{"cells":[{"metadata":{},"cell_type":"markdown","source":"What is a LiDAR?\n\nLiDAR, or light detection and ranging, is a popular remote sensing method used for measuring the exact distance of an object on the earth’s surface. There are three primary components of a LiDAR instrument — the scanner, laser and GPS receiver.\n\n![image.png](attachment:image.png)\n\n\nContinuously rotating LiDAR system sends thousands of laser pulses every second. These pulses collide with the surrounding objects and reflect back. The resulting light reflections are then used to create a 3D point cloud.\n\nWhen it comes to sensing the vehicle’s surroundings with a 360-degree field of view, LIDAR-based systems are highly accurate in object detection and recognition of 3D shapes, even for longer distances (100-200 meters). LIDAR system’s 3D mapping capability also helps in differentiating between cars, pedestrians, trees, people, or other objects, while also calculating and sharing details of their velocity in real time.\n\n\n\n\n","attachments":{"image.png":{"image/png":"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"}}},{"metadata":{},"cell_type":"markdown","source":"**Data Visualization**"},{"metadata":{"trusted":true},"cell_type":"code","source":"!ls ../input/3d-object-detection-for-autonomous-vehicles\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"!pip install lyft_dataset_sdk\n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"**Data**\n\nYou will need the LIDAR, image, map and data files for both train and test (test_images.zip, test_lidar.zip, etc.). You may also need the train.csv, which includes the sample annotations in the form expected for submissions. The sample_submission.csv file contains all of the sample Ids for the test set.\n\nThe data files (test_data.zip, train_data.zip) are in JSON format.\n\n\n\ntrain_data.zip and test_data.zip - contains JSON files with multiple tables. \n\ntrain_images.zip and test_images.zip - contains .jpeg files corresponding to samples in sample_data.json\n\ntrain_lidar.zip and test_lidar.zip - contains .jpeg files corresponding to samples in sample_data.json\n\ntrain_maps.zip and test_maps.zip - contains maps of the entire sample area.\n\ntrain.csv - contains all sample_tokens in the train set, as well as annotations in the required format for all train set objects.\n\nsample_submission.csv - contains all sample_tokens in the test set, with empty predictions."},{"metadata":{},"cell_type":"markdown","source":"**Dataset Structure**\n\nscene - 25-45 seconds snippet of a car's journey.\n\nsample - An annotated snapshot of a scene at a particular timestamp.\n\nsample_data - Data collected from a particular sensor.\n\nsample_annotation - An annotated instance of an object within our interest.\n\ninstance - Enumeration of all object instance we observed.\n\ncategory - Taxonomy of object categories (e.g. vehicle, human).\n\nattribute - Property of an instance that can change while the category remains the same.\n\nvisibility - (currently not used)\n\nsensor - A specific sensor type.\n\ncalibrated sensor - Definition of a particular sensor as calibrated on a particular vehicle.\n\nego_pose - Ego vehicle poses at a particular timestamp.\n\nlog - Log information from which the data was extracted.\n\nmap - Map data that is stored as binary semantic masks from a top-down view."},{"metadata":{},"cell_type":"markdown","source":"**Load all the necessary libraries**"},{"metadata":{"trusted":true},"cell_type":"code","source":"\n\nimport numpy as np\nimport pandas as pd\nimport os\nimport gc\n\n\nimport json\nimport math \nimport sys\nfrom datetime import datetime\nimport time\nfrom typing import Tuple,List\n\nimport matplotlib.pyplot as plt\n%matplotlib inline\nimport cv2\nimport sklearn.metrics\nfrom PIL import Image\n\nfrom matplotlib.axes import Axes\nfrom matplotlib import animation, rc\nimport plotly.graph_objs as go\nimport plotly.tools as ts\nfrom plotly.offline import plot, init_notebook_mode\nimport plotly.figure_factory as ft\ninit_notebook_mode(connected=True)\nfrom pyquaternion import Quaternion\nimport seaborn as sns\nfrom tqdm import tqdm \nimport warnings\n\nfrom lyft_dataset_sdk.utils.map_mask import MapMask\nfrom lyft_dataset_sdk.lyftdataset import LyftDataset\nfrom lyft_dataset_sdk.utils.geometry_utils import view_points, box_in_image, BoxVisibility\nfrom lyft_dataset_sdk.utils.geometry_utils import view_points, transform_matrix\nfrom pathlib import Path\n\nimport struct\nfrom abc import ABC, abstractmethod\nfrom functools import reduce\nfrom typing import Tuple, List, Dict\nimport copy\n\nplt.rcParams['figure.figsize']=[16,10]\nplt.rcParams['font.size']=14\nwarnings.filterwarnings('ignore')\npd.options.display.max_columns = 99\nsns.set_palette(sns.color_palette('tab20', 20))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Path to the dataset\n#Path= '../input/3d-object-detection-for-autonomous-vehicles/'\nDATA_PATH = '../input/3d-object-detection-for-autonomous-vehicles/'\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Load the train dataset\ntrain=pd.read_csv(DATA_PATH+'train.csv')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Load sample submission\nsample_submission=pd.read_csv(DATA_PATH+'sample_submission.csv')\nsample_submission.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(f'Training Data : {train.shape}, Sample Submission Data : {sample_submission.shape}')","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"The annotations in train.csv:\n\ncenter_x, center_y and center_z are the world coordinates of the center of the 3D bounding volume.\n\nwidth, length and height are the dimensions of the volume.\n\nyaw is the angle of the volume around the z axis (where y is forward/back, x is left/right, and z is up/down - making 'yaw' the direction the front of the vehicle / bounding box is \npointing at while on the ground).\n\nclass_name is the type of object contained by the bounding volume.\n\nWe have 638K annotated objects in 22K train samples."},{"metadata":{"trusted":true},"cell_type":"code","source":"#Check the parsing of prediction String\nmax([len(ps.split(' ')) %8 for ps in train.PredictionString.values])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"!ln -s /kaggle/input/3d-object-detection-for-autonomous-vehicles/train_images images\n!ln -s /kaggle/input/3d-object-detection-for-autonomous-vehicles/train_maps maps\n!ln -s /kaggle/input/3d-object-detection-for-autonomous-vehicles/train_lidar lidar","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"level5data = LyftDataset(data_path='.', json_path='/kaggle/input/3d-object-detection-for-autonomous-vehicles/train_data', verbose=True)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"**Group data by Object Category**"},{"metadata":{"trusted":true},"cell_type":"code","source":"object_columns = ['sample_id', 'object_id', 'center_x', 'center_y', 'center_z',\n                  'width', 'length', 'height', 'yaw', 'class_name']\nobjects = []\nfor sample_id, ps in tqdm(train.values[:]):\n    object_params = ps.split()\n    n_objects = len(object_params)\n    for i in range(n_objects // 8):\n        x, y, z, w, l, h, yaw, c = tuple(object_params[i * 8: (i + 1) * 8])\n        objects.append([sample_id, i, x, y, z, w, l, h, yaw, c])\ntrain_objects = pd.DataFrame(\n    objects,\n    columns = object_columns\n)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"objects","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"**Convert numerical features from str to float32**"},{"metadata":{"trusted":true},"cell_type":"code","source":"numerical_cols = ['object_id', 'center_x', 'center_y', 'center_z', 'width', 'length', 'height', 'yaw']\ntrain_objects[numerical_cols] = np.float32(train_objects[numerical_cols].values)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_objects","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"**Data Exploration**"},{"metadata":{},"cell_type":"markdown","source":"centre_x is the x coordinate \n\ncentre_y is the y coordinate"},{"metadata":{"trusted":true},"cell_type":"code","source":"#Lets see the distribution of center_x and center_y\n\nfig,ax=plt.subplots(figsize=(10,10))\nsns.distplot(train_objects['center_x'],color='blue',ax=ax).set_title('center_x and center_y', fontsize=10)\nsns.distplot(train_objects['center_y'],color='pink',ax=ax).set_title('center_x and center_y', fontsize=10)\nplt.xlabel(\"center_x and center_y\")\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Observation : \n\n1. The distributions of both center_x and center_y have multiple peaks, hence its multimodal.\n\n2. The distribution of center_y(pink) has a signficantly higher skew that the the distribution of center_x (blue). \n\n3. The center_x distribution is more evenly spread out.\n\n4. This indicates that objects are spread out very evenly along the x-axis, but not likewise along the y-axis. This is probably because the car's camera can sense objects on either left or right easily (along the x-axis) due to the width of the road being small. But, since the length of the road is much greater than its width, and there is a higher chance of the camera's view being blocked from this angle, the camera can only find objects narrowly ahead or narrowly behind (and not further away)."},{"metadata":{},"cell_type":"markdown","source":"**Relationship between center_x and center_y**"},{"metadata":{"trusted":true},"cell_type":"code","source":"n_train_objects=train_objects.query('class_name==\"car\"')\nsns.jointplot(x=n_train_objects['center_x'][:1000], y=n_train_objects['center_y'][:1000],kind='kde',color=\"green\").set_axis_labels('center_x','center_y',fontsize=10)\nplt.show()\n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Observation : \n\n1. This shows a negative correlation between center_x and center_y.\n\n2. The camera cannot detect objects that are both far ahead and far to the side. Because of this, objects that are far ahead and far to the side are not detected at all, and only objects which satisfy one (or none) of those conditions are detected."},{"metadata":{},"cell_type":"markdown","source":"**Distribution of center_z** : \nz coordinate represents the height of the object above the x-y plane."},{"metadata":{"trusted":true},"cell_type":"code","source":"fig,ax=plt.subplots(figsize=(10,10))\nsns.distplot(train_objects['center_z'],color='blue',ax=ax).set_title('center_x and center_y', fontsize=10)\nplt.xlabel(\"center_z\")\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Observation :  The distribution of center_z has an extremely high positive (rightward) skew and is clustered around the -20 mark (its mean value). Most z coordinates are negative because the camera is attached at the top of the car. So, most of the times, the camera has to \"look down\" to see the objects. Therefore, the height or z-coordinate of the objects relative to the camera are generally negative."},{"metadata":{"trusted":true},"cell_type":"code","source":"fig,ax=plt.subplots(figsize=(10,10))\nsns.distplot(train_objects['yaw'],color='darkorange',ax=ax).set_title('yaw', fontsize=10)\nplt.xlabel(\"yaw\")\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Observations : The distribution of yaw is roughly bimodal and the the mean is between 1 and 2"},{"metadata":{},"cell_type":"markdown","source":"**Width**\n\nwidth is the width of the bounding volume in which the object lies."},{"metadata":{"trusted":true},"cell_type":"code","source":"fig,ax=plt.subplots(figsize=(10,10))\nsns.distplot(train_objects['width'],color='pink',ax=ax).set_title('width', fontsize=10)\nplt.xlabel(\"width\")\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Observations : The width is approximately normally distirbuted with a mean of around 2, with some outliers on either side. The majority of the objects are cars, and these constitute a width of around 2 ( peak). The outliers on the right represent larger objecs like trucks and vans, and the outliers on the left represent smaller objects like pedestrians and bicycles."},{"metadata":{},"cell_type":"markdown","source":"**Length**\n\nlength is the length of the bounding volume in which the object lies.\n\n"},{"metadata":{"trusted":true},"cell_type":"code","source":"fig,ax=plt.subplots(figsize=(10,10))\nsns.distplot(train_objects['length'],color='magenta',ax=ax).set_title('length', fontsize=10)\nplt.xlabel(\"Length\")\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Observations : the length has a distribution with a strong positive (right skewed) with a mean of around 5, with some outliers on either side. The majority of the objects are cars (as we will see later), and these constitute a length of around 5 (at the peak). The outliers on the right represent larger objecs like trucks and vans, and the outliers on the left represent smaller objects like pedestrians and bicycles."},{"metadata":{},"cell_type":"markdown","source":"**Height**\n\nheight is the height of the bounding volume in which the object lies."},{"metadata":{"trusted":true},"cell_type":"code","source":"fig,ax=plt.subplots(figsize=(10,10))\nsns.distplot(train_objects['height'],color='green',ax=ax).set_title('height', fontsize=10)\nplt.xlabel(\"Height\")\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Observations : the height has a distribution with a strong positive (rightward skew) with a mean of around 2, with some outliers on either side. The majority of the objects are cars , and these constitute a length of around 2 ( peak). The outliers on the right represent larger objecs like trucks and vans, and the outliers on the left represent smaller objects like pedestrians and bicycles."},{"metadata":{"trusted":true},"cell_type":"code","source":"#Calculate Object frequencies\n\nfig, ax = plt.subplots(figsize=(10, 10))\nplot = sns.countplot(y=\"class_name\", data=train_objects.query('class_name != \"motorcycle\" and class_name != \"emergency_vehicle\" and class_name != \"animal\"'),\n                     palette=['navy', 'darkblue', 'blue', 'dodgerblue', 'skyblue', 'lightblue']).set_title('Object Frequencies', fontsize=16)\nplt.yticks(fontsize=14)\nplt.xlabel(\"Count\", fontsize=15)\nplt.ylabel(\"Class Name\", fontsize=15)\nplt.show(plot)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Observation : most common object class in the dataset is \"car\""},{"metadata":{},"cell_type":"markdown","source":"**Width vs Class Name**"},{"metadata":{"trusted":true},"cell_type":"code","source":"fig,ax=plt.subplots(figsize=(16,10))\nsns.violinplot(x=\"class_name\", y=\"width\", data= train_objects.query('class_name != \"motorcycle\" and class_name != \"emergency_vehicle\" and class_name != \"animal\"'),\\\n              palette='Set2',split=True, ax=ax).set_title(\"Width vs Class Name\", fontsize=10)\nplt.xlabel(\"Class Name\")\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Observation : The width distributions for large vehicles like cars, buses, and trucks have much larger means as compared to small objects like pedestrians and bicycles."},{"metadata":{},"cell_type":"markdown","source":"**Length vs Class Name**"},{"metadata":{"trusted":true},"cell_type":"code","source":"fig,ax=plt.subplots(figsize=(16,10))\nsns.violinplot(x=\"class_name\", y='length', data=train_objects.query('class_name != \"motorcycle\" and class_name != \"emergency_vehicle\" and class_name != \"animal\"'),\\\n              palette='Set2',split=True, ax=ax).set_title(\"Length vs Class Name\", fontsize=10)\nplt.xlabel(\"Class Name \")\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Observation : the length distributions for large vehicles like cars, buses, and trucks have much larger means as compared to small objects like pedestrians and bicycles."},{"metadata":{},"cell_type":"markdown","source":"**Height vs Class Name**"},{"metadata":{"trusted":true},"cell_type":"code","source":"fig,ax=plt.subplots(figsize=(16,10))\nsns.violinplot(x=\"class_name\", y='height', data=train_objects.query('class_name != \"motorcycle\" and class_name != \"emergency_vehicle\" and class_name != \"animal\"'),\\\n              palette='Set2',split=True, ax=ax).set_title(\"Height vs Class Name\", fontsize=10)\nplt.xlabel(\"Class Name \")\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Observations : the height distributions for large vehicles like buses and trucks have much larger means as compared to small objects like pedestrians and bicycles. "},{"metadata":{},"cell_type":"markdown","source":"**Centre_x, Centre_y, Center_z vs Class Name**"},{"metadata":{"trusted":true},"cell_type":"code","source":"fig,ax=plt.subplots(figsize=(16,10))\nsns.violinplot(x=\"class_name\", y='center_x', data=train_objects.query('class_name != \"motorcycle\" and class_name != \"emergency_vehicle\" and class_name != \"animal\"'),\\\n              palette='Set2',split=True, ax=ax).set_title(\"Center_x vs Class Name\", fontsize=10)\nplt.xlabel(\"Class Name \")\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"fig,ax=plt.subplots(figsize=(16,10))\nsns.violinplot(x=\"class_name\", y='center_y', data=train_objects.query('class_name != \"motorcycle\" and class_name != \"emergency_vehicle\" and class_name != \"animal\"'),\\\n              palette='Set2',split=True, ax=ax).set_title(\"Center_y vs Class Name\", fontsize=10)\nplt.xlabel(\"Class Name \")\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"fig,ax=plt.subplots(figsize=(16,10))\nsns.violinplot(x=\"class_name\", y='center_z', data=train_objects.query('class_name != \"motorcycle\" and class_name != \"emergency_vehicle\" and class_name != \"animal\"'),\\\n              palette='Set2',split=True, ax=ax).set_title(\"Center_z vs Class Name\", fontsize=10)\nplt.xlabel(\"Class Name \")\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Observations :\n\n1. The distributions of center_x for large vehicles including trucks, buses, and other vehicles are well spread. They barely have any skew and have greater means than the distributions for pedestrians and bicycles. This is probably because these large vehicles tend to keep greater distances from the other vehicles, and the smaller vehicles do not stay too close to these large vehicles in order to avoid accidents. Therefore, the mean center_x is clearly greater for larger vehicles like buses and trucks.