Frog marks on image & findcontors.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import cv2\n",
    "from matplotlib import pyplot as plt\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('C:\\\\Users\\\\Chen\\\\Frog ID\\\\Frog data\\\\flat_anotations.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>629.945068</td>\n",
       "      <td>877.188782</td>\n",
       "      <td>Tip of snout</td>\n",
       "      <td>278017541</td>\n",
       "      <td>victorav</td>\n",
       "      <td>{\"50445441\":{\"retired\":null,\"grid\":\"Grid A\",\"c...</td>\n",
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       "      <th>1</th>\n",
       "      <td>1227.169678</td>\n",
       "      <td>910.180786</td>\n",
       "      <td>Vent</td>\n",
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       "      <td>775.934021</td>\n",
       "      <td>Right eye</td>\n",
       "      <td>278017541</td>\n",
       "      <td>victorav</td>\n",
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       "      <th>4</th>\n",
       "      <td>912.628296</td>\n",
       "      <td>750.835022</td>\n",
       "      <td>Right front leg</td>\n",
       "      <td>278017541</td>\n",
       "      <td>victorav</td>\n",
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       "             x           y            label  classification_id user_name  \\\n",
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       "\n",
       "                                        subject_data  subject_ids  \n",
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       "4  {\"50445441\":{\"retired\":null,\"grid\":\"Grid A\",\"c...     50445441  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['data_filename']=df.subject_data.str.replace(\"null\",\"0\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "      <td>629.945068</td>\n",
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       "      <td>1227.169678</td>\n",
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       "      <th>2</th>\n",
       "      <td>733.764771</td>\n",
       "      <td>775.934021</td>\n",
       "      <td>Right eye</td>\n",
       "      <td>278017541</td>\n",
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       "      <td>738.509338</td>\n",
       "      <td>991.895691</td>\n",
       "      <td>Left eye</td>\n",
       "      <td>278017541</td>\n",
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       "      <td>278017541</td>\n",
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       "             x           y            label  classification_id user_name  \\\n",
       "0   629.945068  877.188782     Tip of snout          278017541  victorav   \n",
       "1  1227.169678  910.180786             Vent          278017541  victorav   \n",
       "2   733.764771  775.934021        Right eye          278017541  victorav   \n",
       "3   738.509338  991.895691         Left eye          278017541  victorav   \n",
       "4   912.628296  750.835022  Right front leg          278017541  victorav   \n",
       "\n",
       "                                        subject_data  subject_ids  \\\n",
       "0  {\"50445441\":{\"retired\":null,\"grid\":\"Grid A\",\"c...     50445441   \n",
       "1  {\"50445441\":{\"retired\":null,\"grid\":\"Grid A\",\"c...     50445441   \n",
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       "\n",
       "                                       data_filename  \n",
       "0  {\"50445441\":{\"retired\":0,\"grid\":\"Grid A\",\"capt...  \n",
       "1  {\"50445441\":{\"retired\":0,\"grid\":\"Grid A\",\"capt...  \n",
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     "execution_count": 6,
     "metadata": {},
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    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['fig_name']=df['data_filename'].apply(lambda x: ast.literal_eval(x)[list(eval(x).keys())[0]]['filename'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['frog_id']=df['data_filename'].apply(lambda x: ast.literal_eval(x)[list(eval(x).keys())[0]]['frog_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "      <td>278017541</td>\n",
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       "      <th>1</th>\n",
       "      <td>1227.169678</td>\n",
       "      <td>910.180786</td>\n",
       "      <td>Vent</td>\n",
       "      <td>278017541</td>\n",
       "      <td>victorav</td>\n",
