vkumar24_proj02.ipynb

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   "source": [
    "```python\n",
    "####################################################################################################\n",
    "##### NC State University | Spring 2020\n",
    "##### Course  : ECE-763 Computer Vision \n",
    "##### PROJECT #2 (Haar features, AdaBoost Algorithm, Viola-Jones Face Detection)\n",
    "#####...............................................................................................\n",
    "##### Author  : Vinay Kumar\n",
    "##### UnityID : vkumar24@ncsu.edu\n",
    "#####...............................................................................................\n",
    "##### This is a work of © vkumar24@ncsu.edu\n",
    "####################################################################################################\n",
    "```"
   ]
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/bin/sh: nvidia-smi: command not found\n"
     ]
    }
   ],
   "source": [
    "!nvidia-smi"
   ]
  },
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   "source": [
    "## ```import Necessary Libraries```"
   ]
  },
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    "## |> include_in_final_source <|\n",
    "##\n",
    "\n",
    "import glob\n",
    "import os\n",
    "import pprint\n",
    "import time\n",
    "from itertools import islice\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n",
    "from skimage.feature import haar_like_feature_coord\n",
    "from skimage.feature import draw_haar_like_feature\n",
    "from skimage import io\n",
    "import skimage\n",
    "import cv2\n",
    "from PIL import Image\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import image\n",
    "from matplotlib import cm\n",
    "import matplotlib.gridspec as gridspec\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "\n",
    "import seaborn as sn"
   ]
  },
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    "id": "Lh042emiW3-y"
   },
   "source": [
    "## ```Final Source Code```"
   ]
  },
  {
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    "## |> include_in_final_source <|\n",
    "## \n",
    "## 👍 1. Create Integral Image\n",
    "## 👍 2. Find the haar features\n",
    "##      👍 1. Create discriptions for all the different types of haar-like-features\n",
    "##      👍 2. Write routine to find the features for each image for each feature-type\n",
    "##\n",
    "## 👍 3. Initialize weights for each feature\n",
    "## 👍 4. Find the threshold for each feature\n",
    "## 👍 5. Normalize the weights before each round-t\n",
    "## 👍 6. Find the error_h for each feature\n",
    "## 👍 7. Select the best haar_feature with least error_h for t-th round of AdaBoost"
   ]
  },
  {
   "cell_type": "code",
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    "## |> include_in_final_source <|\n",
    "##\n",
    "## join the pos and neg data into just one \n",
    "# data = {\"id0\": {\"label\":1,\n",
    "#                 \"img\": np.array(),\n",
    "#                 \"ii_img\": np.array(),\n",
    "#                 },\n",
    "#         \"id1\": {\"label\":1,\n",
    "#                 \"img\": np.array(),\n",
    "#                 \"ii_img\": np.array(),\n",
    "#                 },\n",
    "#         ...\n",
    "#         ...\n",
    "#         }\n",
    "\n",
    "def get_data(path, ctype, extension=\"jpg\", shape=(350,350), resolution=(20,20), as_gray=True, verbose=True):\n",
    "    \"\"\"Helper function to create consumable data from the set.\n",
    "    : path       -> path of the folder where images are present\n",
    "    : ctype      -> the type of the class the data belongs to (pos / neg)\n",
    "    : extension  -> extension of the images (.jpg, .png)\n",
    "    : shape      -> cropping shape of the initial image to capture the face only\n",
    "    : resolution -> final resolution of the images like (40x40, 20x20, 60x60) \n",
    "    : as_gray    -> whether to convert to grayscale or not\n",
    "    : verbose ->\n",
    "    \"\"\"\n",
    "    data = {}\n",
    "    idx = 0\n",
    "\n",
    "    for fname in glob.glob(f\"{path}*.{extension}\"):\n",
    "        x_lb = shape[0]\n",
    "        y_lb = shape[1]\n",
    "        img = io.imread(fname, as_gray=as_gray)\n",
    "        if img.shape[0]>=x_lb and img.shape[1]>=y_lb:\n",
    "            # add the file names\n",
    "            _id_ = f\"{ctype}_{idx}\"\n",
    "            idx += 1\n",
    "            data[_id_] = {\"fname\": fname}\n",
    "            \n",
    "            xin = np.int((img.shape[0]-x_lb)/2)\n",
    "            xout = np.int((img.shape[0]+x_lb)/2)\n",
    "            yin = np.int((img.shape[1]-y_lb)/2)\n",
    "            yout = np.int((img.shape[1]+y_lb)/2)\n",
    "            res = cv2.resize(img[xin:xout, yin:yout], dsize=resolution, interpolation=cv2.INTER_CUBIC)\n",
    "            # res = skimage.transform.resize(img, resolution, anti_aliasing=True)\n",
    "            data[_id_][\"img\"] = res\n",
    "\n",
    "            ## add the labels\n",
    "            if ctype == \"pos\":\n",
    "                data[_id_][\"label\"] = 1\n",
    "            elif ctype == \"neg\":\n",
    "                data[_id_][\"label\"] = 0\n",
    "\n",
    "            ## ⚠️can create the integral_image right here too!!\n",
    "            ## ⚠️\n",
    "            ##\n",
    "    if verbose: print(f\"num_data => {len(data.keys())} :: img_shape = {data[_id_]['img'].shape}\")\n",
    "    \n",
    "    return data"
   ]
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    "## |> include_in_final_source <|\n",
    "##\n",
    "## Create Integral Image\n",
    "\n",
    "def create_integral_image(img, do_plot=False):\n",
    "    \"\"\"Create integral_image:\n",
    "    : img -> Image\n",
    "    : do_plot -> Whether to plot the integral_image? or not?\n",
    "    : Returns::\n",
    "    : ii_img -> integral image\n",
    "    \"\"\"\n",
    "    t0 = time.time()\n",
    "    ii_img = np.zeros((img.shape[0], img.shape[1]))\n",
    "    for i in range(img.shape[0]):\n",
    "        for j in range(img.shape[1]):\n",
    "            ii_img[i, j] = np.sum(img[:i+1, :j+1])\n",
    "    # print(f\"took {time.time() - t0} seconds\")\n",
    "\n",
    "    ## padding the edges and corners with zeros (boundary conditions)\n",
    "    tmp = np.zeros((ii_img.shape[0]+2, ii_img.shape[1]+2))\n",
    "    tmp[1:-1, 1:-1] = ii_img\n",
    "    ii_img = tmp\n",
    "\n",
    "    if do_plot:\n",
    "        plt.figure()\n",
    "        plt.imshow(ii_img)\n",
    "\n",
    "    return ii_img\n",
    "\n"
   ]
  },
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    "## |> include_in_final_source <|\n",
    "##\n",
    "\n",
    "def get_coordinates(coordinates):\n",
    "    \"\"\"\n",
    "    : coordinates ->\n",
    "    \"\"\"\n",
    "\n",
    "    # print(\"\\n:::\", coordinates)\n",
    "    coord = coordinates.copy()\n",
    "    for k in range(len(coord)):\n",
    "        if coord[k][0] == coord[k][1]:\n",
    "            coord[k].pop(0)\n",
    "\n",
    "    if coord[0] == coord[1]:\n",
    "        coord.pop(0)\n",
    "\n",
    "    coord = np.array(coord)\n",
    "    # print(\"\\n::\", coord)\n",
    "\n",
    "    return coord\n",
    "\n"
   ]
  },
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    "## |> include_in_final_source <|\n",
    "##\n",
    "## Getting the sum of all pixels in a given coordinate array and the integral_image\n",
    "\n",
    "def get_sum_pixels(coord, ii_img):\n",
    "    \"\"\"\n",
    "    : coord ->\n",
    "    : ii_img ->\n",
    "    \"\"\"\n",
    "\n",
    "    if coord.shape[0]==1 and coord.shape[1]==1:\n",
    "        A = ii_img[coord[0,0,0]-1, coord[0,0,1]-1]\n",
    "        B = ii_img[coord[0,0,0]-1, coord[0,0,1]]\n",
    "        C = ii_img[coord[0,0,0], coord[0,0,1]-1]\n",
    "        D = ii_img[coord[0,0,0], coord[0,0,1]]\n",
    "    if coord.shape[0]==1 and coord.shape[1]!=1:\n",
    "        A = ii_img[coord[0,0,0]-1, coord[0,0,1]-1]\n",
    "        B = ii_img[coord[0,1,0]-1, coord[0,1,1]]\n",
    "        C = ii_img[coord[0,0,0], coord[0,0,1]-1]\n",
    "        D = ii_img[coord[0,1,0], coord[0,1,1]]\n",
    "    if coord.shape[0]!=1 and coord.shape[1]==1:\n",
    "        A = ii_img[coord[0,0,0]-1, coord[0,0,1]-1]\n",
    "        B = ii_img[coord[0,0,0]-1, coord[0,0,1]]\n",
    "        C = ii_img[coord[1,0,0], coord[1,0,1]-1]\n",
    "        D = ii_img[coord[1,0,0], coord[1,0,1]]\n",
    "    if coord.shape[0]!=1 and coord.shape[1]!=1:\n",
    "        A = ii_img[coord[0,0,0]-1, coord[0,0,1]-1]\n",
    "        B = ii_img[coord[0,1,0]-1, coord[0,1,1]]\n",
    "        C = ii_img[coord[1,0,0], coord[1,0,1]-1]\n",
    "        D = ii_img[coord[1,1,0], coord[1,1,1]]\n",
    "    \n",
    "    # print(A, B, C, D, \"\\n-----------\\n\")\n",
    "\n",
    "    ## calculate the feature value using the integral image => (A-B-C+D)\n",
    "    sum_pixels = A-B-C+D\n",
    "    return sum_pixels\n"
   ]
  },
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   "source": [
    "## |> include_in_final_source <|\n",
    "##\n",
    "## Create discriptions for all the different types of haar-like-features\n",
    "\n",
    "class HaarFeatures():\n",
    "    \"\"\"\n",
    "    docstring\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, win_i0, win_j0, win_w, win_h):\n",
    "        \"\"\"\n",
    "        : win_i0 ->\n",
    "        : win_j0 ->\n",
    "        : win_w ->\n",
    "        : win_h ->\n",
    "        \"\"\"\n",
    "        self.feat_type = \"default\"\n",
    "        self.win_i0 = win_i0\n",
    "        self.win_j0 = win_j0\n",
    "        self.win_w = win_w\n",
    "        self.win_h = win_h\n",
    "        self.feats = dict()\n",
    "\n",
    "    def _init_feats(self):\n",
    "        pass\n",
    "\n",
