ensemble_counts.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0773b155",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import glob\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3b6a75cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def txt2df(path,post_process_check=True):\n",
    "    files = glob.glob(path+\"/*.txt\")\n",
    "    df = pd.DataFrame(columns=[\"image_id\",\"worm_type\",\"count\"])\n",
    "    if post_process_check: print(\"post_processing\")\n",
    "    for file in files:\n",
    "        with open(file) as fp:\n",
    "            preds =np.array([i.split()[:] for i in fp.read().split(\"\\n\")[:-1]],dtype=float)\n",
    "            if post_process_check: \n",
    "                preds = post_process(preds)\n",
    "            abw = 0\n",
    "            pbw =0\n",
    "            for pred in preds: \n",
    "                if int(pred[0])==0: abw+=1\n",
    "                else: pbw+=1\n",
    "\n",
    "            df.loc[len(df)] = [os.path.basename(file),\"abw\",abw]\n",
    "            df.loc[len(df)] = [os.path.basename(file),\"pbw\",pbw]\n",
    "    return df\n",
    "\n",
    "\n",
    "def get_df(path):\n",
    "    files = glob.glob(path)\n",
    "    post_process_check = False\n",
    "    if post_process_check: print(\"post processing\")\n",
    "\n",
    "    preds_df = pd.DataFrame(columns=[\"image_id\",\"worm_type\",\"count\"])\n",
    "    for file in tqdm(files):\n",
    "        with open(file) as fp:\n",
    "            preds =np.array([i.split()[:] for i in fp.read().split(\"\\n\")[:-1]],dtype=float)\n",
    "            if post_process_check: \n",
    "                preds = post_process(preds)\n",
    "            abw = 0\n",
    "            pbw =0\n",
    "            for pred in preds: \n",
    "                if int(pred[0])==0: abw+=1\n",
    "                else: pbw+=1\n",
    "\n",
    "            preds_df.loc[len(preds_df)] = [os.path.basename(file),\"abw\",abw]\n",
    "            preds_df.loc[len(preds_df)] = [os.path.basename(file),\"pbw\",pbw]        \n",
    "    return preds_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "462ecd02",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bw_150\r\n",
      "dummy\r\n",
      "ensemble_150_300\r\n",
      "ensemble_150_3002\r\n",
      "ensemble_150_3003\r\n",
      "ensemble_pre_50_300\r\n",
      "ensemble_pre__last_50_300\r\n",
      "exp\r\n",
      "last__bw_150\r\n",
      "temp\r\n",
      "test2.0_last_highres++_aug_100\r\n",
      "test_all_last_highres++_100\r\n",
      "test_best_highres++_100\r\n",
      "test_best_highres++_1002\r\n",
      "test_best_highres_50\r\n",
      "test_best_highres_multires_50\r\n",
      "test_conf0.3_nms0.3_best_highres++_100\r\n",
      "test_conf0.3_nms0.3_best_highres++_iw_150\r\n",
      "test_conf0.3_nms0.3_best_highres++_iw_20\r\n",
      "test_last_highres++_100\r\n",
      "test_last_highres++_aug_100\r\n",
      "test_last_highres_50\r\n",
      "test_last_pre_300\r\n",
      "test_nms0.3_best_highres++_200\r\n",
      "test_nms0.3_last_highres++_100\r\n",
      "test_nms2__last_highres++_100\r\n",
      "test_nms3__last_highres++_100\r\n",
      "test_nms3__last_highres++_1002\r\n",
      "test_nms__last_highres++_100\r\n",
      "test_pretrained_last__bw_150\r\n",
      "test_pretrained_last__bw_1502\r\n",
      "test_pretrained_last__bw_300\r\n",
      "val_best_highres++_100\r\n",
      "val_best_highres_50\r\n",
      "val_best_highres_obj365_50\r\n",
      "val_conf0.3_best_highres++_100\r\n",
      "val_conf0.3_nms0.3_best_highres++_100\r\n",
      "val_conf0.3_nms0.5_best_highres++_100\r\n",
      "val_conf0.4_nms0.3_best_highres++_100\r\n",
      "val_conf0.4_nms0.3_best_highres++_1002\r\n",
      "val_conf0.4_nms0.4_best_highres++_100\r\n",
      "val_conf0.5_best_highres++_100\r\n",
      "val_conf0.5_nms0.3_best_highres++_100\r\n",
      "val_conf_nms0.3_best_highres++_100\r\n",
      "val_last__bw_150\r\n",
      "val_last_bw_pretrained_300\r\n",
      "val_last_highres++_100\r\n",
      "val_last_highres++_aug_100\r\n",
      "val_last_pre_300\r\n",
      "val_nms0.25_last_highres++_100\r\n",
      "val_nms0.35_last_highres++_100\r\n",
      "val_nms0.3_last_highres++_100\r\n",
      "val_nms0.3_last_highres++_1002\r\n"
     ]
    }
   ],
   "source": [
    "!ls detection/yolov5/runs/detect/.old"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2620b39f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 645/645 [00:01<00:00, 434.48it/s]\n"
     ]
    }
   ],
   "source": [