       "      <td>{\"50445441\":{\"retired\":null,\"grid\":\"Grid A\",\"c...</td>\n",
       "      <td>50445441</td>\n",
       "      <td>{\"50445441\":{\"retired\":0,\"grid\":\"Grid A\",\"capt...</td>\n",
       "      <td>00_0-357.jpg</td>\n",
       "      <td>130</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>733.764771</td>\n",
       "      <td>775.934021</td>\n",
       "      <td>Right eye</td>\n",
       "      <td>278017541</td>\n",
       "      <td>victorav</td>\n",
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       "      <td>50445441</td>\n",
       "      <td>{\"50445441\":{\"retired\":0,\"grid\":\"Grid A\",\"capt...</td>\n",
       "      <td>00_0-357.jpg</td>\n",
       "      <td>130</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>738.509338</td>\n",
       "      <td>991.895691</td>\n",
       "      <td>Left eye</td>\n",
       "      <td>278017541</td>\n",
       "      <td>victorav</td>\n",
       "      <td>{\"50445441\":{\"retired\":null,\"grid\":\"Grid A\",\"c...</td>\n",
       "      <td>50445441</td>\n",
       "      <td>{\"50445441\":{\"retired\":0,\"grid\":\"Grid A\",\"capt...</td>\n",
       "      <td>00_0-357.jpg</td>\n",
       "      <td>130</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>912.628296</td>\n",
       "      <td>750.835022</td>\n",
       "      <td>Right front leg</td>\n",
       "      <td>278017541</td>\n",
       "      <td>victorav</td>\n",
       "      <td>{\"50445441\":{\"retired\":null,\"grid\":\"Grid A\",\"c...</td>\n",
       "      <td>50445441</td>\n",
       "      <td>{\"50445441\":{\"retired\":0,\"grid\":\"Grid A\",\"capt...</td>\n",
       "      <td>00_0-357.jpg</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>894.585632</td>\n",
       "      <td>993.976257</td>\n",
       "      <td>Left front leg</td>\n",
       "      <td>278017541</td>\n",
       "      <td>victorav</td>\n",
       "      <td>{\"50445441\":{\"retired\":null,\"grid\":\"Grid A\",\"c...</td>\n",
       "      <td>50445441</td>\n",
       "      <td>{\"50445441\":{\"retired\":0,\"grid\":\"Grid A\",\"capt...</td>\n",
       "      <td>00_0-357.jpg</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>741.838806</td>\n",
       "      <td>1128.011841</td>\n",
       "      <td>Tip of snout</td>\n",
       "      <td>278017631</td>\n",
       "      <td>victorav</td>\n",
       "      <td>{\"50445447\":{\"retired\":null,\"grid\":\"Grid B\",\"c...</td>\n",
       "      <td>50445447</td>\n",
       "      <td>{\"50445447\":{\"retired\":0,\"grid\":\"Grid B\",\"capt...</td>\n",
       "      <td>1100-2578.jpg</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1328.153687</td>\n",
       "      <td>1027.318481</td>\n",
       "      <td>Vent</td>\n",
       "      <td>278017631</td>\n",
       "      <td>victorav</td>\n",
       "      <td>{\"50445447\":{\"retired\":null,\"grid\":\"Grid B\",\"c...</td>\n",
       "      <td>50445447</td>\n",
       "      <td>{\"50445447\":{\"retired\":0,\"grid\":\"Grid B\",\"capt...</td>\n",
       "      <td>1100-2578.jpg</td>\n",
       "      <td>171</td>\n",
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      "text/plain": [
       "             x            y            label  classification_id user_name  \\\n",
       "0   629.945068   877.188782     Tip of snout          278017541  victorav   \n",
       "1  1227.169678   910.180786             Vent          278017541  victorav   \n",
       "2   733.764771   775.934021        Right eye          278017541  victorav   \n",
       "3   738.509338   991.895691         Left eye          278017541  victorav   \n",
       "4   912.628296   750.835022  Right front leg          278017541  victorav   \n",
       "5   894.585632   993.976257   Left front leg          278017541  victorav   \n",
       "6   741.838806  1128.011841     Tip of snout          278017631  victorav   \n",
       "7  1328.153687  1027.318481             Vent          278017631  victorav   \n",
       "\n",
       "                                        subject_data  subject_ids  \\\n",
       "0  {\"50445441\":{\"retired\":null,\"grid\":\"Grid A\",\"c...     50445441   \n",
       "1  {\"50445441\":{\"retired\":null,\"grid\":\"Grid A\",\"c...     50445441   \n",
       "2  {\"50445441\":{\"retired\":null,\"grid\":\"Grid A\",\"c...     50445441   \n",
       "3  {\"50445441\":{\"retired\":null,\"grid\":\"Grid A\",\"c...     50445441   \n",
