{ "metadata": { "name": "", "signature": "sha256:119981e8ee9d19d7b7fc5b70ae497761772a22e4c5d5749c963bd20ee2e3bdb3" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "np.set_printoptions(linewidth=500, precision=1)\n", "\n", "from menpo.rasterize import GLRasterizer\n", "import menpo.io as mio" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "**RENDERING A FACE**" ] }, { "cell_type": "code", "collapsed": false, "input": [ "mesh = mio.import_builtin_asset('james.obj')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib qt\n", "viewer = mesh.view()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "viewer_settings = viewer.renderer_settings" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "np.set_printoptions(linewidth=500, precision=1)\n", "for k, v in viewer_settings.iteritems():\n", " print(\"{}: \".format(k))\n", " print(v)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "projection_matrix: \n", "[[ 4.2e+00 0.0e+00 0.0e+00 0.0e+00]\n", " [ 0.0e+00 3.7e+00 0.0e+00 0.0e+00]\n", " [ 0.0e+00 0.0e+00 -2.2e+00 -1.9e+03]\n", " [ 0.0e+00 0.0e+00 -1.0e+00 0.0e+00]]\n", "width: \n", "1414\n", "model_matrix: \n", "[[ 1. 0. 0. 0.]\n", " [ 0. 1. 0. 0.]\n", " [ 0. 0. 1. 0.]\n", " [ 0. 0. 0. 1.]]\n", "view_matrix: \n", "[[ 7.7e-01 2.2e-02 -6.4e-01 -1.0e+01]\n", " [ 4.9e-02 9.9e-01 9.4e-02 3.2e+01]\n", " [ 6.4e-01 -1.0e-01 7.7e-01 -9.5e+02]\n", " [ 0.0e+00 0.0e+00 0.0e+00 1.0e+00]]\n", "height: \n", "1600\n" ] } ], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "# build a rasterizer configured from the current view\n", "r = GLRasterizer(**viewer_settings)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "# rasterize to produce an RGB image\n", "rgb_img = r.rasterize_mesh(mesh)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline\n", "rgb_img.view()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 8, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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mpDSYRIRI6DzWmsH/zaehI2pUJ77s4LuTlsl6KGUV0mDyKEMBnwCk/g5fuTvP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\n0ud0k86k8esF+Nw8CguqCBYUkc6lSCSTJBNxdNPELPRSWz+Zs676KDM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"text": [ "" ] } ], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "All rasterized images have their mask set to show what the rasterizer actually processed" ] }, { "cell_type": "code", "collapsed": false, "input": [ "rgb_img.mask.view()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 9, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Cyrasterize gives us the ability to rasterize other information though. For instance, we can render out a XYZ floating point shape image" ] }, { "cell_type": "code", "collapsed": false, "input": [ "rgb_img, shape_img = r.rasterize_mesh_with_shape_image(mesh)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "# 3rd channel is z information in model space coordinates\n", "# note that this is NOT camera depth\n", "shape_img.view(channels=2)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 11, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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