    "df1 = get_df(\"detection/yolov5/runs/detect/val_best_1536_100_default/labels/*.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9fe99c8f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 643/643 [00:01<00:00, 329.97it/s]\n"
     ]
    }
   ],
   "source": [
    "df2 = get_df(\"detection/yolov5/runs/detect/.old/val_best_highres_50/labels/*.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "86afb5e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>image_id</th>\n",
       "      <th>worm_type</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>id_095efeae209e071b85c680ce.txt</td>\n",
       "      <td>abw</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>id_095efeae209e071b85c680ce.txt</td>\n",
       "      <td>pbw</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>id_062e434219b5a3612a414996.txt</td>\n",
       "      <td>abw</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>id_062e434219b5a3612a414996.txt</td>\n",
       "      <td>pbw</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>id_07741c21ea0cf05171f394ac.txt</td>\n",
       "      <td>abw</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1291</th>\n",
       "      <td>id_22a36dae9a130ff5d7751e93.txt</td>\n",
       "      <td>pbw</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1292</th>\n",
       "      <td>id_c6af05f4d28a2f31f2735e7d.txt</td>\n",
       "      <td>abw</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1293</th>\n",
       "      <td>id_c6af05f4d28a2f31f2735e7d.txt</td>\n",
       "      <td>pbw</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1294</th>\n",
       "      <td>id_bddefe14f422e0ab79aec5b2.txt</td>\n",
       "      <td>abw</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1295</th>\n",
       "      <td>id_bddefe14f422e0ab79aec5b2.txt</td>\n",
       "      <td>pbw</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1296 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             image_id worm_type  count\n",
       "0     id_095efeae209e071b85c680ce.txt       abw      0\n",
       "1     id_095efeae209e071b85c680ce.txt       pbw     55\n",
       "2     id_062e434219b5a3612a414996.txt       abw      0\n",
       "3     id_062e434219b5a3612a414996.txt       pbw      5\n",
       "4     id_07741c21ea0cf05171f394ac.txt       abw      0\n",
       "...                               ...       ...    ...\n",
       "1291  id_22a36dae9a130ff5d7751e93.txt       pbw     35\n",
       "1292  id_c6af05f4d28a2f31f2735e7d.txt       abw      0\n",
       "1293  id_c6af05f4d28a2f31f2735e7d.txt       pbw      2\n",
       "1294  id_bddefe14f422e0ab79aec5b2.txt       abw      4\n",
       "1295  id_bddefe14f422e0ab79aec5b2.txt       pbw      0\n",
       "\n",
       "[1296 rows x 3 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "og_df = txt2df(\"detection/yolov5/detection/yolo_data/labels/validation\",post_process_check=False)\n",
    "og_df "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1afb1ade",
   "metadata": {},
   "outputs": [],
   "source": [
    "def merge(df,og_df):\n",
    "    merged_df1 = df.merge(og_df,on=[\"image_id\",\"worm_type\"])\n",
    "    merged_df1.columns = [\"image_id\",\"worm_type\",\"pred_count\",\"count\"]\n",
    "    merged_df1[\"diff\"] = merged_df1.apply(lambda x: abs(x[\"count\"]-x[\"pred_count\"]), axis=1)\n",
    "    return merged_df1\n",
    "\n",
    "\n",
    "merge1 = merge(df1,og_df)\n",
    "merge2 = merge(df2,og_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a9dbd560",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pred_count</th>\n",
       "      <th>count</th>\n",
       "      <th>diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1290.000000</td>\n",
       "      <td>1290.000000</td>\n",
       "      <td>1290.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>11.175194</td>\n",
       "      <td>10.211628</td>\n",
       "      <td>1.437984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>33.555974</td>\n",
       "      <td>30.867308</td>\n",
       "      <td>6.628660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>427.000000</td>\n",
       "      <td>453.000000</td>\n",
       "      <td>130.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        pred_count        count         diff\n",
       "count  1290.000000  1290.000000  1290.000000\n",
       "mean     11.175194    10.211628     1.437984\n",
       "std      33.555974    30.867308     6.628660\n",
       "min       0.000000     0.000000     0.000000\n",
       "25%       0.000000     0.000000     0.000000\n",
       "50%       1.000000     1.000000     0.000000\n",