       "4  {\"50445441\":{\"retired\":null,\"grid\":\"Grid A\",\"c...     50445441   \n",
       "5  {\"50445441\":{\"retired\":null,\"grid\":\"Grid A\",\"c...     50445441   \n",
       "6  {\"50445447\":{\"retired\":null,\"grid\":\"Grid B\",\"c...     50445447   \n",
       "7  {\"50445447\":{\"retired\":null,\"grid\":\"Grid B\",\"c...     50445447   \n",
       "\n",
       "                                       data_filename       fig_name frog_id  \n",
       "0  {\"50445441\":{\"retired\":0,\"grid\":\"Grid A\",\"capt...   00_0-357.jpg     130  \n",
       "1  {\"50445441\":{\"retired\":0,\"grid\":\"Grid A\",\"capt...   00_0-357.jpg     130  \n",
       "2  {\"50445441\":{\"retired\":0,\"grid\":\"Grid A\",\"capt...   00_0-357.jpg     130  \n",
       "3  {\"50445441\":{\"retired\":0,\"grid\":\"Grid A\",\"capt...   00_0-357.jpg     130  \n",
       "4  {\"50445441\":{\"retired\":0,\"grid\":\"Grid A\",\"capt...   00_0-357.jpg     130  \n",
       "5  {\"50445441\":{\"retired\":0,\"grid\":\"Grid A\",\"capt...   00_0-357.jpg     130  \n",
       "6  {\"50445447\":{\"retired\":0,\"grid\":\"Grid B\",\"capt...  1100-2578.jpg     171  \n",
       "7  {\"50445447\":{\"retired\":0,\"grid\":\"Grid B\",\"capt...  1100-2578.jpg     171  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.head(8)"
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  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'{\"50445441\":{\"retired\":0,\"grid\":\"Grid A\",\"capture\":\"357\",\"frog_id\":\"130\",\"filename\":\"00_0-357.jpg\"}}'"
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   "outputs": [],
   "source": [
    "path='C:\\\\Users\\\\Chen\\\\Frog ID\\\\whareorino_a\\\\Grid A\\\\Individual Frogs'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# הגדרה של תמונה ספציפית מתוך הדאטהפריים"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "image=cv2.imread(os.path.join(path,df['frog_id'].iloc[25],df['fig_name'].iloc[25]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# הצגה של התמונה שהגדרנו"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv2.imshow('image',image)\n",
    "cv2.cv2.waitKey(0) # משך זמן הצגת תמונה-0 אין סוף  \n",
    "cv2.destroyAllWindows() #"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def open_img(filepath):\n",
    "    \n",
    "    img=cv2.imread(filepath) # התמונה\n",
    "    mark=cv2.circle(img, (int(df['x'].iloc[0]),int(df['y'].iloc[0])),10,(0,0,255))\n",
    "    resize = ResizeWithAspectRatio(mark, width=900) # התמונה מוקטנת\n",
    "    #mark=cv2.circle(resize, (140,140),5,(0,0,255))\n",
    "\n",
    "    \n",
    "    cv2.imshow(\"Image number {}\".format(filepath), resize)\n",
    "    cv2.cv2.waitKey(0) # משך זמן הצגת תמונה-0 אין סוף  \n",
    "    cv2.destroyAllWindows() # "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "def open_img(filepath):\n",
    "    \n",
    "    img=cv2.imread(filepath) # התמונה\n",
    "    mark=cv2.circle(img, (int(df['x'].iloc[0]),int(df['y'].iloc[0])),10,(0,0,255),5)\n",
    "    mark1=cv2.circle(mark, (int(df['x'].iloc[1]),int(df['y'].iloc[1])),10,(0,0,255),5)\n",
    "    mark2=cv2.circle(mark1, (int(df['x'].iloc[2]),int(df['y'].iloc[2])),10,(0,0,255),5)\n",
    "    mark3=cv2.circle(mark2, (int(df['x'].iloc[3]),int(df['y'].iloc[3])),10,(0,0,255),5)\n",
    "    mark4=cv2.circle(mark3, (int(df['x'].iloc[4]),int(df['y'].iloc[4])),10,(0,0,255),5)\n",
    "    mark5=cv2.circle(mark4, (int(df['x'].iloc[5]),int(df['y'].iloc[5])),10,(0,0,255),5)\n",
    "    resize = ResizeWithAspectRatio(mark5, width=900) # התמונה מוקטנת\n",
    "    \n",
    "\n",
    "    \n",
    "    cv2.imshow(\"Image number {}\".format(filepath), resize)\n",
    "    cv2.cv2.waitKey(0) # משך זמן הצגת תמונה-0 אין סוף  \n",
    "    cv2.destroyAllWindows() # "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# שלבי פעולה\n",
    "#1 מגדירים פונקציה שמקטינה תמונה תוך שמירה על הפורפורציות\n",
    "#2 מגדירים פונקציה שמקבלת שם תמונה ודאטהפריים\n",
    "# בתוך הפונקציה יש הגדרה של קורדינטות\n",
    "# קריאה לתמונה ציור מעגלים וקווים ע\"פ הקורדינטות\n",
    "# הקטנת התמונה באופן פורפורצינאלי בעזרת פונקציית ההקטנה שכבר הוגדרה"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#1\n",
    "# פונקציה שמגדירה איך להקטין תמונה תוך שמירה על פורפורציות\n",
    "def ResizeWithAspectRatio(image, width=None, height=None, inter=cv2.INTER_AREA):\n",
    "    dim = None\n",
    "    (h, w) = image.shape[:2]\n",
    "\n",
    "    if width is None and height is None:\n",
    "        return image\n",
