GLCM-Grimp-approach_new.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "084ed149",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "\n",
       "  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
       "        if (callback != null)\n",
       "          callback();\n",
       "      });\n",
       "    } finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(css_urls, js_urls, js_modules, callback) {\n",
       "    if (css_urls == null) css_urls = [];\n",
       "    if (js_urls == null) js_urls = [];\n",
       "    if (js_modules == null) js_modules = [];\n",
       "\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls.length === 0 && js_modules.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "\n",
       "    function on_load() {\n",
       "      root._bokeh_is_loading--;\n",
       "      if (root._bokeh_is_loading === 0) {\n",
       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
       "        run_callbacks()\n",
       "      }\n",
       "    }\n",
       "\n",
       "    function on_error() {\n",
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       "    }\n",
       "\n",
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       "      var url = css_urls[i];\n",
       "      const element = document.createElement(\"link\");\n",
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       "      element.onerror = on_error;\n",
       "      element.rel = \"stylesheet\";\n",
       "      element.type = \"text/css\";\n",
       "      element.href = url;\n",
       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
       "      document.body.appendChild(element);\n",
       "    }\n",
       "\n",
       "    var skip = [];\n",
       "    if (window.requirejs) {\n",
       "      window.requirejs.config({'packages': {}, 'paths': {'gridstack': 'https://cdn.jsdelivr.net/npm/gridstack@4.2.5/dist/gridstack-h5', 'notyf': 'https://cdn.jsdelivr.net/npm/notyf@3/notyf.min'}, 'shim': {'gridstack': {'exports': 'GridStack'}}});\n",
       "      require([\"gridstack\"], function(GridStack) {\n",
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       "\t  on_load();\n",
       "\t}\n",
       "\tcontinue;\n",
       "      }\n",
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       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.async = false;\n",
       "      element.src = url;\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "    for (var i = 0; i < js_modules.length; i++) {\n",
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       "      if (skip.indexOf(url) >= 0) {\n",
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       "\tcontinue;\n",
       "      }\n",
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       "      element.async = false;\n",
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       "      element.type = \"module\";\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "    if (!js_urls.length && !js_modules.length) {\n",
       "      on_load()\n",
       "    }\n",
       "  };\n",
       "\n",
       "  function inject_raw_css(css) {\n",
       "    const element = document.createElement(\"style\");\n",
       "    element.appendChild(document.createTextNode(css));\n",
       "    document.body.appendChild(element);\n",
       "  }\n",
       "\n",
       "  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.3.min.js\", \"https://unpkg.com/@holoviz/panel@0.14.4/dist/panel.min.js\"];\n",
       "  var js_modules = [];\n",
       "  var css_urls = [\"https://cdn.holoviz.org/panel/0.14.4/dist/css/alerts.css\", \"https://cdn.holoviz.org/panel/0.14.4/dist/css/card.css\", \"https://cdn.holoviz.org/panel/0.14.4/dist/css/dataframe.css\", \"https://cdn.holoviz.org/panel/0.14.4/dist/css/debugger.css\", \"https://cdn.holoviz.org/panel/0.14.4/dist/css/json.css\", \"https://cdn.holoviz.org/panel/0.14.4/dist/css/loading.css\", \"https://cdn.holoviz.org/panel/0.14.4/dist/css/markdown.css\", \"https://cdn.holoviz.org/panel/0.14.4/dist/css/widgets.css\"];\n",
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       "      inject_raw_css(\"\\n    .bk.pn-loading.arc:before {\\n      background-image: url(\\\"data:image/svg+xml;base64,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\\\");\\n      background-size: auto calc(min(50%, 400px));\\n    }\\n    \");\n",
       "    },    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "function(Bokeh) {} // ensure no trailing comma for IE\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    if ((root.Bokeh !== undefined) || (force === true)) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i].call(root, root.Bokeh);\n",
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       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
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       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(css_urls, js_urls, js_modules, function() {\n",
       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
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i < css_urls.length; i++) {\n      var url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    var skip = [];\n    if (window.requirejs) {\n      window.requirejs.config({'packages': {}, 'paths': {'gridstack': 'https://cdn.jsdelivr.net/npm/gridstack@4.2.5/dist/gridstack-h5', 'notyf': 'https://cdn.jsdelivr.net/npm/notyf@3/notyf.min'}, 'shim': {'gridstack': {'exports': 'GridStack'}}});\n      require([\"gridstack\"], function(GridStack) {\n\twindow.GridStack = GridStack\n\ton_load()\n      })\n      require([\"notyf\"], function() {\n\ton_load()\n      })\n      root._bokeh_is_loading = css_urls.length + 2;\n    } else {\n      root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length;\n    }    if (((window['GridStack'] !== undefined) && (!(window['GridStack'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/0.14.4/dist/bundled/gridstack/gridstack@4.2.5/dist/gridstack-h5.js'];\n      for (var i = 0; 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i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, js_modules, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
