knn.ipynb

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   "source": [
    "# Evidence of Reasonable Models - kNN\n",
    "\n",
    "We perform a set of experiments to see if the data is classifiable and can be differentiated in the catgories mentioned. In this file, we use the kNN classifier to study the data.\n",
    "\n",
    "The following sections present our process of extracting the combined dataset (combination of data done in data_preprocessing.ipynb) from the relvant train and test files. The train and test split has been pre-done by the providers of the dataset by manually filtering out data from certain drivers (not seen in the train set) as the test set. We sample out 1255 images from the train test consisting of 12555 images and use this randomly generated, label stratified sample for our experiments. The images are preporcessed to extract the figure of the driver and set all the surroundings to black. This preprocessed data is then fed into the model, hyperparameters are tuned and the final best model is tested against the test data. The results are presented as a classification report and a confusion matrix. \n",
    "\n",
    "**Note: The final processed images and their labels have been provided in the \"prcoessed_img.npy\" and \"sampled_labels.npy\" for training data and \"sampled_test_data.npy\" and \"sampled_test_labels.npy\" for test data. Copy these files into the same folder as this kNN.ipynb file. Once you have done that you can skip, i.e., NOT RUN, the Data Preprocessing section and the Preparing Test Data sub-section under Performance Evaluation section.**"
   ]
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     "execution_count": 91,
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     "output_type": "execute_result"
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   ],
   "source": [
    "import os\n",
    "import cv2\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import image\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import classification_report, get_scorer_names\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "import torch\n",
    "from torchvision.models.segmentation import deeplabv3_resnet101, DeepLabV3_ResNet101_Weights\n",
    "from torchvision import transforms\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "# If you do not have a GPU or do not want to run this code on your GPU, comment out the following lines\n",
    "torch.cuda.empty_cache()\n",
    "torch.cuda.is_available()"
   ]
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   "source": [
    "## Extracting the Data\n",
    "\n",
    "The images stored in the combined dataset are extracted into separate train and test set paths.\n",
    "\n",
    "NOTE: Before running this file, please unzip the Distacted Driver Dataset that contains the combined dataset."
   ]
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      "1579\n"
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   "source": [
    "CLASS = [[\"c0\", \"Safe Driving\"], [\"c1\", \"Text\"], [\"c2\", \"Phone\"], \n",
    "         [\"c3\", \"Adjusting Radio\"], [\"c4\", \"Drinking\"], \n",
    "         [\"c5\", \"Reaching Behind\"], [\"c6\", \"Hair or Makeup\"], \n",
    "         [\"c7\", \"Talking to Passenger\"]]\n",
    "TEST_CLS = [os.path.join(os.getcwd(), \"Distracted Driver Dataset\", \"Combined\", \"test\", cls[0]) for cls in CLASS]\n",
    "TRAIN_CLS = [os.path.join(os.getcwd(), \"Distracted Driver Dataset\", \"Combined\", \"train\", cls[0]) for cls in CLASS]\n",
    "for cls_path in TEST_CLS:\n",
    "  print(cls_path)\n",
    "  print(len(os.listdir(cls_path)))\n",
    "for cls_path in TRAIN_CLS:\n",
    "  print(cls_path)\n",
    "  print(len(os.listdir(cls_path)))"
   ]
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   "source": [
    "For each class, the path of the training and test data is stored in separate lists. The corresponding labels are also stored in a different list"
   ]
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  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_paths = []\n",
    "test_paths = []\n",
    "for cls in range(8):\n",
    "  for train_instance in os.listdir(TRAIN_CLS[cls]):\n",
    "    train_paths.append(os.path.join(TRAIN_CLS[cls], train_instance))\n",
    "  for test_instance in os.listdir(TEST_CLS[cls]):\n",
    "    test_paths.append(os.path.join(TEST_CLS[cls], test_instance))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "classes = []\n",
    "test_classes = []\n",
    "for cls in range(8):\n",
    "  for train_instance in os.listdir(TRAIN_CLS[cls]):\n",
