Segmentation_EB.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "269a992c-d672-4b83-b9a1-42910307fd48",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os, sys\n",
    "from PIL import Image\n",
    "\n",
    "import rasterio\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from rasterio.warp import reproject, Resampling, calculate_default_transform\n",
    "import numpy as np\n",
    "from rasterio.enums import Resampling\n",
    "from skimage.morphology import erosion, dilation, diamond, square, opening, disk\n",
    "from scipy import ndimage\n",
    "from datetime import datetime\n",
    "from skimage.filters import threshold_local\n",
    "\n",
    "import skimage\n",
    "from skimage.segmentation import clear_border,watershed\n",
    "from skimage.feature import peak_local_max\n",
    "import cv2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b27700a7-bd02-41a9-a1e4-c2225352e740",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "\n",
    "## i dont think I use either of these functions...\n",
    "\n",
    "def peakdet(v, delta, x = None):\n",
    "    \n",
    "    maxtab = []\n",
    "    mintab = []\n",
    "       \n",
    "    if x is None:\n",
    "        x = np.arange(len(v))\n",
    "    \n",
    "    v = np.asarray(v)\n",
    "    \n",
    "    if len(v) != len(x):\n",
    "        sys.exit('Input vectors v and x must have same length')\n",
    "    \n",
    "    if not np.isscalar(delta):\n",
    "        sys.exit('Input argument delta must be a scalar')\n",
    "    \n",
    "    if delta <= 0:\n",
    "        sys.exit('Input argument delta must be positive')\n",
    "    \n",
    "    mn, mx = np.Inf, -np.Inf\n",
    "    mnpos, mxpos = np.NaN, np.NaN\n",
    "    \n",
    "    lookformax = True\n",
    "   # mintab.append((0, 0.))\n",
    "    \n",
    "    for i in np.arange(len(v)):\n",
    "        this = v[i]\n",
    "        if this > mx:\n",
    "            mx = this\n",
    "            mxpos = x[i]\n",
    "        if this < mn:\n",
    "            mn = this\n",
    "            mnpos = x[i]\n",
    "        \n",
    "        if lookformax:\n",
    "            if this < mx-delta:\n",
    "                maxtab.append((mxpos, mx))\n",
    "                mn = this\n",
    "                mnpos = x[i]\n",
    "                lookformax = False\n",
    "        else:\n",
    "            if this > mn+delta:\n",
    "                mintab.append((mnpos, mn))\n",
    "                mx = this\n",
    "                mxpos = x[i]\n",
    "                lookformax = True\n",
    "    return np.array(maxtab), np.array(mintab)\n",
    "\n",
    "# def reproj_match(infile, match, outfile):\n",
    "#     \"\"\"Reproject a file to match the shape and projection of existing raster. \n",
    "    \n",
    "#     Parameters\n",
    "#     ----------\n",
    "#     infile : (string) path to input file to reproject\n",
    "#     match : (string) path to raster with desired shape and projection \n",
    "#     outfile : (string) path to output file tif\n",
    "#     \"\"\"\n",
    "#     # open input\n",
    "#     with rasterio.open(infile) as src:\n",
    "#         src_transform = src.transform\n",
    "        \n",
    "#         # open input to match\n",