    "    if width is None:\n",
    "        r = height / float(h)\n",
    "        dim = (int(w * r), height)\n",
    "    else:\n",
    "        r = width / float(w)\n",
    "        dim = (width, int(h * r))\n",
    "\n",
    "    return cv2.resize(image, dim, interpolation=inter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def open_img(filepath, df):\n",
    "    \n",
    "    snout=(int(df['x'].iloc[0]),int(df['y'].iloc[0]))\n",
    "    vent=(int(df['x'].iloc[1]),int(df['y'].iloc[1]))\n",
    "    reye=(int(df['x'].iloc[2]),int(df['y'].iloc[2]))\n",
    "    leye=(int(df['x'].iloc[3]),int(df['y'].iloc[3]))\n",
    "    rleg=(int(df['x'].iloc[4]),int(df['y'].iloc[4]))\n",
    "    lleg=(int(df['x'].iloc[5]),int(df['y'].iloc[5]))\n",
    "\n",
    "    img=cv2.imread(filepath) # התמונה\n",
    "    mark=cv2.circle(img, snout,10,(0,0,255),4)\n",
    "    mark1=cv2.circle(mark, vent,10,(0,0,255),4)\n",
    "    mark2=cv2.circle(mark1, reye,10,(0,0,255),4)\n",
    "    mark3=cv2.circle(mark2, leye,10,(0,0,255),4)\n",
    "    mark4=cv2.circle(mark3, rleg,10,(0,0,255),4)\n",
    "    mark5=cv2.circle(mark4, lleg,10,(0,0,255),4)\n",
    "    snout_ventline=cv2.line(mark5, snout, vent, (0,0,255), 3)\n",
    "    eyeline=cv2.line(mark5, reye, leye, (0,0,255), 3)\n",
    "    legline=cv2.line(mark5, rleg, lleg, (0,0,255), 3)\n",
    "    resize = ResizeWithAspectRatio(mark5, width=900) # התמונה מוקטנת\n",
    "    \n",
    "\n",
    "    \n",
    "    cv2.imshow(\"Image number {}\".format(filepath), resize)\n",
    "    cv2.cv2.waitKey(0) # משך זמן הצגת תמונה-0 אין סוף  \n",
    "    cv2.destroyAllWindows() # "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# קריאה לפונקציה תוך הזנת שם הקובץ ומאיזה דטאהפריים לקחת אותו"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "open_img(os.path.join(path,df['frog_id'].iloc[0],df['fig_name'].iloc[0]), df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Image directory\n",
    "directory = r'C:\\Users\\Chen\\Frog ID\\frog on prosses'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change the current directory \n",
    "# to specified directory \n",
    "os.chdir(directory)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# saveing the procssed image in the frog on prosses - directory\n",
    "def save_img(filepath, df):\n",
    "    \n",
    "    snout=(int(df['x'].iloc[0]),int(df['y'].iloc[0]))\n",
    "    vent=(int(df['x'].iloc[1]),int(df['y'].iloc[1]))\n",
    "    reye=(int(df['x'].iloc[2]),int(df['y'].iloc[2]))\n",
    "    leye=(int(df['x'].iloc[3]),int(df['y'].iloc[3]))\n",
    "    rleg=(int(df['x'].iloc[4]),int(df['y'].iloc[4]))\n",
    "    lleg=(int(df['x'].iloc[5]),int(df['y'].iloc[5]))\n",
    "\n",
    "    img=cv2.imread(filepath) # התמונה\n",
    "    mark=cv2.circle(img, snout,10,(0,0,255),4)\n",
    "    mark1=cv2.circle(mark, vent,10,(0,0,255),4)\n",
    "    mark2=cv2.circle(mark1, reye,10,(0,0,255),4)\n",
    "    mark3=cv2.circle(mark2, leye,10,(0,0,255),4)\n",
    "    mark4=cv2.circle(mark3, rleg,10,(0,0,255),4)\n",
    "    mark5=cv2.circle(mark4, lleg,10,(0,0,255),4)\n",
    "    snout_ventline=cv2.line(mark5, snout, vent, (0,0,255), 3)\n",
    "    eyeline=cv2.line(mark5, reye, leye, (0,0,255), 3)\n",
    "    legline=cv2.line(mark5, rleg, lleg, (0,0,255), 3)\n",
    "    resize = ResizeWithAspectRatio(mark5, width=900) # התמונה מוקטנת\n",
    "    \n",
    "\n",
    "    \n",
    "    cv2.imwrite('C:\\\\Users\\\\Chen\\\\Frog ID\\\\frog on prosses\\\\f1.jpg', resize)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_img(os.path.join(path,df['frog_id'].iloc[0],df['fig_name'].iloc[0]), df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Contours found = 349\n"
     ]
    }
   ],
   "source": [
    "# Let's load the image \n",
    "image = cv2.imread('C:\\\\Users\\\\Chen\\\\Frog ID\\\\frog on prosses\\\\f1.jpg')\n",
    "cv2.waitKey(0)\n",
    "\n",
    "# Grayscale\n",
    "gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "# Find Canny edges\n",
    "edged = cv2.Canny(gray, 30, 200)\n",
    "cv2.waitKey(0)\n",
    "\n",
    "# Finding Contours\n",
    "# Use a copy of the image e.g. edged.0copy()\n",
    "# since findContours alters the image\n",
    "\n",
    "cnts, hierarchy = cv2.findContours(edged,cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) \n",
    "#contours, hierarchy = cv2.findContours(edged,cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)\n",
