test_salinasA_gt_mod.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import ListedColormap\n",
    "import arviz as az\n",
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "import json\n",
    "import argparse\n",
    "\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "import pyro\n",
    "import pyro.distributions as dist\n",
    "from pyro.infer import MCMC, NUTS, Predictive, EmpiricalMarginal\n",
    "from pyro.infer.autoguide import init_to_mean, init_to_median, init_to_value\n",
    "from pyro.infer.inspect import get_dependencies\n",
    "from pyro.infer import SVI, TraceEnum_ELBO, config_enumerate, infer_discrete\n",
    "\n",
    "import gempy as gp\n",
    "import gempy_engine\n",
    "import gempy_viewer as gpv\n",
    "from gempy_engine.core.backend_tensor import BackendTensor\n",
    "from gempy_probability.plot_posterior import default_red, default_blue, PlotPosterior\n",
    "\n",
    "import scipy.io\n",
    "from scipy.stats import zscore\n",
    "from sklearn.manifold import TSNE\n",
    "\n",
    "from sklearn.mixture import GaussianMixture\n",
    "from sklearn.mixture import BayesianGaussianMixture\n",
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    " # Load .mat file\n",
    "SalinasA= np.array(scipy.io.loadmat('./HSI_Salinas/SalinasA.mat')['salinasA'])\n",
    "SalinasA_corrected= np.array(scipy.io.loadmat('./HSI_Salinas/SalinasA_corrected.mat')['salinasA_corrected'])\n",
    "SalinasA_gt= np.array(scipy.io.loadmat('./HSI_Salinas/SalinasA_gt.mat')['salinasA_gt'])\n",
    "#Salinas_gt_mod = np.array(scipy.io.loadmat('./HSI_Salinas/SalinasA_mod.nc')['salinasA_gt'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class 'netCDF4._netCDF4.Variable'>\n",
       "float64 data(x, y, z)\n",
       "unlimited dimensions: \n",
       "current shape = (83, 86, 204)\n",
       "filling on, default _FillValue of 9.969209968386869e+36 used"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import netCDF4 as nc\n",
    "SalinasA_gt_mod = nc.Dataset('./HSI_Salinas/SalinasA_mod.nc',mode='r')\n",
    "SalinasA_gt_mod.variables['data']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1040x715 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from skimage import io\n",
    "# Load the image\n",
    "image = io.imread('./HSI_Salinas/SalinasA_mod_gt.png')\n",
    "\n",
    "# Show the image\n",
    "io.imshow(image)\n",
    "io.show()\n",
    "\n",
    "# Access image data as a numpy array\n",
    "#print(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "# Define the RGB to label mapping for 8 labels\n",
    "rgb_to_label = {\n",
    "    (255, 0, 0): 1,       # Red to label 1\n",
    "    (0, 255, 0): 2,       # Green to label 2\n",
    "    (0, 0, 255): 3,       # Blue to label 3\n",
    "    (255, 255, 0): 4,     # Yellow to label 4\n",
    "    (255, 0, 255): 5,     # Magenta to label 5\n",
    "    (0, 255, 255): 6,     # Cyan to label 6\n",
    "    (255, 255, 255): 7,   # White to label 7\n",
    "    (0, 0, 0): 8          # Black to label 8\n",
    "}\n",
    "\n",
    "# Load the image\n",
    "image = Image.open('./HSI_Salinas/SalinasA_mod_gt.png')\n",
    "image = image.convert('RGB')  # Ensure the image is in RGB mode\n",
    "\n",
    "# Convert the image to a numpy array\n",
    "image_array = np.array(image)\n",
    "\n",
    "# Prepare an empty label array with the same dimensions as the image\n",
    "label_array = np.zeros((image_array.shape[0], image_array.shape[1]), dtype=np.int32)\n",
    "\n",
    "# Iterate over the RGB-to-label mappings and apply them to the label array\n",
    "for rgb, label in rgb_to_label.items():\n",
    "    # Create a mask where all pixels match the current RGB value\n",
    "    mask = np.all(image_array == rgb, axis=-1)\n",
    "    # Assign the corresponding label to the label array\n",
    "    label_array[mask] = label\n",
    "\n",
    "# Now `label_array` contains the labels corresponding to the RGB image\n",
    "\n",
    "# Optional: Convert the label array to an image (for visualization)\n",
    "label_image = Image.fromarray(label_array.astype(np.uint8))\n",