    "#         with rasterio.open(match) as match:\n",
    "#             dst_crs = match.crs\n",
    "            \n",
    "#             # calculate the output transform matrix\n",
    "#             dst_transform, dst_width, dst_height = calculate_default_transform(\n",
    "#                 src.crs,     # input CRS\n",
    "#                 dst_crs,     # output CRS\n",
    "#                 match.width,   # input width\n",
    "#                 match.height,  # input height \n",
    "#                 *match.bounds)  # unpacks input outer boundaries (left, bottom, right, top))\n",
    "\n",
    "#         # set properties for output\n",
    "#         dst_kwargs = src.meta.copy()\n",
    "#         dst_kwargs.update({\"crs\": dst_crs,\n",
    "#                            \"transform\": dst_transform,\n",
    "#                            \"width\": dst_width,\n",
    "#                            \"height\": dst_height,\n",
    "#                            \"nodata\": 0})\n",
    "#         print(\"Coregistered to shape:\", dst_height,dst_width,'\\n Affine',dst_transform)\n",
    "#         # open output\n",
    "#         with rasterio.open(outfile, \"w\", **dst_kwargs) as dst:\n",
    "#             # iterate through bands and write using reproject function\n",
    "#             for i in range(1, src.count + 1):\n",
    "#                 reproject(\n",
    "#                     source=rasterio.band(src, i),\n",
    "#                     destination=rasterio.band(dst, i),\n",
    "#                     src_transform=src.transform,\n",
    "#                     src_crs=src.crs,\n",
    "#                     dst_transform=dst_transform,\n",
    "#                     dst_crs=dst_crs,\n",
    "#                     resampling=Resampling.nearest)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5237ea8d-b34c-43e3-bc8d-966655497c55",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/d9/yt5cny_52dg1hlsrpkcxnydr0000gp/T/ipykernel_42966/2339596862.py:128: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). Consider using `matplotlib.pyplot.close()`.\n",
      "  fig,ax=plt.subplots(1,1,figsize=(6,2))\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ZxowZo549e6pnz55KSkqq1/6+++6TzWbzKikpKU0ZGgAAaCE+h4Z169YpPT1dWVlZ+vzzzzVixAglJyfr+PHjDbbPy8vT1KlT9dFHHyk/P1+RkZEaP368vv32W692KSkpOnbsmKesWbOmaTMCAAAtwufQsHjxYs2cOVNpaWkaNmyYcnJy1LVrV61YsaLB9m+++aYefPBBxcXFaejQoXrjjTfkdrvldDq92tntdjkcDk/p2bPnRcdQU1OjyspKrwIAAFqWT6GhtrZWBQUFSkpKutCBn5+SkpKUn59vqY8zZ87o3Llz6tWrl1d9Xl6eQkNDNWTIEM2ePVtlZWUX7SM7O1vBwcGeEhkZ6cs0AABAE/gUGk6ePKm6ujqFhYV51YeFhcnlclnq44knnlBERIRX8EhJSdHq1avldDq1cOFCbd26VRMmTFBdXV2DfWRkZKiiosJTSkpKfJkGAABogla95PL555/X2rVrlZeXp8DAQE/9lClTPD/HxMQoNjZWAwYMUF5ensaNG1evH7vdLrvd3ipjBgAAP/DpSENISIj8/f1VWlrqVV9aWiqHw3HJfV988UU9//zz2rx5s2JjYy/Ztn///goJCVFRUZEvwwMAAC3Ip9AQEBCg+Ph4r5MYfzqpMTEx8aL7LVq0SM8++6w2bdqkUaNGNfp7jhw5orKyMoWHh/syPAAA0IJ8vnoiPT1dy5Yt06pVq7Rv3z7Nnj1b1dXVSktLkyRNmzZNGRkZnvYLFy7UggULtGLFCkVFRcnlcsnlcqmqqkqSVFVVpfnz52vbtm06dOiQnE6nJk2apIEDByo5ObmZpgkAAC6Xz+c0pKam6sSJE8rMzJTL5VJcXJw2bdrkOTmyuLhYfn4Xsshrr72m2tpa3XXXXV79ZGVl6amnnpK/v7/27NmjVatWqby8XBERERo/fryeffZZzlsAAKAdadKJkHPnztXcuXMb3JaXl+f1+NChQ5fsq0uXLvrggw+aMgwAANCKuPcEAACwhLtcXgkauwumxJ0wAQAtjiMNAADAEkIDAACwhNAAAAAsITQAAABLCA0AAMASQgMAALCESy6vFo1dlsklmQCAy8SRBgAAYAmhAQAAWEJoAAAAlnBOQ0fBV1EDAC4TRxoAAIAlhAYAAGAJH0/gAi7bBABcAkcaAACAJRxpgHWcTAkAHRpHGgAAgCUcaWiMlf9d4wLOiwCAqxahAa2rtUIY4QQAmh2hAVcnjngAQLPjnAYAAGAJRxrQMXEkAgB8RmgAGnIlnQBLwAHQSggNwJWOoyYAWgmhAbja8aVcV6/LPSLG3zt81KQTIZcuXaqoqCgFBgYqISFBO3bsuGT79evXa+jQoQoMDFRMTIzef/99r+3GGGVmZio8PFxdunRRUlKS9u/f35ShAWgKm+3yS3twOePv3v1CP927N8+atPQ42nK9rrTnBpqFz6Fh3bp1Sk9PV1ZWlj7//HONGDFCycnJOn78eIPtP/vsM02dOlX333+/du3apcmTJ2vy5Mnau3evp82iRYv08ssvKycnR9u3b1e3bt2UnJyss2fPNn1mAFpXW73J8ubU/vF3e9WwGePb8amEhATdeOONeuWVVyRJbrdbkZGReuihh/THP/6xXvvU1FRVV1dr48aNnrqbbrpJcXFxysnJkTFGERERevTRR/XYY49JkioqKhQWFqbc3FxNmTKlXp81NTWqqanxPK6oqFDfvn1VUlKioKAgX6bTuODg5u0PQLt0plOARs/9pyRpxyv3quv52g49jg6poqKtR9AmKisrFRkZqfLycgU39p5nfFBTU2P8/f3Nhg0bvOqnTZtmfv3rXze4T2RkpPnrX//qVZeZmWliY2ONMcYcOHDASDK7du3yanPrrbeaP/zhDw32mZWVZSRRKBQKhUJpplJSUtJoDvDpRMiTJ0+qrq5OYWFhXvVhYWH6+uuvG9zH5XI12N7lcnm2/1R3sTY/l5GRofT0dM9jt9utU6dOqXfv3rI1w2Gsn1JXixy56IBYz+bHmjY/1rR5sZ7Nr6XW1Bij06dPKyIiotG2V+TVE3a7XXa73auuR48ezf57goKCeLI3I9az+bGmzY81bV6sZ/NriTVt9GOJH/l0ImRISIj8/f1VWlrqVV9aWiqHw9HgPg6H45Ltf/rTlz4BAEDr8yk0BAQEKD4+Xk6n01PndrvldDqVmJjY4D6JiYle7SVpy5YtnvbR0dFyOBxebSorK7V9+/aL9gkAAFqfzx9PpKena/r06Ro1apRGjx6tJUuWqLq6WmlpaZKkadOmqU+fPsrOzpYkPfzww7rtttv00ksvaeLEiVq7dq127typ119/XZJks9k0b948Pffccxo0aJCio6O1YMECRUREaPLkyc03Ux/Y7XZlZWXV+wgETcN6Nj/WtPmxps2L9Wx+7WFNfb7kUpJeeeUVvfDCC3K5XIqLi9PLL7+shIQESdLYsWMVFRWl3NxcT/v169frySef1KFDhzRo0CAtWrRIv/rVrzzbjTHKysrS66+/rvLyct1yyy169dVXNXjw4MufIQAAaBZNCg0AAKDjadLXSAMAgI6H0AAAACwhNAAAAEsIDQAAwBJCw8/4ettvXPDUU0/JZrN5laFDh3q2nz17VnPmzFHv3r3VvXt3/fa3v633pV4d2ccff6w77rhDERERstlseuedd7y2Gwu3kD916pTuueceBQUFqUePHrr//vtVVVXVirNoXxpb0/vuu6/eczYlJcWrDWt6QXZ2tm688UZdc801Cg0N1eTJk1VYWOjVxsrrvLi4WBMnTlTXrl0VGhqq+fPn6/z58605lXbDypqOHTu23vN01qxZXm1aa00JDf+Pr7f9Rn3XX3+9jh075imffPKJZ9sjjzyi9957T+vXr9fWrVt19OhR3XnnnW042valurpaI0aM0NKlSxvcbuUW8vfcc4++/PJLbdmyRRs3btTHH3+sBx54oLWm0O40tqaSlJKS4vWcXbNmjdd21vSCrVu3as6cOdq2bZu2bNmic+fOafz48aqurva0aex1XldXp4kTJ6q2tlafffaZVq1apdzcXGVmZrbFlNqclTWVpJkzZ3o9TxctWuTZ1qpr2ugtrTqQ0aNHmzlz5nge19XVmYiICJOdnd2Go7pyZGVlmREjRjS4rby83HTu3NmsX7/eU7dv3z4jyeTn57fSCK8ckrzuJut2u43D4TAvvPCCp668vNzY7XazZs0aY4wxX331lZFk/vvf/3ra/Pvf/zY2m818++23rTb29urna2qMMdOnTzeTJk266D6s6aUdP37cSDJbt241xlh7nb///vvGz8/PuFwuT5vXXnvNBAUFmZqamtadQDv08zU1xpjbbrvNPPzwwxfdpzXXlCMNP6qtrVVBQYGSkpI8dX5+fkpKSlJ+fn4bjuzKsn//fkVERKh///665557VFxcLEkqKCjQuXPnvNZ36NCh6tu3L+trwcGDB+VyubzWLzg4WAkJCZ71y8/PV48ePTRq1ChPm6SkJPn5+Wn79u2tPuYrRV5enkJDQzVkyBDNnj1bZWVlnm2s6aVVVFRIknr16iXJ2us8Pz9fMTExXnc2Tk5OVmVlpb788stWHH379PM1/cmbb76pkJAQDR8+XBkZGTpz5oxnW2uu6RV5l8uW0JTbfsNbQkKCcnNzNWTIEB07dkxPP/20xowZo71798rlcikgIKDe3UgvdQt0XGDlFvIul0uhoaFe2zt16qRevXqxxheRkpKiO++8U9HR0Tpw4ID+9Kc/acKECcrPz5e/vz9reglut1vz5s3TzTffrOHDh0uSpde5y+Vq8Hn807aOrKE1laTf/e536tevnyIiIrRnzx498cQTKiws1Ntvvy2pddeU0IBmM2HCBM/PsbGxSkhIUL9+/fTWW2+pS5cubTgyoGFTpkzx/BwTE6PY2FgNGDBAeXl5GjduXBuOrP2bM2eO9u7d63XeEi7Pxdb0/59DExMTo/DwcI0bN04HDhzQgAEDWnWMfDzxo6bc9huX1qNHDw0ePFhFRUVyOByqra1VeXm5VxvW1xort5B3OBz1Tto9f/68Tp06xRpb1L9/f4WEhKioqEgSa3oxc+fO1caNG/XRRx/puuuu89RbeZ07HI4Gn8c/beuoLramDfnpXk///3naWmtKaPhRU277jUurqqrSgQMHFB4ervj4eHXu3NlrfQsLC1VcXMz6WmDlFvKJiYkqLy9XQUGBp82HH34ot9vt+UcGl3bkyBGVlZUpPDxcEmv6c8YYzZ07Vxs2bNCHH36o6Ohor+1WXueJiYn64osvvMLYli