{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load in \n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# Any results you write to the current directory are saved as output.","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"pip install -U git+https://github.com/lyft/nuscenes-devkit\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"!pip install moviepy\nimport pdb\nimport cv2\nimport pandas as pd\nimport numpy as np\nfrom PIL import Image\nfrom pathlib import Path\nfrom matplotlib import pyplot as plt\nfrom mpl_toolkits.mplot3d import axes3d, Axes3D\n\n# Load the SDK\nfrom lyft_dataset_sdk.lyftdataset import LyftDataset, LyftDatasetExplorer, Quaternion, view_points\nfrom lyft_dataset_sdk.utils.data_classes import LidarPointCloud\n\nfrom moviepy.editor import ImageSequenceClip\nfrom tqdm import tqdm_notebook as tqdm\n\n%matplotlib inline","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"\n!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\n!ln -s /kaggle/input/3d-object-detection-for-autonomous-vehicles/train_data data","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"lyftdata = LyftDataset(data_path='.', json_path='data/', verbose=True)\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"lyftdata.list_scenes()\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"\nmy_scene = lyftdata.scene[0]\nmy_scene","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"my_sample_token = my_scene[\"first_sample_token\"]\n# my_sample_token = level5data.get(\"sample\", my_sample_token)[\"next\"]  # proceed to next sample\n\nlyftdata.render_sample(my_sample_token)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"my_sample = lyftdata.get('sample', my_sample_token)\nmy_sample","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"lyftdata.list_sample(my_sample['token'])\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"\nlyftdata.render_pointcloud_in_image(sample_token = my_sample[\"token\"],\n                                      dot_size = 1,\n                                      camera_channel = 'CAM_FRONT')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"my_sample['data']\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"\nsensor_channel = 'CAM_FRONT'  # also try this e.g. with 'LIDAR_TOP'\nmy_sample_data = lyftdata.get('sample_data', my_sample['data'][sensor_channel])\nmy_sample_data","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"lyftdata.render_sample_data(my_sample_data['token'])\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"my_annotation_token = my_sample['anns'][16]\nmy_annotation =  my_sample_data.get('sample_annotation', my_annotation_token)\nmy_annotation","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"lyftdata.render_annotation(my_annotation_token)\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"\nmy_instance = lyftdata.instance[100]\nmy_instance\nprint(\"First annotated sample of this instance:\")\nlyftdata.render_annotation(my_instance['first_annotation_token'])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(\"Last annotated sample of this instance\")\nlyftdata.render_annotation(my_instance['last_annotation_token'])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"lyftdata.list_categories()\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"lyftdata.category[2]\n","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}