    "label_image.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4, 4, 4, ..., 1, 1, 1],\n",
       "       [4, 4, 4, ..., 1, 1, 1],\n",
       "       [4, 4, 4, ..., 1, 1, 1],\n",
       "       ...,\n",
       "       [5, 5, 5, ..., 8, 8, 8],\n",
       "       [5, 5, 5, ..., 8, 8, 8],\n",
       "       [5, 5, 5, ..., 8, 8, 8]], dtype=int32)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6], dtype=uint8)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Arrange the label in groundtruth\n",
    "i=0\n",
    "label_data = [0,6,1,5,4,3,2]\n",
    "\n",
    "for ele in np.unique(SalinasA_gt):\n",
    "    mask = SalinasA_gt==ele\n",
    "    SalinasA_gt[mask] = label_data[i]\n",
    "    i=i+1\n",
    "    \n",
    "np.unique(SalinasA_gt)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7, 7, 7, ..., 5, 5, 5],\n",
       "       [7, 7, 7, ..., 5, 5, 5],\n",
       "       [7, 7, 7, ..., 5, 5, 5],\n",
       "       ...,\n",
       "       [3, 3, 3, ..., 1, 1, 1],\n",
       "       [3, 3, 3, ..., 1, 1, 1],\n",
       "       [3, 3, 3, ..., 1, 1, 1]], dtype=uint8)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SalinasA_gt+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 1, 1, 1, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 1, 1, 1, 1, 4,\n",
       "       4, 4, 4, 4, 4, 4, 1, 1, 1, 1, 3, 3, 3, 3, 3, 3, 3], dtype=uint8)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(SalinasA_gt +1)[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 8,\n",
       "       8, 8, 8, 8, 8, 8, 8, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 8, 8, 8, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 8, 8, 8, 8, 2,\n",
       "       2, 2, 2, 2, 2, 2, 8, 8, 8, 8, 5, 5, 5, 5, 5, 5, 5], dtype=int32)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_array[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "SalinasA_gt_mod_ = np.zeros_like(label_array)\n",
    "SalinasA_gt_mod_[label_array==8] =1\n",
    "SalinasA_gt_mod_[label_array==7] =3\n",
    "SalinasA_gt_mod_[label_array==6] =2\n",
    "SalinasA_gt_mod_[label_array==5] =4\n",
    "SalinasA_gt_mod_[label_array==4] =8\n",
    "SalinasA_gt_mod_[label_array==3] =7\n",
    "SalinasA_gt_mod_[label_array==2] =5\n",
    "SalinasA_gt_mod_[label_array==1] =6\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1,\n",
       "       1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1], dtype=int32)"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(SalinasA_gt_mod_ - (SalinasA_gt+1))[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x33516b8b0>"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1040x715 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(SalinasA_gt_mod_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 1, 1, 1, 1, 1, 6,\n",
       "       6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 1, 1, 1, 1, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 1, 1, 1, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 1,\n",
       "       1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2], dtype=int32)"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SalinasA_gt_mod_[:,40]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(83, 86)"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SalinasA_gt_mod_.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-77"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "-83+6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5, 6, 7, 8], dtype=int32)"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(SalinasA_gt_mod_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dict = {\"salinasA_gt_mod\": SalinasA_gt_mod_}\n",
    "scipy.io.savemat('./HSI_Salinas/SalinasA_gt_mod.mat', data_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x335040910>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1040x715 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(SalinasA_gt[:,0:2])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "gempy3",
   "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": "undefined.undefined.undefined"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}