       "75%       7.000000     7.000000     1.000000\n",
       "max     427.000000   453.000000   130.000000"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6b9230b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pred_count</th>\n",
       "      <th>count</th>\n",
       "      <th>diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1286.000000</td>\n",
       "      <td>1286.000000</td>\n",
       "      <td>1286.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>11.089425</td>\n",
       "      <td>10.241058</td>\n",
       "      <td>1.848367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>32.374856</td>\n",
       "      <td>30.910757</td>\n",
       "      <td>8.634085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>435.000000</td>\n",
       "      <td>453.000000</td>\n",
       "      <td>125.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        pred_count        count         diff\n",
       "count  1286.000000  1286.000000  1286.000000\n",
       "mean     11.089425    10.241058     1.848367\n",
       "std      32.374856    30.910757     8.634085\n",
       "min       0.000000     0.000000     0.000000\n",
       "25%       0.000000     0.000000     0.000000\n",
       "50%       1.000000     1.000000     0.000000\n",
       "75%       7.000000     7.000000     1.000000\n",
       "max     435.000000   453.000000   125.000000"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge2.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "31a4b239",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\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>image_id</th>\n",
       "      <th>worm_type</th>\n",
       "      <th>count_x</th>\n",
       "      <th>count_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>id_095efeae209e071b85c680ce.txt</td>\n",
       "      <td>abw</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>id_095efeae209e071b85c680ce.txt</td>\n",
       "      <td>pbw</td>\n",
       "      <td>62</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>id_062e434219b5a3612a414996.txt</td>\n",
       "      <td>abw</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>id_062e434219b5a3612a414996.txt</td>\n",
       "      <td>pbw</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>id_07741c21ea0cf05171f394ac.txt</td>\n",
       "      <td>abw</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1285</th>\n",
       "      <td>id_22a36dae9a130ff5d7751e93.txt</td>\n",
       "      <td>pbw</td>\n",
       "      <td>32</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1286</th>\n",
       "      <td>id_c6af05f4d28a2f31f2735e7d.txt</td>\n",
       "      <td>abw</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1287</th>\n",
       "      <td>id_c6af05f4d28a2f31f2735e7d.txt</td>\n",
       "      <td>pbw</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1288</th>\n",
       "      <td>id_bddefe14f422e0ab79aec5b2.txt</td>\n",
       "      <td>abw</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1289</th>\n",
       "      <td>id_bddefe14f422e0ab79aec5b2.txt</td>\n",
       "      <td>pbw</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1290 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             image_id worm_type  count_x  count_y\n",
       "0     id_095efeae209e071b85c680ce.txt       abw        0      0.0\n",
       "1     id_095efeae209e071b85c680ce.txt       pbw       62     66.0\n",
       "2     id_062e434219b5a3612a414996.txt       abw        0      0.0\n",
       "3     id_062e434219b5a3612a414996.txt       pbw        4      4.0\n",
       "4     id_07741c21ea0cf05171f394ac.txt       abw        0      0.0\n",
       "...                               ...       ...      ...      ...\n",
       "1285  id_22a36dae9a130ff5d7751e93.txt       pbw       32     34.0\n",
       "1286  id_c6af05f4d28a2f31f2735e7d.txt       abw        0      0.0\n",
       "1287  id_c6af05f4d28a2f31f2735e7d.txt       pbw        1      0.0\n",
       "1288  id_bddefe14f422e0ab79aec5b2.txt       abw        2      4.0\n",
       "1289  id_bddefe14f422e0ab79aec5b2.txt       pbw        2      1.0\n",
       "\n",
       "[1290 rows x 4 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_merge = df1.merge(df2,on=[\"image_id\",\"worm_type\"],how='left').fillna(0)\n",
    "\n",
    "count_merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "796bc51c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1285</th>\n",
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       "      <td>34.0</td>\n",
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       "      <th>1286</th>\n",
