creating_datasets.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pickle\n",
    "\n",
    "from os import listdir\n",
    "from os.path import isfile, join\n",
    "from pathlib import Path\n",
    "\n",
    "def rgb2gray(rgb):\n",
    "\n",
    "    r, g, b = rgb[:,:,0], rgb[:,:,1], rgb[:,:,2]\n",
    "    gray = 0.2989 * r + 0.5870 * g + 0.1140 * b\n",
    "\n",
    "    return gray\n",
    "\n",
    "def gray2rgb(gray):\n",
    "\n",
    "    rgb = np.stack((gray,)*3, axis=-1)\n",
    "\n",
    "    return rgb\n",
    "    \n",
    "\n",
    "def rescale_values(image,max_val,min_val):\n",
    "    '''\n",
    "    image - numpy array\n",
    "    max_val/min_val - float\n",
    "    '''\n",
    "    return (image-image.min())/(image.max()-image.min())*(max_val-min_val)+min_val\n",
    "\n",
    "SEED=0\n",
    "np.random.seed(SEED)\n",
    "import os\n",
    "os.environ['PYTHONHASHSEED']=str(SEED)\n",
    "\n",
    "import random\n",
    "random.seed(SEED)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_watermark(background_image_path,watermark_path, intensity_watermark,image_size,white_bool):\n",
    "    #watermark should be jpg with white background\n",
    "    #lower intensity_watermark leads to a more transparent watermark in the final image (less contrast)\n",
    "    # white_bool is a bool that indicated if the watermark to add is white or black\n",
    "    \n",
    "    background_image = Image.open(background_image_path)\n",
    "    background_image = background_image.resize(image_size)\n",
    "    watermark = Image.open(watermark_path)\n",
    "\n",
    "    #resize watermark to cover the entire background width without getting deformed\n",
    "    w=int(background_image.size[0])\n",
    "    h=int(watermark.size[1]*background_image.size[0]/watermark.size[0])\n",
    "    watermark = watermark.resize((w,h))\n",
    "\n",
    "    background_image=np.array(background_image)\n",
    "    watermark=np.array(watermark)\n",
    "\n",
    "    b=rescale_values(background_image,1,0)\n",
    "    rgb=rescale_values(watermark,1,0)\n",
    "\n",
    "    #turning watermark into grayscale\n",
    "    r, g, blue = rgb[:,:,0], rgb[:,:,1], rgb[:,:,2]\n",
    "    gray = 1-(0.2989 * r + 0.5870 * g + 0.1140 * blue)\n",
    "\n",
    "    #padding watermark to be an image the same size as the background image\n",
    "    white=np.ones((b.shape[0],b.shape[1]))\n",
    "    white[0:gray.shape[0],0:gray.shape[1]]=gray\n",
    "\n",
    "    # grayscale watermark with 3 channels\n",
    "    gr=np.repeat(white[..., np.newaxis], 3, axis=2)\n",
    "\n",
    "    if white_bool:\n",
    "        im=1-b\n",
    "        gr_p=rescale_values(gr,1,1-intensity_watermark)\n",
    "        i_2=im*gr_p\n",
    "        output_image=1-(i_2)\n",
    "    else:\n",
    "        im=rescale_values(b,1,0)\n",
    "        gr_p=rescale_values(gr,1,1-intensity_watermark)\n",
    "        output_image=im*gr_p\n",
    "     \n",
    "    #output_image=0\n",
    "    return output_image\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 3000x2000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# folder='D:\\\\escola\\\\tese\\\\ML homework\\\\'\n",
    "folder=''\n",
    "original_image_path='./images/dog/dog.6053.jpg'\n",
    "watermark_path_jpeg=folder+'watermark banner.jpg'\n",
    "output_path=folder+'out\\\\'\n",
    "\n",
    "\n",
    "image_size=(128,128)\n",
    "plt.rcParams['font.size'] = 30\n",
    "intensity_watermark=0.2\n",
    "plt.figure(figsize=(30,20))\n",
    "for i in range(3):\n",
    "    white=1\n",
    "    out_img=add_watermark(original_image_path,watermark_path_jpeg,intensity_watermark,image_size,white)\n",
    "    plt.subplot(2,3,i+1)\n",
    "    plt.imshow(out_img)\n",
    "    plt.title(f'intensity: {intensity_watermark}')\n",
    "    plt.yticks([])\n",
    "    plt.xticks([])\n",
    "    white=0\n",
    "    out_img=add_watermark(original_image_path,watermark_path_jpeg,intensity_watermark,image_size,white)\n",
    "    plt.subplot(2,3,i+4)\n",
    "    plt.imshow(out_img)\n",
    "    plt.title(f'intensity: {intensity_watermark}')\n",
    "    intensity_watermark+=0.3\n",
    "    plt.yticks([])\n",
    "    plt.xticks([])\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creation of the datasets - this didn't work for me as I didn't have the data in images/cat/train form, just images/cat/cat.XXX.jpg (benny)\n",
    "# #cat=0\n",
    "# #dog=0\n",
    "# from os import listdir\n",
    "# from os.path import isfile, join\n",
    "\n",
    "# # folder='D:\\\\escola\\\\tese\\\\ML homework\\\\'\n",
    "# folder=''\n",
    "# image_size=(128,128)\n",
    "# cat_names_train = [folder+'images\\\\cat\\\\train\\\\'+f for f in listdir(folder+'images\\\\cat\\\\train\\\\') if isfile(join(folder+'images\\\\cat\\\\train\\\\', f))]\n",
    "# cat_names_test = [folder+'images\\\\cat\\\\test\\\\'+f for f in listdir(folder+'images\\\\cat\\\\test\\\\') if isfile(join(folder+'images\\\\cat\\\\test\\\\', f))]\n",
    "# cat_names_val = [folder+'images\\\\cat\\\\val\\\\'+f for f in listdir(folder+'images\\\\cat\\\\val\\\\') if isfile(join(folder+'images\\\\cat\\\\val\\\\', f))]\n",
    "\n",
    "\n",
    "# dog_names_train = [folder+'images\\\\dog\\\\train\\\\'+f for f in listdir(folder+'images\\\\dog\\\\train\\\\') if isfile(join(folder+'images\\\\dog\\\\train\\\\', f))]\n",
    "# dog_names_test = [folder+'images\\\\dog\\\\test\\\\'+f for f in listdir(folder+'images\\\\dog\\\\test\\\\') if isfile(join(folder+'images\\\\dog\\\\test\\\\', f))]\n",
    "# dog_names_val = [folder+'images\\\\dog\\\\val\\\\'+f for f in listdir(folder+'images\\\\dog\\\\val\\\\') if isfile(join(folder+'images\\\\dog\\\\val\\\\', f))]\n",
    "\n",
    "# import random\n",
    "# # random.seed(0)\n",
    "# # dataset_images_cats=random.sample(cat_names,k=6000)\n",
    "# # dataset_images_dogs=random.sample(dog_names,k=6000)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total dogs and cats: 6000 6000\n"
     ]
    }
   ],
   "source": [
    "from glob import glob\n",
    "# random.sample(cat_names,k=6000)\n",
    "cat_paths = glob('./images/cat/*')\n",
    "dog_paths = glob('./images/dog/*')\n",
    "print('total dogs and cats:', len(dog_paths), len(cat_paths))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\clark01\\AppData\\Local\\Temp\\1\\ipykernel_10476\\3194630990.py:5: DeprecationWarning: Calling nonzero on 0d arrays is deprecated, as it behaves surprisingly. Use `atleast_1d(cond).nonzero()` if the old behavior was intended. If the context of this warning is of the form `arr[nonzero(cond)]`, just use `arr[cond]`.\n",
      "  inds = list(np.delete(inds, np.where(train_inds==inds)))\n",
      "C:\\Users\\clark01\\AppData\\Local\\Temp\\1\\ipykernel_10476\\3194630990.py:8: DeprecationWarning: Calling nonzero on 0d arrays is deprecated, as it behaves surprisingly. Use `atleast_1d(cond).nonzero()` if the old behavior was intended. If the context of this warning is of the form `arr[nonzero(cond)]`, just use `arr[cond]`.\n",
      "  inds = list(np.delete(inds, np.where(val_inds==inds)))\n"
     ]
    }
   ],
   "source": [
    "inds = list(range(6000))\n",
    "\n",
    "train_inds = random.sample(inds, k=3000)\n",
    "\n",
    "inds = list(np.delete(inds, np.where(train_inds==inds)))\n",
    "\n",
