BayesianMatting.ipynb

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  {
   "source": [
    "<a href=\"https://colab.research.google.com/github/varunjain3/BayesianMatting/blob/main/BayesianMatting.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!git clone https://github.com/varunjain3/BayesianMatting.git"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%cd BayesianMatting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Importing Dependencies\n",
    "\n",
    "import numpy as np\n",
    "from PIL import Image, ImageOps\n",
    "import matplotlib.pyplot as plt\n",
    "import cv2\n",
    "import os\n",
    "\n",
    "# orchard_bouman_clust.py should be in the folder\n",
    "from orchard_bouman_clust import clustFunc\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Some important helper functions\n",
    "\n",
    "# This function displays the image.\n",
    "def show_im(img):\n",
    "    \"\"\"\n",
    "    img - input image should be a numpy array.\n",
    "    \"\"\"\n",
    "    plt.imshow(img)\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "# Provided by Matlab\n",
    "def matlab_style_gauss2d(shape=(3, 3), sigma=0.5):\n",
    "    \"\"\"\n",
    "    2D gaussian mask - should give the same result as MATLAB's\n",
    "    fspecial('gaussian',[shape],[sigma])\n",
    "    \"\"\"\n",
    "    m, n = [(ss-1.)/2. for ss in shape]\n",
    "    y, x = np.ogrid[-m:m+1, -n:n+1]\n",
    "    h = np.exp(-(x*x + y*y)/(2.*sigma*sigma))\n",
    "    h[h < np.finfo(h.dtype).eps*h.max()] = 0\n",
    "    sumh = h.sum()\n",
    "    if sumh != 0:\n",
    "        h /= sumh\n",
    "    return h\n",
    "\n",
    "\n",
    "# To get a window where center is (x,y) that is of size (N,N)\n",
    "def get_window(img,x,y,N=25):\n",
    "    \"\"\"\n",
    "    Extracts a small window of input image, around the center (x,y)\n",
    "    img - input image\n",
    "    x,y - cordinates of center\n",
    "    N - size of window (N,N) {should be odd}\n",
    "    \"\"\"\n",
    "\n",
    "    h, w, c = img.shape             # Extracting Image Dimensions\n",
    "    \n",
    "    arm = N//2                      # Arm from center to get window\n",
    "    window = np.zeros((N,N,c))      \n",
    "\n",
    "    xmin = max(0,x-arm)\n",
    "    xmax = min(w,x+arm+1)\n",
    "    ymin = max(0,y-arm)\n",
    "    ymax = min(h,y+arm+1)\n",
    "\n",
    "    window[arm - (y-ymin):arm +(ymax-y),arm - (x-xmin):arm +(xmax-x)] = img[ymin:ymax,xmin:xmax]\n",
    "\n",
    "    return window\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "## To solve individual pixels\n",
    "def solve(mu_F, Sigma_F, mu_B, Sigma_B, C, Sigma_C, alpha_init, maxIter = 50, minLike = 1e-6):\n",
    "    \"\"\"\n",
    "    mu_F - Mean of foreground pixel\n",
    "    Sigma_F - Covariance Mat of foreground pixel\n",
    "    mu_B, Sigma_B - Mean and Covariance of background pixel\n",
    "    C, Sigma_C - Current pixel, and its variance\n",
    "    alpha_init - starting alpha value\n",
    "    maxIter - Iterations to solve the value of the pixel\n",
    "    minLike - min likelihood to reach to stop before maxIterations. \n",
    "    \"\"\"\n",
    "\n",
    "    # Initializing Matrices\n",
    "    I = np.eye(3)\n",
    "    fg_best = np.zeros(3)\n",
    "    bg_best = np.zeros(3)\n",
    "    a_best = np.zeros(1)\n",
    "    maxlike = -np.inf\n",
    "    \n",
    "    invsgma2 = 1/Sigma_C**2\n",
    "    \n",
    "    for i in range(mu_F.shape[0]):\n",
    "        # Mean of Foreground pixel can have multiple possible values, iterating for all.\n",
    "        mu_Fi = mu_F[i]\n",
    "        invSigma_Fi = np.linalg.inv(Sigma_F[i])\n",
    "\n",
    "        for j in range(mu_B.shape[0]):\n",
    "            # Similarly, multiple mean values be possible for background pixel.\n",
    "            mu_Bj = mu_B[j]\n",
    "            invSigma_Bj = np.linalg.inv(Sigma_B[j])\n",
    "\n",
    "            alpha = alpha_init\n",
    "            myiter = 1\n",
    "            lastLike = -1.7977e+308\n",
    "\n",
    "            # Solving Minimum likelihood through numerical methods\n",
    "            while True:\n",
    "                # Making Equations for AX = b, where we solve for X.abs\n",
    "                # X here has 3 values of forground pixel (RGB) and 3 values for background\n",
    "                A = np.zeros((6,6))\n",
    "                A[:3,:3] = invSigma_Fi + I*alpha**2 * invsgma2\n",
    "                A[:3,3:] = A[3:,:3] = I*alpha*(1-alpha) * invsgma2\n",
    "                A[3:,3:] = invSigma_Bj+I*(1-alpha)**2 * invsgma2\n",
    "                \n",
    "                b = np.zeros((6,1))\n",
    "                b[:3] = np.reshape(invSigma_Fi @ mu_Fi + C*(alpha) * invsgma2,(3,1))\n",
    "                b[3:] = np.reshape(invSigma_Bj @ mu_Bj + C*(1-alpha) * invsgma2,(3,1))\n",
    "\n",
    "                # Solving for X and storing values for Forground and Background Pixels \n",
    "                X = np.linalg.solve(A, b)\n",
    "                F = np.maximum(0, np.minimum(1, X[0:3]))\n",
    "                B = np.maximum(0, np.minimum(1, X[3:6]))\n",
    "                \n",
    "                # Solving for value of alpha once F and B are calculated\n",
    "                alpha = np.maximum(0, np.minimum(1, ((np.atleast_2d(C).T-B).T @ (F-B))/np.sum((F-B)**2)))[0,0]\n",
    "                \n",
    "                # Calculating likelihood value for\n",
    "                like_C = - np.sum((np.atleast_2d(C).T -alpha*F-(1-alpha)*B)**2) * invsgma2\n",
    "                like_fg = (- ((F- np.atleast_2d(mu_Fi).T).T @ invSigma_Fi @ (F-np.atleast_2d(mu_Fi).T))/2)[0,0]\n",
    "                like_bg = (- ((B- np.atleast_2d(mu_Bj).T).T @ invSigma_Bj @ (B-np.atleast_2d(mu_Bj).T))/2)[0,0]\n",
    "                like = (like_C + like_fg + like_bg)\n",
    "\n",
    "                if like > maxlike:\n",
    "                    a_best = alpha\n",
    "                    maxLike = like\n",
    "                    fg_best = F.ravel()\n",
    "                    bg_best = B.ravel()\n",
    "\n",
    "                if myiter >= maxIter or abs(like-lastLike) <= minLike:\n",
    "                    break\n",
    "\n",
    "                lastLike = like\n",
    "                myiter += 1\n",
    "    return fg_best, bg_best, a_best "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def Bayesian_Matte(img,trimap,N = 25,sig = 8,minNeighbours = 10):\n",
    "    '''\n",
    "    img - input image that the user will give to perform the foreground-background mapping\n",
    "    trimap - the alpha mapping that is given with foreground and background determined.\n",
    "    N - Window size, determines how many pixels will be sampled around the pixel to be solved, should be always odd.\n",
    "    sig - wieghts of the neighbouring pixels. less means more centered.\n",
    "    minNeighbours - Neigbour pixels available to solve, should be greater than 0, else inverse wont be calculated\n",
    "    '''\n",
    "    \n",
    "    # We Convert the Images to float so that we are able to play with the pixel values\n",
    "    img = np.array(img,dtype = 'float')\n",
    "    trimap = np.array(image_trimap, dtype = 'float')\n",
    "    \n",
    "    # Here we normalise the Images to range from 0 and 1.\n",
    "    img /= 255\n",
    "    trimap /= 255\n",
    "\n",
    "    # We get the dimensions \n",
    "    h,w,c = img.shape\n",
    "    \n",