    "    def init_feats(self):\n",
    "        self._init_feats()\n",
    "\n"
   ]
  },
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    "## |> include_in_final_source <|\n",
    "##\n",
    "\n",
    "class HaarTypeLR(HaarFeatures):\n",
    "    \"\"\"\n",
    "    Types of haar_features extracted are displayed below:\n",
    "    ______\n",
    "    |*||#|\n",
    "    ------\n",
    "    ________\n",
    "    |**||##|\n",
    "    --------\n",
    "    ________\n",
    "    |*||#|\n",
    "    |*||#|\n",
    "    --------\n",
    "    ____________\n",
    "    |****||####|\n",
    "    ------------\n",
    "    ____________\n",
    "    |****||####|\n",
    "    |****||####|\n",
    "    ------------\n",
    "    and so on ...\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, win_i0, win_j0, win_h, win_w):\n",
    "        \"\"\"\n",
    "        docstring\n",
    "        \"\"\"\n",
    "        super().__init__(win_i0, win_j0, win_h, win_w)\n",
    "        self.feat_type = \"haar_lr\"\n",
    "\n",
    "    \n",
    "    def _init_feats(self):\n",
    "        for i in range(self.win_i0+1, self.win_h):\n",
    "            for j in range(self.win_j0+1, self.win_w):\n",
    "                h = 1\n",
    "                while i+h <= self.win_h:\n",
    "                    w = 1\n",
    "                    while j+2*w <= self.win_w:\n",
    "                        coords = []\n",
    "                        coord_A = [[(i, j), (i, j+w-1)], [(i+h-1, j), (i+h-1, j+w-1)]]\n",
    "                        coord_B = [[(i, j+w), (i, j+2*w-1)], [(i+h-1, j+w), (i+h-1, j+2*w-1)]]\n",
    "\n",
    "                        coords.append(coord_A)\n",
    "                        coords.append(coord_B)\n",
    "\n",
    "                        cx = []\n",
    "                        for t in range(len(coords)):\n",
    "                            c = get_coordinates(coords[t])\n",
    "                            cx.append(c)\n",
    "                        \n",
    "                        self.feats[((i,j,h,w), self.feat_type)] = {\"feat_type\": self.feat_type, \n",
    "                                                                   \"val\": {}, \n",
    "                                                                   \"error\": 0.,\n",
    "                                                                   \"alpha\": 0.,\n",
    "                                                                   \"coord_A\": cx[0], \n",
    "                                                                   \"coord_B\": cx[1],\n",
    "                                                                   }\n",
    "                        \n",
    "                        cx = []     ## reset the coordinates for next round\n",
    "                        w += 1      ## increment the weight\n",
    "                    h += 1          ## increment the height\n",
    "   \n",
    "        \n",
    "    "
   ]
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   "source": [
    "## |> include_in_final_source <|\n",
    "##\n",
    "\n",
    "class HaarTypeLCR(HaarFeatures):\n",
    "    \"\"\"\n",
    "    Types of haar_features extracted are displayed below:\n",
    "    _________\n",
    "    |*||#||*|\n",
    "    ---------\n",
    "    ___________\n",
    "    |**||##||**|\n",
    "    -----------\n",
    "    _________\n",
    "    |*||#||*|\n",
    "    |*||#||*|\n",
    "    ---------\n",
    "    __________________\n",
    "    |****||####||****|\n",
    "    ------------------\n",
    "    __________________\n",
    "    |****||####||****|\n",
    "    |****||####||****|\n",
    "    ------------------\n",
    "    and so on ...\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, win_i0, win_j0, win_h, win_w):\n",
    "        \"\"\"\n",
    "        docstring\n",
    "        \"\"\"\n",
    "        super().__init__(win_i0, win_j0, win_h, win_w)\n",
    "        self.feat_type = \"haar_lcr\"\n",
    "\n",
    "    \n",
    "    def _init_feats(self):\n",
    "        for i in range(self.win_i0+1, self.win_h):\n",
    "            for j in range(self.win_j0+1, self.win_w):\n",
    "                h = 1\n",
    "                while i+h <= self.win_h:\n",
    "                    w = 1\n",
    "                    while j+3*w <= self.win_w:\n",
    "                        coords = []\n",
    "                        coord_A = [[(i, j), (i, j+w-1)], [(i+h-1, j), (i+h-1, j+w-1)]]\n",
    "                        coord_B = [[(i, j+w), (i, j+2*w-1)], [(i+h-1, j+w), (i+h-1, j+2*w-1)]]\n",
    "                        coord_C = [[(i, j+2*w), (i, j+3*w-1)], [(i+h-1, j+2*w), (i+h-1, j+3*w-1)]]\n",
    "\n",
    "                        coords.append(coord_A)\n",
    "                        coords.append(coord_B)\n",
    "                        coords.append(coord_C)\n",
    "\n",
    "                        cx = []\n",
    "                        for t in range(len(coords)):\n",
    "                            c = get_coordinates(coords[t])\n",
    "                            cx.append(c)\n",
    "                        \n",
    "                        self.feats[((i,j,h,w), self.feat_type)] = {\"feat_type\": self.feat_type, \n",
    "                                                                   \"val\": {}, \n",
    "                                                                   \"error\": 0.,\n",
    "                                                                   \"alpha\": 0.,\n",
    "                                                                   \"coord_A\": cx[0], \n",
    "                                                                   \"coord_B\": cx[1],\n",
    "                                                                   \"coord_C\": cx[2],\n",
    "                                                                   }\n",
    "                        \n",
    "                        cx = []     ## reset the coordinates for next round\n",
    "                        w += 1      ## increment the weight\n",
    "                    h += 1          ## increment the height\n",
    "   \n",
    "        \n",
    "    "
   ]
  },
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   "source": [
    "## |> include_in_final_source <|\n",
    "##\n",
    "\n",
    "class HaarTypeTB(HaarFeatures):\n",
    "    \"\"\"\n",
    "    Types of haar_features extracted are displayed below:\n",
    "    ___\n",
    "    |*|\n",
    "    |#|\n",
    "    ---\n",
    "    ___\n",
    "    |*|\n",
    "    |*|\n",
    "    |#|\n",
    "    |#|\n",
    "    ---\n",
    "    ____\n",
    "    |**|\n",
    "    |##|\n",
    "    ----\n",
    "    ____\n",
    "    |**|\n",
    "    |**|\n",
    "    |##|\n",
    "    |##|\n",
    "    ----\n",
    "    _________\n",
    "    |*******|\n",
    "    |#######|\n",
    "    ---------\n",
    "    _________\n",
    "    |*******|\n",
    "    |*******|\n",
    "    |#######|\n",
    "    |#######|\n",
    "    ---------\n",
    "    and so on ...\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, win_i0, win_j0, win_w, win_h):\n",
    "        \"\"\"\n",
    "        docstring\n",
    "        \"\"\"\n",
    "        super().__init__(win_i0, win_j0, win_w, win_h)\n",
    "        self.feat_type = \"haar_tb\"\n",
    "\n",
    "\n",
    "\n",
    "    def _init_feats(self):\n",
    "        for i in range(self.win_i0+1, self.win_h):\n",
    "            for j in range(self.win_j0+1, self.win_w):\n",
    "                h = 1\n",
    "                while i+2*h <= self.win_h:\n",
    "                    w = 1\n",
    "                    while j+w <= self.win_w:\n",
    "                        coords = []\n",
    "                        coord_A = [[(i, j), (i, j+w-1)], [(i+h-1, j), (i+h-1, j+w-1)]]\n",
    "                        coord_B = [[(i+h, j), (i+h, j+w-1)], [(i+2*h-1, j), (i+2*h-1, j+w-1)]]\n",
    "\n",
    "                        coords.append(coord_A)\n",
    "                        coords.append(coord_B)\n",
    "\n",
    "                        cx = []\n",
    "                        for t in range(len(coords)):\n",
    "                            c = get_coordinates(coords[t])\n",
    "                            cx.append(c)\n",
    "                        \n",
    "                        self.feats[((i,j,h,w), self.feat_type)] = {\"feat_type\": self.feat_type,\n",
    "                                                                   \"val\": {}, \n",
    "                                                                   \"error\": 0.,\n",
    "                                                                   \"alpha\": 0.,\n",
    "                                                                   \"coord_A\": cx[0], \n",
    "                                                                   \"coord_B\": cx[1],\n",
    "                                                                   }\n",
    "                        \n",
    "                        cx = []     ## reset the coordinates for next round\n",
    "                        w += 1      ## increment the weight\n",
    "                    h += 1          ## increment the height\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 858,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "aIllajC2cF_4"
   },
   "outputs": [],
   "source": [
    "## |> include_in_final_source <|\n",
    "##\n",
    "\n",
    "class HaarTypeTCB(HaarFeatures):\n",
    "    \"\"\"\n",
    "    Types of haar_features extracted are displayed below:\n",
    "    ___\n",
    "    |*|\n",
    "    |#|\n",
    "    |#|\n",
    "    ---\n",
    "    ___\n",
    "    |*|\n",
    "    |*|\n",
    "    |#|\n",
    "    |#|\n",
    "    |*|\n",
    "    |*|\n",
    "    ---\n",
    "    ____\n",
    "    |**|\n",
    "    |##|\n",
    "    |##|\n",
    "    ----\n",
    "    ____\n",
    "    |**|\n",
    "    |**|\n",