\n\n2. the distributions of center_y for small objects including pedestrians and bicycles have a greater mean value than large objects like trucks and buses. The distributions for the small objects have much greater probability density concentrated at higher values of center_y as compared to large objects. This signifies that small objects, in general, have greater center_y values than large objects.\n\n3. the distributions of center_z for small objects including pedestrians and bicycles have a significantly smaller mean value than large objects like trucks and buses. The distributions for the small objects have much greater probability density concentrated at lower values of center_z as compared to large objects. This signifies that small objects, in general, have smaller center_y values than large objects."},{"metadata":{},"cell_type":"markdown","source":"# LiDAR and Image Data "},{"metadata":{"trusted":true},"cell_type":"code","source":"!pip install lyft-dataset-sdk -q","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from datetime import datetime\nfrom functools import partial\nimport glob\nfrom multiprocessing import Pool\n\n# Disable multiprocesing for numpy/opencv. We already multiprocess ourselves, this would mean every subprocess produces\n# even more threads which would lead to a lot of context switching, slowing things down a lot.\nimport os\nos.environ[\"OMP_NUM_THREADS\"] = \"1\"\n\nimport matplotlib.pyplot as plt\n%matplotlib inline\n\nimport pandas as pd\nimport cv2\nfrom PIL import Image\nimport numpy as np\nfrom tqdm import tqdm, tqdm_notebook\nimport scipy\nimport scipy.ndimage\nimport scipy.special\nfrom scipy.spatial.transform import Rotation as R\nfrom pathlib import Path\n\nfrom lyft_dataset_sdk.lyftdataset import LyftDataset,LyftDatasetExplorer\nfrom lyft_dataset_sdk.utils.data_classes import LidarPointCloud, Box, Quaternion\nfrom lyft_dataset_sdk.utils.geometry_utils import view_points, transform_matrix\nimport time\nfrom lyft_dataset_sdk.utils.map_mask import MapMask","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"!ln -s /kaggle/input/3d-object-detection-for-autonomous-vehicles/test_images images\n!ln -s /kaggle/input/3d-object-detection-for-autonomous-vehicles/test_maps maps\n!ln -s /kaggle/input/3d-object-detection-for-autonomous-vehicles/test_lidar lidar","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"class LyftTestDataset(LyftDataset):\n    \"\"\"Database class for Lyft Dataset to help query and retrieve information from the database.\"\"\"\n\n    def __init__(self, data_path: str, json_path: str, verbose: bool = True, map_resolution: float = 0.1):\n        \"\"\"Loads database and creates reverse indexes and shortcuts.\n        Args:\n            data_path: Path to the tables and data.\n            json_path: Path to the folder with json files\n            verbose: Whether to print status messages during load.\n            map_resolution: Resolution of maps (meters).\n        \"\"\"\n\n        self.data_path = Path(data_path).expanduser().absolute()\n        self.json_path = Path(json_path)\n\n        self.table_names = [\n            \"category\",\n            \"attribute\",\n            \"sensor\",\n            \"calibrated_sensor\",\n            \"ego_pose\",\n            \"log\",\n            \"scene\",\n            \"sample\",\n            \"sample_data\",\n            \"map\",\n        ]\n\n        start_time = time.time()\n\n        # Explicitly assign tables to help the IDE determine valid class members.\n        self.category = self.__load_table__(\"category\")\n        self.attribute = self.__load_table__(\"attribute\")\n        \n        \n        self.sensor = self.__load_table__(\"sensor\")\n        self.calibrated_sensor = self.__load_table__(\"calibrated_sensor\")\n        self.ego_pose = self.__load_table__(\"ego_pose\")\n        self.log = self.__load_table__(\"log\")\n        self.scene = self.__load_table__(\"scene\")\n        self.sample = self.__load_table__(\"sample\")\n        self.sample_data = self.__load_table__(\"sample_data\")\n        \n        self.map = self.__load_table__(\"map\")\n\n        # Initialize map mask for each map record.\n        for map_record in self.map:\n            map_record[\"mask\"] = MapMask(self.data_path / map_record[\"filename\"], resolution=map_resolution)\n\n        if verbose:\n            for table in self.table_names:\n                print(\"{} {},\".format(len(getattr(self, table)), table))\n            print(\"Done loading in {:.1f} seconds.\\n======\".format(time.time() - start_time))\n\n        # Initialize LyftDatasetExplorer class\n        self.explorer = LyftDatasetExplorer(self)\n        # Make reverse indexes for common lookups.\n        self.__make_reverse_index__(verbose)\n        \n    def __make_reverse_index__(self, verbose: bool) -> None:\n        \"\"\"De-normalizes database to create reverse indices for common cases.\n        Args:\n            verbose: Whether to print outputs.\n        \"\"\"\n\n        start_time = time.time()\n        if verbose:\n            print(\"Reverse indexing ...\")\n\n        # Store the mapping from token to table index for each table.\n        self._token2ind = dict()\n        for table in self.table_names:\n            self._token2ind[table] = dict()\n\n            for ind, member in enumerate(getattr(self, table)):\n                self._token2ind[table][member[\"token\"]] = ind\n\n        # Decorate (adds short-cut) sample_data with sensor information.\n        for record in self.sample_data:\n            cs_record = self.get(\"calibrated_sensor\", record[\"calibrated_sensor_token\"])\n            sensor_record = self.get(\"sensor\", cs_record[\"sensor_token\"])\n            record[\"sensor_modality\"] = sensor_record[\"modality\"]\n            record[\"channel\"] = sensor_record[\"channel\"]\n\n        # Reverse-index samples with sample_data and annotations.\n        for record in self.sample:\n            record[\"data\"] = {}\n            record[\"anns\"] = []\n\n        for record in self.sample_data:\n            if record[\"is_key_frame\"]:\n                sample_record = self.get(\"sample\", record[\"sample_token\"])\n                sample_record[\"data\"][record[\"channel\"]] = record[\"token\"]\n\n        # Add reverse indices from log records to map records.\n        if \"log_tokens\" not in self.map[0].keys():\n            raise Exception(\"Error: log_tokens not in map table. This code is not compatible with the teaser dataset.\")\n        log_to_map = dict()\n        for map_record in self.map:\n            for log_token in map_record[\"log_tokens\"]:\n                log_to_map[log_token] = map_record[\"token\"]\n        for log_record in self.log:\n            log_record[\"map_token\"] = log_to_map[log_record[\"token\"]]\n\n        if verbose:\n            print(\"Done reverse indexing in {:.1f} seconds.\\n======\".format(time.time() - start_time))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"level5data = LyftTestDataset(data_path='.', json_path='/kaggle/input/3d-object-detection-for-autonomous-vehicles/test_data', verbose=True)\n# Our code will generate data, visualization and model checkpoints, they will be persisted to disk in this folder\nARTIFACTS_FOLDER = \"./artifacts\"\nos.makedirs(ARTIFACTS_FOLDER, exist_ok=True)\nclasses = [\"car\", \"motorcycle\", \"bus\", \"bicycle\", \"truck\", \"pedestrian\", \"other_vehicle\", \"animal\", \"emergency_vehicle\"]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def create_transformation_matrix_to_voxel_space(shape, voxel_size, offset):\n    \"\"\"\n    Constructs a transformation matrix given an output voxel shape such that (0,0,0) ends up in the center.\n    Voxel_size defines how large every voxel is in world coordinate, (1,1,1) would be the same as Minecraft voxels.\n    \n    An offset per axis in world coordinates (metric) can be provided, this is useful for Z (up-down) in lidar points.\n    \"\"\"\n    \n    shape, voxel_size, offset = np.array(shape), np.array(voxel_size), np.array(offset)\n    \n    tm = np.eye(4, dtype=np.float32)\n    translation = shape/2 + offset/voxel_size\n    \n    tm = tm * np.array(np.hstack((1/voxel_size, [1])))\n    tm[:3, 3] = np.transpose(translation)\n    return tm\n\ndef transform_points(points, transf_matrix):\n    \"\"\"\n    Transform (3,N) or (4,N) points using transformation matrix.\n    \"\"\"\n    if points.shape[0] not in [3,4]:\n        raise Exception(\"Points input should be (3,N) or (4,N) shape, received {}\".format(points.shape))\n    return transf_matrix.dot(np.vstack((points[:3, :], np.ones(points.shape[1]))))[:3, :]\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def car_to_voxel_coords(points, shape, voxel_size, z_offset=0):\n    if len(shape) != 3:\n        raise Exception(\"Voxel volume shape should be 3 dimensions (x,y,z)\")\n        \n    if len(points.shape) != 2 or points.shape[0] not in [3, 4]:\n        raise Exception(\"Input points should be (3,N) or (4,N) in shape, found {}\".format(points.shape))\n\n    tm = create_transformation_matrix_to_voxel_space(shape, voxel_size, (0, 0, z_offset))\n    p = transform_points(points, tm)\n    return p\n\ndef create_voxel_pointcloud(points, shape, voxel_size=(0.5,0.5,1), z_offset=0):\n\n    points_voxel_coords = car_to_voxel_coords(points.copy(), shape, voxel_size, z_offset)\n    points_voxel_coords = points_voxel_coords[:3].transpose(1,0)\n    points_voxel_coords = np.int0(points_voxel_coords)\n    \n    bev = np.zeros(shape, dtype=np.float32)\n    bev_shape = np.array(shape)\n\n    within_bounds = (np.all(points_voxel_coords >= 0, axis=1) * np.all(points_voxel_coords < bev_shape, axis=1))\n    \n    points_voxel_coords = points_voxel_coords[within_bounds]\n    coord, count = np.unique(points_voxel_coords, axis=0, return_counts=True)\n        \n    # Note X and Y are flipped:\n    bev[coord[:,1], coord[:,0], coord[:,2]] = count\n    \n    return bev\n\ndef normalize_voxel_intensities(bev, max_intensity=16):\n    return (bev/max_intensity).clip(0,1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"bev_shape = (336, 336, 3)\ntarget_im = np.zeros(bev_shape, dtype=np.uint8)\n\ndef move_boxes_to_car_space(boxes, ego_pose):\n    \"\"\"\n    Move boxes from world space to car space.\n    Note: mutates input boxes.\n    \"\"\"\n    translation = -np.array(ego_pose['translation'])\n    rotation = Quaternion(ego_pose['rotation']).inverse\n    \n    for box in boxes:\n        # Bring box to car space\n        box.translate(translation)\n        box.rotate(rotation)\n        \ndef scale_boxes(boxes, factor):\n    \"\"\"\n    Note: mutates input boxes\n    \"\"\"\n    for box in boxes:\n        box.wlh = box.wlh * factor\n\ndef draw_boxes(im, voxel_size, boxes, classes, z_offset=0.0):\n    for box in boxes:\n        # We only care about the bottom corners\n        corners = box.bottom_corners()\n        corners_voxel = car_to_voxel_coords(corners, im.shape, voxel_size, z_offset).transpose(1,0)\n        corners_voxel = corners_voxel[:,:2] # Drop z coord\n\n        class_color = classes.index(box.name) + 1\n        \n        if class_color == 0:\n            raise Exception(\"Unknown class: {}\".format(box.name))\n\n        cv2.drawContours(im, np.int0([corners_voxel]), 0, (class_color, class_color, class_color), -1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def visualize_lidar_of_sample(sample_token, axes_limit=80):\n    sample = level5data.get(\"sample\", sample_token)\n    sample_lidar_token = sample[\"data\"][\"LIDAR_TOP\"]\n    level5data.render_sample_data(sample_lidar_token, axes_limit=axes_limit)\n    \n# Don't worry about it being mirrored.\nvisualize_lidar_of_sample(sample_sub.loc[0,'Id'])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Some hyperparameters we'll need to define for the system\nvoxel_size = (0.4, 0.4, 1.5)\nz_offset = -2.0\nbev_shape = (336, 336, 3)\n\n# We scale down each box so they are more separated when projected into our coarse voxel space.\nbox_scale = 0.8\n\nNUM_WORKERS = os.cpu_count() * 3\n\n# \"bev\" stands for birds eye view\n# test_data_folder = os.path.join(ARTIFACTS_FOLDER, \"bev_test_data\")\ntest_data_folder = '/kaggle/working/artifacts'","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def prepare_testing_data_for_scene(sample_token, output_folder=test_data_folder,\n                                   bev_shape=bev_shape, voxel_size=voxel_size, z_offset=z_offset,\n                                   box_scale=box_scale):\n    \"\"\"\n    Given a sample token (in a scene), output rasterized input volumes in birds-eye-view perspective.\n\n    \"\"\"\n    \n#     while sample_token:\n        \n    sample = level5data.get(\"sample\", sample_token)\n    \n\n    sample_lidar_token = sample[\"data\"][\"LIDAR_TOP\"]\n    lidar_data = level5data.get(\"sample_data\", sample_lidar_token)\n    lidar_filepath = level5data.get_sample_data_path(sample_lidar_token)\n    \n    \n\n    ego_pose = level5data.get(\"ego_pose\", lidar_data[\"ego_pose_token\"])\n    calibrated_sensor = level5data.get(\"calibrated_sensor\", lidar_data[\"calibrated_sensor_token\"])\n    \n\n\n    global_from_car = transform_matrix(ego_pose['translation'],\n                                       Quaternion(ego_pose['rotation']), inverse=False)\n    \n\n    car_from_sensor = transform_matrix(calibrated_sensor['translation'], Quaternion(calibrated_sensor['rotation']),\n                                        inverse=False)\n    \n    \n    lidar_pointcloud = LidarPointCloud.from_file(lidar_filepath)\n    \n    lidar_pointcloud.transform(car_from_sensor)\n\n    bev = create_voxel_pointcloud(lidar_pointcloud.points, bev_shape, voxel_size=voxel_size, z_offset=z_offset)\n    bev = normalize_voxel_intensities(bev)\n\n    bev_im = np.round(bev*255).astype(np.uint8)\n\n    cv2.imwrite(os.path.join(output_folder, \"{}_input.png\".format(sample_token)), bev_im)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"for token in tqdm_notebook(sample_sub.loc[:,'Id'].values):\n    prepare_testing_data_for_scene(token)\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"!tar -czf lyft3d_bev_test_data.tar.gz ./artifacts/","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"!du -h lyft3d_bev_test_data.tar.gz","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"!rm -r ./artifacts","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sample_sub = pd.read_csv('../input/3d-object-detection-for-autonomous-vehicles/sample_submission.csv')\nsample_sub.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Lyft Dataset SDK dev-kit.\n# Code written by Oscar Beijbom, 2018.\n# Licensed under the Creative Commons [see licence.txt]\n# Modified by Vladimir Iglovikov 2019.\n\nclass PointCloud(ABC):\n    \"\"\"\n    Abstract class for manipulating and viewing point clouds.\n    Every point cloud (lidar and radar) consists of points where:\n    - Dimensions 0, 1, 2 represent x, y, z coordinates.\n        These are modified when the point cloud is rotated or translated.\n    - All other dimensions are optional. Hence these have to be manually modified if the reference frame changes.\n    \"\"\"\n\n    def __init__(self, points: np.ndarray):\n        \"\"\"\n        Initialize a point cloud and check it has the correct dimensions.\n        :param points: <np.float: d, n>. d-dimensional input point cloud matrix.\n        \"\"\"\n        assert points.shape[0] == self.nbr_dims(), (\n            \"Error: Pointcloud points must have format: %d x n\" % self.nbr_dims()\n        )\n        self.points = points\n\n    @staticmethod\n    @abstractmethod\n    def nbr_dims() -> int:\n        \"\"\"Returns the number of dimensions.\n        Returns: Number of dimensions.\n        \"\"\"\n        pass\n\n    @classmethod\n    @abstractmethod\n    def from_file(cls, file_name: str) -> \"PointCloud\":\n        \"\"\"Loads point cloud from disk.\n        Args:\n            file_name: Path of the pointcloud file on disk.\n        Returns: PointCloud instance.\n        \"\"\"\n        pass\n\n    @classmethod\n    def from_file_multisweep(\n        cls, lyftd, sample_rec: Dict, chan: str, ref_chan: str, num_sweeps: int = 26, min_distance: float = 1.0\n    ) -> Tuple[\"PointCloud\", np.ndarray]:\n        \"\"\"Return a point cloud that aggregates multiple sweeps.\n        As every sweep is in a different coordinate frame, we need to map the coordinates to a single reference frame.\n        As every sweep has a different timestamp, we need to account for that in the transformations and timestamps.\n        Args:\n            lyftd: A LyftDataset instance.\n            sample_rec: The current sample.\n            chan: The radar channel from which we track back n sweeps to aggregate the point cloud.\n            ref_chan: The reference channel of the current sample_rec that the point clouds are mapped to.\n            num_sweeps: Number of sweeps to aggregated.\n            min_distance: Distance below which points are discarded.\n        Returns: (all_pc, all_times). The aggregated point cloud and timestamps.\n        \"\"\"\n\n        # Init\n        points = np.zeros((cls.nbr_dims(), 0))\n        all_pc = cls(points)\n        all_times = np.zeros((1, 0))\n\n        # Get reference pose and timestamp\n        ref_sd_token = sample_rec[\"data\"][ref_chan]\n        ref_sd_rec = lyftd.get(\"sample_data\", ref_sd_token)\n        ref_pose_rec = lyftd.get(\"ego_pose\", ref_sd_rec[\"ego_pose_token\"])\n        ref_cs_rec = lyftd.get(\"calibrated_sensor\", ref_sd_rec[\"calibrated_sensor_token\"])\n        ref_time = 1e-6 * ref_sd_rec[\"timestamp\"]\n\n        # Homogeneous transform from ego car frame to reference frame\n        ref_from_car = transform_matrix(ref_cs_rec[\"translation\"], Quaternion(ref_cs_rec[\"rotation\"]), inverse=True)\n\n        # Homogeneous transformation matrix from global to _current_ ego car frame\n        car_from_global = transform_matrix(\n            ref_pose_rec[\"translation\"], Quaternion(ref_pose_rec[\"rotation\"]), inverse=True\n        )\n\n        # Aggregate current and previous sweeps.\n        sample_data_token = sample_rec[\"data\"][chan]\n        current_sd_rec = lyftd.get(\"sample_data\", sample_data_token)\n        for _ in range(num_sweeps):\n            # Load up the pointcloud.\n            current_pc = cls.from_file(lyftd.data_path / ('train_' + current_sd_rec[\"filename\"]))\n\n            # Get past pose.