{"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\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport matplotlib.pyplot as plt\n%matplotlib inline\nimport cv2\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\ntrain_filename_list = []\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        train_filename_list.append(os.path.join(dirname, filename))\n#         print(os.path.join(dirname, filename))\n\nprint(train_filename_list[0])\ntrain_filename_list = train_filename_list[2:]\n\n\n# You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# import train data\ntrain_df = pd.read_csv(\"/kaggle/input/global-wheat-detection/train.csv\")\n\n# Data path\ndata_path = '/kaggle/input/global-wheat-detection/train/'\n\ntrain_df.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# get unique list of image_id\ntrain_img_ids = train_df[\"image_id\"].unique()\ntrain_img_ids","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# display wheat image and boundingbox\ndef show_img(img_id):\n    img = cv2.cvtColor(cv2.imread(data_path + train_img_ids[img_id] + '.jpg'), cv2.COLOR_BGR2RGB)\n    # plt.imshow(img)\n\n    bboxes = train_df[train_df.image_id==train_img_ids[img_id]].bbox.tolist()\n    J = img.copy()\n    for i in range(len(bboxes)):\n        x = int(str(bboxes[i][1:-1]).split(',')[0][:-2])\n        y = int(str(bboxes[i][1:-1]).split(',')[1][1:-2])\n        xw = x + int(str(bboxes[i][1:-1]).split(',')[2][1:-2])\n        yh = y + int(str(bboxes[i][1:-1]).split(',')[3][1:-2])\n        cv2.rectangle(J,(x,y),(xw,yh),(180,190,0),4)\n    plt.imshow(J);\n    title = \"image id : \" + str(img_id)\n    plt.title(title)\n        \n\nnum_figs = 51 # number of images\nnum_cols = 5\nnum_rows = num_figs // num_cols + 1 if num_figs % num_cols else num_figs // num_cols\n\nplt.figure(figsize=(30,60))\nfor i in range(1,num_figs+1):\n    plt.subplot(num_rows,num_cols,i)\n    plt.grid(False)\n    show_img(i)","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":4}