demo_results_single.ipynb

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 "cells": [
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   "cell_type": "code",
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   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "# %matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# README\n",
    "This demo shows how to evaluate your predictions (read from a file) with the IPD toolkit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 0. Install IPD package & Import\n",
    "Note: Also installs notebook packages for running demos. Omit the optional `notebooks` group if you don't want this.\n",
    "### Option 1 (from source):\n",
    "1. Clone this github repo\n",
    "2. Run `pip install -e .[notebooks]` \n",
    "    1. Make sure you are using a compatible python version (tested with 3.9.2 and 3.10.14)\n",
    "    2. Recommend using a venv\n",
    "\n",
    "### Option 2 (not available yet):\n",
    "1. `pip install intrinsic-ipd[notebooks]`\n",
    "\n",
    "### Option 3:\n",
    "1. `pip install git+[github/http/url]@main[notebooks]`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from intrinsic_ipd import IPDReader, IPDCamera, IPDImage, IPDLightCondition, CameraFramework\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xarray as xr\n",
    "from pprint import pprint\n",
    "np.set_printoptions(suppress=True, precision=4)\n",
    "import itertools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "logging.basicConfig(\n",
    "    level=logging.INFO,  # Set the logging level (INFO, DEBUG, WARNING, etc.)\n",
    "    format=\"%(asctime)s - %(name)s - %(levelname)s - %(filename)s - %(funcName)s - %(message)s\",\n",
    "    datefmt=\"%Y-%m-%d %H:%M:%S\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Read Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-09-21 00:21:07 - root - INFO - reader.py - __init__ - \n",
      "\tDataset:\tdataset_darkbg_0\n",
      "\tDataset Path:\t./datasets/dataset_darkbg_0/test\n",
      "\tCamera Type:\tPhotoneo (ID: 000)\n",
      "\tLighting:\tROOM\n",
      "\tNum Scenes:\t30\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[('gear2', 0),\n",
       " ('gear2', 1),\n",
       " ('gear2', 2),\n",
       " ('gear2', 3),\n",
       " ('gear2', 4),\n",
       " ('hex_manifold', 0),\n",
       " ('hex_manifold', 1)]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reader = IPDReader(\"./datasets\", \"dataset_darkbg_0\", IPDCamera.PHOTONEO, lighting=IPDLightCondition.ROOM, download=True) \n",
    "reader.objects"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Read and Match Predictions to Ground Truth"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Instantiate the matcher\n",
    "If the predicted poses of multiple instances of a part are to be provided in order, use the `ordered=True` setting. Otherwise, matching of predictions to ground truth instances is done based on translation distance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-09-21 00:21:07 - root - INFO - matcher.py - __init__ - Distance thresholds by part:{'corner_bracket': 32.38017666683971, 'corner_bracket0': 39.02804491183019, 'corner_bracket1': 10.877681825412408, 'corner_bracket2': 500, 'corner_bracket3': 26.39645391091472, 'corner_bracket4': 24.395902842373566, 'corner_bracket5': 18.918008035086167, 'corner_bracket6': 17.808145893935368, 'gear1': 25.815695401421685, 'gear2': 29.26397887556897, 'handrail_bracket': 500, 'hex_manifold': 500, 'l_bracket': 64.66071511009928, 'oblong_float': 62.70328076467595, 'pegboard_basket': 126.52277583794437, 'pipe_fitting_unthreaded': 40.67292674500291, 'single_pinch_clamp': 42.761346229284214, 'square_bracket': 43.12704523580799, 't_bracket': 53.88417685702441, 'u_bolt': 71.575479466246, 'wraparound_bracket': 49.71277681037156}\n"
     ]
    }
   ],
   "source": [
    "from intrinsic_ipd.matcher import PoseMatcher\n",
    "\n",
    "ordered_matcher = PoseMatcher(reader, ordered=True) #NOTE: this matches predictions based on list order\n",
    "\n",
    "# matcher = PoseMatcher(reader, match_dist_default_thresh = 10) #NOTE: this matches predictions to instances based on tranlation distance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Register the estimated poses. \n",
    "\n",
    "In the following case, pose estimates are read from a file and registered with the matcher by scene and part. By default `mode=\"override\"`, which will erase any previously registered poses for that scene and part and only save the provided poses. If you would like to append poses rather than override, use `mode=\"append\"`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import yaml\n",