\n            current_pose_rec = lyftd.get(\"ego_pose\", current_sd_rec[\"ego_pose_token\"])\n            global_from_car = transform_matrix(\n                current_pose_rec[\"translation\"], Quaternion(current_pose_rec[\"rotation\"]), inverse=False\n            )\n\n            # Homogeneous transformation matrix from sensor coordinate frame to ego car frame.\n            current_cs_rec = lyftd.get(\"calibrated_sensor\", current_sd_rec[\"calibrated_sensor_token\"])\n            car_from_current = transform_matrix(\n                current_cs_rec[\"translation\"], Quaternion(current_cs_rec[\"rotation\"]), inverse=False\n            )\n\n            # Fuse four transformation matrices into one and perform transform.\n            trans_matrix = reduce(np.dot, [ref_from_car, car_from_global, global_from_car, car_from_current])\n            current_pc.transform(trans_matrix)\n\n            # Remove close points and add timevector.\n            current_pc.remove_close(min_distance)\n            time_lag = ref_time - 1e-6 * current_sd_rec[\"timestamp\"]  # positive difference\n            times = time_lag * np.ones((1, current_pc.nbr_points()))\n            all_times = np.hstack((all_times, times))\n\n            # Merge with key pc.\n            all_pc.points = np.hstack((all_pc.points, current_pc.points))\n\n            # Abort if there are no previous sweeps.\n            if current_sd_rec[\"prev\"] == \"\":\n                break\n            else:\n                current_sd_rec = lyftd.get(\"sample_data\", current_sd_rec[\"prev\"])\n\n        return all_pc, all_times\n\n    def nbr_points(self) -> int:\n        \"\"\"Returns the number of points.\"\"\"\n        return self.points.shape[1]\n\n    def subsample(self, ratio: float) -> None:\n        \"\"\"Sub-samples the pointcloud.\n        Args:\n            ratio: Fraction to keep.\n        \"\"\"\n        selected_ind = np.random.choice(np.arange(0, self.nbr_points()), size=int(self.nbr_points() * ratio))\n        self.points = self.points[:, selected_ind]\n\n    def remove_close(self, radius: float) -> None:\n        \"\"\"Removes point too close within a certain radius from origin.\n        Args:\n            radius: Radius below which points are removed.\n        Returns:\n        \"\"\"\n        x_filt = np.abs(self.points[0, :]) < radius\n        y_filt = np.abs(self.points[1, :]) < radius\n        not_close = np.logical_not(np.logical_and(x_filt, y_filt))\n        self.points = self.points[:, not_close]\n\n    def translate(self, x: np.ndarray) -> None:\n        \"\"\"Applies a translation to the point cloud.\n        Args:\n            x: <np.float: 3, 1>. Translation in x, y, z.\n        \"\"\"\n        for i in range(3):\n            self.points[i, :] = self.points[i, :] + x[i]\n\n    def rotate(self, rot_matrix: np.ndarray) -> None:\n        \"\"\"Applies a rotation.\n        Args:\n            rot_matrix: <np.float: 3, 3>. Rotation matrix.\n        Returns:\n        \"\"\"\n        self.points[:3, :] = np.dot(rot_matrix, self.points[:3, :])\n\n    def transform(self, transf_matrix: np.ndarray) -> None:\n        \"\"\"Applies a homogeneous transform.\n        Args:\n            transf_matrix: transf_matrix: <np.float: 4, 4>. Homogenous transformation matrix.\n        \"\"\"\n        self.points[:3, :] = transf_matrix.dot(np.vstack((self.points[:3, :], np.ones(self.nbr_points()))))[:3, :]\n\n    def render_height(\n        self,\n        ax: Axes,\n        view: np.ndarray = np.eye(4),\n        x_lim: Tuple = (-20, 20),\n        y_lim: Tuple = (-20, 20),\n        marker_size: float = 1,\n    ) -> None:\n        \"\"\"Simple method that applies a transformation and then scatter plots the points colored by height (z-value).\n        Args:\n            ax: Axes on which to render the points.\n            view: <np.float: n, n>. Defines an arbitrary projection (n <= 4).\n            x_lim: (min <float>, max <float>). x range for plotting.\n            y_lim: (min <float>, max <float>). y range for plotting.\n            marker_size: Marker size.\n        \"\"\"\n        self._render_helper(2, ax, view, x_lim, y_lim, marker_size)\n\n    def render_intensity(\n        self,\n        ax: Axes,\n        view: np.ndarray = np.eye(4),\n        x_lim: Tuple = (-20, 20),\n        y_lim: Tuple = (-20, 20),\n        marker_size: float = 1,\n    ) -> None:\n        \"\"\"Very simple method that applies a transformation and then scatter plots the points colored by intensity.\n        Args:\n            ax: Axes on which to render the points.\n            view: <np.float: n, n>. Defines an arbitrary projection (n <= 4).\n            x_lim: (min <float>, max <float>).\n            y_lim: (min <float>, max <float>).\n            marker_size: Marker size.\n        Returns:\n        \"\"\"\n        self._render_helper(3, ax, view, x_lim, y_lim, marker_size)\n\n    def _render_helper(\n        self, color_channel: int, ax: Axes, view: np.ndarray, x_lim: Tuple, y_lim: Tuple, marker_size: float\n    ) -> None:\n        \"\"\"Helper function for rendering.\n        Args:\n            color_channel: Point channel to use as color.\n            ax: Axes on which to render the points.\n            view: <np.float: n, n>. Defines an arbitrary projection (n <= 4).\n            x_lim: (min <float>, max <float>).\n            y_lim: (min <float>, max <float>).\n            marker_size: Marker size.\n        \"\"\"\n        points = view_points(self.points[:3, :], view, normalize=False)\n        ax.scatter(points[0, :], points[1, :], c=self.points[color_channel, :], s=marker_size)\n        ax.set_xlim(x_lim[0], x_lim[1])\n        ax.set_ylim(y_lim[0], y_lim[1])\n\n\nclass LidarPointCloud(PointCloud):\n    @staticmethod\n    def nbr_dims() -> int:\n        \"\"\"Returns the number of dimensions.\n        Returns: Number of dimensions.\n        \"\"\"\n        return 4\n\n    @classmethod\n    def from_file(cls, file_name: Path) -> \"LidarPointCloud\":\n        \"\"\"Loads LIDAR data from binary numpy format. Data is stored as (x, y, z, intensity, ring index).\n        Args:\n            file_name: Path of the pointcloud file on disk.\n        Returns: LidarPointCloud instance (x, y, z, intensity).\n        \"\"\"\n\n        assert file_name.suffix == \".bin\", \"Unsupported filetype {}\".format(file_name)\n\n        scan = np.fromfile(str(file_name), dtype=np.float32)\n        points = scan.reshape((-1, 5))[:, : cls.nbr_dims()]\n        return cls(points.T)\n\n\nclass RadarPointCloud(PointCloud):\n\n    # Class-level settings for radar pointclouds, see from_file().\n    invalid_states = [0]  # type: List[int]\n    dynprop_states = range(7)  # type: List[int] # Use [0, 2, 6] for moving objects only.\n    ambig_states = [3]  # type: List[int]\n\n    @staticmethod\n    def nbr_dims() -> int:\n        \"\"\"Returns the number of dimensions.\n        Returns: Number of dimensions.\n        \"\"\"\n        return 18\n\n    @classmethod\n    def from_file(\n        cls,\n        file_name: Path,\n        invalid_states: List[int] = None,\n        dynprop_states: List[int] = None,\n        ambig_states: List[int] = None,\n    ) -> \"RadarPointCloud\":\n        \"\"\"Loads RADAR data from a Point Cloud Data file. See details below.\n        Args:\n            file_name: The path of the pointcloud file.\n            invalid_states: Radar states to be kept. See details below.\n            dynprop_states: Radar states to be kept. Use [0, 2, 6] for moving objects only. See details below.\n            ambig_states: Radar states to be kept. See details below. To keep all radar returns,\n                set each state filter to range(18).\n        Returns: <np.float: d, n>. Point cloud matrix with d dimensions and n points.\n        Example of the header fields:\n        # .PCD v0.7 - Point Cloud Data file format\n        VERSION 0.7\n        FIELDS x y z dyn_prop id rcs vx vy vx_comp vy_comp is_quality_valid ambig_\n                                                            state x_rms y_rms invalid_state pdh0 vx_rms vy_rms\n        SIZE 4 4 4 1 2 4 4 4 4 4 1 1 1 1 1 1 1 1\n        TYPE F F F I I F F F F F I I I I I I I I\n        COUNT 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n        WIDTH 125\n        HEIGHT 1\n        VIEWPOINT 0 0 0 1 0 0 0\n        POINTS 125\n        DATA binary\n        Below some of the fields are explained in more detail:\n        x is front, y is left\n        vx, vy are the velocities in m/s.\n        vx_comp, vy_comp are the velocities in m/s compensated by the ego motion.\n        We recommend using the compensated velocities.\n        invalid_state: state of Cluster validity state.\n        (Invalid states)\n        0x01\tinvalid due to low RCS\n        0x02\tinvalid due to near-field artefact\n        0x03\tinvalid far range cluster because not confirmed in near range\n        0x05\treserved\n        0x06\tinvalid cluster due to high mirror probability\n        0x07\tInvalid cluster because outside sensor field of view\n        0x0d\treserved\n        0x0e\tinvalid cluster because it is a harmonics\n        (Valid states)\n        0x00\tvalid\n        0x04\tvalid cluster with low RCS\n        0x08\tvalid cluster with azimuth correction due to elevation\n        0x09\tvalid cluster with high child probability\n        0x0a\tvalid cluster with high probability of being a 50 deg artefact\n        0x0b\tvalid cluster but no local maximum\n        0x0c\tvalid cluster with high artefact probability\n        0x0f\tvalid cluster with above 95m in near range\n        0x10\tvalid cluster with high multi-target probability\n        0x11\tvalid cluster with suspicious angle\n        dynProp: Dynamic property of cluster to indicate if is moving or not.\n        0: moving\n        1: stationary\n        2: oncoming\n        3: stationary candidate\n        4: unknown\n        5: crossing stationary\n        6: crossing moving\n        7: stopped\n        ambig_state: State of Doppler (radial velocity) ambiguity solution.\n        0: invalid\n        1: ambiguous\n        2: staggered ramp\n        3: unambiguous\n        4: stationary candidates\n        pdh0: False alarm probability of cluster (i.e. probability of being an artefact caused\n                                                                                    by multipath or similar).\n        0: invalid\n        1: <25%\n        2: 50%\n        3: 75%\n        4: 90%\n        5: 99%\n        6: 99.9%\n        7: <=100%\n        \"\"\"\n\n        assert file_name.suffix == \".pcd\", \"Unsupported filetype {}\".format(file_name)\n\n        meta = []\n        with open(str(file_name), \"rb\") as f:\n            for line in f:\n                line = line.strip().decode(\"utf-8\")\n                meta.append(line)\n                if line.startswith(\"DATA\"):\n                    break\n\n            data_binary = f.read()\n\n        # Get the header rows and check if they appear as expected.\n        assert meta[0].startswith(\"#\"), \"First line must be comment\"\n        assert meta[1].startswith(\"VERSION\"), \"Second line must be VERSION\"\n        sizes = meta[3].split(\" \")[1:]\n        types = meta[4].split(\" \")[1:]\n        counts = meta[5].split(\" \")[1:]\n        width = int(meta[6].split(\" \")[1])\n        height = int(meta[7].split(\" \")[1])\n        data = meta[10].split(\" \")[1]\n        feature_count = len(types)\n        assert width > 0\n        assert len([c for c in counts if c != c]) == 0, \"Error: COUNT not supported!\"\n        assert height == 1, \"Error: height != 0 not supported!\"\n        assert data == \"binary\"\n\n        # Lookup table for how to decode the binaries.\n        unpacking_lut = {\n            \"F\": {2: \"e\", 4: \"f\", 8: \"d\"},\n            \"I\": {1: \"b\", 2: \"h\", 4: \"i\", 8: \"q\"},\n            \"U\": {1: \"B\", 2: \"H\", 4: \"I\", 8: \"Q\"},\n        }\n        types_str = \"\".join([unpacking_lut[t][int(s)] for t, s in zip(types, sizes)])\n\n        # Decode each point.\n        offset = 0\n        point_count = width\n        points = []\n        for i in range(point_count):\n            point = []\n            for p in range(feature_count):\n                start_p = offset\n                end_p = start_p + int(sizes[p])\n                assert end_p < len(data_binary)\n                point_p = struct.unpack(types_str[p], data_binary[start_p:end_p])[0]\n                point.append(point_p)\n                offset = end_p\n            points.append(point)\n\n        # A NaN in the first point indicates an empty pointcloud.\n        point = np.array(points[0])\n        if np.any(np.isnan(point)):\n            return cls(np.zeros((feature_count, 0)))\n\n        # Convert to numpy matrix.\n        points = np.array(points).transpose()\n\n        # If no parameters are provided, use default settings.\n        invalid_states = cls.invalid_states if invalid_states is None else invalid_states\n        dynprop_states = cls.dynprop_states if dynprop_states is None else dynprop_states\n        ambig_states = cls.ambig_states if ambig_states is None else ambig_states\n\n        # Filter points with an invalid state.\n        valid = [p in invalid_states for p in points[-4, :]]\n        points = points[:, valid]\n\n        # Filter by dynProp.\n        valid = [p in dynprop_states for p in points[3, :]]\n        points = points[:, valid]\n\n        # Filter by ambig_state.\n        valid = [p in ambig_states for p in points[11, :]]\n        points = points[:, valid]\n\n        return cls(points)\n\n\nclass Box:\n    \"\"\" Simple data class representing a 3d box including, label, score and velocity. \"\"\"\n\n    def __init__(\n        self,\n        center: List[float],\n        size: List[float],\n        orientation: Quaternion,\n        label: int = np.nan,\n        score: float = np.nan,\n        velocity: Tuple = (np.nan, np.nan, np.nan),\n        name: str = None,\n        token: str = None,\n    ):\n        \"\"\"\n        Args:\n            center: Center of box given as x, y, z.\n            size: Size of box in width, length, height.\n            orientation: Box orientation.\n            label: Integer label, optional.\n            score: Classification score, optional.\n            velocity: Box velocity in x, y, z direction.\n            name: Box name, optional. Can be used e.g. for denote category name.\n            token: Unique string identifier from DB.\n        \"\"\"\n        assert not np.any(np.isnan(center))\n        assert not np.any(np.isnan(size))\n        assert len(center) == 3\n        assert len(size) == 3\n        assert type(orientation) == Quaternion\n\n        self.center = np.array(center)\n        self.wlh = np.array(size)\n        self.orientation = orientation\n        self.label = int(label) if not np.isnan(label) else label\n        self.score = float(score) if not np.isnan(score) else score\n        self.velocity = np.array(velocity)\n        self.name = name\n        self.token = token\n\n    def __eq__(self, other):\n        center = np.allclose(self.center, other.center)\n        wlh = np.allclose(self.wlh, other.wlh)\n        orientation = np.allclose(self.orientation.elements, other.orientation.elements)\n        label = (self.label == other.label) or (np.isnan(self.label) and np.isnan(other.label))\n        score = (self.score == other.score) or (np.isnan(self.score) and np.isnan(other.score))\n        vel = np.allclose(self.velocity, other.velocity) or (\n            np.all(np.isnan(self.velocity)) and np.all(np.isnan(other.velocity))\n        )\n\n        return center and wlh and orientation and label and score and vel\n\n    def __repr__(self):\n        repr_str = (\n            \"label: {}, score: {:.2f}, xyz: [{:.2f}, {:.2f}, {:.2f}], wlh: [{:.2f}, {:.2f}, {:.2f}], \"\n            \"rot axis: [{:.2f}, {:.2f}, {:.2f}], ang(degrees): {:.2f}, ang(rad): {:.2f}, \"\n            \"vel: {:.2f}, {:.2f}, {:.2f}, name: {}, token: {}\"\n        )\n\n        return repr_str.format(\n            self.label,\n            self.score,\n            self.center[0],\n            self.center[1],\n            self.center[2],\n            self.wlh[0],\n            self.wlh[1],\n            self.wlh[2],\n            self.orientation.axis[0],\n            self.orientation.axis[1],\n            self.orientation.axis[2],\n            self.orientation.degrees,\n            self.orientation.radians,\n            self.velocity[0],\n            self.velocity[1],\n            self.velocity[2],\n            self.name,\n            self.token,\n        )\n\n    @property\n    def rotation_matrix(self) -> np.ndarray:\n        \"\"\"Return a rotation matrix.\n        Returns: <np.float: 3, 3>. The box's rotation matrix.\n        \"\"\"\n        return self.orientation.rotation_matrix\n\n    def translate(self, x: np.ndarray) -> None:\n        \"\"\"Applies a translation.\n        Args:\n            x: <np.float: 3, 1>. Translation in x, y, z direction.\n        \"\"\"\n        self.center += x\n\n    def rotate(self, quaternion: Quaternion) -> None:\n        \"\"\"Rotates box.\n        Args:\n            quaternion: Rotation to apply.\n        \"\"\"\n        self.center = np.dot(quaternion.rotation_matrix, self.center)\n        self.orientation = quaternion * self.orientation\n        self.velocity = np.dot(quaternion.rotation_matrix, self.velocity)\n\n    def corners(self, wlh_factor: float = 1.0) -> np.ndarray:\n        \"\"\"Returns the bounding box corners.\n        Args:\n            wlh_factor: Multiply width, length, height by a factor to scale the box.\n        Returns: First four corners are the ones facing forward.\n                The last four are the ones facing backwards.\n        \"\"\"\n\n        width, length, height = self.wlh * wlh_factor\n\n        # 3D bounding box corners. (Convention: x points forward, y to the left, z up.)\n        x_corners = length / 2 * np.array([1, 1, 1, 1, -1, -1, -1, -1])\n        y_corners = width / 2 * np.array([1, -1, -1, 1, 1, -1, -1, 1])\n        z_corners = height / 2 * np.array([1, 1, -1, -1, 1, 1, -1, -1])\n        corners = np.vstack((x_corners, y_corners, z_corners))\n\n        # Rotate\n        corners = np.dot(self.orientation.rotation_matrix, corners)\n\n        # Translate\n        x, y, z = self.center\n        corners[0, :] = corners[0, :] + x\n        corners[1, :] = corners[1, :] + y\n        corners[2, :] = corners[2, :] + z\n\n        return corners\n\n    def bottom_corners(self) -> np.ndarray:\n        \"\"\"Returns the four bottom corners.