{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"d34b68eb-fdad-512d-e1a3-42396d34d997"},"outputs":[],"source":"import pandas as pd\nimport numpy as np\nimport tifffile as tif\nimport gc\nfrom shapely.wkt import loads as wkt_loads\nimport cv2\nimport random\nfrom keras.layers import Input, merge, Convolution2D, MaxPooling2D, UpSampling2D, Reshape, core, Dropout\n\nfrom keras.models import Model\n\nfrom keras.optimizers import Adam\nfrom keras.callbacks import ModelCheckpoint, LearningRateScheduler\nfrom keras import backend as K\nimport matplotlib.pyplot as plt"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"d633f7c4-c50f-a36d-d551-9855b400373f"},"outputs":[],"source":"for _ in range(6010,6020,10):\n    print('Doing '+str(_))\n    \n    big_pic = []\n    \n    for i in range(5):\n        hstrip = []\n        for j in range(5):\n            print(str(_)+'_'+str(i)+'_'+str(j))\n            im_3 = tif.imread('/kaggle/input/three_band/'+str(_)+'_'+str(i)+'_'+str(j)+'.tif')\n            im_P = tif.imread('/kaggle/input/sixteen_band/'+str(_)+'_'+str(i)+'_'+str(j)+'_P.tif')\n            print(im_3.shape)\n            print(im_P.shape)\n            hstrip.append(im_3)\n        hstrip = np.concatenate(tuple(hstrip), axis = 2)\n        big_pic.append(hstrip)\n    \n    big_pic = np.concatenate(tuple(big_pic), axis=1)\n    print(big_pic.shape)\n    \n    "},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"f86e075b-dab5-e72a-4051-52f2b62f3cf7"},"outputs":[],"source":""}],"metadata":{"_change_revision":0,"_is_fork":false,"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.6.0"}},"nbformat":4,"nbformat_minor":0}