_in_img.range()
    av_xy_r = (x_r + y_r) / 2.0
    trimesh_3d_in_img = Scale([1, 1, av_xy_r / z_r]).apply(trimesh_3d_in_img)
    mesh_in_img_lit = lambertian_shading(as_colouredtrimesh(trimesh_3d_in_img))
    return rasterize_mesh(mesh_in_img_lit, img_shape)


def render_overlay_of_mesh_in_img(trimesh_3d_in_img, img):
    mesh_in_img = render_mesh_in_img(trimesh_3d_in_img, img.shape)
    return overlay_image(img, mesh_in_img.as_greyscale(), (0, 0))


def render_initialization(images, mm, id_indices, exp_indices, template_camera,
                          p, qs, cs, img_index):
    c_i = cs[img_index]
    q_i = qs[img_index]
    i_in_img = instance_for_params(mm, id_indices, exp_indices,
                                   template_camera,
                                   p, q_i, c_i)['instance_in_img']
    [x_r, y_r, z_r] = i_in_img.range()
    av_xy_r = (x_r + y_r) / 2.0
    i_in_img = Scale([1, 1, av_xy_r / z_r]).apply(i_in_img)
    mesh_in_img_lit = lambertian_shading(as_colouredtrimesh(i_in_img))
    return rasterize_mesh(mesh_in_img_lit, images[0].shape).as_unmasked()


def render_iteration(mm, id_ind, exp_ind, img_shape, camera, params,
                     img_index, iteration):
    params_i = params[iteration]
    c_i = params_i['cs'][img_index]
    p_i = params_i['p']
    q_i = params_i['qs'][img_index]
    i_in_img = instance_for_params(mm, id_ind, exp_ind, camera,
                                   p_i, q_i, c_i)['instance_in_img']
    [x_r, y_r, z_r] = i_in_img.range()
    av_xy_r = (x_r + y_r) / 2.0
    i_in_img = Scale([1, 1, av_xy_r / z_r]).apply(i_in_img)
    mesh_in_img_lit = lambertian_shading(as_colouredtrimesh(i_in_img))
    return rasterize_mesh(mesh_in_img_lit, img_shape).as_unmasked()


def overlay_image(background, overlay, offset_vector):
    output = background.copy()

    indices = overlay.indices() + offset_vector

    new_mask = np.zeros_like(background.pixels[0], dtype=np.bool)

    max_ = np.array(background.bounds()[1])
    in_img = ~np.logical_or(np.any(indices < 0, axis=1),
                            np.any(indices > max_, axis=1))
    new_mask[indices[in_img, 0], indices[in_img, 1]] = True
    output.pixels[:, new_mask] = overlay.as_vector(
        keep_channels=True)[:, in_img]
    return output


================================================
FILE: notebooks/1. Building an "in-the-wild" texture model.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Building an \"in-the-wild\" texture model\n",
    "\n",
    "#### Prerequisites\n",
    "\n",
    "A collection of \"in-the-wild\" images with 3D mesh fits.\n",
    "\n",
    "The authors of:\n",
    "\n",
    "> Zhu, Xiangyu, et al. \"Face alignment across large poses: A 3d solution.\" CVPR 2016\n",
    "\n",
    "Provide data [on their website](http://www.cbsr.ia.ac.cn/users/xiangyuzhu/projects/3DDFA/main.htm) which can be used to bootstrap this. As an example, we show loading this data into the format we need to proceed, and then demonstrate building the texture model. The two files this example follows are:\n",
    "```\n",
    "300W-3D.zip\n",
    "300W-3D-Face.zip\n",
    "```\n",
    "unzip them next to each other in a folder and replace `DATA_PATH` with the parent folder path. \n",
    "\n",
    "If you have your own collection of \"in-the-wild\" images, you can easily replace this first cell with your own loading code - as long as you load each image with a 3D `TriMesh` that menpo visualizes the 2D projection of correctly, you are good. A good sanity check is therefore:\n",
    "```\n",
    "%matplotlib inline\n",
    "img.view()\n",
    "PointCloud(fit_3d.points[:, :2]).view()\n",
    "```\n",
    "You should see that Menpo renders the points of the mesh on the face in the image."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Loading the data\n",
    "\n",
    "Firstly we load the data we need in the right format. We need to produce:\n",
    "\n",
    "- A list of tuples of `(feature_img, fit_trimesh_3d)`, where\n",
    "  - `feature_img` has already had a dense image feature e.g. `fast_dist` extracted from it\n",
    "  - All instances of `feature_img` are scaled such that the face occupies the same size in each image\n",