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     "output_type": "display_data"
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     "data": {
      "text/html": [
       "<style>.bk-root, .bk-root .bk:before, .bk-root .bk:after {\n",
       "  font-family: var(--jp-ui-font-size1);\n",
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     "text": [
      "Getting login from ~/.netrc\n",
      "Already logged in. Proceed.\n"
     ]
    }
   ],
   "source": [
    "#%matplotlib ipympl\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "import grimpfunc as grimp\n",
    "import nisardev as nisar\n",
    "import panel as pn\n",
    "pn.extension()\n",
    "#from matplotlib import colors\n",
    "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import dask\n",
    "from dask.diagnostics import ProgressBar\n",
    "ProgressBar().register()\n",
    "dask.config.set(num_workers=2)  # Avoid too many NSIDC connections\n",
    "import matplotlib.gridspec as gridspec\n",
    "\n",
    "# Set path for gdal \n",
    "env = dict(GDAL_HTTP_COOKIEFILE = os.path.expanduser('~/.grimp_download_cookiejar.txt'),\n",
    "            GDAL_HTTP_COOKIEJAR = os.path.expanduser('~/.grimp_download_cookiejar.txt'))\n",
    "os.environ.update(env)\n",
    "# Get login\n",
    "myLogin = grimp.NASALogin()  # If login appears not to work, try rerunning this cell\n",
    "myLogin.username = '###' # Your username\n",
    "myLogin.password = '###' # Your password\n",
    "myLogin.view()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "80e2ad55",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\r134p694_a\\AppData\\Local\\Temp\\ipykernel_37328\\3397143139.py:5: FutureWarning: Currently, index_parts defaults to True, but in the future, it will default to False to be consistent with Pandas. Use `index_parts=True` to keep the current behavior and True/False to silence the warning.\n",
      "  gdf2=gdf1.explode()\n"
     ]
    }
   ],
   "source": [
    "import geopandas as gpd\n",
    "\n",
    "gdf=gpd.read_file(\"C:/Users/r134p694_a/practice/SEG_Fjords/SEG_Fjords_v10.shp\")\n",
    "gdf1=gdf\n",
    "gdf2=gdf1.explode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5c1e4896",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <th>0</th>\n",
       "      <td>49</td>\n",
       "      <td>None</td>\n",
       "      <td>99.882650</td>\n",
       "      <td>4</td>\n",
       "      <td>1.594527</td>\n",
       "      <td>0.022652</td>\n",
       "      <td>POLYGON ((777729.789 -2109310.903, 778128.658 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <th>0</th>\n",
       "      <td>50</td>\n",
       "      <td>None</td>\n",
       "      <td>117.619453</td>\n",
       "      <td>3</td>\n",
       "      <td>1.531864</td>\n",
       "      <td>0.026914</td>\n",
       "      <td>POLYGON ((804329.371 -2088078.345, 804336.687 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <th>0</th>\n",
       "      <td>51</td>\n",
       "      <td>None</td>\n",
       "      <td>110.475607</td>\n",
       "      <td>2</td>\n",
       "      <td>1.919556</td>\n",
       "      <td>0.025471</td>\n",
       "      <td>POLYGON ((799521.626 -2065790.718, 799748.594 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">51</th>\n",
       "      <th>0</th>\n",
       "      <td>52</td>\n",
       "      <td>None</td>\n",
       "      <td>200.494347</td>\n",
       "      <td>1</td>\n",
       "      <td>2.866816</td>\n",
       "      <td>0.046837</td>\n",
       "      <td>POLYGON ((832320.132 -2036506.395, 832020.249 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>52</td>\n",
       "      <td>None</td>\n",
       "      <td>200.494347</td>\n",
       "      <td>1</td>\n",
       "      <td>2.866816</td>\n",
       "      <td>0.046837</td>\n",
       "      <td>POLYGON ((832320.132 -2036506.395, 832364.893 ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>95 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      OBJECTID_1          Name       AreaKM  FjordID  Shape_Leng  Shape_Area  \\\n",
       "0  0           1        KIALEQ   348.063647       23    6.254204    0.070007   \n",
       "   1           1        KIALEQ   348.063647       23    6.254204    0.070007   \n",
       "1  0           2  Tingmiarmiut  1079.015818       40   19.926715    0.179119   \n",
       "2  0           3      Koge Bay   352.066193       32    3.352452    0.064703   \n",
       "3  0           4     Jens Munk   979.173202       33    9.551306    0.178193   \n",
       "...          ...           ...          ...      ...         ...         ...   \n",
       "48 0          49          None    99.882650        4    1.594527    0.022652   \n",
       "49 0          50          None   117.619453        3    1.531864    0.026914   \n",
       "50 0          51          None   110.475607        2    1.919556    0.025471   \n",
       "51 0          52          None   200.494347        1    2.866816    0.046837   \n",
       "   1          52          None   200.494347        1    2.866816    0.046837   \n",