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       "      <td>4.0</td>\n",
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       "      <th>1289</th>\n",
       "      <td>id_bddefe14f422e0ab79aec5b2.txt</td>\n",
       "      <td>pbw</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1290 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             image_id worm_type  count_x  count_y  mean\n",
       "0     id_095efeae209e071b85c680ce.txt       abw        0      0.0     0\n",
       "1     id_095efeae209e071b85c680ce.txt       pbw       62     66.0    64\n",
       "2     id_062e434219b5a3612a414996.txt       abw        0      0.0     0\n",
       "3     id_062e434219b5a3612a414996.txt       pbw        4      4.0     4\n",
       "4     id_07741c21ea0cf05171f394ac.txt       abw        0      0.0     0\n",
       "...                               ...       ...      ...      ...   ...\n",
       "1285  id_22a36dae9a130ff5d7751e93.txt       pbw       32     34.0    33\n",
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       "1287  id_c6af05f4d28a2f31f2735e7d.txt       pbw        1      0.0     1\n",
       "1288  id_bddefe14f422e0ab79aec5b2.txt       abw        2      4.0     3\n",
       "1289  id_bddefe14f422e0ab79aec5b2.txt       pbw        2      1.0     1\n",
       "\n",
       "[1290 rows x 5 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_merge[\"mean\"] = count_merge.apply(lambda x: x[\"count_x\"] if x[\"count_y\"]==0 else int(0.5*x[\"count_x\"]+0.5*x[\"count_y\"]),axis=1)\n",
    "count_merge\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "91f43348",
   "metadata": {},
   "outputs": [],
   "source": [
    "count_merge_df = count_merge[[\"image_id\",\"worm_type\",\"mean\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c589246c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<p>1286 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             image_id worm_type  pred_count  count  diff\n",
       "0     id_095efeae209e071b85c680ce.txt       abw           0      0     0\n",
       "1     id_095efeae209e071b85c680ce.txt       pbw          58     55     3\n",
       "2     id_062e434219b5a3612a414996.txt       abw           0      0     0\n",
       "3     id_062e434219b5a3612a414996.txt       pbw           4      5     1\n",
       "4     id_07741c21ea0cf05171f394ac.txt       abw           0      0     0\n",
       "...                               ...       ...         ...    ...   ...\n",
       "1281  id_02d739e1c393e30c003b7c41.txt       pbw          89     94     5\n",
       "1282  id_22a36dae9a130ff5d7751e93.txt       abw           0      0     0\n",
       "1283  id_22a36dae9a130ff5d7751e93.txt       pbw          31     35     4\n",
       "1284  id_bddefe14f422e0ab79aec5b2.txt       abw           3      4     1\n",
       "1285  id_bddefe14f422e0ab79aec5b2.txt       pbw           1      0     1\n",
       "\n",
       "[1286 rows x 5 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge2 = merge(count_merge_df,og_df)\n",
    "merge2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5de77745",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1286.000000</td>\n",
       "      <td>1286.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>10.318040</td>\n",
       "      <td>10.241058</td>\n",
       "      <td>1.182737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>30.370538</td>\n",
       "      <td>30.910757</td>\n",
       "      <td>5.777324</td>\n",
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       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>max</th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "        pred_count        count         diff\n",
       "count  1286.000000  1286.000000  1286.000000\n",
       "mean     10.318040    10.241058     1.182737\n",
       "std      30.370538    30.910757     5.777324\n",
       "min       0.000000     0.000000     0.000000\n",
       "25%       0.000000     0.000000     0.000000\n",
       "50%       1.000000     1.000000     0.000000\n",
       "75%       7.000000     7.000000     0.000000\n",
       "max     384.000000   453.000000    94.000000"
      ]
     },
     "execution_count": 16,
     "metadata": {},
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    "merge2.describe()"
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f5a2f88",
   "metadata": {},
   "outputs": [],
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f697ae4f",
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   "outputs": [],
   "source": []
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