    "import numpy as np\n",
    "\n",
    "file = \"./demo_results/dataset_darkbg_0_PHOTONEO.yml\"\n",
    "with open(file) as stream:\n",
    "    poses_by_part_by_scene = yaml.safe_load(stream)\n",
    "poses_by_object_by_scene = {}\n",
    "for scene in poses_by_part_by_scene:\n",
    "    poses_by_part = poses_by_part_by_scene[scene]\n",
    "    poses_by_object_by_scene[scene] = {}\n",
    "    for part in poses_by_part:\n",
    "        poses = poses_by_part[part] #dict\n",
    "        poses = [poses[key] for key in sorted(poses.keys(), reverse=False)] #NOTE: sorted values by instance\n",
    "        poses = np.array(poses)\n",
    "        poses[:,:3, 3] *= 1e3\n",
    "        ordered_matcher.register_poses(scene, part, poses, mode=\"override\")\n",
    "        # matcher.register_poses(scene, part, poses, mode=\"override\")\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "matched_poses_xarr = ordered_matcher.get_matched_poses()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Evaluate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Instantiate the Evaluator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from intrinsic_ipd.evaluator import Evaluator\n",
    "evaluator = Evaluator(reader)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Evaluate using annotated ground truth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "part          instance\n",
       "gear2         0            4.713135\n",
       "              1            4.269179\n",
       "              2            4.309647\n",
       "              3            4.547010\n",
       "              4            4.489904\n",
       "hex_manifold  0            8.417651\n",
       "              1           70.674926\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluator.measure_ground_truth_accuracy(matched_poses_xarr).to_pandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Evaluate using robot consistency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "part          instance\n",
       "gear2         0            1.118934\n",
       "              1            1.305575\n",
       "              2            1.456735\n",
       "              3            1.512205\n",
       "              4            1.350184\n",
       "hex_manifold  0            4.354976\n",
       "              1           84.739032\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluator.measure_robot_consistency(ordered_matcher.get_matched_poses()).to_pandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Getting prediction stats after matching"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (counts: 3, part: 2)&gt;\n",
       "array([[150,  60],\n",
       "       [150,  60],\n",
       "       [150,  60]])\n",
       "Coordinates:\n",
       "  * part     (part) object &#x27;gear2&#x27; &#x27;hex_manifold&#x27;\n",
       "  * counts   (counts) object &#x27;true_positive&#x27; &#x27;test_positive&#x27; &#x27;actual_positive&#x27;\n",
       "    dataset  &lt;U16 &#x27;dataset_darkbg_0&#x27;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>counts</span>: 3</li><li><span class='xr-has-index'>part</span>: 2</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-fa55b20e-2987-4336-a3fd-236220eafed3' class='xr-array-in' type='checkbox' checked><label for='section-fa55b20e-2987-4336-a3fd-236220eafed3' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>150 60 150 60 150 60</span></div><div class='xr-array-data'><pre>array([[150,  60],\n",
       "       [150,  60],\n",
       "       [150,  60]])</pre></div></div></li><li class='xr-section-item'><input id='section-bafb45c9-6076-4c83-9f52-85057ee2f1f8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bafb45c9-6076-4c83-9f52-85057ee2f1f8' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>part</span></div><div class='xr-var-dims'>(part)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;gear2&#x27; &#x27;hex_manifold&#x27;</div><input id='attrs-e32a2430-7db2-4c3c-a3fc-9198a1f7cd7c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e32a2430-7db2-4c3c-a3fc-9198a1f7cd7c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1bc251d3-ded8-4075-8fe3-dbc3cf6c4cd2' class='xr-var-data-in' type='checkbox'><label for='data-1bc251d3-ded8-4075-8fe3-dbc3cf6c4cd2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;gear2&#x27;, &#x27;hex_manifold&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>counts</span></div><div class='xr-var-dims'>(counts)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;true_positive&#x27; ... &#x27;actual_posi...