{"cells":[{"metadata":{},"cell_type":"markdown","source":"This is a fork of [this existing kernel](https://www.kaggle.com/hanjoonchoe/resize-and-load-with-feather-format-much-faster) with just resize function changed. I've referenced the resize function from [my existing kernel](https://www.kaggle.com/kaushal2896/bengali-graphemes-starter-eda-multi-output-cnn)"},{"metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","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\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.\nimport cv2\nimport gc\nimport matplotlib.pyplot as plt\nfrom tqdm import tqdm_notebook as tqdm\nimport time","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### Resize Train set (64x64) and Convert feather format"},{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","trusted":true},"cell_type":"code","source":"start_time = time.time()\ndata0 = pd.read_parquet('/kaggle/input/bengaliai-cv19/train_image_data_0.parquet')\ndata1 = pd.read_parquet('/kaggle/input/bengaliai-cv19/train_image_data_1.parquet')\ndata2 = pd.read_parquet('/kaggle/input/bengaliai-cv19/train_image_data_2.parquet')\ndata3 = pd.read_parquet('/kaggle/input/bengaliai-cv19/train_image_data_3.parquet')\nprint(\"--- %s seconds ---\" % (time.time() - start_time))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def resize(df, size=128):\n    resized = {}\n    resize_size=128\n    df = df.set_index('image_id')\n    for i in tqdm(range(df.shape[0])):\n        image=df.loc[df.index[i]].values.reshape(137,236)\n        _, thresh = cv2.threshold(image, 30, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)\n        contours, _ = cv2.findContours(thresh,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)[-2:]\n\n        idx = 0 \n        ls_xmin = []\n        ls_ymin = []\n        ls_xmax = []\n        ls_ymax = []\n        for cnt in contours:\n            idx += 1\n            x,y,w,h = cv2.boundingRect(cnt)\n            ls_xmin.append(x)\n            ls_ymin.append(y)\n            ls_xmax.append(x + w)\n            ls_ymax.append(y + h)\n        xmin = min(ls_xmin)\n        ymin = min(ls_ymin)\n        xmax = max(ls_xmax)\n        ymax = max(ls_ymax)\n\n        roi = image[ymin:ymax,xmin:xmax]\n        resized_roi = cv2.resize(roi, (resize_size, resize_size),interpolation=cv2.INTER_AREA)\n        resized[df.index[i]] = resized_roi.reshape(-1)\n    resized = pd.DataFrame(resized).T.reset_index()\n    resized.columns = resized.columns.astype(str)\n    resized.rename(columns={'index':'image_id'},inplace=True)\n    return resized","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"data0 = resize(data0)\ndata0.to_feather('train_data_0.feather')\ndel data0\ndata1 = resize(data1)\ndata1.to_feather('train_data_1.feather')\ndel data1\ndata2 = resize(data2)\ndata2.to_feather('train_data_2.feather')\ndel data2\ndata3 = resize(data3)\ndata3.to_feather('train_data_3.feather')\ndel data3","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### Resize test set (64x64) and Convert feather format"},{"metadata":{"trusted":true},"cell_type":"code","source":"start_time = time.time()\ndata0 = pd.read_parquet('/kaggle/input/bengaliai-cv19/test_image_data_0.parquet')\ndata1 = pd.read_parquet('/kaggle/input/bengaliai-cv19/test_image_data_1.parquet')\ndata2 = pd.read_parquet('/kaggle/input/bengaliai-cv19/test_image_data_2.parquet')\ndata3 = pd.read_parquet('/kaggle/input/bengaliai-cv19/test_image_data_3.parquet')\nprint(\"--- %s seconds ---\" % (time.time() - start_time))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"data0 = resize(data0)\ndata0.to_feather('test_data_0.feather')\ndel data0\ndata1 = resize(data1)\ndata1.to_feather('test_data_1.feather')\ndel data1\ndata2 = resize(data2)\ndata2.to_feather('test_data_2.feather')\ndel data2\ndata3 = resize(data3)\ndata3.to_feather('test_data_3.feather')\ndel data3","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### Reload trainset and Check the images"},{"metadata":{"trusted":true},"cell_type":"code","source":"start_time = time.time()\ndata0 = pd.read_feather('train_data_0.feather')\ndata1 = pd.read_feather('train_data_1.feather')\ndata2 = pd.read_feather('train_data_2.feather')\ndata3 = pd.read_feather('train_data_3.feather')\nprint(\"--- %s seconds ---\" % (time.time() - start_time))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def Grapheme_plot(df):\n    df_sample = df.sample(15)\n    im_id, img = df_sample.iloc[:,0].values,df_sample.iloc[:,1:].values.astype(np.float)\n    \n    fig,ax = plt.subplots(3,5,figsize=(20,20))\n    for i in range(15):\n        j=i%3\n        k=i//3\n        ax[j,k].imshow(img[i].reshape(128,128), cmap='gray')\n        ax[j,k].set_title(im_id[i],fontsize=20)\n    plt.tight_layout()\n        ","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"Grapheme_plot(data0)\nGrapheme_plot(data1)\nGrapheme_plot(data2)\nGrapheme_plot(data3)","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 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