    "    classes.append(cls)\n",
    "  for test_instance in os.listdir(TEST_CLS[cls]):\n",
    "    test_classes.append(cls)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then separate dataframes storing the train and test paths with their corresponding labels are created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
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       "\n",
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      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df = pd.DataFrame({\n",
    "  'filename': train_paths,\n",
    "  'class': classes\n",
    "})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "\n",
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     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test = pd.DataFrame({\n",
    "  'filename': test_paths,\n",
    "  'class': test_classes\n",
    "})\n",
    "df_test"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The below plots show us the frequency of each class in the dataset. As can be seen from the plots, the dataset is not balanced. Keeping this in mind, we use evaluation metrics like recall and precision along with accuracy to get better insights on performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['class'].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_test['class'].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>filename</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>c:\\Users\\wamik\\Documents\\Notes Y4S2\\CS3244\\Pro...</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>c:\\Users\\wamik\\Documents\\Notes Y4S2\\CS3244\\Pro...</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>c:\\Users\\wamik\\Documents\\Notes Y4S2\\CS3244\\Pro...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>c:\\Users\\wamik\\Documents\\Notes Y4S2\\CS3244\\Pro...</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>14473</th>\n",
       "      <td>c:\\Users\\wamik\\Documents\\Notes Y4S2\\CS3244\\Pro...</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14474</th>\n",
       "      <td>c:\\Users\\wamik\\Documents\\Notes Y4S2\\CS3244\\Pro...</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14475</th>\n",
       "      <td>c:\\Users\\wamik\\Documents\\Notes Y4S2\\CS3244\\Pro...</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>14476</th>\n",
       "      <td>c:\\Users\\wamik\\Documents\\Notes Y4S2\\CS3244\\Pro...</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14477</th>\n",
       "      <td>c:\\Users\\wamik\\Documents\\Notes Y4S2\\CS3244\\Pro...</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14478 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                filename  class\n",
       "0      c:\\Users\\wamik\\Documents\\Notes Y4S2\\CS3244\\Pro...      0\n",
       "1      c:\\Users\\wamik\\Documents\\Notes Y4S2\\CS3244\\Pro...      0\n",
       "2      c:\\Users\\wamik\\Documents\\Notes Y4S2\\CS3244\\Pro...      0\n",
       "3      c:\\Users\\wamik\\Documents\\Notes Y4S2\\CS3244\\Pro...      0\n",
       "4      c:\\Users\\wamik\\Documents\\Notes Y4S2\\CS3244\\Pro...      0\n",
       "...                                                  ...    ...\n",
       "14473  c:\\Users\\wamik\\Documents\\Notes Y4S2\\CS3244\\Pro...      7\n",
       "14474  c:\\Users\\wamik\\Documents\\Notes Y4S2\\CS3244\\Pro...      7\n",
       "14475  c:\\Users\\wamik\\Documents\\Notes Y4S2\\CS3244\\Pro...      7\n",
       "14476  c:\\Users\\wamik\\Documents\\Notes Y4S2\\CS3244\\Pro...      7\n",
       "14477  c:\\Users\\wamik\\Documents\\Notes Y4S2\\CS3244\\Pro...      7\n",
       "\n",
       "[14478 rows x 2 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all_data = pd.concat([df, df_test], ignore_index=True)\n",
    "df_all_data"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preprocessing\n",
    "\n",
    "First, we convert the images from RGB to Gray and then perform appropriate resizing based on a calculated scale. Then, we use pytorch's DeeplabV3 neural network to extract the shape of the driver, these are represented as pixels marked true in _mask_. [source](https://v-hramchenko.medium.com/how-to-cut-out-a-person-in-an-image-with-open-source-projects-e5e7f8798d5c)\n",
    "\n",
    "The pixels in the gray resized image corresponding to these true pixels are then extracted and the rest of the pixels are set to black. As such we get images with the driver cropped out and background set to black. This will help the model to focus on the expected regions of the image to appropriately detect distracted behaviour.\n",
    "\n",