    "    |##|\n",
    "    |##|\n",
    "    |**|\n",
    "    |**|\n",
    "    ----\n",
    "    _________\n",
    "    |*******|\n",
    "    |#######|\n",
    "    |*******|\n",
    "    ---------\n",
    "    _________\n",
    "    |*******|\n",
    "    |*******|\n",
    "    |#######|\n",
    "    |#######|\n",
    "    |*******|\n",
    "    |*******|\n",
    "    ---------\n",
    "    and so on ...\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, win_i0, win_j0, win_w, win_h):\n",
    "        \"\"\"\n",
    "        docstring\n",
    "        \"\"\"\n",
    "        super().__init__(win_i0, win_j0, win_w, win_h)\n",
    "        self.feat_type = \"haar_tcb\"\n",
    "\n",
    "\n",
    "\n",
    "    def _init_feats(self):\n",
    "        for i in range(self.win_i0+1, self.win_h):\n",
    "            for j in range(self.win_j0+1, self.win_w):\n",
    "                h = 1\n",
    "                while i+3*h <= self.win_h:\n",
    "                    w = 1\n",
    "                    while j+w <= self.win_w:\n",
    "                        coords = []\n",
    "                        coord_A = [[(i, j), (i, j+w-1)], [(i+h-1, j), (i+h-1, j+w-1)]]\n",
    "                        coord_B = [[(i+h, j), (i+h, j+w-1)], [(i+2*h-1, j), (i+2*h-1, j+w-1)]]\n",
    "                        coord_C = [[(i+2*h, j), (i+2*h, j+w-1)], [(i+3*h-1, j), (i+3*h-1, j+w-1)]]\n",
    "                        \n",
    "\n",
    "                        coords.append(coord_A)\n",
    "                        coords.append(coord_B)\n",
    "                        coords.append(coord_C)\n",
    "\n",
    "                        cx = []\n",
    "                        for t in range(len(coords)):\n",
    "                            c = get_coordinates(coords[t])\n",
    "                            cx.append(c)\n",
    "                        \n",
    "                        self.feats[((i,j,h,w), self.feat_type)] = {\"feat_type\": self.feat_type,\n",
    "                                                                   \"val\": {}, \n",
    "                                                                   \"error\": 0.,\n",
    "                                                                   \"alpha\": 0.,\n",
    "                                                                   \"coord_A\": cx[0], \n",
    "                                                                   \"coord_B\": cx[1], \n",
    "                                                                   \"coord_C\": cx[2],\n",
    "                                                                   }\n",
    "                        \n",
    "                        cx = []     ## reset the coordinates for next round\n",
    "                        w += 1      ## increment the weight\n",
    "                    h += 1          ## increment the height\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 874,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "LswVXjeZcF9S"
   },
   "outputs": [],
   "source": [
    "## |>UNDER_CONSTRUCTION<|\n",
    "## |> include_in_final_source <|\n",
    "##\n",
    "\n",
    "class HaarTypeDD(HaarFeatures):\n",
    "    \"\"\"\n",
    "    |>UNDER_CONSTRUCTION<|\n",
    "    |>Define the rules for changing the h, w <|\n",
    "    Types of haar_features extracted are displayed below:\n",
    "    _____\n",
    "    |*|#|\n",
    "    |#|*|\n",
    "    -----\n",
    "    _____\n",
    "    |*|#|\n",
    "    |*|#|\n",
    "    |#|*|\n",
    "    |#|*|\n",
    "    -----\n",
    "    _______\n",
    "    |**|##|\n",
    "    |##|**|\n",
    "    -------\n",
    "    _______\n",
    "    |**|##|\n",
    "    |**|##|\n",
    "    |##|**|\n",
    "    |##|**|\n",
    "    -------\n",
    "    and so on ...\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, win_i0, win_j0, win_w, win_h):\n",
    "        \"\"\"\n",
    "        docstring\n",
    "        \"\"\"\n",
    "        super().__init__(win_i0, win_j0, win_w, win_h)\n",
    "        self.feat_type = \"haar_dd\"\n",
    "\n",
    "\n",
    "    def _init_feats(self):\n",
    "        for i in range(self.win_i0+1, self.win_h):\n",
    "            for j in range(self.win_j0+1, self.win_w):\n",
    "                h = 1\n",
    "                while i+2*h <= self.win_h:\n",
    "                    w = 1\n",
    "                    while j+2*w <= self.win_w:\n",
    "                        coords = []\n",
    "                        coord_A = [[(i, j), (i, j+w-1)], [(i+h-1, j), (i+h-1, j+w-1)]]\n",
    "                        coord_B = [[(i, j+w), (i, j+2*w-1)], [(i+h, j+w), (i+h, j+2*w-1)]]\n",
    "                        coord_C = [[(i+h, j), (i+h, j+w-1)], [(i+2*h-1, j), (i+2*h-1, j+w-1)]]\n",
    "                        coord_D = [[(i+h, j+w), (i+h, j+2*w-1)], [(i+2*h-1, j+w), (i+2*h-1, j+2*w-1)]]\n",
    "\n",
    "                        coords.append(coord_A)\n",
    "                        coords.append(coord_B)\n",
    "                        coords.append(coord_C)\n",
    "                        coords.append(coord_D)\n",
    "\n",
    "                        cx = []\n",
    "                        for t in range(len(coords)):\n",
    "                            c = get_coordinates(coords[t])\n",
    "                            cx.append(c)\n",
    "                        \n",
    "                        self.feats[((i,j,h,w), self.feat_type)] = {\"feat_type\": self.feat_type,\n",
    "                                                                   \"val\": {}, \n",
    "                                                                   \"error\": 0.,\n",
    "                                                                   \"alpha\": 0.,\n",
    "                                                                   \"coord_A\": cx[0], \n",
    "                                                                   \"coord_B\": cx[1], \n",
    "                                                                   \"coord_C\": cx[2],\n",
    "                                                                   \"coord_D\": cx[3],\n",
    "                                                                   }\n",
    "                        \n",
    "                        cx = []     ## reset the coordinates for next round\n",
    "                        w += 1      ## increment the weight\n",
    "                    h += 1          ## increment the height\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 860,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "np5Yp8kacF43"
   },
   "outputs": [],
   "source": [
    "## |> include_in_final_source <|\n",
    "##\n",
    "## Initialize weights for the data\n",
    "\n",
    "def init_weights(data, keys, dist=\"Uniform\"):\n",
    "    \"\"\"\n",
    "    : data ->\n",
    "    : keys ->\n",
    "    : dist ->\n",
    "    \"\"\"\n",
    "    \n",
    "    if dist == \"Uniform\":\n",
    "        num_train = len(keys)\n",
    "        for k in keys:\n",
    "            data[k][\"weight\"] = 1. / num_train\n",
    "    if dist == \"XUniform\":\n",
    "        C = len(keys)                                   ## total number of classes\n",
    "        for c_k in keys:\n",
    "            num = len(c_k)                              ## total number of data items in each class\n",
    "            for k in c_k:\n",
    "                data[k][\"weight\"] = 1. / (C * num)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 861,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "G2eoO8DCcF2U"
   },
   "outputs": [],
   "source": [
    "## |> include_in_final_source <|\n",
    "##\n",
    "def create_weak_classifiers(ctypes, i0, j0, w, h):\n",
    "    \"\"\"\n",
    "    : ctypes ->\n",
    "    : i0 ->\n",
    "    : j0 ->\n",
    "    : w ->\n",
    "    : h ->\n",
    "    \"\"\"\n",
    "    classifiers = {}\n",
    "    for c in ctypes:\n",
    "        # print(c)\n",
    "        if c == \"haar_lr\":\n",
    "            h_lr = HaarTypeLR(i0, j0, w, h)\n",
    "            h_lr.init_feats()\n",
    "            classifiers.update(h_lr.feats)\n",
    "        elif c == \"haar_lcr\":\n",
    "            h_lcr = HaarTypeLCR(i0, j0, w, h)\n",
    "            h_lcr.init_feats()\n",
    "            classifiers.update(h_lcr.feats)\n",
    "        elif c == \"haar_tb\":\n",
    "            h_tb = HaarTypeTB(i0, j0, w, h)\n",
    "            h_tb.init_feats()\n",
    "            classifiers.update(h_tb.feats)\n",
    "        elif c == \"haar_tcb\":\n",
    "            h_tcb = HaarTypeTCB(i0, j0, w, h)\n",
    "            h_tcb.init_feats()\n",
    "            classifiers.update(h_tcb.feats)\n",
    "        elif c == \"haar_dd\":\n",
    "            h_dd = HaarTypeDD(i0, j0, w, h)\n",
    "            h_dd.init_feats()\n",
    "            classifiers.update(h_dd.feats)\n",
    "    return classifiers\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 862,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "oOR3TneYcFzG"
   },
   "outputs": [],
   "source": [
    "## |> include_in_final_source <|\n",
    "##\n",
    "def calc_feat_val(data, classifiers, i_id, c_id):\n",
    "    \"\"\"\n",
    "    : data ->\n",
    "    : classifiers ->\n",
    "    : i_id -> index(ID) of the image\n",
    "    : c_id -> index(ID) of the weak-classifier => ((i0,j0,h,w), ctype)\n",
    "    \"\"\"\n",
    "    if classifiers[c_id][\"feat_type\"] == \"haar_lr\" or classifiers[c_id][\"feat_type\"] == \"haar_tb\":\n",
    "        sum_A = get_sum_pixels(coord=classifiers[c_id][\"coord_A\"], ii_img=data[i_id][\"ii_img\"])\n",
    "        sum_B = get_sum_pixels(coord=classifiers[c_id][\"coord_B\"], ii_img=data[i_id][\"ii_img\"])\n",
    "        val = sum_A - sum_B\n",
    "        classifiers[c_id][\"val\"][i_id] = val\n",
    "        \n",
    "    elif classifiers[c_id][\"feat_type\"] == \"haar_lcr\" or classifiers[c_id][\"feat_type\"] == \"haar_tcb\":\n",