    "    # Preparing the gaussian weights for window\n",
    "    gaussian_weights = matlab_style_gauss2d((N,N),sig)\n",
    "    gaussian_weights /= np.max(gaussian_weights)\n",
    "\n",
    "    # We seperate the foreground specified in the trimap from the main image.\n",
    "    fg_map = trimap == 1\n",
    "    fg_actual = np.zeros((h,w,c))\n",
    "    fg_actual = img * np.reshape(fg_map,(h,w,1))\n",
    "\n",
    "    # We seperate the background specified in the trimap from the main image. \n",
    "    bg_map = trimap == 0\n",
    "    bg_actual = np.zeros((h,w,c))\n",
    "    bg_actual = img * np.reshape(bg_map,(h,w,1))\n",
    "    \n",
    "    # Creating empty alpha channel to fill in by the program\n",
    "    unknown_map = np.logical_or(fg_map,bg_map) == False\n",
    "    a_channel = np.zeros(unknown_map.shape)\n",
    "    a_channel[fg_map] = 1\n",
    "    a_channel[unknown_map] = np.nan\n",
    "\n",
    "    # Finding total number of unkown pixels to be calculated\n",
    "    n_unknown = np.sum(unknown_map)\n",
    "\n",
    "    # Making the datastructure for finding pixel values and saving id they have been solved yet or not.\n",
    "    A,B = np.where(unknown_map == True)\n",
    "    not_visited = np.vstack((A,B,np.zeros(A.shape))).T\n",
    "\n",
    "    print(\"Solving Image with {} unsovled pixels... Please wait...\".format(len))\n",
    "\n",
    "    # running till all the pixels are solved.\n",
    "    while(sum(not_visited[:,2]) != n_unknown):\n",
    "        last_n = sum(not_visited[:,2])\n",
    "\n",
    "        # iterating for all pixels\n",
    "        for i in range(n_unknown): \n",
    "            # checking if solved or not\n",
    "            if not_visited[i,2] == 1:\n",
    "                continue\n",
    "            \n",
    "            # If not solved, we try to solve\n",
    "            else:\n",
    "                # We get the location of the unsolved pixel\n",
    "                y,x = map(int,not_visited[i,:2])\n",
    "                \n",
    "                # Creating an window which states what pixels around it are solved(forground/background)\n",
    "                a_window = get_window(a_channel[:, :, np.newaxis], x, y, N)[:,:,0]\n",
    "                \n",
    "                # Creating a window and weights of solved foreground window\n",
    "                fg_window = get_window(fg_actual,x,y,N)\n",
    "                fg_weights = np.reshape(a_window**2 * gaussian_weights,-1)\n",
    "                values_to_keep = np.nan_to_num(fg_weights) > 0\n",
    "                fg_pixels = np.reshape(fg_window,(-1,3))[values_to_keep,:]\n",
    "                fg_weights = fg_weights[values_to_keep]\n",
    "        \n",
    "                # Creating a window and weights of solved background window\n",
    "                bg_window = get_window(bg_actual,x,y,N)\n",
    "                bg_weights = np.reshape((1-a_window)**2 * gaussian_weights,-1)\n",
    "                values_to_keep = np.nan_to_num(bg_weights) > 0\n",
    "                bg_pixels = np.reshape(bg_window,(-1,3))[values_to_keep,:]\n",
    "                bg_weights = bg_weights[values_to_keep]\n",
    "                \n",
    "                # We come back to this pixel later if it doesnt has enough solved pixels around it.\n",
    "                if len(bg_weights) < minNeighbours or len(fg_weights) < minNeighbours:\n",
    "                    continue\n",
    "                \n",
    "                # If enough pixels, we cluster these pixels to get clustered colour centers and their covariance    matrices\n",
    "                mean_fg, cov_fg = clustFunc(fg_pixels,fg_weights)\n",
    "                mean_bg, cov_bg = clustFunc(bg_pixels,bg_weights)\n",