    "**Note: The code in this section  needs to be run only once. If you already have \"first_half_processed_imgs.npy\", \"scnd_half_processed_imgs.npy\", \"processed_imgs.npy\", and \"sampled_labels.npy\", DO NOT run this section. Skip to [Loading Final Data](#loading-final-data)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_deeplab(device):\n",
    "    deeplab = deeplabv3_resnet101(weights=DeepLabV3_ResNet101_Weights.DEFAULT).to(device)\n",
    "    deeplab.eval()\n",
    "    return deeplab\n",
    "\n",
    "device = torch.device(\"cuda\") # If you do not have a GPU or do not want to run the code on your GPU, replace \"cuda\" with \"cpu\"\n",
    "deeplab = make_deeplab(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "deeplab_preprocess = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n",
    "])\n",
    "\n",
    "def apply_deeplab(deeplab, img, device):\n",
    "    input_tensor = deeplab_preprocess(img)\n",
    "    input_batch = input_tensor.unsqueeze(0)\n",
    "    with torch.no_grad():\n",
    "        output = deeplab(input_batch.to(device))['out'][0]\n",
    "    # If you do not have a GPU or do not want to run this code on your GPU, replace the next two lines with one line: \n",
    "    # output_predictions = output.argmax(0).cpu().numpy()\n",
    "    output_predictions = output.argmax(0).cuda().to(\"cpu\")\n",
    "    output_predictions = output_predictions.numpy()\n",
    "    return (output_predictions == 15)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first test out our proposed idea on a sample image, the results of which are shown below. It can be seen that the driver is cropped out of the image and the background is removed (set to black)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(210, 340, 3)\n",
      "(210, 340, 3)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1f5892f7550>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img_orig = cv2.imread(df['filename'].iloc[0], cv2.COLOR_RGB2GRAY)\n",
    "plt.imshow(img_orig)\n",
    "plt.show()\n",
    "k = min(1.0, 1024/max(img_orig.shape[0], img_orig.shape[1]))\n",
    "img = cv2.resize(img_orig, (340, 210), fx=k, fy=k, interpolation=cv2.INTER_LANCZOS4)\n",
    "\n",
    "# Running the deeplab model on a sample image\n",
    "mask = apply_deeplab(deeplab, img, device)\n",
    "\n",
    "plt.imshow(mask, cmap='gray')\n",
    "plt.show()\n",
    "\n",
    "# Only choose pixels of the gray image that correspond to true pixels of the mask. Rest are set to rgb 0, 0, 0, i.e., black.\n",
    "print(np.asarray(img).shape)\n",
    "img_seg = []\n",
    "for i in range(len(mask)):\n",
    "    pixels = []\n",
    "    for j in range(len(mask[i])):\n",
    "        if mask[i][j]:\n",
    "            pixels.append(img[i][j])\n",
    "        else:\n",
    "            pixels.append([0, 0, 0])\n",
    "    img_seg.append(pixels)\n",
    "    \n",
    "img_seg = np.asarray(img_seg)\n",
    "print(img_seg.shape)\n",
    "plt.imshow(img_seg)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now perform a similar process on all sets of the training data. Before doing so, we first convert the images into 2D pixels with the appropriate color to gray transformation and resizing. This is done so that the 1255 samples can be randomly selected using _train_test_split()_."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed  135 images. Continuing...\n",
      "Processed  270 images. Continuing...\n",
      "Processed  405 images. Continuing...\n",
      "Processed  540 images. Continuing...\n",
      "Processed  675 images. Continuing...\n",
      "Processed  810 images. Continuing...\n",
      "Processed  945 images. Continuing...\n",
      "Processed  1080 images. Continuing...\n",
      "Processed  1215 images. Continuing...\n",
      "Processed  1350 images. Continuing...\n",
      "Processed  1485 images. Continuing...\n",
      "Processed  1620 images. Continuing...\n",
      "Processed  1755 images. Continuing...\n",
      "Processed  1890 images. Continuing...\n",
      "Processed  2025 images. Continuing...\n",
      "Processed  2160 images. Continuing...\n",
      "Processed  2295 images. Continuing...\n",
      "Processed  2430 images. Continuing...\n",
      "Processed  2565 images. Continuing...\n",
      "Processed  2700 images. Continuing...\n",
      "Processed  2835 images. Continuing...\n",
      "Processed  2970 images. Continuing...\n",
      "Processed  3105 images. Continuing...\n",
      "Processed  3240 images. Continuing...\n",
      "Processed  3375 images. Continuing...\n",
      "Processed  3510 images. Continuing...\n",
      "Processed  3645 images. Continuing...\n",
      "Processed  3780 images. Continuing...\n",