    "  - `fit_trimesh_3d` lies directly on the dense feature image - i.e. Y, X are in units of pixels, and Z is of an arbitrary scale (so long as depth is meaningfully conveyed for z-buffering)\n",
    "  - A good test for this last condition is to visualize the mesh using Menpo (see the example below)\n",
    "  \n",
    "In order to be memory efficient, we choose to construct a `LazyList` rather than a literal `list` here. This means each feature/fit pair is generated from disk on the fly when we access each element, keeping memory usage low."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from functools import lru_cache\n",
    "from pathlib import Path\n",
    "import numpy as np\n",
    "\n",
    "import scipy.io as sio\n",
    "import menpo.io as mio\n",
    "\n",
    "from menpo.base import LazyList\n",
    "from menpo.feature import fast_dsift\n",
    "from menpo.shape import PointCloud, TriMesh\n",
    "\n",
    "from itwmm import (render_overlay_of_mesh_in_img, \n",
    "                   generate_texture_model_from_image_3d_fits)\n",
    "\n",
    "# Replace DATA_PATH with the path to your data. It should have subdirectories:\n",
    "#  300W-3D/\n",
    "#  300W-3D-Face/\n",
    "DATA_PATH = Path('~/Dropbox/itwmm_src_data/').expanduser()\n",
    "\n",
    "# The diagonal range of the projected fit\n",
    "DIAGONAL_RANGE = 180\n",
    "\n",
    "# The image feature we wish to employ\n",
    "FEATURE_F = fast_dsift"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "@lru_cache(1)\n",
    "def load_trilist():\n",
    "    trilist = sio.loadmat(str(DATA_PATH / '300W-3D' / 'Code' / 'ModelGeneration' / 'model_info.mat'))['tri']\n",
    "    # flip triangle faces and matlab -> Python indexing\n",
    "    return trilist[[1, 0, 2]].T - 1     \n",
    "\n",
    "\n",
    "def convert_raw_3d_fit_to_img_trimesh_fit(raw_3d_fit, img):\n",
    "    img_points_2d = raw_3d_fit[:2][::-1].copy() - 1  # matlab 1 indexing\n",
    "    img_points_2d[0] = img.shape[0] - img_points_2d[0]\n",
    "    # Depth needs to be flipped\n",
    "    img_points_3d = np.vstack([img_points_2d, raw_3d_fit[2] * -1]).T\n",
    "    img_trimesh_3d = TriMesh(img_points_3d, trilist=load_trilist())\n",
    "    return img_trimesh_3d\n",
    "\n",
    "\n",
    "def load_img_and_fit(img_path):\n",
    "    fit_rel_path = img_path.relative_to(DATA_PATH / '300W-3D').with_suffix('.mat')\n",
    "    fit_path = DATA_PATH / '300W-3D-Face' / fit_rel_path\n",
    "\n",
    "    img = mio.import_image(img_path).as_greyscale()\n",
    "    raw_3d_fit = sio.loadmat(str(fit_path))['Fitted_Face']\n",
    "    fit_trimesh_3d = convert_raw_3d_fit_to_img_trimesh_fit(raw_3d_fit, img)\n",
    "    return img, fit_trimesh_3d\n",
    "\n",
    "\n",
    "def load_data_with_feature_sample(img_path, err_proportion=0.0001):\n",
    "    img, fit_trimesh_3d = load_img_and_fit(img_path)\n",
    "    \n",
    "    # rescale the image to the diagonal range we are using\n",
    "    img.landmarks['fit_2d'] = PointCloud(fit_trimesh_3d.points[:, :2])\n",
    "    img, tr = img.rescale_landmarks_to_diagonal_range(DIAGONAL_RANGE, return_transform=True)\n",
    "    # note that we use the rescaled landmarks here\n",
    "    fit_trimesh_3d.points[:, :2] = img.landmarks['fit_2d'].points\n",
    "    fit_trimesh_3d.points[:, 2] = fit_trimesh_3d.points[:, 2] / tr.scale[0]\n",
    "    # take the feature on the rescaled image\n",
    "    feat = FEATURE_F(img)\n",
    "    return img, feat, fit_trimesh_3d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_paths = list(mio.image_paths(DATA_PATH / '300W-3D' / '**/*'))\n",
    "print('{} images with fits to read.'.format(len(img_paths)))\n",
    "\n",
    "# we can make a LazyList which returns image/feature/fit triples when\n",
    "# we iterate. This saves us loading everything into memory at once.\n",
    "imgs_features_and_fits = LazyList.init_from_iterable(img_paths).map(\n",
    "    load_data_with_feature_sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "# Let's test one.\n",
    "img, feat, fit_trimesh_3d = imgs_features_and_fits[100]\n",
    "\n",
    "# Visual check that everything looks sensible\n",
    "img.view()\n",
    "feat.view(channels=0, new_figure=True)\n",
    "render_overlay_of_mesh_in_img(fit_trimesh_3d, img).view(new_figure=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# the routine to build the ITW texture model only requires \n",
