{
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   "source": [
    "# McCormik Validation\n",
    "\n",
    "## Import Statement :\n",
    "\n",
    "First make sure you have installed pandas and ipyvolume. When using virtualenv and working in an activated virtual environment, the --sys-prefix option may be required to enable the extension and keep the environment isolated (i.e. jupyter nbextension enable --py widgetsnbextension --sys-prefix)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy\n",
    "import pandas\n",
    "\n",
    "\n",
    "import openalea.phenomenal.object as phm_obj\n",
    "import openalea.phenomenal.mesh as phm_mesh\n",
    "import openalea.phenomenal.segmentation as phm_seg\n",
    "import openalea.phenomenal.display.notebook as phm_display_notebook\n",
    "import openalea.phenomenal.data as phm_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Import McCormik data\n",
    "\n",
    "### 1.1 Select plant_number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "vertices, faces, colors = phm_data.mesh_mccormik_plant(\n",
    "    \"data/mccormick_plant_2\"\n",
    ")  # only 1 or 2 available\n",
    "colors = colors[:, :3].astype(float) / 255.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 Viewing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "phm_display_notebook.show_mesh(vertices, faces, color=colors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Phenomenal Measurements\n",
    "\n",
    "### 2.1 Voxelization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "voxels_size = 4.0\n",
    "voxels_position = phm_mesh.from_vertices_faces_to_voxels_position(\n",
    "    vertices, faces, voxels_size=voxels_size\n",
    ")\n",
    "voxels_position = numpy.array(voxels_position)\n",
    "voxel_grid = phm_obj.VoxelGrid(voxels_position, voxels_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 Viewing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "phm_display_notebook.show_voxel_grid(voxel_grid, size=0.4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Skeletonization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "graph = phm_seg.graph_from_voxel_grid(voxel_grid)\n",
    "voxel_skeleton = phm_seg.skeletonize(voxel_grid, graph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Display it\n",
    "phm_display_notebook.show_skeleton(voxel_skeleton, with_voxel=True, size=0.4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4 Cereals Segmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "vms = phm_seg.maize_segmentation(voxel_skeleton, graph)\n",
    "vmsi = phm_seg.maize_analysis(vms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Phenomenal measurements of each organs\n",
    "pm_rows = [vo.info for vo in vmsi.voxel_organs] + [vmsi.info]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Display it\n",
    "phm_display_notebook.show_segmentation(vmsi, size=0.4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. McCormik measurement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "stem_color = (0, 1, 1)\n",
    "index = numpy.apply_along_axis(numpy.array_equal, 1, colors, stem_color)\n",
    "v = vertices[index]\n",
    "\n",
    "mccm_rows = list()\n",
    "row = dict()\n",
    "row[\"mccm_label\"] = \"plant\"\n",
    "row[\"mccm_number_of_leaf\"] = len(set(map(tuple, colors))) - 1\n",
    "mccm_rows.append(row)\n",
    "\n",
    "row = dict()\n",
    "row[\"mccm_label\"] = \"stem\"\n",
    "row[\"mccm_length\"] = numpy.max(v[:, 2]) - numpy.min(v[:, 0])\n",
    "mccm_rows.append(row)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Measures registration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def registration_row(pm_rows, sm_rows):\n",
    "    def same_label(pm_row, sm_row):\n",
    "        for label in [\"stem\", \"plant\"]:\n",
    "            if pm_row[\"pm_label\"] == label and sm_row[\"mccm_label\"] == label:\n",
    "                return True\n",
    "        return False\n",
    "\n",
    "    registered_row = list()\n",
    "    for pm_row in pm_rows:\n",
    "        for sm_row in sm_rows:\n",
    "            if same_label(pm_row, sm_row):\n",
    "                registered_row.append((pm_row, sm_row, 0))\n",
    "                continue\n",
    "            else:\n",
    "                continue\n",
    "\n",
    "    return registered_row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = [\"pm_label\", \"pm_length\", \"pm_number_of_leaf\"]\n",
    "\n",
    "rows = list()\n",
    "for pm_row, mccm_row, d in registration_row(pm_rows, mccm_rows):\n",
    "    for label in labels:\n",
    "        if label in pm_row:\n",
    "            mccm_row[label] = pm_row[label]\n",
    "    rows.append(mccm_row)\n",
    "\n",
    "df = pandas.DataFrame(rows)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Measures Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_filtred_label = df[df[\"pm_label\"] == \"stem\"]\n",
    "plt.plot(df_filtred_label[\"mccm_length\"], df_filtred_label[\"pm_length\"], \"g+\")\n",
    "df_filtred_label = df[df[\"pm_label\"] == \"plant\"]\n",
    "plt.plot(\n",
    "    df_filtred_label[\"mccm_number_of_leaf\"], df_filtred_label[\"pm_number_of_leaf\"], \"k+\"\n",
    ")\n",
    "\n",
    "plt.xlabel(\"Phenomenal measurements\")\n",
    "plt.ylabel(\"McCormik measurements\")\n",
    "\n",
    "m = int(max([df[k].max() for k in [\"pm_length\", \"mccm_length\"]]))\n",
    "\n",
    "plt.plot(range(m), range(m), \"k--\")"
   ]
  }
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