{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Load Dataset\n",
    "\n",
    "When working with Parallel Domain's synthetic data, the standard output format is [Dataset Governance Policy (DGP)](https://github.com/TRI-ML/dgp/blob/master/dgp/proto/README.md).\n",
    "In general, the PD SDK can load from any format, as long as a custom decoder exists adhering to the `DatasetDecoderProtocol`.\n",
    "Out of the box, PD SDK comes with a pre-configured `DGPDatasetDecoder` which we can leverage to load data.\n",
    "\n",
    "In this tutorial, we are going to load and access a dataset and its scenes.\n",
    "\n",
    "Initially, we need to select the fitting decoder (in this case: `DGPDatasetDecoder`) and then tell it where our dataset is stored. The location can be either a local filesystem path or an s3 address."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [],
   "source": [
    "from paralleldomain.decoding.dgp.decoder import DGPDatasetDecoder\n",
    "from paralleldomain.model.dataset import Dataset  # optional import, just for type reference in this tutorial\n",
    "\n",
    "dataset_path = \"s3://pd-sdk-c6b4d2ea-0301-46c9-8b63-ef20c0d014e9/testset_dgp\"\n",
    "dgp_decoder = DGPDatasetDecoder(dataset_path=dataset_path)\n",
    "\n",
    "dataset: Dataset = dgp_decoder.get_dataset()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Alternatively you can also use the decode_dataset helper method."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "from paralleldomain.decoding.helper import decode_dataset\n",
    "from paralleldomain.decoding.common import DecoderSettings\n",
    "\n",
    "# To deactivate caching of certain data types use the DecoderSettings\n",
    "settings = DecoderSettings(cache_images=False)\n",
    "# decode dgp dataset\n",
    "dgp_dataset: Dataset = decode_dataset(dataset_path=dataset_path, dataset_format=\"dgp\", settings=settings)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "If you want to load a dataset which is stored in Cityscapes or NuImages format simply change the dataset_format to \"cityscapes\" or \"nuimages\":"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "nu_images_dataset_path = \"some/path/to/a/nuimages/root/folder\"\n",
    "nu_images_dataset: Dataset = decode_dataset(dataset_path=nu_images_dataset_path, dataset_format=\"nuimages\")\n",
    "\n",
    "cityscapes_dataset_path = \"some/path/to/a/cityscapes/root/folder\"\n",
    "cityscapes_dataset: Dataset = decode_dataset(dataset_path=nu_images_dataset_path, dataset_format=\"cityscapes\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Dataset Information\n",
    "\n",
    "Now that the dataset information has been loaded, we query a couple of metadata from it:"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset Metadata:\n",
      "Name: DefaultDatasetName\n",
      "Available Annotation Types:\n",
      "\t<class 'paralleldomain.model.annotation.BoundingBoxes2D'>\n",
      "\t<class 'paralleldomain.model.annotation.BoundingBoxes3D'>\n",
      "\t<class 'paralleldomain.model.annotation.SemanticSegmentation2D'>\n",
      "\t<class 'paralleldomain.model.annotation.SemanticSegmentation3D'>\n",
      "\t<class 'paralleldomain.model.annotation.InstanceSegmentation2D'>\n",
      "\t<class 'paralleldomain.model.annotation.InstanceSegmentation3D'>\n",
      "\t<class 'paralleldomain.model.annotation.Depth'>\n",
      "\t<class 'paralleldomain.model.annotation.Annotation'>\n",
      "\t<class 'paralleldomain.model.annotation.Annotation'>\n",
      "\t<class 'paralleldomain.model.annotation.OpticalFlow'>\n",
      "\t<class 'paralleldomain.model.annotation.Annotation'>\n",
      "Custom Attributes:\n",
      "\torigin: INTERNAL\n",
      "\tname: DefaultDatasetName\n",
      "\tcreator: \n",
      "\tavailable_annotation_types: [0, 1, 2, 3, 4, 5, 6, 10, 7, 8, 9]\n",
      "\tcreation_date: 2021-06-22T15:16:21.317Z\n",
      "\tversion: \n",
      "\tdescription: \n"
     ]
    }
   ],
   "source": [
    "print(\"Dataset Metadata:\")\n",
    "print(\"Name:\", dataset.metadata.name)\n",