    "# feat/fit pairs, so we get rid of the redundent images\n",
    "features_and_fits = imgs_features_and_fits.map(lambda x: x[1:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Confirm we get the format we need\n",
    "print(features_and_fits[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Building the ITW texture model\n",
    "\n",
    "Now we are able to build the texture model. Given data in the right format, this is just a single function call.\n",
    "\n",
    "Note however that this process is quite memory intensive. We've done all we can in loading the data lazily, we still need to constract a large data matrix X and a mask matrix, and solve RPCA which has some memory overhead.\n",
    "\n",
    "As a rough guide, with the recommended settings here, around 1.8GB of RAM will be required for every 100 images used.\n",
    "\n",
    "Feel free to tweak `n_imgs_for_rpca` and choose wheather to use the original 64-bit feature images or 32-bit conversions (which will half RAM requirements).\n",
    "\n",
    "Also a deprecation warning will appear in red, but it's only a notice of a future deprecation - this will be addressed in a future release of `menpo3d`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def as_float32(img):\n",
    "    img = img.copy()\n",
    "    img.pixels = img.pixels.astype(np.float32)\n",
    "    return img\n",
    "\n",
    "features_and_fits_32bit = features_and_fits.map(lambda x: (as_float32(x[0]), x[1]))\n",
    "n_imgs_for_rpca = 100\n",
    "\n",
    "itw_texture_model, X, m = generate_texture_model_from_image_3d_fits(features_and_fits_32bit[:n_imgs_for_rpca])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(itw_texture_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('The matrices used for R-PCA were of shape: {} & {}'.format(X.shape, m.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib qt\n",
    "from itwmm.base import as_colouredtrimesh\n",
    "# As a sanity check, visualize the masks found on the original geometry\n",
    "# with the texture value extracted (mask = red)\n",
    "i = 4\n",
    "img, feat, fit_trimesh_3d = imgs_features_and_fits[i]\n",
    "img_sampled = np.repeat(img.sample(PointCloud(fit_trimesh_3d.points[:, :2])), 3, axis=0).T\n",
    "mask = m[i].reshape([fit_trimesh_3d.n_points, -1])[:, 0]\n",
    "img_sampled[~mask, 0] = 1.\n",
    "as_colouredtrimesh(fit_trimesh_3d, colours=img_sampled).view()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Exporting a ITW texture model\n",
    "\n",
    "To use this model we just need to save it out and load it in to the fitting notebooks. Note that we need to save out some metadata along with the raw PCA basis to be able to use this again in the future. In particular, we need:\n",
    "\n",
    "- The rescaling diagonal. We will need this at fit time to ensure the features are meaningful.\n",
    "- The feature extraction function. Again we need to call this at fit time to produce a meaningful feature image to compare against in our LK algorithm.\n",
    "\n",
    "We also need the shape model we use to be in perfect correspondence with the fits used here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mio.export_pickle(\n",
    "    {\n",
    "        'texture_model': itw_texture_model,\n",
    "        'diagonal_range': 180,\n",
    "        'feature_function': fast_dsift\n",
    "    }, \n",
    "    DATA_PATH / 'itw_texture_model.pkl',\n",
    "    protocol=4, overwrite=True)"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}


================================================
FILE: notebooks/2. Creating an expressive 3DMM.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Creating an Expressive 3D Morphable Model\n",
    "\n",
    "#### Prerequisites\n",
    "\n",
    "- Access to a 3D facial shape model of identity\n",
    "- Access to a 3D facial shape model of expression (a 'blendshape' model)\n",
    "\n",
    "The authors of:\n",
    "\n",
    "> Zhu, Xiangyu, et al. \"Face alignment across large poses: A 3d solution.\" CVPR 2016\n",
    "\n",