       "\n",
       "                                               geometry  \n",
       "0  0  POLYGON ((480891.016 -2499872.703, 481054.310 ...  \n",
       "   1  POLYGON ((502354.865 -2456479.479, 502943.293 ...  \n",
       "1  0  POLYGON ((177152.550 -2975052.409, 177147.140 ...  \n",
       "2  0  POLYGON ((210055.129 -2724033.108, 210145.162 ...  \n",
       "3  0  POLYGON ((205440.776 -2750397.737, 206918.528 ...  \n",
       "...                                                 ...  \n",
       "48 0  POLYGON ((777729.789 -2109310.903, 778128.658 ...  \n",
       "49 0  POLYGON ((804329.371 -2088078.345, 804336.687 ...  \n",
       "50 0  POLYGON ((799521.626 -2065790.718, 799748.594 ...  \n",
       "51 0  POLYGON ((832320.132 -2036506.395, 832020.249 ...  \n",
       "   1  POLYGON ((832320.132 -2036506.395, 832364.893 ...  \n",
       "\n",
       "[95 rows x 7 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf2 = gdf2.to_crs(epsg=3413)\n",
    "gdf2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f6516b14",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "<POLYGON ((5.05e+05 -2.29e+06, 5.05e+05 -2.29e+06, 5.05e+05 -2.29e+06, 5.05e...>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf2['geometry'][34][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7305cea8",
   "metadata": {},
   "source": [
    "# For fjord 30 training image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "cbb4f3ee",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed | 30.84 s\n"
     ]
    }
   ],
   "source": [
    "myImageUrls = grimp.cmrUrls(mode='image')  # mode image restricts search to the image products\n",
    "myImageUrls.initialSearch(firstDate='2022-01-01', lastDate='2022-12-31', productFilter='sigma0') # change the time window as needed\n",
    "# selecting productFilter='sigma0' or 'gamma0' gives you Sigma0 or Gamma0 images which might be better for digital processing\n",
    "# \"image\" is a \"visualization\" product\n",
    "\n",
    "myImageSeries = nisar.nisarImageSeries()  # Instantiate the series object\n",
    "myImageSeries.readSeriesFromTiff(myImageUrls.getCogs(), chunkSize=2048/2)  # In this case increasing the chunksize can improve performance\n",
    "myImageSeries.subset  \n",
    "\n",
    "bnds=gdf2[\"geometry\"][27][3].bounds\n",
    "\n",
    "bbox = {'minx': bnds[0], 'miny': bnds[1], 'maxx': bnds[2], 'maxy': bnds[3]} \n",
    "xbox = np.array([bbox[x] for x in ['minx', 'minx', 'maxx', 'maxx', 'minx']]) * 0.001\n",
    "ybox = np.array([bbox[y] for y in ['miny', 'maxy', 'maxy', 'miny', 'miny']]) * 0.001\n",
    "\n",
    "myImageSeries.subSetImage(bbox)\n",
    "myImageSeries.loadRemote()\n",
    "myImageSeries.subset\n",
    "year=2022\n",
    "fjord=30\n",
    "gpd2 = gpd.GeoDataFrame(gdf2[\"geometry\"][27][3:4])\n",
    "temp=myImageSeries.subset.rio.clip(gpd2.geometry.values,gpd2.crs,drop=True,invert=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "5f8bf7cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018-12-31\n"
     ]
    }
   ],
   "source": [
    "print(temp.time[0].values.astype('datetime64[D]'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "4f4ea7de",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\r134p694_a\\AppData\\Local\\Temp\\ipykernel_37328\\2356335019.py:9: RuntimeWarning: invalid value encountered in cast\n",
      "  return np.clip(norm_data, 0, 255).astype(np.uint8)\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from skimage import io, color, filters, exposure\n",
    "import numpy as np\n",
    "import cv2\n",
    "\n",
    "# Normalize function to convert dB values to 8-bit range\n",
    "def normalize_to_8bit(data, min_val=-29.99, max_val=29.9):\n",
    "    norm_data = (data - min_val) / (max_val - min_val) * 255\n",
    "    return np.clip(norm_data, 0, 255).astype(np.uint8)\n",
    "\n",
    "# Function to check if an image is blank\n",
    "def is_blank_image(image):\n",
    "    return np.all(image == image[0, 0, :])\n",
    "\n",
    "for i in range(len(temp.time)):\n",
    "    temp1 = temp.sel(time=temp.time[i]).values.transpose(1, 2, 0)\n",
    "    tbi = np.repeat(temp1, 3, axis=2)  # Create a three-band image\n",
    "\n",
    "    # Normalize the sigma naught values to 8-bit range\n",
    "    tbi_normalized = normalize_to_8bit(tbi)\n",
    "\n",
    "    # Check if the image is blank\n",
    "    if is_blank_image(tbi_normalized):\n",
    "        print(f\"Image {i} is blank. Skipping save.\")\n",
    "        continue\n",
    "\n",
    "    # Save the image using matplotlib\n",
    "    cv2.imwrite(f\"Test Images/image_{i}_{temp.time[i].values.astype('datetime64[D]')}_{fjord}.png\", tbi_normalized)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9611afc",
   "metadata": {},
   "source": [
    "## window mask applied "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c10fd5b4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\r134p694_a\\anaconda3\\Lib\\site-packages\\paramiko\\transport.py:219: CryptographyDeprecationWarning: Blowfish has been deprecated\n",
      "  \"class\": algorithms.Blowfish,\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from skimage.color import rgb2gray\n",
    "from skimage.feature import graycomatrix, graycoprops\n",
    "from skimage.io import imread\n",
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Function to load images from a folder\n",
    "def load_images(folder_path):\n",
    "    images = []\n",
    "    file_names = []\n",
    "    for file_name in os.listdir(folder_path):\n",
