example_buffer.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from water_mask_funcs import create_buffer_mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "og_mask = cv2.imread('/data_dir/Shield_Water_Mask/20200709_171108_48_106c_3B_AnalyticMS_SR_no_buffer_mask.tif').astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f9887a89b70>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(og_mask[:,:,0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "some random subset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "og_mask = og_mask[3000:4000,2000:3000, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f98861fc898>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(og_mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "buffered = create_buffer_mask(og_mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f9885a1a1d0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(buffered)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}