    "Provide data [on their website](http://www.cbsr.ia.ac.cn/users/xiangyuzhu/projects/3DDFA/main.htm) which can be used to bootstrap the expression model, and the LSFM model can be used for identity variation. As an example, we show loading this data into the format we need to proceed, and then demonstrate building the combined expression/identity model. The file this example follows is:\n",
    "```\n",
    "300W-3D.zip\n",
    "```\n",
    "and also requires access to the following file (the LSFM model):\n",
    "```\n",
    "all_all_all.mat\n",
    "```\n",
    "place the two files together in a directory and unzip the zip file in place. Replace `DATA_PATH` with the parent folder path."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "import numpy as np\n",
    "\n",
    "import scipy.io as sio\n",
    "import menpo.io as mio\n",
    "import menpo3d.io as m3io\n",
    "\n",
    "from menpo.shape import TriMesh, PointCloud\n",
    "from menpo.model import PCAModel\n",
    "\n",
    "# Replace DATA_PATH with the path to your data. It should have subdirectories:\n",
    "#  300W-3D/\n",
    "#  all_all_all.mat\n",
    "DATA_PATH = Path('~/Dropbox/itwmm_src_data/').expanduser()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],   
   "source": [
    "def generate_id_exp_model(data_path):\n",
    "    # Load the LSFM ID model\n",
    "    lsfm_model = m3io.import_lsfm_model(data_path / 'all_all_all.mat')\n",
    "    \n",
    "    # The number of ID and expression deformation components\n",
    "    n_id = lsfm_model.n_active_components\n",
    "    n_exp = 28 \n",
    "    \n",
    "    # Multiply the LSFM components with a scale factor, and bake in the eigenvalues.\n",
    "    # The scale factor is to normalize weightings between ID and expression models.\n",
    "    scale = np.sqrt(lsfm_model.eigenvalues[:n_id])\n",
    "    i_raw = lsfm_model.components[:n_id] * scale[:, None]\n",
    "    \n",
    "    # Load the expression deformations from the data, and again scale for balancing\n",
    "    scale_exp_mean = 1.0006846894227463e-05\n",
    "    scale_exp_comps = scale_exp_mean / 2.5\n",
    "    e_raw = sio.loadmat(str(data_path / '300W-3D' / 'Code' / 'Model_Exp.mat'))['w_exp'].T[:-1] * scale_exp_comps \n",
    "    e_mean = sio.loadmat(str(data_path / '300W-3D' / 'Code' / 'Model_Exp.mat'))['mu_exp'].T * scale_exp_mean\n",
    "    \n",
    "    # We now just need to combine the two models.\n",
    "    id_ind = np.arange(n_id)\n",
    "    exp_ind = np.arange(n_id, n_id + n_exp)\n",
    "    components = np.zeros([n_id + n_exp, i_raw.shape[1]])\n",
    "    components[id_ind] = i_raw\n",
    "    components[exp_ind] = e_raw\n",
    "    mean_combined = TriMesh(lsfm_model.mean().points + e_mean.reshape((-1,3)), lsfm_model.mean().trilist) \n",
    "    \n",
    "    # iBUG 68 landmarks \n",
    "    lms_vertex_indices = [\n",
    "        21868, 22404, 22298, 22327, 43430, 45175, 46312, 47132, 47911,\n",
    "        48692, 49737, 51376, 53136, 32516, 32616, 32205, 32701, 38910,\n",
    "        39396, 39693, 39934, 40131, 40843, 41006, 41179, 41430, 13399,\n",
    "        8161,  8172,  8179,  8185,  5622,  6881,  8202,  9403, 10764,\n",
    "        1831,  3887,  5049,  6214,  4805,  3643,  9955, 11095, 12255,\n",
    "        14197, 12397, 11366,  5779,  6024,  7014,  8215,  9294, 10267,\n",
    "        10922,  9556,  8836,  8236,  7636,  6794,  5905,  7264,  8223,\n",
    "        9063, 10404,  8828,  8228,  7509\n",
    "    ]\n",
    "    lms = PointCloud(mean_combined.points[lms_vertex_indices])\n",
    "\n",
    "    # Initialize a Menpo PCA Model from the raw data.\n",
    "    # Note that we fix the eigenvalues to 1, as we choose to rescale\n",
    "    # the components to 'bake-in' the eigenvalues above.\n",
    "    shape_model = PCAModel.init_from_components(components, \n",
    "                                                np.ones(components.shape[1]), \n",
    "                                                mean_combined, \n",
    "                                                lsfm_model.n_samples, \n",
    "                                                True)\n",
    "    \n",
    "    return shape_model, lms, id_ind, exp_ind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Blend the ID and expression models into one, and note\n",
    "# The indices into the model for both indivual components.\n",
