eval_icl.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# Copyright (c) 2022, Gonzalo Ferrer, Dmitrii Iarosh, Anastasiia Kornilova\n",
    "#\n",
    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "# http://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jupyter environment detected. Enabling Open3D WebVisualizer.\n",
      "[Open3D INFO] WebRTC GUI backend enabled.\n",
      "[Open3D INFO] WebRTCWindowSystem: HTTP handshake server disabled.\n"
     ]
    }
   ],
   "source": [
    "import copy\n",
    "import pickle\n",
    "import mrob\n",
    "import numpy as np\n",
    "import open3d as o3d\n",
    "import os\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from pathlib import Path\n",
    "from tqdm import tqdm"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "from dataset_loaders.CameraParameters import CameraParameters\n",
    "from dataset_loaders.IclTumCompatLoader import IclTumCompatLoader\n",
    "\n",
    "from plane_extractor.plane_extractor import extract_planes_from_pcd_colors\n",
    "\n",
    "from plane_backends.solver_factory import create_solver_by_name\n",
    "\n",
    "from metrics.rpe import rpe\n",
    "from metrics.ape import ape\n",
    "\n",
    "from perturbation.perturbation import Perturbation, generate_random_pose_shift"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "def get_map(pcds, Ts):\n",
    "    map_pcd = o3d.geometry.PointCloud()\n",
    "    i = 0\n",
    "    for pcd, T in zip(pcds, Ts):\n",
    "        map_pcd += copy.deepcopy(pcd).transform(T)\n",
    "        map_pcd = map_pcd.voxel_down_sample(0.03)\n",
    "\n",
    "    return map_pcd"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Load ICL data with planes"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "depth_data_path = 'depth'\n",
    "label_data_path = 'labels'"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [],
   "source": [
    "depth_paths = [f for f in Path(depth_data_path).iterdir() if f.is_file()]\n",
    "label_paths = [f for f in Path(label_data_path).iterdir() if f.is_file()]\n",
    "\n",
    "# Order depth and labels by timestamps\n",
    "frame_timestamps = [int(file.split('.')[0]) for file in os.listdir(depth_data_path)]\n",
    "depth_paths = [depth_paths[i] for i in np.argsort(frame_timestamps)]\n",
    "label_paths = [label_paths[i] for i in np.argsort(frame_timestamps)]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [],
   "source": [
    "# Taken from https://www.doc.ic.ac.uk/~ahanda/VaFRIC/codes.html\n",
    "camera_parameters = CameraParameters(\n",
    "    width=640,\n",
    "    height=480,\n",
    "    cx=319.50,\n",
    "    cy=239.50,\n",
    "    fx=481.20,\n",
    "    fy=-480.00,\n",
    "    scale=5000,\n",
    ")\n",
    "\n",
    "loader = IclTumCompatLoader(camera_parameters)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 99/99 [00:38<00:00,  2.58it/s]\n"
     ]
    }
   ],
   "source": [
    "pcds = []\n",
    "observations = []\n",
    "\n",
    "OBSERVATIONS_COUNT = 100\n",
    "\n",
    "for i in tqdm(range(1, OBSERVATIONS_COUNT)):\n",
    "    pcd = loader.load_point_cloud(depth_paths[i], label_paths[i])\n",
    "    pcds.append(pcd)\n",
    "    # Plane labels are stored as colors of pcd (single color --- single plane)\n",
    "    color_to_points = extract_planes_from_pcd_colors(pcd)\n",
    "    observations.append(color_to_points)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Load gt poses"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [],
   "source": [
    "def read_poses_icl_tum_compat(traj_path):\n",
    "    poses_quaternions = []\n",
    "    with open(traj_path, 'r') as file:\n",
    "        for line in file:\n",
    "            poses_quaternions.append([float(i) for i in line.strip().split(' ')])\n",
    "\n",
    "    transform_matrices = []\n",
    "    for i, pose in enumerate(poses_quaternions):\n",
    "        translation = pose[1:4]\n",
    "        rotation = mrob.geometry.quat_to_so3(pose[4:8])\n",
    "        transform_matrix = np.eye(4)\n",
    "        transform_matrix[:3, :3] = rotation\n",
    "        transform_matrix[:3, 3] = translation\n",
    "        transform_matrices.append(transform_matrix)\n",
    "\n",
    "    return transform_matrices"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded 1508 poses\n"
     ]
    }
   ],
   "source": [
    "gt_poses = read_poses_icl_tum_compat('livingRoom0.gt.freiburg')\n",
    "# Remap poses as the first pose is the origin point\n",
    "gt_poses = [np.linalg.inv(gt_poses[0]) @ pose for pose in gt_poses]\n",
    "\n",