    "        sum_A = get_sum_pixels(coord=classifiers[c_id][\"coord_A\"], ii_img=data[i_id][\"ii_img\"])\n",
    "        sum_B = get_sum_pixels(coord=classifiers[c_id][\"coord_B\"], ii_img=data[i_id][\"ii_img\"])\n",
    "        sum_C = get_sum_pixels(coord=classifiers[c_id][\"coord_C\"], ii_img=data[i_id][\"ii_img\"])\n",
    "        val = sum_A - sum_B + sum_C\n",
    "        classifiers[c_id][\"val\"][i_id] = val\n",
    "        \n",
    "    elif classifiers[c_id][\"feat_type\"] == \"haar_dd\":\n",
    "        sum_A = get_sum_pixels(coord=classifiers[c_id][\"coord_A\"], ii_img=data[i_id][\"ii_img\"])\n",
    "        sum_B = get_sum_pixels(coord=classifiers[c_id][\"coord_B\"], ii_img=data[i_id][\"ii_img\"])\n",
    "        sum_C = get_sum_pixels(coord=classifiers[c_id][\"coord_C\"], ii_img=data[i_id][\"ii_img\"])\n",
    "        sum_D = get_sum_pixels(coord=classifiers[c_id][\"coord_D\"], ii_img=data[i_id][\"ii_img\"])\n",
    "        val = sum_A - sum_B + sum_C - sum_D\n",
    "        classifiers[c_id][\"val\"][i_id] = val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 863,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Bu2N1iurdtbm"
   },
   "outputs": [],
   "source": [
    "## |> include_in_final_source <|\n",
    "##\n",
    "## get threshold for each weak-classifier\n",
    "\n",
    "def calc_threshold_and_polarity(data, classifiers):\n",
    "    \"\"\"\n",
    "    : data ->\n",
    "    : classifiers ->\n",
    "    \"\"\"\n",
    "    \n",
    "    for c_id in classifiers:\n",
    "        ## sort the feat_val for each image in ascending order of the feat_val\n",
    "        sorted_vals = sorted(classifiers[c_id][\"val\"].items(), key=lambda x:x[1])\n",
    "        ## calculate the weights of LEFT=(neg_till + pos_all - pos_till) & RIGHT=(pos_till + neg_all -neg_till)\n",
    "        sum_weights_neg_all = 0\n",
    "        sum_weights_pos_all = 0\n",
    "        sum_weights_neg_till = 0\n",
    "        sum_weights_pos_till = 0\n",
    "        errs = {}\n",
    "        polarity = {}\n",
    "        for i_id in data:\n",
    "            if data[i_id][\"label\"] == 0:\n",
    "                sum_weights_neg_all += data[i_id][\"weight\"]\n",
    "            elif data[i_id][\"label\"] == 1:\n",
    "                sum_weights_pos_all += data[i_id][\"weight\"]\n",
    "\n",
    "        for item in sorted_vals:\n",
    "            ## item -> (i_id, f_val)\n",
    "            i_id = item[0]\n",
    "            if data[i_id][\"label\"] == 0:\n",
    "                sum_weights_neg_till += data[i_id][\"weight\"]\n",
    "            elif data[i_id][\"label\"] == 1:\n",
    "                sum_weights_pos_till += data[i_id][\"weight\"]\n",
    "            left = sum_weights_neg_till + sum_weights_pos_all - sum_weights_pos_till\n",
    "            right = sum_weights_pos_till + sum_weights_neg_all - sum_weights_neg_till\n",
    "            # errs[i_id] = min(left, right)\n",
    "            if left <= right:\n",
    "                errs[i_id] = left\n",
    "                polarity[i_id] = -1\n",
    "            elif left > right:\n",
    "                errs[i_id] = right\n",
    "                polarity[i_id] = 1\n",
    "        \n",
    "        thres_iid = sorted(errs.items(), key=lambda x:x[1])[0][0]\n",
    "        classifiers[c_id][\"threshold\"] = classifiers[c_id][\"val\"][thres_iid]\n",
    "        classifiers[c_id][\"polarity\"] = polarity[thres_iid]\n",
    "\n",
    "def calc_pred_and_error(data, classifiers):\n",
    "    for c_id in classifiers:\n",
    "        ## initialize predicted_labels\n",
    "        classifiers[c_id][\"pred_labels\"] = {}\n",
    "        error = 0\n",
    "        for i_id in classifiers[c_id][\"val\"]:\n",
    "            polarity = classifiers[c_id][\"polarity\"]\n",
    "            if polarity * classifiers[c_id][\"val\"][i_id] >= polarity * classifiers[c_id][\"threshold\"]:\n",
    "                classifiers[c_id][\"pred_labels\"][i_id] = 1\n",
    "            else:\n",
    "                classifiers[c_id][\"pred_labels\"][i_id] = 0\n",
    "\n",
    "            if classifiers[c_id][\"pred_labels\"][i_id] != data[i_id][\"label\"]:\n",
    "                error += data[i_id][\"weight\"]\n",
    "        classifiers[c_id][\"error\"] = error\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 864,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "teJmhPVOdtYN"
   },
   "outputs": [],
   "source": [
    "## |> include_in_final_source <|\n",
    "##\n",
    "\n",
    "def get_best_classifier(classifiers):\n",
    "    \"\"\"\n",
    "    : classifiers ->\n",
    "    \"\"\"\n",
    "\n",
    "    sorted_errors = sorted([(c_id, classifiers[c_id][\"error\"]) for c_id in classifiers], key=lambda x:x[1])\n",
    "    # sorted_errors = sorted(error_all_feats.items(), key=lambda x:x[1])\n",
    "    best_c_id = sorted_errors[0][0]\n",
    "    best_error = sorted_errors[0][1]\n",
    "    best_alpha = 0.5 * np.log((1-best_error)/best_error)\n",
    "    classifiers[best_c_id][\"alpha\"] = best_alpha\n",
    "    return best_c_id\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 865,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CgiKJ4kEf4K2"
   },
   "outputs": [],
   "source": [
    "## |> include_in_final_source <|\n",
    "##\n",
    "\n",
    "def update_normalized_weights(data, classifiers, c_id):\n",
    "    \"\"\"\n",
    "    : data ->\n",
    "    : classifiers ->\n",
    "    : c_id -> best_c_id\n",
    "    \"\"\"\n",
    "    \n",
    "    ## calculate the weight normalization factor\n",
    "    norm_factor = 0\n",
    "    for i_id in data:\n",
    "        if classifiers[c_id][\"pred_labels\"][i_id] != data[i_id][\"label\"]:\n",
    "            norm_factor += data[i_id][\"weight\"] * np.exp(classifiers[c_id][\"alpha\"])\n",
    "        elif classifiers[c_id][\"pred_labels\"][i_id] == data[i_id][\"label\"]:\n",
    "            norm_factor += data[i_id][\"weight\"] * np.exp(-1 * classifiers[c_id][\"alpha\"])\n",
    "\n",
    "    ## normalize and update the weights of all data\n",
    "    for i_id in data:        \n",
    "        if classifiers[c_id][\"pred_labels\"][i_id] != data[i_id][\"label\"]:\n",
    "            data[i_id][\"weight\"] *= np.exp(classifiers[c_id][\"alpha\"])\n",
    "            data[i_id][\"weight\"] /= norm_factor\n",
    "        elif classifiers[c_id][\"pred_labels\"][i_id] == data[i_id][\"label\"]:\n",
    "            data[i_id][\"weight\"] *= np.exp(-1 * classifiers[c_id][\"alpha\"])\n",
    "            data[i_id][\"weight\"] /= norm_factor\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9UlckaQtdtM_"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 886,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Dl-5OLtkdXM8"
   },
   "outputs": [],
   "source": [
    "## |> include_in_final_source <|\n",
    "##\n",
    "##\n",
    "## 👍 for iter t <= T:\n",
    "##      👍 Calculate the vals for each image for each weak-classifier\n",
    "##      👍 Get threshold\n",
    "##      👍 Get error\n",
    "##      👍 Get the best-feature (weak-classifier)\n",
    "##      👍 Get the best-features's ALPHA\n",
    "##      👍 Update the weights with normalization\n",
    "##      👍 Return the (best-feature, ALPHA)\n",
    "##\n",
    "##\n",
    "def run_adaboost(data, classifiers, num_epochs, verbose=True):\n",
    "    \"\"\"\n",
    "    : data ->\n",
    "    : classifiers ->\n",
    "    : num_steps ->\n",
    "    : verbose ->\n",
    "    \"\"\"\n",
    "    \n",
    "    strong_classifier = []          ## a strong-classifier is an array of multiple weak-classifiers\n",
    "    for epoch in range(num_epochs):\n",
    "        cid0 = list(calssifiers.keys())[0]\n",
    "        if epoch==0 and (len(classifiers[cid0][\"val\"].keys()) == 0):\n",
    "            ## calculate \"val\" only for the 1st epoch (as it remains same after)\n",
    "            t0 = time.time()\n",
    "            for c_id in classifiers:\n",
    "                for i_id in data:\n",
    "                    ## 👍 calculate the vals for all data\n",
    "                    calc_feat_val(data=data, classifiers=classifiers, i_id=i_id, c_id=c_id)\n",
    "            print(f\"⏰calc_feat_val() took => {time.time()-t0} seconds @epoch[{epoch}]!!\")\n",
    "        ## 👍 get threshold and error for each weak-classifier\n",
    "        calc_threshold_and_polarity(data, classifiers)\n",
    "        calc_pred_and_error(data, classifiers)\n",
    "        ## 👍 Get the best-feature (weak-classifier), its ALPHA & store it in strong-classifier\n",
    "        best_c_id = get_best_classifier(classifiers)\n",
    "        # strong_classifier[best_c_id] = classifiers[best_c_id].copy()\n",
    "        strong_classifier.append({best_c_id: classifiers[best_c_id].copy()})\n",
    "        # strong_classifier[t] = classifiers[best_c_id].copy()\n",
    "        ## 👍 Normalize & update the weights\n",
    "        update_normalized_weights(data=data, classifiers=classifiers, c_id=best_c_id)\n",
    "        if verbose:\n",
    "            print(f\"epoch[{epoch}/{num_epochs}] => {best_c_id} \" \\\n",
    "                f\":: (error->{classifiers[best_c_id]['error']}) \" \\\n",
    "                f\":: (alpha->{classifiers[best_c_id]['alpha']})\")\n",
    "    return strong_classifier\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 887,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "kGW4hz7pcFv2"
   },
   "outputs": [],
   "source": [
    "## |> include_in_final_source <|\n",
    "##\n",
    "def draw_classifier(data, strong_classifier, win_h, win_w, i_id):\n",
    "    \"\"\"\n",
    "    : data ->\n",
    "    : strong_classifier -> \n",
    "    : win_h ->\n",
    "    : win_w ->\n",
    "    : i_id ->\n",
    "    \"\"\"\n",
    "\n",
    "    for wc in strong_classifier:\n",
    "        c_id = list(wc.keys())[0]\n",