    "print(\"Available Annotation Types:\", *[f\"\\t{a}\" for a in dataset.available_annotation_types], sep=\"\\n\")\n",
    "print(\"Custom Attributes:\", *[f\"\\t{k}: {v}\" for k,v in dataset.metadata.custom_attributes.items()], sep=\"\\n\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "As you can see, the property `.available_annotation_types` includes classes from `paralleldomain.model.annotation`. In tutorials around reading annotations from a dataset, these exact classes will be re-used, which allows for a consistent type-check across objects."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Access available Scenes\n",
    "\n",
    "Every dataset consists of scenes. These can contain ordered (usually by time) or unordered data.\n",
    "In this example, we are looking to receive a list of scene names by type that have been found within the loaded dataset."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found scene pd-sdk_test_set\n",
      "Found unordered scene pd-sdk_test_set\n"
     ]
    }
   ],
   "source": [
    "for sn in dataset.scene_names:\n",
    "    print(f\"Found scene {sn}\")\n",
    "\n",
    "for usn in dataset.unordered_scene_names:\n",
    "    print(f\"Found unordered scene {usn}\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Load Scene\n",
    "\n",
    "After having retrieved all scene names from a dataset, we get the actual `Scene` object and access a couple of properties as well as child objects.\n",
    "Let's start with scene properties:\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{ 'PD': { '@type': 'type.googleapis.com/dgp.proto.ParallelDomainSceneMetadata',\n",
      "          'batch_id': 0,\n",
      "          'cloud_cover': 0.10000000149011612,\n",
      "          'fog_intensity': 0.0,\n",
      "          'location': 'SF_6thAndMission_medium',\n",
      "          'rain_intensity': 0.0,\n",
      "          'region_type': 'NORTHERN_CALIFORNIA',\n",
      "          'scene_type': 'URBAN',\n",
      "          'street_lights': 0.0,\n",
      "          'sun_azimuth': 0,\n",
      "          'sun_elevation': 0,\n",
      "          'time_of_day': 'LS_sky_noon_partlyCloudy_1113_HDS024',\n",
      "          'version': 0,\n",
      "          'wetness': 0}}\n"
     ]
    }
   ],
   "source": [
    "from paralleldomain.model.scene import Scene  # optional import, just for type reference in this tutorial\n",
    "from pprint import PrettyPrinter\n",
    "\n",
    "\n",
    "selected_scene = dataset.scene_names[0]  # for future\n",
    "scene: Scene = dataset.get_scene(scene_name=selected_scene)\n",
    "\n",
    "# Use prettyprint for nested dictionaries\n",
    "pp = PrettyPrinter(indent=2)\n",
    "pp.pprint(scene.metadata)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Scene metadata usually contains any variables that changes with each scene and are not necessarily consistent across a whole dataset.\n",
    "In many cases these are environment variables like weather, time of day and location.\n",
    "\n",
    "A `Scene` object also includes information about the available annotation types. In most datasets, these will be consistent with the ones at the `Dataset` level, but there is the possibility to vary them."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ <class 'paralleldomain.model.annotation.BoundingBoxes2D'>,\n",
      "  <class 'paralleldomain.model.annotation.BoundingBoxes3D'>,\n",
      "  <class 'paralleldomain.model.annotation.SemanticSegmentation2D'>,\n",
      "  <class 'paralleldomain.model.annotation.SemanticSegmentation3D'>,\n",
      "  <class 'paralleldomain.model.annotation.InstanceSegmentation2D'>,\n",
      "  <class 'paralleldomain.model.annotation.InstanceSegmentation3D'>,\n",
      "  <class 'paralleldomain.model.annotation.Depth'>,\n",
      "  <class 'paralleldomain.model.annotation.Annotation'>,\n",
      "  <class 'paralleldomain.model.annotation.Annotation'>,\n",
      "  <class 'paralleldomain.model.annotation.OpticalFlow'>,\n",
      "  <class 'paralleldomain.model.annotation.Annotation'>]\n"
     ]
    }