    "shape_model, lms, id_ind, exp_ind = generate_id_exp_model(DATA_PATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib qt\n",
    "# generate a random expression to see if it's sensible\n",
    "n_c = shape_model.n_components\n",
    "\n",
    "# The std. dev weighting to use\n",
    "sigma = 1\n",
    "\n",
    "weights = np.random.multivariate_normal(np.zeros(n_c), np.eye(n_c) * sigma, 1).ravel()\n",
    "\n",
    "synthesized_vector = shape_model.mean_vector + np.sum(weights[:, None] * shape_model.components, axis=0)\n",
    "synthesized_instance = shape_model.template_instance.from_vector(synthesized_vector)\n",
    "synthesized_instance.view()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exporting the combined ID/expression shape model\n",
    "\n",
    "To use this model we just need to save it out and load it in to the fitting notebooks. Note that we need to save out some metadata along with the raw PCA basis to be able to use this again in the future. In particular, we need:\n",
    "\n",
    "- The record of which components are identitiy and which are expression (`id_ind` and `exp_ind`)\n",
    "- The vertex indices of the sparse set of landmarks on the model\n",
    "\n",
    "We also need the ITW texture model we use to be in perfect correspondence with the shape model provided here, and for the landmarks in data we fit to be of the same configuration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mio.export_pickle(\n",
    "    {\n",
    "        'shape_model': shape_model,\n",
    "        'id_ind': id_ind,\n",
    "        'exp_ind': exp_ind,\n",
    "        'lms': lms\n",
    "    }, \n",
    "    DATA_PATH / 'id_exp_shape_model.pkl',\n",
    "    protocol=4, overwrite=True)"
   ]
  }
 ],
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================================================
FILE: notebooks/3. Fitting "in-the-wild" images.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Fitting \"in-the-wild\" images\n",
    "\n",
    "#### Prerequisites\n",
    "\n",
    "- Access to a combined 3D facial shape model of identity and expression\n",
    "- Access to an \"in-the-wild\" texture model\n",
    "- Access to an \"in-the-wild\" image with landmarks to fit. The landmarks should be given as input and should follow the iBUG 68 mark-up. It is highly recommended that these landmarks are \"3D-Aware-2D\" (3DA-2D) landmarks, as e.g. described in the paper (Zafeiriou et al., \"The 3D menpo facial landmark tracking challenge\", ICCV-W 2017).\n",
    "\n",
    "The first two of these can be generated by following the two previous notebooks in this folder:\n",
    "\n",
    "- `1. Building an \"in-the-wild\" texture model.ipynb`\n",
    "- `2. Creating an expressive 3DMM.ipynb`\n",
    "\n",
    "Running these notebooks to completion will lead to the following two files being generated in the `DATA_PATH` folder:\n",
    "\n",
    "- `itw_texture_model.pkl`\n",
    "- `id_exp_shape_model.pkl`\n",
    "\n",
    "This script shows how to load these directly and use them in fitting.\n",
    "\n",
    "You could of course understand the required formats by studying the aformentinoed scripts, and instead load your own shape and texture models instead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "import numpy as np\n",
    "\n",
    "import menpo.io as mio\n",
    "from menpo.base import LazyList\n",
    "from menpo.shape import PointCloud\n",
    "from menpo.visualize import print_progress\n",
    "from menpo3d.morphablemodel import ColouredMorphableModel\n",
    "\n",
    "from itwmm import (\n",
    "    initialize_camera_from_params, initialize_camera,\n",
    "    fit_image, instance_for_params,\n",
    "    render_initialization, render_iteration,\n",
    ")\n",
    "\n",
    "# Replace DATA_PATH with the path to your data. It should have files:\n",
    "#  itw_texture_model.pkl\n",
    "#  id_exp_shape_model.pkl\n",
    "# As generated from Notebooks 1. and 2.)\n",
    "DATA_PATH = Path('~/Dropbox/itwmm_src_data/').expanduser()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def prepare_image_and_return_transforms(diagonal, feature_f, image):\n",
    "    # this variation is needed if we need to know how the imput image \n",
    "    # is transformed\n",
    "    img, t = image.crop_to_landmarks_proportion(0.4, return_transform=True)\n",