\n        Returns: <np.float: 3, 4>. Bottom corners. First two face forward, last two face backwards.\n        \"\"\"\n        return self.corners()[:, [2, 3, 7, 6]]\n\n    def render(\n        self,\n        axis: Axes,\n        view: np.ndarray = np.eye(3),\n        normalize: bool = False,\n        colors: Tuple = (\"b\", \"r\", \"k\"),\n        linewidth: float = 2,\n    ):\n        \"\"\"Renders the box in the provided Matplotlib axis.\n        Args:\n            axis: Axis onto which the box should be drawn.\n            view: <np.array: 3, 3>. Define a projection in needed (e.g. for drawing projection in an image).\n            normalize: Whether to normalize the remaining coordinate.\n            colors: (<Matplotlib.colors>: 3). Valid Matplotlib colors (<str> or normalized RGB tuple) for front,\n            back and sides.\n            linewidth: Width in pixel of the box sides.\n        \"\"\"\n        corners = view_points(self.corners(), view, normalize=normalize)[:2, :]\n\n        def draw_rect(selected_corners, color):\n            prev = selected_corners[-1]\n            for corner in selected_corners:\n                axis.plot([prev[0], corner[0]], [prev[1], corner[1]], color=color, linewidth=linewidth)\n                prev = corner\n\n        # Draw the sides\n        for i in range(4):\n            axis.plot(\n                [corners.T[i][0], corners.T[i + 4][0]],\n                [corners.T[i][1], corners.T[i + 4][1]],\n                color=colors[2],\n                linewidth=linewidth,\n            )\n\n        # Draw front (first 4 corners) and rear (last 4 corners) rectangles(3d)/lines(2d)\n        draw_rect(corners.T[:4], colors[0])\n        draw_rect(corners.T[4:], colors[1])\n\n        # Draw line indicating the front\n        center_bottom_forward = np.mean(corners.T[2:4], axis=0)\n        center_bottom = np.mean(corners.T[[2, 3, 7, 6]], axis=0)\n        axis.plot(\n            [center_bottom[0], center_bottom_forward[0]],\n            [center_bottom[1], center_bottom_forward[1]],\n            color=colors[0],\n            linewidth=linewidth,\n        )\n\n    def render_cv2(\n        self,\n        image: np.ndarray,\n        view: np.ndarray = np.eye(3),\n        normalize: bool = False,\n        colors: Tuple = ((0, 0, 255), (255, 0, 0), (155, 155, 155)),\n        linewidth: int = 2,\n    ) -> None:\n        \"\"\"Renders box using OpenCV2.\n        Args:\n            image: <np.array: width, height, 3>. Image array. Channels are in BGR order.\n            view: <np.array: 3, 3>. Define a projection if needed (e.g. for drawing projection in an image).\n            normalize: Whether to normalize the remaining coordinate.\n            colors: ((R, G, B), (R, G, B), (R, G, B)). Colors for front, side & rear.\n            linewidth: Linewidth for plot.\n        Returns:\n        \"\"\"\n        corners = view_points(self.corners(), view, normalize=normalize)[:2, :]\n\n        def draw_rect(selected_corners, color):\n            prev = selected_corners[-1]\n            for corner in selected_corners:\n                cv2.line(image, (int(prev[0]), int(prev[1])), (int(corner[0]), int(corner[1])), color, linewidth)\n                prev = corner\n\n        # Draw the sides\n        for i in range(4):\n            cv2.line(\n                image,\n                (int(corners.T[i][0]), int(corners.T[i][1])),\n                (int(corners.T[i + 4][0]), int(corners.T[i + 4][1])),\n                colors[2][::-1],\n                linewidth,\n            )\n\n        # Draw front (first 4 corners) and rear (last 4 corners) rectangles(3d)/lines(2d)\n        draw_rect(corners.T[:4], colors[0][::-1])\n        draw_rect(corners.T[4:], colors[1][::-1])\n\n        # Draw line indicating the front\n        center_bottom_forward = np.mean(corners.T[2:4], axis=0)\n        center_bottom = np.mean(corners.T[[2, 3, 7, 6]], axis=0)\n        cv2.line(\n            image,\n            (int(center_bottom[0]), int(center_bottom[1])),\n            (int(center_bottom_forward[0]), int(center_bottom_forward[1])),\n            colors[0][::-1],\n            linewidth,\n        )\n\n    def copy(self) -> \"Box\":\n        \"\"\"        Create a copy of self.\n        Returns: A copy.\n        \"\"\"\n        return copy.deepcopy(self)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Lyft Dataset SDK dev-kit.\n# Code written by Oscar Beijbom, 2018.\n# Licensed under the Creative Commons [see licence.txt]\n# Modified by Vladimir Iglovikov 2019.\n\nPYTHON_VERSION = sys.version_info[0]\n\nif not PYTHON_VERSION == 3:\n    raise ValueError(\"LyftDataset sdk only supports Python version 3.\")\n\n\nclass LyftDataset:\n    \"\"\"Database class for Lyft Dataset to help query and retrieve information from the database.\"\"\"\n\n    def __init__(self, data_path: str, json_path: str, verbose: bool = True, map_resolution: float = 0.1):\n        \"\"\"Loads database and creates reverse indexes and shortcuts.\n        Args:\n            data_path: Path to the tables and data.\n            json_path: Path to the folder with json files\n            verbose: Whether to print status messages during load.\n            map_resolution: Resolution of maps (meters).\n        \"\"\"\n\n        self.data_path = Path(data_path).expanduser().absolute()\n        self.json_path = Path(json_path)\n\n        self.table_names = [\n            \"category\",\n            \"attribute\",\n            \"visibility\",\n            \"instance\",\n            \"sensor\",\n            \"calibrated_sensor\",\n            \"ego_pose\",\n            \"log\",\n            \"scene\",\n            \"sample\",\n            \"sample_data\",\n            \"sample_annotation\",\n            \"map\",\n        ]\n\n        start_time = time.time()\n\n        # Explicitly assign tables to help the IDE determine valid class members.\n        self.category = self.__load_table__(\"category\")\n        self.attribute = self.__load_table__(\"attribute\")\n        self.visibility = self.__load_table__(\"visibility\")\n        self.instance = self.__load_table__(\"instance\")\n        self.sensor = self.__load_table__(\"sensor\")\n        self.calibrated_sensor = self.__load_table__(\"calibrated_sensor\")\n        self.ego_pose = self.__load_table__(\"ego_pose\")\n        self.log = self.__load_table__(\"log\")\n        self.scene = self.__load_table__(\"scene\")\n        self.sample = self.__load_table__(\"sample\")\n        self.sample_data = self.__load_table__(\"sample_data\")\n        self.sample_annotation = self.__load_table__(\"sample_annotation\")\n        self.map = self.__load_table__(\"map\")\n\n        # Initialize map mask for each map record.\n        for map_record in self.map:\n            map_record[\"mask\"] = MapMask(self.data_path / 'train_maps/map_raster_palo_alto.png', resolution=map_resolution)\n\n        if verbose:\n            for table in self.table_names:\n                print(\"{} {},\".format(len(getattr(self, table)), table))\n            print(\"Done loading in {:.1f} seconds.\\n======\".format(time.time() - start_time))\n\n        # Make reverse indexes for common lookups.\n        self.__make_reverse_index__(verbose)\n\n        # Initialize LyftDatasetExplorer class\n        self.explorer = LyftDatasetExplorer(self)\n\n    def __load_table__(self, table_name) -> dict:\n        \"\"\"Loads a table.\"\"\"\n        with open(str(self.json_path.joinpath(\"{}.json\".format(table_name)))) as f:\n            table = json.load(f)\n        return table\n\n    def __make_reverse_index__(self, verbose: bool) -> None:\n        \"\"\"De-normalizes database to create reverse indices for common cases.\n        Args:\n            verbose: Whether to print outputs.\n        \"\"\"\n\n        start_time = time.time()\n        if verbose:\n            print(\"Reverse indexing ...\")\n\n        # Store the mapping from token to table index for each table.\n        self._token2ind = dict()\n        for table in self.table_names:\n            self._token2ind[table] = dict()\n\n            for ind, member in enumerate(getattr(self, table)):\n                self._token2ind[table][member[\"token\"]] = ind\n\n        # Decorate (adds short-cut) sample_annotation table with for category name.\n        for record in self.sample_annotation:\n            inst = self.get(\"instance\", record[\"instance_token\"])\n            record[\"category_name\"] = self.get(\"category\", inst[\"category_token\"])[\"name\"]\n\n        # Decorate (adds short-cut) sample_data with sensor information.\n        for record in self.sample_data:\n            cs_record = self.get(\"calibrated_sensor\", record[\"calibrated_sensor_token\"])\n            sensor_record = self.get(\"sensor\", cs_record[\"sensor_token\"])\n            record[\"sensor_modality\"] = sensor_record[\"modality\"]\n            record[\"channel\"] = sensor_record[\"channel\"]\n\n        # Reverse-index samples with sample_data and annotations.\n        for record in self.sample:\n            record[\"data\"] = {}\n            record[\"anns\"] = []\n\n        for record in self.sample_data:\n            if record[\"is_key_frame\"]:\n                sample_record = self.get(\"sample\", record[\"sample_token\"])\n                sample_record[\"data\"][record[\"channel\"]] = record[\"token\"]\n\n        for ann_record in self.sample_annotation:\n            sample_record = self.get(\"sample\", ann_record[\"sample_token\"])\n            sample_record[\"anns\"].append(ann_record[\"token\"])\n\n        # Add reverse indices from log records to map records.\n        if \"log_tokens\" not in self.map[0].keys():\n            raise Exception(\"Error: log_tokens not in map table. This code is not compatible with the teaser dataset.\")\n        log_to_map = dict()\n        for map_record in self.map:\n            for log_token in map_record[\"log_tokens\"]:\n                log_to_map[log_token] = map_record[\"token\"]\n        for log_record in self.log:\n            log_record[\"map_token\"] = log_to_map[log_record[\"token\"]]\n\n        if verbose:\n            print(\"Done reverse indexing in {:.1f} seconds.\\n======\".format(time.time() - start_time))\n\n    def get(self, table_name: str, token: str) -> dict:\n        \"\"\"Returns a record from table in constant runtime.\n        Args:\n            table_name: Table name.\n            token: Token of the record.\n        Returns: Table record.\n        \"\"\"\n\n        assert table_name in self.table_names, \"Table {} not found\".format(table_name)\n\n        return getattr(self, table_name)[self.getind(table_name, token)]\n\n    def getind(self, table_name: str, token: str) -> int:\n        \"\"\"Returns the index of the record in a table in constant runtime.\n        Args:\n            table_name: Table name.\n            token: The index of the record in table, table is an array.\n        Returns:\n        \"\"\"\n        return self._token2ind[table_name][token]\n\n    def field2token(self, table_name: str, field: str, query) -> List[str]:\n        \"\"\"Query all records for a certain field value, and returns the tokens for the matching records.\n        Runs in linear time.\n        Args:\n            table_name: Table name.\n            field: Field name.\n            query: Query to match against. Needs to type match the content of the query field.\n        Returns: List of tokens for the matching records.\n        \"\"\"\n        matches = []\n        for member in getattr(self, table_name):\n            if member[field] == query:\n                matches.append(member[\"token\"])\n        return matches\n\n    def get_sample_data_path(self, sample_data_token: str) -> Path:\n        \"\"\"Returns the path to a sample_data.\n        Args:\n            sample_data_token:\n        Returns:\n        \"\"\"\n\n        sd_record = self.get(\"sample_data\", sample_data_token)\n        return self.data_path / sd_record[\"filename\"]\n\n    def get_sample_data(\n        self,\n        sample_data_token: str,\n        box_vis_level: BoxVisibility = BoxVisibility.ANY,\n        selected_anntokens: List[str] = None,\n        flat_vehicle_coordinates: bool = False,\n    ) -> Tuple[Path, List[Box], np.array]:\n        \"\"\"Returns the data path as well as all annotations related to that sample_data.\n        The boxes are transformed into the current sensor's coordinate frame.\n        Args:\n            sample_data_token: Sample_data token.\n            box_vis_level: If sample_data is an image, this sets required visibility for boxes.\n            selected_anntokens: If provided only return the selected annotation.\n            flat_vehicle_coordinates: Instead of current sensor's coordinate frame, use vehicle frame which is\n        aligned to z-plane in world\n        Returns: (data_path, boxes, camera_intrinsic <np.array: 3, 3>)\n        \"\"\"\n\n        # Retrieve sensor & pose records\n        sd_record = self.get(\"sample_data\", sample_data_token)\n        cs_record = self.get(\"calibrated_sensor\", sd_record[\"calibrated_sensor_token\"])\n        sensor_record = self.get(\"sensor\", cs_record[\"sensor_token\"])\n        pose_record = self.get(\"ego_pose\", sd_record[\"ego_pose_token\"])\n\n        data_path = self.get_sample_data_path(sample_data_token)\n\n        if sensor_record[\"modality\"] == \"camera\":\n            cam_intrinsic = np.array(cs_record[\"camera_intrinsic\"])\n            imsize = (sd_record[\"width\"], sd_record[\"height\"])\n        else:\n            cam_intrinsic = None\n            imsize = None\n\n        # Retrieve all sample annotations and map to sensor coordinate system.\n        if selected_anntokens is not None:\n            boxes = list(map(self.get_box, selected_anntokens))\n        else:\n            boxes = self.get_boxes(sample_data_token)\n\n        # Make list of Box objects including coord system transforms.\n        box_list = []\n        for box in boxes:\n            if flat_vehicle_coordinates:\n                # Move box to ego vehicle coord system parallel to world z plane\n                ypr = Quaternion(pose_record[\"rotation\"]).yaw_pitch_roll\n                yaw = ypr[0]\n\n                box.translate(-np.array(pose_record[\"translation\"]))\n                box.rotate(Quaternion(scalar=np.cos(yaw / 2), vector=[0, 0, np.sin(yaw / 2)]).inverse)\n\n            else:\n                # Move box to ego vehicle coord system\n                box.translate(-np.array(pose_record[\"translation\"]))\n                box.rotate(Quaternion(pose_record[\"rotation\"]).inverse)\n\n                #  Move box to sensor coord system\n                box.translate(-np.array(cs_record[\"translation\"]))\n                box.rotate(Quaternion(cs_record[\"rotation\"]).inverse)\n\n            if sensor_record[\"modality\"] == \"camera\" and not box_in_image(\n                box, cam_intrinsic, imsize, vis_level=box_vis_level\n            ):\n                continue\n\n            box_list.append(box)\n\n        return data_path, box_list, cam_intrinsic\n\n    def get_box(self, sample_annotation_token: str) -> Box:\n        \"\"\"Instantiates a Box class from a sample annotation record.\n        Args:\n            sample_annotation_token: Unique sample_annotation identifier.\n        Returns:\n        \"\"\"\n        record = self.get(\"sample_annotation\", sample_annotation_token)\n        return Box(\n            record[\"translation\"],\n            record[\"size\"],\n            Quaternion(record[\"rotation\"]),\n            name=record[\"category_name\"],\n            token=record[\"token\"],\n        )\n\n    def get_boxes(self, sample_data_token: str) -> List[Box]:\n        \"\"\"Instantiates Boxes for all annotation for a particular sample_data record. If the sample_data is a\n        keyframe, this returns the annotations for that sample. But if the sample_data is an intermediate\n        sample_data, a linear interpolation is applied to estimate the location of the boxes at the time the\n        sample_data was captured.\n        Args:\n            sample_data_token: Unique sample_data identifier.\n        Returns:\n        \"\"\"\n\n        # Retrieve sensor & pose records\n        sd_record = self.get(\"sample_data\", sample_data_token)\n        curr_sample_record = self.get(\"sample\", sd_record[\"sample_token\"])\n\n        if curr_sample_record[\"prev\"] == \"\" or sd_record[\"is_key_frame\"]:\n            # If no previous annotations available, or if sample_data is keyframe just return the current ones.\n            boxes = list(map(self.get_box, curr_sample_record[\"anns\"]))\n\n        else:\n            prev_sample_record = self.get(\"sample\", curr_sample_record[\"prev\"])\n\n            curr_ann_recs = [self.get(\"sample_annotation\", token) for token in curr_sample_record[\"anns\"]]\n            prev_ann_recs = [self.get(\"sample_annotation\", token) for token in prev_sample_record[\"anns\"]]\n\n            # Maps instance tokens to prev_ann records\n            prev_inst_map = {entry[\"instance_token\"]: entry for entry in prev_ann_recs}\n\n            t0 = prev_sample_record[\"timestamp\"]\n            t1 = curr_sample_record[\"timestamp\"]\n            t = sd_record[\"timestamp\"]\n\n            # There are rare situations where the timestamps in the DB are off so ensure that t0 < t < t1.\n            t = max(t0, min(t1, t))\n\n            boxes = []\n            for curr_ann_rec in curr_ann_recs:\n\n                if curr_ann_rec[\"instance_token\"] in prev_inst_map:\n                    # If the annotated instance existed in the previous frame, interpolate center & orientation.\n                    prev_ann_rec = prev_inst_map[curr_ann_rec[\"instance_token\"]]\n\n                    # Interpolate center.\n                    center = [\n                        np.interp(t, [t0, t1], [c0, c1])\n                        for c0, c1 in zip(prev_ann_rec[\"translation\"], curr_ann_rec[\"translation\"])\n                    ]\n\n                    # Interpolate orientation.