merge_unet_segformer.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import PIL\n",
    "from PIL import Image, ImageOps, ImageDraw\n",
    "import os\n",
    "import numpy as np\n",
    "from tqdm.notebook import tqdm\n",
    "import subprocess\n",
    "import multiprocess\n",
    "from multiprocess import Pool\n",
    "import math\n",
    "import seaborn as sns\n",
    "import plotly.express as px\n",
    "\n",
    "from bus_data import *\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "if os.uname()[1] == \"media\":\n",
    "    # Florin's gaming PC\n",
    "    data_volume = \"/home/florin/data\"\n",
    "else:\n",
    "    # Google Colab\n",
    "    from google.colab import drive\n",
    "\n",
    "    gdrive_path = \"/content/gdrive\"\n",
    "    drive.mount(gdrive_path, force_remount=False)\n",
    "    data_volume = gdrive_path + \"/MyDrive\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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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>dataset</th>\n",
       "      <th>class</th>\n",
       "      <th>video</th>\n",
       "      <th>image</th>\n",
       "      <th>str_index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BUV_dataset</td>\n",
       "      <td>benign</td>\n",
       "      <td>263b86b85a58f270</td>\n",
       "      <td>/home/florin/data/BUS Project Home/Datasets/BUV_dataset/rawframes/benign/263b86b85a58f270/000000.png</td>\n",
       "      <td>00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BUV_dataset</td>\n",
       "      <td>benign</td>\n",
       "      <td>263b86b85a58f270</td>\n",
       "      <td>/home/florin/data/BUS Project Home/Datasets/BUV_dataset/rawframes/benign/263b86b85a58f270/000001.png</td>\n",
       "      <td>00001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BUV_dataset</td>\n",
       "      <td>benign</td>\n",
       "      <td>263b86b85a58f270</td>\n",
       "      <td>/home/florin/data/BUS Project Home/Datasets/BUV_dataset/rawframes/benign/263b86b85a58f270/000002.png</td>\n",
       "      <td>00002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BUV_dataset</td>\n",
       "      <td>benign</td>\n",
       "      <td>263b86b85a58f270</td>\n",
       "      <td>/home/florin/data/BUS Project Home/Datasets/BUV_dataset/rawframes/benign/263b86b85a58f270/000003.png</td>\n",
       "      <td>00003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BUV_dataset</td>\n",
       "      <td>benign</td>\n",
       "      <td>263b86b85a58f270</td>\n",
       "      <td>/home/florin/data/BUS Project Home/Datasets/BUV_dataset/rawframes/benign/263b86b85a58f270/000004.png</td>\n",
       "      <td>00004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25267</th>\n",
       "      <td>BUV_dataset</td>\n",
       "      <td>malignant</td>\n",
       "      <td>xf7f83b2a576016b</td>\n",
       "      <td>/home/florin/data/BUS Project Home/Datasets/BUV_dataset/rawframes/malignant/xf7f83b2a576016b/000186.png</td>\n",
       "      <td>25267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25268</th>\n",
       "      <td>BUV_dataset</td>\n",
       "      <td>malignant</td>\n",
       "      <td>xf7f83b2a576016b</td>\n",
       "      <td>/home/florin/data/BUS Project Home/Datasets/BUV_dataset/rawframes/malignant/xf7f83b2a576016b/000187.png</td>\n",
       "      <td>25268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25269</th>\n",
       "      <td>BUV_dataset</td>\n",
       "      <td>malignant</td>\n",
       "      <td>xf7f83b2a576016b</td>\n",
       "      <td>/home/florin/data/BUS Project Home/Datasets/BUV_dataset/rawframes/malignant/xf7f83b2a576016b/000188.png</td>\n",