    "    img, scale = img.rescale_landmarks_to_diagonal_range(diagonal, return_transform=True)\n",
    "    return {\n",
    "        'image': feature_f(img),\n",
    "        't': t.translation_component,\n",
    "        'scale': scale.scale[0]\n",
    "    }\n",
    "\n",
    "\n",
    "def load_id_exp_shape_model(path):\n",
    "    sm_dict = mio.import_pickle(path)\n",
    "    shape_model = sm_dict['shape_model']\n",
    "    lms = sm_dict['lms']\n",
    "    id_ind = sm_dict['id_ind']\n",
    "    exp_ind = sm_dict['exp_ind']\n",
    "    return shape_model, lms, id_ind, exp_ind\n",
    "\n",
    "\n",
    "def load_itw_texture_model(path):\n",
    "    tm_dict = mio.import_pickle(path)\n",
    "    texture_model  = tm_dict['texture_model']\n",
    "    diagonal_range = tm_dict['diagonal_range']\n",
    "    feature_function = tm_dict['feature_function']\n",
    "    return texture_model, diagonal_range, feature_function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare data and model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# LOAD SHAPE MODEL\n",
    "# note that id_ind and exp_ind are two index mappings into the components of\n",
    "# this special combined shape model. The first records the index position of\n",
    "# components that are related to identitiy, the second an index of the (remaining)\n",
    "# components which are related to shape.\n",
    "shape_model, lms, id_ind, exp_ind = load_id_exp_shape_model(DATA_PATH / 'id_exp_shape_model.pkl')\n",
    "\n",
    "# record the number of ID / EXP params\n",
    "n_p, n_q = id_ind.shape[0], exp_ind.shape[0]\n",
    "\n",
    "# LOAD ITW TEXTURE MODEL\n",
    "# Note we have to know the diagonal setting and feature used in the texture model.\n",
    "texture_model, diagonal_range, feature_function = load_itw_texture_model(DATA_PATH / 'itw_texture_model.pkl')\n",
    "\n",
    "# construct our Morphable Model that we can use in the fitting approaches below\n",
    "mm = ColouredMorphableModel(shape_model, texture_model, lms, \n",
    "                            holistic_features=feature_function,\n",
    "                            diagonal=diagonal_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# load some images and prepare them for fitting.\n",
    "# Note that we have to rescale the images/extract the feature we used for the model\n",
    "# ourselves. Unlike previous menpo fitting routines, fit_image is a simpler implementation.\n",
    "# it requires us to explicitly do more before we call fit_image, but it is much simpler to\n",
    "# follow what is being done in the code.\n",
    "frame = mio.import_image('itw_image.jpg')\n",
    "\n",
    "transform_info = prepare_image_and_return_transforms(diagonal_range, feature_function, frame)\n",
    "image = transform_info.pop('image')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialize parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "# initialize the shape weights to zero (mean)\n",
    "p = np.zeros(n_p)\n",
    "q = np.zeros(n_q)\n",
    "\n",
    "# initialize the camera with a large focal length (orthogathic)\n",
    "camera = initialize_camera_from_params(image, mm, id_ind, exp_ind, p, q, focal_length=99999999)\n",
    "c = camera.as_vector()\n",
    "\n",
    "# Check the initialization looks sensible\n",
    "# Note that most methods in ITWMM expect video inputs. We just need to wrap\n",
    "# Single-frame parameters in a single list to re-use them for images.\n",
    "render_initialization([image], mm, id_ind, exp_ind, camera, p, [q], [c], 0).view()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ITW Image fitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Actually run the optimisation.\n",
    "# Return is a list of parameters recovered per-iteration per-frame.\n",
    "params = fit_image(image, mm, id_ind, exp_ind, camera, \n",
    "                   p, q, c,\n",
    "                   lm_group='PTS', n_iters=10, \n",
    "                   c_f=3.,\n",
    "                   c_id=1.,\n",
    "                   c_exp=3.,\n",
    "                   c_l=1.,\n",
    "                   n_samples=1000, compute_costs=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inspect results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# now we render the fitting.\n",