    "print(f\"Loaded {len(gt_poses)} poses\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Evaluate APE and RPE"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [],
   "source": [
    "def save_data_for_balm(pcds, gt_poses, perturbed_poses, folder_name):\n",
    "    os.mkdir(folder_name)\n",
    "    for i, pcd in enumerate(pcds):\n",
    "        o3d.io.write_point_cloud(os.path.join(folder_name, f'{i}.pcd'), pcd)\n",
    "\n",
    "    with open(os.path.join(folder_name, 'gt_poses.pkl'), 'wb') as f:\n",
    "        pickle.dump(gt_poses, f)\n",
    "\n",
    "    with open(os.path.join(folder_name, 'pert_poses.pkl'), 'wb') as f:\n",
    "        pickle.dump(perturbed_poses, f)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 50/50 [01:20<00:00,  1.61s/it]\n",
      "100%|██████████| 50/50 [01:23<00:00,  1.67s/it]\n",
      "100%|██████████| 50/50 [01:28<00:00,  1.77s/it]\n",
      "100%|██████████| 50/50 [01:34<00:00,  1.89s/it]\n",
      "100%|██████████| 50/50 [01:37<00:00,  1.96s/it]\n"
     ]
    }
   ],
   "source": [
    "solvers = [\n",
    "    'bareg',\n",
    "    'pi-factor',\n",
    "    'ef-centered',\n",
    "]\n",
    "\n",
    "df_stat = pd.DataFrame()\n",
    "perturbations = [\n",
    "    Perturbation(rotation_shift=1, translation_shift=0.01),\n",
    "    Perturbation(rotation_shift=5, translation_shift=0.05),\n",
    "    Perturbation(rotation_shift=10, translation_shift=0.1),\n",
    "    Perturbation(rotation_shift=15, translation_shift=0.15),\n",
    "    Perturbation(rotation_shift=20, translation_shift=0.2),\n",
    "]\n",
    "\n",
    "ind = 0\n",
    "SEQUENCE_SIZE = 10\n",
    "SEQUENCES_COUNT = 50\n",
    "for perturbation in perturbations:\n",
    "    for sequence_index in tqdm(range(SEQUENCES_COUNT)):\n",
    "        start = np.random.randint(0, OBSERVATIONS_COUNT - SEQUENCE_SIZE)\n",
    "        sequence_gt_poses = gt_poses[start:start + SEQUENCE_SIZE]\n",
    "        sequence_observations = observations[start:start + SEQUENCE_SIZE]\n",
    "\n",
    "        perturbed_poses = []\n",
    "        for i, _ in enumerate(sequence_gt_poses):\n",
    "            perturbed_poses.append(\n",
    "                sequence_gt_poses[i] @ generate_random_pose_shift(\n",
    "                    perturbation.rotation_shift / 180 * np.pi,\n",
    "                    perturbation.translation_shift\n",
    "                )\n",
    "            )\n",
    "        name = '_'.join(\n",
    "            [\n",
    "                str(SEQUENCE_SIZE),\n",
    "                str(sequence_index),\n",
    "                str(perturbation.rotation_shift),\n",
    "                str(perturbation.translation_shift)\n",
    "            ]\n",
    "        )\n",
    "\n",
    "        save_data_for_balm(pcds[start:start + SEQUENCE_SIZE], sequence_gt_poses, perturbed_poses, name)\n",
    "\n",
    "        for solver_name in solvers:\n",
    "            solver = create_solver_by_name(solver_name, iterations_count=300)\n",
    "            refined_poses, _, _ = solver.solve(sequence_observations, perturbed_poses)\n",
    "            ape_translation, ape_rotation = ape(sequence_gt_poses, refined_poses[:SEQUENCE_SIZE])\n",
    "            rpe_translation, rpe_rotation = rpe(sequence_gt_poses, refined_poses[:SEQUENCE_SIZE])\n",
    "            stat = {\n",
    "                'solver': solver_name,\n",
    "                'sample': sequence_index,\n",
    "                'pose_perturbation': '{0}_{1}'.format(perturbation.rotation_shift, perturbation.translation_shift),\n",
    "                'ape_rotation': ape_rotation,\n",
    "                'rpe_rotation': rpe_rotation,\n",
    "                'ape_translation': ape_translation,\n",
    "                'rpe_translation': rpe_translation\n",
    "            }\n",
    "\n",
    "            df_stat = pd.concat([df_stat, pd.DataFrame(stat, index=[ind])])\n",
    "            ind += 1"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dimaj\\AppData\\Local\\Temp\\ipykernel_4300\\743787268.py:16: FutureWarning: The default value of numeric_only in DataFrameGroupBy.quantile is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  d = df[df['solver'] == solver].groupby(['pose_perturbation'], as_index=False).quantile(0.5)\n",
      "C:\\Users\\dimaj\\AppData\\Local\\Temp\\ipykernel_4300\\743787268.py:16: FutureWarning: The default value of numeric_only in DataFrameGroupBy.quantile is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  d = df[df['solver'] == solver].groupby(['pose_perturbation'], as_index=False).quantile(0.5)\n",