       "      <td>25269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25270</th>\n",
       "      <td>BUV_dataset</td>\n",
       "      <td>malignant</td>\n",
       "      <td>xf7f83b2a576016b</td>\n",
       "      <td>/home/florin/data/BUS Project Home/Datasets/BUV_dataset/rawframes/malignant/xf7f83b2a576016b/000189.png</td>\n",
       "      <td>25270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25271</th>\n",
       "      <td>BUV_dataset</td>\n",
       "      <td>malignant</td>\n",
       "      <td>xf7f83b2a576016b</td>\n",
       "      <td>/home/florin/data/BUS Project Home/Datasets/BUV_dataset/rawframes/malignant/xf7f83b2a576016b/000190.png</td>\n",
       "      <td>25271</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25272 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           dataset      class             video  \\\n",
       "0      BUV_dataset     benign  263b86b85a58f270   \n",
       "1      BUV_dataset     benign  263b86b85a58f270   \n",
       "2      BUV_dataset     benign  263b86b85a58f270   \n",
       "3      BUV_dataset     benign  263b86b85a58f270   \n",
       "4      BUV_dataset     benign  263b86b85a58f270   \n",
       "...            ...        ...               ...   \n",
       "25267  BUV_dataset  malignant  xf7f83b2a576016b   \n",
       "25268  BUV_dataset  malignant  xf7f83b2a576016b   \n",
       "25269  BUV_dataset  malignant  xf7f83b2a576016b   \n",
       "25270  BUV_dataset  malignant  xf7f83b2a576016b   \n",
       "25271  BUV_dataset  malignant  xf7f83b2a576016b   \n",
       "\n",
       "                                                                                                         image  \\\n",
       "0         /home/florin/data/BUS Project Home/Datasets/BUV_dataset/rawframes/benign/263b86b85a58f270/000000.png   \n",
       "1         /home/florin/data/BUS Project Home/Datasets/BUV_dataset/rawframes/benign/263b86b85a58f270/000001.png   \n",
       "2         /home/florin/data/BUS Project Home/Datasets/BUV_dataset/rawframes/benign/263b86b85a58f270/000002.png   \n",
       "3         /home/florin/data/BUS Project Home/Datasets/BUV_dataset/rawframes/benign/263b86b85a58f270/000003.png   \n",
       "4         /home/florin/data/BUS Project Home/Datasets/BUV_dataset/rawframes/benign/263b86b85a58f270/000004.png   \n",
       "...                                                                                                        ...   \n",
       "25267  /home/florin/data/BUS Project Home/Datasets/BUV_dataset/rawframes/malignant/xf7f83b2a576016b/000186.png   \n",
       "25268  /home/florin/data/BUS Project Home/Datasets/BUV_dataset/rawframes/malignant/xf7f83b2a576016b/000187.png   \n",
       "25269  /home/florin/data/BUS Project Home/Datasets/BUV_dataset/rawframes/malignant/xf7f83b2a576016b/000188.png   \n",
       "25270  /home/florin/data/BUS Project Home/Datasets/BUV_dataset/rawframes/malignant/xf7f83b2a576016b/000189.png   \n",
       "25271  /home/florin/data/BUS Project Home/Datasets/BUV_dataset/rawframes/malignant/xf7f83b2a576016b/000190.png   \n",
       "\n",
       "      str_index  \n",
       "0         00000  \n",
       "1         00001  \n",
       "2         00002  \n",
       "3         00003  \n",
       "4         00004  \n",
       "...         ...  \n",
       "25267     25267  \n",
       "25268     25268  \n",
       "25269     25269  \n",
       "25270     25270  \n",
       "25271     25271  \n",
       "\n",
       "[25272 rows x 5 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "buv_df = buv_dataset_make(data_volume)\n",
    "buv_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "basedir = \"frames\"\n",