1bFBQUpGHDhrXORNqRxta0Ibt375Ykr+dpq61ps55WeYVbu3atsdvtJjc313z11VfmgQceMD169PA6IxUX9+ijj5q8vDxz8OBB8+mnn5qkpCQTEhJijh8/bowxZtasWaZv377mww8/NDt37jSJiYkmMTGxjUfdfpw+fdrs2rXL7Nq1y0gyixcvNrt27TKHDx82xhjz/PPPmx49eph3333X7Nmzx0yaNMlER0eb77//3tNHSkqKGTlypNm+fbv55JNPzKBBg8zUqVPbakpt7lJrevr0afPYY4+Z/Px8c/DgQfOf//zH3HDDDWbQoEHm7Nmznj5Y0wtmz55tgoODTV5enjl27JinnDlzxtOmsdf5+fPnzfDhw8348ePN7t27zaZNm8y1115rMjIy2mJKba6xNS0qKjLPPPOM2blzpzl48KB59913Tf/+/c2tt97q6aM115TQ8DN///vfTd++fU1AQIAZPXq02bZtW1sP6YqRmppqwsPDTUBAgOnTp49JTU01RUVFnu3ff/+9efDBB03Pnj1N165dzW9+8xtz7NixNhxx+/LRRx8ZSfXK9OnTjTE/XHa5YMECExYWZux2uxk3bpwpLCz06qOsrMxMnTrVdO/e3QQFBZm0tDRz+vTpNphN+3CpNT1z5owZP368ufbaa03nzp1Nv379zMyZM+v9J4E1vaChtZRkVq5c6Wlj5XV+6NAhM2HCBNOlSxcTEhJiHn30UXPu3LlWnk370NiaFhcXm1tvvdX06tXL2O12M3DgQDN//nxTUVHh1U9rrSm3xgYAAJZwTgMAALCE0AAAACwhNAAAAEsIDQAAwBJCAwAAsITQAAAALCE0AAAASwgNAADAEkIDAACwhNAAAAAsITQAAABL/g/oSS5DmZ2DyAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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4IXsYPfUs+ul59NSz6KfndVRPWz1V8D9uXVQYHBwsPz8/VVVVucyvqqpSWFhYs+uEhYW5NR4AAHQ+twKBv7+/xo0bp/z8fOc8h8Oh/Px8JScnN7tOcnKyy3hJysvLa3E8AADofG6fMsjMzFRGRobGjx+vxMREbdy4UfX19Vq4cKEkacGCBRo0aJCys7MlScuXL9fkyZO1bt06zZgxQ9u3b9eRI0f0xz/+0bN70kZWq1WrV6++5ZQE2o+eehb99Dx66ln00/O6Qk/d/tqhJL344otau3at7Ha7xo4dqxdeeEFJSUmSpJSUFA0ZMkQ5OTnO8bm5ufrZz36mjz/+WCNGjNCaNWv0ta99zWM7AQAA7ky7AgEAALi7tOuniwEAwN2FQAAAAAgEAACAQAAAANQNA4G7t27GTb/4xS9ksVhcplGjRjmXX7t2TUuXLtWAAQPUr18/ffOb37zlB6m6u/3792vmzJmKiIiQxWLRzp07XZYbY7Rq1SqFh4erT58+Sk1N1enTp13GXLp0SfPnz5fNZlNgYKAeffRR1dXVdeJedB236+f3v//9W16z06dPdxlDP7+QnZ2tCRMmqH///goJCdHs2bNVUlLiMqYt7/Py8nLNmDFDffv2VUhIiJ555hnduHGjM3ely2hLT1NSUm55nS5ZssRlTGf1tFsFAndv3QxXDzzwgCorK53TgQMHnMueeuopvfHGG8rNzdW+fft0/vx5zZkzx4vVdj319fWKj4/Xpk2bml2+Zs0avfDCC9q8ebMOHz6se+65R2lpabp27ZpzzPz583Xq1Cnl5eVp9+7d2r9/vx577LHO2oUu5Xb9lKTp06e7vGa3bdvmspx+fmHfvn1aunSp3n33XeXl5en69euaNm2a6uvrnWNu9z5vamrSjBkz1NjYqEOHDunPf/6zcnJytGrVKm/skte1paeStHjxYpfX6Zo1a5zLOrWnt7nB4V0lMTHRLF261Pm4qanJREREmOzsbC9W5RtWr15t4uPjm11WXV1tevXqZXJzc53zPvjgAyPJFBYWdlKFvkWSy11DHQ6HCQsLM2vXrnXOq66uNlar1Wzbts0YY8z7779vJJl//etfzjH/+Mc/jMViMefOneu02ruiL/fTGGMyMjLMrFmzWlyHfrbuwoULRpLZt2+fMaZt7/M333zT9OjRw9jtdueY3//+98Zms5mGhobO3YEu6Ms9NcaYyZMnm+XLl7e4Tmf2tNscIWhsbFRRUZFSU1Od83r06KHU1FQVFhZ6sTLfcfr0aUVERGjo0KGaP3++ysvLJUlFRUW6fv26S29HjRqlqKgoettGZWVlstvtLj0MCAhQUlKSs4eFhYUKDAzU+PHjnWNSU1PVo0cPHT58uNNr9gUFBQUKCQlRTEyMHn/8cV28eNG5jH62rqamRpIUFBQkqW3v88LCQsXGxrrc4TYtLU21tbU6depUJ1bfNX25p5/729/+puDgYI0ZM0ZZWVm6cuWKc1ln9rRL3u2wI7Tn1s34QlJSknJychQTE6PKyko9++yzmjRpkk6ePCm73S5/f/9b7kh5u9tc4wuf96m1W4Xb7XaFhIS4LO/Zs6eCgoLoczOmT5+uOXPmKDo6WmfOnNFPfvITpaenq7CwUH5+fvSzFQ6HQ08++aQefvhhjRkzRpLa9D5v6Xb3ny/rzprrqSR997vf1eDBgxUREaHjx4/rRz/6kUpKSvT6669L6tyedptAgDuTnp7u/HNcXJySkpI0ePBgvfrqq+rTp48XKwOa953vfMf559jYWMXFxWnYsGEqKCjQ1KlTvVhZ17d06VKdPHnS5Toh3JmWevr/16zExsYqPDxcU6dO1ZkzZzRs2LBOrbHbnDJoz62b0bLAwECNHDlSpaWlCgsLU2Njo6qrq13G0Nu2+7xPrb0+w8LCbrkA9saNG7p06RJ9boOhQ4cqODhYpaWlkuhnS5YtW6bdu3frnXfe0X333eec35b3eUu3u/98WXfVUk+b8/l9gf7/ddpZPe02gaA9t25Gy+rq6nTmzBmFh4dr3Lhx6tWrl0tvS0pKVF5eTm/bKDo6WmFhYS49rK2t1eHDh509TE5OVnV1tYqKipxj9u7dK4fD4fxHBC375JNPdPHiRYWHh0uin19mjNGyZcu0Y8cO7d27V9HR0S7L2/I+T05O1okTJ1yCVl5enmw2m0aPHt05O9KF3K6nzSkuLpYkl9dpp/XUo5codnHbt283VqvV5OTkmPfff9889thjJjAw0OXqTTRvxYoVpqCgwJSVlZmDBw+a1NRUExwcbC5cuGCMMWbJkiUmKirK7N271xw5csQkJyeb5ORkL1fdtVy+fNkcO3bMHDt2zEgy69evN8eOHTNnz541xhjz61//2gQGBppdu3aZ48ePm1mzZpno6Ghz9epV53NMnz7dJCQkmMOHD5sDBw6YESNGmHnz5nlrl7yqtX5evnzZPP3006awsNCUlZWZt99+2zz44INmxIgR5tq1a87noJ9fePzxx01AQIApKCgwlZWVzunKlSvOMbd7n9+4ccOMGTPGTJs2zRQXF5u33nrLDBw40GRlZXljl7zudj0tLS01zz33nDly5IgpKyszu3btMkOHDjWPPPKI8zk6s6fdKhAYY8xvf/tbExUVZfz9/U1iYqJ59913vV2ST5g7d64JDw83/v7+ZtCgQWbu3LmmtLTUufzq1avmhz/8obn33ntN3759zTe+8Q1TWVnpxYq7nnfeecdIumXKyMgwxtz86uHPf/5zExoaaqxWq5k6daopKSlxeY6LFy+aefPmmX79+hmbzWYWLlxoLl++7IW98b7W+nnlyhUzbdo0M3DgQNOrVy8zePBgs3jx4lvCP/38QnO9lGS2bNniHNOW9/nHH39s0tPTTZ8+fUxwcLBZsWKFuX79eifvTddwu56Wl5ebRx55xAQFBRmr1WqGDx9unnnmGVNTU+PyPJ3VU25/DAAAus81BAAAoGUEAgAAQCAAAAAEAgAAIAIBAAAQgQAAAIhAAAAARCAAAAAiEAAAABEIAACACAQAAEDSfwHQN6wgA8jXPQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "# ## land mask\n",
    "# this is the same landmask as the original IFT- can be downloaded w SOIT\n",
    "outfile='/Users/ellenbuckley/Documents/Analysis/FSD/for_ccv/reproj_land.tiff'\n",
    "s=rasterio.open(outfile)\n",
    "land_mask=(s.read()[0])==75\n",
    "\n",
    "\n",
    "# ## load files\n",
    "\n",
    "\n",
    "data_direc='/Users/ellenbuckley/Documents/Analysis/FSD/for_ccv/input/'\n",
    "save_direc='/Users/ellenbuckley/Documents/Analysis/FSD/for_ccv/output/'\n",
    "ftci_direc=data_direc+'tci/'\n",
    "#ffci_direc=data_direc+'fci/'\n",
    "fcloud_direc=data_direc+'cloud/'\n",
    "\n",
    "#option to save figs after each step\n",
    "save_figs=True\n",
    "\n",
    "#year=2006\n",
    "for y in [2012]:# in np.arange(2012,2016,1):\n",
    "    year=y\n",
    "    for i in np.arange(214,245,1):# 94,274range of julian days where there is light in the Beaufort Sea\n",
    "\n",
    "        doy=i\n",
    "        aq=False # aqua or terra\n",
    "\n",
    "        doy_str = str(doy)\n",
    "        year_str = str(year)\n",
    "\n",
    "        # adjusting day num\n",
    "        doy_str.rjust(3 + len(doy_str), '0')\n",
    "        \n",
    "        # converting to date\n",
    "        res = datetime.strptime(year_str + \"-\" + doy_str, \"%Y-%j\").strftime(\"%Y-%m-%d\")\n",
    "\n",
    "        if aq:\n",
    "            sat='aqua'\n",
    "        else:\n",
    "            sat='terra'\n",
    "\n",
    "        fcloud=fcloud_direc+'cloud_'+res+'_'+str(doy)+'_'+sat+'.tiff'\n",
    "        ftci=ftci_direc+'tci_'+res+'_'+str(doy)+'_'+sat+'.tiff'\n",
    "        \n",
    "        \n",
    "        #open all files\n",
    "        cloud=rasterio.open(fcloud)\n",
    "        tci=rasterio.open(ftci)\n",
    "\n",
    "        # this pulls in the cloud mask file where red indicates >95% cloud cover\n",
    "        # so that is what I am masking out here\n",
    "        # but I think could just change to reading in false color and doing a...\n",
    "        # simple cloud mask from the first (NIR) channel as in original IFT\n",
    "        \n",
    "        cloud_mask=(cloud.read()[0])==255\n",
    "\n",
    "        red_c=np.copy(tci.read()[0])\n",
    "        green_c=np.copy(tci.read()[1])\n",
    "        blue_c=np.copy(tci.read()[2])\n",
    "        rgb=np.dstack([red_c,green_c,blue_c])\n",
    "\n",
    "        \n",