    "\n",
    "\n",
    "cv2.imshow('Canny Edges After Contouring', edged)\n",
    "cv2.waitKey(0)\n",
    "\n",
    "print(\"Number of Contours found = \" + str(len(cnts)))\n",
    "\n",
    "# Draw all contours\n",
    "# -1 signifies drawing all contours\n",
    "cv2.drawContours(image, cnts, -1, (0, 255, 0), 3)\n",
    "\n",
    "cv2.imshow('Contours', image)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import imutils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnts = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)\n",
    "cnts = imutils.grab_contours(cnts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "cnts "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# find contours in thresholded image, then grab the largest\n",
    "c = max(cnts, key=cv2.contourArea)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "left = tuple(c[c[:, :, 0].argmin()][0])\n",
    "right = tuple(c[c[:, :, 0].argmax()][0])\n",
    "top = tuple(c[c[:, :, 1].argmin()][0])\n",
    "bottom = tuple(c[c[:, :, 1].argmax()][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# draw the outline of the object, then draw each of the\n",
    "# extreme points, where the left-most is red, right-most\n",
    "# is green, top-most is blue, and bottom-most is teal\n",
    "cv2.drawContours(image, [c], -1, (0, 255, 255), 2)\n",
    "cv2.circle(image, left, 8, (0, 0, 255), -1)\n",
    "cv2.circle(image, right, 8, (0, 255, 0), -1)\n",
    "cv2.circle(image, top, 8, (255, 0, 0), -1)\n",
    "cv2.circle(image, bottom, 8, (255, 255, 0), -1)\n",
    "# show the output image\n",
    "cv2.imshow(\"Image\", image)\n",
    "cv2.waitKey(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "index 1 is out of bounds for axis 0 with size 1",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-42-93b02ac7822b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m#l = [(0, 0.0083553666846612491), (1, 0.0054999681509501821)]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0msmallest\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcnts\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0many\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0msmallest\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-42-93b02ac7822b>\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m#l = [(0, 0.0083553666846612491), (1, 0.0054999681509501821)]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0msmallest\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcnts\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0many\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0msmallest\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mIndexError\u001b[0m: index 1 is out of bounds for axis 0 with size 1"
     ]
    }
   ],
   "source": [
    " \n",
    "smallest = min(cnts, key=lambda x: x[1].any())[0]\n",
    "smallest\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df = pd.DataFrame.from_records(data)\n",
    "cnts_df = pd.DataFrame.from_records(cnts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
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       "      <th>1</th>\n",
       "      <td>[[70, 603]]</td>\n",
       "      <td>[[68, 605]]</td>\n",
       "      <td>[[67, 605]]</td>\n",
       "      <td>[[62, 610]]</td>\n",
       "      <td>[[61, 610]]</td>\n",
       "      <td>[[56, 615]]</td>\n",
       "      <td>[[55, 615]]</td>\n",
       "      <td>[[50, 620]]</td>\n",
       "      <td>[[49, 620]]</td>\n",
       "      <td>[[45, 624]]</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[[288, 574]]</td>\n",
       "      <td>[[286, 576]]</td>\n",
       "      <td>[[286, 577]]</td>\n",
       "      <td>[[287, 578]]</td>\n",
       "      <td>[[285, 580]]</td>\n",
       "      <td>[[284, 580]]</td>\n",
       "      <td>[[281, 583]]</td>\n",
       "      <td>[[280, 583]]</td>\n",
       "      <td>[[279, 584]]</td>\n",
       "      <td>[[277, 584]]</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[[296, 573]]</td>\n",
       "      <td>[[296, 574]]</td>\n",
       "      <td>[[307, 574]]</td>\n",
       "      <td>[[308, 575]]</td>\n",
       "      <td>[[309, 574]]</td>\n",
       "      <td>[[310, 575]]</td>\n",
       "      <td>[[323, 575]]</td>\n",
       "      <td>[[310, 575]]</td>\n",
       "      <td>[[309, 574]]</td>\n",
       "      <td>[[308, 575]]</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[[113, 568]]</td>\n",
       "      <td>[[112, 569]]</td>\n",