    "        if file_name.endswith('.png'):  # Adjust the file extension if necessary\n",
    "            file_path = os.path.join(folder_path, file_name)\n",
    "            image = np.array(Image.open(file_path))  # Load the image as a numpy array\n",
    "            images.append(image)\n",
    "            file_names.append(file_name)\n",
    "    return images, file_names\n",
    "\n",
    "folder_path = 'Train Images'\n",
    "images, file_names = load_images(folder_path)\n",
    "\n",
    "def extract_windows_and_features(image, mask, window_size, quantization, displacement, angles):\n",
    "    if image.ndim == 3:\n",
    "        image = rgb2gray(image)\n",
    "    \n",
    "    image_normalized = (image - image.min()) / (image.max() - image.min())\n",
    "    image_quantized = (image_normalized * (quantization - 1)).astype(np.uint8)\n",
    "    \n",
    "    windows = []\n",
    "    features_list = []\n",
    "    positions = []\n",
    "    \n",
    "    for i in range(0, image.shape[0] - window_size + 1, window_size):\n",
    "        for j in range(0, image.shape[1] - window_size + 1, window_size):\n",
    "            window = image_quantized[i:i + window_size, j:j + window_size]\n",
    "            window_mask = mask[i:i + window_size, j:j + window_size]\n",
    "            \n",
    "            if np.sum(window_mask) < window_size * window_size * 0.5:\n",
    "                continue\n",
    "            \n",
    "            glcm = graycomatrix(window, distances=[displacement], angles=angles, levels=quantization, symmetric=True, normed=True)\n",
    "            dissimilarity = graycoprops(glcm, 'dissimilarity').mean(axis=1)\n",
    "            homogeneity = graycoprops(glcm, 'homogeneity').mean(axis=1)\n",
    "            glcm_mean = np.mean(window)\n",
    "            \n",
    "            features = np.hstack([dissimilarity, homogeneity, glcm_mean])\n",
    "            \n",
    "            windows.append(window)\n",
    "            features_list.append(features)\n",
    "            positions.append((i, j))\n",
    "    \n",
    "    return np.array(windows), np.array(features_list), positions\n",
    "\n",
    "def prepare_dataset(images, window_size, quantization, displacement, angles):\n",
    "    windows_list = []\n",
    "    features_list = []\n",
    "    positions_list = []\n",
    "    \n",
    "    for image in images:\n",
    "        mask = image != 0\n",
    "        windows, features, positions = extract_windows_and_features(image, mask, window_size, quantization, displacement, angles)\n",
    "        windows_list.extend(windows)\n",
    "        features_list.extend(features)\n",
    "        positions_list.extend(positions)\n",
    "    \n",
    "    return np.array(windows_list), np.array(features_list), positions_list\n",
    "\n",
    "window_size = 16\n",
    "quantization = 64\n",
    "displacement = 1\n",
    "angles = [0, np.pi/4, np.pi/2, 3*np.pi/4]\n",
    "\n",
    "windows, features, positions = prepare_dataset(images, window_size, quantization, displacement, angles)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fff2b92d",
   "metadata": {},
   "source": [
    "### Additional GLCM features included(if at all needed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "2d21d019",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from skimage.color import rgb2gray\n",
    "from skimage.feature import graycomatrix, graycoprops\n",
    "from skimage.io import imread\n",
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Function to load images from a folder\n",
    "def load_images(folder_path):\n",
    "    images = []\n",
    "    file_names = []\n",
    "    for file_name in os.listdir(folder_path):\n",
    "        if file_name.endswith('.png'):  # Adjust the file extension if necessary\n",
    "            file_path = os.path.join(folder_path, file_name)\n",
    "            image = np.array(Image.open(file_path))  # Load the image as a numpy array\n",
    "            images.append(image)\n",
    "            file_names.append(file_name)\n",
    "    return images, file_names\n",
    "\n",
    "folder_path = 'Train Images'\n",
    "images, file_names = load_images(folder_path)\n",
    "\n",
    "def extract_windows_and_features(image, mask, window_size, quantization, displacement, angles):\n",
    "    if image.ndim == 3:\n",
    "        image = rgb2gray(image)\n",
    "    \n",
    "    image_normalized = (image - image.min()) / (image.max() - image.min())\n",
    "    image_quantized = (image_normalized * (quantization - 1)).astype(np.uint8)\n",
    "    \n",
    "    windows = []\n",
    "    features_list = []\n",
    "    positions = []\n",
    "    \n",
    "    for i in range(0, image.shape[0] - window_size + 1, window_size):\n",
    "        for j in range(0, image.shape[1] - window_size + 1, window_size):\n",
    "            window = image_quantized[i:i + window_size, j:j + window_size]\n",
    "            window_mask = mask[i:i + window_size, j:j + window_size]\n",
    "            \n",
    "            if np.sum(window_mask) < window_size * window_size * 0.5:\n",
    "                continue\n",
    "            \n",
    "            glcm = graycomatrix(window, distances=[displacement], angles=angles, levels=quantization, symmetric=True, normed=True)\n",
    "            \n",
    "            # Extract GLCM features\n",
    "            contrast = graycoprops(glcm, 'contrast').mean(axis=1)\n",
    "            dissimilarity = graycoprops(glcm, 'dissimilarity').mean(axis=1)\n",