\n                    rotation = Quaternion.slerp(\n                        q0=Quaternion(prev_ann_rec[\"rotation\"]),\n                        q1=Quaternion(curr_ann_rec[\"rotation\"]),\n                        amount=(t - t0) / (t1 - t0),\n                    )\n\n                    box = Box(\n                        center,\n                        curr_ann_rec[\"size\"],\n                        rotation,\n                        name=curr_ann_rec[\"category_name\"],\n                        token=curr_ann_rec[\"token\"],\n                    )\n                else:\n                    # If not, simply grab the current annotation.\n                    box = self.get_box(curr_ann_rec[\"token\"])\n\n                boxes.append(box)\n        return boxes\n\n    def box_velocity(self, sample_annotation_token: str, max_time_diff: float = 1.5) -> np.ndarray:\n        \"\"\"Estimate the velocity for an annotation.\n        If possible, we compute the centered difference between the previous and next frame.\n        Otherwise we use the difference between the current and previous/next frame.\n        If the velocity cannot be estimated, values are set to np.nan.\n        Args:\n            sample_annotation_token: Unique sample_annotation identifier.\n            max_time_diff: Max allowed time diff between consecutive samples that are used to estimate velocities.\n        Returns: <np.float: 3>. Velocity in x/y/z direction in m/s.\n        \"\"\"\n\n        current = self.get(\"sample_annotation\", sample_annotation_token)\n        has_prev = current[\"prev\"] != \"\"\n        has_next = current[\"next\"] != \"\"\n\n        # Cannot estimate velocity for a single annotation.\n        if not has_prev and not has_next:\n            return np.array([np.nan, np.nan, np.nan])\n\n        if has_prev:\n            first = self.get(\"sample_annotation\", current[\"prev\"])\n        else:\n            first = current\n\n        if has_next:\n            last = self.get(\"sample_annotation\", current[\"next\"])\n        else:\n            last = current\n\n        pos_last = np.array(last[\"translation\"])\n        pos_first = np.array(first[\"translation\"])\n        pos_diff = pos_last - pos_first\n\n        time_last = 1e-6 * self.get(\"sample\", last[\"sample_token\"])[\"timestamp\"]\n        time_first = 1e-6 * self.get(\"sample\", first[\"sample_token\"])[\"timestamp\"]\n        time_diff = time_last - time_first\n\n        if has_next and has_prev:\n            # If doing centered difference, allow for up to double the max_time_diff.\n            max_time_diff *= 2\n\n        if time_diff > max_time_diff:\n            # If time_diff is too big, don't return an estimate.\n            return np.array([np.nan, np.nan, np.nan])\n        else:\n            return pos_diff / time_diff\n\n    def list_categories(self) -> None:\n        self.explorer.list_categories()\n\n    def list_attributes(self) -> None:\n        self.explorer.list_attributes()\n\n    def list_scenes(self) -> None:\n        self.explorer.list_scenes()\n\n    def list_sample(self, sample_token: str) -> None:\n        self.explorer.list_sample(sample_token)\n\n    def render_pointcloud_in_image(\n        self,\n        sample_token: str,\n        dot_size: int = 5,\n        pointsensor_channel: str = \"LIDAR_TOP\",\n        camera_channel: str = \"CAM_FRONT\",\n        out_path: str = None,\n    ) -> None:\n        self.explorer.render_pointcloud_in_image(\n            sample_token,\n            dot_size,\n            pointsensor_channel=pointsensor_channel,\n            camera_channel=camera_channel,\n            out_path=out_path,\n        )\n\n    def render_sample(\n        self,\n        sample_token: str,\n        box_vis_level: BoxVisibility = BoxVisibility.ANY,\n        nsweeps: int = 1,\n        out_path: str = None,\n    ) -> None:\n        self.explorer.render_sample(sample_token, box_vis_level, nsweeps=nsweeps, out_path=out_path)\n\n    def render_sample_data(\n        self,\n        sample_data_token: str,\n        with_anns: bool = True,\n        box_vis_level: BoxVisibility = BoxVisibility.ANY,\n        axes_limit: float = 40,\n        ax: Axes = None,\n        nsweeps: int = 1,\n        out_path: str = None,\n        underlay_map: bool = False,\n    ) -> None:\n        return self.explorer.render_sample_data(\n            sample_data_token,\n            with_anns,\n            box_vis_level,\n            axes_limit,\n            ax,\n            num_sweeps=nsweeps,\n            out_path=out_path,\n            underlay_map=underlay_map,\n        )\n\n    def render_annotation(\n        self,\n        sample_annotation_token: str,\n        margin: float = 10,\n        view: np.ndarray = np.eye(4),\n        box_vis_level: BoxVisibility = BoxVisibility.ANY,\n        out_path: str = None,\n    ) -> None:\n        self.explorer.render_annotation(sample_annotation_token, margin, view, box_vis_level, out_path)\n\n    def render_instance(self, instance_token: str, out_path: str = None) -> None:\n        self.explorer.render_instance(instance_token, out_path=out_path)\n\n    def render_scene(self, scene_token: str, freq: float = 10, imwidth: int = 640, out_path: str = None) -> None:\n        self.explorer.render_scene(scene_token, freq, image_width=imwidth, out_path=out_path)\n\n    def render_scene_channel(\n        self,\n        scene_token: str,\n        channel: str = \"CAM_FRONT\",\n        freq: float = 10,\n        imsize: Tuple[float, float] = (640, 360),\n        out_path: str = None,\n    ) -> None:\n        self.explorer.render_scene_channel(\n            scene_token=scene_token, channel=channel, freq=freq, image_size=imsize, out_path=out_path\n        )\n\n    def render_egoposes_on_map(self, log_location: str, scene_tokens: List = None, out_path: str = None) -> None:\n        self.explorer.render_egoposes_on_map(log_location, scene_tokens, out_path=out_path)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"class LyftDatasetExplorer:\n    \"\"\"Helper class to list and visualize Lyft Dataset data. These are meant to serve as tutorials and templates for\n    working with the data.\"\"\"\n\n    def __init__(self, lyftd: LyftDataset):\n        self.lyftd = lyftd\n\n    @staticmethod\n    def get_color(category_name: str) -> Tuple[int, int, int]:\n        \"\"\"Provides the default colors based on the category names.\n        This method works for the general Lyft Dataset categories, as well as the Lyft Dataset detection categories.\n        Args:\n            category_name:\n        Returns:\n        \"\"\"\n        if \"bicycle\" in category_name or \"motorcycle\" in category_name:\n            return 255, 61, 99  # Red\n        elif \"vehicle\" in category_name or category_name in [\"bus\", \"car\", \"construction_vehicle\", \"trailer\", \"truck\"]:\n            return 255, 158, 0  # Orange\n        elif \"pedestrian\" in category_name:\n            return 0, 0, 230  # Blue\n        elif \"cone\" in category_name or \"barrier\" in category_name:\n            return 0, 0, 0  # Black\n        else:\n            return 255, 0, 255  # Magenta\n\n    def list_categories(self) -> None:\n        \"\"\"Print categories, counts and stats.\"\"\"\n\n        print(\"Category stats\")\n\n        # Add all annotations\n        categories = dict()\n        for record in self.lyftd.sample_annotation:\n            if record[\"category_name\"] not in categories:\n                categories[record[\"category_name\"]] = []\n            categories[record[\"category_name\"]].append(record[\"size\"] + [record[\"size\"][1] / record[\"size\"][0]])\n\n        # Print stats\n        for name, stats in sorted(categories.items()):\n            stats = np.array(stats)\n            print(\n                \"{:27} n={:5}, width={:5.2f}\\u00B1{:.2f}, len={:5.2f}\\u00B1{:.2f}, height={:5.2f}\\u00B1{:.2f}, \"\n                \"lw_aspect={:5.2f}\\u00B1{:.2f}\".format(\n                    name[:27],\n                    stats.shape[0],\n                    np.mean(stats[:, 0]),\n                    np.std(stats[:, 0]),\n                    np.mean(stats[:, 1]),\n                    np.std(stats[:, 1]),\n                    np.mean(stats[:, 2]),\n                    np.std(stats[:, 2]),\n                    np.mean(stats[:, 3]),\n                    np.std(stats[:, 3]),\n                )\n            )\n\n    def list_attributes(self) -> None:\n        \"\"\"Prints attributes and counts.\"\"\"\n        attribute_counts = dict()\n        for record in self.lyftd.sample_annotation:\n            for attribute_token in record[\"attribute_tokens\"]:\n                att_name = self.lyftd.get(\"attribute\", attribute_token)[\"name\"]\n                if att_name not in attribute_counts:\n                    attribute_counts[att_name] = 0\n                attribute_counts[att_name] += 1\n\n        for name, count in sorted(attribute_counts.items()):\n            print(\"{}: {}\".format(name, count))\n\n    def list_scenes(self) -> None:\n        \"\"\" Lists all scenes with some meta data. \"\"\"\n\n        def ann_count(record):\n            count = 0\n            sample = self.lyftd.get(\"sample\", record[\"first_sample_token\"])\n            while not sample[\"next\"] == \"\":\n                count += len(sample[\"anns\"])\n                sample = self.lyftd.get(\"sample\", sample[\"next\"])\n            return count\n\n        recs = [\n            (self.lyftd.get(\"sample\", record[\"first_sample_token\"])[\"timestamp\"], record)\n            for record in self.lyftd.scene\n        ]\n\n        for start_time, record in sorted(recs):\n            start_time = self.lyftd.get(\"sample\", record[\"first_sample_token\"])[\"timestamp\"] / 1000000\n            length_time = self.lyftd.get(\"sample\", record[\"last_sample_token\"])[\"timestamp\"] / 1000000 - start_time\n            location = self.lyftd.get(\"log\", record[\"log_token\"])[\"location\"]\n            desc = record[\"name\"] + \", \" + record[\"description\"]\n            if len(desc) > 55:\n                desc = desc[:51] + \"...\"\n            if len(location) > 18:\n                location = location[:18]\n\n            print(\n                \"{:16} [{}] {:4.0f}s, {}, #anns:{}\".format(\n                    desc,\n                    datetime.utcfromtimestamp(start_time).strftime(\"%y-%m-%d %H:%M:%S\"),\n                    length_time,\n                    location,\n                    ann_count(record),\n                )\n            )\n\n    def list_sample(self, sample_token: str) -> None:\n        \"\"\"Prints sample_data tokens and sample_annotation tokens related to the sample_token.\"\"\"\n\n        sample_record = self.lyftd.get(\"sample\", sample_token)\n        print(\"Sample: {}\\n\".format(sample_record[\"token\"]))\n        for sd_token in sample_record[\"data\"].values():\n            sd_record = self.lyftd.get(\"sample_data\", sd_token)\n            print(\n                \"sample_data_token: {}, mod: {}, channel: {}\".format(\n                    sd_token, sd_record[\"sensor_modality\"], sd_record[\"channel\"]\n                )\n            )\n        print(\"\")\n        for ann_token in sample_record[\"anns\"]:\n            ann_record = self.lyftd.get(\"sample_annotation\", ann_token)\n            print(\"sample_annotation_token: {}, category: {}\".format(ann_record[\"token\"], ann_record[\"category_name\"]))\n\n    def map_pointcloud_to_image(self, pointsensor_token: str, camera_token: str) -> Tuple:\n        \"\"\"Given a point sensor (lidar/radar) token and camera sample_data token, load point-cloud and map it to\n        the image plane.\n        Args:\n            pointsensor_token: Lidar/radar sample_data token.\n            camera_token: Camera sample_data token.\n        Returns: (pointcloud <np.float: 2, n)>, coloring <np.float: n>, image <Image>).\n        \"\"\"\n\n        cam = self.lyftd.get(\"sample_data\", camera_token)\n        pointsensor = self.lyftd.get(\"sample_data\", pointsensor_token)\n        pcl_path = self.lyftd.data_path / ('train_' + pointsensor[\"filename\"])\n        if pointsensor[\"sensor_modality\"] == \"lidar\":\n            pc = LidarPointCloud.from_file(pcl_path)\n        else:\n            pc = RadarPointCloud.from_file(pcl_path)\n        im = Image.open(str(self.lyftd.data_path / ('train_' + cam[\"filename\"])))\n\n        # Points live in the point sensor frame. So they need to be transformed via global to the image plane.\n        # First step: transform the point-cloud to the ego vehicle frame for the timestamp of the sweep.\n        cs_record = self.lyftd.get(\"calibrated_sensor\", pointsensor[\"calibrated_sensor_token\"])\n        pc.rotate(Quaternion(cs_record[\"rotation\"]).rotation_matrix)\n        pc.translate(np.array(cs_record[\"translation\"]))\n\n        # Second step: transform to the global frame.\n        poserecord = self.lyftd.get(\"ego_pose\", pointsensor[\"ego_pose_token\"])\n        pc.rotate(Quaternion(poserecord[\"rotation\"]).rotation_matrix)\n        pc.translate(np.array(poserecord[\"translation\"]))\n\n        # Third step: transform into the ego vehicle frame for the timestamp of the image.\n        poserecord = self.lyftd.get(\"ego_pose\", cam[\"ego_pose_token\"])\n        pc.translate(-np.array(poserecord[\"translation\"]))\n        pc.rotate(Quaternion(poserecord[\"rotation\"]).rotation_matrix.T)\n\n        # Fourth step: transform into the camera.\n        cs_record = self.lyftd.get(\"calibrated_sensor\", cam[\"calibrated_sensor_token\"])\n        pc.translate(-np.array(cs_record[\"translation\"]))\n        pc.rotate(Quaternion(cs_record[\"rotation\"]).rotation_matrix.T)\n\n        # Fifth step: actually take a \"picture\" of the point cloud.\n        # Grab the depths (camera frame z axis points away from the camera).\n        depths = pc.points[2, :]\n\n        # Retrieve the color from the depth.\n        coloring = depths\n\n        # Take the actual picture (matrix multiplication with camera-matrix + renormalization).\n        points = view_points(pc.points[:3, :], np.array(cs_record[\"camera_intrinsic\"]), normalize=True)\n\n        # Remove points that are either outside or behind the camera. Leave a margin of 1 pixel for aesthetic reasons.\n        mask = np.ones(depths.shape[0], dtype=bool)\n        mask = np.logical_and(mask, depths > 0)\n        mask = np.logical_and(mask, points[0, :] > 1)\n        mask = np.logical_and(mask, points[0, :] < im.size[0] - 1)\n        mask = np.logical_and(mask, points[1, :] > 1)\n        mask = np.logical_and(mask, points[1, :] < im.size[1] - 1)\n        points = points[:, mask]\n        coloring = coloring[mask]\n\n        return points, coloring, im\n\n    def render_pointcloud_in_image(\n        self,\n        sample_token: str,\n        dot_size: int = 2,\n        pointsensor_channel: str = \"LIDAR_TOP\",\n        camera_channel: str = \"CAM_FRONT\",\n        out_path: str = None,\n    ) -> None:\n        \"\"\"Scatter-plots a point-cloud on top of image.\n        Args:\n            sample_token: Sample token.\n            dot_size: Scatter plot dot size.\n            pointsensor_channel: RADAR or LIDAR channel name, e.g. 'LIDAR_TOP'.\n            camera_channel: Camera channel name, e.g. 'CAM_FRONT'.\n            out_path: Optional path to save the rendered figure to disk.\n        Returns:\n        \"\"\"\n        sample_record = self.lyftd.get(\"sample\", sample_token)\n\n        # Here we just grab the front camera and the point sensor.\n        pointsensor_token = sample_record[\"data\"][pointsensor_channel]\n        camera_token = sample_record[\"data\"][camera_channel]\n\n        points, coloring, im = self.map_pointcloud_to_image(pointsensor_token, camera_token)\n        plt.figure(figsize=(9, 16))\n        plt.imshow(im)\n        plt.scatter(points[0, :], points[1, :], c=coloring, s=dot_size)\n        plt.axis(\"off\")\n\n        if out_path is not None:\n            plt.savefig(out_path)\n\n    def render_sample(\n        self, token: str, box_vis_level: BoxVisibility = BoxVisibility.ANY, nsweeps: int = 1, out_path: str = None\n    ) -> None:\n        \"\"\"Render all LIDAR and camera sample_data in sample along with annotations.\n        Args:\n            token: Sample token.\n            box_vis_level: If sample_data is an image, this sets required visibility for boxes.\n            nsweeps: Number of sweeps for lidar and radar.\n            out_path: Optional path to save the rendered figure to disk.\n        Returns:\n        \"\"\"\n        record = self.lyftd.get(\"sample\", token)\n\n        # Separate RADAR from LIDAR and vision.\n        radar_data = {}\n        nonradar_data = {}\n        for channel, token in record[\"data\"].items():\n            sd_record = self.lyftd.get(\"sample_data\", token)\n            sensor_modality = sd_record[\"sensor_modality\"]\n            if sensor_modality in [\"lidar\", \"camera\"]:\n                nonradar_data[channel] = token\n            else:\n                radar_data[channel] = token\n\n        num_radar_plots = 1 if len(radar_data) > 0 else 0\n\n        # Create plots.\n        n = num_radar_plots + len(nonradar_data)\n        cols = 2\n        fig, axes = plt.subplots(int(np.ceil(n / cols)), cols, figsize=(16, 24))\n\n        if len(radar_data) > 0:\n            # Plot radar into a single subplot.\n            ax = axes[0, 0]\n            for i, (_, sd_token) in enumerate(radar_data.items()):\n                self.render_sample_data(\n                    sd_token, with_anns=i == 0, box_vis_level=box_vis_level, ax=ax, num_sweeps=nsweeps\n                )\n            ax.set_title(\"Fused RADARs\")\n\n        # Plot camera and lidar in separate subplots.\n        for (_, sd_token), ax in zip(nonradar_data.items(), axes.flatten()[num_radar_plots:]):\n            self.render_sample_data(sd_token, box_vis_level=box_vis_level, ax=ax, num_sweeps=nsweeps)\n\n        axes.flatten()[-1].axis(\"off\")\n        plt.tight_layout()\n        fig.subplots_adjust(wspace=0, hspace=0)\n\n        if out_path is not None:\n            plt.savefig(out_path)\n\n    def render_ego_centric_map(self, sample_data_token: str, axes_limit: float = 40, ax: Axes = None) -> None:\n        \"\"\"Render map centered around the associated ego pose.