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September 26, 2016 13:48
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| { | |
| "cells": [ | |
| { | |
| "metadata": { | |
| "collapsed": true, | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "%matplotlib qt\nimport pickle\nimport os.path as op\nfrom mayavi import mlab\nfrom scipy.io import loadmat\nfrom scipy.spatial import KDTree\nfrom pathlib import Path\nfrom tqdm import tqdm_notebook, tnrange\n\nimport menpo.io as mio\nimport menpo3d.io as m3dio\nimport numpy as np\nfrom menpo.shape import PointCloud, TriMesh\nfrom menpo.model import PCAModel\n\nnp.set_printoptions(suppress=True, precision=3, linewidth=1000)", | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "collapsed": false, | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "surrey_model = mio.import_pickle('/vol/construct3dmm/experiments/src/static/other_models/surrey/shape_model.pkl', \n encoding='latin1')", | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "collapsed": false, | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "tddfa_expression = loadmat(op.expanduser('~/Dropbox/phd/3ddfa/Matlab/Model_Expression.mat'))\ntddfa_identity = loadmat(op.expanduser('~/Dropbox/phd/3ddfa/Matlab/ModelGeneration/Model_Shape.mat'))", | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "collapsed": false, | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "tddfa_identity_mean = np.ascontiguousarray(tddfa_identity['mu_shape'].reshape(-1, 3))\ntddfa_identity_basis = np.ascontiguousarray(tddfa_identity['w'].T)\ntddfa_identity_eigenvalues = np.ascontiguousarray(tddfa_identity['sigma'].ravel()) ** 2\ntddfa_landmark_indices = np.ascontiguousarray(tddfa_identity['keypoints'].ravel()) - 1\ntddfa_trilist = np.ascontiguousarray(tddfa_identity['tri'].T) - 1\n\ntddfa_identity_template = TriMesh(tddfa_identity_mean, trilist=tddfa_trilist, copy=False)\ntddfa_identity_template.landmarks['gt'] = PointCloud(tddfa_identity_mean[tddfa_landmark_indices])\n\n\ntddfa_expression_mean = np.ascontiguousarray(tddfa_expression['mu_exp'].reshape(-1, 3))\ntddfa_expression_basis = np.ascontiguousarray(tddfa_expression['w_exp'].T)\ntddfa_expression_eigenvalues = np.ascontiguousarray(tddfa_expression['sigma_exp'].ravel())\ntddfa_expression_template = TriMesh(tddfa_expression_mean, trilist=tddfa_trilist, copy=False)", | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "collapsed": false, | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "map_tddfa_to_basel = loadmat(op.expanduser('~/Dropbox/phd/3ddfa/Matlab/ModelGeneration/model_info.mat'))['trimIndex'].ravel() - 1", | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "collapsed": false, | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "tddfa_identity_model = PCAModel.init_from_components(tddfa_identity_basis, \n tddfa_identity_eigenvalues, \n tddfa_identity_template,\n tddfa_identity_eigenvalues.shape[0] + 1, \n True)\n\ntddfa_expression_model = PCAModel.init_from_components(tddfa_expression_basis, \n tddfa_expression_eigenvalues, \n tddfa_expression_template,\n tddfa_expression_eigenvalues.shape[0] + 1, \n True)", | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "# Load the basel model" | |
| }, | |
| { | |
| "metadata": { | |
| "collapsed": false, | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "basel_model_matlab = loadmat(op.expanduser('~/Dropbox/phd/face_flow/01_MorphableModel.mat'))\nbasel_mean = basel_model_matlab['shapeMU']\nbasel_trilist = basel_model_matlab['tl'] - 1\n\nbasel_template = TriMesh(basel_mean.reshape(-1, 3), trilist=basel_trilist)\n\ndel basel_model_matlab", | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "# Load the MeIn3D Model" | |
| }, | |
| { | |
| "metadata": { | |
