itercrop.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import dlib\n",
    "from skimage import io\n",
    "import numpy as np\n",
    "import cv2\n",
    "import matplotlib.pylab as plt\n",
    "import math\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def arc_points(point1, point2, num_of_points):\n",
    "    points = []\n",
    "    center_x = (point1[0] + point2[0])/2\n",
    "    center_y = (point1[1] + point2[1])/2\n",
    "    radius = abs((point1[0] - point2[0])/2)\n",
    "    for i in range(num_of_points):\n",
    "        if i == 0:\n",
    "            continue\n",
    "    \n",
    "        point = []\n",
    "        x = center_x + radius * math.cos(math.pi + i * math.pi / num_of_points)\n",
    "        y = center_y + radius * math.sin(math.pi + i * math.pi / num_of_points)\n",
    "        point.append(x)\n",
    "        point.append(y)\n",
    "        \n",
    "        points.append(point)\n",
    "    \n",
    "    return points\n",
    "\n",
    "\n",
    "def get_landmarks(img):\n",
    "    dets = detector(img, 1)\n",
    "    landmarks = np.zeros((34, 2))\n",
    "    for k, d in enumerate(dets):\n",
    "        shape = predictor(img, d)\n",
    "        for i in range(17):\n",
    "            landmarks[i] = (shape.part(i).x, shape.part(i).y)\n",
    "        \n",
    "        point1 = [shape.part(0).x, shape.part(0).y]\n",
    "        point2 = [shape.part(16).x, shape.part(16).y]\n",
    "        points = arc_points(point1, point2, 18)\n",
    "        for i in range(len(points)):\n",
    "            landmarks[33 - i] = (points[i][0], points[i][1])\n",
    "    \n",
    "    return landmarks\n",
    "\n",
    "\n",
    "def inside(X,Y,Region): \n",
    "    j=len(Region)-1\n",
    "    flag=False\n",
    "    for i in range(len(Region)):\n",
    "        if (Region[i][1]<Y and Region[j][1]>=Y or Region[j][1]<Y and Region[i][1]>=Y):  \n",
    "            if (Region[i][0] + (Y - Region[i][1]) / (Region[j][1] - Region[i][1]) * (Region[j][0] - Region[i][0]) < X):\n",
    "                flag =not flag\n",
    "        j=i\n",
    "    return flag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/rishit/Desktop/BGU/fc\r\n"
     ]
    }
   ],
   "source": [
    "!pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "n_arr = []\n",
    "p_arr = []\n",
    "directory = '0_frames'\n",
    "for filename in os.scandir(directory):\n",
    "    if filename.is_file():\n",
    "        print(filename.path[-12:])\n",
    "        p_arr.append(filename.path)\n",
    "        n_arr.append(filename.path[-12:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'np' 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<ipython-input-1-aa7d98719e5b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_arr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'np' is not defined"
     ]
    }
   ],
   "source": [
    "np.info(n_arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'00000000.jpg'"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_arr = np.sort(n_arr)\n",
    "n_arr[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0_frames/00000000.jpg'"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_arr = np.sort(p_arr)\n",
    "p_arr[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "detector = dlib.get_frontal_face_detector() \n",
    "# the .dat file can be downloaded following this link:\n",
    "# https://sourceforge.net/projects/dclib/files/dlib/v18.10/shape_predictor_68_face_landmarks.dat.bz2/download\n",
    "predictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "input recieved - file 00000000.jpg\n",
      "now cropping   - file 00000000.jpg\n",
      "cropped \n",
      "00000000.jpg done\n",
      "***\n",
      "input recieved - file 00000001.jpg\n",
      "now cropping   - file 00000001.jpg\n",
      "cropped \n",
      "00000001.jpg done\n",
      "***\n",
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      "now cropping   - file 00000002.jpg\n",
      "cropped \n",
      "00000002.jpg done\n",
      "***\n",
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      "now cropping   - file 00000003.jpg\n",
      "cropped \n",
      "00000003.jpg done\n",
      "***\n",
      "input recieved - file 00000004.jpg\n",
      "now cropping   - file 00000004.jpg\n",
      "cropped \n",
      "00000004.jpg done\n",
      "***\n",
      "input recieved - file 00000005.jpg\n",
      "now cropping   - file 00000005.jpg\n",
      "cropped \n",
