dense_correspondence/evaluation/evaluation_quantitative_tutorial.ipynb

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 "cells": [
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   "cell_type": "markdown",
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   "source": [
    "# Example quantitative plots\n",
    "\n",
    "How to plot the results of the quantitative evaluation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "import fnmatch\n",
    "import pandas as pd\n",
    "import sklearn.metrics as sm\n",
    "import scipy.stats as ss\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import dense_correspondence_manipulation.utils.utils as utils\n",
    "utils.add_dense_correspondence_to_python_path()\n",
    "\n",
    "from dense_correspondence.evaluation.evaluation import DenseCorrespondenceEvaluationPlotter as DCEP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you have multiple networks trained, you can add them to the `nets_list` below, and they will be plotted together."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "folder_name = \"tutorials\"\n",
    "path_to_nets = os.path.join(\"code/data_volume/pdc/trained_models\", folder_name)\n",
    "path_to_nets = utils.convert_to_absolute_path(path_to_nets)\n",
    "all_nets = sorted(os.listdir(path_to_nets))\n",
    "nets_to_plot = []\n",
    "\n",
    "nets_list = [\"caterpillar_3\"]\n",
    "for net in nets_list:\n",
    "    nets_to_plot.append(os.path.join(folder_name,net))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training \n",
    "Evaluate the network on the training scenes. Correspondences are all within scene"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = DCEP()\n",
    "dc_source_dir = utils.getDenseCorrespondenceSourceDir()\n",
    "\n",
    "network_name = nets_to_plot[0]\n",
    "path_to_csv = os.path.join(dc_source_dir, \"data_volume\", \"pdc\", \"trained_models\", network_name, \"analysis/train/data.csv\")\n",
    "fig_axes = DCEP.run_on_single_dataframe(path_to_csv, label=network_name, save=False)\n",
    "\n",
    "for network_name in nets_to_plot[1:]:\n",
    "    path_to_csv = os.path.join(dc_source_dir, \"data_volume\", \"pdc\", \"trained_models\", network_name, \"analysis/train/data.csv\")\n",
    "    fig_axes = DCEP.run_on_single_dataframe(path_to_csv, label=network_name, previous_fig_axes=fig_axes, save=False)\n",
    "\n",
    "_, axes = fig_axes\n",
    "# axes[0].set_title(\"Training Set\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test\n",
    "Evaluate the network on the test scenes. Correspondences are all within scene"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = DCEP()\n",
    "dc_source_dir = utils.getDenseCorrespondenceSourceDir()\n",
    "\n",
    "network_name = nets_to_plot[0]\n",
    "path_to_csv = os.path.join(dc_source_dir, \"data_volume\", \"pdc\", \"trained_models\", network_name, \"analysis/test/data.csv\")\n",
    "fig_axes = DCEP.run_on_single_dataframe(path_to_csv, label=network_name, save=False)\n",
    "\n",
    "for network_name in nets_to_plot[1:]:\n",
    "    path_to_csv = os.path.join(dc_source_dir, \"data_volume\", \"pdc\", \"trained_models\", network_name, \"analysis/test/data.csv\")\n",
    "    fig_axes = DCEP.run_on_single_dataframe(path_to_csv, label=network_name, previous_fig_axes=fig_axes, save=False)\n",
    "\n",
    "_, axes = fig_axes\n",
    "# axes[0].set_title(\"Test Set\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cross Scene Single Object\n",
    "Evaluate the network on correspondences that come from different scenes. These correspondences were manually annotated only for evaluation purposes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = DCEP()\n",
    "dc_source_dir = utils.getDenseCorrespondenceSourceDir()\n",
    "\n",
    "network_name = nets_to_plot[0]\n",
    "path_to_csv = os.path.join(dc_source_dir, \"data_volume\", \"pdc\", \"trained_models\", network_name, \"analysis/cross_scene/data.csv\")\n",
    "fig_axes = DCEP.run_on_single_dataframe(path_to_csv, label=network_name, save=False)\n",
    "\n",
    "for network_name in nets_to_plot[1:]:\n",
    "    path_to_csv = os.path.join(dc_source_dir, \"data_volume\", \"pdc\", \"trained_models\", network_name, \"analysis/cross_scene/data.csv\")\n",
    "    fig_axes = DCEP.run_on_single_dataframe(path_to_csv, label=network_name, previous_fig_axes=fig_axes, save=False)\n",
    "\n",
    "_, axes = fig_axes\n",
    "# axes[0].set_title(\"Cross Scene Set\")\n",
    "plt.show()"
   ]
  }
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