{"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# 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\nfor 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":{"trusted":true},"cell_type":"code","source":"%matplotlib inline\nimport matplotlib.pyplot as plt\nimport seaborn as sns","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Get the data"},{"metadata":{},"cell_type":"markdown","source":"The CSV data is simple with columns\n- image_id - the unique image ID\n- width, height - the width and height of the images\n- bbox - a bounding box, formatted as a Python-style list of [xmin, ymin, width, height]"},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"data = pd.read_csv('/kaggle/input/global-wheat-detection/train.csv')\ndata.head()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### Uinque Image IDS and number of sampels in train.csv file "},{"metadata":{"trusted":true},"cell_type":"code","source":"print(f'there are {data.image_id.nunique()}  unique images in the data')\nprint(f'Total number of samples{data.shape[0]}')","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Image Understanding\n- Read an image\n- Dispaly an image\n- Dispaly part of the image where we have wheat heads"},{"metadata":{"trusted":true},"cell_type":"code","source":"sample_img = plt.imread('/kaggle/input/global-wheat-detection/train/b6ab77fd7.jpg')\nplt.imshow(sample_img)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"x_min,y_min,width,height = list(map(int,[377.0, 504.0, 74.0, 160.0]))\nplt.imshow(sample_img[x_min:x_min+width, y_min:y_min+height,:])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"x_min,y_min,width,height = list(map(int,[834.0, 222.0, 56.0, 36.0]))\nplt.imshow(sample_img[x_min:x_min+width, y_min:y_min+height,:])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"x_min,y_min,width,height = list(map(int,[226.0, 548.0, 130.0, 58.0]))\nplt.imshow(sample_img[x_min:x_min+width, y_min:y_min+height,:])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"data.isnull().sum()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Images in train Folder"},{"metadata":{"trusted":true},"cell_type":"code","source":"len(os.listdir('/kaggle/input/global-wheat-detection/train'))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"data.image_id.unique()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Images without wheat heads"},{"metadata":{"trusted":true,"collapsed":true},"cell_type":"code","source":"train_image_ids = [s.strip('.jpg') for s in os.listdir('/kaggle/input/global-wheat-detection/train')]\ntrain_image_ids","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"len(train_image_ids) - data.image_id.nunique()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"image_ids_without_wheat = list(set(train_image_ids).difference(set(data.image_id.unique())))\nlen(image_ids_without_wheat)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sample_image_without_wheat = plt.imread('/kaggle/input/global-wheat-detection/train/'+image_ids_without_wheat[0]+'.jpg')\nplt.imshow(sample_image_without_wheat)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"for i in range(1,10):\n    plt.subplot(3,3,i)  \n    plt.imshow(plt.imread('/kaggle/input/global-wheat-detection/train/'+image_ids_without_wheat[i-1]+'.jpg'))","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Images with wheat heads using bounding boxes"},{"metadata":{"trusted":true},"cell_type":"code","source":"import cv2","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"cv2.imread()","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}