   ],
   "source": [
    "pp.pprint(scene.available_annotation_types)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Normally, in a scene, we expect to have more than one frame available, especially when we work with sequential data.\n",
    "These can be accessed through their frame IDs. In DGP datasets, these are usually string representations of increasing integers, but they could also be more explicit identifiers for other datasets, e.g., a string representation of a UNIX time or details of the recording vehicle.\n",
    "\n",
    "In our example, the frame IDs follow the pattern of integers in string representation:"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pd-sdk_test_set has 10 frames available.\n",
      "['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']\n"
     ]
    }
   ],
   "source": [
    "print(f\"{scene.name} has {len(scene.frame_ids)} frames available.\")\n",
    "print(scene.frame_ids)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Load Frame + Sensor\n",
    "\n",
    "### Frames\n",
    "A `Frame` object is like a timestamp-bracket around different sensor data. If we have multiple sensors mounted on our recording vehicle, then the single data recordings are usually grouped into specific timestamps.\n",
    "We can retrieve a `Frame` object and actually see what the \"grouping datetime\" is:"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-06-22 15:16:21.367000+00:00\n"
     ]
    }
   ],
   "source": [
    "frame_0_id = \"0\"\n",
    "frame_0 = scene.get_frame(frame_id=frame_0_id)\n",
    "print(frame_0.date_time)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Date/Times are presented as Python's std library `datetime` objects. When decoding data, the PD SDK also adds timezone information to these objects.\n",
    "\n",
    "### Sensors\n",
    "\n",
    "As a next step, we want to see what sensor are available within that scene. In general, sensors are divided into `CameraSensor` and `LidarSensor`."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cameras:\n",
      "camera_front\n",
      "camera_rear\n",
      "virtual_lidar_front_camera_0\n",
      "virtual_lidar_front_camera_1\n",
      "virtual_lidar_front_camera_2\n",
      "virtual_lidar_rear_camera_0\n",
      "virtual_lidar_rear_camera_1\n",
      "virtual_lidar_rear_camera_2\n",
      "\n",
      "\n",
      "LiDARs:\n",
      "lidar_front\n",
      "lidar_rear\n"
     ]
    }
   ],
   "source": [
    "print(\"Cameras:\", *scene.camera_names, sep='\\n')\n",
    "print(\"\\n\")\n",
    "print(\"LiDARs:\", *scene.lidar_names, sep='\\n')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Similar to how we used this information to get a scene from a dataset, we can use this information to get a sensor from a scene."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [],
   "source": [
    "camera_0_name = scene.camera_names[0]\n",
    "camera_0 = scene.get_camera_sensor(camera_name=camera_0_name)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Knowing which frames and sensors are available allows us to now query for the actual sensor data.\n",
    "As described above, a `Frame` is the time-grouping bracket around different sensor recordings. The actual data for a specific sensor assigned to this frame is represented in a `SensorFrame`.\n",
    "This is where sensor data and annotations live.\n",
    "\n",
    "We can either first select a `Frame` and then pick a `Sensor` or the other way around. They will return the same `SensorFrame` instance."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Both objects are equal: 140255650227008 == 140255650227008\n"
     ]
    }
   ],
   "source": [
    "camera_frame_via_frame = frame_0.get_camera(camera_name=camera_0_name)\n",
    "camera_frame_via_camera = camera_0.get_frame(frame_id=frame_0_id)\n",
    "\n",
    "assert(camera_frame_via_camera is camera_frame_via_camera)\n",
    "print(f\"Both objects are equal: {id(camera_frame_via_frame)} == {id(camera_frame_via_camera)}\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Load Sensor Frames\n",