    "            homogeneity = graycoprops(glcm, 'homogeneity').mean(axis=1)\n",
    "            ASM = graycoprops(glcm, 'ASM').mean(axis=1)\n",
    "            energy = graycoprops(glcm, 'energy').mean(axis=1)\n",
    "            correlation = graycoprops(glcm, 'correlation').mean(axis=1)\n",
    "            \n",
    "            # Combine all features into one array\n",
    "            features = np.hstack([contrast, dissimilarity, homogeneity, ASM, energy, correlation])\n",
    "            \n",
    "            windows.append(window)\n",
    "            features_list.append(features)\n",
    "            positions.append((i, j))\n",
    "    \n",
    "    return np.array(windows), np.array(features_list), positions\n",
    "\n",
    "\n",
    "def prepare_dataset(images, window_size, quantization, displacement, angles):\n",
    "    windows_list = []\n",
    "    features_list = []\n",
    "    positions_list = []\n",
    "    \n",
    "    for image in images:\n",
    "        mask = image != 0\n",
    "        windows, features, positions = extract_windows_and_features(image, mask, window_size, quantization, displacement, angles)\n",
    "        windows_list.extend(windows)\n",
    "        features_list.extend(features)\n",
    "        positions_list.extend(positions)\n",
    "    \n",
    "    return np.array(windows_list), np.array(features_list), positions_list\n",
    "\n",
    "window_size = 16\n",
    "quantization = 64\n",
    "displacement = 1\n",
    "angles = [0, np.pi/4, np.pi/2, 3*np.pi/4]\n",
    "\n",
    "windows, features, positions = prepare_dataset(images, window_size, quantization, displacement, angles)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "0b0da22d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                Output Shape                 Param #   Connected to                  \n",
      "==================================================================================================\n",
      " input_3 (InputLayer)        [(None, 16, 16, 1)]          0         []                            \n",
      "                                                                                                  \n",
      " conv2d_4 (Conv2D)           (None, 16, 16, 64)           640       ['input_3[0][0]']             \n",
      "                                                                                                  \n",
      " batch_normalization_4 (Bat  (None, 16, 16, 64)           256       ['conv2d_4[0][0]']            \n",
      " chNormalization)                                                                                 \n",
      "                                                                                                  \n",
      " max_pooling2d_3 (MaxPoolin  (None, 8, 8, 64)             0         ['batch_normalization_4[0][0]'\n",
      " g2D)                                                               ]                             \n",
      "                                                                                                  \n",
      " dropout_10 (Dropout)        (None, 8, 8, 64)             0         ['max_pooling2d_3[0][0]']     \n",
      "                                                                                                  \n",
      " conv2d_5 (Conv2D)           (None, 8, 8, 128)            73856     ['dropout_10[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_5 (Bat  (None, 8, 8, 128)            512       ['conv2d_5[0][0]']            \n",
      " chNormalization)                                                                                 \n",
      "                                                                                                  \n",
      " max_pooling2d_4 (MaxPoolin  (None, 4, 4, 128)            0         ['batch_normalization_5[0][0]'\n",
      " g2D)                                                               ]                             \n",
      "                                                                                                  \n",
      " dropout_11 (Dropout)        (None, 4, 4, 128)            0         ['max_pooling2d_4[0][0]']     \n",
      "                                                                                                  \n",
      " conv2d_6 (Conv2D)           (None, 4, 4, 128)            147584    ['dropout_11[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_6 (Bat  (None, 4, 4, 128)            512       ['conv2d_6[0][0]']            \n",
      " chNormalization)                                                                                 \n",
      "                                                                                                  \n",
      " dropout_12 (Dropout)        (None, 4, 4, 128)            0         ['batch_normalization_6[0][0]'\n",
      "                                                                    ]                             \n",
      "                                                                                                  \n",
      " conv2d_7 (Conv2D)           (None, 4, 4, 64)             73792     ['dropout_12[0][0]']          \n",
      "                                                                                                  \n",
      " input_4 (InputLayer)        [(None, 6)]                  0         []                            \n",
      "                                                                                                  \n",
      " batch_normalization_7 (Bat  (None, 4, 4, 64)             256       ['conv2d_7[0][0]']            \n",