    "        if save_figs==True:\n",
    "            \n",
    "            # save cloud mask on RGB\n",
    "            red_cmasked=np.copy(tci.read()[0])\n",
    "            green_cmasked=np.copy(tci.read()[1])\n",
    "            blue_cmasked=np.copy(tci.read()[2])\n",
    "\n",
    "            red_cmasked[cloud_mask]=0\n",
    "            green_cmasked[cloud_mask]=0\n",
    "            blue_cmasked[cloud_mask]=0\n",
    "            rgb=np.dstack([red_cmasked,green_cmasked,blue_cmasked])\n",
    "            with rasterio.Env():\n",
    "                profile = tci.profile\n",
    "                profile.update(\n",
    "                    dtype=rasterio.uint8,\n",
    "                    count=3,\n",
    "                    compress='lzw')\n",
    "\n",
    "                with rasterio.open(save_direc+str(doy)+'cloud_mask_on_rgb.tif', 'w', **profile) as dst:\n",
    "                    dst.write(np.rollaxis(rgb, axis=2))\n",
    "            \n",
    "            \n",
    "            # save land AND cloud mask on RGB\n",
    "            red_cmasked[land_mask]=0\n",
    "            green_cmasked[land_mask]=0\n",
    "            blue_cmasked[land_mask]=0\n",
    "            rgb=np.dstack([red_cmasked,green_cmasked,blue_cmasked])\n",
    "            with rasterio.Env():\n",
    "                profile = tci.profile\n",
    "                profile.update(\n",
    "                    dtype=rasterio.uint8,\n",
    "                    count=3,\n",
    "                    compress='lzw')\n",
    "\n",
    "                with rasterio.open(save_direc+str(doy)+'land_cloud_mask_on_rgb.tif', 'w', **profile) as dst:\n",
    "                    dst.write(np.rollaxis(rgb, axis=2))\n",
    "\n",
    "                    \n",
    "        ## adaptive threshold for ice mask\n",
    "        red_masked=red_c=np.copy(tci.read()[0])\n",
    "        image = red_masked\n",
    "        thresh_adaptive = threshold_local(image, block_size=399)\n",
    "        \n",
    "        # make sure land and cloud are already masked out\n",
    "        red_masked[(land_mask|cloud_mask)]=0\n",
    "    \n",
    "        # here just determining the min and max values for the adaptive threshold\n",
    "        binz=np.arange(1,256,5)\n",
    "        rn,rbins=np.histogram(red_masked.flatten(),bins=binz)\n",
    "        dx=.01*np.mean(rn)\n",
    "        rmaxtab,rmintab=peakdet(rn,dx,x=None)\n",
    "        rmax_n=rbins[rmaxtab[-1,0]]\n",
    "        rm1_n=rbins[rmaxtab[0,0]]\n",
    "        rhm_high=rmaxtab[-1,1]/2\n",
    "        rhm_low=rmaxtab[0,1]/4\n",
    "        \n",
    "        if ~np.any(rmintab):\n",
    "            ow_cut_min=100\n",
    "        else:\n",
    "            ow_cut_min=rbins[rmintab[-1,0]]\n",
    "        if np.any(np.where((rbins[:-1]<rmax_n)&(rn<=rhm_high))):     \n",
    "            ow_cut_max=rbins[np.where((rbins[:-1]<rmax_n)&(rn<=rhm_high))[0][-1]]#fwhm to left of ice max\n",
    "        else:\n",
    "            ow_cut_max=rmax_n-10\n",
    "        if save_figs==True:\n",
    "            fig,ax=plt.subplots(1,1,figsize=(6,2))\n",
    "            plt.hist(red_masked.flatten(),bins=binz,color='r')\n",
    "            plt.axvline(ow_cut_max)\n",