    "\n",
    "Now that we know how to retrieve `SensorFrame` object for specific sensors and timestamps, we can use those to extract the actual sensor data.\n",
    "\n",
    "### Accessing shared properties\n",
    "While there are `CameraSensorFrame` and `LidarSensorFrame` objects with sensor specific data, there are certain properties which are common to any `SensorFrame`.\n",
    "\n",
    "We are going to print the most basic attributes on a `SensorFrame`, using the example of a `CameraSensorFrame` object."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "camera_front recorded at 2021-06-22 15:16:21.367000+00:00\n",
      "lidar_front recorded at 2021-06-22 15:16:21.367000+00:00\n"
     ]
    }
   ],
   "source": [
    "# Get `CameraSensorFrame` for the first camera on the first frame within the scene.\n",
    "lidar_0_name = scene.lidar_names[0]\n",
    "\n",
    "camera_0_frame_0 = frame_0.get_camera(camera_name=camera_0_name)\n",
    "lidar_0_frame_0 = frame_0.get_lidar(lidar_name=lidar_0_name)\n",
    "\n",
    "print(f\"{camera_0_frame_0.sensor_name} recorded at {camera_0_frame_0.date_time}\")\n",
    "print(f\"{lidar_0_frame_0.sensor_name} recorded at {lidar_0_frame_0.date_time}\")\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Every `SensorFrame` always has information about the sensor pose (where is it in the world coordinate system?) and sensor extrinsic (how is the sensor positioned relative to the ego-vehicle reference coordinate system?).\n",
    "Poses and Extrinsics are represented as instances of the `Transformation` object. It allows storing 6-DoF information and allows for easy combination with each other.\n",
    "In the example below, we are going to calculate the difference between the camera and the lidar sensor. The difference should be the same when using pose or extrinsic."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R: [89.31, -45.03, -176.82],t: [-143.43, 151.38, 12.22]  ->  R: [75.19, -44.71, 173.19],t: [-143.12, 151.8, 13.46]\n",
      "R: [0.0, 90.0, -90.0],t: [1.5, 0.0, 1.5]  ->  R: [0.0, 80.0, -90.0],t: [1.0, 0.0, 2.75]\n"
     ]
    }
   ],
   "source": [
    "print(camera_0_frame_0.pose, \" -> \", lidar_0_frame_0.pose)\n",
    "camera_to_lidar_pose = camera_0_frame_0.pose.inverse @ lidar_0_frame_0.pose\n",
    "\n",
    "print(camera_0_frame_0.extrinsic, \" -> \", lidar_0_frame_0.extrinsic)\n",
    "camera_to_lidar_extrinsic = camera_0_frame_0.extrinsic.inverse @ lidar_0_frame_0.extrinsic"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "We can use the associated homogenous transformation matrix to compare both results."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Diff Pose: R: [10.0, 0.0, -0.0],t: [-0.0, -1.25, -0.5]\n",
      "Diff Extrinsic: R: [10.0, 0.0, -0.0],t: [0.0, -1.25, -0.5]\n",
      "If you see this, the difference are close to equal.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "print(\"Diff Pose:\", camera_to_lidar_pose)\n",
    "print(\"Diff Extrinsic:\", camera_to_lidar_extrinsic)\n",
    "\n",
    "assert np.all(np.isclose(camera_to_lidar_pose.transformation_matrix, camera_to_lidar_extrinsic.transformation_matrix, atol=1e-05))\n",
    "print(\"If you see this, the difference are close to equal.\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "In the same manner, it is easily possible to calculate the relative location between two sensors. Let's calculate the difference between two camera sensors."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R: [0.0, 90.0, -90.0],t: [1.5, 0.0, 1.5]  ->  R: [116.57, -90.0, -153.43],t: [-1.5, 0.0, 1.5]\n",
      "Diff Extrinsic:  R: [180.0, 0.0, -180.0],t: [0.0, -0.0, -3.0]\n"
     ]
    }
   ],
   "source": [
    "camera_1_name = scene.camera_names[1]\n",
    "camera_1_frame_0 = frame_0.get_camera(camera_name=camera_1_name)\n",
    "\n",
    "print(camera_0_frame_0.extrinsic, \" -> \", camera_1_frame_0.extrinsic)\n",
