{"cells": [{"cell_type": "code", "source": "__author__ = \"n01z3\"\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport cv2\nimport pandas as pd\nfrom shapely.wkt import loads as wkt_loads\nimport tifffile as tiff\nimport os\nimport random\nfrom keras.models import Model\nfrom keras.layers import Input, merge, Convolution2D, MaxPooling2D, UpSampling2D, Reshape, core, Dropout\nfrom keras.optimizers import Adam\nfrom keras.callbacks import ModelCheckpoint, LearningRateScheduler\nfrom keras import backend as K\nfrom sklearn.metrics import jaccard_similarity_score\nfrom shapely.geometry import MultiPolygon, Polygon\nimport shapely.wkt\nimport shapely.affinity\nfrom collections import defaultdict\n\nN_Cls = 10\nDF = pd.read_csv('../input/train_wkt_v4.csv')\nGS = pd.read_csv('../input/grid_sizes.csv', names=['ImageId', 'Xmax', 'Ymin'], skiprows=1)\nSB = pd.read_csv('../input/sample_submission.csv')\nISZ = 160\nsmooth = 1e-12", "outputs": [], "execution_count": null, "metadata": {"_uuid": "32cb25543174ca788e85f9062e2ff72dc5563908", "trusted": true, "_cell_guid": "ed571bc7-fd6b-47f1-874c-77baedcdbe8c"}}, {"cell_type": "code", "source": "print(\"let's stick all imgs together\")\ns = 835\nx = np.zeros((5 * s, 5 * s, 8))\ny = np.zeros((5 * s, 5 * s, N_Cls))\nids = sorted(DF.ImageId.unique())\nprint(len(ids))\nfor i in range(5):\n    for j in range(5):\n        id = ids[5 * i + j]\n        img = tiff.imread(\"../input/three_band/{}_M.tif\".format(id))\n        img = np.rollaxis(img, 0, 3)", "outputs": [], "execution_count": null, "metadata": {"_uuid": "c4d1a1d379a1c5b373fed0a3969dcf6eff671354", "trusted": true, "_cell_guid": "5b3aa33f-2d8e-47bf-8492-2b23a572d2bf"}}], "nbformat": 4, "metadata": {"kernelspec": {"display_name": "Python 3", "name": "python3", "language": "python"}, "language_info": {"mimetype": "text/x-python", "name": "python", "version": "3.6.1", "pygments_lexer": "ipython3", "codemirror_mode": {"version": 3, "name": "ipython"}, "nbconvert_exporter": "python", "file_extension": ".py"}}, "nbformat_minor": 1}