    "            plt.axvline(ow_cut_min)\n",
    "            plt.savefig(save_direc+'ice_mask_hist.png')\n",
    "\n",
    "        thresh_adaptive[thresh_adaptive<ow_cut_min]=ow_cut_min\n",
    "        thresh_adaptive[thresh_adaptive>ow_cut_max]=ow_cut_max\n",
    "        ice_mask=image> thresh_adaptive\n",
    "        \n",
    "        \n",
    "        # a simple text file with columns: 'doy','ice_area','unmasked','sic'\n",
    "        with open(save_direc+str(doy)+'mask_values_'+str(year)+'.txt', 'a') as f:\n",
    "            f.write(str(doy)+ '\\t' +str(sum(sum(ice_mask)))+ '\\t'+str(sum(sum(~(land_mask+cloud_mask))))+ '\\t'+str(sum(sum(ice_mask))/sum(sum(~(land_mask+cloud_mask))))+'\\n')\n",
    "\n",
    "        # saving ice mask\n",
    "        if save_figs==True:\n",
    "            with rasterio.Env():\n",
    "                profile = tci.profile\n",
    "                profile.update(\n",
    "                    dtype=rasterio.uint8,\n",
    "                    count=1,\n",
    "                    compress='lzw')\n",
    "\n",
    "                with rasterio.open(save_direc+str(doy)+'ice_mask_bw.tif', 'w', **profile) as dst:\n",
    "                    dst.write(ice_mask.astype(rasterio.uint8), 1)\n",
    "        \n",
    "        \n",
    "        # here dialating the land and cloud mask so any floes that are adjacent to the mask can be removed later\n",
    "        lmd=land_mask+cloud_mask\n",
    "        kernel = diamond(10)#np.ones((3,3), np.uint8)\n",
    "        lmd= skimage.morphology.binary_dilation((land_mask+cloud_mask).astype(int), kernel)\n",
    "        \n",
    "        # setting up different kernel for erosion-expansion algo\n",
    "        kernel_er1=diamond(1)\n",
    "        kernel_er2=cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(5,5))\n",
    "\n",
    "        inp=ice_mask\n",
    "        input_no= ice_mask\n",
    "        r=0\n",
    "        output=np.zeros((np.shape(ice_mask)))\n",
    "        \n",
    "        \n",
    "        for n in np.arange(8,2,-1):\n",
    "            it=n\n",
    "            # erode a lot at first, decrease number of iterations each time\n",
    "            im3=cv2.erode(inp.astype(np.uint8),kernel_er1,iterations=it)\n",
    "            im3= ndimage.binary_fill_holes(im3.astype(np.uint8))\n",
    "           \n",
    "            im2=cv2.dilate(inp.astype(np.uint8),kernel_er1,iterations=it)\n",
    "            \n",
    "            # label floes remaining after erosion\n",
    "            ret, markers = cv2.connectedComponents(im3.astype(np.uint8))\n",
    "\n",
    "            # Add one to all labels so that sure background is not 0, but 1\n",
    "            markers = markers+1\n",
    "\n",
    "\n",
    "            unknown = cv2.subtract(im2.astype(np.uint8),im3.astype(np.uint8))\n",
    "            \n",
    "            # Now, mark the region of unknown with zero\n",
    "            markers[unknown==255] = 0\n",
    "\n",
    "            #dilate each marker\n",
    "            for a in np.arange(0,it+1,1):\n",
    "                markers=skimage.morphology.dilation(markers,kernel_er1)\n",
    "\n",
    "\n",
    "            #rewatershed\n",
    "            im4 = cv2.watershed(rgb,markers)\n",