    "sfdir = basedir + \"/masks_segformer\"\n",
    "unetdir = basedir + \"/masks_unet\"\n",
    "outdir = basedir + \"/video\"\n",
    "\n",
    "shutil.rmtree(outdir, ignore_errors=True)\n",
    "os.makedirs(outdir, exist_ok=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def eval_performance(src_df):\n",
    "    pixel_pred = pd.DataFrame(\n",
    "        [[0, 0, 0], [0, 0, 0]],\n",
    "        index=[\"unet\", \"segformer\"],\n",
    "        columns=[\"benign\", \"malignant\", \"total\"],\n",
    "    )\n",
    "    first_index = src_df.index.to_list()[0]\n",
    "    video_id = src_df.loc[first_index, \"video\"]\n",
    "    video_class = src_df.loc[first_index, \"class\"]\n",
    "\n",
    "    for _, row in src_df.iterrows():\n",
    "        arr_unet = np.array(\n",
    "            Image.open(unetdir + \"/f\" + row[\"str_index\"] + \".png\")\n",
    "        ).astype(np.uint8)\n",
    "        pixel_pred.loc[\"unet\", \"total\"] += (arr_unet != 0).sum()\n",
    "        pixel_pred.loc[\"unet\", \"benign\"] += (arr_unet == 1).sum()\n",
    "        pixel_pred.loc[\"unet\", \"malignant\"] += (arr_unet == 2).sum()\n",
    "\n",
    "        arr_sf = np.array(Image.open(sfdir + \"/f\" + row[\"str_index\"] + \".png\")).astype(\n",
    "            np.uint8\n",
    "        )\n",
    "        pixel_pred.loc[\"segformer\", \"total\"] += (arr_sf != 0).sum()\n",
    "        pixel_pred.loc[\"segformer\", \"benign\"] += (arr_sf == 1).sum()\n",
    "        pixel_pred.loc[\"segformer\", \"malignant\"] += (arr_sf == 2).sum()\n",
    "\n",
    "    unet_pred = pixel_pred.loc[\"unet\", video_class]\n",
    "    unet_total = pixel_pred.loc[\"unet\", \"total\"]\n",
    "    sf_pred = pixel_pred.loc[\"segformer\", video_class]\n",
    "    sf_total = pixel_pred.loc[\"segformer\", \"total\"]\n",
    "\n",
    "    return (\n",
    "        video_id,\n",
    "        unet_pred,\n",
    "        unet_total,\n",
    "        sf_pred,\n",
    "        sf_total,\n",
    "        video_class,\n",
    "    )\n",
    "\n",
    "\n",
    "def process_img_mask(func_input):\n",
    "    src_df, perf_metrics = func_input\n",
    "    img_size = 256\n",
    "    img_size2 = 2 * img_size\n",
    "    img_size3 = 3 * img_size\n",
    "\n",
    "    for _, row in src_df.iterrows():\n",
    "        img = (\n",
    "            Image.open(row[\"image\"])\n",
    "            .resize((img_size, img_size), resample=PIL.Image.Resampling.BILINEAR)\n",
    "            .convert(mode=\"RGB\")\n",
    "        )\n",
    "        mask_unet = Image.open(unetdir + \"/f\" + row[\"str_index\"] + \".png\").resize(\n",
    "            (img_size, img_size), resample=PIL.Image.Resampling.BILINEAR\n",
    "        )\n",
    "        mask_sf = Image.open(sfdir + \"/f\" + row[\"str_index\"] + \".png\").resize(\n",
    "            (img_size, img_size), resample=PIL.Image.Resampling.BILINEAR\n",
    "        )\n",
    "\n",
    "        mask_col_unet = ImageOps.colorize(\n",
    "            mask_unet,\n",
    "            black=(0, 0, 127),\n",
    "            white=(255, 127, 127),\n",
    "            mid=(127, 255, 127),\n",
    "            blackpoint=0,\n",
    "            whitepoint=2,\n",
    "            midpoint=1,\n",
    "        )\n",
    "\n",
    "        mask_col_sf = ImageOps.colorize(\n",
    "            mask_sf,\n",
    "            black=(0, 0, 127),\n",
    "            white=(255, 127, 127),\n",
    "            mid=(127, 255, 127),\n",
    "            blackpoint=0,\n",
    "            whitepoint=2,\n",
    "            midpoint=1,\n",
    "        )\n",
    "\n",
    "        blend_unet = Image.blend(img, mask_col_unet, 0.2)\n",