</div><input id='attrs-5afbef2b-6d0f-4076-9ae1-f18a871a568b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5afbef2b-6d0f-4076-9ae1-f18a871a568b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-324639fa-9ffa-4578-bf26-390a55d02d82' class='xr-var-data-in' type='checkbox'><label for='data-324639fa-9ffa-4578-bf26-390a55d02d82' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;true_positive&#x27;, &#x27;test_positive&#x27;, &#x27;actual_positive&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dataset</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U16</div><div class='xr-var-preview xr-preview'>&#x27;dataset_darkbg_0&#x27;</div><input id='attrs-1570457a-3251-4e53-931a-0664bd3b3f12' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1570457a-3251-4e53-931a-0664bd3b3f12' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d6bf6f2b-bdc5-47b0-9642-afebfd01c83b' class='xr-var-data-in' type='checkbox'><label for='data-d6bf6f2b-bdc5-47b0-9642-afebfd01c83b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;dataset_darkbg_0&#x27;, dtype=&#x27;&lt;U16&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3e9c6f31-eced-4096-8743-cb847960ba54' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3e9c6f31-eced-4096-8743-cb847960ba54' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (counts: 3, part: 2)>\n",
       "array([[150,  60],\n",
       "       [150,  60],\n",
       "       [150,  60]])\n",
       "Coordinates:\n",
       "  * part     (part) object 'gear2' 'hex_manifold'\n",
       "  * counts   (counts) object 'true_positive' 'test_positive' 'actual_positive'\n",
       "    dataset  <U16 'dataset_darkbg_0'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ordered_matcher.get_stats()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "  * part     (part) object &#x27;gear2&#x27; &#x27;hex_manifold&#x27;\n",
       "    dataset  &lt;U16 &#x27;dataset_darkbg_0&#x27;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>part</span>: 2</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-a9821df7-14de-42b9-8135-10d09f3b7d3e' class='xr-array-in' type='checkbox' checked><label for='section-a9821df7-14de-42b9-8135-10d09f3b7d3e' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>1.0 1.0</span></div><div class='xr-array-data'><pre>array([1., 1.])</pre></div></div></li><li class='xr-section-item'><input id='section-2b4f1a6a-6386-4513-9893-90e29735e7fe' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2b4f1a6a-6386-4513-9893-90e29735e7fe' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>part</span></div><div class='xr-var-dims'>(part)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;gear2&#x27; &#x27;hex_manifold&#x27;</div><input id='attrs-b076e0df-9715-43dc-8802-b811a3a9845c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b076e0df-9715-43dc-8802-b811a3a9845c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc295bf8-1333-4149-a7d5-f53f1591137e' class='xr-var-data-in' type='checkbox'><label for='data-bc295bf8-1333-4149-a7d5-f53f1591137e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;gear2&#x27;, &#x27;hex_manifold&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dataset</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U16</div><div class='xr-var-preview xr-preview'>&#x27;dataset_darkbg_0&#x27;</div><input id='attrs-6936d14e-aca1-4637-8201-4fd949cb3e1d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6936d14e-aca1-4637-8201-4fd949cb3e1d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d2926986-8c54-4a52-b635-d7418a0e9f45' class='xr-var-data-in' type='checkbox'><label for='data-d2926986-8c54-4a52-b635-d7418a0e9f45' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;dataset_darkbg_0&#x27;, dtype=&#x27;&lt;U16&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8474e92f-aac2-499b-8112-343721da27c1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8474e92f-aac2-499b-8112-343721da27c1' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
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       "<xarray.DataArray (part: 2)>\n",
       "array([1., 1.])\n",
       "Coordinates:\n",
       "  * part     (part) object 'gear2' 'hex_manifold'\n",
       "    dataset  <U16 'dataset_darkbg_0'"
      ]
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     "execution_count": 12,
