{"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)\n\n%matplotlib inline\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\nimport matplotlib.patches as patches\nfrom PIL import Image\nfrom ast import literal_eval\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\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# 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":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"PATH = \"/kaggle/input/global-wheat-detection/\"\ntrain_csv = pd.read_csv(PATH + \"train.csv\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_csv.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"for i in range(0, 10):\n    location = i*30\n\n    img_path = PATH + \"train/\" + train_csv[\"image_id\"].iloc[location] + \".jpg\"\n    bbox = train_csv[\"bbox\"].iloc[location]\n    print(img_path)\n    \n    bbox = literal_eval(bbox)\n\n    im = np.array(Image.open(img_path), dtype=np.uint8)\n    im = im[:, :, 0]\n    im[im<100] = 0\n    print(im)\n    \n    # Create figure and axes\n    fig,ax = plt.subplots(1)\n\n    # Display the image\n    ax.imshow(im, cmap = 'hot')\n\n    # Create a Rectangle patch\n    rect = patches.Rectangle((bbox[0],bbox[1]),bbox[2],bbox[3],linewidth=1,edgecolor='y',facecolor='none')\n\n    # Add the patch to the Axes\n    ax.add_patch(rect)\n\n    plt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","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}