crop_dataset.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "source": [
    "import numpy as np\r\n",
    "import os\r\n",
    "import imutils\r\n",
    "import dlib\r\n",
    "import cv2\r\n",
    "\r\n",
    "import imageio\r\n",
    "from imutils import face_utils\r\n",
    "import os"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "source": [
    "def shape_to_np(shape, dtype=\"int\"):\r\n",
    "    # initialize the list of (x, y)-coordinates\r\n",
    "    coords = np.zeros((68, 2), dtype=dtype)\r\n",
    "\r\n",
    "    # loop over the 68 facial landmarks and convert them\r\n",
    "    # to a 2-tuple of (x, y)-coordinates\r\n",
    "    for i in range(0, 68):\r\n",
    "    \tcoords[i] = (shape.part(i).x, shape.part(i).y)\r\n",
    "\r\n",
    "    # return the list of (x, y)-coordinates\r\n",
    "    return coords"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "def crop_and_save_image(img, img_path, write_img_path, img_name):\r\n",
    "    detector = dlib.get_frontal_face_detector()\r\n",
    "    predictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat')\r\n",
    "    # load the input image, resize it, and convert it to grayscale\r\n",
    "\r\n",
    "    image = cv2.imread(img_path)\r\n",
    "    image = imutils.resize(image, width=500)\r\n",
    "    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\r\n",
    "\r\n",
    "    # detect faces in the grayscale image\r\n",
    "    rects = detector(gray, 1)\r\n",
    "    if len(rects) > 1:\r\n",
    "    \tprint( \"ERROR: more than one face detected\")\r\n",
    "    \treturn\r\n",
    "    if len(rects) < 1:\r\n",
    "    \tprint( \"ERROR: no faces detected\")\r\n",
    "    \treturn\r\n",
    "\r\n",
    "    for (i, rect) in enumerate(rects):\r\n",
    "        shape = predictor(gray, rect)\r\n",
    "        shape = face_utils.shape_to_np(shape)\r\n",
    "        name, i, j = 'mouth', 48, 68\r\n",
    "        # clone = gray.copy()\r\n",
    "\r\n",
    "        (x, y, w, h) = cv2.boundingRect(np.array([shape[i:j]]))        \r\n",
    "        roi = gray[y:y+h, x:x+w]\r\n",
    "        roi = imutils.resize(roi, width = 250, inter=cv2.INTER_CUBIC)        \r\n",
    "        print('cropped/' + write_img_path)\r\n",
    "        cv2.imwrite('cropped/' + write_img_path, roi)\r\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "source": [
    "people = ['F01','F02','F04','F05','F06','F07','F08','F09', 'F10','F11','M01','M02','M04','M07','M08']\r\n",
    "data_types = ['words']\r\n",
    "folder_enum = ['01','02','03','04','05','06','07','08', '09', '10']\r\n",
    "instances = ['01','02','03','04','05','06','07','08', '09', '10']\r\n",
    "\r\n",
    "words = ['Begin', 'Choose', 'Connection', 'Navigation', 'Next', 'Previous', 'Start', 'Stop', 'Hello', 'Web']          \r\n",
    "words_di = {i:words[i] for i in range(len(words))}\r\n",
    "\r\n",
    "if not os.path.exists('cropped'):\r\n",
    "    os.mkdir('cropped')\r\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "source": [
    "import shutil\r\n",
    "\r\n",
    "def crop_one_person():      \r\n",
    "    if not os.path.exists('cropped'):\r\n",
    "      os.mkdir('cropped')\r\n",
    "\r\n",
    "    people = ['M08']\r\n",
    "    data_types = ['words']\r\n",
    "    folder_enum = ['01','02','03','04','05','06','07','08','09','10']  #,'02','03','04','05','06','07','08','09','10'\r\n",
    "    instances = ['01','02','03','04','05','06','07','08', '09', '10']\r\n",
    "\r\n",
    "    i = 1\r\n",
    "    for person_ID in people:\r\n",
    "        if not os.path.exists('cropped/' + person_ID ):\r\n",
    "            os.mkdir('cropped/' + person_ID + '/')\r\n",
    "\r\n",
    "        for data_type in data_types:\r\n",
    "            if not os.path.exists('cropped/' + person_ID + '/' + data_type):\r\n",
    "                os.mkdir('cropped/' + person_ID + '/' + data_type)\r\n",
    "\r\n",
    "            for phrase_ID in folder_enum:\r\n",
    "                if not os.path.exists('cropped/' + person_ID + '/' + data_type + '/' + phrase_ID):\r\n",
    "                    # F01/phrases/01\r\n",
    "                    os.mkdir('cropped/' + person_ID + '/' + data_type + '/' + phrase_ID)\r\n",
    "\r\n",
    "                for instance_ID in instances:\r\n",
    "                    # F01/phrases/01/01\r\n",
    "                    directory = 'F:/MIRACLE-V1/dataset/dataset/' + person_ID + '/' + data_type + '/' + phrase_ID + '/' + instance_ID + '/'\r\n",
    "                    dir_temp = person_ID + '/' + data_type + '/' + phrase_ID + '/' + instance_ID + '/'\r\n",
    "    #                 print(directory)\r\n",
    "                    filelist = os.listdir(directory)\r\n",
    "                    if not os.path.exists('cropped/' + person_ID + '/' + data_type + '/' + phrase_ID + '/' + instance_ID):\r\n",
    "                        os.mkdir('cropped/' + person_ID + '/' + data_type + '/' + phrase_ID + '/' + instance_ID)\r\n",
    "\r\n",
    "                        for img_name in filelist:\r\n",
    "                            if img_name.startswith('color'):\r\n",
    "                                image = imageio.imread(directory + '' + img_name)\r\n",
    "                                crop_and_save_image(image, directory + '' + img_name,\r\n",
    "                                                    dir_temp + '' + img_name, img_name)\r\n",
    "\r\n",
    "    print(f'Iteration : {i}')\r\n",
    "    i += 1\r\n",
    "    #shutil.rmtree('cropped')\r\n",
    "\r\n",
    "crop_one_person()"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
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