\n        Args:\n            sample_data_token: Sample_data token.\n            axes_limit: Axes limit measured in meters.\n            ax: Axes onto which to render.\n        \"\"\"\n\n        def crop_image(image: np.array, x_px: int, y_px: int, axes_limit_px: int) -> np.array:\n            x_min = int(x_px - axes_limit_px)\n            x_max = int(x_px + axes_limit_px)\n            y_min = int(y_px - axes_limit_px)\n            y_max = int(y_px + axes_limit_px)\n\n            cropped_image = image[y_min:y_max, x_min:x_max]\n\n            return cropped_image\n\n        sd_record = self.lyftd.get(\"sample_data\", sample_data_token)\n\n        # Init axes.\n        if ax is None:\n            _, ax = plt.subplots(1, 1, figsize=(9, 9))\n\n        sample = self.lyftd.get(\"sample\", sd_record[\"sample_token\"])\n        scene = self.lyftd.get(\"scene\", sample[\"scene_token\"])\n        log = self.lyftd.get(\"log\", scene[\"log_token\"])\n        map = self.lyftd.get(\"map\", log[\"map_token\"])\n        map_mask = map[\"mask\"]\n\n        pose = self.lyftd.get(\"ego_pose\", sd_record[\"ego_pose_token\"])\n        pixel_coords = map_mask.to_pixel_coords(pose[\"translation\"][0], pose[\"translation\"][1])\n\n        scaled_limit_px = int(axes_limit * (1.0 / map_mask.resolution))\n        mask_raster = map_mask.mask()\n\n        cropped = crop_image(mask_raster, pixel_coords[0], pixel_coords[1], int(scaled_limit_px * math.sqrt(2)))\n\n        ypr_rad = Quaternion(pose[\"rotation\"]).yaw_pitch_roll\n        yaw_deg = -math.degrees(ypr_rad[0])\n\n        rotated_cropped = np.array(Image.fromarray(cropped).rotate(yaw_deg))\n        ego_centric_map = crop_image(\n            rotated_cropped, rotated_cropped.shape[1] / 2, rotated_cropped.shape[0] / 2, scaled_limit_px\n        )\n        ax.imshow(\n            ego_centric_map, extent=[-axes_limit, axes_limit, -axes_limit, axes_limit], cmap=\"gray\", vmin=0, vmax=150\n        )\n\n    def render_sample_data(\n        self,\n        sample_data_token: str,\n        with_anns: bool = True,\n        box_vis_level: BoxVisibility = BoxVisibility.ANY,\n        axes_limit: float = 40,\n        ax: Axes = None,\n        num_sweeps: int = 1,\n        out_path: str = None,\n        underlay_map: bool = False,\n    ):\n        \"\"\"Render sample data onto axis.\n        Args:\n            sample_data_token: Sample_data token.\n            with_anns: Whether to draw annotations.\n            box_vis_level: If sample_data is an image, this sets required visibility for boxes.\n            axes_limit: Axes limit for lidar and radar (measured in meters).\n            ax: Axes onto which to render.\n            num_sweeps: Number of sweeps for lidar and radar.\n            out_path: Optional path to save the rendered figure to disk.\n            underlay_map: When set to true, LIDAR data is plotted onto the map. This can be slow.\n        \"\"\"\n\n        # Get sensor modality.\n        sd_record = self.lyftd.get(\"sample_data\", sample_data_token)\n        sensor_modality = sd_record[\"sensor_modality\"]\n\n        if sensor_modality == \"lidar\":\n            # Get boxes in lidar frame.\n            _, boxes, _ = self.lyftd.get_sample_data(\n                sample_data_token, box_vis_level=box_vis_level, flat_vehicle_coordinates=True\n            )\n\n            # Get aggregated point cloud in lidar frame.\n            sample_rec = self.lyftd.get(\"sample\", sd_record[\"sample_token\"])\n            chan = sd_record[\"channel\"]\n            ref_chan = \"LIDAR_TOP\"\n            pc, times = LidarPointCloud.from_file_multisweep(\n                self.lyftd, sample_rec, chan, ref_chan, num_sweeps=num_sweeps\n            )\n\n            # Compute transformation matrices for lidar point cloud\n            cs_record = self.lyftd.get(\"calibrated_sensor\", sd_record[\"calibrated_sensor_token\"])\n            pose_record = self.lyftd.get(\"ego_pose\", sd_record[\"ego_pose_token\"])\n            vehicle_from_sensor = np.eye(4)\n            vehicle_from_sensor[:3, :3] = Quaternion(cs_record[\"rotation\"]).rotation_matrix\n            vehicle_from_sensor[:3, 3] = cs_record[\"translation\"]\n\n            ego_yaw = Quaternion(pose_record[\"rotation\"]).yaw_pitch_roll[0]\n            rot_vehicle_flat_from_vehicle = np.dot(\n                Quaternion(scalar=np.cos(ego_yaw / 2), vector=[0, 0, np.sin(ego_yaw / 2)]).rotation_matrix,\n                Quaternion(pose_record[\"rotation\"]).inverse.rotation_matrix,\n            )\n\n            vehicle_flat_from_vehicle = np.eye(4)\n            vehicle_flat_from_vehicle[:3, :3] = rot_vehicle_flat_from_vehicle\n\n            # Init axes.\n            if ax is None:\n                _, ax = plt.subplots(1, 1, figsize=(9, 9))\n\n            if underlay_map:\n                self.render_ego_centric_map(sample_data_token=sample_data_token, axes_limit=axes_limit, ax=ax)\n\n            # Show point cloud.\n            points = view_points(\n                pc.points[:3, :], np.dot(vehicle_flat_from_vehicle, vehicle_from_sensor), normalize=False\n            )\n            dists = np.sqrt(np.sum(pc.points[:2, :] ** 2, axis=0))\n            colors = np.minimum(1, dists / axes_limit / np.sqrt(2))\n            ax.scatter(points[0, :], points[1, :], c=colors, s=0.2)\n\n            # Show ego vehicle.\n            ax.plot(0, 0, \"x\", color=\"red\")\n\n            # Show boxes.\n            if with_anns:\n                for box in boxes:\n                    c = np.array(self.get_color(box.name)) / 255.0\n                    box.render(ax, view=np.eye(4), colors=(c, c, c))\n\n            # Limit visible range.\n            ax.set_xlim(-axes_limit, axes_limit)\n            ax.set_ylim(-axes_limit, axes_limit)\n\n        elif sensor_modality == \"radar\":\n            # Get boxes in lidar frame.\n            sample_rec = self.lyftd.get(\"sample\", sd_record[\"sample_token\"])\n            lidar_token = sample_rec[\"data\"][\"LIDAR_TOP\"]\n            _, boxes, _ = self.lyftd.get_sample_data(lidar_token, box_vis_level=box_vis_level)\n\n            # Get aggregated point cloud in lidar frame.\n            # The point cloud is transformed to the lidar frame for visualization purposes.\n            chan = sd_record[\"channel\"]\n            ref_chan = \"LIDAR_TOP\"\n            pc, times = RadarPointCloud.from_file_multisweep(\n                self.lyftd, sample_rec, chan, ref_chan, num_sweeps=num_sweeps\n            )\n\n            # Transform radar velocities (x is front, y is left), as these are not transformed when loading the point\n            # cloud.\n            radar_cs_record = self.lyftd.get(\"calibrated_sensor\", sd_record[\"calibrated_sensor_token\"])\n            lidar_sd_record = self.lyftd.get(\"sample_data\", lidar_token)\n            lidar_cs_record = self.lyftd.get(\"calibrated_sensor\", lidar_sd_record[\"calibrated_sensor_token\"])\n            velocities = pc.points[8:10, :]  # Compensated velocity\n            velocities = np.vstack((velocities, np.zeros(pc.points.shape[1])))\n            velocities = np.dot(Quaternion(radar_cs_record[\"rotation\"]).rotation_matrix, velocities)\n            velocities = np.dot(Quaternion(lidar_cs_record[\"rotation\"]).rotation_matrix.T, velocities)\n            velocities[2, :] = np.zeros(pc.points.shape[1])\n\n            # Init axes.\n            if ax is None:\n                _, ax = plt.subplots(1, 1, figsize=(9, 9))\n\n            # Show point cloud.\n            points = view_points(pc.points[:3, :], np.eye(4), normalize=False)\n            dists = np.sqrt(np.sum(pc.points[:2, :] ** 2, axis=0))\n            colors = np.minimum(1, dists / axes_limit / np.sqrt(2))\n            sc = ax.scatter(points[0, :], points[1, :], c=colors, s=3)\n\n            # Show velocities.\n            points_vel = view_points(pc.points[:3, :] + velocities, np.eye(4), normalize=False)\n            max_delta = 10\n            deltas_vel = points_vel - points\n            deltas_vel = 3 * deltas_vel  # Arbitrary scaling\n            deltas_vel = np.clip(deltas_vel, -max_delta, max_delta)  # Arbitrary clipping\n            colors_rgba = sc.to_rgba(colors)\n            for i in range(points.shape[1]):\n                ax.arrow(points[0, i], points[1, i], deltas_vel[0, i], deltas_vel[1, i], color=colors_rgba[i])\n\n            # Show ego vehicle.\n            ax.plot(0, 0, \"x\", color=\"black\")\n\n            # Show boxes.\n            if with_anns:\n                for box in boxes:\n                    c = np.array(self.get_color(box.name)) / 255.0\n                    box.render(ax, view=np.eye(4), colors=(c, c, c))\n\n            # Limit visible range.\n            ax.set_xlim(-axes_limit, axes_limit)\n            ax.set_ylim(-axes_limit, axes_limit)\n\n        elif sensor_modality == \"camera\":\n            # Load boxes and image.\n            data_path, boxes, camera_intrinsic = self.lyftd.get_sample_data(\n                sample_data_token, box_vis_level=box_vis_level\n            )\n\n            data = Image.open(str(data_path)[:len(str(data_path)) - 46] + 'train_images/' +\\\n                              str(data_path)[len(str(data_path)) - 39 : len(str(data_path))])\n\n            # Init axes.\n            if ax is None:\n                _, ax = plt.subplots(1, 1, figsize=(9, 16))\n\n            # Show image.\n            ax.imshow(data)\n\n            # Show boxes.\n            if with_anns:\n                for box in boxes:\n                    c = np.array(self.get_color(box.name)) / 255.0\n                    box.render(ax, view=camera_intrinsic, normalize=True, colors=(c, c, c))\n\n            # Limit visible range.\n            ax.set_xlim(0, data.size[0])\n            ax.set_ylim(data.size[1], 0)\n\n        else:\n            raise ValueError(\"Error: Unknown sensor modality!\")\n\n        ax.axis(\"off\")\n        ax.set_title(sd_record[\"channel\"])\n        ax.set_aspect(\"equal\")\n\n        if out_path is not None:\n            num = len([name for name in os.listdir(out_path)])\n            out_path = out_path + str(num).zfill(5) + \"_\" + sample_data_token + \".png\"\n            plt.savefig(out_path)\n            plt.close(\"all\")\n            return out_path\n\n    def render_annotation(\n        self,\n        ann_token: str,\n        margin: float = 10,\n        view: np.ndarray = np.eye(4),\n        box_vis_level: BoxVisibility = BoxVisibility.ANY,\n        out_path: str = None,\n    ) -> None:\n        \"\"\"Render selected annotation.\n        Args:\n            ann_token: Sample_annotation token.\n            margin: How many meters in each direction to include in LIDAR view.\n            view: LIDAR view point.\n            box_vis_level: If sample_data is an image, this sets required visibility for boxes.\n            out_path: Optional path to save the rendered figure to disk.\n        \"\"\"\n\n        ann_record = self.lyftd.get(\"sample_annotation\", ann_token)\n        sample_record = self.lyftd.get(\"sample\", ann_record[\"sample_token\"])\n        assert \"LIDAR_TOP\" in sample_record[\"data\"].keys(), \"No LIDAR_TOP in data, cant render\"\n\n        fig, axes = plt.subplots(1, 2, figsize=(18, 9))\n\n        # Figure out which camera the object is fully visible in (this may return nothing)\n        boxes, cam = [], []\n        cams = [key for key in sample_record[\"data\"].keys() if \"CAM\" in key]\n        for cam in cams:\n            _, boxes, _ = self.lyftd.get_sample_data(\n                sample_record[\"data\"][cam], box_vis_level=box_vis_level, selected_anntokens=[ann_token]\n            )\n            if len(boxes) > 0:\n                break  # We found an image that matches. Let's abort.\n        assert len(boxes) > 0, \"Could not find image where annotation is visible. Try using e.g. BoxVisibility.ANY.\"\n        assert len(boxes) < 2, \"Found multiple annotations. Something is wrong!\"\n\n        cam = sample_record[\"data\"][cam]\n\n        # Plot LIDAR view\n        lidar = sample_record[\"data\"][\"LIDAR_TOP\"]\n        data_path, boxes, camera_intrinsic = self.lyftd.get_sample_data(lidar, selected_anntokens=[ann_token])\n        LidarPointCloud.from_file(Path(str(data_path)[:len(str(data_path)) - 46] + 'train_lidar/' +\\\n                                       str(data_path)[len(str(data_path)) - 40 : len(str(data_path))])).render_height(axes[0], view=view)\n        for box in boxes:\n            c = np.array(self.get_color(box.name)) / 255.0\n            box.render(axes[0], view=view, colors=(c, c, c))\n            corners = view_points(boxes[0].corners(), view, False)[:2, :]\n            axes[0].set_xlim([np.min(corners[0, :]) - margin, np.max(corners[0, :]) + margin])\n            axes[0].set_ylim([np.min(corners[1, :]) - margin, np.max(corners[1, :]) + margin])\n            axes[0].axis(\"off\")\n            axes[0].set_aspect(\"equal\")\n\n        # Plot CAMERA view\n        data_path, boxes, camera_intrinsic = self.lyftd.get_sample_data(cam, selected_anntokens=[ann_token])\n        im = Image.open(Path(str(data_path)[:len(str(data_path)) - 46] + 'train_images/' +\\\n                             str(data_path)[len(str(data_path)) - 39 : len(str(data_path))]))\n        axes[1].imshow(im)\n        axes[1].set_title(self.lyftd.get(\"sample_data\", cam)[\"channel\"])\n        axes[1].axis(\"off\")\n        axes[1].set_aspect(\"equal\")\n        for box in boxes:\n            c = np.array(self.get_color(box.name)) / 255.0\n            box.render(axes[1], view=camera_intrinsic, normalize=True, colors=(c, c, c))\n\n        if out_path is not None:\n            plt.savefig(out_path)\n\n    def render_instance(self, instance_token: str, out_path: str = None) -> None:\n        \"\"\"Finds the annotation of the given instance that is closest to the vehicle, and then renders it.\n        Args:\n            instance_token: The instance token.\n            out_path: Optional path to save the rendered figure to disk.\n        Returns:\n        \"\"\"\n\n        ann_tokens = self.lyftd.field2token(\"sample_annotation\", \"instance_token\", instance_token)\n        closest = [np.inf, None]\n        for ann_token in ann_tokens:\n            ann_record = self.lyftd.get(\"sample_annotation\", ann_token)\n            sample_record = self.lyftd.get(\"sample\", ann_record[\"sample_token\"])\n            sample_data_record = self.lyftd.get(\"sample_data\", sample_record[\"data\"][\"LIDAR_TOP\"])\n            pose_record = self.lyftd.get(\"ego_pose\", sample_data_record[\"ego_pose_token\"])\n            dist = np.linalg.norm(np.array(pose_record[\"translation\"]) - np.array(ann_record[\"translation\"]))\n            if dist < closest[0]:\n                closest[0] = dist\n                closest[1] = ann_token\n        self.render_annotation(closest[1], out_path=out_path)\n\n    def render_scene(self, scene_token: str, freq: float = 10, image_width: int = 640, out_path: Path = None) -> None:\n        \"\"\"Renders a full scene with all surround view camera channels.\n        Args:\n            scene_token: Unique identifier of scene to render.\n            freq: Display frequency (Hz).\n            image_width: Width of image to render. Height is determined automatically to preserve aspect ratio.\n            out_path: Optional path to write a video file of the rendered frames.\n        \"\"\"\n\n        if out_path is not None:\n            assert out_path.suffix == \".avi\"\n\n        # Get records from DB.\n        scene_rec = self.lyftd.get(\"scene\", scene_token)\n        first_sample_rec = self.lyftd.get(\"sample\", scene_rec[\"first_sample_token\"])\n        last_sample_rec = self.lyftd.get(\"sample\", scene_rec[\"last_sample_token\"])\n\n        channels = [\"CAM_FRONT_LEFT\", \"CAM_FRONT\", \"CAM_FRONT_RIGHT\", \"CAM_BACK_LEFT\", \"CAM_BACK\", \"CAM_BACK_RIGHT\"]\n\n        horizontal_flip = [\"CAM_BACK_LEFT\", \"CAM_BACK\", \"CAM_BACK_RIGHT\"]  # Flip these for aesthetic reasons.\n\n        time_step = 1 / freq * 1e6  # Time-stamps are measured in micro-seconds.\n\n        window_name = \"{}\".format(scene_rec[\"name\"])\n        cv2.namedWindow(window_name)\n        cv2.moveWindow(window_name, 0, 0)\n\n        # Load first sample_data record for each channel\n        current_recs = {}  # Holds the current record to be displayed by channel.\n        prev_recs = {}  # Hold the previous displayed record by channel.\n        for channel in channels:\n            current_recs[channel] = self.lyftd.get(\"sample_data\", first_sample_rec[\"data\"][channel])\n            prev_recs[channel] = None\n\n        # We assume that the resolution is the same for all surround view cameras.\n        image_height = int(image_width * current_recs[channels[0]][\"height\"] / current_recs[channels[0]][\"width\"])\n        image_size = (image_width, image_height)\n\n        # Set some display parameters\n        layout = {\n            \"CAM_FRONT_LEFT\": (0, 0),\n            \"CAM_FRONT\": (image_size[0], 0),\n            \"CAM_FRONT_RIGHT\": (2 * image_size[0], 0),\n            \"CAM_BACK_LEFT\": (0, image_size[1]),\n            \"CAM_BACK\": (image_size[0], image_size[1]),\n            \"CAM_BACK_RIGHT\": (2 * image_size[0], image_size[1]),\n        }\n\n        canvas = np.ones((2 * image_size[1], 3 * image_size[0], 3), np.uint8)\n        if out_path is not None:\n            fourcc = cv2.VideoWriter_fourcc(*\"MJPG\")\n            out = cv2.VideoWriter(out_path, fourcc, freq, canvas.shape[1::-1])\n        else:\n            out = None\n\n        current_time = first_sample_rec[\"timestamp\"]\n\n        while current_time < last_sample_rec[\"timestamp\"]:\n\n            current_time += time_step\n\n            # For each channel, find first sample that has time > current_time.\n            for channel, sd_rec in current_recs.items():\n                while sd_rec[\"timestamp\"] < current_time and sd_rec[\"next\"] != \"\":\n                    sd_rec = self.lyftd.get(\"sample_data\", sd_rec[\"next\"])\n                    current_recs[channel] = sd_rec\n\n            # Now add to canvas\n            for channel, sd_rec in current_recs.items():\n\n                # Only update canvas if we have not already rendered this one.\n                if not sd_rec == prev_recs[channel]:\n\n                    # Get annotations and params from DB.