These lines can help some one to transform grayscale mask to multichannel (equal to number of classes) one hot mask.

gray2hot.py

def gray2hot(graymask, num_classes=2, include_background=True):                           
    '''Convert grayscale image of mask to multichannel mask                               
    if include_background=True number of channels in output will be             
    num_classes + 1                                                             
    '''                                                                         
    graymask = graymask.astype(np.int32)                                                  
    onehot_mask = np.zeros((graymask.shape[0], graymask.shape[1], num_classes+include_background), dtype=np.int32) # +1 relate to background                                  
    if include_background:                                                                
        onehot_mask[:, :, 0] = 1 # initialize all as background at first                  
    if np.all(graymask == 0):                                                             
        return onehot_mask                                                                
    indx = list(np.nonzero(graymask))                                                     
    indx.append((graymask[np.nonzero(graymask)]-(not include_background)))                
    onehot_mask[tuple(indx)] = 1                                                          
    if include_background:                                                                
        onehot_mask[:, :, 0] = onehot_mask[:, :, 0] - np.sum(onehot_mask[...,1:], axis=-1) # remove backgound from where foreground exist
    return onehot_mask   

test_gray2hot.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import ipdb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gray2hot(graymask, num_classes=2, include_background=True):                                             \n",
    "    '''Convert grayscale image of mask to multichannel mask'''\n",
    "    graymask = graymask.astype(np.int32)\n",
    "    onehot_mask = np.zeros((graymask.shape[0], graymask.shape[1], num_classes+include_background), dtype=np.int32) # +1 relate to background\n",
    "    if include_background:\n",
    "        onehot_mask[:, :, 0] = 1 # initialize all as background at first\n",
    "    if np.all(graymask == 0):\n",
    "        return onehot_mask\n",
    "    indx = list(np.nonzero(graymask))                                                       \n",
    "    indx.append((graymask[np.nonzero(graymask)]-(not include_background)))                                           \n",
    "    onehot_mask[tuple(indx)] = 1\n",
    "    if include_background:\n",
    "        onehot_mask[:, :, 0] = onehot_mask[:, :, 0] - np.sum(onehot_mask[...,1:], axis=-1) # remove backgound from where foreground exist\n",
    "    return onehot_mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0. 1. 2.]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fabf150acf8>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_csv('dataframe.csv')\n",