| "collapsed": false, | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "with Path('~/Dropbox/phd/mein3d_all_200.pkl').expanduser().open(mode='rb') as f:\n mein3d_model = pickle.load(f, encoding='latin1')['model']", | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "collapsed": false, | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "james_original_template = m3dio.import_mesh('~/Dropbox/phd/face_flow/original_james_template.obj')\nwith Path('~/Dropbox/phd/face_flow/broken_james_template.pkl').expanduser().open(mode='rb') as f:\n screwed_james_original_template = pickle.load(f, encoding='latin1')\n# masked_template_mask = mio.import_pickle('/Users/pts08/Downloads/template_cropped_mask.pkl')", | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "# Fix the MeIn3D Model" | |
| }, | |
| { | |
| "metadata": { | |
| "collapsed": false, | |
| "trusted": false, | |
| "scrolled": true | |
| }, | |
| "cell_type": "code", | |
| "source": "print('Basel:', basel_template.n_points)\nprint('MeIn3D:', mein3d_model.mean().n_points)\nprint('JamesTemplate:', screwed_james_original_template.n_points)\nprint('3DDFA:', tddfa_identity_model.mean().n_points)", | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "collapsed": false, | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "# Find correspondances from MeIn3D to Basel\ntree = KDTree(screwed_james_original_template.points)", | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "collapsed": false, | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "distances, map_basel_to_mein3d = tree.query(basel_template.points)\nbroken_distance_map = distances > 1\nmap_basel_to_mein3d[broken_distance_map] = -1", | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "collapsed": true, | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "map_tddfa_to_mein3d = map_basel_to_mein3d[map_tddfa_to_basel]", | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "collapsed": false, | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "(map_tddfa_to_mein3d == -1).sum()", | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "collapsed": false, | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "fixed_mein3d_model = mein3d_model.copy()\nfixed_mein3d_model.__dict__['_mean'] = fixed_mein3d_model.__dict__['_mean'].reshape(-1, 3)[map_tddfa_to_mein3d].ravel()\nfixed_mein3d_model.__dict__['_components'] = fixed_mein3d_model.__dict__['_components'].reshape(200, -1, 3)[:, map_tddfa_to_mein3d, :].reshape(200, -1)\nfixed_mein3d_model.__dict__['template_instance'] = TriMesh(fixed_mein3d_model.__dict__['_mean'].reshape(-1, 3),\n trilist=tddfa_trilist)", | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "collapsed": false, | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "print('Fixed MeIn3D:', fixed_mein3d_model.n_features // 3)", | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "# Save Models" | |
| }, | |
| { | |
| "metadata": { | |
| "collapsed": false, | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "mio.export_pickle(fixed_mein3d_model, '~/Dropbox/phd/mein3d_fw_correspond_all_200.pkl.gz', overwrite=True)\nmio.export_pickle(tddfa_expression_model, '~/Dropbox/phd/3ddfa_fw_29.pkl.gz', overwrite=True)\nmio.export_pickle(tddfa_identity_model, '~/Dropbox/phd/3ddfa_basel_200.pkl.gz', overwrite=True)", | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "# Save correspondance info" | |
| }, | |
| { | |
| "metadata": { | |
| "collapsed": true, | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "mio.export_pickle({'map_tddfa_to_basel': map_tddfa_to_basel,\n 'map_basel_to_mein3d': map_basel_to_mein3d,\n 'tddfa_trilist': tddfa_trilist},\n '~/Dropbox/phd/mapping_mein3d_to_tddfa.pkl.gz')", | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "# Visualize Video" | |
| }, | |
| { | |
| "metadata": { | |