       "      <td>[[111, 569]]</td>\n",
       "      <td>[[107, 573]]</td>\n",
       "      <td>[[106, 572]]</td>\n",
       "      <td>[[106, 571]]</td>\n",
       "      <td>[[106, 572]]</td>\n",
       "      <td>[[107, 573]]</td>\n",
       "      <td>[[106, 574]]</td>\n",
       "      <td>[[105, 574]]</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</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",
       "      <td>...</td>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>[[666, 13]]</td>\n",
       "      <td>[[665, 14]]</td>\n",
       "      <td>[[665, 16]]</td>\n",
       "      <td>[[664, 17]]</td>\n",
       "      <td>[[664, 18]]</td>\n",
       "      <td>[[663, 19]]</td>\n",
       "      <td>[[663, 21]]</td>\n",
       "      <td>[[662, 22]]</td>\n",
       "      <td>[[662, 24]]</td>\n",
       "      <td>[[661, 25]]</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>[[631, 13]]</td>\n",
       "      <td>[[630, 14]]</td>\n",
       "      <td>[[630, 35]]</td>\n",
       "      <td>[[642, 35]]</td>\n",
       "      <td>[[643, 34]]</td>\n",
       "      <td>[[645, 34]]</td>\n",
       "      <td>[[647, 32]]</td>\n",
       "      <td>[[647, 30]]</td>\n",
       "      <td>[[648, 29]]</td>\n",
       "      <td>[[648, 19]]</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>[[622, 13]]</td>\n",
       "      <td>[[622, 35]]</td>\n",
       "      <td>[[626, 35]]</td>\n",
       "      <td>[[626, 14]]</td>\n",
       "      <td>[[625, 13]]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>[[546, 13]]</td>\n",
       "      <td>[[545, 14]]</td>\n",
       "      <td>[[545, 35]]</td>\n",
       "      <td>[[549, 35]]</td>\n",
       "      <td>[[549, 34]]</td>\n",
       "      <td>[[550, 33]]</td>\n",
       "      <td>[[550, 27]]</td>\n",
       "      <td>[[551, 26]]</td>\n",
       "      <td>[[557, 26]]</td>\n",
       "      <td>[[559, 24]]</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>[[14, 6]]</td>\n",
       "      <td>[[14, 8]]</td>\n",
       "      <td>[[13, 9]]</td>\n",
       "      <td>[[15, 11]]</td>\n",
       "      <td>[[15, 12]]</td>\n",
       "      <td>[[13, 14]]</td>\n",
       "      <td>[[13, 17]]</td>\n",
       "      <td>[[14, 18]]</td>\n",
       "      <td>[[13, 19]]</td>\n",
       "      <td>[[12, 19]]</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>344 rows × 1583 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             0             1             2             3             4     \\\n",
       "0    [[763, 623]]  [[762, 624]]  [[762, 625]]  [[763, 626]]  [[763, 628]]   \n",
       "1     [[70, 603]]   [[68, 605]]   [[67, 605]]   [[62, 610]]   [[61, 610]]   \n",
       "2    [[288, 574]]  [[286, 576]]  [[286, 577]]  [[287, 578]]  [[285, 580]]   \n",
       "3    [[296, 573]]  [[296, 574]]  [[307, 574]]  [[308, 575]]  [[309, 574]]   \n",
       "4    [[113, 568]]  [[112, 569]]  [[111, 569]]  [[107, 573]]  [[106, 572]]   \n",
       "..            ...           ...           ...           ...           ...   \n",
       "339   [[666, 13]]   [[665, 14]]   [[665, 16]]   [[664, 17]]   [[664, 18]]   \n",
       "340   [[631, 13]]   [[630, 14]]   [[630, 35]]   [[642, 35]]   [[643, 34]]   \n",
       "341   [[622, 13]]   [[622, 35]]   [[626, 35]]   [[626, 14]]   [[625, 13]]   \n",
       "342   [[546, 13]]   [[545, 14]]   [[545, 35]]   [[549, 35]]   [[549, 34]]   \n",
       "343     [[14, 6]]     [[14, 8]]     [[13, 9]]    [[15, 11]]    [[15, 12]]   \n",
       "\n",
       "             5             6             7             8             9     \\\n",
       "0    [[762, 629]]  [[761, 629]]  [[761, 631]]  [[763, 631]]  [[764, 632]]   \n",
       "1     [[56, 615]]   [[55, 615]]   [[50, 620]]   [[49, 620]]   [[45, 624]]   \n",
       "2    [[284, 580]]  [[281, 583]]  [[280, 583]]  [[279, 584]]  [[277, 584]]   \n",
       "3    [[310, 575]]  [[323, 575]]  [[310, 575]]  [[309, 574]]  [[308, 575]]   \n",
       "4    [[106, 571]]  [[106, 572]]  [[107, 573]]  [[106, 574]]  [[105, 574]]   \n",
       "..            ...           ...           ...           ...           ...   \n",
       "339   [[663, 19]]   [[663, 21]]   [[662, 22]]   [[662, 24]]   [[661, 25]]   \n",
       "340   [[645, 34]]   [[647, 32]]   [[647, 30]]   [[648, 29]]   [[648, 19]]   \n",
       "341          None          None          None          None          None   \n",
