data_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "from glob import glob"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read all tables for each cell with its time records."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read all tables from the folder\n",
    "tables = glob('tables/*.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# parse each table name to get coords\n",
    "coords = [table.split('/')[-1].split('(')[1].split(')')[0] for table in tables]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "     Unnamed: 0       angle  time  angle_change\n",
       "0             0  145.527786     0           NaN\n",
       "1             1  141.900328     1      3.627458\n",
       "2             2  141.340192     2      0.560136\n",
       "3             3  142.305760     3      0.965568\n",
       "4             4  142.997473     4      0.691714\n",
       "..          ...         ...   ...           ...\n",
       "198         198  141.842773   198      0.000000\n",
       "199         199  142.125016   199      0.282243\n",
       "200         200  144.904183   200      2.779167\n",
       "201         201  141.842773   201      3.061410\n",
       "202         202  142.125016   202      0.282243\n",
       "\n",
       "[203 rows x 4 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# open each table and create a dataframe\n",
    "dfs = [pd.read_csv(table) for table in tables]\n",
    "dfs[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create rolling avarage to smooth statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <th>angle</th>\n",
       "      <th>time</th>\n",
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       "      <th>avg_7</th>\n",
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       "      <td>142.125016</td>\n",
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       "      <td>NaN</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>203 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0       angle  time  angle_change       avg_7\n",
       "0             0  145.527786     0           NaN         NaN\n",
       "1             1  141.900328     1      3.627458         NaN\n",
       "2             2  141.340192     2      0.560136         NaN\n",
       "3             3  142.305760     3      0.965568  142.116063\n",
       "4             4  142.997473     4      0.691714  141.517835\n",
       "..          ...         ...   ...           ...         ...\n",
       "198         198  141.842773   198      0.000000  142.278083\n",
       "199         199  142.125016   199      0.282243  142.360759\n",
       "200         200  144.904183   200      2.779167         NaN\n",
       "201         201  141.842773   201      3.061410         NaN\n",
       "202         202  142.125016   202      0.282243         NaN\n",
       "\n",
       "[203 rows x 5 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create new column with 7 time units average\n",
    "for df in dfs:\n",
    "    df['avg_7'] = df.angle.rolling(7).mean().shift(-3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/septentrionalem/python/miniconda3/envs/m1/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "/Users/septentrionalem/python/miniconda3/envs/m1/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "/Users/septentrionalem/python/miniconda3/envs/m1/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "/Users/septentrionalem/python/miniconda3/envs/m1/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "/Users/septentrionalem/python/miniconda3/envs/m1/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "/Users/septentrionalem/python/miniconda3/envs/m1/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "/Users/septentrionalem/python/miniconda3/envs/m1/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "/Users/septentrionalem/python/miniconda3/envs/m1/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "/var/folders/gw/9m0zb5rx7vv69h7z1804vcc00000gn/T/ipykernel_10242/1793051683.py:12: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  fig.show()\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1152x1152 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# for each table, create a plot using seaborn ploting the angle column against the time \n",
    "# create figure 4 by 4\n",
    "fig, axes = plt.subplots(nrows=4, ncols=2, figsize=(16, 16))\n",
    "\n",
    "colors = ('orange', 'green', 'green', 'darkviolet', 'pink', 'skyblue', 'red', 'grey')\n",
    "for i, (df, c) in enumerate(zip(dfs, colors)):\n",
    "    sns.lineplot(df['time'], df['avg_7'], ax=axes[i//2, i%2], label=f'cell {i} avg 7', color=c)\n",
    "    # set y axis range\n",
    "    axes[i//2, i%2].set_ylim(0, 180)\n",
    "    \n",
    "fig.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Following code can be used to visualize which cells were measured. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import napari and open viewer\n",
    "import napari\n",
    "viewer = napari.Viewer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Image layer 'frames' at 0x2a93de1c0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from utils import *\n",
    "# open original data\n",
    "filepath = 'video_sperm/Jeko1_videa/FcRL3_siRNAIII.avi'\n",
    "image_data = get_image_data(filepath)\n",
    "viewer.add_image(image_data, name='frames')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'dfs' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/gw/9m0zb5rx7vv69h7z1804vcc00000gn/T/ipykernel_29895/936790198.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# add points layers with all coords\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mpoints\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdfs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m     \u001b[0mpoint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcoord\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcoord\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcoords\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m','\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mpoints\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpoint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'dfs' is not defined"
     ]
    }
   ],
   "source": [