      "Processed  3915 images. Continuing...\n",
      "Processed  4050 images. Continuing...\n",
      "Processed  4185 images. Continuing...\n",
      "Processed  4320 images. Continuing...\n",
      "Processed  4455 images. Continuing...\n",
      "Processed  4590 images. Continuing...\n",
      "Processed  4725 images. Continuing...\n",
      "Processed  4860 images. Continuing...\n",
      "Processed  4995 images. Continuing...\n",
      "Processed  5130 images. Continuing...\n",
      "Processed  5265 images. Continuing...\n",
      "Processed  5400 images. Continuing...\n",
      "Processed  5535 images. Continuing...\n",
      "Processed  5670 images. Continuing...\n",
      "Processed  5805 images. Continuing...\n",
      "Processed  5940 images. Continuing...\n",
      "Processed  6075 images. Continuing...\n",
      "Processed  6210 images. Continuing...\n",
      "Processed  6345 images. Continuing...\n",
      "Processed  6480 images. Continuing...\n",
      "Processed  6615 images. Continuing...\n",
      "Processed  6750 images. Continuing...\n",
      "Processed  6885 images. Continuing...\n",
      "Processed  7020 images. Continuing...\n",
      "Processed  7155 images. Continuing...\n",
      "Processed  7290 images. Continuing...\n",
      "Processed  7425 images. Continuing...\n",
      "Processed  7560 images. Continuing...\n",
      "Processed  7695 images. Continuing...\n",
      "Processed  7830 images. Continuing...\n",
      "Processed  7965 images. Continuing...\n",
      "Processed  8100 images. Continuing...\n",
      "Processed  8235 images. Continuing...\n",
      "Processed  8370 images. Continuing...\n",
      "Processed  8505 images. Continuing...\n",
      "Processed  8640 images. Continuing...\n",
      "Processed  8775 images. Continuing...\n",
      "Processed  8910 images. Continuing...\n",
      "Processed  9045 images. Continuing...\n",
      "Processed  9180 images. Continuing...\n",
      "Processed  9315 images. Continuing...\n",
      "Processed  9450 images. Continuing...\n",
      "Processed  9585 images. Continuing...\n",
      "Processed  9720 images. Continuing...\n",
      "Processed  9855 images. Continuing...\n",
      "Processed  9990 images. Continuing...\n",
      "Processed  10125 images. Continuing...\n",
      "Processed  10260 images. Continuing...\n",
      "Processed  10395 images. Continuing...\n",
      "Processed  10530 images. Continuing...\n",
      "Processed  10665 images. Continuing...\n",
      "Processed  10800 images. Continuing...\n",
      "Processed  10935 images. Continuing...\n",
      "Processed  11070 images. Continuing...\n",
      "Processed  11205 images. Continuing...\n",
      "Processed  11340 images. Continuing...\n",
      "Processed  11475 images. Continuing...\n",
      "Processed  11610 images. Continuing...\n",
      "Processed  11745 images. Continuing...\n",
      "Processed  11880 images. Continuing...\n",
      "Processed  12015 images. Continuing...\n",
      "Processed  12150 images. Continuing...\n",
      "Processed  12285 images. Continuing...\n",
      "Processed  12420 images. Continuing...\n",
      "Processed  12555 images. Continuing...\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "X_train_final = []\n",
    "Y_test = []\n",
    "for index, path in df.iterrows():\n",
    "    img_orig = cv2.imread(path[0], cv2.COLOR_RGB2GRAY)\n",
    "    k = min(1.0, 1024/max(img_orig.shape[0], img_orig.shape[1]))\n",
    "    img = cv2.resize(img_orig, (340, 210), fx=k, fy=k, interpolation=cv2.INTER_LANCZOS4)\n",
    "    if (index+1) % 135 == 0:\n",
    "        print(\"Processed \", index + 1, \"images. Continuing...\")\n",
    "    X_train_final.append(np.asarray(img))\n",
    "    Y_test.append(path[1])\n",
    "\n",
    "print(\"Done!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12555\n"
     ]
    }
   ],
   "source": [
    "print(len(Y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(12555, 210, 340, 3)\n"
     ]
    }
   ],
   "source": [
    "data_arr = np.asarray(X_train_final)\n",
    "print(data_arr.shape)\n",
    "# data_arr = data_arr.reshape(data_arr.shape[0], -1)\n",
    "labels_arr = np.asarray(Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1255, 210, 340, 3)\n",
      "(1255,)\n"
     ]
    }
   ],
   "source": [
    "(X_train, X_test, Y_train, Y_test) = train_test_split(data_arr, labels_arr, test_size=0.9, stratify=Y_test, random_state=42)\n",
    "X_train = np.asarray(X_train)\n",
    "Y_train = np.asarray(Y_train)\n",
    "X_test = np.asarray(X_test)\n",
    "Y_test = np.asarray(Y_test)\n",