    "df = df[(df['count_one'] != 0) & (df['count_two'] !=0)]\n",
    "# df = df[(df['count_one'] !=0)]\n",
    "subject, slice, _, _, _ = df.iloc[10]\n",
    "PATH =  'converted_images/train_images/' + '{}_{}.png'.format(subject.split('.')[0], slice)\n",
    "mask = np.array(Image.open(PATH.replace('train_images', 'train_labels')), dtype=np.float32)\n",
    "print(np.unique(mask))\n",
    "plt.imshow(mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask_hot = gray2hot(mask, num_classes=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1]\n"
     ]
    },
    {
     "data": {
      "image/png": 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h559KzFydraZ72+OB4/vNOH/2XvkoqTPtqYSkGWQwSOoYDJI6BoOkjsEgqWMwSOoYDJI6BoOkzv8D4NIQrvWgELAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1]\n"
     ]
    },
    {
     "data": {
      "image/png": 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b878AbGj9G9v0Upv/mhn8HpzHLz+VmLs6W033tMf9R7abaf7be+WjpM6sDyUkzSGDQVLHYJDUMRgkdQwGSR2DQVLHYJDUMRgkdf4fxmkLDfnIQY8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQYAAAD8CAYAAACVSwr3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4xLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvDW2N/gAADX1JREFUeJzt3H3MnXV9x/H3Z33CRyrImq5tVoxNDH9sSBoeglkcxA07Y/kDDcbMxjRpsrFE4xJXtmSLyf6Q/SFKsuiaYVYXH2A+hIawMSyYZX+IVHmQhyE3REI7tFEBWYgM9Ls/zq/u0F/xPu19rvucW96v5OT8ns59fU9799Prus51nVQVkjTuN2ZdgKT5YzBI6hgMkjoGg6SOwSCpYzBI6gwSDEkuS/JwkoUke4fYhqThZNrXMSRZBXwPeAdwGLgLeF9VPTjVDUkazBB7DOcDC1X1WFX9L/AlYOcA25E0kNUD/MxNwBNj/cPABb/qBWuzrk7jNQOUIumYZ3nqR1V11iRrhwiGiSTZA+wBOI1Xc0EunVUp0ivC1+vLj0+6dohDiSPAlrH+5jb2ElW1r6q2V9X2NawboAxJp2qIYLgL2Jbk7CRrgSuBAwNsR9JApn4oUVUvJvkz4FZgFfDZqnpg2tuRNJxBzjFU1S3ALUP8bEnD88pHSR2DQVLHYJDUMRgkdQwGSR2DQVLHYJDUMRgkdQwGSR2DQVLHYJDUMRgkdQwGSR2DQVLHYJDUMRgkdQwGSR2DQVLHYJDUMRgkdQwGSR2DQVLHYJDUMRgkdQwGSR2DQVLHYJDUMRgkdQwGSR2DQVLHYJDUMRgkdQwGSR2DQVJn0WBI8tkkR5PcPzZ2RpLbkjzSnt/QxpPkuiQLSe5Lct6QxUsaxiR7DP8EXHbc2F7gYFVtAw62PsA7gW3tsQf49HTKlLScFg2GqvoP4CfHDe8E9rf2fuDysfHP1cg3gfVJNk6rWEnL41TPMWyoqidb+wfAhtbeBDwxtu5wG+sk2ZPkUJJDL/D8KZYhaQhLPvlYVQXUKbxuX1Vtr6rta1i31DIkTdGpBsMPjx0itOejbfwIsGVs3eY2JmkFOdVgOADsau1dwE1j4x9on05cCDwzdsghaYVYvdiCJF8E3g68Mclh4G+AjwM3JtkNPA68ty2/BdgBLADPAR8coGZJA1s0GKrqfS8zdekJ1hZw1VKLkjRbXvkoqWMwSOoYDJI6BoOkjsEgqWMwSOoYDJI6BoOkjsEgqWMwSOoYDJI6BoOkjsEgqWMwSOoYDJI6BoOkjsEgqWMwSOoYDJI6BoOkjsEgqWMwSOoYDJI6BoOkjsEgqWMwSOoYDJI6BoOkjsEgqWMwSOoYDJI6BoOkjsEgqbNoMCTZkuSOJA8meSDJh9r4GUluS/JIe35DG0+S65IsJLkvyXlDvwlJ0zXJHsOLwJ9X1TnAhcBVSc4B9gIHq2obcLD1Ad4JbGuPPcCnp161pEEtGgxV9WRVfae1nwUeAjYBO4H9bdl+4PLW3gl8rka+CaxPsnHqlUsazEmdY0iyFXgrcCewoaqebFM/ADa09ibgibGXHW5jklaIiYMhyWuBrwAfrqqfjs9VVQF1MhtOsifJoSSHXuD5k3mppIFNFAxJ1jAKhc9X1Vfb8A+PHSK056Nt/AiwZezlm9vYS1TVvqraXlXb17DuVOuXNIBJPpUIcD3wUFV9YmzqALCrtXcBN42Nf6B9OnEh8MzYIYekFWD1BGsuBv4Y+G6Se9rYXwIfB25Msht4HHhvm7sF2AEsAM8BH5xqxZIGt2gwVNV/AnmZ6UtPsL6Aq5ZYl6QZ8spHSR2DQVLHYJDUMRgkdQwGSR2DQVLHYJDUMRgkdQwGSR2DQVLHYJDUMRgkdQwGSR2DQVLHYJDUMRgkdQwGSR2DQVLHYJDUMRgkdQwGSR2DQVLHYJDUMRgkdQwGSR2DQVLHYJDUMRgkdQwGSR2DQVLHYJDUMRgkdQwGSR2DQVJn0WBIclqSbyW5N8kDST7Wxs9OcmeShSQ3JFnbxte1/kKb3zrsW5A0bZPsMTwPXFJVvwucC1yW5ELgGuDaqnoz8BSwu63fDTzVxq9t6yStIIsGQ438T+uuaY8CLgG+3Mb3A5e39s7Wp81fmiRTq1jS4CY6x5BkVZJ7gKPAbcCjwNNV9WJbchjY1NqbgCcA2vwzwJkn+Jl7khxKcugFnl/au5A0VRMFQ1X9vKrOBTYD5wNvWeqGq2pfVW2vqu1rWLfUHydpik7qU4mqehq4A7gIWJ9kdZvaDBxp7SPAFoA2fzrw46lUK2lZTPKpxFlJ1rf2q4B3AA8xCogr2rJdwE2tfaD1afO3V1VNs2hJw1q9+BI2AvuTrGIUJDdW1c1JHgS+lORvgbuB69v664F/TrIA/AS4coC6JQ1o0WCoqvuAt55g/DFG5xuOH/8Z8J6pVCdpJrzyUVLHYJDUMRgkdQwGSR2DQVLHYJDUMRgkdQwGSR2DQVLHYJDUMRgkdQwGSR2DQVLHYJDUMRgkdSb5ohYJgFv/+56X9P/wt86dUSUamnsMOmXHB4V+fRgMkjoGgybmocMrh8GgiXmO4ZXDYNDExoPAUPj15qcSOikGwiuDewySOgaDpI7BIKljMEjqGAySOgaDpI7BIKljMEjqGAySOgaDpI7BIKkzcTAkWZXk7iQ3t/7ZSe5MspDkhiRr2/i61l9o81uHKV3SUE5mj+FDwENj/WuAa6vqzcBTwO42vht4qo1f29ZJWkEmCoYkm4E/Av6x9QNcAny5LdkPXN7aO1ufNn9pWy9phZh0j+GTwEeBX7T+mcDTVfVi6x8GNrX2JuAJgDb/TFv/Ekn2JDmU5NALPH+K5UsawqLBkORdwNGq+vY0N1xV+6pqe1VtX8O6af5oSUs0yRe1XAy8O8kO4DTg9cCngPVJVre9gs3Akbb+CLAFOJxkNXA68OOpVy5pMIvuMVTV1VW1uaq2AlcCt1fV+4E7gCvasl3ATa19oPVp87dXVU21akmDWsp1DH8BfCTJAqNzCNe38euBM9v4R4C9SytR0nI7qe98rKpvAN9o7ceA80+w5mfAe6ZQm6QZ8cpHSR2DQVLHYJDUMRgkdQwGSR2DQVLHYJDUMRgkdQwGSR2DQVLHYJDUMRgkdQwGSR2DQVLHYJDUMRgkdQwGSR2DQVLHYJDUMRgkdQwGSR2DQVLHYJDUMRgkdQwGSR2DQVLHYJDUMRgkdQwGSR2DQVLHYJDUMRgkdQwGSR2DQVJnomBI8v0k301yT5JDbeyMJLcleaQ9v6GNJ8l1SRaS3JfkvCHfgKTpO5k9ht+vqnOranvr7wUOVtU24GDrA7wT2NYee4BPT6tYSctjKYcSO4H9rb0fuHxs/HM18k1gfZKNS9iOpGU2aTAU8O9Jvp1kTxvbUFVPtvYPgA2tvQl4Yuy1h9vYSyTZk+RQkkMv8PwplC5pKKsnXPe2qjqS5DeB25L81/hkVVWSOpkNV9U+YB/A63PGSb1W0rAm2mOoqiPt+SjwNeB84IfHDhHa89G2/AiwZezlm9uYpBVi0WBI8pokrzvWBv4AuB84AOxqy3YBN7X2AeAD7dOJC4Fnxg45JK0AkxxKbAC+luTY+i9U1b8luQu4Mclu4HHgvW39LcAOYAF4Dvjg1KuWNKhUzf7wPsmzwMOzrmNCbwR+NOsiJrBS6oSVU+tKqRNOXOtvV9VZk7x40pOPQ3t47PqIuZbk0EqodaXUCSun1pVSJyy9Vi+JltQxGCR15iUY9s26gJOwUmpdKXXCyql1pdQJS6x1Lk4+Spov87LHIGmOzDwYklyW5OF2m/bexV8xaC2fTXI0yf1jY3N5e3mSLUnuSPJgkgeSfGge601yWpJvJbm31fmxNn52kjtbPTckWdvG17X+Qpvfuhx1jtW7KsndSW6e8zqH/SqEqprZA1gFPAq8CVgL3AucM8N6fg84D7h/bOzvgL2tvRe4prV3AP8KBLgQuHOZa90InNfarwO+B5wzb/W27b22tdcAd7bt3whc2cY/A/xJa/8p8JnWvhK4YZn/XD8CfAG4ufXntc7vA288bmxqf/fL9kZe5s1dBNw61r8auHrGNW09LhgeBja29kZG11wA/APwvhOtm1HdNwHvmOd6gVcD3wEuYHTxzerjfw+AW4GLWnt1W5dlqm8zo+8WuQS4uf1Dmrs62zZPFAxT+7uf9aHERLdoz9iSbi9fDm039q2M/jeeu3rb7vk9jG60u43RXuLTVfXiCWr5ZZ1t/hngzOWoE/gk8FHgF61/5pzWCQN8FcK4ebnycUWoOvnby4eW5LXAV4APV9VP2z0twPzUW1U/B85Nsp7R3blvmXFJnSTvAo5W1beTvH3W9Uxg6l+FMG7Wewwr4Rbtub29PMkaRqHw+ar6ahue23qr6mngDka75OuTHPuPabyWX9bZ5k8HfrwM5V0MvDvJ94EvMTqc+NQc1gkM/1UIsw6Gu4Bt7czvWkYncQ7MuKbjzeXt5RntGlwPPFRVn5jXepOc1fYUSPIqRudBHmIUEFe8TJ3H6r8CuL3agfGQqurqqtpcVVsZ/R7eXlXvn7c6YZm+CmG5Tpb8ipMoOxidUX8U+KsZ1/JF4EngBUbHYbsZHTceBB4Bvg6c0dYG+PtW93eB7ctc69sYHWfeB9zTHjvmrV7gd4C7W533A3/dxt8EfIvR7fn/Aqxr46e1/kKbf9MMfg/ezv9/KjF3dbaa7m2PB479u5nm371XPkrqzPpQQtIcMhgkdQwGSR2DQVLHYJDUMRgkdQwGSR2DQVLn/wDCNKPZDsPpTAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for img in np.transpose(mask_hot, (2, 0, 1)):\n",
    "    print(np.unique(img))\n",
    "    plt.imshow(img,vmax=1, vmin=0)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}