    "        blend_sf = Image.blend(img, mask_col_sf, 0.2)\n",
    "\n",
    "        img_text = row[\"class\"]\n",
    "        if img_text == \"benign\":\n",
    "            img_text_color = (127, 255, 127)\n",
    "        elif img_text == \"malignant\":\n",
    "            img_text_color = (255, 127, 127)\n",
    "        else:\n",
    "            img_text_color = (255, 255, 255)\n",
    "\n",
    "        draw_img = ImageDraw.Draw(img)\n",
    "        draw_img.text(xy=(2, img_size - 12), text=img_text, fill=img_text_color)\n",
    "        draw_img.text(xy=(2, 0), text=\"id=\" + row[\"video\"], fill=(255, 255, 255))\n",
    "\n",
    "        draw_unet = ImageDraw.Draw(blend_unet)\n",
    "        draw_unet.text(\n",
    "            xy=(2, img_size - 12),\n",
    "            text=\"perf=\" + str(perf_metrics[0]),\n",
    "            fill=img_text_color,\n",
    "        )\n",
    "        draw_unet.text(xy=(2, 0), text=\"U-Net / ResNet34\", fill=(255, 255, 255))\n",
    "\n",
    "        draw_sf = ImageDraw.Draw(blend_sf)\n",
    "        draw_sf.text(\n",
    "            xy=(2, img_size - 12),\n",
    "            text=\"perf=\" + str(perf_metrics[1]),\n",
    "            fill=img_text_color,\n",
    "        )\n",
    "        draw_sf.text(xy=(2, 0), text=\"SegFormer MiT-b3\", fill=(255, 255, 255))\n",
    "\n",
    "        final_frame = Image.new(mode=\"RGB\", size=(img_size3, img_size))\n",
    "        final_frame.paste(img, (0, 0))\n",
    "        final_frame.paste(blend_unet, (img_size, 0))\n",
    "        final_frame.paste(blend_sf, (img_size2, 0))\n",
    "        final_frame.save(outdir + \"/f\" + row[\"str_index\"] + \".png\", format=\"png\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a7ba950f6a384d31aba67581a718d793",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "performance evaluation:   0%|          | 0/16 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "16509703b0d447f2954398184c5679bc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "create video frames:   0%|          | 0/186 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if __name__ == \"__main__\":\n",
    "    n_cpu = multiprocess.cpu_count()\n",
    "    p = Pool(processes=n_cpu)\n",
    "    video_list = buv_df[\"video\"].unique()\n",
    "    perf_results = []\n",
    "    for i in tqdm(\n",
    "        list(range(0, len(video_list), n_cpu)), desc=\"performance evaluation\"\n",
    "    ):\n",
    "        video_to_measure = []\n",
    "        for v in range(i, i + n_cpu):\n",
    "            if v >= len(video_list):\n",
    "                break\n",
    "            vid_df = buv_df[buv_df[\"video\"] == video_list[v]]\n",
    "            vid_df.reset_index(inplace=True)\n",
    "            video_to_measure.append(vid_df)\n",
    "        work_out = p.map(eval_performance, video_to_measure)\n",
    "        perf_results += work_out\n",
    "\n",
    "    for v in tqdm(video_list, desc=\"create video frames\"):\n",
    "        vid_df = buv_df[buv_df[\"video\"] == v]\n",
    "        vid_df.reset_index(inplace=True)\n",
    "        df_step = math.ceil(len(vid_df) / n_cpu)\n",
    "        perf_metrics = (0, 0)\n",
    "        for r in perf_results:\n",
    "            if r[0] == v:\n",
    "                perf_metrics = (round(r[1], 4), round(r[2], 4))\n",
    "        index_ranges = []\n",
    "        for start in range(0, len(vid_df), df_step):\n",
    "            index_ranges.append((vid_df.iloc[start : start + df_step], perf_metrics))\n",
    "        p.map(process_img_mask, index_ranges)\n",
    "\n",
    "    p.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "y4m [info]: 768x256p 1:1 @ 25/1 fps (cfr)\n",