    "print(X_train.shape)\n",
    "print(Y_train.shape)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We process the data in 2 parts due to processing time and power constraints. Each half is stored in a separate .npy file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed  1  images. Continuing...\n",
      "Processed  2  images. Continuing...\n",
      "Processed  3  images. Continuing...\n",
      "Processed  4  images. Continuing...\n",
      "Processed  5  images. Continuing...\n",
      "Processed  6  images. Continuing...\n",
      "Processed  7  images. Continuing...\n",
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      "Processed  517  images. Continuing...\n",
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      "Processed  617  images. Continuing...\n",
      "Processed  618  images. Continuing...\n",
      "Processed  619  images. Continuing...\n",
      "Processed  620  images. Continuing...\n",
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      "Processed  622  images. Continuing...\n",
      "Processed  623  images. Continuing...\n",
      "Processed  624  images. Continuing...\n",
      "Processed  625  images. Continuing...\n",
      "Processed  626  images. Continuing...\n",
      "Processed  627  images. Continuing...\n",
      "(627, 210, 340, 3)\n"
     ]
    }
   ],
   "source": [
    "X_train_processed = []\n",
    "done = 0\n",
    "for img in X_train[0:627]:\n",
    "    done+=1\n",
    "    mask = apply_deeplab(deeplab, img, device)\n",
    "    img_seg = []\n",
    "    for i in range(len(mask)):\n",
    "        pixels = []\n",
    "        for j in range(len(mask[i])):\n",
    "            if mask[i][j]:\n",
    "                pixels.append(img[i][j])\n",
    "            else:\n",
    "                pixels.append([0, 0, 0])\n",
    "        img_seg.append(pixels)\n",
    "    X_train_processed.append(img_seg)\n",
    "    print(\"Processed \", done, \" images. Continuing...\") \n",
    "\n",
    "X_train_processed = np.asarray(X_train_processed)\n",
    "print(X_train_processed.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save(\"first_half_processed_imgs.npy\", X_train_processed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "(628, 210, 340, 3)\n"
     ]
    }
   ],
   "source": [
    "X_train_processed = []\n",
    "done = 0\n",
    "for img in X_train[627:]:\n",
    "    done+=1\n",
    "    mask = apply_deeplab(deeplab, img, device)\n",
    "    img_seg = []\n",
    "    for i in range(len(mask)):\n",
    "        pixels = []\n",
    "        for j in range(len(mask[i])):\n",
    "            if mask[i][j]:\n",
    "                pixels.append(img[i][j])\n",
    "            else:\n",
    "                pixels.append([0, 0, 0])\n",
    "        img_seg.append(pixels)\n",
    "    X_train_processed.append(img_seg)\n",
    "    print(\"Processed \", done, \" images. Continuing...\") \n",
    "\n",
    "X_train_processed = np.asarray(X_train_processed)\n",
    "print(X_train_processed.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save(\"scnd_half_processed_imgs.npy\", X_train_processed)\n",
    "np.save(\"sampled_labels.npy\", Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1255, 214200)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_1 = np.load(\"first_half_processed_imgs.npy\")\n",
    "X_train_2 = np.load(\"scnd_half_processed_imgs.npy\")\n",
    "Y_train = np.load(\"sampled_labels.npy\")\n",
    "X_train_final = np.concatenate((X_train_1, X_train_2))\n",
    "X_train_final = X_train_final.reshape(X_train_final.shape[0], -1)\n",
    "X_train_final.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We reduce the dimensions of the data using Principal Component Analysis (PCA)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca_dims = PCA()\n",
    "pca_dims.fit(X_train_final)\n",
    "cumsum = np.cumsum(pca_dims.explained_variance_ratio_)\n",
    "d = np.argmax(cumsum >= 0.95) + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "514"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=d)\n",
    "X_train_reduced = pca.fit_transform(X_train_final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save(\"processed_imgs.npy\", X_train_reduced)\n",
    "np.save(\"sampled_labels.npy\", Y_train)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading Final Data\n",
    "\n",
    "Note: Ensure that the below files being loaded are in the same location as this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1255, 514)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_train = np.load(\"Data/Initial sampled images/sampled_labels.npy\")\n",