    "        classifier_img = np.ones((win_h+2, win_w+2))*255\n",
    "        # print(classifier_img)\n",
    "\n",
    "        i0 = c_id[0][0]\n",
    "        j0 = c_id[0][1]\n",
    "        h = c_id[0][2]\n",
    "        w = c_id[0][3]\n",
    "        c_type = c_id[1]\n",
    "        if c_type==\"haar_lr\":\n",
    "            for i in range(i0, i0+h):\n",
    "                for j in range(j0, j0+w):\n",
    "                    classifier_img[i,j] = 125          ## A\n",
    "                for j in range(j0+w, j0+2*w):\n",
    "                    classifier_img[i,j] = 0            ## B\n",
    "        elif c_type==\"haar_lcr\":\n",
    "            for i in range(i0, i0+h):\n",
    "                for j in range(j0, j0+w):\n",
    "                    classifier_img[i,j] = 125          ## A\n",
    "                for j in range(j0+w, j0+2*w):\n",
    "                    classifier_img[i,j] = 0            ## B\n",
    "                for j in range(j0+2*w, j0+3*w):\n",
    "                    classifier_img[i,j] = 125          ## C\n",
    "        elif c_type==\"haar_tb\":\n",
    "            for j in range(j0, j0+w):\n",
    "                for i in range(i0, i0+h):\n",
    "                    classifier_img[i,j] = 125          ## A\n",
    "                for i in range(i0+h, i0+2*h):\n",
    "                    classifier_img[i,j] = 0            ## B\n",
    "        elif c_type==\"haar_tcb\":\n",
    "            for j in range(j0, j0+w):\n",
    "                for i in range(i0, i0+h):\n",
    "                    classifier_img[i,j] = 125          ## A\n",
    "                for i in range(i0+h, i0+2*h):\n",
    "                    classifier_img[i,j] = 0            ## B\n",
    "                for i in range(i0+2*h, i0+3*h):\n",
    "                    classifier_img[i,j] = 125          ## C\n",
    "        elif c_type==\"haar_dd\":\n",
    "            for i in range(i0, i0+h):\n",
    "                for j in range(j0, j0+w):\n",
    "                    classifier_img[i,j] = 125          ## A\n",
    "                for j in range(j0+w, j0+2*w):\n",
    "                    classifier_img[i,j] = 0            ## B\n",
    "            for i in range(i0+h, i0+2*h):\n",
    "                for j in range(j0, j0+w):\n",
    "                    classifier_img[i,j] = 0            ## C\n",
    "                for j in range(j0+w, j0+2*w):\n",
    "                    classifier_img[i,j] = 125          ## D\n",
    "\n",
    "        plt.figure()\n",
    "        plt.imshow(data[i_id][\"img\"], cmap=\"gray\")\n",
    "        plt.imshow(classifier_img[1:-1, 1:-1], cmap=\"hot\", alpha=0.7)\n",
    "        plt.title(f\"{c_id}, error={wc[c_id]['error']}, alpha={wc[c_id]['alpha']}\")\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 888,
   "metadata": {},
   "outputs": [],
   "source": [
    "## |> include_in_final_source <|\n",
    "##\n",
    "\n",
    "class Cascade():\n",
    "    def __init__(self, strong_classifier=None):\n",
    "        self.strong_classifier = strong_classifier\n",
    "    \n",
    "    def train(self, data, keys, num_epochs, c_types, weight_dist=\"Uniform\", template=(0,0,20,20), verbose=False):\n",
    "        \"\"\"\n",
    "        : data ->\n",
    "        : c_types -> different types of classifiers (haar-type-classifiers) :: [\"haar_lr\", \"haar_tb\", \"haar_lcr\", \"haar_tcb\", \"haar_dd\"]\n",
    "        : weight_dist -> ::\"Uniform\" or \"XUniform\"\n",
    "        : template -> (i0, j0, h, w) of the window  :: (0,0,20,20)\n",
    "        : verbose -> :: True or False\n",
    "        \"\"\"\n",
    "        self.num_epochs = num_epochs\n",
    "        \n",
    "        t0 = time.time()\n",
    "        ## 👍 initialize the weights\n",
    "        # keys = data.keys()\n",
    "        init_weights(data=data, keys=keys, dist=weight_dist)\n",
    "\n",
    "        ## 👍 create ALL haar-like-features\n",
    "        classifiers = create_weak_classifiers(ctypes=c_types, \n",
    "                                                   i0=template[0],\n",
    "                                                   j0=template[1],\n",
    "                                                   w=template[2],\n",
    "                                                   h=template[3])\n",
    "        \n",
    "        ## write rules for training in each mode-type\n",
    "        \n",
    "        if verbose: print(f\"<|CASCADE|>|> #classifiers=>{len(classifiers)}\")\n",
    "        self.strong_classifier = run_adaboost(data=data, classifiers=classifiers, num_epochs=self.num_epochs, verbose=verbose)\n",
    "        if verbose: print(f\"Time taken => {time.time()-t0} seconds!!\")\n",
    "        \n",
    "    \n",
    "    def test(self, data, mode, verbose=False):\n",
    "        \"\"\"\n",
    "        : data ->\n",
    "        : mode -> ::[\"sequential\", \"committe\", \"attentional\"]\n",
    "        : verbose ->\n",
    "        \"\"\"\n",
    "        assert len(self.strong_classifier) != 0\n",
    "        self.mode=mode\n",
    "        if mode == \"sequential\":\n",
    "            allowed_iids = list(data.keys())\n",
    "            stage = 0\n",
    "            for wc in self.strong_classifier:\n",
    "                c_id = list(wc.keys())[0]\n",
    "                wc[c_id][\"val\"] = {}           ## reset the garbage from training process\n",
    "                wc[c_id][\"pred_labels\"] = {}   ## reset the garbage from training process\n",
    "                error = 0\n",
    "                new_allowed_iids = []\n",
    "                \n",
    "                for i_id in allowed_iids:\n",
    "                    calc_feat_val(data=data, classifiers=wc, i_id=i_id, c_id=c_id)\n",
    "                    polarity = wc[c_id][\"polarity\"]\n",
    "                    if polarity * wc[c_id][\"val\"][i_id] >= polarity * wc[c_id][\"threshold\"]:\n",
    "                        wc[c_id][\"pred_labels\"][i_id] = 1\n",
    "                        new_allowed_iids.append(i_id)             ## only faces are sent to the next weak-clasifier\n",
    "                    else:\n",
    "                        wc[c_id][\"pred_labels\"][i_id] = 0\n",
    "\n",
    "                    if wc[c_id][\"pred_labels\"][i_id] != data[i_id][\"label\"]:\n",
    "                        error += 1\n",
    "                wc[c_id][\"stage_test_error\"] = error/len(allowed_iids)\n",
    "\n",
    "                ## update the allowed_iids to be classified by next weak-classifier\n",
    "                allowed_iids = new_allowed_iids\n",
    "\n",
    "                if verbose: \n",
    "                    print(f\"mode=[{mode}]: stage[{stage}]=> passed = [{len(allowed_iids)}/{len(data)}] \" \\\n",
    "                        f\":: stage_test_error -> {wc[c_id]['stage_test_error']} \" \\\n",
    "                        f\":: overall_test_error -> {error/len(data)}\")\n",
    "                stage += 1\n",
    "                \n",
    "        elif mode == \"committee\":\n",
    "            ## if (sum_t(alpha_t * polarity_t * val_t_i) >= sum_t(alpha_t * polarity_t * thresh_t)) => 1 else 0\n",
    "            pred_xvals = {}\n",
    "            weighted_committe_threshold = 0\n",
    "            error = 0\n",
    "            for wc in self.strong_classifier:\n",
    "                c_id = list(wc.keys())[0]\n",
    "                wc[c_id][\"val\"] = {}           ## reset the garbage from training process\n",
    "                wc[c_id][\"pred_labels\"] = {}   ## reset the garbage from training process\n",
    "                weighted_committe_threshold += wc[c_id][\"alpha\"] * wc[c_id][\"polarity\"] * wc[c_id][\"threshold\"]\n",
    "                \n",
    "                for i_id in data.keys():\n",
    "                    calc_feat_val(data=data, classifiers=wc, i_id=i_id, c_id=c_id)\n",
    "                    if i_id not in pred_xvals.keys():\n",
    "                        pred_xvals[i_id] = [wc[c_id][\"alpha\"] * wc[c_id][\"polarity\"] * wc[c_id][\"val\"][i_id]]\n",
    "                    else:\n",
    "                        pred_xvals[i_id].append(wc[c_id][\"alpha\"] * wc[c_id][\"polarity\"] * wc[c_id][\"val\"][i_id])\n",
    "            \n",
    "            for i_id in data.keys():\n",
    "                pred_xvals[i_id].append(np.sum(pred_xvals[i_id]))\n",
    "                \n",
    "                ## calculate the predicted labels\n",
    "                if pred_xvals[i_id][-1] >= weighted_committe_threshold:\n",
    "                    pred_xvals[i_id].append(1)\n",
    "                    if pred_xvals[i_id][-1] != data[i_id][\"label\"]: error += 1\n",
    "                else:\n",
    "                    pred_xvals[i_id].append(0)\n",
    "                    if pred_xvals[i_id][-1] != data[i_id][\"label\"]: error += 1\n",
    "                    \n",
    "            ## calculate the \"error\" for the commitee\n",
    "            error /= len(data.keys())\n",
    "            if verbose: \n",
    "                    print(f\"mode=[{mode}] => overall_test_error -> {error}\")\n",
    "            res = {}\n",
    "            for iid in pred_xvals.keys():\n",
    "                res[iid] = pred_xvals[iid][-2]\n",
    "            return res\n",
    "\n",
    "    def draw(self, data, i_id, win_h, win_w):\n",
    "        \"\"\"\n",
    "        : data -> used to draw the background overlay\n",
    "        : i_id -> which image to draw on the background of the classifiers \n",
    "        : win_h -> window width\n",
    "        : win_w -> window height\n",
    "        \"\"\"\n",
    "        draw_classifier(data=data, strong_classifier=self.strong_classifier, win_h=win_h, win_w=win_w, i_id=i_id)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 890,
   "metadata": {},
   "outputs": [],
   "source": [
    "## |> include_in_final_source <|\n",
