{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"_kg_hide-output":true,"collapsed":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":{"trusted":true},"cell_type":"code","source":"import os\nimport cv2\nimport matplotlib.pyplot as plt","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"train_df = pd.read_csv('../input/pku-autonomous-driving/train.csv')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_df.info()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_expanded = pd.concat([train_df, train_df['PredictionString'].str.split(' ', expand=True).astype(float)], axis=1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_expanded = train_expanded.drop(['PredictionString'], axis=1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"columns = []\nfor col in train_expanded:\n    if isinstance(col, int):\n        if col % 7 == 0:\n            columns.append(f'modeltype_{col//7:d}')\n        elif col % 7 == 1:\n            columns.append(f'yaw_{col//7:d}')\n        elif col % 7 == 2:\n            columns.append(f'pitch_{col//7:d}')\n        elif col % 7 == 3:\n            columns.append(f'roll_{col//7:d}')\n        elif col % 7 == 4:\n            columns.append(f'x_{col//7:d}')\n        elif col % 7 == 5:\n            columns.append(f'y_{col//7:d}')\n        elif col % 7 == 6:\n            columns.append(f'z_{col//7:d}')            \n    else:\n        columns.append(col)\ntrain_expanded.columns = columns","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_expanded['CarAmount'] = (train_df['PredictionString'].str.split(' ').apply(len) // 7).astype(int)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"col = train_expanded.columns.tolist()\ncol = col[0:1] + col[-1:] + col[1:-1]\ntrain_expanded = train_expanded[col]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_expanded = train_expanded.sort_values(by=['ImageId'])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_expanded['CarAmount'].plot(kind='hist', figsize=(15, 3), bins=100, title='Distribution of cars in each image')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_image_path = '../input/pku-autonomous-driving/train_images/'\ntrain_mask_path = '../input/pku-autonomous-driving/train_masks/'","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_images = next(os.walk(train_image_path))[2]\ntrain_images.sort()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_masks = next(os.walk(train_mask_path))[2]\ntrain_masks.sort()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sample_image = cv2.cvtColor(cv2.imread(train_image_path + train_images[0]), cv2.COLOR_BGR2RGB)\nprint(sample_image.shape)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sample_mask = cv2.cvtColor(cv2.imread(train_mask_path + train_masks[0]), cv2.COLOR_BGR2RGB)\nprint(sample_mask.shape)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"plt.figure(figsize=(15,15))\nplt.imshow(sample_image)\nplt.imshow(sample_mask, alpha=0.65)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","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}