    "print(\"Diff Extrinsic: \", camera_0_frame_0.extrinsic.inverse @ camera_1_frame_0.extrinsic)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "It is important to remember that a sensor extrinsic is provided in the ego-vehicles reference coordinate system. For DGP dataset, that is FLU (Front (x), Left (y), Up (z)).\n",
    "So the translation difference between both sensors in ego-vehicle coordinate system is approx x=-3. When calculating the difference between both extrinsics, we will receive though a value of approx. z=-3. That is because we receive the difference in the camera coordinate system (RDF). In this example, we have two cameras (one front, one rear facing) that are perfectly aligned with the ego-vehicle's longitudinal axis x.\n",
    "\n",
    "If we want to have the camera sensor in a FLU coordinate system, we can simply leverage the `CoordinateSystem` class to take of it for us. Objects of that class can also be combined with `Transformation` objects."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R: [90.0, -90.0, -180.0],t: [-3.0, -0.0, 0.0]\n"
     ]
    }
   ],
   "source": [
    "from paralleldomain.utilities.coordinate_system import CoordinateSystem\n",
    "\n",
    "\n",
    "extrinsic_diff = (camera_0_frame_0.extrinsic.inverse @ camera_1_frame_0.extrinsic)\n",
    "RDF_to_FLU = (CoordinateSystem(\"RDF\") > CoordinateSystem(\"FLU\"))\n",
    "\n",
    "print( RDF_to_FLU @ extrinsic_diff)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Accessing Annotations\n",
    "\n",
    "While both `CameraSensorFrame` and `LidarSensorFrame` have the property `.available_annotation_types`, the content will most likely be different.\n",
    "There are shared annotation types which are available for both sensor types, but for example 2D Bounding Boxes are something just available for camera data, or point cloud segmentation only for LiDAR data.\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ <class 'paralleldomain.model.annotation.BoundingBoxes2D'>,\n",
      "  <class 'paralleldomain.model.annotation.BoundingBoxes3D'>,\n",
      "  <class 'paralleldomain.model.annotation.Annotation'>,\n",
      "  <class 'paralleldomain.model.annotation.SemanticSegmentation2D'>,\n",
      "  <class 'paralleldomain.model.annotation.InstanceSegmentation2D'>,\n",
      "  <class 'paralleldomain.model.annotation.Depth'>,\n",
      "  <class 'paralleldomain.model.annotation.OpticalFlow'>]\n",
      "[ <class 'paralleldomain.model.annotation.BoundingBoxes3D'>,\n",
      "  <class 'paralleldomain.model.annotation.SemanticSegmentation3D'>,\n",
      "  <class 'paralleldomain.model.annotation.InstanceSegmentation3D'>,\n",
      "  <class 'paralleldomain.model.annotation.Annotation'>]\n"
     ]
    }
   ],
   "source": [
    "pp.pprint(camera_0_frame_0.available_annotation_types)\n",
    "pp.pprint(lidar_0_frame_0.available_annotation_types)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "To actually the annotations into memory and use them for further analysis, we can leverage the `AnnotationTypes` class.\n",
    "In the example below, we are going to load the 2D Bounding Boxes from a camera frame.\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class ID: 5, Instance ID: 6\n",
      "Class ID: 5, Instance ID: 15\n",
      "Class ID: 5, Instance ID: 62\n",
      "Class ID: 5, Instance ID: 79\n",
      "Class ID: 5, Instance ID: 109\n",
      "Class ID: 5, Instance ID: 118\n",
      "Class ID: 5, Instance ID: 154\n",
      "Class ID: 5, Instance ID: 177\n",
      "Class ID: 5, Instance ID: 178\n",
      "Class ID: 5, Instance ID: 187\n"
     ]
    }
   ],
   "source": [
    "from paralleldomain.model.annotation import AnnotationTypes\n",
    "from paralleldomain.model.annotation import BoundingBoxes2D  # optional import, just for type reference in this tutorial\n",
    "\n",
    "\n",
    "# Quick check if `BoundingBoxes2D` is an available annotation type. If not, and we do not check for it, we will receive a `ValueError` exception.\n",
    "if AnnotationTypes.BoundingBoxes2D in camera_0_frame_0.available_annotation_types:\n",
    "    boxes2d: BoundingBoxes2D = camera_0_frame_0.get_annotations(annotation_type=AnnotationTypes.BoundingBoxes2D)\n",