       "342   [[550, 33]]   [[550, 27]]   [[551, 26]]   [[557, 26]]   [[559, 24]]   \n",
       "343    [[13, 14]]    [[13, 17]]    [[14, 18]]    [[13, 19]]    [[12, 19]]   \n",
       "\n",
       "     ...  1573  1574  1575  1576  1577  1578  1579  1580  1581  1582  \n",
       "0    ...  None  None  None  None  None  None  None  None  None  None  \n",
       "1    ...  None  None  None  None  None  None  None  None  None  None  \n",
       "2    ...  None  None  None  None  None  None  None  None  None  None  \n",
       "3    ...  None  None  None  None  None  None  None  None  None  None  \n",
       "4    ...  None  None  None  None  None  None  None  None  None  None  \n",
       "..   ...   ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  \n",
       "339  ...  None  None  None  None  None  None  None  None  None  None  \n",
       "340  ...  None  None  None  None  None  None  None  None  None  None  \n",
       "341  ...  None  None  None  None  None  None  None  None  None  None  \n",
       "342  ...  None  None  None  None  None  None  None  None  None  None  \n",
       "343  ...  None  None  None  None  None  None  None  None  None  None  \n",
       "\n",
       "[344 rows x 1583 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnts_df.head(-5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnts_df.to_csv('C:\\\\Users\\\\Chen\\\\Frog ID\\\\Frog data\\\\cnts_df.csv',index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#https://github.com/jrosebr1/imutils/blob/master/demos/sorting_contours.py\n",
    "#https://gist.github.com/divyaprabha123/bfa1e44ebdfc6b578fd9715818f07aec\n",
    "#https://github.com/jrosebr1/imutils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imutils import contours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "ename": "Exception",
     "evalue": "Contours tuple must have length 2 or 3, otherwise OpenCV changed their cv2.findContours return signature yet again. Refer to OpenCV's documentation in that case",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mException\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-26-29c97f054487>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mcnts\u001b[0m\u001b[1;33m=\u001b[0m \u001b[0mimutils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgrab_contours\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcnts\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;31m#cnts = imutils.grab_contours(cnts)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\imutils\\convenience.py\u001b[0m in \u001b[0;36mgrab_contours\u001b[1;34m(cnts)\u001b[0m\n\u001b[0;32m    167\u001b[0m     \u001b[1;31m# signature yet again and I have no idea WTH is going on\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    168\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 169\u001b[1;33m         raise Exception((\"Contours tuple must have length 2 or 3, \"\n\u001b[0m\u001b[0;32m    170\u001b[0m             \u001b[1;34m\"otherwise OpenCV changed their cv2.findContours return \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    171\u001b[0m             \u001b[1;34m\"signature yet again. Refer to OpenCV's documentation \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mException\u001b[0m: Contours tuple must have length 2 or 3, otherwise OpenCV changed their cv2.findContours return signature yet again. Refer to OpenCV's documentation in that case"
     ]
    }
   ],
   "source": [
    "cnts= imutils.grab_contours(cnts)\n",
    "\n",
    "#cnts = imutils.grab_contours(cnts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prediction(self, image):\n",
    "        image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
    "        image = cv2.GaussianBlur(image, (21, 21), 0)\n",
    "        if self.avg is None:\n",
    "            self.avg = image.copy().astype(float)\n",
    "        cv2.accumulateWeighted(image, self.avg, 0.5)\n",
    "        frameDelta = cv2.absdiff(image, cv2.convertScaleAbs(self.avg))\n",
    "        thresh = cv2.threshold(\n",
    "                frameDelta, DELTA_THRESH, 255,\n",
    "                cv2.THRESH_BINARY)[1]\n",
    "        thresh = cv2.dilate(thresh, None, iterations=2)\n",
    "        cnts = cv2.findContours(\n",
    "                thresh.copy(), cv2.RETR_EXTERNAL,\n",
    "                cv2.CHAIN_APPROX_SIMPLE)\n",
    "        cnts = imutils.grab_contours(cnts)\n",
    "        self.avg = image.copy().astype(float)\n",