| "collapsed": false, | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "f = mlab.figure(figure='test', bgcolor=(1.0, 1.0, 1.0), size=(640, 480))", | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "collapsed": false, | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "fixed_mein3d_model.mean().view(figure_id=f.name)\nmesh = f.children[0]\npolydata = mesh.children[0]", | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "collapsed": true, | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "surrey_model.mean().view(figure_id=f.name)\nmesh = f.children[0]\npolydata = mesh.children[0]", | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "collapsed": false, | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "# ID only\nout_path = Path('~/Downloads/models_rendering/identity').expanduser()\nfname_gen = lambda j, k, i: '{:03d}_{:03d}_{:.2f}'.format(j, k, i).replace('-', 'm').replace('.', '_')\nfname_gen = lambda i: '{:05d}'.format(i)\nn_steps = 6\nid_range = (-3, 3)\n\nid_linspace_neg = np.linspace(0, id_range[0], num=n_steps)\nid_linspace_plus = np.linspace(0, id_range[1], num=n_steps)\nid_linspace = np.concatenate(\n [id_linspace_neg, np.flipud(id_linspace_neg),\n id_linspace_plus, np.flipud(id_linspace_plus)], axis=0)\n\nn_identities = 10\nlinear_index = 0\n\nfor j in tnrange(n_identities, desc='n_identities'):\n identity_w = np.zeros(n_identities)\n for i in tqdm_notebook(id_linspace, desc='weights', leave=False, total=id_linspace.shape[0]):\n identity_w[j % n_identities] = i\n id_instance = surrey_model.instance(identity_w, \n normalized_weights=True)\n polydata.mlab_source.points = id_instance.points\n f.scene.save_jpg(str(out_path / (fname_gen(linear_index) + '.jpg')), \n quality=100, progressive=False)\n linear_index += 1", | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "collapsed": false, | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "# Mixed\nout_path = Path('/Users/pts08/Downloads/models_rendering/expression')\nfname_gen = lambda j, k, i, e: '{:03d}_{:03d}_{:.2f}_{:.2f}'.format(j, k, i, e).replace('-', 'm').replace('.', '_')\nfname_gen = lambda i: '{:05d}'.format(i)\nn_steps = 6\nid_range = (-3, 3)\nexp_range = (-0.3, 0.3)\n\nid_linspace_neg = np.linspace(0, id_range[0], num=n_steps)\nid_linspace_plus = np.linspace(0, id_range[1], num=n_steps)\nid_linspace = np.concatenate(\n [id_linspace_neg, np.flipud(id_linspace_neg),\n id_linspace_plus, np.flipud(id_linspace_plus)], axis=0)\n\nexp_linspace_neg = np.linspace(0, exp_range[0], num=n_steps)\nexp_linspace_plus = np.linspace(0, exp_range[1], num=n_steps)\nexp_linspace = np.concatenate(\n [exp_linspace_neg, np.flipud(exp_linspace_neg),\n exp_linspace_plus, np.flipud(exp_linspace_plus)], axis=0)\n\nn_identities = 29\nn_exp = 29\nlinear_index = 0\nfor j in tnrange(n_exp, desc='n_expressions'):\n identity_w = np.zeros(n_identities)\n exp_w = np.zeros(n_exp)\n for i, e in tqdm_notebook(zip(id_linspace, exp_linspace), desc='weights', leave=False, total=id_linspace.shape[0]):\n identity_w[j % n_identities] = i\n exp_w[j] = e\n id_instance = tddfa_identity_model.instance([0], \n normalized_weights=True)\n exp_instance = tddfa_expression_model.instance(exp_w,\n normalized_weights=True)\n polydata.mlab_source.points = id_instance.points + exp_instance.points\n f.scene.save_jpg(str(out_path / (fname_gen(linear_index) + '.jpg')), \n quality=100, progressive=False)\n linear_index += 1", | |
| "execution_count": null, | |
| "outputs": [] | |
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| "version": "3.5.1", | |
| "codemirror_mode": { | |
| "name": "ipython", | |
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| "nbconvert_exporter": "python", | |
| "pygments_lexer": "ipython3", | |
| "mimetype": "text/x-python" | |
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