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    "evaluator.recall(ordered_matcher)"
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  {
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       "Coordinates:\n",
       "  * part     (part) object &#x27;gear2&#x27; &#x27;hex_manifold&#x27;\n",
       "    dataset  &lt;U16 &#x27;dataset_darkbg_0&#x27;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>part</span>: 2</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-006e9bb4-ff36-44b0-84ce-e1ccb71002c7' class='xr-array-in' type='checkbox' checked><label for='section-006e9bb4-ff36-44b0-84ce-e1ccb71002c7' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>1.0 1.0</span></div><div class='xr-array-data'><pre>array([1., 1.])</pre></div></div></li><li class='xr-section-item'><input id='section-e2cc2ece-d1ad-4ba9-9694-383a7e1fd6a2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e2cc2ece-d1ad-4ba9-9694-383a7e1fd6a2' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>part</span></div><div class='xr-var-dims'>(part)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;gear2&#x27; &#x27;hex_manifold&#x27;</div><input id='attrs-42b2c5e5-319c-4096-8307-a2eb28fde093' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-42b2c5e5-319c-4096-8307-a2eb28fde093' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9eb052f8-7337-43c6-ba47-9951ad944f68' class='xr-var-data-in' type='checkbox'><label for='data-9eb052f8-7337-43c6-ba47-9951ad944f68' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;gear2&#x27;, &#x27;hex_manifold&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dataset</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U16</div><div class='xr-var-preview xr-preview'>&#x27;dataset_darkbg_0&#x27;</div><input id='attrs-9462e168-11f1-4d9b-8f8e-5ae23045fad9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9462e168-11f1-4d9b-8f8e-5ae23045fad9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2191179d-2ef3-4877-9f2a-866c29587cf1' class='xr-var-data-in' type='checkbox'><label for='data-2191179d-2ef3-4877-9f2a-866c29587cf1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;dataset_darkbg_0&#x27;, dtype=&#x27;&lt;U16&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5a50d2de-1e5f-4e1f-a694-2f1540e6eb29' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5a50d2de-1e5f-4e1f-a694-2f1540e6eb29' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (part: 2)>\n",
       "array([1., 1.])\n",
       "Coordinates:\n",
       "  * part     (part) object 'gear2' 'hex_manifold'\n",
       "    dataset  <U16 'dataset_darkbg_0'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluator.precision(ordered_matcher)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Render the estimate vs ground truth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-09-21 00:21:11 - OpenGL.acceleratesupport - INFO - acceleratesupport.py - <module> - No OpenGL_accelerate module loaded: No module named 'OpenGL_accelerate'\n",
      "2024-09-21 00:21:11 - root - WARNING - render.py - render_scene - Set PYOPENGL_PLATFORM environment variable before importing pyrender or any other OpenGL library. \n",
      "\tSetting to PYOPENGL_PLATFORM=`egl`. \n",
      "\tSee https://pyrender.readthedocs.io/en/latest/examples/offscreen.html \n",
      "2024-09-21 00:21:11 - root - INFO - reader.py - get_img - Opening image from ./datasets/dataset_darkbg_0/test/000030/000/rgb/000000.png\n",
      "2024-09-21 00:21:12 - root - INFO - reader.py - get_img - Opening image from ./datasets/dataset_darkbg_0/test/000030/000/rgb/000000.png\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f7ad816f940>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from intrinsic_ipd.render import render_scene\n",
    "import cv2\n",
    "\n",
    "image, rendered_gt, overlay_gt = render_scene(reader, 30)\n",
    "image, rendered_pred, overlay_pred = render_scene(reader, 30, poses=ordered_matcher.get_matched_poses())\n",
    "\n",
    "# set rendered prediction to red\n",
    "rendered_pred = np.array(rendered_pred)\n",
    "black_pixels = np.where(\n",
    "    (rendered_pred[:, :, 0] == 0) & \n",
    "    (rendered_pred[:, :, 1] == 0) & \n",
    "    (rendered_pred[:, :, 2] == 0)\n",
    ")\n",
    "rendered_pred[black_pixels] = [255, 0, 0]\n",
    "\n",
    "\n",
    "alpha = 0.5  # Transparency of the overlay\n",
    "gt_vs_est = cv2.addWeighted(rendered_gt, 1 - alpha, rendered_pred, alpha, 0)\n",
    "plt.imshow(gt_vs_est)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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