      "00000005.jpg done\n",
      "***\n",
      "input recieved - file 00000006.jpg\n",
      "now cropping   - file 00000006.jpg\n",
      "cropped \n",
      "00000006.jpg done\n",
      "***\n",
      "input recieved - file 00000007.jpg\n",
      "now cropping   - file 00000007.jpg\n",
      "cropped \n",
      "00000007.jpg done\n",
      "***\n",
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      "now cropping   - file 00000008.jpg\n",
      "cropped \n",
      "00000008.jpg done\n",
      "***\n",
      "input recieved - file 00000009.jpg\n",
      "now cropping   - file 00000009.jpg\n",
      "cropped \n",
      "00000009.jpg done\n",
      "***\n",
      "input recieved - file 00000010.jpg\n",
      "now cropping   - file 00000010.jpg\n",
      "cropped \n",
      "00000010.jpg done\n",
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      "***\n",
      "input recieved - file 00000385.jpg\n",
      "now cropping   - file 00000385.jpg\n",
      "cropped \n",
      "00000385.jpg done\n",
      "***\n",
      "input recieved - file 00000386.jpg\n",
      "now cropping   - file 00000386.jpg\n",
      "cropped \n",
      "00000386.jpg done\n",
      "***\n",
      "input recieved - file 00000387.jpg\n",
      "now cropping   - file 00000387.jpg\n",
      "cropped \n",
      "00000387.jpg done\n",
      "***\n",
      "input recieved - file 00000388.jpg\n",
      "now cropping   - file 00000388.jpg\n",
      "cropped \n",
      "00000388.jpg done\n",
      "***\n",
      "input recieved - file 00000389.jpg\n",
      "now cropping   - file 00000389.jpg\n",
      "cropped \n",
      "00000389.jpg done\n",
      "***\n",
      "input recieved - file 00000390.jpg\n",
      "now cropping   - file 00000390.jpg\n",
      "cropped \n",
      "00000390.jpg done\n",
      "***\n",
      "input recieved - file 00000391.jpg\n",
      "now cropping   - file 00000391.jpg\n",
      "cropped \n",
      "00000391.jpg done\n",
      "***\n",
      "input recieved - file 00000392.jpg\n",
      "now cropping   - file 00000392.jpg\n",
      "cropped \n",
      "00000392.jpg done\n",
      "***\n",
      "input recieved - file 00000393.jpg\n",
      "now cropping   - file 00000393.jpg\n",
      "cropped \n",
      "00000393.jpg done\n",
      "***\n",
      "input recieved - file 00000394.jpg\n",
      "now cropping   - file 00000394.jpg\n",
      "cropped \n",
      "00000394.jpg done\n",
      "***\n",
      "input recieved - file 00000395.jpg\n",
      "now cropping   - file 00000395.jpg\n",
      "cropped \n",
      "00000395.jpg done\n",
      "***\n",
      "input recieved - file 00000396.jpg\n",
      "now cropping   - file 00000396.jpg\n",
      "cropped \n",
      "00000396.jpg done\n",
      "***\n",
      "input recieved - file 00000397.jpg\n",
      "now cropping   - file 00000397.jpg\n",
      "cropped \n",
      "00000397.jpg done\n",
      "***\n",
      "input recieved - file 00000398.jpg\n",
      "now cropping   - file 00000398.jpg\n",
      "cropped \n",
      "00000398.jpg done\n",
      "***\n",
      "input recieved - file 00000399.jpg\n",
      "now cropping   - file 00000399.jpg\n",
      "cropped \n",
      "00000399.jpg done\n",
      "***\n",
      "input recieved - file 00000400.jpg\n",
      "now cropping   - file 00000400.jpg\n",
      "cropped \n",
      "00000400.jpg done\n",
      "***\n",
      "input recieved - file 00000401.jpg\n",
      "now cropping   - file 00000401.jpg\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(p_arr)):\n",
    "    path = p_arr[i]\n",
    "    name = n_arr[i]\n",
    "    print(\"input recieved - file {}\".format(name))\n",
    "    img = io.imread(path)\n",
    "    region = get_landmarks(img)\n",
    "    shape = list(img.shape) \n",
    "    cropped_img = img.copy()\n",
    "    print(\"now cropping   - file {}\".format(name))\n",
    "    for i in range(shape[0]):\n",
    "        for j in range(shape[1]):\n",
    "            if not inside(j, i, region): \n",
    "                cropped_img[i, j] = (img[0,0][0], img[0,0][1], img[0,0][2]) # the RGB values of the background\n",
    "    print(\"cropped \")\n",
    "    cropped_img = cv2.cvtColor(cropped_img, cv2.COLOR_BGR2RGB)\n",
    "    cv2.imwrite('/Users/rishit/Desktop/BGU/fc/c_frames/{}cropped_.jpg'.format(name), cropped_img)\n",
    "    print('{} done'.format(name))\n",
    "    print(\"***\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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