    "                alpha_init = np.nanmean(a_window.ravel())\n",
    "                \n",
    "                # We try to solve our 3 equation 7 variable problem with minimum likelihood estimation\n",
    "                fg_pred,bg_pred,alpha_pred = solve(mean_fg,cov_fg,mean_bg,cov_bg,img[y,x],0.7,alpha_init)\n",
    "\n",
    "                # storing the predicted values in appropriate windows for use for later pixels.\n",
    "                fg_actual[y, x] = fg_pred.ravel()\n",
    "                bg_actual[y, x] = bg_pred.ravel()\n",
    "                a_channel[y, x] = alpha_pred\n",
    "                not_visited[i,2] = 1\n",
    "                if(np.sum(not_visited[:,2])%1000 == 0):\n",
    "                    print(\"Solved {} out of {}.\".format(np.sum(not_visited[:,2]),len(not_visited)))\n",
    "\n",
    "        if sum(not_visited[:,2]) == last_n:\n",
    "            # ChangingWindow Size\n",
    "            # Preparing the gaussian weights for window\n",
    "            N += 2\n",
    "            # sig += 1 \n",
    "            gaussian_weights = matlab_style_gauss2d((N,N),sig)\n",
    "            gaussian_weights /= np.max(gaussian_weights)\n",
    "            print(N)\n",
    "\n",
    "    return a_channel,n_unknown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Solving Image with 29357 unsovled pixels... Please wait...\n",
      "Solved 1000.0 out of 29357.\n",
      "Solved 2000.0 out of 29357.\n",
      "Solved 3000.0 out of 29357.\n",
      "Solved 4000.0 out of 29357.\n",
      "Solved 5000.0 out of 29357.\n",
      "Solved 6000.0 out of 29357.\n",
      "Solved 7000.0 out of 29357.\n",
      "Solved 8000.0 out of 29357.\n",
      "Solved 9000.0 out of 29357.\n",
      "Solved 10000.0 out of 29357.\n",
      "Solved 11000.0 out of 29357.\n",
      "Solved 12000.0 out of 29357.\n",
      "Solved 13000.0 out of 29357.\n",
      "Solved 14000.0 out of 29357.\n",
      "Solved 15000.0 out of 29357.\n",
      "Solved 16000.0 out of 29357.\n",
      "Solved 17000.0 out of 29357.\n",
      "Solved 18000.0 out of 29357.\n",
      "Solved 19000.0 out of 29357.\n",
      "Solved 20000.0 out of 29357.\n",
      "Solved 21000.0 out of 29357.\n",
      "Solved 22000.0 out of 29357.\n",
      "Solved 23000.0 out of 29357.\n",
      "Solved 24000.0 out of 29357.\n",
      "Solved 25000.0 out of 29357.\n",
      "Solved 26000.0 out of 29357.\n",
      "Solved 27000.0 out of 29357.\n",
      "Solved 28000.0 out of 29357.\n",
      "Solved 29000.0 out of 29357.\n"
     ]
    },
    {
     "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": "stream",
     "name": "stdout",
     "text": [
      "Absolute Loss with ground truth -  0.895618214758068\n"
     ]
    }
   ],
   "source": [
    "name = \"GT05\"\n",
    "\n",
    "os.path.join(\"data\",\"gt_training_lowres\",\"{}.png\".format(name))\n",
    "\n",
    "image = np.array(Image.open(os.path.join(\"data\",\"input_training_lowres\",\"{}.png\".format(name))))\n",
    "image_trimap = np.array(ImageOps.grayscale(Image.open(os.path.join(\"data\",\"trimap_training_lowres\",\"{}.png\".format(name)))))\n",
    "\n",
    "alpha,pixel_count = Bayesian_Matte(image,image_trimap) \n",
    "alpha *= 255\n",
    "\n",
    "image_alpha = np.array(ImageOps.grayscale(Image.open(os.path.join(\"data\",\"gt_training_lowres\",\"{}.png\".format(name))\n",
    ")))\n",
    "\n",
    "alpha_int8 = np.array(alpha,dtype = int)\n",
    "\n",
    "plt.imsave(\"{}_alpha.png\".format(name), alpha, cmap='gray')\n",
    "show_im(alpha)\n",
    "\n",
    "print(\"Absolute Loss with ground truth - \", np.sum(np.abs(alpha - image_alpha))/(alpha.shape[0]*alpha.shape[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}