{"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\n# for 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":{"trusted":true},"cell_type":"code","source":"base_path = \"/kaggle/input/3d-object-detection-for-autonomous-vehicles/\"","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"data = pd.read_csv(\"/kaggle/input/3d-object-detection-for-autonomous-vehicles/train.csv\")\ndata","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"os.listdir(base_path + \"train_images\")[:5]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import cv2\nfrom PIL import Image\nimport matplotlib.pyplot as plt","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"for i in os.listdir(base_path + \"train_images\")[:5]:\n    path = base_path + \"train_images/\" + i\n    img = cv2.imread(path)\n    img1 = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)\n    plt.imshow(img1)\n    plt.axis(\"off\")\n    plt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"os.listdir(base_path + \"/train_data\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import json\nwith open(base_path + \"/train_data/sample_data.json\") as f:\n    data_sample = json.load(f)\n    print(len(data_sample), \"\\n\", data_sample[0], \"\\n\", data_sample[3])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"with open(base_path + \"/train_data/sensor.json\") as f:\n    data_sensor = json.load(f)\n    print(len(data_sensor), \"\\n\", data_sensor[0])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"jpeg_data = []\nothers_data = []\nfor i in data_sample:\n    if i[\"fileformat\"] == \"jpeg\":\n        jpeg_data.append(i)\n    else:\n        others_data.append(i)\nprint(len(jpeg_data), len(others_data))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"jpeg_df = pd.DataFrame(jpeg_data)\nothers_df = pd.DataFrame(others_data)\nprint(jpeg_df.shape, others_df.shape)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"jpeg_df.head(5)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"others_df.head(5)","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}