      "x264 [info]: using SAR=1/1\n",
      "x264 [info]: using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2\n",
      "x264 [info]: profile High, level 2.1, 4:2:0, 8-bit\n",
      "x264 [info]: frame I:294   Avg QP:18.16  size: 40214                           \n",
      "x264 [info]: frame P:19650 Avg QP:20.95  size: 14551\n",
      "x264 [info]: frame B:5328  Avg QP:23.86  size:  6388\n",
      "x264 [info]: consecutive B-frames: 70.5%  3.0%  3.3% 23.2%\n",
      "x264 [info]: mb I  I16..4:  0.7% 71.3% 28.0%\n",
      "x264 [info]: mb P  I16..4:  0.0%  4.4%  1.5%  P16..4: 40.5% 31.3% 20.1%  0.0%  0.0%    skip: 2.1%\n",
      "x264 [info]: mb B  I16..4:  0.0%  0.9%  0.9%  B16..8: 18.3%  4.6%  4.2%  direct:35.1%  skip:36.0%  L0:35.2% L1:34.3% BI:30.6%\n",
      "x264 [info]: 8x8 transform intra:72.1% inter:56.4%\n",
      "x264 [info]: coded y,uvDC,uvAC intra: 96.7% 14.3% 12.3% inter: 80.1% 3.3% 1.8%\n",
      "x264 [info]: i16 v,h,dc,p: 52% 21% 20%  8%\n",
      "x264 [info]: i8 v,h,dc,ddl,ddr,vr,hd,vl,hu:  5% 31% 11%  6%  7%  4% 14%  5% 17%\n",
      "x264 [info]: i4 v,h,dc,ddl,ddr,vr,hd,vl,hu:  7% 26% 10%  7%  9%  5% 16%  5% 16%\n",
      "x264 [info]: i8c dc,h,v,p: 86%  7%  6%  1%\n",
      "x264 [info]: Weighted P-Frames: Y:8.9% UV:0.2%\n",
      "x264 [info]: ref P L0: 64.9% 32.0%  2.4%  0.6%  0.1%\n",
      "x264 [info]: ref B L0: 95.8%  3.1%  1.1%\n",
      "x264 [info]: ref B L1: 99.4%  0.6%\n",
      "x264 [info]: kb/s:2625.72\n",
      "\n",
      "encoded 25272 frames, 305.19 fps, 2625.72 kb/s\n",
      "\u001b[32mTrack Importing MPEG-4 AVC - Width 768 Height 256 FPS 30/1 SAR 1/1\n",
      "\u001b[32mAVC|H264 Import results: 25272 samples (25811 NALUs) - Slices: 294 I 19650 P 5328 B - 1 SEI - 269 IDR - 0 CRA\n",
      "\u001b[0m\u001b[32mAVC|H264 Stream uses forward prediction - stream CTS offset: 3 frames\n",
      "\u001b[0m\u001b[32mAVC|H264 Max NALU size is 56953 - stream could be optimized by setting nal_length=2\n",
      "\u001b[0m\u001b[37mSaving video-20221205-195121.mp4: \u001b[0m\u001b[37m0.500 secs Interleaving\n",
      "                                                        \r"
     ]
    },
    {
     "data": {
      "text/plain": [
       "CompletedProcess(args='./make_video.sh', returncode=0)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subprocess.run(\"./make_video.sh\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>video</th>\n",
       "      <th>unet_pred</th>\n",
       "      <th>unet_total</th>\n",
       "      <th>segformer_pred</th>\n",
       "      <th>segformer_total</th>\n",
       "      <th>class</th>\n",
       "      <th>unet</th>\n",
       "      <th>segformer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>263b86b85a58f270</td>\n",
       "      <td>209231</td>\n",
       "      <td>242432</td>\n",
       "      <td>1261203</td>\n",
       "      <td>1261203</td>\n",
       "      <td>benign</td>\n",
       "      <td>0.863050</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2cda21c3aab26332</td>\n",
       "      <td>62973</td>\n",
       "      <td>660411</td>\n",
       "      <td>438776</td>\n",
       "      <td>3466823</td>\n",
       "      <td>benign</td>\n",
       "      <td>0.095354</td>\n",
       "      <td>0.126564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>32a6532065f51cec</td>\n",
       "      <td>725739</td>\n",
       "      <td>817660</td>\n",
       "      <td>4144535</td>\n",
       "      <td>4164626</td>\n",
       "      <td>benign</td>\n",
       "      <td>0.887580</td>\n",
       "      <td>0.995176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>391ae00452fe2e99</td>\n",
       "      <td>20557</td>\n",