    "\n",
    "    for b in boxes2d.boxes[:10]:\n",
    "        print(b)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "For the LiDAR sensor, we are going to retrieve the 3D Semantic Segmentation of the point cloud and count objects by class ID. Instead of checking explicitly if the annotation type is available, we are going to use try/catch on a `ValueError`. To see if it works, we will try to receive 2D Bounding Boxes from the LiDAR sensor."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LiDAR Frame doesn't have <class 'paralleldomain.model.annotation.BoundingBoxes2D'> as annotation type available. Original exception below:\n",
      "The annotation type <class 'paralleldomain.model.annotation.BoundingBoxes2D'> is not available in this sensor frame!\n",
      "{ 3: 1906,\n",
      "  5: 89,\n",
      "  8: 3,\n",
      "  11: 47,\n",
      "  12: 6,\n",
      "  21: 9,\n",
      "  22: 8,\n",
      "  24: 1404,\n",
      "  26: 81,\n",
      "  27: 12,\n",
      "  28: 332,\n",
      "  31: 89,\n",
      "  33: 4,\n",
      "  34: 1,\n",
      "  37: 83,\n",
      "  38: 21,\n",
      "  255: 61}\n"
     ]
    }
   ],
   "source": [
    "from paralleldomain.model.annotation import SemanticSegmentation3D  # optional import, just for type reference in this tutorial\n",
    "\n",
    "\n",
    "annotation_type = AnnotationTypes.BoundingBoxes2D\n",
    "\n",
    "try:\n",
    "    boxes2d: BoundingBoxes2D = lidar_0_frame_0.get_annotations(annotation_type=annotation_type)\n",
    "except ValueError as e:\n",
    "    print(f\"LiDAR Frame doesn't have {annotation_type} as annotation type available. Original exception below:\")\n",
    "    print(str(e))\n",
    "\n",
    "\n",
    "# Move on to the actual task:\n",
    "\n",
    "annotation_type = AnnotationTypes.SemanticSegmentation3D\n",
    "\n",
    "count_by_class_id = {}\n",
    "\n",
    "try:\n",
    "    semseg3d: SemanticSegmentation3D = lidar_0_frame_0.get_annotations(annotation_type=annotation_type)\n",
    "    u_class_ids, u_counts = np.unique(semseg3d.class_ids, return_counts=True)\n",
    "    count_by_class_id = {u_class_ids[idx]: u_counts[idx] for idx in range(len(u_class_ids))}\n",
    "    pp.pprint(count_by_class_id)\n",
    "\n",
    "except ValueError as e:\n",
    "    print(f\"LiDAR Frame doesn't have {annotation_type} as annotation type available.\")\n",
    "    print(str(e))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Instead of showing just class IDs, we can show the actual class labels quite easily. On the `Scene` object we can retrieve the `ClassMap` for each annotation style.\n",
    "Let's get the one for 3D Semantic Segmentation and print the labels for better readability."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{ 3: 'Building [1906]',\n",
      "  5: 'Car [89]',\n",
      "  8: 'CrossWalk [3]',\n",
      "  11: 'LaneMarking [47]',\n",
      "  12: 'LimitLine [6]',\n",
      "  21: 'ParkingMeter [9]',\n",
      "  22: 'Pedestrian [8]',\n",
      "  24: 'Road [1404]',\n",
      "  26: 'RoadBoundary(Curb) [81]',\n",
      "  27: 'RoadMarking [12]',\n",
      "  28: 'SideWalk [332]',\n",
      "  31: 'Terrain [89]',\n",
      "  33: 'TrafficLight [4]',\n",
      "  34: 'TrafficSign [1]',\n",
      "  37: 'Vegetation [83]',\n",
      "  38: 'VerticalPole [21]',\n",
      "  255: 'Void [61]'}\n"
     ]
    }
   ],
   "source": [
    "from paralleldomain.model.class_mapping import ClassMap  # optional import, just for type reference in this tutorial\n",
    "\n",
    "semseg3d_classmap: ClassMap = scene.get_class_map(annotation_type=AnnotationTypes.SemanticSegmentation3D)\n",
    "\n",
    "count_by_class_label = {k: f\"{semseg3d_classmap[k].name} [{v}]\" for k,v in count_by_class_id.items()}\n",
    "\n",
    "pp.pprint(count_by_class_label)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Access Camera Data\n",
    "\n",
    "As mentioned above, sensor-specific sensor frames, like `CameraSensorFrame` have additional properties to the shared ones described above.\n",