    "##\n",
    "## Draw ROC curve & AUC\n",
    "\n",
    "def draw_roc_curve(data, mode, classifiers=None, predictions=None):\n",
    "    \"\"\"\n",
    "    : classifiers -> [optional]\n",
    "    : predictions -> [optional]\n",
    "    \"\"\"\n",
    "    plt.figure()\n",
    "    plt.title(\"ROC Curve for all classifiers\")\n",
    "    \n",
    "    if mode == \"sequential\":\n",
    "        idx = 0\n",
    "        for wc in classifiers:\n",
    "            c_id = list(wc.keys())[0]\n",
    "            polarity = wc[c_id][\"polarity\"]\n",
    "            iids = sorted(list(wc[c_id][\"val\"].keys()))\n",
    "            labels = []\n",
    "            preds_vals = []\n",
    "            val_min = 1e6\n",
    "            val_max = -1e6\n",
    "            for i_id in iids:\n",
    "                labels.append(data[i_id][\"label\"])\n",
    "                val = wc[c_id][\"val\"][i_id]\n",
    "                preds_vals.append(val)\n",
    "                if val_min > val:\n",
    "                    val_min = val\n",
    "                if val_max < val:\n",
    "                    val_max = val\n",
    "\n",
    "            D = np.zeros((100, len(iids)+6))\n",
    "            for i, t in enumerate(np.linspace(val_min, val_max, 100)):\n",
    "                preds = [int(polarity*p > polarity*t) for p in preds_vals]\n",
    "                D[i][:-6] = preds\n",
    "\n",
    "                for j in range(len(D[i])-6):\n",
    "                    if labels[j] == 1 and D[i, j] == 1:   ## TP\n",
    "                        D[i,-6] += 1\n",
    "                    if labels[j] == 1 and D[i, j] == 0:   ## FP\n",
    "                        D[i,-5] += 1\n",
    "                    if labels[j] == 0 and D[i, j] == 0:   ## TN\n",
    "                        D[i,-4] += 1\n",
    "                    if labels[j] == 0 and D[i, j] == 1:   ## FN\n",
    "                        D[i,-3] += 1\n",
    "                if (D[i,-6] + D[i,-3]) != 0:\n",
    "                    D[i][-2] = D[i,-6] / (D[i,-6] + D[i,-3])   ## TPR\n",
    "                if (D[i,-5] + D[i,-4]) != 0:\n",
    "                    D[i][-1] = D[i,-5] / (D[i,-5] + D[i,-4])   ## FPR\n",
    "\n",
    "            tpr = list(D[:, -2])\n",
    "            fpr = list(D[:, -1])\n",
    "            rates = [[f, t] for f, t in zip(fpr, tpr)]\n",
    "            sorted_rates = np.array(sorted(rates, key=lambda x:x[0])).T\n",
    "\n",
    "            auc = sklearn.metrics.auc(sorted_rates[0], sorted_rates[1])\n",
    "\n",
    "            plt.plot(sorted_rates[0], sorted_rates[1], label=f\"stage-{idx}: AUC={auc}\")\n",
    "            plt.xlabel(\"FPR\")\n",
    "            plt.ylabel(\"TPR\")\n",
    "            plt.legend()\n",
    "            idx += 1\n",
    "    elif mode==\"committee\":\n",
    "        # predictions -> {i_id: pred_xvals}\n",
    "        labels = []\n",
    "        iids = list(sorted(predictions.keys()))\n",
    "        preds_xvals = []\n",
    "        val_min = 1e6\n",
    "        val_max = -1e6\n",
    "        D = np.zeros((100, len(iids)+6))\n",
    "        \n",
    "        for i_id in iids:\n",
    "            labels.append(data[i_id][\"label\"])\n",
    "            xval = predictions[i_id]\n",
    "            preds_xvals.append(xval)\n",
    "            if val_min > xval:\n",
    "                val_min = xval\n",
    "            if val_max < xval:\n",
    "                val_max = xval\n",
    "        for i, t in enumerate(np.linspace(val_min, val_max, 100)):\n",
    "            preds = [int(p >= t) for p in preds_xvals]\n",
    "            D[i][:-6] = preds\n",
    "\n",
    "            for j in range(len(D[i])-6):\n",
    "                if labels[j] == 1 and D[i, j] == 1:   ## TP\n",
    "                    D[i,-6] += 1\n",
    "                if labels[j] == 1 and D[i, j] == 0:   ## FP\n",
    "                    D[i,-5] += 1\n",
    "                if labels[j] == 0 and D[i, j] == 0:   ## TN\n",
    "                    D[i,-4] += 1\n",
    "                if labels[j] == 0 and D[i, j] == 1:   ## FN\n",
    "                    D[i,-3] += 1\n",
    "            if (D[i,-6] + D[i,-3]) != 0:\n",
    "                D[i][-2] = D[i,-6] / (D[i,-6] + D[i,-3])   ## TPR\n",
    "            if (D[i,-5] + D[i,-4]) != 0:\n",
    "                D[i][-1] = D[i,-5] / (D[i,-5] + D[i,-4])   ## FPR\n",
    "        tpr = list(D[:, -2])\n",
    "        fpr = list(D[:, -1])\n",
    "        rates = [[f, t] for f, t in zip(fpr, tpr)]\n",
    "        sorted_rates = np.array(sorted(rates, key=lambda x:x[0])).T\n",
    "\n",
    "        auc = sklearn.metrics.auc(sorted_rates[0], sorted_rates[1])\n",
    "\n",
    "        plt.plot(sorted_rates[0], sorted_rates[1], label=f\"{mode}: AUC={auc}\")\n",
    "        plt.xlabel(\"FPR\")\n",
    "        plt.ylabel(\"TPR\")\n",
    "        plt.legend()\n",
    "    plt.show()\n",
    "\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 871,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "9sVm9RYgff75",
    "outputId": "d00d519e-f3a4-441d-f6c6-cf7966ed4cd6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1584919948.5908759"
      ]
     },
     "execution_count": 871,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 891,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "HI2cjd2bcFsk"
   },
   "outputs": [],
   "source": [
    "## 🥶🥵⚠️🎉👍⏰"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 873,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Fqgf1AthcFpg"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "num_data => 300 :: img_shape = (20, 20)\n",
      "num_data => 879 :: img_shape = (20, 20)\n"
     ]
    }
   ],
   "source": [
    "## |> include_in_final_source <|\n",
    "## 🎉🎉 MASTER CODE 🎉🎉 PART-1 (DATALOADING) 🎉🎉\n",
    "## \n",
    "## 👍 grab the pos & neg training data (windows) and their labels\n",
    "\n",
    "path_pos_data_train = f\"../data/face_train/\"\n",
    "path_neg_data_train = f\"../data/bg_train/\"\n",
    "shape=(350, 350)\n",
    "resolution=(20, 20)\n",
    "\n",
    "pos_data_train = get_data(path=path_pos_data_train, shape=shape, resolution=resolution, as_gray=True, ctype=\"pos\")\n",
    "neg_data_train = get_data(path=path_neg_data_train, shape=(20, 20), resolution=resolution, as_gray=True, ctype=\"neg\")\n",
    "data_train = {**pos_data_train, **neg_data_train}\n",
    "\n",
    "## 🥵 calculate integral-image for all data\n",
    "for i_id in data_train.keys():\n",
    "    ii_img = create_integral_image(data_train[i_id][\"img\"])\n",
    "    data_train[i_id][\"ii_img\"] = ii_img\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 896,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<|CASCADE|>|> #classifiers=>55860\n",
      "⏰calc_feat_val() took => 838.880529165268 seconds @epoch[0]!!\n",
      "epoch[0/5] => ((1, 2, 3, 3), 'haar_lr') :: (error->0.06700593723494487) :: (alpha->1.3168088031597203)\n",
      "epoch[1/5] => ((1, 13, 3, 1), 'haar_tcb') :: (error->0.09353279631760517) :: (alpha->1.1356213564669195)\n",
      "epoch[2/5] => ((1, 16, 6, 2), 'haar_lr') :: (error->0.11284739107754822) :: (alpha->1.0309903168286074)\n",
      "epoch[3/5] => ((8, 16, 4, 1), 'haar_tcb') :: (error->0.16761127545433152) :: (alpha->0.8013260434728907)\n",
      "epoch[4/5] => ((9, 2, 5, 2), 'haar_lr') :: (error->0.15162335430268106) :: (alpha->0.8609625906865277)\n",
      "Time taken => 2547.447574853897 seconds!!\n"
     ]
    }
   ],
   "source": [
    "## Cascade|> include_in_final_source <|\n",
    "## 🚀🚀 MASTER CODE 🚀🚀 PART-2.new (CASCADE training) 🚀🚀\n",
    "## \n",
    "num_epochs = 5\n",
    "c_types = [\"haar_lr\", \"haar_lcr\", \"haar_tb\", \"haar_tcb\"]\n",
    "weight_dist = \"Uniform\"\n",
    "# i_id_keys = [list(pos_data_train), list(neg_data_train)]\n",
    "i_id_keys = data_train.keys()\n",
    "template = (0,0,20,20)\n",
    "mode = \"sequential\"   ## otherwise can use these aswell -> [\"committe\", \"attentional\"]\n",
    "\n",
    "## Build & train a cascade\n",
    "cascade = Cascade()\n",
    "cascade.train(data=data_train, keys=i_id_keys, num_epochs=num_epochs, c_types=c_types, weight_dist=weight_dist, template=template, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 897,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "JH7f_qJX5Un2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "num_data => 116 :: img_shape = (20, 20)\n",
      "num_data => 50 :: img_shape = (20, 20)\n"
     ]
    }
   ],
   "source": [
    "## |> include_in_final_source <|\n",
    "## 🚀🚀 MASTER CODE 🚀🚀 PART-3 (TEST DATA-LOADING) 🚀🚀\n",
    "## \n",
    "## 👍 grab the pos & neg training data (windows) and their labels\n",
    "\n",
    "path_pos_data_test = f\"../data/face_test/\"\n",
    "path_neg_data_test = f\"../data/bg_test/\"\n",
    "shape=(350, 350)\n",
    "resolution=(20, 20)\n",
    "\n",
    "pos_data_test = get_data(path=path_pos_data_test, shape=shape, resolution=resolution, as_gray=True, ctype=\"pos\")\n",
    "neg_data_test = get_data(path=path_neg_data_test, shape=(20,20), resolution=resolution, as_gray=True, ctype=\"neg\")\n",
    "data_test = {**pos_data_test, **neg_data_test}\n",
    "\n",
    "## 🥵 calculate integral-image for all data\n",
    "for i_id in data_test.keys():\n",
    "    ii_img = create_integral_image(data_test[i_id][\"img\"])\n",
    "    data_test[i_id][\"ii_img\"] = ii_img\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 898,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fybTfoaC5Ukf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mode=[sequential]: stage[0]=> passed = [114/166] :: stage_test_error -> 0.24096385542168675 :: overall_test_error -> 0.24096385542168675\n",
      "mode=[sequential]: stage[1]=> passed = [58/166] :: stage_test_error -> 0.32456140350877194 :: overall_test_error -> 0.22289156626506024\n",