    "        return cnts "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'module' object is not iterable",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-36-304c26d4ba5a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mimutils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcontours\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msort_contours\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcontours\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\imutils\\contours.py\u001b[0m in \u001b[0;36msort_contours\u001b[1;34m(cnts, method)\u001b[0m\n\u001b[0;32m     21\u001b[0m     \u001b[1;31m# construct the list of bounding boxes and sort them from top to\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     22\u001b[0m     \u001b[1;31m# bottom\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 23\u001b[1;33m     \u001b[0mboundingBoxes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mboundingRect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mc\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcnts\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     24\u001b[0m     (cnts, boundingBoxes) = zip(*sorted(zip(cnts, boundingBoxes),\n\u001b[0;32m     25\u001b[0m                                         key=lambda b: b[1][i], reverse=reverse))\n",
      "\u001b[1;31mTypeError\u001b[0m: 'module' object is not iterable"
     ]
    }
   ],
   "source": [
    "imutils.contours.sort_contours(contours)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'4.5.1'"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv2.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['00_0-357.jpg', '1100-2578.jpg', '1100-8058.JPG', ...,\n",
       "       '0110-8723.JPG', '0000-149.jpg', '1000-464.jpg'], dtype=object)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['fig_name'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "df149=df[df['fig_name']=='0000-149.jpg']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "open_img(os.path.join(path,df149['frog_id'].iloc[0],df149['fig_name'].iloc[0]), df149)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#A function that measures an angle between 2 lines:\n",
    "\n",
    "def dot(vA, vB):\n",
    "    return vA[0]*vB[0]+vA[1]*vB[1]\n",
    "\n",
    "def ang(lineA, lineB):\n",
    "    # Get nicer vector form\n",
    "    vA = [(lineA[0][0]-lineA[1][0]), (lineA[0][1]-lineA[1][1])]\n",
    "    vB = [(lineB[0][0]-lineB[1][0]), (lineB[0][1]-lineB[1][1])]\n",
    "    # Get dot prod\n",
    "    dot_prod = dot(vA, vB)\n",
    "    # Get magnitudes\n",
    "    magA = dot(vA, vA)**0.5\n",
    "    magB = dot(vB, vB)**0.5\n",
    "    # Get cosine value\n",
    "    cos_ = dot_prod/magA/magB\n",
    "    # Get angle in radians and then convert to degrees\n",
    "    angle = math.acos(dot_prod/magB/magA)\n",
    "    # Basically doing angle <- angle mod 360\n",
    "    ang_deg = math.degrees(angle)%360\n",
    "\n",
    "    if ang_deg-180>=0:\n",
    "        # As in if statement\n",
    "        return 360 - ang_deg\n",
    "    else: \n",
    "\n",
    "        return ang_deg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 6 points are defined :\n",
    "def comput_ang(df):\n",
    "#change to float\n",
    "    snout=(float(df['x'].iloc[0]),float(df['y'].iloc[0]))\n",
    "    vent=(float(df['x'].iloc[1]),float(df['y'].iloc[1]))\n",
    "    reye=(float(df['x'].iloc[2]),float(df['y'].iloc[2]))\n",
    "    leye=(float(df['x'].iloc[3]),float(df['y'].iloc[3]))\n",
    "    rleg=(float(df['x'].iloc[4]),float(df['y'].iloc[4]))\n",
    "    lleg=(float(df['x'].iloc[5]),float(df['y'].iloc[5]))\n",
    "\n",
    "    snout_ventline=(snout, vent)\n",
    "    eyeline=(reye, leye)\n",
    "    legline=(rleg, lleg)\n",
    "    return snout_ventline, eyeline, legline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "ventline149, eyeline149, legline149 = comput_ang(df149)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "95.46476764417719"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ang(ventline149, eyeline149)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "99.23213553501175"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ang(ventline149, legline149)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "image=cv2.imread(os.path.join(path,df['frog_id'].iloc[25],df['fig_name'].iloc[25]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv2.imwrite()"
   ]
  }
 ],
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