    "# add points layers with all coords\n",
    "points = []\n",
    "for i, df in enumerate(dfs):\n",
    "    point = [int((coord.split('.'))[:4][0]) for coord in coords[i].split(',')]\n",
    "    points.append(point)\n",
    "    \n",
    "viewer.add_points(points, name=f'cell points', size=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Labels layer 'sperm' at 0x2a20a6df0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# visualize masks for selected cells and show vector\n",
    "from tifffile import imread\n",
    "\n",
    "# open segmentation\n",
    "path = 'cells_labels.tiff'\n",
    "cells = imread(path)\n",
    "viewer.add_labels(cells, name='cells')\n",
    "\n",
    "path = 'sperm_labels.tiff'\n",
    "sperm = imread(path)\n",
    "viewer.add_labels(sperm, name='sperm')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "\n",
    "for i, point in enumerate(points):\n",
    "    # take cells value from point layer\n",
    "    cell_id = cells[0][point[0]][point[1]]\n",
    "\n",
    "    # take only cell with cell_id\n",
    "    cell = cells.copy()\n",
    "    cell[cells != cell_id] = 0\n",
    "    viewer.add_labels(cell, name=f'cell {i}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 12028  39770 204088 210684]\n"
     ]
    }
   ],
   "source": [
    "# for some cell add vector in first frame\n",
    "cell = viewer.layers['cell 7'].data\n",
    "cell = cell[0]\n",
    "\n",
    "# dilate cell by 3 pixels\n",
    "cell_dilated = np.zeros_like(cell)\n",
    "kernel = np.ones((3, 3), np.uint8)\n",
    "cell_dilated = cv2.dilate(cell.astype(np.uint8), kernel=kernel, iterations=4)\n",
    "\n",
    "# if any sperm is touching cell dilated, then sperm is interacting with cell\n",
    "\n",
    "sperm_interacting = np.multiply(sperm, cell_dilated)\n",
    "sperm_interacting_id = np.unique(sperm_interacting)\n",
    "sperm_interacting_id = sperm_interacting_id[1:]\n",
    "print(sperm_interacting_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/gw/9m0zb5rx7vv69h7z1804vcc00000gn/T/ipykernel_98929/2418761533.py:1: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.\n",
      "  sperm_coords = np.array(np.where(sperm_interacting == sperm_interacting_id)).T[0]\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "index 0 is out of bounds for axis 0 with size 0",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/gw/9m0zb5rx7vv69h7z1804vcc00000gn/T/ipykernel_98929/2418761533.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msperm_coords\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msperm_interacting\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0msperm_interacting_id\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0msperm_cell_value\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msperm\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msperm_coords\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msperm_coords\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# create sperm cell\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0msperm_cell\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msperm\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: index 0 is out of bounds for axis 0 with size 0"
     ]
    }
   ],
   "source": [
    "sperm_coords = np.array(np.where(sperm_interacting == sperm_interacting_id)).T[0]\n",
    "sperm_cell_value = sperm[0][sperm_coords[0], sperm_coords[1]]\n",
    "\n",
    "# create sperm cell \n",
    "sperm_cell = sperm[0].copy()\n",
    "sperm_cell[sperm_cell != sperm_cell_value] = 0\n",
    "viewer.add_labels(sperm_cell, name=f'sperm_cell')\n",
    "\n",
    "# now do angle measurements for all time frames\n",
    "cell = cell_dilated\n",
    "\n",
    "# find stack where the overlap is present\n",
    "overlap = sperm_interacting.copy()\n",
    "overlap[overlap != sperm_interacting_id] = 0\n",
    "\n",
    "overlap_coords = np.array(np.where(overlap != 0))\n",
    "\n",
    "# compute cell line and cell vector\n",
    "cell_line, cell_vector = compute_cell_vector(overlap_coords)\n",
    "sperm_line, sperm_vector = compute_sperm_cell_vector(sperm_cell)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Shapes layer 'sperm_line' at 0x2cdbfd580>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "viewer.add_shapes([cell_line[0], cell_line[1]], name='cell_line', shape_type='line', edge_width=1)\n",
    "viewer.add_shapes([sperm_line[0], sperm_line[1]], name='sperm_line', shape_type='line', edge_width=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'viewer' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/gw/9m0zb5rx7vv69h7z1804vcc00000gn/T/ipykernel_55149/131506667.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# take all layers from the viewer of cells\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mlayers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mviewer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mlayer\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlayers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'cell '\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlayer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m         \u001b[0mim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlayer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'viewer' is not defined"
     ]
    }
   ],
   "source": [
    "# take all layers from the viewer of cells\n",
    "layers = viewer.layers\n",
    "for layer in layers:\n",
    "    if 'cell ' in layer.name:\n",
    "        im = layer.data\n",
    "        # add values to the image\n",
    "        \n",
    "        print(im.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<VideoCapture 0x111b02fb0>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import cv2\n",
    "\n",
    "cap = cv2.VideoCapture('video.mov')\n",
    "cap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n",
    "frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n",
    "fps = int(cap.get(cv2.CAP_PROP_FPS))\n",
    "\n",
    "# initialize the FourCC and a video writer object\n",
    "fourcc = cv2.VideoWriter_fourcc(*'XVID')\n",
    "output = cv2.VideoWriter('output.avi', fourcc, fps, (frame_width, frame_height))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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