    "iter_no = -1\n",
    "frame.view()\n",
    "render_iteration(mm, id_ind, exp_ind, image.shape, \n",
    "                 camera, params, 0, iter_no).view(new_figure=True)"
   ]
  }
 ],
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================================================
FILE: notebooks/4. Fitting "in-the-wild" videos.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Fitting \"in-the-wild\" videos\n",
    "\n",
    "#### Prerequisites\n",
    "\n",
    "- Access to a combined 3D facial shape model of identity and expression\n",
    "- Access to an \"in-the-wild\" texture model\n",
    "- Access to an \"in-the-wild\" video with landmarks to fit. The per-frame landmarks should be given as input and should follow the iBUG 68 mark-up. It is highly recommended that these landmarks are \"3D-Aware-2D\" (3DA-2D) landmarks, as e.g. described in the paper (Zafeiriou et al., \"The 3D menpo facial landmark tracking challenge\", ICCV-W 2017).\n",
    "\n",
    "The first two of these can be generated by following the two previous notebooks in this folder:\n",
    "\n",
    "- `1. Building an \"in-the-wild\" texture model.ipynb`\n",
    "- `2. Creating an expressive 3DMM.ipynb`\n",
    "\n",
    "Running these notebooks to completion will lead to the following two files being generated in the `DATA_DIR` folder:\n",
    "\n",
    "- `itw_texture_model.pkl`\n",
    "- `id_exp_shape_model.pkl`\n",
    "\n",
    "This script shows how to load these directly and use them in fitting.\n",
    "\n",
    "You could of course understand the required formats by studying the aformentinoed scripts, and instead load your own shape and texture models instead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "import numpy as np\n",
    "\n",
    "import menpo.io as mio\n",
    "from menpo.base import LazyList\n",
    "from menpo.shape import PointCloud\n",
    "from menpo.visualize import print_progress\n",
    "from menpo3d.morphablemodel import ColouredMorphableModel\n",
    "\n",
    "from itwmm import (\n",
    "    initialize_camera_from_params, initialize_camera,\n",
    "    fit_video, instance_for_params,\n",
    "    render_initialization, render_iteration,\n",
    ")\n",
    "\n",
    "# Replace DATA_PATH with the path to your data. It should have files:\n",
    "#  itw_texture_model.pkl\n",
    "#  id_exp_shape_model.pkl\n",
    "# As generated from Notebooks 1. and 2.)\n",
    "DATA_PATH = Path('~/Dropbox/itwmm_src_data/').expanduser()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def prepare_image_and_return_transforms(diagonal, feature_f, image):\n",
    "    # this variation is needed if we need to know how the imput image \n",
    "    # is transformed\n",
    "    img, t = image.crop_to_landmarks_proportion(0.4, return_transform=True)\n",
    "    img, scale = img.rescale_landmarks_to_diagonal_range(diagonal, return_transform=True)\n",
    "    return {\n",
    "        'image': feature_f(img),\n",
    "        't': t.translation_component,\n",
    "        'scale': scale.scale[0]\n",
    "    }\n",
    "\n",
    "\n",
    "def load_id_exp_shape_model(path):\n",
    "    sm_dict = mio.import_pickle(path)\n",
    "    shape_model = sm_dict['shape_model']\n",
    "    lms = sm_dict['lms']\n",
    "    id_ind = sm_dict['id_ind']\n",
    "    exp_ind = sm_dict['exp_ind']\n",
    "    return shape_model, lms, id_ind, exp_ind\n",
    "\n",
    "\n",
    "def load_itw_texture_model(path):\n",
    "    tm_dict = mio.import_pickle(path)\n",
    "    texture_model  = tm_dict['texture_model']\n",
    "    diagonal_range = tm_dict['diagonal_range']\n",
    "    feature_function = tm_dict['feature_function']\n",
    "    return texture_model, diagonal_range, feature_function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare data and model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# LOAD SHAPE MODEL\n",
    "# note that id_ind and exp_ind are two index mappings into the components of\n",
    "# this special combined shape model. The first records the index position of\n",
    "# components that are related to identitiy, the second an index of the (remaining)\n",
    "# components which are related to shape.\n",