    "For a camera that is especially the RGB mask, as well as, camera intrinsics and distortion parameters.\n",
    "\n",
    "**Note:** Whenever we work with image data (including masks that represent an image-encoded representation), we work with `np.ndarray` of shape (h, w, 3) or (h, w, 4).\n",
    "The last axis is defined in the following index order: 0: Red, 1: Green, 2: Blue, [3: Alpha]. When using OpenCV directly, we need to make explicitly convert the image into BGR[A] order. If you use methods from within the PD SDK, e.g., from `utilities`, any required conversion is handled for you."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Below is an image with 4 channels and resolution 1920x1080 sqpx\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{ 'camera_model': 'opencv_pinhole',\n",
      "  'cx': 959.5,\n",
      "  'cy': 539.5,\n",
      "  'fov': 85,\n",
      "  'fx': 1047.65625,\n",
      "  'fy': 1047.65625,\n",
      "  'k1': 0,\n",
      "  'k2': 0,\n",
      "  'k3': 0,\n",
      "  'k4': 0,\n",
      "  'k5': 0,\n",
      "  'k6': 0,\n",
      "  'p1': 0,\n",
      "  'p2': 0,\n",
      "  'skew': 0}\n"
     ]
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "from paralleldomain.model.sensor import Image  # optional import, just for type reference in this tutorial\n",
    "\n",
    "\n",
    "image_data: Image = camera_0_frame_0.image\n",
    "\n",
    "print(f\"Below is an image with {image_data.channels} channels and resolution {image_data.width}x{image_data.height} sqpx\")\n",
    "\n",
    "plt.imshow(image_data.rgba)  # `.rgba` returns image including alpha-channel, otherwise `.rgb` can be used for convenience.\n",
    "plt.title(camera_0_frame_0.sensor_name)\n",
    "plt.show()\n",
    "\n",
    "pp.pprint(vars(camera_0_frame_0.intrinsic))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Access LiDAR Data\n",
    "\n",
    "Similar to a camera, LiDAR sensors have their dedicated sensor frame object `LidarSensorFrame`.\n",
    "There we can access different point cloud properties like points in Cartesian coordinates, their intensity or timing offsets.\n",
    "\n",
    "The simple example below creates an orthographic topdown projection of the point cloud in ego-vehicle coordinate system by leveraging the extrinsic information.\n",
    "The colorization will be done by height, and the size of points will be defined by reflection intensity."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [
    {
     "data": {
      "text/plain": "Text(0.5, 1.0, 'lidar_front')"
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from paralleldomain.model.sensor import PointCloud  # optional import, just for type reference in this tutorial\n",
    "\n",
    "\n",
    "pc_data: PointCloud = lidar_0_frame_0.point_cloud\n",
    "\n",
    "pc_xyz_one: np.ndarray = pc_data.xyz_one # Returns the xyz coordinates with an additional column full of \"1\" to allow for direct transformation\n",
    "pc_intensity: np.ndarray = pc_data.intensity\n",
    "\n",
    "pc_ego = (lidar_0_frame_0.extrinsic @ pc_xyz_one.T).T\n",
    "pc_ego = pc_ego[:,:3]  # throw away \"1\" - we are done transforming\n",
    "\n",
    "subset_slice = slice(None, None, 5)  # we want a slice of every 5th point to reduce rendering time\n",
    "\n",
    "pc_ego_subset = pc_ego[subset_slice]\n",
    "pc_intensity_subset = pc_intensity[subset_slice]\n",
    "\n",
    "plt.scatter(x=pc_ego_subset[:,0], y=pc_ego_subset[:,1], s=pc_intensity_subset, c=pc_ego_subset[:,2])\n",
    "plt.grid(True)\n",
    "plt.title(lidar_0_frame_0.sensor_name)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "In the scatter plot above we can see that the test point cloud is quite sparse (in fact it has only 3 lasers vertically). Nevertheless, outlines of buildings and objects are clearly visible.\n",
    "Also, there appears to be a couple of highly reflective object in the ego-vehicle's proximity. By applying the LiDAR's extrinsic, we have put (0,0,0) to the ego-vehicle's reference point - here it is the center of the bottom-face.\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}