       "      <td>237485</td>\n",
       "      <td>214091</td>\n",
       "      <td>908546</td>\n",
       "      <td>benign</td>\n",
       "      <td>0.086561</td>\n",
       "      <td>0.235641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4414661bcb60d5cf</td>\n",
       "      <td>35194</td>\n",
       "      <td>37915</td>\n",
       "      <td>207514</td>\n",
       "      <td>207514</td>\n",
       "      <td>benign</td>\n",
       "      <td>0.928234</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>x9c1c965d274c457</td>\n",
       "      <td>895589</td>\n",
       "      <td>912003</td>\n",
       "      <td>4733823</td>\n",
       "      <td>6181461</td>\n",
       "      <td>malignant</td>\n",
       "      <td>0.982002</td>\n",
       "      <td>0.765810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>xb60099fde76c0ea</td>\n",
       "      <td>2551537</td>\n",
       "      <td>2876927</td>\n",
       "      <td>13281628</td>\n",
       "      <td>15652084</td>\n",
       "      <td>malignant</td>\n",
       "      <td>0.886897</td>\n",
       "      <td>0.848553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>xc92f65176f4b7a7</td>\n",
       "      <td>1223961</td>\n",
       "      <td>1262357</td>\n",
       "      <td>6563842</td>\n",
       "      <td>8020740</td>\n",
       "      <td>malignant</td>\n",
       "      <td>0.969584</td>\n",
       "      <td>0.818359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>xe60a9301eb8c62f</td>\n",
       "      <td>620402</td>\n",
       "      <td>633248</td>\n",
       "      <td>3414556</td>\n",
       "      <td>3423733</td>\n",
       "      <td>malignant</td>\n",
       "      <td>0.979714</td>\n",
       "      <td>0.997320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>xf7f83b2a576016b</td>\n",
       "      <td>438346</td>\n",
       "      <td>657152</td>\n",
       "      <td>2789045</td>\n",
       "      <td>3629001</td>\n",
       "      <td>malignant</td>\n",
       "      <td>0.667039</td>\n",
       "      <td>0.768543</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>186 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                video  unet_pred  unet_total  segformer_pred  segformer_total  \\\n",
       "0    263b86b85a58f270     209231      242432         1261203          1261203   \n",
       "1    2cda21c3aab26332      62973      660411          438776          3466823   \n",
       "2    32a6532065f51cec     725739      817660         4144535          4164626   \n",
       "3    391ae00452fe2e99      20557      237485          214091           908546   \n",
       "4    4414661bcb60d5cf      35194       37915          207514           207514   \n",
       "..                ...        ...         ...             ...              ...   \n",
       "181  x9c1c965d274c457     895589      912003         4733823          6181461   \n",
       "182  xb60099fde76c0ea    2551537     2876927        13281628         15652084   \n",
       "183  xc92f65176f4b7a7    1223961     1262357         6563842          8020740   \n",
       "184  xe60a9301eb8c62f     620402      633248         3414556          3423733   \n",
       "185  xf7f83b2a576016b     438346      657152         2789045          3629001   \n",
       "\n",
       "         class      unet  segformer  \n",
       "0       benign  0.863050   1.000000  \n",
       "1       benign  0.095354   0.126564  \n",
       "2       benign  0.887580   0.995176  \n",
       "3       benign  0.086561   0.235641  \n",
       "4       benign  0.928234   1.000000  \n",
       "..         ...       ...        ...  \n",
       "181  malignant  0.982002   0.765810  \n",