    "\n",
    "            # get rid of floes that intersect the dilated land mask\n",
    "            im4[np.isin(im4,np.unique(im4[(lmd==True)&(im4>1)]))]=1\n",
    "            \n",
    "            # set the open water and already identified floes to no\n",
    "            im4[input_no==False]=1\n",
    "\n",
    "\n",
    "           # get rid of ones that are too small\n",
    "            area_lim=(it)**4\n",
    "            props = skimage.measure.regionprops_table(im4,properties=['label', 'area'])\n",
    "            df=pd.DataFrame.from_dict(props)\n",
    "            im4[np.isin(im4,df[df.area<area_lim].label.values)]=1\n",
    "            \n",
    "            if save_figs==True:\n",
    "                with rasterio.Env():\n",
    "\n",
    "                    # Write an array as a raster band to a new 8-bit file. For\n",
    "                    # the new file's profile, we start with the profile of the source\n",
    "                    profile = tci.profile\n",
    "\n",
    "                    # And then change the band count to 1, set the\n",
    "                    # dtype to uint8, and specify LZW compression.\n",
    "                    profile.update(\n",
    "                        dtype=rasterio.uint8,\n",
    "                        count=1,\n",
    "                        compress='lzw')\n",
    "\n",
    "                    with rasterio.open(save_direc+str(doy)+'identification_round_'+str(r)+'_not_sharp8_m1_nows.tif', 'w', **profile) as dst:\n",
    "                        dst.write(im4.astype(rasterio.uint8), 1)\n",
    "            \n",
    "           \n",
    "            r+=1  # r is just for tracking the iteration round for naming figures\n",
    "            \n",
    "            input_no=ice_mask+inp\n",
    "            inp=((im4==1)&(inp==1)&ice_mask)\n",
    "            im4[im4<2]=0\n",
    "            output=im4+output\n",
    "        \n",
    "        \n",
    "        # saving the props table\n",
    "        output=opening(output)\n",
    "        props = skimage.measure.regionprops_table(output.astype(int),red_c,properties=['label', 'area','centroid',\n",
    "                                                                                  'axis_major_length','axis_minor_length',\n",
    "                                                                                'orientation','perimeter','intensity_mean'])\n",
    "        df=pd.DataFrame.from_dict(props)\n",
    "        df.to_csv(save_direc+res+'_'+str(doy)+'_'+sat+'.csv')\n",
    "\n",
    "        \n",
    "        # saving the label floes tif\n",
    "        with rasterio.Env():\n",
    "                profile = tci.profile\n",
    "                profile.update(\n",
    "                    dtype=rasterio.uint8,\n",
    "                    count=1,\n",
    "                    compress='lzw')\n",
    "                with rasterio.open(save_direc+res+'_'+str(doy)+'_'+sat+'_final.tif', 'w', **profile) as dst:\n",
    "                    dst.write(output.astype(rasterio.uint8), 1)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23d6466b-5969-4dfb-a0dc-8826a0de5c6e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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