    "val_inds = random.sample(inds, k=1500)\n",
    "inds = list(np.delete(inds, np.where(val_inds==inds)))\n",
    "\n",
    "test_inds = random.sample(inds, k=1500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " 4174,\n",
       " ...]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_inds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([], dtype=int64),)\n",
      "(array([], dtype=int64),)\n",
      "(array([], dtype=int64),)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\clark01\\AppData\\Local\\Temp\\1\\ipykernel_10476\\3422988351.py:2: DeprecationWarning: Calling nonzero on 0d arrays is deprecated, as it behaves surprisingly. Use `atleast_1d(cond).nonzero()` if the old behavior was intended. If the context of this warning is of the form `arr[nonzero(cond)]`, just use `arr[cond]`.\n",
      "  print(np.where(train_inds == val_inds))\n",
      "C:\\Users\\clark01\\AppData\\Local\\Temp\\1\\ipykernel_10476\\3422988351.py:3: DeprecationWarning: Calling nonzero on 0d arrays is deprecated, as it behaves surprisingly. Use `atleast_1d(cond).nonzero()` if the old behavior was intended. If the context of this warning is of the form `arr[nonzero(cond)]`, just use `arr[cond]`.\n",
      "  print(np.where(train_inds == test_inds))\n",
      "C:\\Users\\clark01\\AppData\\Local\\Temp\\1\\ipykernel_10476\\3422988351.py:4: DeprecationWarning: Calling nonzero on 0d arrays is deprecated, as it behaves surprisingly. Use `atleast_1d(cond).nonzero()` if the old behavior was intended. If the context of this warning is of the form `arr[nonzero(cond)]`, just use `arr[cond]`.\n",
      "  print(np.where(test_inds == val_inds))\n"
     ]
    }
   ],
   "source": [
    "# no overlapping inds between sets\n",
    "print(np.where(train_inds == val_inds))\n",
    "print(np.where(train_inds == test_inds))\n",
    "print(np.where(test_inds == val_inds))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_names_train = list(np.array(cat_paths)[train_inds])\n",
    "cat_names_val = list(np.array(cat_paths)[val_inds])\n",
    "cat_names_test = list(np.array(cat_paths)[test_inds])\n",
    "\n",
    "dog_names_train = list(np.array(dog_paths)[train_inds])\n",
    "dog_names_val = list(np.array(dog_paths)[val_inds])\n",
    "dog_names_test = list(np.array(dog_paths)[test_inds])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def save_images(percentage_watermark,list_files,watermark_path,image_size,intensity_watermak,output_folder,white_bool):\n",
    "    with_watermark=random.sample(list_files,k=int(len(list_files)*percentage_watermark))\n",
    "    Path(output_folder).mkdir(parents=True, exist_ok=True)\n",
    "    n_water=0\n",
    "    n_n_water=0\n",
    "\n",
    "    for image in list_files:\n",
    "        if image in with_watermark:\n",
    "            out_im=add_watermark(image,watermark_path,intensity_watermak,image_size,white_bool)\n",
    "            n_water+=1\n",
    "        else:\n",
    "            out_im=Image.open(image)\n",
    "            out_im = out_im.resize(image_size)\n",
    "            out_im=np.array(out_im)\n",
    "            n_n_water+=1\n",
    "            \n",
    "        n=image.rfind('\\\\')\n",
    "        # plt.imsave(output_folder+image[n+1:],out_im)\n",
    "\n",
    "    print('number of images with watermark:',n_water)\n",
    "    print('number of images without watermark:',n_n_water)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Supressor \n",
    "+  50% each class has the watermark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# folder='D:\\\\escola\\\\tese\\\\ML homework\\\\'\n",
    "folder=''\n",
    "watermark_path_jpeg=folder+'watermark banner.jpg'\n",
    "image_size=(128,128)\n",
    "i=0.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[12], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# output_folder='D:\\\\escola\\\\tese\\\\ML homework\\\\dataset_supressor\\\\'\u001b[39;00m\n\u001b[0;32m      2\u001b[0m output_folder\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m./artifacts/test/dataset_supressor/\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m----> 3\u001b[0m \u001b[43msave_images\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0.5\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43mcat_names_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43mwatermark_path_jpeg\u001b[49m\u001b[43m,\u001b[49m\u001b[43mimage_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43mi\u001b[49m\u001b[43m,\u001b[49m\u001b[43moutput_folder\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtrain/\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m      4\u001b[0m save_images(\u001b[38;5;241m0.5\u001b[39m,cat_names_test,watermark_path_jpeg,image_size,i,output_folder\u001b[38;5;241m+\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtest/\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m      5\u001b[0m save_images(\u001b[38;5;241m0.5\u001b[39m,cat_names_val,watermark_path_jpeg,image_size,i,output_folder\u001b[38;5;241m+\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mval/\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;241m1\u001b[39m)\n",
      "Cell \u001b[1;32mIn[10], line 9\u001b[0m, in \u001b[0;36msave_images\u001b[1;34m(percentage_watermark, list_files, watermark_path, image_size, intensity_watermak, output_folder, white_bool)\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m image \u001b[38;5;129;01min\u001b[39;00m list_files:\n\u001b[0;32m      8\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m image \u001b[38;5;129;01min\u001b[39;00m with_watermark:\n\u001b[1;32m----> 9\u001b[0m         out_im\u001b[38;5;241m=\u001b[39m\u001b[43madd_watermark\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimage\u001b[49m\u001b[43m,\u001b[49m\u001b[43mwatermark_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43mintensity_watermak\u001b[49m\u001b[43m,\u001b[49m\u001b[43mimage_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43mwhite_bool\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     10\u001b[0m         n_water\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m\n\u001b[0;32m     11\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "Cell \u001b[1;32mIn[2], line 13\u001b[0m, in \u001b[0;36madd_watermark\u001b[1;34m(background_image_path, watermark_path, intensity_watermark, image_size, white_bool)\u001b[0m\n\u001b[0;32m     11\u001b[0m w\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mint\u001b[39m(background_image\u001b[38;5;241m.\u001b[39msize[\u001b[38;5;241m0\u001b[39m])\n\u001b[0;32m     12\u001b[0m h\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mint\u001b[39m(watermark\u001b[38;5;241m.\u001b[39msize[\u001b[38;5;241m1\u001b[39m]\u001b[38;5;241m*\u001b[39mbackground_image\u001b[38;5;241m.\u001b[39msize[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m/\u001b[39mwatermark\u001b[38;5;241m.\u001b[39msize[\u001b[38;5;241m0\u001b[39m])\n\u001b[1;32m---> 13\u001b[0m watermark \u001b[38;5;241m=\u001b[39m \u001b[43mwatermark\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresize\u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[43mw\u001b[49m\u001b[43m,\u001b[49m\u001b[43mh\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     15\u001b[0m background_image\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39marray(background_image)\n\u001b[0;32m     16\u001b[0m watermark\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39marray(watermark)\n",
      "File \u001b[1;32m~\\AppData\\Local\\miniforge-pypy3\\envs\\debugging\\Lib\\site-packages\\PIL\\Image.py:2138\u001b[0m, in \u001b[0;36mImage.resize\u001b[1;34m(self, size, resample, box, reducing_gap)\u001b[0m\n\u001b[0;32m   2134\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(msg)\n\u001b[0;32m   2136\u001b[0m size \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(size)\n\u001b[1;32m-> 2138\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   2139\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m box \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   2140\u001b[0m     box \u001b[38;5;241m=\u001b[39m (\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m0\u001b[39m) \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msize\n",
      "File \u001b[1;32m~\\AppData\\Local\\miniforge-pypy3\\envs\\debugging\\Lib\\site-packages\\PIL\\ImageFile.py:269\u001b[0m, in \u001b[0;36mImageFile.load\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    266\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m(msg)\n\u001b[0;32m    268\u001b[0m b \u001b[38;5;241m=\u001b[39m b \u001b[38;5;241m+\u001b[39m s\n\u001b[1;32m--> 269\u001b[0m n, err_code \u001b[38;5;241m=\u001b[39m \u001b[43mdecoder\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecode\u001b[49m\u001b[43m(\u001b[49m\u001b[43mb\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    270\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m n \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m    271\u001b[0m     \u001b[38;5;28;01mbreak\u001b[39;00m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "\n",
    "# output_folder='D:\\\\escola\\\\tese\\\\ML homework\\\\dataset_supressor\\\\'\n",
    "output_folder='./artifacts/test/dataset_supressor/'\n",
    "save_images(0.5,cat_names_train,watermark_path_jpeg,image_size,i,output_folder+'train/',1)\n",
    "save_images(0.5,cat_names_test,watermark_path_jpeg,image_size,i,output_folder+'test/',1)\n",
    "save_images(0.5,cat_names_val,watermark_path_jpeg,image_size,i,output_folder+'val/',1)\n",
    "print()\n",
    "save_images(0.5,dog_names_train,watermark_path_jpeg,image_size,i,output_folder+'train/',1)\n",
    "save_images(0.5,dog_names_test,watermark_path_jpeg,image_size,i,output_folder+'test/',1)\n",
    "save_images(0.5,dog_names_val,watermark_path_jpeg,image_size,i,output_folder+'val/',1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Confounder \n",
    "+ 20% of cat class has watermark\n",
    "+ 80% of dog class has watermark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of images with watermark: 600\n",
      "number of images without watermark: 2400\n",
      "number of images with watermark: 300\n",
      "number of images without watermark: 1200\n",
      "number of images with watermark: 300\n",
      "number of images without watermark: 1200\n",
      "\n",
      "number of images with watermark: 2400\n",
      "number of images without watermark: 600\n",
      "number of images with watermark: 1200\n",
      "number of images without watermark: 300\n",
      "number of images with watermark: 1200\n",
      "number of images without watermark: 300\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# output_folder='D:\\\\escola\\\\tese\\\\ML homework\\\\dataset_confounder\\\\'\n",
    "output_folder='./artifacts/dataset_confounder/'\n",
    "\n",
    "save_images(0.2,cat_names_train,watermark_path_jpeg,image_size,i,output_folder+'train/',1)\n",
    "save_images(0.2,cat_names_test,watermark_path_jpeg,image_size,i,output_folder+'test/',1)\n",
    "save_images(0.2,cat_names_val,watermark_path_jpeg,image_size,i,output_folder+'val/',1)\n",
    "print()\n",
    "save_images(0.8,dog_names_train,watermark_path_jpeg,image_size,i,output_folder+'train/',1)\n",
    "save_images(0.8,dog_names_test,watermark_path_jpeg,image_size,i,output_folder+'test/',1)\n",
    "save_images(0.8,dog_names_val,watermark_path_jpeg,image_size,i,output_folder+'val/',1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of images with watermark: 0\n",
      "number of images without watermark: 3000\n",
      "number of images with watermark: 0\n",
      "number of images without watermark: 1500\n",
      "number of images with watermark: 0\n",
      "number of images without watermark: 1500\n",
      "\n",
      "number of images with watermark: 0\n",
      "number of images without watermark: 3000\n",
      "number of images with watermark: 0\n",
      "number of images without watermark: 1500\n",
      "number of images with watermark: 0\n",
      "number of images without watermark: 1500\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# output_folder='D:\\\\escola\\\\tese\\\\ML homework\\\\no_watermark\\\\'\n",
    "output_folder='./artifacts/no_watermark/'\n",
    "\n",
    "save_images(0,cat_names_train,watermark_path_jpeg,image_size,i,output_folder+'train/',1)\n",
    "save_images(0,cat_names_test,watermark_path_jpeg,image_size,i,output_folder+'test/',1)\n",
    "save_images(0,cat_names_val,watermark_path_jpeg,image_size,i,output_folder+'val/',1)\n",
    "print()\n",
    "save_images(0,dog_names_train,watermark_path_jpeg,image_size,i,output_folder+'train/',1)\n",
    "save_images(0,dog_names_test,watermark_path_jpeg,image_size,i,output_folder+'test/',1)\n",
    "save_images(0,dog_names_val,watermark_path_jpeg,image_size,i,output_folder+'val/',1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of images with watermark: 3000\n",
      "number of images without watermark: 0\n",
      "number of images with watermark: 1500\n",
      "number of images without watermark: 0\n",
      "number of images with watermark: 1500\n",
      "number of images without watermark: 0\n",
      "\n",
      "number of images with watermark: 3000\n",
      "number of images without watermark: 0\n",
      "number of images with watermark: 1500\n",
      "number of images without watermark: 0\n",
      "number of images with watermark: 1500\n",
      "number of images without watermark: 0\n"
     ]
    }
   ],
   "source": [
    "# output_folder='D:\\\\escola\\\\tese\\\\ML homework\\\\dataset_supressor\\\\'\n",
    "output_folder='./artifacts/dataset_allwatermark/'\n",
    "save_images(1,cat_names_train,watermark_path_jpeg,image_size,i,output_folder+'train/',1)\n",
    "save_images(1,cat_names_test,watermark_path_jpeg,image_size,i,output_folder+'test/',1)\n",
    "save_images(1,cat_names_val,watermark_path_jpeg,image_size,i,output_folder+'val/',1)\n",
    "print()\n",
    "save_images(1,dog_names_train,watermark_path_jpeg,image_size,i,output_folder+'train/',1)\n",
    "save_images(1,dog_names_test,watermark_path_jpeg,image_size,i,output_folder+'test/',1)\n",
    "save_images(1,dog_names_val,watermark_path_jpeg,image_size,i,output_folder+'val/',1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['./images/cat\\\\cat.1877.jpg',\n",
       " './images/cat\\\\cat.3645.jpg',\n",
       " './images/cat\\\\cat.3844.jpg',\n",
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       " './images/cat\\\\cat.3247.jpg',\n",
       " './images/cat\\\\cat.2888.jpg',\n",
       " './images/cat\\\\cat.838.jpg',\n",
       " './images/cat\\\\cat.5805.jpg',\n",
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       " './images/cat\\\\cat.4068.jpg',\n",
       " './images/cat\\\\cat.1201.jpg',\n",
       " './images/cat\\\\cat.6157.jpg',\n",
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       " './images/cat\\\\cat.11.jpg',\n",
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       " './images/cat\\\\cat.194.jpg',\n",
       " './images/cat\\\\cat.3973.jpg',\n",
       " './images/cat\\\\cat.3894.jpg',\n",
       " './images/cat\\\\cat.561.jpg',\n",
       " './images/cat\\\\cat.2292.jpg',\n",
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       " './images/cat\\\\cat.6031.jpg',\n",
       " './images/cat\\\\cat.5215.jpg',\n",
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       " './images/cat\\\\cat.1218.jpg',\n",
       " './images/cat\\\\cat.13.jpg',\n",
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       " './images/cat\\\\cat.3865.jpg',\n",
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       " './images/cat\\\\cat.3862.jpg',\n",
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       " './images/cat\\\\cat.5310.jpg',\n",
       " './images/cat\\\\cat.5879.jpg',\n",
       " './images/cat\\\\cat.4372.jpg',\n",
       " './images/cat\\\\cat.5806.jpg',\n",
       " './images/cat\\\\cat.674.jpg',\n",
       " './images/cat\\\\cat.3725.jpg',\n",
       " './images/cat\\\\cat.1337.jpg',\n",
       " './images/cat\\\\cat.597.jpg',\n",
       " './images/cat\\\\cat.5758.jpg',\n",
       " './images/cat\\\\cat.5027.jpg',\n",
       " './images/cat\\\\cat.2680.jpg',\n",
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       " './images/cat\\\\cat.213.jpg',\n",
       " './images/cat\\\\cat.2218.jpg',\n",
       " './images/cat\\\\cat.3258.jpg',\n",
       " './images/cat\\\\cat.1205.jpg',\n",
       " './images/cat\\\\cat.3380.jpg',\n",
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       " './images/cat\\\\cat.573.jpg',\n",
       " './images/cat\\\\cat.2995.jpg',\n",
       " './images/cat\\\\cat.416.jpg',\n",
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       " './images/cat\\\\cat.5543.jpg',\n",
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       " './images/cat\\\\cat.2424.jpg',\n",
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       " './images/cat\\\\cat.477.jpg',\n",
       " './images/cat\\\\cat.3790.jpg',\n",
       " './images/cat\\\\cat.913.jpg',\n",
       " './images/cat\\\\cat.1427.jpg',\n",
       " './images/cat\\\\cat.2956.jpg',\n",
       " './images/cat\\\\cat.737.jpg',\n",
       " './images/cat\\\\cat.1795.jpg',\n",
       " './images/cat\\\\cat.3697.jpg',\n",
       " './images/cat\\\\cat.3107.jpg',\n",
       " './images/cat\\\\cat.2436.jpg',\n",
       " './images/cat\\\\cat.355.jpg',\n",
       " './images/cat\\\\cat.2716.jpg',\n",
       " './images/cat\\\\cat.278.jpg',\n",
       " './images/cat\\\\cat.952.jpg',\n",
       " './images/cat\\\\cat.956.jpg',\n",
       " './images/cat\\\\cat.646.jpg',\n",
       " './images/cat\\\\cat.3849.jpg',\n",
       " './images/cat\\\\cat.1898.jpg',\n",
       " './images/cat\\\\cat.5110.jpg',\n",
       " './images/cat\\\\cat.2856.jpg',\n",
       " './images/cat\\\\cat.614.jpg',\n",
       " './images/cat\\\\cat.2245.jpg',\n",
       " './images/cat\\\\cat.3598.jpg',\n",
       " './images/cat\\\\cat.3951.jpg',\n",
       " './images/cat\\\\cat.1197.jpg',\n",
       " './images/cat\\\\cat.510.jpg',\n",
       " './images/cat\\\\cat.6171.jpg',\n",
       " './images/cat\\\\cat.903.jpg',\n",
       " './images/cat\\\\cat.5108.jpg',\n",
       " './images/cat\\\\cat.4298.jpg',\n",
       " './images/cat\\\\cat.645.jpg',\n",
       " './images/cat\\\\cat.1808.jpg',\n",
       " './images/cat\\\\cat.2875.jpg',\n",
       " './images/cat\\\\cat.882.jpg',\n",
       " './images/cat\\\\cat.4919.jpg',\n",
       " './images/cat\\\\cat.3237.jpg',\n",
       " './images/cat\\\\cat.1631.jpg',\n",
       " './images/cat\\\\cat.1771.jpg',\n",
       " './images/cat\\\\cat.483.jpg',\n",
       " './images/cat\\\\cat.4206.jpg',\n",
       " './images/cat\\\\cat.6213.jpg',\n",
       " './images/cat\\\\cat.5253.jpg',\n",
       " './images/cat\\\\cat.365.jpg',\n",
       " './images/cat\\\\cat.2323.jpg',\n",
       " './images/cat\\\\cat.2087.jpg',\n",
       " './images/cat\\\\cat.19.jpg',\n",
       " './images/cat\\\\cat.3125.jpg',\n",
       " './images/cat\\\\cat.3186.jpg',\n",
       " './images/cat\\\\cat.2283.jpg',\n",
       " './images/cat\\\\cat.4720.jpg',\n",
       " './images/cat\\\\cat.3183.jpg',\n",
       " './images/cat\\\\cat.2823.jpg',\n",
       " './images/cat\\\\cat.5142.jpg',\n",
       " './images/cat\\\\cat.5474.jpg',\n",
       " './images/cat\\\\cat.682.jpg',\n",
       " './images/cat\\\\cat.2775.jpg',\n",
       " './images/cat\\\\cat.988.jpg',\n",
       " './images/cat\\\\cat.4649.jpg',\n",
       " './images/cat\\\\cat.2420.jpg',\n",
       " './images/cat\\\\cat.369.jpg',\n",
       " './images/cat\\\\cat.4238.jpg',\n",
       " './images/cat\\\\cat.2984.jpg',\n",
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       " './images/cat\\\\cat.1957.jpg',\n",
       " './images/cat\\\\cat.1460.jpg',\n",
       " './images/cat\\\\cat.4520.jpg',\n",
       " './images/cat\\\\cat.4031.jpg',\n",
       " './images/cat\\\\cat.469.jpg',\n",
       " './images/cat\\\\cat.3379.jpg',\n",
       " './images/cat\\\\cat.5702.jpg',\n",
       " './images/cat\\\\cat.3322.jpg',\n",
       " './images/cat\\\\cat.4705.jpg',\n",
       " './images/cat\\\\cat.2039.jpg',\n",
       " './images/cat\\\\cat.3097.jpg',\n",
       " './images/cat\\\\cat.1334.jpg',\n",
       " './images/cat\\\\cat.4106.jpg',\n",
       " './images/cat\\\\cat.910.jpg',\n",
       " './images/cat\\\\cat.1488.jpg',\n",
       " './images/cat\\\\cat.735.jpg',\n",
       " './images/cat\\\\cat.5578.jpg',\n",
       " './images/cat\\\\cat.2309.jpg',\n",
       " './images/cat\\\\cat.1500.jpg',\n",
       " './images/cat\\\\cat.1832.jpg',\n",
       " './images/cat\\\\cat.3977.jpg',\n",
       " './images/cat\\\\cat.2706.jpg',\n",
       " './images/cat\\\\cat.3163.jpg',\n",
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       " './images/cat\\\\cat.1791.jpg',\n",
       " './images/cat\\\\cat.423.jpg',\n",
       " './images/cat\\\\cat.5839.jpg',\n",
       " './images/cat\\\\cat.5499.jpg',\n",
       " './images/cat\\\\cat.3191.jpg',\n",
       " './images/cat\\\\cat.516.jpg',\n",
       " './images/cat\\\\cat.1228.jpg',\n",
       " './images/cat\\\\cat.5359.jpg',\n",
       " './images/cat\\\\cat.136.jpg',\n",
       " './images/cat\\\\cat.4055.jpg',\n",
       " './images/cat\\\\cat.4120.jpg',\n",
       " './images/cat\\\\cat.5017.jpg',\n",
       " './images/cat\\\\cat.3764.jpg',\n",
       " './images/cat\\\\cat.2678.jpg',\n",
       " './images/cat\\\\cat.2810.jpg',\n",
       " './images/cat\\\\cat.2201.jpg',\n",
       " './images/cat\\\\cat.6052.jpg',\n",
       " './images/cat\\\\cat.604.jpg',\n",
       " './images/cat\\\\cat.18.jpg',\n",
       " './images/cat\\\\cat.1860.jpg',\n",
       " './images/cat\\\\cat.5113.jpg',\n",
       " './images/cat\\\\cat.5669.jpg',\n",
       " './images/cat\\\\cat.2072.jpg',\n",
       " './images/cat\\\\cat.3196.jpg',\n",
       " './images/cat\\\\cat.740.jpg',\n",
       " './images/cat\\\\cat.4715.jpg',\n",
       " './images/cat\\\\cat.4916.jpg',\n",
       " './images/cat\\\\cat.2816.jpg',\n",
       " './images/cat\\\\cat.4281.jpg',\n",
       " './images/cat\\\\cat.926.jpg',\n",
       " './images/cat\\\\cat.5613.jpg',\n",
       " './images/cat\\\\cat.5619.jpg',\n",
       " './images/cat\\\\cat.2379.jpg',\n",
       " './images/cat\\\\cat.3558.jpg',\n",
       " './images/cat\\\\cat.2337.jpg',\n",
       " './images/cat\\\\cat.609.jpg',\n",
       " './images/cat\\\\cat.1159.jpg',\n",
       " './images/cat\\\\cat.3504.jpg',\n",
       " './images/cat\\\\cat.2104.jpg',\n",
       " './images/cat\\\\cat.1759.jpg',\n",
       " './images/cat\\\\cat.4937.jpg',\n",
       " './images/cat\\\\cat.3344.jpg',\n",
       " './images/cat\\\\cat.5679.jpg',\n",
       " './images/cat\\\\cat.2829.jpg',\n",
       " './images/cat\\\\cat.1754.jpg',\n",
       " './images/cat\\\\cat.5328.jpg',\n",
       " './images/cat\\\\cat.1767.jpg',\n",
       " './images/cat\\\\cat.633.jpg',\n",
       " './images/cat\\\\cat.4648.jpg',\n",
       " './images/cat\\\\cat.5011.jpg',\n",
       " './images/cat\\\\cat.5745.jpg',\n",
       " './images/cat\\\\cat.478.jpg',\n",
       " './images/cat\\\\cat.312.jpg',\n",
       " './images/cat\\\\cat.3507.jpg',\n",
       " './images/cat\\\\cat.5753.jpg',\n",
       " './images/cat\\\\cat.1669.jpg',\n",
       " './images/cat\\\\cat.254.jpg',\n",
       " './images/cat\\\\cat.1302.jpg',\n",
       " './images/cat\\\\cat.5895.jpg',\n",
       " './images/cat\\\\cat.4053.jpg',\n",
       " './images/cat\\\\cat.2911.jpg',\n",
       " './images/cat\\\\cat.4929.jpg',\n",
       " './images/cat\\\\cat.3546.jpg',\n",
       " './images/cat\\\\cat.2621.jpg',\n",
       " './images/cat\\\\cat.969.jpg',\n",
       " './images/cat\\\\cat.3548.jpg',\n",
       " './images/cat\\\\cat.2921.jpg',\n",
       " './images/cat\\\\cat.718.jpg',\n",
       " './images/cat\\\\cat.4435.jpg',\n",
       " './images/cat\\\\cat.1939.jpg',\n",
       " './images/cat\\\\cat.1280.jpg',\n",
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       " ...]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_names_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
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       " ...]"
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     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "no_mark_names = [i.split('\\\\')[-1] for i in glob('./artifacts/no_watermark/test/cat*')]\n",
    "no_mark_names"
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  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
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       " 'cat.4760.jpg',\n",
       " ...]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yes_mark_names = [i.split('\\\\')[-1] for i in glob('./artifacts/dataset_allwatermark/test/cat*')]\n",
    "yes_mark_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1500"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(np.where(np.array(no_mark_names) == np.array(yes_mark_names))[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# create train, val, test datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def create_datasets(folder,files):\n",
    "    dataset=[]\n",
    "    for img in files:\n",
    "        if img.find('cat')!=-1:\n",
    "            image = np.asarray(Image.open(folder+img))\n",
    "            dataset.append([image,0])\n",
    "\n",
    "        elif img.find('dog')!=-1:\n",
    "            image = np.asarray(Image.open(folder+img))\n",
    "            dataset.append([image,1])\n",
    "\n",
    "    return dataset\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## confounder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "from os import listdir\n",
    "from os.path import isfile, join"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# folder_test='D:\\\\escola\\\\tese\\\\ML homework\\\\dataset_confounder\\\\test\\\\'\n",
    "folder_test='./artifacts/dataset_confounder/test/'\n",
    "# folder_train='D:\\\\escola\\\\tese\\\\ML homework\\\\dataset_confounder\\\\train\\\\'\n",
    "folder_train='./artifacts/dataset_confounder/train/'\n",
    "# folder_val='D:\\\\escola\\\\tese\\\\ML homework\\\\dataset_confounder\\\\val\\\\'\n",
    "folder_val='./artifacts/dataset_confounder/val/'\n",
    "\n",
    "files_train = [f for f in listdir(folder_train) if isfile(join(folder_train, f))]\n",
    "files_test = [f for f in listdir(folder_test) if isfile(join(folder_test, f))]\n",
    "files_val = [f for f in listdir(folder_val) if isfile(join(folder_val, f))]\n",
    "\n",
    "confounder_train=create_datasets(folder_train,files_train)\n",
    "confounder_test=create_datasets(folder_test,files_test)\n",
    "confounder_val=create_datasets(folder_val,files_val)\n",
    "\n",
    "with open('confounder_train128.pkl', 'wb') as f:\n",
    "    pickle.dump(confounder_train, f)\n",
    "\n",
    "with open('confounder_test128.pkl', 'wb') as f:\n",
    "    pickle.dump(confounder_test, f)\n",
    "\n",
    "with open('confounder_val128.pkl', 'wb') as f:\n",
    "    pickle.dump(confounder_val, f)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## supressor\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# folder_test='D:\\\\escola\\\\tese\\\\ML homework\\\\dataset_supressor\\\\test\\\\'\n",
    "# folder_train='D:\\\\escola\\\\tese\\\\ML homework\\\\dataset_supressor\\\\train\\\\'\n",
    "# folder_val='D:\\\\escola\\\\tese\\\\ML homework\\\\dataset_supressor\\\\val\\\\'\n",
    "\n",
    "folder_test='./artifacts/dataset_supressor/test/'\n",
    "folder_train='./artifacts/dataset_supressor/train/'\n",
    "folder_val='./artifacts/dataset_supressor/val/'\n",
    "\n",
    "files_train = [f for f in listdir(folder_train) if isfile(join(folder_train, f))]\n",
    "files_test = [f for f in listdir(folder_test) if isfile(join(folder_test, f))]\n",
    "files_val = [f for f in listdir(folder_val) if isfile(join(folder_val, f))]\n",
    "\n",
    "supressor_train=create_datasets(folder_train,files_train)\n",
    "supressor_validation=create_datasets(folder_val,files_val)\n",
    "supressor_test=create_datasets(folder_test,files_test)\n",
    "\n",
    "\n",
    "with open('suppressor_train128.pkl', 'wb') as f:\n",
    "    pickle.dump(supressor_train, f)\n",
    "\n",
    "with open('suppressor_validation128.pkl', 'wb') as f:\n",
    "    pickle.dump(supressor_validation, f)\n",
    "\n",
    "with open('suppressor_test128.pkl', 'wb') as f:\n",
    "    pickle.dump(supressor_test, f)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## nothing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# folder_test='D:\\\\escola\\\\tese\\\\ML homework\\\\no_watermark\\\\test\\\\'\n",
    "# folder_train='D:\\\\escola\\\\tese\\\\ML homework\\\\no_watermark\\\\train\\\\'\n",
    "# folder_val='D:\\\\escola\\\\tese\\\\ML homework\\\\no_watermark\\\\val\\\\'\n",
    "\n",
    "folder_test='./artifacts/no_watermark/test/'\n",
    "folder_train='./artifacts/no_watermark/train/'\n",
    "folder_val='./artifacts/no_watermark/val/'\n",
    "\n",
    "files_train = [f for f in listdir(folder_train) if isfile(join(folder_train, f))]\n",
    "files_test = [f for f in listdir(folder_test) if isfile(join(folder_test, f))]\n",
    "files_val = [f for f in listdir(folder_val) if isfile(join(folder_val, f))]\n",
    "\n",
    "no_mark_train=create_datasets(folder_train,files_train)\n",
    "no_mark_validation=create_datasets(folder_val,files_val)\n",
    "no_mark_test=create_datasets(folder_test,files_test)\n",
    "\n",
    "with open('no_mark_train128.pkl', 'wb') as f:\n",
    "    pickle.dump(no_mark_train, f)\n",
    "\n",
    "with open('no_mark_validation128.pkl', 'wb') as f:\n",
    "    pickle.dump(no_mark_validation, f)\n",
    "\n",
    "with open('no_mark_test128.pkl', 'wb') as f:\n",
    "    pickle.dump(no_mark_test, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## All marks\n",
    "For the first experiment of comparing with vs without marks, equivalent to no_mark test set... but with a watermark in every sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "folder_test='./artifacts/dataset_allwatermark/test/'\n",
    "\n",
    "files_test = [f for f in listdir(folder_test) if isfile(join(folder_test, f))]\n",
    "\n",
    "yes_mark_test=create_datasets(folder_test,files_test)\n",
    "\n",
    "with open('mark_all128.pkl', 'wb') as f:\n",
    "    pickle.dump(yes_mark_test, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each of the datasets:\n",
    "+ the training set has 3000 cats and 3000 dog images.\n",
    "+ the test set has 1500 cats and 1500 dogs images.\n",
    "+ the validation set has 1500 cats and 1500 dogs images.\n",
    "\n",
    "The images are 128x128.\n",
    "\n",
    "The watermark intensity is 0.8. \n",
    "\n",
    "The confounder sets 80% of the images that are dogs have a watermark and only 20% of the cat images have the watermark.\n",
    "\n",
    "The surpressor sets have 50% images with watermark for both cats and dogs.\n",
    "\n",
    "The no_mark sets have no watermarks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_images(percentage_watermark,list_files,watermark_path,image_size,intensity_watermak,output_folder,white_bool):\n",
    "    with_watermark=random.sample(list_files,k=int(len(list_files)*percentage_watermark))\n",
    "    Path(output_folder).mkdir(parents=True, exist_ok=True)\n",
    "    n_water=0\n",
    "    n_n_water=0\n",
    "\n",
    "    data = np.zeros((len(list_files), 128, 128, 3))\n",
    "\n",
    "    for i, image in enumerate(list_files):\n",
    "        if image in with_watermark:\n",
    "            out_im=add_watermark(image,watermark_path,intensity_watermak,image_size,white_bool)\n",
    "            n_water+=1\n",
    "        else:\n",
    "            out_im=Image.open(image)\n",
    "            out_im = out_im.resize(image_size)\n",
    "            out_im=np.array(out_im)\n",
    "            n_n_water+=1\n",
    "            \n",
    "        n=image.rfind('\\\\')\n",
    "        plt.imsave(output_folder+image[n+1:],out_im)\n",
    "        data[i] = out_im\n",
    "\n",
    "    print('number of images with watermark:',n_water)\n",
    "    print('number of images without watermark:',n_n_water)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total dogs and cats: 6000 6000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\clark01\\AppData\\Local\\Temp\\1\\ipykernel_10476\\57238992.py:14: DeprecationWarning: Calling nonzero on 0d arrays is deprecated, as it behaves surprisingly. Use `atleast_1d(cond).nonzero()` if the old behavior was intended. If the context of this warning is of the form `arr[nonzero(cond)]`, just use `arr[cond]`.\n",
      "  inds = list(np.delete(inds, np.where(train_inds==inds)))\n",
      "C:\\Users\\clark01\\AppData\\Local\\Temp\\1\\ipykernel_10476\\57238992.py:17: DeprecationWarning: Calling nonzero on 0d arrays is deprecated, as it behaves surprisingly. Use `atleast_1d(cond).nonzero()` if the old behavior was intended. If the context of this warning is of the form `arr[nonzero(cond)]`, just use `arr[cond]`.\n",
      "  inds = list(np.delete(inds, np.where(val_inds==inds)))\n"
     ]
    }
   ],
   "source": [
    "from glob import glob\n",
    "# random.sample(cat_names,k=6000)\n",
    "cat_paths = glob('./images/cat/*')\n",
    "dog_paths = glob('./images/dog/*')\n",
    "print('total dogs and cats:', len(dog_paths), len(cat_paths))\n",
    "\n",
    "\n",
    "N = 60\n",
    "\n",
    "inds = list(range(N))\n",
    "\n",
    "train_inds = random.sample(inds, k=int(N/2))\n",
    "\n",
    "inds = list(np.delete(inds, np.where(train_inds==inds)))\n",
    "\n",
    "val_inds = random.sample(inds, k=int(N/4))\n",
    "inds = list(np.delete(inds, np.where(val_inds==inds)))\n",
    "\n",
    "test_inds = random.sample(inds, k=int(N/4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_names_train = list(np.array(cat_paths)[train_inds])\n",
    "cat_names_val = list(np.array(cat_paths)[val_inds])\n",
    "cat_names_test = list(np.array(cat_paths)[test_inds])\n",
    "\n",
    "dog_names_train = list(np.array(dog_paths)[train_inds])\n",
    "dog_names_val = list(np.array(dog_paths)[val_inds])\n",
    "dog_names_test = list(np.array(dog_paths)[test_inds])\n",
    "\n",
    "# folder='D:\\\\escola\\\\tese\\\\ML homework\\\\'\n",
    "folder=''\n",
    "watermark_path_jpeg=folder+'watermark banner.jpg'\n",
    "image_size=(128,128)\n",
    "intensity=0.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of images with watermark: 15\n",
      "number of images without watermark: 15\n"
     ]
    }
   ],
   "source": [
    "# output_folder='D:\\\\escola\\\\tese\\\\ML homework\\\\dataset_supressor\\\\'\n",
    "output_folder='./artifacts/split_1/dataset_supressor/'\n",
    "save_images(0.5,cat_names_train,watermark_path_jpeg,image_size,intensity,output_folder+'train/',1)\n",
    "# save_images(0.5,cat_names_test,watermark_path_jpeg,image_size,intensity,output_folder+'test/',1)\n",
    "# save_images(0.5,cat_names_val,watermark_path_jpeg,image_size,intensity,output_folder+'val/',1)\n",
    "# print()\n",
    "# save_images(0.5,dog_names_train,watermark_path_jpeg,image_size,intensity,output_folder+'train/',1)\n",
    "# save_images(0.5,dog_names_test,watermark_path_jpeg,image_size,intensity,output_folder+'test/',1)\n",
    "# save_images(0.5,dog_names_val,watermark_path_jpeg,image_size,intensity,output_folder+'val/',1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3000"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(cat_names_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total dogs and cats: 6000 6000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\clark01\\AppData\\Local\\Temp\\1\\ipykernel_12328\\1268904633.py:17: DeprecationWarning: Calling nonzero on 0d arrays is deprecated, as it behaves surprisingly. Use `atleast_1d(cond).nonzero()` if the old behavior was intended. If the context of this warning is of the form `arr[nonzero(cond)]`, just use `arr[cond]`.\n",
      "  inds = list(np.delete(inds, np.where(train_inds==inds)))\n",
      "C:\\Users\\clark01\\AppData\\Local\\Temp\\1\\ipykernel_12328\\1268904633.py:20: DeprecationWarning: Calling nonzero on 0d arrays is deprecated, as it behaves surprisingly. Use `atleast_1d(cond).nonzero()` if the old behavior was intended. If the context of this warning is of the form `arr[nonzero(cond)]`, just use `arr[cond]`.\n",
      "  inds = list(np.delete(inds, np.where(val_inds==inds)))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of images with watermark: 1500\n",
      "number of images without watermark: 1500\n",
      "number of images with watermark: 750\n",
      "number of images without watermark: 750\n",
      "number of images with watermark: 750\n",
      "number of images without watermark: 750\n",
      "number of images with watermark: 1500\n",
      "number of images without watermark: 1500\n",
      "number of images with watermark: 750\n",
      "number of images without watermark: 750\n",
      "number of images with watermark: 750\n",
      "number of images without watermark: 750\n",
      "number of images with watermark: 600\n",
      "number of images without watermark: 2400\n",
      "number of images with watermark: 300\n",
      "number of images without watermark: 1200\n",
      "number of images with watermark: 300\n",
      "number of images without watermark: 1200\n",
      "\n",
      "number of images with watermark: 2400\n",
      "number of images without watermark: 600\n",
      "number of images with watermark: 1200\n",
      "number of images without watermark: 300\n",
      "number of images with watermark: 1200\n",
      "number of images without watermark: 300\n",
      "number of images with watermark: 0\n",
      "number of images without watermark: 3000\n",
      "number of images with watermark: 0\n",
      "number of images without watermark: 1500\n",
      "number of images with watermark: 0\n",
      "number of images without watermark: 1500\n",
      "\n",
      "number of images with watermark: 0\n",
      "number of images without watermark: 3000\n",
      "number of images with watermark: 0\n",
      "number of images without watermark: 1500\n",
      "number of images with watermark: 0\n",
      "number of images without watermark: 1500\n",
      "number of images with watermark: 1500\n",
      "number of images without watermark: 1500\n",
      "number of images with watermark: 750\n",
      "number of images without watermark: 750\n",
      "number of images with watermark: 750\n",
      "number of images without watermark: 750\n",
      "number of images with watermark: 1500\n",
      "number of images without watermark: 1500\n",
      "number of images with watermark: 750\n",
      "number of images without watermark: 750\n",
      "number of images with watermark: 750\n",
      "number of images without watermark: 750\n",
      "number of images with watermark: 600\n",
      "number of images without watermark: 2400\n",
      "number of images with watermark: 300\n",
      "number of images without watermark: 1200\n",
      "number of images with watermark: 300\n",
      "number of images without watermark: 1200\n",
      "\n",
      "number of images with watermark: 2400\n",
      "number of images without watermark: 600\n",
      "number of images with watermark: 1200\n",
      "number of images without watermark: 300\n",
      "number of images with watermark: 1200\n",
      "number of images without watermark: 300\n",
      "number of images with watermark: 0\n",
      "number of images without watermark: 3000\n",
      "number of images with watermark: 0\n",
      "number of images without watermark: 1500\n",
      "number of images with watermark: 0\n",
      "number of images without watermark: 1500\n",
      "\n",
      "number of images with watermark: 0\n",
      "number of images without watermark: 3000\n",
      "number of images with watermark: 0\n",
      "number of images without watermark: 1500\n",
      "number of images with watermark: 0\n",
      "number of images without watermark: 1500\n",
      "number of images with watermark: 1500\n",
      "number of images without watermark: 1500\n",
      "number of images with watermark: 750\n",
      "number of images without watermark: 750\n",
      "number of images with watermark: 750\n",
      "number of images without watermark: 750\n",
      "number of images with watermark: 1500\n",
      "number of images without watermark: 1500\n",
      "number of images with watermark: 750\n",
      "number of images without watermark: 750\n",
      "number of images with watermark: 750\n",
      "number of images without watermark: 750\n",
      "number of images with watermark: 600\n",
      "number of images without watermark: 2400\n",
      "number of images with watermark: 300\n",
      "number of images without watermark: 1200\n",
      "number of images with watermark: 300\n",
      "number of images without watermark: 1200\n",
      "\n",
      "number of images with watermark: 2400\n",
      "number of images without watermark: 600\n",
      "number of images with watermark: 1200\n",
      "number of images without watermark: 300\n",
      "number of images with watermark: 1200\n",
      "number of images without watermark: 300\n",
      "number of images with watermark: 0\n",
      "number of images without watermark: 3000\n",
      "number of images with watermark: 0\n",
      "number of images without watermark: 1500\n",
      "number of images with watermark: 0\n",
      "number of images without watermark: 1500\n",
      "\n",
      "number of images with watermark: 0\n",
      "number of images without watermark: 3000\n",
      "number of images with watermark: 0\n",
      "number of images without watermark: 1500\n",
      "number of images with watermark: 0\n",
      "number of images without watermark: 1500\n",
      "number of images with watermark: 1500\n",
      "number of images without watermark: 1500\n",
      "number of images with watermark: 750\n",
      "number of images without watermark: 750\n",
      "number of images with watermark: 750\n",
      "number of images without watermark: 750\n",
      "number of images with watermark: 1500\n",
      "number of images without watermark: 1500\n",
      "number of images with watermark: 750\n",
      "number of images without watermark: 750\n",
      "number of images with watermark: 750\n",
      "number of images without watermark: 750\n",
      "number of images with watermark: 600\n",
      "number of images without watermark: 2400\n",
      "number of images with watermark: 300\n",
      "number of images without watermark: 1200\n",
      "number of images with watermark: 300\n",
      "number of images without watermark: 1200\n",
      "\n",
      "number of images with watermark: 2400\n",
      "number of images without watermark: 600\n",
      "number of images with watermark: 1200\n",
      "number of images without watermark: 300\n",
      "number of images with watermark: 1200\n",
      "number of images without watermark: 300\n",
      "number of images with watermark: 0\n",
      "number of images without watermark: 3000\n",
      "number of images with watermark: 0\n",
      "number of images without watermark: 1500\n",
      "number of images with watermark: 0\n",
      "number of images without watermark: 1500\n",
      "\n",
      "number of images with watermark: 0\n",
      "number of images without watermark: 3000\n",
      "number of images with watermark: 0\n",
      "number of images without watermark: 1500\n",
      "number of images with watermark: 0\n",
      "number of images without watermark: 1500\n",
      "number of images with watermark: 1500\n",
      "number of images without watermark: 1500\n",
      "number of images with watermark: 750\n",
      "number of images without watermark: 750\n",
      "number of images with watermark: 750\n",
      "number of images without watermark: 750\n",
      "number of images with watermark: 1500\n",
      "number of images without watermark: 1500\n",
      "number of images with watermark: 750\n",
      "number of images without watermark: 750\n",
      "number of images with watermark: 750\n",
      "number of images without watermark: 750\n",
      "number of images with watermark: 600\n",
      "number of images without watermark: 2400\n",
      "number of images with watermark: 300\n",
      "number of images without watermark: 1200\n",
      "number of images with watermark: 300\n",
      "number of images without watermark: 1200\n",
      "\n",
      "number of images with watermark: 2400\n",
      "number of images without watermark: 600\n",
      "number of images with watermark: 1200\n",
      "number of images without watermark: 300\n",
      "number of images with watermark: 1200\n",
      "number of images without watermark: 300\n",
      "number of images with watermark: 0\n",
      "number of images without watermark: 3000\n",
      "number of images with watermark: 0\n",
      "number of images without watermark: 1500\n",
      "number of images with watermark: 0\n",
      "number of images without watermark: 1500\n",
      "\n",
      "number of images with watermark: 0\n",
      "number of images without watermark: 3000\n",
      "number of images with watermark: 0\n",
      "number of images without watermark: 1500\n",
      "number of images with watermark: 0\n",
      "number of images without watermark: 1500\n"
     ]
    }
   ],
   "source": [
    "from glob import glob\n",
    "# random.sample(cat_names,k=6000)\n",
    "cat_paths = glob('./images/cat/*')\n",
    "dog_paths = glob('./images/dog/*')\n",
    "print('total dogs and cats:', len(dog_paths), len(cat_paths))\n",
    "\n",
    "folder=''\n",
    "watermark_path_jpeg=folder+'watermark banner.jpg'\n",
    "image_size=(128,128)\n",
    "intensity=0.8\n",
    "\n",
    "for i in range(5):\n",
    "    N = 6000\n",
    "    inds = list(range(N))\n",
    "    \n",
    "    train_inds = random.sample(inds, k=int(N/2))\n",
    "    inds = list(np.delete(inds, np.where(train_inds==inds)))\n",
    "    \n",
    "    val_inds = random.sample(inds, k=int(N/4))\n",
    "    inds = list(np.delete(inds, np.where(val_inds==inds)))\n",
    "    \n",
    "    test_inds = random.sample(inds, k=int(N/4))\n",
    "\n",
    "    cat_names_train = list(np.array(cat_paths)[train_inds])\n",
    "    cat_names_val = list(np.array(cat_paths)[val_inds])\n",
    "    cat_names_test = list(np.array(cat_paths)[test_inds])\n",
    "    \n",
    "    dog_names_train = list(np.array(dog_paths)[train_inds])\n",
    "    dog_names_val = list(np.array(dog_paths)[val_inds])\n",
    "    dog_names_test = list(np.array(dog_paths)[test_inds])\n",
    "\n",
    "    output_folder=f'./artifacts/split_{i}/dataset_suppressor/'\n",
    "    save_images(0.5,cat_names_train,watermark_path_jpeg,image_size,intensity,output_folder+'train/',1)\n",
    "    save_images(0.5,cat_names_test,watermark_path_jpeg,image_size,intensity,output_folder+'test/',1)\n",
    "    save_images(0.5,cat_names_val,watermark_path_jpeg,image_size,intensity,output_folder+'val/',1)\n",
    "\n",
    "    save_images(0.5,dog_names_train,watermark_path_jpeg,image_size,intensity,output_folder+'train/',1)\n",
    "    save_images(0.5,dog_names_test,watermark_path_jpeg,image_size,intensity,output_folder+'test/',1)\n",
    "    save_images(0.5,dog_names_val,watermark_path_jpeg,image_size,intensity,output_folder+'val/',1)\n",
    "\n",
    "    output_folder=f'./artifacts/split_{i}/dataset_confounder/'\n",
    "    \n",
    "    save_images(0.2,cat_names_train,watermark_path_jpeg,image_size,intensity,output_folder+'train/',1)\n",
    "    save_images(0.2,cat_names_test,watermark_path_jpeg,image_size,intensity,output_folder+'test/',1)\n",
    "    save_images(0.2,cat_names_val,watermark_path_jpeg,image_size,intensity,output_folder+'val/',1)\n",
    "    print()\n",
    "    save_images(0.8,dog_names_train,watermark_path_jpeg,image_size,intensity,output_folder+'train/',1)\n",
    "    save_images(0.8,dog_names_test,watermark_path_jpeg,image_size,intensity,output_folder+'test/',1)\n",
    "    save_images(0.8,dog_names_val,watermark_path_jpeg,image_size,intensity,output_folder+'val/',1)\n",
    "    \n",
    "    output_folder=f'./artifacts/split_{i}/no_watermark/'\n",
    "    \n",
    "    save_images(0,cat_names_train,watermark_path_jpeg,image_size,intensity,output_folder+'train/',1)\n",
    "    save_images(0,cat_names_test,watermark_path_jpeg,image_size,intensity,output_folder+'test/',1)\n",
    "    save_images(0,cat_names_val,watermark_path_jpeg,image_size,intensity,output_folder+'val/',1)\n",
    "    print()\n",
    "    save_images(0,dog_names_train,watermark_path_jpeg,image_size,intensity,output_folder+'train/',1)\n",
    "    save_images(0,dog_names_test,watermark_path_jpeg,image_size,intensity,output_folder+'test/',1)\n",
    "    save_images(0,dog_names_val,watermark_path_jpeg,image_size,intensity,output_folder+'val/',1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_datasets(folder,files):\n",
    "    dataset=[]\n",
    "    for img in files:\n",
    "        if img.find('cat')!=-1:\n",
    "            image = np.asarray(Image.open(folder+img))\n",
    "            dataset.append([image,0])\n",
    "\n",
    "        elif img.find('dog')!=-1:\n",
    "            image = np.asarray(Image.open(folder+img))\n",
    "            dataset.append([image,1])\n",
    "\n",
    "    return dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(5):\n",
    "    for dataset in ['dataset_confounder', 'dataset_suppressor', 'no_watermark']:\n",
    "        # folder_test='D:\\\\escola\\\\tese\\\\ML homework\\\\dataset_confounder\\\\test\\\\'\n",
    "        folder_test=f'./artifacts/split_{i}/{dataset}/test/'\n",
    "        # folder_train='D:\\\\escola\\\\tese\\\\ML homework\\\\dataset_confounder\\\\train\\\\'\n",
    "        folder_train=f'./artifacts/split_{i}/{dataset}/train/'\n",
    "        # folder_val='D:\\\\escola\\\\tese\\\\ML homework\\\\dataset_confounder\\\\val\\\\'\n",
    "        folder_val=f'./artifacts/split_{i}/{dataset}/val/'\n",
    "        \n",
    "        files_train = [f for f in listdir(folder_train) if isfile(join(folder_train, f))]\n",
    "        files_test = [f for f in listdir(folder_test) if isfile(join(folder_test, f))]\n",
    "        files_val = [f for f in listdir(folder_val) if isfile(join(folder_val, f))]\n",
    "        \n",
    "        confounder_train=create_datasets(folder_train,files_train)\n",
    "        confounder_test=create_datasets(folder_test,files_test)\n",
    "        confounder_val=create_datasets(folder_val,files_val)\n",
    "        \n",
    "        with open(f'{dataset}_train128_{i}.pkl', 'wb') as f:\n",
    "            pickle.dump(confounder_train, f)\n",
    "        \n",
    "        with open(f'{dataset}_test128_{i}.pkl', 'wb') as f:\n",
    "            pickle.dump(confounder_test, f)\n",
    "        \n",
    "        with open(f'{dataset}_val128_{i}.pkl', 'wb') as f:\n",
    "            pickle.dump(confounder_val, f)"
   ]
  }
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