      " chNormalization)                                                                                 \n",
      "                                                                                                  \n",
      " dense_8 (Dense)             (None, 128)                  896       ['input_4[0][0]']             \n",
      "                                                                                                  \n",
      " max_pooling2d_5 (MaxPoolin  (None, 2, 2, 64)             0         ['batch_normalization_7[0][0]'\n",
      " g2D)                                                               ]                             \n",
      "                                                                                                  \n",
      " dropout_15 (Dropout)        (None, 128)                  0         ['dense_8[0][0]']             \n",
      "                                                                                                  \n",
      " dropout_13 (Dropout)        (None, 2, 2, 64)             0         ['max_pooling2d_5[0][0]']     \n",
      "                                                                                                  \n",
      " dense_9 (Dense)             (None, 256)                  33024     ['dropout_15[0][0]']          \n",
      "                                                                                                  \n",
      " flatten_1 (Flatten)         (None, 256)                  0         ['dropout_13[0][0]']          \n",
      "                                                                                                  \n",
      " dropout_16 (Dropout)        (None, 256)                  0         ['dense_9[0][0]']             \n",
      "                                                                                                  \n",
      " dense_7 (Dense)             (None, 512)                  131584    ['flatten_1[0][0]']           \n",
      "                                                                                                  \n",
      " dense_10 (Dense)            (None, 128)                  32896     ['dropout_16[0][0]']          \n",
      "                                                                                                  \n",
      " dropout_14 (Dropout)        (None, 512)                  0         ['dense_7[0][0]']             \n",
      "                                                                                                  \n",
      " dropout_17 (Dropout)        (None, 128)                  0         ['dense_10[0][0]']            \n",
      "                                                                                                  \n",
      " concatenate_1 (Concatenate  (None, 640)                  0         ['dropout_14[0][0]',          \n",
      " )                                                                   'dropout_17[0][0]']          \n",
      "                                                                                                  \n",
      " dense_11 (Dense)            (None, 128)                  82048     ['concatenate_1[0][0]']       \n",
      "                                                                                                  \n",
      " dropout_18 (Dropout)        (None, 128)                  0         ['dense_11[0][0]']            \n",
      "                                                                                                  \n",
      " dense_12 (Dense)            (None, 64)                   8256      ['dropout_18[0][0]']          \n",
      "                                                                                                  \n",
      " dropout_19 (Dropout)        (None, 64)                   0         ['dense_12[0][0]']            \n",
      "                                                                                                  \n",
      " dense_13 (Dense)            (None, 1)                    65        ['dropout_19[0][0]']          \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 586177 (2.24 MB)\n",
      "Trainable params: 585409 (2.23 MB)\n",
      "Non-trainable params: 768 (3.00 KB)\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, Flatten, Dense, Concatenate, BatchNormalization, Dropout\n",
    "from tensorflow.keras.models import Model\n",
    "\n",
    "# Define input shapes\n",
    "image_input_shape = (window_size, window_size, 1)  # Image windows\n",
    "features_input_shape = (features.shape[1],)  # GLCM features\n",
    "\n",
    "# Image window branch\n",
    "image_input = Input(shape=image_input_shape)\n",
    "x = Conv2D(64, (3, 3), activation='relu', padding='same')(image_input)\n",
    "x = BatchNormalization()(x)\n",
    "x = MaxPooling2D((2, 2))(x)\n",
    "x = Dropout(0.2)(x)\n",
    "\n",
    "x = Conv2D(128, (3, 3), activation='relu', padding='same')(x)\n",
    "x = BatchNormalization()(x)\n",
    "x = MaxPooling2D((2, 2))(x)\n",
    "x = Dropout(0.2)(x)\n",
    "\n",
    "x = Conv2D(128, (3, 3), activation='relu', padding='same')(x)\n",
    "x = BatchNormalization()(x)\n",
    "x = Dropout(0.2)(x)\n",
    "\n",
    "x = Conv2D(64, (3, 3), activation='relu', padding='same')(x)\n",
    "x = BatchNormalization()(x)\n",
    "x = MaxPooling2D((2, 2))(x)\n",
    "x = Dropout(0.2)(x)\n",
    "\n",
    "x = Flatten()(x)\n",
    "x = Dense(512, activation='relu')(x)\n",
    "x = Dropout(0.2)(x)\n",
    "\n",
    "# GLCM features branch\n",
    "features_input = Input(shape=features_input_shape)\n",
