Example of loading 3D data from .mat file into Menpo and converting to our types for landmarking

shiyang_load_mat.ipynb

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   "source": [
    "# All the imports we will need\n",
    "import scipy.io as sio\n",
    "import menpo3d.io as m3io\n",
    "from menpo.landmark import LandmarkGroup\n",
    "from menpo.shape import TriMesh, PointCloud"
   ]
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     "text": [
      "['tri', 'vertex', '__header__', '__globals__', 'landmark', '__version__']\n"
     ]
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   "source": [
    "# 1. Load the mat file and extract what we need\n",
    "d = sio.loadmat('./simple_F015_Happy_019_fit.mat')\n",
    "print(d.keys())\n",
    "\n",
    "# extract the data we need\n",
    "trilist = d['tri']\n",
    "points = d['vertex']\n",
    "lm_points = d['landmark']"
   ]
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     "text": [
      "TriMesh: n_points: 9909, n_dims: 3, n_tris: 19520\n",
      "LandmarkGroup: n_labels: 1, n_points: 33\n"
     ]
    }
   ],
   "source": [
    "# 2. Build Menpo types for the Mesh and the landmarks\n",
    "mesh = TriMesh(points, trilist=trilist)\n",
    "landmarks = LandmarkGroup.init_with_all_label(PointCloud(lm_points))\n",
    "\n",
    "print(mesh)\n",
    "print(landmarks)"
   ]
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   "source": [
    "# 3. Export the data as .obj and .ljson\n",
    "m3io.export_mesh(mesh, './simple_F015_Happy_019_fit.obj')\n",
    "m3io.export_landmark_file(landmarks, './simple_F015_Happy_019_fit.ljson')"
   ]
  }
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