2- One-hot maker.ipynb

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 "cells": [
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   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "import time"
   ]
  },
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      "0 remaining\n"
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    "'''\n",
    "    Use this cell to convert any dimension masks into a one-hot array.\n",
    "    Plug-and-play Requirements:\n",
    "        - Masks must have desired class value assigned to the Red value of the image.\n",
    "        - Background class must be white\n",
    "        - If R value of background class is the actual class value comment out the highlighted line\n",
    "        - Change num_classes to classes\n",
    "    \n",
    "'''\n",
    "\n",
    "#mask_path = 'images/masks/'\n",
    "mask_path = 'images/validation/masks/'\n",
    "#array_path = 'images/one-hot-masks/'\n",
    "array_path = 'images/validation/one-hot-masks/'\n",
    "\n",
    "\n",
    "num_classes = 6\n",
    "\n",
    "remain = len(os.listdir(mask_path))\n",
    "for i,file in enumerate(os.listdir(mask_path)):\n",
    "    img = keras.preprocessing.image.load_img(mask_path+file,target_size=(512,512))\n",
    "    array = keras.preprocessing.image.img_to_array(img)\n",
    "    r_img = array[:,:,0]\n",
    "    #########################\n",
    "    r_img[r_img > 10] = 5  #####<----Remove this line if the background Red value is its class value\n",
    "    #########################\n",
    "    Y = tf.one_hot(r_img,num_classes,dtype='int8')\n",
    "    np.save(array_path+file[:-3]+'npy',Y)\n",
    "    remain -= 1\n",
    "    if remain % 100 == 0:\n",
    "        print(remain,'remaining')"
   ]
  }
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