    "X_train_final = np.load(\"Data/Initial sampled images/processed_imgs.npy\")\n",
    "X_train_final.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1255, 514)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_final = X_train_final.reshape(X_train_final.shape[0], -1)\n",
    "X_train_final.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## kNN Model\n",
    "\n",
    "We choose to do trial and error for choosing the best hyperparameters due to the nature of the data. The training set has drivers that the model has already seen, hence doing a validation split on the training set may result in overfitting to drivers' facial features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(get_scorer_names())\n",
    "# parameters_knn = {'n_neighbors': range(5, 25), 'weights': ['uniform', 'distance'], 'metric': ['euclidean', 'chebyshev', 'manhattan']}\n",
    "\n",
    "# clf_knn = GridSearchCV(KNeighborsClassifier(), param_grid=parameters_knn, cv=5, n_jobs=-1, scoring=\"v_measure_score\", verbose=10)\n",
    "\n",
    "# clf_knn.fit(X=X_train_final, y=Y_train)\n",
    "\n",
    "# print (\"The best acc for KNN:\", clf_knn.best_score_ * 100, \"%, with\", clf_knn.best_params_) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3 4 5 6 7]\n"
     ]
    }
   ],
   "source": [
    "clf_knn = KNeighborsClassifier(n_neighbors = 10, weights = 'distance')\n",
    "clf_knn.fit(X_train_final, Y_train)\n",
    "print(clf_knn.classes_)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performance Evaluation\n",
    "\n",
    "### Preparing Test Data\n",
    "\n",
    "Test data is prepared in the same manner as the training data. Run this section ONLY ONCE to generate the .npy files.\n",
    "\n",
    "**Note: if you have the \"sampled_test_data.npy\" and \"sampled_test_labels.npy\" files, DO NOT run this section, skip to [Evaluating Performance](#evaluating-performance) section.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed  135 images. Continuing...\n",
      "Processed  270 images. Continuing...\n",
      "Processed  405 images. Continuing...\n",
      "Processed  540 images. Continuing...\n",
      "Processed  675 images. Continuing...\n",
      "Processed  810 images. Continuing...\n",
      "Processed  945 images. Continuing...\n",
      "Processed  1080 images. Continuing...\n",
      "Processed  1215 images. Continuing...\n",
      "Processed  1350 images. Continuing...\n",
      "Processed  1485 images. Continuing...\n",
      "Processed  1620 images. Continuing...\n",
      "Processed  1755 images. Continuing...\n",
      "Processed  1890 images. Continuing...\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "X_test_processed = []\n",
    "Y_test = []\n",
    "for index, path in df_test.iterrows():\n",
    "    img_orig = cv2.imread(path[0], cv2.COLOR_RGB2GRAY)\n",
    "    k = min(1.0, 1024/max(img_orig.shape[0], img_orig.shape[1]))\n",
    "    img = cv2.resize(img_orig, (340, 210), fx=k, fy=k, interpolation=cv2.INTER_LANCZOS4)\n",
    "    if (index+1) % 135 == 0:\n",
    "        print(\"Processed \", index + 1, \"images. Continuing...\")\n",
    "    X_test_processed.append(np.asarray(img))\n",
    "    Y_test.append(path[1])\n",
    "\n",
    "print(\"Done!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1923, 210, 340, 3)\n"
     ]
    }
   ],
   "source": [
    "tdata_arr = np.asarray(X_test_processed)\n",
    "print(tdata_arr.shape)\n",
    "# data_arr = data_arr.reshape(data_arr.shape[0], -1)\n",
    "tlabels_arr = np.asarray(Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(193, 210, 340, 3)\n",
      "(193,)\n"
     ]
    }
   ],
   "source": [
    "(X_train, X_test, Y_test, Y_test) = train_test_split(tdata_arr, tlabels_arr, test_size=0.1, stratify=tlabels_arr, random_state=42)\n",
    "X_test = np.asarray(X_test)\n",
    "Y_test = np.asarray(Y_test)\n",
    "print(X_test.shape)\n",
    "print(Y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed  1  images. Continuing...\n",
      "Processed  2  images. Continuing...\n",
      "Processed  3  images. Continuing...\n",
      "Processed  4  images. Continuing...\n",
      "Processed  5  images. Continuing...\n",
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      "Processed  193  images. Continuing...\n",
      "(193, 214200)\n"
     ]
    }
   ],
   "source": [
    "X_test_processed = []\n",
    "done = 0\n",
    "for img in X_test:\n",
    "    done+=1\n",
    "    mask = apply_deeplab(deeplab, img, device)\n",
    "    img_seg = []\n",
    "    for i in range(len(mask)):\n",
    "        pixels = []\n",
    "        for j in range(len(mask[i])):\n",
