{"cells":[{"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)\nimport time, gc, math\nimport cv2\n\nfrom tensorflow import keras\nstart = time.process_time()","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","trusted":true},"cell_type":"code","source":"# PATH = 'C:\\\\Users\\\\Gaurav\\\\Documents\\\\Bengali.Ai\\\\bengaliai-cv19\\\\'\nPATH = '/kaggle/input/bengaliai-cv19/'\nPATH1 = '/kaggle/input/bengali-png-model/'\n# train_df_ = pd.read_csv(PATH+'train.csv')\n# test_df_ = pd.read_csv(PATH+'test.csv')\n# class_map_df = pd.read_csv(PATH+'class_map.csv')\n# sample_sub_df = pd.read_csv(PATH+'sample_submission.csv')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"HEIGHT = 137\nWIDTH = 236\n\nBATCH_SIZE = 128\n\nIMG_SIZE=64\nN_CHANNELS=3","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# def resize(df, size=IMG_SIZE, need_progress_bar=True):\n#     resized = {}\n#     X = df.values.reshape(df.shape[0], HEIGHT, WIDTH)\n#     for i in range(df.shape[0]):\n#         img = X[i]\n# #         img = (img*(255.0/img.max())).astype(np.uint8) \n# #         img = crop_resize(img)\n# #         stacked_img = np.stack((img,)*3, axis=-1)\n# #         resized[df.index[i]] = stacked_img.reshape(-1)\n#         img = cv2.resize(img,(size,size),interpolation=cv2.INTER_AREA)\n#         resized[df.index[i]] = img.reshape(-1)\n#     resized = pd.DataFrame(resized).T\n#     return resized","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def bbox(img):\n    rows = np.any(img, axis=1)\n    cols = np.any(img, axis=0)\n    rmin, rmax = np.where(rows)[0][[0, -1]]\n    cmin, cmax = np.where(cols)[0][[0, -1]]\n    return rmin, rmax, cmin, cmax\n\ndef crop_resize(img0, size=IMG_SIZE, pad=16):\n    #crop a box around pixels large than the threshold \n    #some images contain line at the sides\n    ymin,ymax,xmin,xmax = bbox(img0[5:-5,5:-5] > 80)\n    #cropping may cut too much, so we need to add it back\n    xmin = xmin - 13 if (xmin > 13) else 0\n    ymin = ymin - 10 if (ymin > 10) else 0\n    xmax = xmax + 13 if (xmax < WIDTH - 13) else WIDTH\n    ymax = ymax + 10 if (ymax < HEIGHT - 10) else HEIGHT\n    img = img0[ymin:ymax,xmin:xmax]\n    #remove lo intensity pixels as noise\n    img[img < 28] = 0\n    lx, ly = xmax-xmin,ymax-ymin\n    l = max(lx,ly) + pad\n    #make sure that the aspect ratio is kept in rescaling\n    img = np.pad(img, [((l-ly)//2,), ((l-lx)//2,)], mode='constant')\n    return cv2.resize(img,(size,size))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from keras.models import load_model\n# load model\nmodel = load_model(PATH1+'08_ResNet101_0.hdf5')\n# summarize model.\nmodel.summary()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"components = ['consonant_diacritic', 'grapheme_root', 'vowel_diacritic']\ntarget=[] # model predictions placeholder\nrow_id=[] # row_id place holder\nfor i in range(4):\n    df_test_img = pd.read_parquet('/kaggle/input/bengaliai-cv19/test_image_data_{}.parquet'.format(i)) \n    index = df_test_img['image_id']\n    index_count = 0\n    df_test_img = 255 - df_test_img.iloc[:, 1:].values.reshape(-1, HEIGHT, WIDTH).astype(np.uint8)\n    \n    if df_test_img.shape[0] < 10000:\n        split_len = 1\n    else:\n        split_len = 5\n    split_data = {}\n    split = math.ceil(df_test_img.shape[0]/split_len)\n    while split > 15000:\n        split_len += 1\n        split = math.ceil(df_test_img.shape[0]/split_len)\n        \n    for j in range(split_len):\n        split_data[j] = df_test_img[0:split]\n        df_test_img = np.delete(df_test_img, range(split_data[j].shape[0]), 0)\n    \n    del df_test_img\n    gc.collect()\n    \n    for k in range(split_len):\n        preds_dict = {\n            'grapheme_root': [],\n            'vowel_diacritic': [],\n            'consonant_diacritic': []\n        }\n        resized={}\n        \n        for l in range(split_data[k].shape[0]):\n            img = split_data[k][l]\n            img = (img*(255.0/img.max())).astype(np.uint8) \n            img = crop_resize(img)\n            img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)\n            resized[l] = img.reshape(-1)   \n        \n        resized = pd.DataFrame(resized).T\n        X_test = resized.values.reshape(-1, IMG_SIZE, IMG_SIZE, N_CHANNELS)\n\n        preds = model.predict(X_test/255)\n\n        for m, p in enumerate(preds_dict):\n            preds_dict[p] = np.argmax(preds[m], axis=1)\n        \n        for n,id in enumerate(index[index_count:(index_count+split_data[k].shape[0])].values):\n            for i,comp in enumerate(components):\n                id_sample=id+'_'+comp\n                row_id.append(id_sample)\n                target.append(preds_dict[comp][n])\n        \n        \n        index_count += split_data[k].shape[0]\n        del split_data[k]\n        del resized\n        del preds_dict\n        del preds\n        del X_test\n        gc.collect()\n\ndf_sample = pd.DataFrame(\n    {\n        'row_id': row_id,\n        'target':target\n    },\n    columns = ['row_id','target'] \n)\ndf_sample.to_csv('submission.csv',index=False)\ndf_sample.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(time.process_time() - 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