       "182  malignant  0.886897   0.848553  \n",
       "183  malignant  0.969584   0.818359  \n",
       "184  malignant  0.979714   0.997320  \n",
       "185  malignant  0.667039   0.768543  \n",
       "\n",
       "[186 rows x 8 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perf_df = pd.DataFrame(\n",
    "    perf_results,\n",
    "    columns=[\n",
    "        \"video\",\n",
    "        \"unet_pred\",\n",
    "        \"unet_total\",\n",
    "        \"segformer_pred\",\n",
    "        \"segformer_total\",\n",
    "        \"class\",\n",
    "    ],\n",
    ")\n",
    "\n",
    "perf_df[\"unet\"] = perf_df[\"unet_pred\"] / perf_df[\"unet_total\"]\n",
    "perf_df[\"segformer\"] = perf_df[\"segformer_pred\"] / perf_df[\"segformer_total\"]\n",
    "perf_df.to_csv(basedir + \"/perf_results.csv\", index=False)\n",
    "\n",
    "perf_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "perf_df = pd.read_csv(basedir + \"/perf_results.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unet</th>\n",
       "      <th>segformer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>benign</th>\n",
       "      <td>0.345397</td>\n",
       "      <td>0.581847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>malignant</th>\n",
       "      <td>0.905014</td>\n",
       "      <td>0.854745</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               unet  segformer\n",
       "benign     0.345397   0.581847\n",
       "malignant  0.905014   0.854745"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "consistency_df = pd.DataFrame(\n",
    "    index=[\"benign\", \"malignant\"], columns=[\"unet\", \"segformer\"], dtype=np.float64\n",
    ")\n",
    "\n",
    "consistency_df.loc[\"benign\", \"unet\"] = (\n",
    "    perf_df[perf_df[\"class\"] == \"benign\"][\"unet_pred\"].sum()\n",
    "    / perf_df[perf_df[\"class\"] == \"benign\"][\"unet_total\"].sum()\n",
    ")\n",
    "consistency_df.loc[\"malignant\", \"unet\"] = (\n",
    "    perf_df[perf_df[\"class\"] == \"malignant\"][\"unet_pred\"].sum()\n",
    "    / perf_df[perf_df[\"class\"] == \"malignant\"][\"unet_total\"].sum()\n",
    ")\n",
    "\n",
    "consistency_df.loc[\"benign\", \"segformer\"] = (\n",
    "    perf_df[perf_df[\"class\"] == \"benign\"][\"segformer_pred\"].sum()\n",
    "    / perf_df[perf_df[\"class\"] == \"benign\"][\"segformer_total\"].sum()\n",
    ")\n",
    "consistency_df.loc[\"malignant\", \"segformer\"] = (\n",
    "    perf_df[perf_df[\"class\"] == \"malignant\"][\"segformer_pred\"].sum()\n",
    "    / perf_df[perf_df[\"class\"] == \"malignant\"][\"segformer_total\"].sum()\n",
    ")\n",
    "\n",
    "consistency_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(consistency_df);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "alignmentgroup": "True",
         "bingroup": "x",
         "hovertemplate": "variable=unet<br>value=%{x}<br>count=%{y}<extra></extra>",
         "legendgroup": "unet",
         "marker": {
          "color": "#636efa",
          "pattern": {
           "shape": ""
          }
         },
         "name": "unet",
         "nbinsx": 200,
         "offsetgroup": "unet",
         "orientation": "v",
         "showlegend": true,
         "type": "histogram",
         "x": [
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    "px.histogram(\n",
    "    data_frame=perf_df[[\"unet\", \"segformer\"]][perf_df[\"class\"] == \"malignant\"],\n",
    "    barmode=\"relative\",\n",
    "    title=\"Histogram: malignant prediction consistency\",\n",
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    "    width=800,\n",
    "    height=400,\n",
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