Creating-the-dataset.ipynb

{
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     "text": [
      "['Barack Obama', 'Donald Trump', 'George W Bush']\n",
      "[0 1 2]\n",
      "{'Barack Obama': 0, 'Donald Trump': 1, 'George W Bush': 2}\n"
     ]
    }
   ],
   "source": [
    "import os # os files handling\n",
    "import numpy as np\n",
    "\n",
    "# import dataset folder\n",
    "data_path = r'train_data'\n",
    "\n",
    "# the file names in the dataset are added to an list\n",
    "labels = os.listdir(data_path)\n",
    "print(labels)\n",
    "\n",
    "# numbers are generated according to the number of files\n",
    "categories = np.arange(len(labels))\n",
    "print(categories)\n",
    "\n",
    "# an dictionary is created using categories (file names) & labels (number of files)\n",
    "categories_dict = dict(zip(labels,categories))\n",
    "print(categories_dict)"
   ]
  },
  {
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      "If you accept this cropped face press y or else n :\n",
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      "If you accept this cropped face press y or else n :\n",
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      "If you accept this cropped face press y or else n :\n",
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      "If you accept this cropped face press y or else n :\n",
      "If you accept this cropped face press y or else n :\n",
      "If you accept this cropped face press y or else n :\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If you accept this cropped face press y or else n :\n",
      "If you accept this cropped face press y or else n :\n",
      "If you accept this cropped face press y or else n :\n",
      "If you accept this cropped face press y or else n :\n",
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      "If you accept this cropped face press y or else n :\n",
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      "If you accept this cropped face press y or else n :\n",
      "If you accept this cropped face press y or else n :\n",
      "If you accept this cropped face press y or else n :\n",
      "If you accept this cropped face press y or else n :\n",
      "If you accept this cropped face press y or else n :\n",
      "If you accept this cropped face press y or else n :\n",
      "If you accept this cropped face press y or else n :\n",
      "If you accept this cropped face press y or else n :\n",
      "If you accept this cropped face press y or else n :\n",
      "If you accept this cropped face press y or else n :\n",
      "If you accept this cropped face press y or else n :\n",
      "If you accept this cropped face press y or else n :\n",
      "If you accept this cropped face press y or else n :\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If you accept this cropped face press y or else n :\n",
      "If you accept this cropped face press y or else n :\n",
      "If you accept this cropped face press y or else n :\n",
      "If you accept this cropped face press y or else n :\n",
      "If you accept this cropped face press y or else n :\n",
      "If you accept this cropped face press y or else n :\n",
      "If you accept this cropped face press y or else n :\n",
      "If you accept this cropped face press y or else n :\n",
      "If you accept this cropped face press y or else n :\n",
      "If you accept this cropped face press y or else n :\n",
      "If you accept this cropped face press y or else n :\n",
      "If you accept this cropped face press y or else n :\n",
      "If you accept this cropped face press y or else n :\n",
      "If you accept this cropped face press y or else n :\n",
      "If you accept this cropped face press y or else n :\n",
      "If you accept this cropped face press y or else n :\n",
      "If you accept this cropped face press y or else n :\n",
      "If you accept this cropped face press y or else n :\n",
      "If you accept this cropped face press y or else n :\n",
      "If you accept this cropped face press y or else n :\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "\n",
    "data=[]\n",
    "target=[]\n",
    "\n",
    "# load the casecade classifier\n",
    "face_classifier = cv2.CascadeClassifier('cascades/haarcascade_frontalface_default.xml')\n",
    "\n",
    "# go to all files for one by one\n",
    "for label in labels:\n",
    "    \n",
    "    # create path by useing data_path & label\n",
    "    imgs_path = os.path.join(data_path,label)\n",
    "    \n",
    "    # image names in the created path are taken into a list.\n",
    "    img_names = os.listdir(imgs_path)\n",
    "    \n",
    "    # go to all images for one by one\n",
    "    for img_name in img_names:\n",
    "        \n",
    "        # get the paths of each image, because we want to load each images one by one\n",
    "        img_path = os.path.join(imgs_path,img_name)\n",
    "        img = cv2.imread(img_path) # load images in img_path\n",
    "        \n",
    "        gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) # convert to gray image (BGR to GRAY)\n",
    "        \n",
    "        faces = face_classifier.detectMultiScale(gray) # get rectangle values (faces->2d array)\n",
    "        \n",
    "        # This loop runs according to the number of faces in the image\n",
    "        for(x,y,w,h) in faces:\n",
    "            \n",
    "            #cv2.rectangle(img,(x,y),(x+w,y+h),(0,255,0),2) # drow a rectangle box\n",
    "            cropped_face=gray[y:y+h,x:x+w] # crop faces \n",
    "            cv2.imshow('cropped_face',cropped_face) # show images (cropped faces) in window\n",
    "            \n",
    "            print('If you accept this cropped face press y or else n :')\n",
    "            \n",
    "            k=cv2.waitKey(0) # time to wait for images (holding)\n",
    "            \n",
    "            # check the y or n\n",
    "            if(k==121):\n",
    "                \n",
    "                # if you press y, the image will be resized as a 50*50 image\n",
    "                cropped_face = cv2.resize(cropped_face,(50,50))\n",
    "\n",
    "                # if an image is accepted, add it to the data and target array.\n",
    "                data.append(cropped_face)\n",
    "                target.append(categories_dict[label])\n",
    "            \n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "54ffdfca",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(324, 50, 50)\n",
      "(324,)\n"
     ]
    }
   ],
   "source": [
    "# convert numpy array\n",
    "data = np.array(data)\n",
    "target = np.array(target)\n",
    "\n",
    "print(data.shape)\n",
    "print(target.shape)\n",
    "\n",
    "data = data.reshape(data.shape[0],data.shape[1]*data.shape[2]) # convert 1d (flattening)\n",
    "\n",
    "# save physical array\n",
    "np.save('data',data)\n",
    "np.save('target',target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9d4b7243",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({1: 125, 2: 109, 0: 90})\n"
     ]
    }
   ],
   "source": [
    "import collections\n",
    "print(collections.Counter(target))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2624735a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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