      "mode=[sequential]: stage[2]=> passed = [55/166] :: stage_test_error -> 0.05172413793103448 :: overall_test_error -> 0.018072289156626505\n",
      "mode=[sequential]: stage[3]=> passed = [9/166] :: stage_test_error -> 0.8363636363636363 :: overall_test_error -> 0.27710843373493976\n",
      "mode=[sequential]: stage[4]=> passed = [5/166] :: stage_test_error -> 0.4444444444444444 :: overall_test_error -> 0.024096385542168676\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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cNoF0nbr/7x3Az4C9c/nxwJF5ubuR7uFYxpYb3s4h3U8ynfRJdzFbbrQaap0OJm3Tr8rLfyep27a/2/V+4Pzc1pNJ+8dXK/OPz+39Y+BP8+MxuexEUk/cIfl9/xEDvzXwZdLNuZNIvSd3seXGtZm0ubSTy2rtl6R9bVF+jd1J9898tNM+TGXfJF0mODM/Pio//0rNbb//WwPvyO/1eQz+rYGh2vx6Uu/JBNKHhRXkmy4L2vxI0vF2N9Ix+seVst1Jvcxn5m3hVNIJrn//fw/pE/0+beo81PF/qPPST0mJ3UTSsaz6rYHnuo0O1l4nkZJJSMel24EP5OdDnpcqx+m2l0zzNreRDjcrtp2nYIOaSupO2aky7VzytcX8/DTSQWINcBvw6pZl/JJ8faTN8r9Euma0uvK3uFJ+CfCRDvP+bn7jn8kN/R3gwEr5+2lzLTaXvYK0069pee2XV2IWAUeU1KXN8pfQORHYl3SCW026Rvk2Knd35/ZdnjfMfyF1K3U9ESB9QlxK5WAx2HuXy9+U26n/Duf5lbIhE4E87WWk621P5f8vq5Rdx7YnAhOA+bluy4F3Vcpm5PafkZ/PzHWr/i3JZdNI32ZYld+Tn7a2SZt5o6X8g6TrhCvzezolTz8zxz/Tsh321+sj+T15Jv+fx5a776eREoMn8zouonK9m3Rd9oE878Okk+DeHdrqLAZ+ffANpJPk6lznqxj49SmREtrH898n2HKNdNB1yjGvJ90A9XR+j6sH6Fl52hOkg/c3gT1btonW9p5TKe/vhn6adHyZUCnbjbQ/rSJtr39TqffLSfvIuA5tVLxfMvBbA0+SunB37rQPMzAROJV00l5FSloupGYikJdzJGl/WgvcysCv5b0X+H6NNp9Ouiy1mpZvTBS0+eWkfbv/21N7tsz7ctK2u5r0ldDqMThId/dXt6P35rKhjv9DnZdm5nVem5fzqpby57KNdmwvUvKxPNf7ftKlgf7kpeS8NJ10ou/0bYn3UPNbe/07wKAkfRRYERGfzc8nkL7eclxEPDzEvPsB34yIlw75Qu3nvy2/zmNDBm897zWkm3nu3IZ5/y/wpog4bTjqsiOQ9CnSDU2fz8+f03tntiOR9Nek+28uHjJ46GUtISWw//mcK2bWZUWJgJmZlXMiYDsS/+jQDk5bDznZ//f9XtfNzMy2f+4RMDMzazD3CJiZmTVY22ENzbbV1KlTY+bMmb2uxrCr03O2evXqoYOyzZs3b0t1embg19IHN2ZM+eeMOrETJ04siqtT1zqx24Nbbrnl0YiY1ut62OjgRMCG1cyZM1mwYEFRbJ2Ta7cO1KUn4g0bNhQv8yc/+cnQQdmaNWuKY+ucLOskGHWWO3Zs+SGj9IQNsOuuuxbHHnRQ2a8G16lrndg6urXd9vX1tY7GabbNfGnAzMyswZwINJikEyXdLem+yq+WVcsnSPpGLv9Zm1/2MjOzHZwTgYbSlp+gPYk0NOgZkg5pCTub9KuILwQ+Q/kPv5iZ2Q7CiUBzHQXcF+mXsJ4lDZd6SkvMKWz5dat/A47TjnZXlZmZDcqJQHNNZ+DPpC5l4E//DoiJ9JOpT5F+xGMASXMlLZC0YOXKlV2qrpmZdYMTgeZq98m+9Tb+khgiYl5EzI6I2dOm+RtNZmY7EicCzbWUgb+Xvh9b/176r2MkjSX9zPDjI1I7MzMbEU4Emutm4EBJz5M0nvS79le2xFxJ+klZSD+N+qPwmNRmZqOKBxRqqIjYKOk84GqgD5gfEYslfQhYEBFXApcA/yLpPlJPwOm9q7GZmXWDf3TIhtXs2bPj5ptvLoqt8wWEOttpndjrr7++KK7OyHNr164tjq1j/fr1xbF12mDcuHHFsd0asXDChAnFsZMnTy6KKx2BEKCvr68rsd0akrmvr++WiJhdPIPZIHxpwMzMrMGcCJiZmTWYEwEzM7MGcyJgZmbWYE4EzMzMGsyJgJmZWYM5ETAzM2swJwJmZmYN5kTAzMyswZwImJmZNZh/a8BGnTrD65YO67phw4biZW7cuLE4dtOmTcWxdYYCrjMMbh11hgJ+5plnimPrDMtc+v6uW7eueJk77bRTcWydYYPrxJr1insEzMzMGsyJgJmZWYM5ETAzM2swJwJmZmYN5kTAzMyswZwImJmZNZgTATMzswZzItBQkvaX9F+S7pS0WNI72sTMkfSUpNvy39/0oq5mZtY9HlCouTYC746IWyVNAm6RdG1E3NESd0NEvKYH9TMzsxHgHoGGioiHI+LW/HgVcCcwvbe1MjOzkeYeAUPSTOBI4Gdtil8q6RfAMuAvImJxm/nnAnMBZsyYUfy6mzdv3obaDm358uXDvsw6dd15552LY+sMwzt16tTi2DrDLNcZErnOMMd1hiOuM4Tz+vXri+JWrVpVvMw661U6LHVd3dofzIbiHoGGk7Qr8C3gzyPi6ZbiW4EDIuII4J+A77RbRkTMi4jZETF72rRp3a2wmZkNKycCDSZpHCkJ+GpEfLu1PCKejojV+fFVwDhJ5R9Lzcxsu+dEoKGUfhbtEuDOiPh0h5i9cxySjiJtL4+NXC3NzKzbfI9Acx0DvAlYJOm2PO29wAyAiLgIOBV4u6SNwFrg9Khz8dnMzLZ7TgQaKiJuBAb9sfSIuBC4cGRqZGZmveBLA2ZmZg3mRMDMzKzBnAiYmZk1mBMBMzOzBnMiYGZm1mD+1oANu9JvGOYhCoZ1mQB33313cWzp0LZ1hvft6+srjt1jjz2KY8eOLd9d67Ttpk2bimPrDAVc5z2rMxxx6ZDIDzzwQPEyd9ttt+LYOu9DnW3BrFfcI2BmZtZgTgTMzMwazImAmZlZgzkRMDMzazAnAmZmZg3mRMDMzKzBnAiYmZk1mBMBMzOzBnMiYGZm1mAeWdB6ZvPmzV2JPfDAA4tj77jjjqK4OiP11TFmTHkuXie2TnvVGf2uW+1QOlpgnTrUGbWxTnvVGTGxTmy32tZsKO4RMDMzazAnAmZmZg3mRKDBJC2RtEjSbZIWtCmXpH+UdJ+khZJe3It6mplZ9/geAXtlRDzaoewk4MD8dzTwhfzfzMxGCfcI2GBOAb4cyU+ByZL26XWlzMxs+DgRaLYArpF0i6S5bcqnAw9Vni/N0waQNFfSAkkLVq5c2aWqmplZNzgRaLZjIuLFpEsA50o6tqW83feZtvo+VETMi4jZETF72rRp3ainmZl1iROBBouIZfn/CuAK4KiWkKXA/pXn+wHLRqZ2ZmY2EpwINJSkXSRN6n8MnADc3hJ2JfDm/O2BlwBPRcTDI1xVMzPrIn9roLn2Aq7Io5mNBb4WET+QdA5ARFwEXAWcDNwHrAH+pEd1NTOzLnEi0FARcT9wRJvpF1UeB3DuSNarkzrDr06ZMqU4tnRo2TrD8I4d253dqs4wvHVs2rSpOPaxxx4rjt2wYUNxbJ02mzRpUlHc0qVLi5e57777FsfWGTa4jm4t12wovjRgZmbWYE4EzMzMGsyJgJmZWYM5ETAzM2swJwJmZmYN5kTAzMyswZwImJmZNZgTATMzswZzImBmZtZgTgTMzMwazEMM27ArHSq1zpCqdWLHjCnPb0uH7a0zBO4TTzwx7K8P5cMhA+yxxx7FsatXry6OrVPftWvXFsfuvPPOxbGlwz2XDkUM9YZZ7tZ2W2cYbbPh5B4BMzOzBnMiYGZm1mBOBMzMzBrMiYCZmVmDOREwMzNrMCcCZmZmDeZEwMzMrMGcCDSUpIMl3Vb5e1rSn7fEzJH0VCXmb3pVXzMz6w4PKNRQEXE3MAtAUh/wK+CKNqE3RMRrRrJuZmY2ctwjYADHAb+MiAd7XREzMxtZ7hEwgNOByzuUvVTSL4BlwF9ExOLWAElzgbkAM2bMKH/Vj7+2PLbGUK2n3VA+XOxNN91UFHddYRzA+vXri2PXrVtXHDt58uTi2DpDIk+cOLE4ts6QuePHjy+O3WWXXYpjS4eQ7tbwzXVizXYE7hFoOEnjgdcC32xTfCtwQEQcAfwT8J12y4iIeRExOyJmT5s2rXuVNTOzYedEwE4Cbo2I5a0FEfF0RKzOj68CxkmaOtIVNDOz7nEiYGfQ4bKApL2VfxJN0lGk7eWxEaybmZl1me8RaDBJOwPHA2+rTDsHICIuAk4F3i5pI7AWOD3qXCQ2M7PtnhOBBouINcAeLdMuqjy+ELhwpOtlZmYjx5cGzMzMGsyJgJmZWYM5ETAzM2swJwJmZmYN5kTAzMyswfytAeudNERBWWiXqrDTTjsVxZUOawswZcqU4tg6w9XWqcOmTeXDLNepQ52hgOt807ROfUtjuzUUcJ33oU4b+Ju51ivuETAzM2swJwJmZmYN5kTAzMyswZwImJmZNZgTATMzswZzImBmZtZgTgTMzMwazImAmZlZgzkRMDMzazAnAmZmZg3mIYZth9CtwVdVOMxxaRzAuHHjimPrDFe7bt264tg6w+vWGdr22WefLY6t0w51lNZ31113LV5mnfehzrZQJ9asV9wjYGZm1mBOBEY5SfMlrZB0e2Xa7pKulXRv/t/2V3IknZlj7pV05sjV2szMRooTgdHvUuDElmnnAz+MiAOBH+bnA0jaHfgAcDRwFPCBTgmDmZntuJwIjHIRcT3weMvkU4DL8uPLgNe1mfXVwLUR8XhEPAFcy9YJhZmZ7eCcCDTTXhHxMED+v2ebmOnAQ5XnS/M0MzMbRZwIWCftbndue7u2pLmSFkhasHLlyi5Xy8zMhpMTgWZaLmkfgPx/RZuYpcD+lef7AcvaLSwi5kXE7IiYPW3atGGvrJmZdY8TgWa6Euj/FsCZwHfbxFwNnCBpSr5J8IQ8zczMRhEnAqOcpMuBm4CDJS2VdDbwceB4SfcCx+fnSJot6Z8BIuJx4MPAzfnvQ3mamZmNIh5ZcJSLiDM6FB3XJnYB8NbK8/nA/C5VzczMtgNOBKxn6gy+WmcY3DF9fcWxe++9d1Hcxo0bi5e5adOm4tg66gzZu2bNmuLYOm07YcKE4tg6bdYNpe8twNix5YdCDxtso40vDZiZmTWYEwEzM7MGcyJgZmbWYE4EzMzMGsyJgJmZWYM5ETAzM2swJwJmZmYN5kTAzMyswZwImJmZNZgTATMzswbzEMPWM3WGtq03rGv5cvfcc8+iuBkzZhQvc8mSJcWxGzZsKI7dvHlzV2LXrVtXHFtn6OI6wxHXGT65dDjgOsMGbw88dLH1insEzMzMGsyJgJmZWYM5ETAzM2swJwJmZmYN5kTAzMyswZwImJmZNZgTATMzswZzIjDKSZovaYWk2yvTPinpLkkLJV0haXKHeZdIWiTpNkkLRq7WZmY2UpwIjH6XAie2TLsWOCwiDgfuAd4zyPyvjIhZETG7S/UzM7MeciIwykXE9cDjLdOuiYiN+elPgf1GvGJmZrZd2LHG4LRueAvwjQ5lAVwjKYCLI2JeuyBJc4G5kIbiLR46+Pwra1e2xLdqxG7cuHHoIGDTpk3bVplhVFpXgL6+vuLYOsP71hkWesyY8s8ZdYZE7sbr12kvs9HGPQINJul9wEbgqx1CjomIFwMnAedKOrZdUETMi4jZETF76tSpXaqtmZl1gxOBhpJ0JvAa4I3R4WNeRCzL/1cAVwBHjVwNzcxsJDgRaCBJJwJ/Bbw2Itr+nJykXSRN6n8MnADc3i7WzMx2XE4ERjlJlwM3AQdLWirpbOBCYBJwbf5q4EU5dl9JV+VZ9wJulPQL4OfA9yLiBz1YBTMz6yLfLDjKRcQZbSZf0iF2GXByfnw/cEQXq2ZmZtsB9wiYmZk1mBMBMzOzBnMiYGZm1mBOBMzMzBrMiYCZmVmD+VsDNuxKh6GtMwRsnaFt65A0rHFQb7jcOsvt1jC4EydOLI6tM9RynXaoE1tah261bZ3l1ok16xX3CJiZmTWYEwEzM7MGcyJgZmbWYE4EzMzMGsyJgJmZWYM5ETAzM2swJwJmZmYN5kTAzMyswZwImJmZNZhHFrRhJanWiIF1lluqGyP71Xn9OqMg1hmpr85yu9Ve3RpZcOzY8kNRaR26sR1C90YW9CiE1ivuETAzM2swJwJmZmYN5kRglJM0X9IKSbdXpl0g6VeSbst/J3eY90RJd0u6T9L5I1drMzMbKU4ERr9LgTtNmxgAAAbUSURBVBPbTP9MRMzKf1e1FkrqAz4HnAQcApwh6ZCu1tTMzEacE4FRLiKuBx7fhlmPAu6LiPsj4lng68Apw1o5MzPrOScCzXWepIX50sGUNuXTgYcqz5fmaVuRNFfSAkkLVq5c2Y26mplZlzgRaKYvAC8AZgEPA59qE9Puu0xtv78WEfMiYnZEzJ42bdrw1dLMzLrOiUADRcTyiNgUEZuBL5IuA7RaCuxfeb4fsGwk6mdmZiPHiUADSdqn8vT3gdvbhN0MHCjpeZLGA6cDV45E/czMbOR4ZMFRTtLlwBxgqqSlwAeAOZJmkbr6lwBvy7H7Av8cESdHxEZJ5wFXA33A/IhY3INVMDOzLnIiMMpFxBltJl/SIXYZcHLl+VXAVl8t7IVuDa9bqs5wtXWG1q2z3I0bNxbHbtiwoTi2jjrvQ51hg7sxhPO6deuKlzl+/Pji2DrvWZ1YDzFsveJLA2ZmZg3mRMDMzKzBnAiYmZk1mBMBMzOzBnMiYGZm1mBOBMzMzBrMiYCZmVmDOREwMzNrMCcCZmZmDeZEwMzMrME8xLANq4goHgK2W0Oq1hkGtzR20aJFxcuss151YseNG1ccW2do2zpDAa9fv744tk596wyfXDqE8z333FO8zMMOO6w4tq+vrzh206ZNxbEeYth6xT0CZmZmDeZEwMzMrMGcCJiZmTWYEwEzM7MGcyJgZmbWYE4EzMzMGsyJgJmZWYN5HIFRTtJ84DXAiog4LE/7BnBwDpkMPBkRs9rMuwRYBWwCNkbE7BGptJmZjRgnAqPfpcCFwJf7J0TEH/Y/lvQp4KlB5n9lRDzatdqZmVlPOREY5SLiekkz25UpDWV2GvC7I1knMzPbfjgRaLaXA8sj4t4O5QFcIymAiyNiXrsgSXOBuQAzZswofvE6QwF3S2kdnnzyyeJlTp48uTi2zrCy3Ro2uM4wuBMmTCiOrTNscJ1tobTNjj766GFfJsCGDRuKY+u07fawP1gz+WbBZjsDuHyQ8mMi4sXAScC5ko5tFxQR8yJidkTMnjp1ajfqaWZmXeJEoKEkjQVeD3yjU0xELMv/VwBXAEeNTO3MzGykOBForlcBd0XE0naFknaRNKn/MXACcPsI1s/MzEaAE4FRTtLlwE3AwZKWSjo7F51Oy2UBSftKuio/3Qu4UdIvgJ8D34uIH4xUvc3MbGT4ZsFRLiLO6DD9rDbTlgEn58f3A0d0tXJmZtZz7hEwMzNrMCcCZmZmDeZEwMzMrMGcCJiZmTWYEwEzM7MG87cGbNiVDpVaZ/jVzZs3dyW2dNjeOXPmFC+zznC169atK4598MEHi2PrDINbZ9jgOkNI1xnmuM571tfXVxT37LPPFi+zTnvVUWdbqFNfs+HkHgEzM7MGcyJgZmbWYE4EzMzMGsyJgJmZWYM5ETAzM2swJwJmZmYN5kTAzMyswZwImJmZNZgTATMzswZzImBmZtZgKh0O1qyEpJVAu7FwpwKPjnB1RsJoXS8Yves2GtbrgIiY1utK2OjgRMBGhKQFETG71/UYbqN1vWD0rttoXS+zbeVLA2ZmZg3mRMDMzKzBnAjYSJnX6wp0yWhdLxi96zZa18tsm/geATMzswZzj4CZmVmDOREwMzNrMCcC1lWSTpR0t6T7JJ3f6/oMJ0lLJC2SdJukBb2uz7aSNF/SCkm3V6btLulaSffm/1N6Wcdt1WHdLpD0q/y+3Sbp5F7W0azXnAhY10jqAz4HnAQcApwh6ZDe1mrYvTIiZu3g30u/FDixZdr5wA8j4kDgh/n5juhStl43gM/k921WRFw1wnUy2644EbBuOgq4LyLuj4hnga8Dp/S4TtYiIq4HHm+ZfApwWX58GfC6Ea3UMOmwbmZW4UTAumk68FDl+dI8bbQI4BpJt0ia2+vKDLO9IuJhgPx/zx7XZ7idJ2lhvnSwQ172MBsuTgSsm9Rm2mj6vuoxEfFi0qWPcyUd2+sKWZEvAC8AZgEPA5/qbXXMesuJgHXTUmD/yvP9gGU9qsuwi4hl+f8K4ArSpZDRYrmkfQDy/xU9rs+wiYjlEbEpIjYDX2R0vW9mtTkRsG66GThQ0vMkjQdOB67scZ2GhaRdJE3qfwycANw++Fw7lCuBM/PjM4Hv9rAuw6o/wcl+n9H1vpnVNrbXFbDRKyI2SjoPuBroA+ZHxOIeV2u47AVcIQnSfvS1iPhBb6u0bSRdDswBpkpaCnwA+Djwr5LOBv4XeEPvarjtOqzbHEmzSJeplgBv61kFzbYDHmLYzMyswXxpwMzMrMGcCJiZmTWYEwEzM7MGcyJgZmbWYE4EzMzMGsyJgJmZWYM5ETAzM2uw/w+UP8ks603ifwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## |> include_in_final_source <|\n",
    "## 🚀🚀 MASTER CODE 🚀🚀 PART-4 (TEST using trained CASCADE) 🚀🚀\n",
    "## \n",
    "# sc = cascade.strong_classifier              ## <- 🚫CAREFUL: used only for speed-testing to avoid re-training. DONOT include in final_source\n",
    "# cascade = Cascade(strong_classifier=sc)     ## <- 🚫CAREFUL: used only for speed-testing to avoid re-training. DONOT include in final_source\n",
    "\n",
    "cascade.test(data=data_test, mode=\"sequential\", verbose=True)\n",
    "# predictions = cascade.test(data=data_test, mode=\"committee\", verbose=True)\n",
    "\n",
    "cascade.draw(data_test, \"pos_0\", 20, 20)\n",
    "\n",
    "draw_roc_curve(data=data_test, mode=\"sequential\", classifiers=cascade.strong_classifier)\n",
    "# draw_roc_curve(data=data_test, mode=\"committee\", predictions=predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HRrnmSiQiqQa"
   },
   "source": [