    "shape_model, lms, id_ind, exp_ind = load_id_exp_shape_model(DATA_PATH / 'id_exp_shape_model.pkl')\n",
    "\n",
    "# record the number of ID / EXP params\n",
    "n_p, n_q = id_ind.shape[0], exp_ind.shape[0]\n",
    "\n",
    "# LOAD ITW TEXTURE MODEL\n",
    "# Note we have to know the diagonal setting and feature used in the texture model.\n",
    "texture_model, diagonal_range, feature_function = load_itw_texture_model(DATA_PATH / 'itw_texture_model.pkl')\n",
    "\n",
    "# construct our Morphable Model that we can use in the fitting approaches below\n",
    "mm = ColouredMorphableModel(shape_model, texture_model, lms, \n",
    "                            holistic_features=feature_function,\n",
    "                            diagonal=diagonal_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load some images and prepare them for fitting.\n",
    "# Note that we have to rescale the images/extract the feature we used for the model\n",
    "# ourselves. Unlike previous menpo fitting routines, fit_video is a simpler implementation.\n",
    "# it requires us to explicitly do more before we call fit_video, but it is much simpler to\n",
    "# follow what is being done in the code.\n",
    "lim_frames = 100\n",
    "\n",
    "frame_ids = LazyList.init_from_iterable(\n",
    "    [p.stem for p in mio.image_paths('video_dir/')][:lim_frames])\n",
    "frames = frame_ids.map(lambda fid: mio.import_image('video_dir/{}.png'.format(fid)))\n",
    "\n",
    "transform_info = [prepare_image_and_return_transforms(diagonal_range, feature_function, i) for i in \n",
    "                  print_progress(frames, prefix='processing images')]\n",
    "images = [x.pop('image') for x in transform_info]\n",
    "\n",
    "n_images = len(images)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialize parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "# initialize the shape weights to zero (mean)\n",
    "p = np.zeros(n_p)\n",
    "qs = np.zeros([n_images, n_q])\n",
    "\n",
    "# initialize all cameras with a large focal length (orthogathic)\n",
    "cameras = [initialize_camera_from_params(img, mm, id_ind, exp_ind, p, q, focal_length=99999999) \n",
    "           for img, q in zip(images, qs)]\n",
    "cs = np.vstack([camera.as_vector() for camera in cameras])\n",
    "template_camera = cameras[0]\n",
    "\n",
    "# Check the initialization looks sensible (for first and last frame)\n",
    "render_initialization(images, mm, id_ind, exp_ind, template_camera, p, qs, cs, 0).view()\n",
    "render_initialization(images, mm, id_ind, exp_ind, template_camera, p, qs, cs, -1).view(new_figure=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ITW Video fitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Actually run the optimisation.\n",
    "# Return is a list of parameters recovered per-iteration per-frame.\n",
    "#\n",
    "# E.g. to access the 3'rd frame's parameters at the 6th iteration:\n",
    "#   params[5][2]  # (lists are 0-based in Python)\n",
    "#\n",
    "params = fit_video(images, mm, id_ind, exp_ind, template_camera, \n",
    "                   p, qs, cs,\n",
    "                   lm_group='PTS', n_iters=10, \n",
    "                   c_f=3.,\n",
    "                   c_id=1. * n_images,\n",
    "                   c_exp=3.,\n",
    "                   c_l=1.,\n",
    "                   c_sm=6.,\n",
    "                   n_samples=1000, compute_costs=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inspect results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# now we render the fitting.\n",
    "frame_no = 5\n",
    "iter_no = -1\n",
    "frames[frame_no].view()\n",
    "render_iteration(mm, id_ind, exp_ind, images[0].shape, \n",
    "                 template_camera, params, frame_no, iter_no).view(new_figure=True)"
   ]
  }
 ],
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================================================
FILE: setup.py
================================================
from setuptools import setup, find_packages

install_requires = ['menpo3d>=0.6.0']

setup(name='itwmm',
      version='1.0.0',
      description='ITW Morphable Model fitting',
      author='Menpo Authors',
      author_email='hello@menpo.org',
      packages=find_packages(),
      install_requires=install_requires
      )