\n                    image_path, boxes, camera_intrinsic = self.lyftd.get_sample_data(\n                        sd_rec[\"token\"], box_vis_level=BoxVisibility.ANY\n                    )\n\n                    # Load and render\n                    if not image_path.exists():\n                        raise Exception(\"Error: Missing image %s\" % image_path)\n                    im = cv2.imread(str(image_path))\n                    for box in boxes:\n                        c = self.get_color(box.name)\n                        box.render_cv2(im, view=camera_intrinsic, normalize=True, colors=(c, c, c))\n\n                    im = cv2.resize(im, image_size)\n                    if channel in horizontal_flip:\n                        im = im[:, ::-1, :]\n\n                    canvas[\n                        layout[channel][1] : layout[channel][1] + image_size[1],\n                        layout[channel][0] : layout[channel][0] + image_size[0],\n                        :,\n                    ] = im\n\n                    prev_recs[channel] = sd_rec  # Store here so we don't render the same image twice.\n\n            # Show updated canvas.\n            cv2.imshow(window_name, canvas)\n            if out_path is not None:\n                out.write(canvas)\n\n            key = cv2.waitKey(1)  # Wait a very short time (1 ms).\n\n            if key == 32:  # if space is pressed, pause.\n                key = cv2.waitKey()\n\n            if key == 27:  # if ESC is pressed, exit.\n                cv2.destroyAllWindows()\n                break\n\n        cv2.destroyAllWindows()\n        if out_path is not None:\n            out.release()\n\n    def render_scene_channel(\n        self,\n        scene_token: str,\n        channel: str = \"CAM_FRONT\",\n        freq: float = 10,\n        image_size: Tuple[float, float] = (640, 360),\n        out_path: Path = None,\n    ) -> None:\n        \"\"\"Renders a full scene for a particular camera channel.\n        Args:\n            scene_token: Unique identifier of scene to render.\n            channel: Channel to render.\n            freq: Display frequency (Hz).\n            image_size: Size of image to render. The larger the slower this will run.\n            out_path: Optional path to write a video file of the rendered frames.\n        \"\"\"\n\n        valid_channels = [\n            \"CAM_FRONT_LEFT\",\n            \"CAM_FRONT\",\n            \"CAM_FRONT_RIGHT\",\n            \"CAM_BACK_LEFT\",\n            \"CAM_BACK\",\n            \"CAM_BACK_RIGHT\",\n        ]\n\n        assert image_size[0] / image_size[1] == 16 / 9, \"Aspect ratio should be 16/9.\"\n        assert channel in valid_channels, \"Input channel {} not valid.\".format(channel)\n\n        if out_path is not None:\n            assert out_path.suffix == \".avi\"\n\n        # Get records from DB\n        scene_rec = self.lyftd.get(\"scene\", scene_token)\n        sample_rec = self.lyftd.get(\"sample\", scene_rec[\"first_sample_token\"])\n        sd_rec = self.lyftd.get(\"sample_data\", sample_rec[\"data\"][channel])\n\n        # Open CV init\n        name = \"{}: {} (Space to pause, ESC to exit)\".format(scene_rec[\"name\"], channel)\n        cv2.namedWindow(name)\n        cv2.moveWindow(name, 0, 0)\n\n        if out_path is not None:\n            fourcc = cv2.VideoWriter_fourcc(*\"MJPG\")\n            out = cv2.VideoWriter(out_path, fourcc, freq, image_size)\n        else:\n            out = None\n\n        has_more_frames = True\n        while has_more_frames:\n\n            # Get data from DB\n            image_path, boxes, camera_intrinsic = self.lyftd.get_sample_data(\n                sd_rec[\"token\"], box_vis_level=BoxVisibility.ANY\n            )\n\n            # Load and render\n            if not image_path.exists():\n                raise Exception(\"Error: Missing image %s\" % image_path)\n            image = cv2.imread(str(image_path))\n            for box in boxes:\n                c = self.get_color(box.name)\n                box.render_cv2(image, view=camera_intrinsic, normalize=True, colors=(c, c, c))\n\n            # Render\n            image = cv2.resize(image, image_size)\n            cv2.imshow(name, image)\n            if out_path is not None:\n                out.write(image)\n\n            key = cv2.waitKey(10)  # Images stored at approx 10 Hz, so wait 10 ms.\n            if key == 32:  # If space is pressed, pause.\n                key = cv2.waitKey()\n\n            if key == 27:  # if ESC is pressed, exit\n                cv2.destroyAllWindows()\n                break\n\n            if not sd_rec[\"next\"] == \"\":\n                sd_rec = self.lyftd.get(\"sample_data\", sd_rec[\"next\"])\n            else:\n                has_more_frames = False\n\n        cv2.destroyAllWindows()\n        if out_path is not None:\n            out.release()\n\n    def render_egoposes_on_map(\n        self,\n        log_location: str,\n        scene_tokens: List = None,\n        close_dist: float = 100,\n        color_fg: Tuple[int, int, int] = (167, 174, 186),\n        color_bg: Tuple[int, int, int] = (255, 255, 255),\n        out_path: Path = None,\n    ) -> None:\n        \"\"\"Renders ego poses a the map. These can be filtered by location or scene.\n        Args:\n            log_location: Name of the location, e.g. \"singapore-onenorth\", \"singapore-hollandvillage\",\n                             \"singapore-queenstown' and \"boston-seaport\".\n            scene_tokens: Optional list of scene tokens.\n            close_dist: Distance in meters for an ego pose to be considered within range of another ego pose.\n            color_fg: Color of the semantic prior in RGB format (ignored if map is RGB).\n            color_bg: Color of the non-semantic prior in RGB format (ignored if map is RGB).\n            out_path: Optional path to save the rendered figure to disk.\n        Returns:\n        \"\"\"\n\n        # Get logs by location\n        log_tokens = [l[\"token\"] for l in self.lyftd.log if l[\"location\"] == log_location]\n        assert len(log_tokens) > 0, \"Error: This split has 0 scenes for location %s!\" % log_location\n\n        # Filter scenes\n        scene_tokens_location = [e[\"token\"] for e in self.lyftd.scene if e[\"log_token\"] in log_tokens]\n        if scene_tokens is not None:\n            scene_tokens_location = [t for t in scene_tokens_location if t in scene_tokens]\n        if len(scene_tokens_location) == 0:\n            print(\"Warning: Found 0 valid scenes for location %s!\" % log_location)\n\n        map_poses = []\n        map_mask = None\n\n        print(\"Adding ego poses to map...\")\n        for scene_token in tqdm(scene_tokens_location):\n\n            # Get records from the database.\n            scene_record = self.lyftd.get(\"scene\", scene_token)\n            log_record = self.lyftd.get(\"log\", scene_record[\"log_token\"])\n            map_record = self.lyftd.get(\"map\", log_record[\"map_token\"])\n            map_mask = map_record[\"mask\"]\n\n            # For each sample in the scene, store the ego pose.\n            sample_tokens = self.lyftd.field2token(\"sample\", \"scene_token\", scene_token)\n            for sample_token in sample_tokens:\n                sample_record = self.lyftd.get(\"sample\", sample_token)\n\n                # Poses are associated with the sample_data. Here we use the lidar sample_data.\n                sample_data_record = self.lyftd.get(\"sample_data\", sample_record[\"data\"][\"LIDAR_TOP\"])\n                pose_record = self.lyftd.get(\"ego_pose\", sample_data_record[\"ego_pose_token\"])\n\n                # Calculate the pose on the map and append\n                map_poses.append(\n                    np.concatenate(\n                        map_mask.to_pixel_coords(pose_record[\"translation\"][0], pose_record[\"translation\"][1])\n                    )\n                )\n\n        # Compute number of close ego poses.\n        print(\"Creating plot...\")\n        map_poses = np.vstack(map_poses)\n        dists = sklearn.metrics.pairwise.euclidean_distances(map_poses * map_mask.resolution)\n        close_poses = np.sum(dists < close_dist, axis=0)\n\n        if len(np.array(map_mask.mask()).shape) == 3 and np.array(map_mask.mask()).shape[2] == 3:\n            # RGB Colour maps\n            mask = map_mask.mask()\n        else:\n            # Monochrome maps\n            # Set the colors for the mask.\n            mask = Image.fromarray(map_mask.mask())\n            mask = np.array(mask)\n\n            maskr = color_fg[0] * np.ones(np.shape(mask), dtype=np.uint8)\n            maskr[mask == 0] = color_bg[0]\n            maskg = color_fg[1] * np.ones(np.shape(mask), dtype=np.uint8)\n            maskg[mask == 0] = color_bg[1]\n            maskb = color_fg[2] * np.ones(np.shape(mask), dtype=np.uint8)\n            maskb[mask == 0] = color_bg[2]\n            mask = np.concatenate(\n                (np.expand_dims(maskr, axis=2), np.expand_dims(maskg, axis=2), np.expand_dims(maskb, axis=2)), axis=2\n            )\n\n        # Plot.\n        _, ax = plt.subplots(1, 1, figsize=(10, 10))\n        ax.imshow(mask)\n        title = \"Number of ego poses within {}m in {}\".format(close_dist, log_location)\n        ax.set_title(title, color=\"k\")\n        sc = ax.scatter(map_poses[:, 0], map_poses[:, 1], s=10, c=close_poses)\n        color_bar = plt.colorbar(sc, fraction=0.025, pad=0.04)\n        plt.rcParams[\"figure.facecolor\"] = \"black\"\n        color_bar_ticklabels = plt.getp(color_bar.ax.axes, \"yticklabels\")\n        plt.setp(color_bar_ticklabels, color=\"k\")\n        plt.rcParams[\"figure.facecolor\"] = \"white\"  # Reset for future plots\n\n        if out_path is not None:\n            plt.savefig(out_path)\n            plt.close(\"all\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#DATA_PATH = '../input/3d-object-detection-for-autonomous-vehicles/'\nlyft_dataset = LyftDataset(data_path=DATA_PATH,json_path=DATA_PATH+'train_data')\nmy_scene = lyft_dataset.scene[0]\nmy_scene","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"lyft_dataset.list_scenes()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"**Render Scenes**"},{"metadata":{"trusted":true},"cell_type":"code","source":"def render_scene(index):\n    my_scene=lyft_dataset.scene[index]\n    lyft_dataset.render_sample(my_scene[\"first_sample_token\"])\n    ","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"render_scene(0)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"render_scene(1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#For point cloud image\nmy_sample_token = my_scene[\"first_sample_token\"]\nmy_sample = lyft_dataset.get('sample', my_sample_token)\nlyft_dataset.render_pointcloud_in_image(sample_token = my_sample[\"token\"],\n                                        dot_size = 1,\n                                        camera_channel = 'CAM_FRONT')\nmy_sample['data']","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Front Camera\nsensor_channel = 'CAM_FRONT'\nmy_sample_data = lyft_dataset.get('sample_data', my_sample['data'][sensor_channel])\nlyft_dataset.render_sample_data(my_sample_data['token'])\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Back Camera\nsensor_channel = 'CAM_BACK'\nmy_sample_data = lyft_dataset.get('sample_data', my_sample['data'][sensor_channel])\nlyft_dataset.render_sample_data(my_sample_data['token'])\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#CAM FRont LEft Camera\nsensor_channel = 'CAM_FRONT_LEFT'\nmy_sample_data = lyft_dataset.get('sample_data', my_sample['data'][sensor_channel])\nlyft_dataset.render_sample_data(my_sample_data['token'])\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#CAM Front Right Camera\nsensor_channel = 'CAM_FRONT_RIGHT'\nmy_sample_data = lyft_dataset.get('sample_data', my_sample['data'][sensor_channel])\nlyft_dataset.render_sample_data(my_sample_data['token'])\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Pick annotations\nmy_annotation_token = my_sample['anns'][30]\nmy_annotation =  my_sample_data.get('sample_annotation', my_annotation_token)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"lyft_dataset.render_annotation(my_annotation_token)\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"my_scene = lyft_dataset.scene[0]\nmy_sample_token = my_scene[\"first_sample_token\"]\nmy_sample = lyft_dataset.get('sample', my_sample_token)\nlyft_dataset.render_sample_data(my_sample['data']['LIDAR_TOP'], nsweeps=5)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from functools import partial\nimport glob\nfrom multiprocessing import Pool\nimport os\nos.environ[\"OMP_NUM_THREADS\"] = \"1\"\nfrom tqdm import tqdm, tqdm_notebook\nimport scipy\nimport scipy.ndimage\nimport scipy.special\nfrom scipy.spatial.transform import Rotation as R\nfrom lyft_dataset_sdk.utils.map_mask import MapMask\nfrom pathlib import Path\nfrom lyft_dataset_sdk.lyftdataset import LyftDataset,LyftDatasetExplorer","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"!ln -s /kaggle/input/3d-object-detection-for-autonomous-vehicles/test_images images\n!ln -s /kaggle/input/3d-object-detection-for-autonomous-vehicles/test_maps maps\n!ln -s /kaggle/input/3d-object-detection-for-autonomous-vehicles/test_lidar lidar\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"classes = [\"car\", \"motorcycle\", \"bus\", \"bicycle\", \"truck\", \"pedestrian\", \"other_vehicle\", \"animal\", \"emergency_vehicle\"]\ntrain_dataset = LyftDataset(data_path='.', json_path='../input/3d-object-detection-for-autonomous-vehicles/train_data', verbose=True)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_dataset.list_categories()\ndel train_dataset;","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"class_heights = {'animal':0.51,'bicycle':1.44,'bus':3.44,'car':1.72,'emergency_vehicle':2.39,'motorcycle':1.59,\n                'other_vehicle':3.23,'pedestrian':1.78,'truck':3.44}\nlevel5data = LyftDataset(data_path='.', json_path='../input/3d-object-detection-for-autonomous-vehicles/test_data', verbose=True)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def move_boxes_to_car_space(boxes, ego_pose):\n    \"\"\"\n    Move boxes from world space to car space.\n    Note: mutates input boxes.\n    \"\"\"\n    translation = -np.array(ego_pose['translation'])\n    rotation = Quaternion(ego_pose['rotation']).inverse\n    \n    for box in boxes:\n        # Bring box to car space\n        box.translate(translation)\n        box.rotate(rotation)\n        \ndef scale_boxes(boxes, factor):\n    \"\"\"\n    Note: mutates input boxes\n    \"\"\"\n    for box in boxes:\n        box.wlh = box.wlh * factor\n\ndef draw_boxes(im, voxel_size, boxes, classes, z_offset=0.0):\n    for box in boxes:\n        # We only care about the bottom corners\n        corners = box.bottom_corners()\n        corners_voxel = car_to_voxel_coords(corners, im.shape, voxel_size, z_offset).transpose(1,0)\n        corners_voxel = corners_voxel[:,:2] # Drop z coord\n\n        class_color = classes.index(box.name) + 1\n        \n        if class_color == 0:\n            raise Exception(\"Unknown class: {}\".format(box.name))\n\n        cv2.drawContours(im, np.int0([corners_voxel]), 0, (class_color, class_color, class_color), -1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Some hyperparameters we'll need to define for the system\nvoxel_size = (0.4, 0.4, 1.5)\nz_offset = -2.0\nbev_shape = (336, 336, 3)\n\n# We scale down each box so they are more separated when projected into our coarse voxel space.\nbox_scale = 0.8","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def visualize_lidar_of_sample(sample_token, axes_limit=80):\n    sample = level5data.get(\"sample\", sample_token)\n    sample_lidar_token = sample[\"data\"][\"LIDAR_TOP\"]\n    level5data.render_sample_data(sample_lidar_token, axes_limit=axes_limit)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.utils.data\n\ntrain_data_folder = '../input/3d-object-detection-for-autonomous-vehicles/test_data'\n\nclass BEVImageDataset(torch.utils.data.Dataset):\n    def __init__(self, input_filepaths, map_filepaths=None):\n        self.input_filepaths = input_filepaths\n        self.map_filepaths = map_filepaths\n        \n        if map_filepaths is not None:\n            assert len(input_filepaths) == len(map_filepaths)\n        \n\n    def __len__(self):\n        return len(self.input_filepaths)\n\n    def __getitem__(self, idx):\n        input_filepath = self.input_filepaths[idx]\n        \n        sample_token = input_filepath.split(\"/\")[-1].replace(\"_input.png\",\"\")\n        \n        im = cv2.imread(input_filepath, cv2.IMREAD_UNCHANGED)\n        \n        if self.map_filepaths:\n            map_filepath = self.map_filepaths[idx]\n            map_im = cv2.imread(map_filepath, cv2.IMREAD_UNCHANGED)\n            im = np.concatenate((im, map_im), axis=2)\n        \n        \n        im = im.astype(np.float32)/255\n        \n        im = torch.from_numpy(im.transpose(2,0,1))\n        \n        return im, sample_token\n\n    \ntest_data_folder = './artifacts/'\ninput_filepaths = sorted(glob.glob(os.path.join(train_data_folder, \"*_input.png\")))\nmap_filepaths = sorted(glob.glob(os.path.join(train_data_folder, \"*_map.png\")))\n\ntest_dataset = BEVImageDataset(input_filepaths,map_filepaths)\n    \nim, sample_token = test_dataset[1]\nim = im.numpy()\n\nplt.figure(figsize=(16,8))\n\n# Transpose the input volume CXY to XYC order, which is what matplotlib requires.\n# plt.imshow(np.hstack((im.transpose(1,2,0)[...,:3], target_as_rgb)))\nplt.imshow(im.transpose(1,2,0)[...,:3])\nplt.title(sample_token)\nplt.show()\n\nvisualize_lidar_of_sample(sample_token)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# This implementation was copied from https://github.com/jvanvugt/pytorch-unet, it is MIT licensed.\nimport torch.nn as nn\nclass UNet(nn.Module):\n    def __init__(\n        self,\n        in_channels=1,\n        n_classes=2,\n        depth=5,\n        wf=6,\n        padding=False,\n        batch_norm=False,\n        up_mode='upconv',\n    ):\n        \"\"\"\n        Implementation of\n        U-Net: Convolutional Networks for Biomedical Image Segmentation\n        (Ronneberger et al., 2015)\n        https://arxiv.org/abs/1505.04597\n        Using the default arguments will yield the exact version used\n        in the original paper\n        Args:\n            in_channels (int): number of input channels\n            n_classes (int): number of output channels\n            depth (int): depth of the network\n            wf (int): number of filters in the first layer is 2**wf\n            padding (bool): if True, apply padding such that the input shape\n                            is the same as the output.