      "C:\\Users\\dimaj\\AppData\\Local\\Temp\\ipykernel_4300\\743787268.py:16: FutureWarning: The default value of numeric_only in DataFrameGroupBy.quantile is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  d = df[df['solver'] == solver].groupby(['pose_perturbation'], as_index=False).quantile(0.5)\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 600x400 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dimaj\\AppData\\Local\\Temp\\ipykernel_4300\\743787268.py:16: FutureWarning: The default value of numeric_only in DataFrameGroupBy.quantile is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  d = df[df['solver'] == solver].groupby(['pose_perturbation'], as_index=False).quantile(0.5)\n",
      "C:\\Users\\dimaj\\AppData\\Local\\Temp\\ipykernel_4300\\743787268.py:16: FutureWarning: The default value of numeric_only in DataFrameGroupBy.quantile is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  d = df[df['solver'] == solver].groupby(['pose_perturbation'], as_index=False).quantile(0.5)\n",
      "C:\\Users\\dimaj\\AppData\\Local\\Temp\\ipykernel_4300\\743787268.py:16: FutureWarning: The default value of numeric_only in DataFrameGroupBy.quantile is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  d = df[df['solver'] == solver].groupby(['pose_perturbation'], as_index=False).quantile(0.5)\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 600x400 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dimaj\\AppData\\Local\\Temp\\ipykernel_4300\\743787268.py:16: FutureWarning: The default value of numeric_only in DataFrameGroupBy.quantile is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  d = df[df['solver'] == solver].groupby(['pose_perturbation'], as_index=False).quantile(0.5)\n",
      "C:\\Users\\dimaj\\AppData\\Local\\Temp\\ipykernel_4300\\743787268.py:16: FutureWarning: The default value of numeric_only in DataFrameGroupBy.quantile is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  d = df[df['solver'] == solver].groupby(['pose_perturbation'], as_index=False).quantile(0.5)\n",
      "C:\\Users\\dimaj\\AppData\\Local\\Temp\\ipykernel_4300\\743787268.py:16: FutureWarning: The default value of numeric_only in DataFrameGroupBy.quantile is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  d = df[df['solver'] == solver].groupby(['pose_perturbation'], as_index=False).quantile(0.5)\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 600x400 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dimaj\\AppData\\Local\\Temp\\ipykernel_4300\\743787268.py:16: FutureWarning: The default value of numeric_only in DataFrameGroupBy.quantile is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  d = df[df['solver'] == solver].groupby(['pose_perturbation'], as_index=False).quantile(0.5)\n",
      "C:\\Users\\dimaj\\AppData\\Local\\Temp\\ipykernel_4300\\743787268.py:16: FutureWarning: The default value of numeric_only in DataFrameGroupBy.quantile is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  d = df[df['solver'] == solver].groupby(['pose_perturbation'], as_index=False).quantile(0.5)\n",
      "C:\\Users\\dimaj\\AppData\\Local\\Temp\\ipykernel_4300\\743787268.py:16: FutureWarning: The default value of numeric_only in DataFrameGroupBy.quantile is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  d = df[df['solver'] == solver].groupby(['pose_perturbation'], as_index=False).quantile(0.5)\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 600x400 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dimaj\\AppData\\Local\\Temp\\ipykernel_4300\\190755569.py:16: FutureWarning: The default value of numeric_only in DataFrameGroupBy.quantile is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  d = df[df['solver'] == solver].groupby(['pose_perturbation'], as_index=False).quantile(0.5)\n",
      "C:\\Users\\dimaj\\AppData\\Local\\Temp\\ipykernel_4300\\190755569.py:16: FutureWarning: The default value of numeric_only in DataFrameGroupBy.quantile is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  d = df[df['solver'] == solver].groupby(['pose_perturbation'], as_index=False).quantile(0.5)\n",
      "C:\\Users\\dimaj\\AppData\\Local\\Temp\\ipykernel_4300\\190755569.py:16: FutureWarning: The default value of numeric_only in DataFrameGroupBy.quantile is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  d = df[df['solver'] == solver].groupby(['pose_perturbation'], as_index=False).quantile(0.5)\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 600x400 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dimaj\\AppData\\Local\\Temp\\ipykernel_4300\\190755569.py:16: FutureWarning: The default value of numeric_only in DataFrameGroupBy.quantile is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  d = df[df['solver'] == solver].groupby(['pose_perturbation'], as_index=False).quantile(0.5)\n",