    "# ```sandbox (rough work for development)```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 786,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<|CASCADE|>|> #classifiers=>8100\n",
      "epoch[0/1] => ((5, 1, 2, 3), 'haar_dd') :: (error->0.03901611535199324) :: (alpha->1.6019914322694553)\n",
      "Time taken => 185.82044792175293 seconds!!\n"
     ]
    }
   ],
   "source": [
    "\n",
    "num_epochs = 1\n",
    "c_types = [\"haar_dd\"]\n",
    "weight_dist = \"Uniform\"\n",
    "template = (0,0,20,20)\n",
    "mode = \"sequential\"   ## otherwise can use these aswell -> [\"committe\", \"attentional\"]\n",
    "\n",
    "## Build & train a cascade\n",
    "cascade1 = Cascade()\n",
    "cascade1.train(data=data_train, num_epochs=num_epochs, c_types=c_types, weight_dist=weight_dist, template=template, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 846,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mode=[sequential]: stage[0]=> passed = [113/166] :: stage_test_error -> 0.10240963855421686 :: overall_test_error -> 0.10240963855421686\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sc = cascade.strong_classifier              ## <- 🚫CAREFUL: used only for speed-testing to avoid re-training. DONOT include in final_source\n",
    "cascade1 = Cascade(strong_classifier=sc)     ## <- 🚫CAREFUL: used only for speed-testing to avoid re-training. DONOT include in final_source\n",
    "\n",
    "cascade1.test(data=data_test, mode=\"sequential\", verbose=True)\n",
    "# predictions = cascade.test(data=data_test, mode=\"committee\", verbose=True)\n",
    "\n",
    "cascade1.draw(data_test, \"pos_0\", 20, 20)\n",
    "\n",
    "draw_roc_curve(data=data_test, mode=\"sequential\", classifiers=cascade1.strong_classifier)\n",
    "# draw_roc_curve(data=data_test, mode=\"committee\", predictions=predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 792,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['feat_type', 'val', 'error', 'alpha', 'coord_A', 'coord_B', 'coord_C', 'coord_D', 'threshold', 'polarity', 'pred_labels', 'stage_test_error'])"
      ]
     },
     "execution_count": 792,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cascade1.strong_classifier[0][((5, 1, 2, 3), 'haar_dd')].keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 551,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 545
    },
    "colab_type": "code",
    "id": "X23geaa75Uf-",
    "outputId": "77d0e3a1-e870-43e3-a20d-4f014ec1896e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_classifier(strong_classifier=cascade.strong_classifier, win_h=template[2], win_w=template[3])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "Gqg5NEGP5UP_",
    "outputId": "b9941906-9de3-46c7-b43d-d1a5acaa8bd5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34200"
      ]
     },
     "execution_count": 159,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(classifiers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fDUiV4itNr4i"
   },
   "outputs": [],
   "source": [
    "from skimage import io\n",
    "img = io.imread(f\"../data/face_test/151-11.jpg\", as_gray=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 286
    },
    "colab_type": "code",
    "id": "r0T8_LxQN-0f",
    "outputId": "00cfee2a-4b75-4f59-9e2e-98764fb8bf3f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(480, 640)"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.imshow(img, cmap=\"gray\")\n",
    "img.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20, 20)"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import skimage\n",
    "res = skimage.transform.resize(img, (20,20), anti_aliasing=True)\n",
    "plt.figure()\n",
    "plt.imshow(res,cmap=\"gray\")\n",
    "res.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20, 20)"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "res = skimage.transform.resize(img, (20,20), anti_aliasing=False)\n",
    "plt.figure()\n",
    "plt.imshow(data_train[\"pos_0\"][\"img\"],cmap=\"gray\")\n",
    "res.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "e_UY4IgucIna"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Jea_UaH7iolz"
   },
   "outputs": [],
   "source": [
    "def draw_classifierX(c_id):\n",
    "    classifier_img = np.zeros((7,7))\n",
    "    i0 = c_id[0][0]\n",
    "    j0 = c_id[0][1]\n",
    "    h = c_id[0][2]\n",
    "    w = c_id[0][3]\n",
    "    c_type = c_id[1]\n",
    "    if c_type==\"haar_lr\":\n",
    "        for i in range(i0, i0+h):\n",
    "            for j in range(j0, j0+w):\n",
    "                classifier_img[i,j] = 255\n",
    "            for j in range(j0+w, j0+2*w):\n",
    "                classifier_img[i,j] = 200\n",
    "    elif c_type==\"haar_tb\":\n",
    "        for j in range(j0, j0+w):\n",
    "            for i in range(i0, i0+h):\n",
    "                classifier_img[i,j] = 255\n",
    "            for i in range(i0+h, i0+2*h):\n",
    "                classifier_img[i,j] = 200\n",
    "\n",
    "    plt.figure()\n",
    "    plt.imshow(classifier_img)\n",
    "    plt.title(c_id)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 545
    },
    "colab_type": "code",
    "id": "nXh2NrZTMRKT",
    "outputId": "e8924937-3c45-4ed9-fb4f-47710725b454"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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w5ggDt0taJmlRh3UbDWM1EcMU6l9oknYHbgDOtf1yV3Vtv2H7EGAusEBSJ6cfkk4C1tle\n1kW9bTjC9mHA8cCf16dgXWg0jNVEDFOo1wL790zPrZ8rXn0+ewNwte0bJ6OH+hDwLmBhRyUPB06u\nz22vA46W9I2OamN7bf19HXAT1elfF1ofxmqYQv0AcKCkA+qLB6cA353knlpXX6y6FFhl+8KOa+8t\naVb9eBeqi5SPd1Hb9udsz7U9j+p3faft07uoLWm3+qIk9aHvR4BO3vnoYhirpsPutM72ZklnAbcB\nU4DLbD/WRW1J1wJHAbMlrQH+1valXdSm2mOdATxSn9sC/LXtmzuovR9wZf3Ow07A9bY7fWtpkuwL\n3FS9nrIzcI3tWzus3+owVkPzllZEDMYwHX5HxAAk1BGFSagjCpNQRxQmoY4oTEIdUZiEOqIw/wca\nHSm5zd/AGwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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oGCvbG3q1/UHaUy8EnrK9uh7a51rgk/1o2Pa9wEv9aGuEtp+1/WD9fCOwEpjbp7Zte1M9\nObV+9O1bXtI84BPARf1qc7J1DGN1MVTDWPUy0DBYoZ4LPN0xvZY+fbgHhaT5wEHA/W+/ZE/bnCJp\nBbAeuLNj0IZ+uAD4ErC1j20OMXCHpOWSFvex3UbDWE3EIIX6XU3STsANwJm2X+lXu7bfsn0gMA9Y\nKKkv3Q9JxwPrbS/vR3sjONz2wcCxwOfrLlg/NBrGaiIGKdTrgD07pufVrxWv7s/eAFxl+7uTUUN9\nCHg3sKhPTR4GnFj3ba8FjpJ0ZZ/axva6+s/1wI1U3b9+aH0Yq0EK9QPAByXtXZ88OAn4/iTX1Lr6\nZNXFwErb5/e57V0lzamf70B1knJVP9q2/RXb82zPp/q3vsv2Kf1oW9LM+qQk9aHvMUBfrnz0Yxir\npsPutM72FklfAG4HpgCX2H68H21LugY4EthF0lrgHNsX96Ntqj3WqcCjdd8W4Gzbt/ah7d2By+sr\nD9sB19nu66WlSbIbcGP1fcr2wNW2b+tj+60OYzUwl7QiojcG6fA7InogoY4oTEIdUZiEOqIwCXVE\nYRLqiMIk1BGF+V+GUH4AsxAkxAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_classifierX(((2,3,3,1), \"haar_lr\"))\n",
    "draw_classifierX(((0,1,2,2), \"haar_tb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "StUlWBLEa9Kj",
    "outputId": "597f9cef-7f71-45bb-ed11-e506e1f208c3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[((2, 2, 1, 3), 'haar_tb'), ((1, 3, 2, 2), 'haar_tb')]"
      ]
     },
     "execution_count": 42,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strong_classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3f66IgzTiohv"
   },
   "outputs": [],
   "source": [
    "classifier_img = np.zeros((5,5))\n",
    "classifier_img[0,0] = 300\n",
    "classifier_img[1,1] = 200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "colab_type": "code",
    "id": "JzwsGr6Ximrz",
    "outputId": "23f1d491-90b4-475e-d4e8-a950be0a231b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f387289b128>"
      ]
     },
     "execution_count": 41,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPUAAAD4CAYAAAA0L6C7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n0n6lPIZAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.imshow(classifier_img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 534
    },
    "colab_type": "code",
    "id": "bv188PwVKDQP",
    "outputId": "a97bdd9b-e996-492c-c919-a9b3a27ff928"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40, 40)"
      ]
     },
     "execution_count": 7,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SMY4JaQAwl2hxai4ha9wNkbFs2HsZGVKscXpWt24CgLmcXl99cPssHXu0tEPF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\n3XFSgie746QET3bHSQn/H1TeA+iMLUSlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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d+5pLH8BIWQs7bVEuwBRatQCTHPbc8/6dnldc/IWew5xx/DCOkDnb7Tv4/G6m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\nGCHBgt0wQoIFu2GEhP8H2GaUPF4ucnMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## |> include_in_final_source <|\n",
    "##\n",
    "## Plotting the face and background training data\n",
    "plt.figure()\n",
    "plt.imshow(np.reshape(face_data[:,0], (40,40,3))[:,:,0])\n",
    "np.reshape(face_data[:,0], (40,40,3))[:,:,0].shape\n",
    "\n",
    "plt.figure()\n",
    "plt.imshow(np.reshape(bg_data[:,0], (40,40,3))[:,:,0])\n",
    "np.reshape(bg_data[:,0], (40,40,3))[:,:,0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "JzrhKuIzb5-x"
   },
   "outputs": [],
   "source": [
    "## |> include_in_final_source <|\n",
    "##\n",
    "img = np.reshape(face_data[:,0], (40,40,3))[:,:,0]\n",
    "bg  = np.reshape(bg_data[:,0], (40,40,3))[:,:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 136
    },
    "colab_type": "code",
    "id": "tTRqaRcikk4m",
    "outputId": "18eb273b-1b26-406c-d6bd-e455720f5041"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[171, 174, 173, ..., 167, 167, 164],\n",
       "       [175, 174, 176, ..., 165, 164, 166],\n",
       "       [173, 174, 170, ..., 165, 164, 166],\n",
       "       ...,\n",
       "       [150, 153, 154, ..., 121, 128, 128],\n",
       "       [149, 157, 148, ..., 122, 123, 121],\n",
       "       [153, 152, 146, ..., 116, 121, 124]], dtype=uint8)"
      ]
     },
     "execution_count": 9,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "machine_shape": "hm",
   "name": "ece763_project_2.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}