\n                            This may introduce artifacts\n            batch_norm (bool): Use BatchNorm after layers with an\n                               activation function\n            up_mode (str): one of 'upconv' or 'upsample'.\n                           'upconv' will use transposed convolutions for\n                           learned upsampling.\n                           'upsample' will use bilinear upsampling.\n        \"\"\"\n        super(UNet, self).__init__()\n        assert up_mode in ('upconv', 'upsample')\n        self.padding = padding\n        self.depth = depth\n        prev_channels = in_channels\n        self.down_path = nn.ModuleList()\n        for i in range(depth):\n            self.down_path.append(\n                UNetConvBlock(prev_channels, 2 ** (wf + i), padding, batch_norm)\n            )\n            prev_channels = 2 ** (wf + i)\n\n        self.up_path = nn.ModuleList()\n        for i in reversed(range(depth - 1)):\n            self.up_path.append(\n                UNetUpBlock(prev_channels, 2 ** (wf + i), up_mode, padding, batch_norm)\n            )\n            prev_channels = 2 ** (wf + i)\n\n        self.last = nn.Conv2d(prev_channels, n_classes, kernel_size=1)\n\n    def forward(self, x):\n        blocks = []\n        for i, down in enumerate(self.down_path):\n            x = down(x)\n            if i != len(self.down_path) - 1:\n                blocks.append(x)\n                x = F.max_pool2d(x, 2)\n\n        for i, up in enumerate(self.up_path):\n            x = up(x, blocks[-i - 1])\n\n        return self.last(x)\n\n\nclass UNetConvBlock(nn.Module):\n    def __init__(self, in_size, out_size, padding, batch_norm):\n        super(UNetConvBlock, self).__init__()\n        block = []\n\n        block.append(nn.Conv2d(in_size, out_size, kernel_size=3, padding=int(padding)))\n        block.append(nn.ReLU())\n        if batch_norm:\n            block.append(nn.BatchNorm2d(out_size))\n\n        block.append(nn.Conv2d(out_size, out_size, kernel_size=3, padding=int(padding)))\n        block.append(nn.ReLU())\n        if batch_norm:\n            block.append(nn.BatchNorm2d(out_size))\n\n        self.block = nn.Sequential(*block)\n\n    def forward(self, x):\n        out = self.block(x)\n        return out\n\n\nclass UNetUpBlock(nn.Module):\n    def __init__(self, in_size, out_size, up_mode, padding, batch_norm):\n        super(UNetUpBlock, self).__init__()\n        if up_mode == 'upconv':\n            self.up = nn.ConvTranspose2d(in_size, out_size, kernel_size=2, stride=2)\n        elif up_mode == 'upsample':\n            self.up = nn.Sequential(\n                nn.Upsample(mode='bilinear', scale_factor=2),\n                nn.Conv2d(in_size, out_size, kernel_size=1),\n            )\n\n        self.conv_block = UNetConvBlock(in_size, out_size, padding, batch_norm)\n\n    def center_crop(self, layer, target_size):\n        _, _, layer_height, layer_width = layer.size()\n        diff_y = (layer_height - target_size[0]) // 2\n        diff_x = (layer_width - target_size[1]) // 2\n        return layer[\n            :, :, diff_y : (diff_y + target_size[0]), diff_x : (diff_x + target_size[1])\n        ]\n\n    def forward(self, x, bridge):\n        up = self.up(x)\n        crop1 = self.center_crop(bridge, up.shape[2:])\n        out = torch.cat([up, crop1], 1)\n        out = self.conv_block(out)\n\n        return out","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def get_unet_model(in_channels=6, num_output_classes=2):\n    model = UNet(in_channels=in_channels, n_classes=num_output_classes, wf=5, depth=4, padding=True, up_mode='upsample')\n    \n    # Optional, for multi GPU training and inference\n    model = nn.DataParallel(model)\n    return model","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def visualize_predictions(input_image, prediction, n_images=2, apply_softmax=True):\n    \"\"\"\n    Takes as input 3 PyTorch tensors, plots the input image, predictions and targets.\n    \"\"\"\n    # Only select the first n images\n    prediction = prediction[:n_images]\n\n    input_image = input_image[:n_images]\n\n    prediction = prediction.detach().cpu().numpy()\n    if apply_softmax:\n        prediction = scipy.special.softmax(prediction, axis=1)\n    class_one_preds = np.hstack(1-prediction[:,0])\n\n\n    class_rgb = np.repeat(class_one_preds[..., None], 3, axis=2)\n    class_rgb[...,2] = 0\n\n    \n    input_im = np.hstack(input_image.cpu().numpy().transpose(0,2,3,1))\n    \n    if input_im.shape[2] == 3:\n        input_im_grayscale = np.repeat(input_im.mean(axis=2)[..., None], 3, axis=2)\n        overlayed_im = (input_im_grayscale*0.6 + class_rgb*0.7).clip(0,1)\n    else:\n        input_map = input_im[...,3:]\n        overlayed_im = (input_map*0.6 + class_rgb*0.7).clip(0,1)\n\n    thresholded_pred = np.repeat(class_one_preds[..., None] > 0.5, 3, axis=2)\n\n    fig = plt.figure(figsize=(12,26))\n    plot_im = np.vstack([class_rgb, input_im[...,:3], overlayed_im, thresholded_pred]).clip(0,1).astype(np.float32)\n    plt.imshow(plot_im)\n    plt.axis(\"off\")\n    plt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\nclass_weights = torch.from_numpy(np.array([0.2] + [1.0]*len(classes), dtype=np.float32))\nclass_weights = class_weights.to(device)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"batch_size = 8\nepochs = 15 # Note: We may be able to train for longer and expect better results, the reason this number is low is to keep the runtime short.\n\nmodel = get_unet_model(num_output_classes=len(classes)+1)\n\nstate = torch.load('../input/lyft3d-mask-training/unet_checkpoint_epoch_10.pth')\nmodel.load_state_dict(state)\nmodel = model.to(device)\nmodel.eval();","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def calc_detection_box(prediction_opened,class_probability):\n\n    sample_boxes = []\n    sample_detection_scores = []\n    sample_detection_classes = []\n    \n    contours, hierarchy = cv2.findContours(prediction_opened, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) \n    \n    for cnt in contours:\n        rect = cv2.minAreaRect(cnt)\n        box = cv2.boxPoints(rect)\n        \n        # Let's take the center pixel value as the confidence value\n        box_center_index = np.int0(np.mean(box, axis=0))\n        \n        for class_index in range(len(classes)):\n            box_center_value = class_probability[class_index+1, box_center_index[1], box_center_index[0]]\n            \n            # Let's remove candidates with very low probability\n            if box_center_value < 0.01:\n                continue\n            \n            box_center_class = classes[class_index]\n\n            box_detection_score = box_center_value\n            sample_detection_classes.append(box_center_class)\n            sample_detection_scores.append(box_detection_score)\n            sample_boxes.append(box)\n            \n    return np.array(sample_boxes),sample_detection_scores,sample_detection_classes","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(3,3))\n    \ndef open_preds(predictions_non_class0):\n\n    predictions_opened = np.zeros((predictions_non_class0.shape), dtype=np.uint8)\n\n    for i, p in enumerate(tqdm(predictions_non_class0)):\n        thresholded_p = (p > background_threshold).astype(np.uint8)\n        predictions_opened[i] = cv2.morphologyEx(thresholded_p, cv2.MORPH_OPEN, kernel)\n        \n    return predictions_opened","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import gc\ngc.collect()\ntest_loader = torch.utils.data.DataLoader(test_dataset, batch_size, shuffle=True, num_workers=os.cpu_count()*2)\nprogress_bar = tqdm_notebook(test_loader)\n\n# We quantize to uint8 here to conserve memory. We're allocating >20GB of memory otherwise.\n# predictions = np.zeros((len(test_loader), 1+len(classes), 336, 336), dtype=np.uint8)\n\nsample_tokens = []\nall_losses = []\n\ndetection_boxes = []\ndetection_scores = []\ndetection_classes = []\n\n# Arbitrary threshold in our system to create a binary image to fit boxes around.\nbackground_threshold = 225\n\nwith torch.no_grad():\n    model.eval()\n    for ii, (X, batch_sample_tokens) in enumerate(progress_bar):\n\n        sample_tokens.extend(batch_sample_tokens)\n        \n        X = X.to(device)  # [N, 1, H, W]\n        prediction = model(X)  # [N, 2, H, W]\n        \n        prediction = F.softmax(prediction, dim=1)\n        \n        prediction_cpu = prediction.cpu().numpy()\n        predictions = np.round(prediction_cpu*255).astype(np.uint8)\n        \n        # Get probabilities for non-background\n        predictions_non_class0 = 255 - predictions[:,0]\n        \n        predictions_opened = np.zeros((predictions_non_class0.shape), dtype=np.uint8)\n\n        for i, p in enumerate(predictions_non_class0):\n            thresholded_p = (p > background_threshold).astype(np.uint8)\n            predictions_opened[i] = cv2.morphologyEx(thresholded_p, cv2.MORPH_OPEN, kernel)\n    \n            sample_boxes,sample_detection_scores,sample_detection_classes = calc_detection_box(predictions_opened[i],\n                                                                                              predictions[i])\n        \n            detection_boxes.append(np.array(sample_boxes))\n            detection_scores.append(sample_detection_scores)\n            detection_classes.append(sample_detection_classes)\n        \n#         # Visualize the first prediction\n#         if ii == 0:\n#             visualize_predictions(X, prediction, apply_softmaxiii=False)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(\"Total amount of boxes:\", np.sum([len(x) for x in detection_boxes]))\n    \n\n# Visualize the boxes in the first sample\nt = np.zeros_like(predictions_opened[0])\nfor sample_boxes in detection_boxes[0]:\n    box_pix = np.int0(sample_boxes)\n    cv2.drawContours(t,[box_pix],0,(255),2)\nplt.imshow(t)\nplt.show()\n\n# Visualize their probabilities\nplt.hist(detection_scores[0], bins=20)\nplt.xlabel(\"Detection Score\")\nplt.ylabel(\"Count\")\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def create_transformation_matrix_to_voxel_space(shape, voxel_size, offset):\n    \"\"\"\n    Constructs a transformation matrix given an output voxel shape such that (0,0,0) ends up in the center.\n    Voxel_size defines how large every voxel is in world coordinate, (1,1,1) would be the same as Minecraft voxels.\n    \n    An offset per axis in world coordinates (metric) can be provided, this is useful for Z (up-down) in lidar points.\n    \"\"\"\n    \n    shape, voxel_size, offset = np.array(shape), np.array(voxel_size), np.array(offset)\n    \n    tm = np.eye(4, dtype=np.float32)\n    translation = shape/2 + offset/voxel_size\n    \n    tm = tm * np.array(np.hstack((1/voxel_size, [1])))\n    tm[:3, 3] = np.transpose(translation)\n    return tm\n\ndef transform_points(points, transf_matrix):\n    \"\"\"\n    Transform (3,N) or (4,N) points using transformation matrix.\n    \"\"\"\n    if points.shape[0] not in [3,4]:\n        raise Exception(\"Points input should be (3,N) or (4,N) shape, received {}\".format(points.shape))\n    return transf_matrix.dot(np.vstack((points[:3, :], np.ones(points.shape[1]))))[:3, :]\n\n\ndef car_to_voxel_coords(points, shape, voxel_size, z_offset=0):\n    if len(shape) != 3:\n        raise Exception(\"Voxel volume shape should be 3 dimensions (x,y,z)\")\n        \n    if len(points.shape) != 2 or points.shape[0] not in [3, 4]:\n        raise Exception(\"Input points should be (3,N) or (4,N) in shape, found {}\".format(points.shape))\n\n    tm = create_transformation_matrix_to_voxel_space(shape, voxel_size, (0, 0, z_offset))\n    p = transform_points(points, tm)\n    return p\n\ndef create_voxel_pointcloud(points, shape, voxel_size=(0.5,0.5,1), z_offset=0):\n\n    points_voxel_coords = car_to_voxel_coords(points.copy(), shape, voxel_size, z_offset)\n    points_voxel_coords = points_voxel_coords[:3].transpose(1,0)\n    points_voxel_coords = np.int0(points_voxel_coords)\n    \n    bev = np.zeros(shape, dtype=np.float32)\n    bev_shape = np.array(shape)\n\n    within_bounds = (np.all(points_voxel_coords >= 0, axis=1) * np.all(points_voxel_coords < bev_shape, axis=1))\n    \n    points_voxel_coords = points_voxel_coords[within_bounds]\n    coord, count = np.unique(points_voxel_coords, axis=0, return_counts=True)\n        \n    # Note X and Y are flipped:\n    bev[coord[:,1], coord[:,0], coord[:,2]] = count\n    \n    return bev\n\ndef normalize_voxel_intensities(bev, max_intensity=16):\n    return (bev/max_intensity).clip(0,1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from lyft_dataset_sdk.eval.detection.mAP_evaluation import Box3D, recall_precision\npred_box3ds = []\n\n# This could use some refactoring..\nfor (sample_token, sample_boxes, sample_detection_scores, sample_detection_class) in tqdm_notebook(zip(sample_tokens, detection_boxes, detection_scores, detection_classes), total=len(sample_tokens)):\n    sample_boxes = sample_boxes.reshape(-1, 2) # (N, 4, 2) -> (N*4, 2)\n    sample_boxes = sample_boxes.transpose(1,0) # (N*4, 2) -> (2, N*4)\n\n    # Add Z dimension\n    sample_boxes = np.vstack((sample_boxes, np.zeros(sample_boxes.shape[1]),)) # (2, N*4) -> (3, N*4)\n\n    sample = level5data.get(\"sample\", sample_token)\n    sample_lidar_token = sample[\"data\"][\"LIDAR_TOP\"]\n    lidar_data = level5data.get(\"sample_data\", sample_lidar_token)\n    lidar_filepath = level5data.get_sample_data_path(sample_lidar_token)\n    ego_pose = level5data.get(\"ego_pose\", lidar_data[\"ego_pose_token\"])\n    ego_translation = np.array(ego_pose['translation'])\n\n    global_from_car = transform_matrix(ego_pose['translation'],\n                                       Quaternion(ego_pose['rotation']), inverse=False)\n\n    car_from_voxel = np.linalg.inv(create_transformation_matrix_to_voxel_space(bev_shape, voxel_size, (0, 0, z_offset)))\n\n\n    global_from_voxel = np.dot(global_from_car, car_from_voxel)\n    sample_boxes = transform_points(sample_boxes, global_from_voxel)\n\n    # We don't know at where the boxes are in the scene on the z-axis (up-down), let's assume all of them are at\n    # the same height as the ego vehicle.\n    sample_boxes[2,:] = ego_pose[\"translation\"][2]\n\n\n    # (3, N*4) -> (N, 4, 3)\n    sample_boxes = sample_boxes.transpose(1,0).reshape(-1, 4, 3)\n\n#     box_height = 1.75\n    box_height = np.array([class_heights[cls] for cls in sample_detection_class])\n\n    # Note: Each of these boxes describes the ground corners of a 3D box.\n    # To get the center of the box in 3D, we'll have to add half the height to it.\n    sample_boxes_centers = sample_boxes.mean(axis=1)\n    sample_boxes_centers[:,2] += box_height/2\n\n    # Width and height is arbitrary - we don't know what way the vehicles are pointing from our prediction segmentation\n    # It doesn't matter for evaluation, so no need to worry about that here.\n    # Note: We scaled our targets to be 0.8 the actual size, we need to adjust for that\n    sample_lengths = np.linalg.norm(sample_boxes[:,0,:] - sample_boxes[:,1,:], axis=1) * 1/box_scale\n    sample_widths = np.linalg.norm(sample_boxes[:,1,:] - sample_boxes[:,2,:], axis=1) * 1/box_scale\n    \n    sample_boxes_dimensions = np.zeros_like(sample_boxes_centers) \n    sample_boxes_dimensions[:,0] = sample_widths\n    sample_boxes_dimensions[:,1] = sample_lengths\n    sample_boxes_dimensions[:,2] = box_height\n\n    for i in range(len(sample_boxes)):\n        translation = sample_boxes_centers[i]\n        size = sample_boxes_dimensions[i]\n        class_name = sample_detection_class[i]\n        ego_distance = float(np.linalg.norm(ego_translation - translation))\n    \n        \n        # Determine the rotation of the box\n        v = (sample_boxes[i,0] - sample_boxes[i,1])\n        v /= np.linalg.norm(v)\n        r = R.from_dcm([\n            [v[0], -v[1], 0],\n            [v[1],  v[0], 0],\n            [   0,     0, 1],\n        ])\n        quat = r.as_quat()\n        # XYZW -> WXYZ order of elements\n        quat = quat[[3,0,1,2]]\n        \n        detection_score = float(sample_detection_scores[i])\n\n        \n        box3d = Box3D(\n            sample_token=sample_token,\n            translation=list(translation),\n            size=list(size),\n            rotation=list(quat),\n            name=class_name,\n            score=detection_score\n        )\n        pred_box3ds.append(box3d)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"pred_box3ds[0]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sub = {}\nfor i in tqdm_notebook(range(len(pred_box3ds))):\n#     yaw = -np.arctan2(pred_box3ds[i].rotation[2], pred_box3ds[i].rotation[0])\n    yaw = 2*np.arccos(pred_box3ds[i].rotation[0]);\n    pred =  str(pred_box3ds[i].score/255) + ' ' + str(pred_box3ds[i].center_x)  + ' '  + \\\n    str(pred_box3ds[i].center_y) + ' '  + str(pred_box3ds[i].center_z) + ' '  + \\\n    str(pred_box3ds[i].width) + ' ' \\\n    + str(pred_box3ds[i].length) + ' '  + str(pred_box3ds[i].height) + ' ' + str(yaw) + ' ' \\\n    + str(pred_box3ds[i].name) + ' ' \n        \n    if pred_box3ds[i].sample_token in sub.keys():     \n        sub[pred_box3ds[i].sample_token] += pred\n    else:\n        sub[pred_box3ds[i].sample_token] = pred        \n    \nsample_sub = pd.read_csv('../input/3d-object-detection-for-autonomous-vehicles/sample_submission.csv')\nfor token in set(sample_sub.Id.values).difference(sub.keys()):\n    print(token)\n    sub[token] = ''","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sub = pd.DataFrame(list(sub.items()))\nsub.columns = sample_sub.columns\nsub.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sub.to_csv('lyft3d_pred.csv',index=False)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"ls","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"!rm -r ./artifacts/","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":1}