      "C:\\Users\\dimaj\\AppData\\Local\\Temp\\ipykernel_4300\\190755569.py:16: FutureWarning: The default value of numeric_only in DataFrameGroupBy.quantile is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  d = df[df['solver'] == solver].groupby(['pose_perturbation'], as_index=False).quantile(0.5)\n",
      "C:\\Users\\dimaj\\AppData\\Local\\Temp\\ipykernel_4300\\190755569.py:16: FutureWarning: The default value of numeric_only in DataFrameGroupBy.quantile is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  d = df[df['solver'] == solver].groupby(['pose_perturbation'], as_index=False).quantile(0.5)\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 600x400 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dimaj\\AppData\\Local\\Temp\\ipykernel_4300\\190755569.py:16: FutureWarning: The default value of numeric_only in DataFrameGroupBy.quantile is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  d = df[df['solver'] == solver].groupby(['pose_perturbation'], as_index=False).quantile(0.5)\n",
      "C:\\Users\\dimaj\\AppData\\Local\\Temp\\ipykernel_4300\\190755569.py:16: FutureWarning: The default value of numeric_only in DataFrameGroupBy.quantile is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  d = df[df['solver'] == solver].groupby(['pose_perturbation'], as_index=False).quantile(0.5)\n",
      "C:\\Users\\dimaj\\AppData\\Local\\Temp\\ipykernel_4300\\190755569.py:16: FutureWarning: The default value of numeric_only in DataFrameGroupBy.quantile is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  d = df[df['solver'] == solver].groupby(['pose_perturbation'], as_index=False).quantile(0.5)\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 600x400 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dimaj\\AppData\\Local\\Temp\\ipykernel_4300\\190755569.py:16: FutureWarning: The default value of numeric_only in DataFrameGroupBy.quantile is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  d = df[df['solver'] == solver].groupby(['pose_perturbation'], as_index=False).quantile(0.5)\n",
      "C:\\Users\\dimaj\\AppData\\Local\\Temp\\ipykernel_4300\\190755569.py:16: FutureWarning: The default value of numeric_only in DataFrameGroupBy.quantile is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  d = df[df['solver'] == solver].groupby(['pose_perturbation'], as_index=False).quantile(0.5)\n",
      "C:\\Users\\dimaj\\AppData\\Local\\Temp\\ipykernel_4300\\190755569.py:16: FutureWarning: The default value of numeric_only in DataFrameGroupBy.quantile is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  d = df[df['solver'] == solver].groupby(['pose_perturbation'], as_index=False).quantile(0.5)\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 600x400 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "metric_names = {\n",
    "    'rpe_rotation': 'RPE (rotation), deg',\n",
    "    'rpe_translation': 'RPE (translation), m',\n",
    "    'ape_rotation': 'APE (rotation), rad',\n",
    "    'ape_translation': 'APE (translation), m',\n",
    "}\n",
    "\n",
    "df = df_stat\n",
    "\n",
    "colors = ['r', 'g', 'b', 'y']\n",
    "\n",
    "for metric_name in metric_names:\n",
    "    plt.figure(figsize=(6, 4))\n",
    "    for i, solver in enumerate(solvers):\n",
    "        stat = []\n",
    "        d = df[df['solver'] == solver].groupby(['pose_perturbation'], as_index=False).quantile(0.5)\n",
    "        for perturbation in perturbations:\n",
    "            stat.append(d[d['pose_perturbation'] == str(perturbation)][metric_name])\n",
    "\n",
    "        stat = np.array(stat)\n",
    "        if metric_name.endswith(\"rotation\"):\n",
    "            plt.plot([str(perturbation) for perturbation in perturbations], stat * 180 / np.pi, '{0}-'.format(colors[i]))\n",
    "        else:\n",
    "            plt.plot([str(perturbation) for perturbation in perturbations], stat, '{0}-'.format(colors[i]))\n",
    "\n",
    "    plt.xlabel('Perturbation magnitude per pose, (deg/meters)')\n",
    "    plt.ylabel(metric_names[metric_name])\n",
    "    plt.legend(['BAREG', 'Pi-factor', 'EF'])\n",
    "    plt.semilogy()\n",
    "    plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  },
  "vscode": {
   "interpreter": {
    "hash": "d82a00f7a1c37fa80c71108086a15d9831aa2b6918fd1ad0b12feb221af8f869"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}