    "y = Dense(128, activation='relu')(features_input)\n",
    "y = Dropout(0.2)(y)\n",
    "y = Dense(256, activation='relu')(y)\n",
    "y = Dropout(0.2)(y)\n",
    "y = Dense(128, activation='relu')(y)\n",
    "y = Dropout(0.2)(y)\n",
    "\n",
    "# Combine both branches\n",
    "combined = Concatenate()([x, y])\n",
    "z = Dense(128, activation='relu')(combined)\n",
    "z = Dropout(0.2)(z)\n",
    "z = Dense(64, activation='relu')(z)\n",
    "z = Dropout(0.2)(z)\n",
    "output = Dense(1, activation='sigmoid')(z)  # Binary classification\n",
    "\n",
    "# Define the model\n",
    "model = Model(inputs=[image_input, features_input], outputs=output)\n",
    "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "# Print model summary\n",
    "model.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "7eb03a2c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\r134p694_a\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "# Combine windows and features into a single dataset for clustering\n",
    "combined_data = np.concatenate([windows.reshape(windows.shape[0], -1), features], axis=1)\n",
    "\n",
    "# Apply K-means clustering\n",
    "kmeans = KMeans(n_clusters=2, random_state=42)\n",
    "pseudo_labels = kmeans.fit_predict(combined_data)\n",
    "\n",
    "# Map cluster labels to sea ice (1) and water (0) based on some initial inspection or domain knowledge\n",
    "# For simplicity, assuming cluster 0 is water and cluster 1 is sea ice\n",
    "pseudo_labels = (pseudo_labels == 0).astype(int)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "dc2294f6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "8932/8932 [==============================] - 99s 11ms/step - loss: 0.1198 - accuracy: 0.9504\n",
      "Epoch 2/10\n",
      "8932/8932 [==============================] - 101s 11ms/step - loss: 0.1035 - accuracy: 0.9566\n",
      "Epoch 3/10\n",
      "8932/8932 [==============================] - 102s 11ms/step - loss: 0.0964 - accuracy: 0.9592\n",
      "Epoch 4/10\n",
      "8932/8932 [==============================] - 102s 11ms/step - loss: 0.0910 - accuracy: 0.9619\n",
      "Epoch 5/10\n",
      "8932/8932 [==============================] - 103s 11ms/step - loss: 0.0838 - accuracy: 0.9646\n",
      "Epoch 6/10\n",
      "8932/8932 [==============================] - 104s 12ms/step - loss: 0.0791 - accuracy: 0.9661\n",
      "Epoch 7/10\n",
      "8932/8932 [==============================] - 100s 11ms/step - loss: 0.0736 - accuracy: 0.9690\n",
      "Epoch 8/10\n",
      "8932/8932 [==============================] - 100s 11ms/step - loss: 0.0724 - accuracy: 0.9693\n",
      "Epoch 9/10\n",
      "8932/8932 [==============================] - 100s 11ms/step - loss: 0.0713 - accuracy: 0.9701\n",
      "Epoch 10/10\n",
      "8932/8932 [==============================] - 101s 11ms/step - loss: 0.0663 - accuracy: 0.9720\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.History at 0x242b071b110>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the model with pseudo-labels\n",
    "model.fit([windows, features], pseudo_labels, epochs=10, batch_size=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "id": "1907d1a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_windows_and_features_for_prediction(image, window_size, quantization, displacement, angles):\n",
    "    if image.ndim == 3:\n",
    "        image = rgb2gray(image)\n",
    "\n",
    "    image_normalized = (image - image.min()) / (image.max() - image.min())\n",
    "    image_quantized = (image_normalized * (quantization - 1)).astype(np.uint8)\n",
    "\n",
    "    windows = []\n",
    "    features_list = []\n",
    "    positions = []\n",
    "\n",
    "    for i in range(0, image.shape[0] - window_size + 1, window_size):\n",
    "        for j in range(0, image.shape[1] - window_size + 1, window_size):\n",
    "            window = image_quantized[i:i + window_size, j:j + window_size]\n",
    "            glcm = graycomatrix(window, distances=[displacement], angles=angles, levels=quantization, symmetric=True, normed=True)\n",
    "            dissimilarity = graycoprops(glcm, 'dissimilarity').mean(axis=1)\n",
    "            homogeneity = graycoprops(glcm, 'homogeneity').mean(axis=1)\n",
    "            glcm_mean = np.mean(window)\n",
    "            features = np.hstack([dissimilarity, homogeneity, glcm_mean])\n",
    "            windows.append(window)\n",
    "            features_list.append(features)\n",
    "            positions.append((i, j))\n",
    "\n",
    "    return np.array(windows), np.array(features_list), positions\n",
    "\n",
    "# Example: Extracting windows and features for a test image\n",
    "test_image = np.array(Image.open('Train Images/image_0_2019_30.png'))\n",
    "test_windows, test_features, positions = extract_windows_and_features_for_prediction(test_image, window_size, quantization, displacement, angles)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "f4cb17c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_windows_and_features_for_prediction(image, mask, window_size, quantization, displacement, angles):\n",
    "    if image.ndim == 3:\n",
    "        image = rgb2gray(image)\n",
    "\n",
    "    image_normalized = (image - image.min()) / (image.max() - image.min())\n",
    "    image_quantized = (image_normalized * (quantization - 1)).astype(np.uint8)\n",
    "\n",
    "    windows = []\n",
    "    features_list = []\n",
    "    positions = []\n",
    "\n",
    "    for i in range(0, image.shape[0] - window_size + 1, window_size):\n",
