Update nd_parser.ipynb with TensorFlow optimization and NDVI calculation

notebooks/nd_parser.ipynb

@@ -11,11 +11,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-01-30 20:03:19.555982: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
+      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
+     ]
+    }
+   ],
    "source": [
     "import os\n",
+    "import cv2\n",
     "import pathlib\n",
     "import numpy as np\n",
     "import tensorflow as tf\n",
@@ -24,7 +34,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -35,7 +45,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -66,16 +76,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f8841523640>"
+       "<matplotlib.image.AxesImage at 0x7f8360886e20>"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -112,24 +122,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# NDVI calculated from NIR and RGB bands\n",
+    "# NDVI calculated from NIR and RED bands\n",
     "`NDVI = (NIR - RED) / (NIR + RED)`\n",
     "\n",
     "Normalised difference vegetation index (NDVI) is a simple graphical indicator that can be used to analyse remote sensing measurements, typically but not necessarily from a space platform, and assess whether the target being observed contains live green vegetation or not."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.colorbar.Colorbar at 0x7f8841d05b50>"
+       "<matplotlib.colorbar.Colorbar at 0x7f8360c88970>"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -164,16 +174,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.colorbar.Colorbar at 0x7f8891590310>"
+       "<matplotlib.colorbar.Colorbar at 0x7f83406da370>"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -206,16 +216,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f8852141a90>"
+       "<matplotlib.image.AxesImage at 0x7f8371e0bcd0>"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -246,16 +256,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f8841d66d00>"
+       "<matplotlib.image.AxesImage at 0x7f82f0d18490>"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -293,7 +303,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -317,7 +327,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {