PSNR & SSIM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e2294c45",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4e1bd17f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['tune_batches', 'tune_epochs', 'tune_lr']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "root = \"results\"\n",
    "dir_type = \"colored\"\n",
    "path_to_hypes = os.path.join(root, dir_type)\n",
    "tunes = os.listdir(path_to_hyperparameters)\n",
    "tunes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8d54b2ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'tune_batches': ['ncd_pix2pix_batch_1',\n",
       "  'ncd_pix2pix_batch_16',\n",
       "  'ncd_pix2pix_batch_2',\n",
       "  'ncd_pix2pix_batch_32',\n",
       "  'ncd_pix2pix_batch_4',\n",
       "  'ncd_pix2pix_batch_64',\n",
       "  'ncd_pix2pix_batch_8'],\n",
       " 'tune_epochs': ['ncd_pix2pix_epochs_2_8',\n",
       "  'ncd_pix2pix_epochs_4_6',\n",
       "  'ncd_pix2pix_epochs_5_5',\n",
       "  'ncd_pix2pix_epochs_6_4',\n",
       "  'ncd_pix2pix_epochs_8_2'],\n",
       " 'tune_lr': ['ncd_pix2pix_lr_0.0001',\n",
       "  'ncd_pix2pix_lr_0.0002',\n",
       "  'ncd_pix2pix_lr_0.0005',\n",
       "  'ncd_pix2pix_lr_2e-05']}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tunes_dict = {}\n",
    "for t in tunes:\n",
    "    tunes_dict[t] = os.listdir(os.path.join(path_to_hypes, t))\n",
    "\n",
    "tunes_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8b162e01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'results\\\\colored\\\\tune_batches\\\\ncd_pix2pix_batch_1\\\\test_latest\\\\images'"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imgs_dir = os.path.join(path_to_hypes, 'tune_batches', tunes_dict['tune_batches'][0], 'test_latest', 'images')\n",
    "imgs = os.listdir(imgs_dir)\n",
    "imgs_dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "1654f3dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'psnr': [18.798, 3.885], 'ssim': [0.861, 0.049]}\n",
      "{'psnr': [18.193, 2.94], 'ssim': [0.834, 0.052]}\n",
      "{'psnr': [18.408, 2.625], 'ssim': [0.838, 0.048]}\n",
      "{'psnr': [18.216, 2.383], 'ssim': [0.829, 0.049]}\n",
      "{'psnr': [18.295, 2.234], 'ssim': [0.83, 0.047]}\n",
      "{'psnr': [18.243, 2.107], 'ssim': [0.825, 0.048]}\n",
      "{'psnr': [18.261, 2.031], 'ssim': [0.825, 0.046]}\n",
      "{'psnr': [18.497, 4.085], 'ssim': [0.856, 0.048]}\n",
      "{'psnr': [18.568, 3.857], 'ssim': [0.857, 0.048]}\n",
      "{'psnr': [18.561, 3.745], 'ssim': [0.857, 0.047]}\n",
      "{'psnr': [18.724, 3.762], 'ssim': [0.858, 0.047]}\n",
      "{'psnr': [18.75, 3.819], 'ssim': [0.858, 0.047]}\n",
      "{'psnr': [18.506, 3.794], 'ssim': [0.846, 0.047]}\n",
      "{'psnr': [18.738, 3.565], 'ssim': [0.849, 0.047]}\n",
      "{'psnr': [18.711, 3.854], 'ssim': [0.856, 0.05]}\n",
      "{'psnr': [18.964, 3.775], 'ssim': [0.849, 0.051]}\n"
     ]
    }
   ],
   "source": [
    "from skimage.metrics import peak_signal_noise_ratio, structural_similarity\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "import statistics\n",
    "\n",
    "# create a dictionary\n",
    "hypes = {}\n",
    "# for each hyperparameter (i.e. tune_batches)\n",
    "for t in tunes:\n",
    "    # create PSNR list & SSIM list\n",
    "    PSNR = []\n",
    "    SSIM = []\n",
    "    # for each run (i.e. batch_1)\n",
    "    for experiment in tunes_dict[t]:\n",
    "        # get list of all images (i.e. Apple10)\n",
    "        path = os.path.join(path_to_hypes, t, experiment, 'test_latest', 'images')\n",
    "        images = os.listdir(path)\n",
    "        # get the unique names after splitting them\n",
    "        unique_names = set([i.split('_')[0] for i in images])        \n",
    "        # for each unique set of images\n",
    "        for name in unique_names:\n",
    "            # append fake_B_rgb.png to get fake_img\n",
    "            assert os.path.isfile(os.path.join(path, name+'_fake_B_rgb.png'))\n",
    "            fake_img = cv2.imread(os.path.join(path, name+'_fake_B_rgb.png'))\n",
    "            \n",
    "            # append real_B_rgb.png to get real_img\n",
    "            assert os.path.isfile(os.path.join(path, name+'_real_B_rgb.png'))\n",
    "            real_img = cv2.imread(os.path.join(path, name+'_real_B_rgb.png'))\n",
    "            \n",
    "            # calculate PSNR\n",
    "            psnr = peak_signal_noise_ratio(real_img, fake_img)\n",
    "            # calculate SSIM\n",
    "            ssim =  structural_similarity(real_img, fake_img, channel_axis=2)\n",
    "            # append to respective lists\n",
    "            PSNR.append(psnr)\n",
    "            SSIM.append(ssim)\n",
    "        # average values & get std dev\n",
    "        stats_psnr = [round(statistics.mean(PSNR), 3), round(statistics.stdev(PSNR),3)]\n",
    "        stats_ssim = [round(statistics.mean(SSIM), 3), round(statistics.stdev(SSIM),3)]\n",
    "#         stats_psnr = str(round(statistics.mean(PSNR), 3)) + '/' + str(round(statistics.stdev(PSNR),3))\n",
    "#         stats_ssim = str(round(statistics.mean(SSIM), 3)) + '/' + str(round(statistics.stdev(SSIM),3))\n",
    "        \n",
    "        # add to dictionary value specific to each run\n",
    "        hypes[experiment] = {'psnr': stats_psnr, 'ssim': stats_ssim}\n",
    "        print(hypes[experiment])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "777154a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PSNR value: 11.605895230834676\n",
      "SSIM value: 0.6840249614455058\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# # for i in range(3):\n",
    "# #     plt.axis(\"off\")\n",
    "# #     image = cv2.imread(os.path.join(os.path.join(path_to_hypes, 'tune_batches', tunes_dict['tune_batches'][0], 'test_latest', 'images'), imgs[i]))\n",
    "# #     plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))\n",
    "# #     plt.show()\n",
    "# image_real = cv2.imread(os.path.join(os.path.join(path_to_hypes, 'tune_batches', tunes_dict['tune_batches'][0], 'test_latest', 'images'), imgs[0]))\n",
    "\n",
    "# image_fake = cv2.imread(os.path.join(os.path.join(path_to_hypes, 'tune_batches', tunes_dict['tune_batches'][0], 'test_latest', 'images'), imgs[3]))\n",
    "\n",
    "# print('PSNR value:', peak_signal_noise_ratio(image_real, image_fake))\n",
    "# print('SSIM value:', structural_similarity(image_real, image_fake, channel_axis=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "c38cbe99",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import pandas as pd\n",
    "# string_hypes = pd.DataFrame(hypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "8c9f26ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for t in tunes:\n",
    "#     # create PSNR list & SSIM list\n",
    "#     # for each run (i.e. batch_1)\n",
    "#     psnrs = []\n",
    "#     ssims = []\n",
    "#     max_psnr = ('', -1)\n",
    "#     max_ssim = ('', -1)\n",
    "    \n",
    "#     min_psnr = ('', 1000)\n",
    "#     min_ssim = ('', 1000)\n",
    "# #     plt.figure()\n",
    "#     fig, (ax1, ax2) = plt.subplots(1, 2)\n",
    "#     fig.suptitle(t.split('_')[1])\n",
    "#     for experiment in tunes_dict[t]:\n",
    "#         if hypes[experiment]['psnr'][0] > max_psnr[1]:\n",
    "#             max_psnr = (experiment, hypes[experiment]['psnr'][0])\n",
    "            \n",
    "            \n",
    "#         if hypes[experiment]['ssim'][0] > max_ssim[1]:\n",
    "#             max_ssim = (experiment, hypes[experiment]['ssim'][0])\n",
    "            \n",
    "            \n",
    "#         if hypes[experiment]['psnr'][0] < min_psnr[1]:\n",
    "#             min_psnr = (experiment, hypes[experiment]['psnr'][0])\n",
    "\n",
    "#         if hypes[experiment]['ssim'][0] < min_ssim[1]:\n",
    "#             min_ssim = (experiment, hypes[experiment]['ssim'][0])\n",
    "            \n",
    "#         psnrs.append((experiment, hypes[experiment]['psnr'][0]))\n",
    "#         ssims.append((experiment, hypes[experiment]['ssim'][0]))\n",
    "#     print(t.split('_')[1],'(max psnr):', max_psnr)\n",
    "#     print(t.split('_')[1],'(max ssim):', max_ssim)\n",
    "    \n",
    "    \n",
    "#     print(t.split('_')[1],'(min psnr):', min_psnr)\n",
    "#     print(t.split('_')[1],'(min ssim):', min_ssim)\n",
    "#     ax1.plot(psnrs)\n",
    "#     ax2.plot(ssims)\n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "707aede1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ncd_pix2pix_batch_1</th>\n",
       "      <th>ncd_pix2pix_batch_16</th>\n",
       "      <th>ncd_pix2pix_batch_2</th>\n",
       "      <th>ncd_pix2pix_batch_32</th>\n",
       "      <th>ncd_pix2pix_batch_4</th>\n",
       "      <th>ncd_pix2pix_batch_64</th>\n",
       "      <th>ncd_pix2pix_batch_8</th>\n",
       "      <th>ncd_pix2pix_epochs_2_8</th>\n",
       "      <th>ncd_pix2pix_epochs_4_6</th>\n",
       "      <th>ncd_pix2pix_epochs_5_5</th>\n",
       "      <th>ncd_pix2pix_epochs_6_4</th>\n",
       "      <th>ncd_pix2pix_epochs_8_2</th>\n",
       "      <th>ncd_pix2pix_lr_0.0001</th>\n",
       "      <th>ncd_pix2pix_lr_0.0002</th>\n",
       "      <th>ncd_pix2pix_lr_0.0005</th>\n",
       "      <th>ncd_pix2pix_lr_2e-05</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>psnr</th>\n",
       "      <td>18.798/3.885</td>\n",
       "      <td>18.193/2.94</td>\n",
       "      <td>18.408/2.625</td>\n",
       "      <td>18.216/2.383</td>\n",
       "      <td>18.295/2.234</td>\n",
       "      <td>18.243/2.107</td>\n",
       "      <td>18.261/2.031</td>\n",
       "      <td>18.497/4.085</td>\n",
       "      <td>18.568/3.857</td>\n",
       "      <td>18.561/3.745</td>\n",
       "      <td>18.724/3.762</td>\n",
       "      <td>18.75/3.819</td>\n",
       "      <td>18.506/3.794</td>\n",
       "      <td>18.738/3.565</td>\n",
       "      <td>18.711/3.854</td>\n",
       "      <td>18.964/3.775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ssim</th>\n",
       "      <td>0.861/0.049</td>\n",
       "      <td>0.834/0.052</td>\n",
       "      <td>0.838/0.048</td>\n",
       "      <td>0.829/0.049</td>\n",
       "      <td>0.83/0.047</td>\n",
       "      <td>0.825/0.048</td>\n",
       "      <td>0.825/0.046</td>\n",
       "      <td>0.856/0.048</td>\n",
       "      <td>0.857/0.048</td>\n",
       "      <td>0.857/0.047</td>\n",
       "      <td>0.858/0.047</td>\n",
       "      <td>0.858/0.047</td>\n",
       "      <td>0.846/0.047</td>\n",
       "      <td>0.849/0.047</td>\n",
       "      <td>0.856/0.05</td>\n",
       "      <td>0.849/0.051</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ncd_pix2pix_batch_1 ncd_pix2pix_batch_16 ncd_pix2pix_batch_2  \\\n",
       "psnr        18.798/3.885          18.193/2.94        18.408/2.625   \n",
       "ssim         0.861/0.049          0.834/0.052         0.838/0.048   \n",
       "\n",
       "     ncd_pix2pix_batch_32 ncd_pix2pix_batch_4 ncd_pix2pix_batch_64  \\\n",
       "psnr         18.216/2.383        18.295/2.234         18.243/2.107   \n",
       "ssim          0.829/0.049          0.83/0.047          0.825/0.048   \n",
       "\n",
       "     ncd_pix2pix_batch_8 ncd_pix2pix_epochs_2_8 ncd_pix2pix_epochs_4_6  \\\n",
       "psnr        18.261/2.031           18.497/4.085           18.568/3.857   \n",
       "ssim         0.825/0.046            0.856/0.048            0.857/0.048   \n",
       "\n",
       "     ncd_pix2pix_epochs_5_5 ncd_pix2pix_epochs_6_4 ncd_pix2pix_epochs_8_2  \\\n",
       "psnr           18.561/3.745           18.724/3.762            18.75/3.819   \n",
       "ssim            0.857/0.047            0.858/0.047            0.858/0.047   \n",
       "\n",
       "     ncd_pix2pix_lr_0.0001 ncd_pix2pix_lr_0.0002 ncd_pix2pix_lr_0.0005  \\\n",
       "psnr          18.506/3.794          18.738/3.565          18.711/3.854   \n",
       "ssim           0.846/0.047           0.849/0.047            0.856/0.05   \n",
       "\n",
       "     ncd_pix2pix_lr_2e-05  \n",
       "psnr         18.964/3.775  \n",
       "ssim          0.849/0.051  "
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "string_hypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "4b9d68a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.ticker import FormatStrFormatter\n",
    "for t in tunes:\n",
    "    # create PSNR list & SSIM list\n",
    "    # for each run (i.e. batch_1)\n",
    "    psnrs = []\n",
    "    ssims = []\n",
    "#     plt.figure()\n",
    "    fig, (ax1, ax2) = plt.subplots(1, 3, figsize=(7,3), sharex=False, sharey=False)\n",
    "    fig.suptitle('PSNR')\n",
    "    plt.subplots_adjust(left=0, bottom=None, right=1.2, top=None, wspace=None, hspace=None)\n",
    "    \n",
    "    for experiment in tunes_dict[t]:\n",
    "        psnrs.append(hypes[experiment]['psnr'][0])\n",
    "        ssims.append(hypes[experiment]['ssim'][0])\n",
    "    ax1.boxplot(psnrs)\n",
    "    ax1.set_xlabel('PSNR')\n",
    "    ax1.set_ylabel('dB')\n",
    "    ax1.yaxis.set_major_formatter(FormatStrFormatter('%.1f'))\n",
    "    \n",
    "    ax2.boxplot(ssims)\n",
    "    ax2.set_xlabel('SSIM')\n",
    "    ax2.set_ylabel('index')\n",
    "    ax2.yaxis.set_major_formatter(FormatStrFormatter('%.3f'))\n",
    "    \n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "373e121b",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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3JnkwyZ8neWNfNUqSJLWqz2vyNgKXTGi7Dri2qpYDV3evJ/o68DNV9dJu/xuSLOqvTEmSpPb0drq2qjYnOX1iM7D/kQgLgV2T7PcXB2zvSrIbWAo82k+lkiRJ7Rn2NXlXAHcmuZ7BKuL503XuTuceD/xl/6VJkiS1Y9hfoXI5cGVVnQpcCWyYqmOS7wV+D3hrVX1rij5rumv7xvfs2dNLwVIrnC/SzDhX1Iphh7xLgdu67VuBZ9x4AZDkZOCPgLVVtXWqwarqpqoaq6qxpUuXznmx80WSSX+me28YX8eiueV8kWbGuaJWDDvk7QIu6LYvBh6e2CHJ8cDtwC1V9ZEh1jZvVdWsfiRJ0pGrt2vykmwCLgROSbITWAe8DbgxyQLgCWBN13cMeHtVXQb8K+CfAN+dZFU33Kqq2tZXrZIkSa3p8+7alVO8dc4kfceBy7rtDwEf6qsuSZKk+cBn10qSJDXIkCdJktQgQ54kSVKDDHmSJEkNSitfhZFkD/DlUdfRmFOAr466iMacVlUj/+It58ucc67MPedKu5wvc2vKudJMyNPcSzJeVWOjrkM60jlXpJlzvgyPp2slSZIaZMiTJElqkCFP07lp1AVIRwnnijRzzpch8Zo8SZKkBrmSJ0mS1CBDnp4hyc1JdifZPupapCOZc0WaOefL8BnyNJmNwCWjLkI6CmzEuSLN1EacL0NlyNMzVNVmYO+o65COdM4VaeacL8NnyJMkSWqQIU+SJKlBhjxJkqQGGfIkSZIaZMjTMyTZBNwLnJlkZ5LVo65JOhI5V6SZc74Mn0+8kCRJapAreZIkSQ0y5EmSJDXIkCdJktQgQ54kSVKDDHmSJEkNMuQ1JsnpSbYfRv9VSZ4/gz7v/86rk+aXJBcm+Z+jrkMatSSPj7qG+ciQp1XAtCFPkqS5lmTBqGtonSGvTQuSfDjJjiQfSXJCkquT3J9ke5KbMvAGYAz4cJJtSZ6d5Nwk/yfJnyW5L8lzujGfn+SPkzyc5Lr9B0ryY0nuTfKZJLcmOalr/5UkDyX58yTXj+C/gTRjSd7c/b5vS/JbSY5N8niS/5LkwSSfSrK067s8ydbud/v2JIu79hcl+WQ3dz6T5Ae74U/q5uHnunmZrr9zRPNOt7r9v5N8FHho1PW0zpDXpjOBD1TVMuAx4B3A+6vq3Ko6C3g28Nqq+ggwDrypqpYDTwP/Hfi3VfVy4NXAN7oxlwNvBH4YeGOSU5OcAlwFvLqqzu7G+vkk3w28HnhpVb0M+OVhfGhpNpIsY/C7/coD5sGbgBOB8ap6KXAPsK7b5RbgP3S/2589oP3DwG92c+d84Ctd+48AVwAvAX4AeKVzRPPc2Qz+njlj1IW0zqXSNj1SVZ/utj8EvBv4qyT/HjgBWAI8CHxswn5nAl+pqvsBquoxgG7h4VNV9ffd64eA04BFDP7i+nTX53gGj6z5e+AJYEN3PZLXJOlI9k+Bc4D7u9/jZwO7gW8x+EcPDObRbUkWAouq6p6u/XeBW7sV7xdU1e0AVfUE/P+5c19V7exebwNOB7biHNH8dV9V/dWoi5gPDHltmvisugI+AIxV1SNJrgGedZhj/uMB208z+N0JcFdVrZzYOcl5DP7yfAPwTuDiwzyeNCwBfreqfuGgxuR9E/rN9hmQz5g7VfVN54jmsX8YdQHzhadr2/T9SV7Rbf80sKXb/mp3zdwbDuj7NWD/dXefB743ybkASZ5ziAtjtzI49fSirv+JSc7ojrGwqu4ArgRePiefSurHp4A3JHkuQJIlSU5j8P/H/XPlp4Et3Wr2viSv6trfAtxTVV8DdiZ5XTfGdyU5YaoDOkckDYMreW36PPBzSW5mcGHrfwUWA9uBvwXuP6DvRuCDSb4BvILBtUm/keTZDK7He/VUB6mqPUlWAZuSfFfXfBWD4PiHSZ7FYJXk5+fuo0lzq6oeSnIV8IkkxwBPAT/HYLXhvO693QzmBsClDObMCcAXgbd27W8BfivJL3Zj/NQ0h30OzhFJPUvVbM9ASFK7kjxeVSeNug5Jmi1P10qSJDXIlTxJkqQGuZInSZLUIEOeJElSgwx5kiRJDTLkSZIkNciQJ0mS1CBDniRJUoP+HxVV5P1X+K3pAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.ticker import FormatStrFormatter\n",
    "psnrs = []\n",
    "ssims = []\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(8,3), sharey=True)\n",
    "fig.suptitle('PSNR')\n",
    "plt.subplots_adjust(left=0, bottom=None, right=1, top=None, wspace=None, hspace=None)\n",
    "\n",
    "fig1, (ax11, ax22, ax33) = plt.subplots(1, 3, figsize=(8,3), sharey=True)\n",
    "fig1.suptitle('SSIM')\n",
    "plt.subplots_adjust(left=0, bottom=None, right=1, top=None, wspace=None, hspace=None)\n",
    "\n",
    "for t in tunes:\n",
    "    # create PSNR list & SSIM list\n",
    "    # for each run (i.e. batch_1)\n",
    "    ps = []\n",
    "    ss = []\n",
    "#     plt.figure()\n",
    "    \n",
    "    for experiment in tunes_dict[t]:\n",
    "        ps.append(hypes[experiment]['psnr'][0])\n",
    "        ss.append(hypes[experiment]['ssim'][0])\n",
    "    \n",
    "    psnrs.append(ps)\n",
    "    ssims.append(ss)\n",
    "\n",
    "ax1.boxplot(psnrs[0])\n",
    "ax1.set_xlabel('batches')\n",
    "ax1.set_ylabel('dB')\n",
    "ax1.yaxis.set_major_formatter(FormatStrFormatter('%.1f'))\n",
    "\n",
    "ax2.boxplot(psnrs[1])\n",
    "ax2.set_xlabel('epochs')\n",
    "ax2.yaxis.set_major_formatter(FormatStrFormatter('%.1f'))\n",
    "\n",
    "ax3.boxplot(psnrs[2])\n",
    "ax3.set_xlabel('lr')\n",
    "ax3.yaxis.set_major_formatter(FormatStrFormatter('%.1f'))\n",
    "\n",
    "\n",
    "ax11.boxplot(ssims[0])\n",
    "ax11.set_xlabel('batches')\n",
    "ax11.set_ylabel('index')\n",
    "ax11.yaxis.set_major_formatter(FormatStrFormatter('%.3f'))\n",
    "\n",
    "ax22.boxplot(ssims[1])\n",
    "ax22.set_xlabel('epochs')\n",
    "ax22.yaxis.set_major_formatter(FormatStrFormatter('%.3f'))\n",
    "\n",
    "ax33.boxplot(ssims[2])\n",
    "ax33.set_xlabel('lr')\n",
    "ax33.yaxis.set_major_formatter(FormatStrFormatter('%.3f'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d275a073",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# for i in range(3):\n",
    "#     plt.axis(\"off\")\n",
    "#     image = cv2.imread(os.path.join(os.path.join(path_to_hypes, 'tune_batches', tunes_dict['tune_batches'][0], 'test_latest', 'images'), imgs[i]))\n",
    "#     plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))\n",
    "#     plt.show()\n",
    "image_real = cv2.imread(os.path.join(os.path.join(path_to_hypes, 'tune_batches', tunes_dict['tune_batches'][0], 'test_latest', 'images'), imgs[0]))\n",
    "\n",
    "image_fake = cv2.imread(os.path.join(os.path.join(path_to_hypes, 'tune_batches', tunes_dict['tune_batches'][0], 'test_latest', 'images'), imgs[3]))\n",
    "\n",
    "print('PSNR value:', peak_signal_noise_ratio(image_real, image_fake))\n",
    "print('SSIM value:', structural_similarity(image_real, image_fake, channel_axis=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "8c08313f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19.878384757329624 13.586817103629087 13.946928724973484\n",
      "0.8456309151606144 0.8645946719971733 0.8336671030000545\n"
     ]
    },
    {
     "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": [
    "pix2pix = cv2.imread('Apple 12/Apple12_fake_B_rgb.png')\n",
    "plt.imshow(cv2.cvtColor(im, cv2.COLOR_BGR2RGB))\n",
    "eccv16 = cv2.imread('Apple 12/saved_eccv16.png')\n",
    "siggraph17 = cv2.imread('Apple 12/saved_siggraph17.png')\n",
    "real = cv2.imread('Apple 12/Apple12_real_B_rgb.png')\n",
    "real1 = cv2.imread('Apple 12/Apple12-2.jpg')\n",
    "print(peak_signal_noise_ratio(real, pix2pix), peak_signal_noise_ratio(real1, eccv16), peak_signal_noise_ratio(real1, siggraph17))\n",
    "print(structural_similarity(real, pix2pix, channel_axis=2), structural_similarity(real1, eccv16, channel_axis=2), structural_similarity(real1, siggraph17, channel_axis=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d0ec310b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61b37447",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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