    "            if mask[i][j]:\n",
    "                pixels.append(img[i][j])\n",
    "            else:\n",
    "                pixels.append([0, 0, 0])\n",
    "        img_seg.append(pixels)\n",
    "    X_test_processed.append(img_seg)\n",
    "    print(\"Processed \", done, \" images. Continuing...\") \n",
    "\n",
    "X_test_processed = np.asarray(X_test_processed)\n",
    "X_test_processed = X_test_processed.reshape(X_test_processed.shape[0], -1)\n",
    "print(X_test_processed.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_final = pca.transform(X_test_processed)\n",
    "np.save(\"sampled_test_data.npy\", X_test_final)\n",
    "np.save(\"sampled_test_labels.npy\", Y_test)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluating Performance\n",
    "\n",
    "Since accuracy may not be the best indicator of model performance due to the imbalance in the dataset, we also use confusion matrix and classification report to further support our analysis.\n",
    "\n",
    "Note: Ensure that the below files being loaded are in the same location as this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(193, 514)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_processed = np.load(\"Data/Initial sampled images/sampled_test_data.npy\")\n",
    "Y_test = np.load(\"Data/Initial sampled images/sampled_test_labels.npy\")\n",
    "X_test_processed.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.35751295336787564\n"
     ]
    }
   ],
   "source": [
    "print(clf_knn.score(X_test_processed, Y_test))\n",
    "predictions = clf_knn.predict(X_test_processed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_test_new = []\n",
    "for l in Y_test:\n",
    "    if l!=0:\n",
    "        Y_test_new.append(1)\n",
    "    else:\n",
    "        Y_test_new.append(0)\n",
    "predictions_new = []\n",
    "for pred in predictions:\n",
    "    if pred!=0:\n",
    "        predictions_new.append(1)\n",
    "    else:\n",
    "        predictions_new.append(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.50      0.20      0.29        35\n",
      "           1       0.84      0.96      0.90       158\n",
      "\n",
      "    accuracy                           0.82       193\n",
      "   macro avg       0.67      0.58      0.59       193\n",
      "weighted avg       0.78      0.82      0.79       193\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(Y_test_new, predictions_new))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x1abaa17f9a0>"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix\n",
    "\n",
    "\n",
    "cm_knn = confusion_matrix(Y_test_new, predictions_new, labels=[0, 1])\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm_knn,\n",
    "                               display_labels=[0, 1])  \n",
    "disp.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.50      0.20      0.29        35\n",
      "           1       0.35      0.69      0.46        39\n",
      "           2       0.25      0.35      0.30        37\n",
      "           3       0.73      0.47      0.57        17\n",
      "           4       0.60      0.21      0.32        14\n",
      "           5       1.00      0.43      0.60        14\n",
      "           6       0.23      0.20      0.21        15\n",
      "           7       0.13      0.09      0.11        22\n",
      "\n",
      "    accuracy                           0.36       193\n",
      "   macro avg       0.47      0.33      0.36       193\n",
      "weighted avg       0.42      0.36      0.35       193\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x1abaa1cf820>"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(classification_report(Y_test, predictions))\n",
    "cm_knn = confusion_matrix(Y_test, predictions, labels=clf_knn.classes_)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm_knn,\n",
    "                               display_labels=clf_knn.classes_)  \n",
    "disp.plot()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We get an accuracy of around 36% which is greater than the accuracy we can expect to get on random selection. Probability of getting a label right with random selection = 1/8 = 0.125. 12.5% < 36%. Moreover, the confusion matrix and classification report show that the model has f1 score > 30% for most labels and >50% for some. This provides further evidence that we more data preprocessing and feature engineering, we can classify the data. Hence, we have evidence that we can build a reasonable model for classifying the above dataset."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ml",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}