    "        for j in range(0, image.shape[1] - window_size + 1, window_size):\n",
    "            window = image_quantized[i:i + window_size, j:j + window_size]\n",
    "            window_mask = mask[i:i + window_size, j:j + window_size]\n",
    "            \n",
    "            if np.sum(window_mask) < window_size * window_size * 0.5:\n",
    "                continue  # Skip windows where the mask is not sufficiently covered\n",
    "            \n",
    "            glcm = graycomatrix(window, distances=[displacement], angles=angles, levels=quantization, symmetric=True, normed=True)\n",
    "            dissimilarity = graycoprops(glcm, 'dissimilarity').mean(axis=1)\n",
    "            homogeneity = graycoprops(glcm, 'homogeneity').mean(axis=1)\n",
    "            glcm_mean = np.mean(window)\n",
    "            features = np.hstack([dissimilarity, homogeneity, glcm_mean])\n",
    "            \n",
    "            windows.append(window)\n",
    "            features_list.append(features)\n",
    "            positions.append((i, j))\n",
    "\n",
    "    return np.array(windows), np.array(features_list), positions\n",
    "\n",
    "\n",
    "test_image = np.array(Image.open('Test Images/image_21_2022-09-08_30.png'))\n",
    "test_mask = test_image != 0 \n",
    "test_windows, test_features, positions = extract_windows_and_features_for_prediction(test_image, test_mask, window_size, quantization, displacement, angles)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31cf0e03",
   "metadata": {},
   "source": [
    "### With added GLCM Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "8300cbd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_windows_and_features_for_prediction(image, mask, window_size, quantization, displacement, angles):\n",
    "    if image.ndim == 3:\n",
    "        image = rgb2gray(image)\n",
    "\n",
    "    image_normalized = (image - image.min()) / (image.max() - image.min())\n",
    "    image_quantized = (image_normalized * (quantization - 1)).astype(np.uint8)\n",
    "\n",
    "    windows = []\n",
    "    features_list = []\n",
    "    positions = []\n",
    "\n",
    "    for i in range(0, image.shape[0] - window_size + 1, window_size):\n",
    "        for j in range(0, image.shape[1] - window_size + 1, window_size):\n",
    "            window = image_quantized[i:i + window_size, j:j + window_size]\n",
    "            window_mask = mask[i:i + window_size, j:j + window_size]\n",
    "            \n",
    "            if np.sum(window_mask) < window_size * window_size * 0.5:\n",
    "                continue  # Skip windows where the mask is not sufficiently covered\n",
    "            \n",
    "            glcm = graycomatrix(window, distances=[displacement], angles=angles, levels=quantization, symmetric=True, normed=True)\n",
    "            \n",
    "            # Extract GLCM features\n",
    "            contrast = graycoprops(glcm, 'contrast').mean(axis=1)\n",
    "            dissimilarity = graycoprops(glcm, 'dissimilarity').mean(axis=1)\n",
    "            homogeneity = graycoprops(glcm, 'homogeneity').mean(axis=1)\n",
    "            ASM = graycoprops(glcm, 'ASM').mean(axis=1)\n",
    "            energy = graycoprops(glcm, 'energy').mean(axis=1)\n",
    "            correlation = graycoprops(glcm, 'correlation').mean(axis=1)\n",
    "            \n",
    "            # Combine all features into one array\n",
    "            features = np.hstack([contrast, dissimilarity, homogeneity, ASM, energy, correlation])\n",
    "            \n",
    "            windows.append(window)\n",
    "            features_list.append(features)\n",
    "            positions.append((i, j))\n",
    "\n",
    "    return np.array(windows), np.array(features_list), positions\n",
    "\n",
    "\n",
    "# Example usage with a test image\n",
    "test_image = np.array(Image.open('Test Images/image_21_2022-09-08_30.png'))\n",
    "test_mask = test_image != 0 \n",
    "test_windows, test_features, positions = extract_windows_and_features_for_prediction(test_image, test_mask, window_size, quantization, displacement, angles)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "8cd62b4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "78/78 [==============================] - 0s 4ms/step\n"
     ]
    }
   ],
   "source": [
    "# Predict the labels for the test windows\n",
    "test_predictions = model.predict([test_windows, test_features])\n",
    "\n",
    "# Convert predictions to binary labels\n",
    "test_predicted_labels = (test_predictions > 0.5).astype(int)\n",
    "\n",
    "def map_predictions_to_image(predicted_labels, positions, image_shape, window_size):\n",
    "    prediction_map = np.zeros(image_shape)\n",
    "\n",
    "    for (i, j), label in zip(positions, predicted_labels):\n",
    "        prediction_map[i:i + window_size, j:j + window_size] = label\n",
    "\n",
    "    return prediction_map\n",
    "\n",
    "# Create a prediction map for the test image\n",
    "prediction_map = map_predictions_to_image(test_predicted_labels, positions, test_image.shape, window_size)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "c984c80b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(20,10))\n",
    "\n",
    "#plt.subplot(2, 1, 1)\n",
    "plt.imshow(test_image, cmap='gray')\n",
    "plt.title('Original Test Image')\n",
    "\n",
    "#plt.subplot(2, 1, 2)\n",
    "plt.imshow(prediction_map, cmap='gray')\n",
    "plt.title('Prediction Map (0: Water, 1: Sea Ice)')\n",
    "\n",
    "plt.show()\n"
   ]
  }
 ],
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