Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _if_near(point, mask, nearest_neighbor):\n",
    "    nn = nearest_neighbor\n",
    "    w,h = mask.shape\n",
    "    x,y = point\n",
    "    mask = np.pad(mask,nn,'edge')\n",
    "    x += nn\n",
    "    y += nn\n",
    "    if(w+nn>x and h+nn>y):\n",
    "        x_i,y_i = int(x+0.5),int(y+0.5)\n",
    "        #return True\n",
    "        near = mask[x_i-nn:x_i+nn,y_i-nn:y_i+nn]\n",
    "        if near.max()-near.min() != 0:\n",
    "            if(x<w and y<h):\n",
    "                return True\n",
    "    return False\n",
    "\n",
    "\n",
    "# ***\n",
    "# *n* It's an example of 1-neighbor\n",
    "# ***\n",
    "#\n",
    "# *****\n",
    "# *****\n",
    "# **n** It's an example of 2-neighbor\n",
    "# *****\n",
    "# *****\n",
    "#\n",
    "# Did you get any of that?\n",
    "\n",
    "def _get_edge_k_neighbor(img,k):\n",
    "    '''\n",
    "    I will say the idea is identical to the\n",
    "    the original _is_near, but this implement save the\n",
    "    temporal result and thus speed up the whole\n",
    "    process by a massive margin when a big amount of\n",
    "    points requires calculation.\n",
    "\n",
    "    This will return a array sized (w,h), \n",
    "    store the max-min value in its neighbor.\n",
    "    '''\n",
    "    w,h = img.shape\n",
    "    padded = np.pad(img, k, 'edge')\n",
    "    # this is the result image array\n",
    "    res = np.zeros(img.shape)\n",
    "    \n",
    "    # This is the main process\n",
    "    for i in range(w):\n",
    "        for j in range(h):\n",
    "            neighbor = padded[i:i+2*k,j:j+2*k]\n",
    "            _max = neighbor.max()\n",
    "            _min = neighbor.min()\n",
    "            res[i-k,j-k] = (_max-_min)\n",
    "    \n",
    "    return res\n",
    "\n",
    "def _new_if_near(point, edge_k_neighbor):\n",
    "    x, y = point\n",
    "    x, y = int(x), int(y)\n",
    "    return edge_k_neighbor[x][y]>0\n",
    "    \n",
    "\n",
    "def getpoint(mask_img, k, beta, training = True, nearest_neighbor=3, new_if_near = True):\n",
    "    w,h = mask_img.shape\n",
    "    N = int(beta*k*w*h)\n",
    "    xy_min = [0, 0]\n",
    "    xy_max = [w-1, h-1]\n",
    "    points = np.random.uniform(low=xy_min, high=xy_max, size=(N,2))\n",
    "    #print(points)\n",
    "    if(beta>1 or beta<0): \n",
    "        print(\"beta should be in range [0,1]\")\n",
    "        return NULL\n",
    "    \n",
    "    # for the training, the mask is a hard mask\n",
    "    if training == True:\n",
    "        if beta ==0: return points\n",
    "        res = []\n",
    "        if new_if_near:\n",
    "            edge_k_neighbor = _get_edge_k_neighbor(mask_img,nearest_neighbor)\n",
    "            for p in points:\n",
    "                if _new_if_near(p,edge_k_neighbor):\n",
    "                    res.append(p)\n",
    "        else:\n",
    "            for p in points:\n",
    "                if _if_near(p,mask_img,nearest_neighbor):\n",
    "                    res.append(p)\n",
    "\n",
    "        others = int((1-beta)*k*w*h)\n",
    "        not_edge_points = np.random.uniform(low=xy_min, high=xy_max, size=(others,2))\n",
    "        for p in not_edge_points:\n",
    "            res.append(p)\n",
    "        return res\n",
    "    \n",
    "    # for the inference, the mask is a soft mask\n",
    "    if training == False:\n",
    "        res = []\n",
    "        for i in range(w):\n",
    "            for j in range(h):\n",
    "                if mask_img[i,j] > 0:\n",
    "                    res.append((i,j))\n",
    "        return res\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _generate_mask(size, func = lambda x:x*x):\n",
    "    w,h = size\n",
    "    res = np.zeros((w,h))\n",
    "    for x in range(w):\n",
    "        for y in range(h):\n",
    "            if y> func(x): res[x,y] = 255\n",
    "    return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_mask = _generate_mask((14,14), )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.image.AxesImage at 0x265063ddc08>"
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      "text/plain": "<Figure size 432x288 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(my_mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "267 ms ± 12.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
    }
   ],
   "source": [
    "%%timeit\n",
    "#plt.imshow(my_mask,cmap=\"Purples\")\n",
    "points = getpoint(mask_img=my_mask,k=1000,beta=0.8,nearest_neighbor=2,new_if_near=True)\n",
    "# points = list(zip(*points))\n",
    "# plt.scatter(points[1],points[0],c='black',s=4)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.collections.PathCollection at 0x2657f5d1488>"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
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      "text/plain": "<Figure size 432x288 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(my_mask,cmap=\"Purples\")\n",
    "points = getpoint(my_mask,1,1,nearest_neighbor=2)\n",
    "points = list(zip(*points))\n",
    "plt.scatter(points[1],points[0],c='black',s=4)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1c82139b348>"
      ]
     },
     "execution_count": 28,
     "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.imshow(my_mask,cmap=\"Purples\")\n",
    "points = getpoint(my_mask,10,1,nearest_neighbor=2)\n",
    "points = list(zip(*points))\n",
    "plt.scatter(points[1],points[0],c='black',s=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_mask = np.asarray(Image.open(\"tree_mask.jpg\").resize((32,32)))\n",
    "my_mask = my_mask[:,:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32, 32)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_mask.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1c82400b488>"
      ]
     },
     "execution_count": 47,
     "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.imshow(my_mask,cmap=\"Purples\")\n",
    "points = getpoint(my_mask,1,1,nearest_neighbor=1)\n",
    "points = list(zip(*points))\n",
    "plt.scatter(points[1],points[0],c='black',s=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pointGenerate import getpoint\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "resolution = 128\n",
    "sz = (resolution,resolution)\n",
    "my_mask = np.asarray(Image.open(\"tree_mask.jpg\").resize(sz))\n",
    "my_img = np.asarray(Image.open(\"tree.jpg\").resize(sz))\n",
    "my_mask = my_mask[:,:,0]\n",
    "\n",
    "points = getpoint(my_mask,0.25,0.95,nearest_neighbor=1)\n",
    "points = list(zip(*points))\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.imshow(my_mask,cmap=\"Purples\")\n",
    "plt.scatter(points[1],points[0],c='black',s=4)\n",
    "plt.title('k=0.25, beta=0.95')\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.imshow(my_img,cmap=\"Purples\")\n",
    "plt.scatter(points[1],points[0],c='black',s=4)\n",
    "plt.title('k=0.25, beta=0.95')\n",
    "\n",
    "plt.savefig('resolution=128.jpg',dpi=400)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "negative dimensions are not allowed",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-17-f7ba7ea58046>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mpoints\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetpoint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmy_mask\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m0.95\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mnearest_neighbor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\OneDrive - sjtu.edu.cn\\PointRend\\pointGenerate.py\u001b[0m in \u001b[0;36mgetpoint\u001b[1;34m(mask_img, k, beta, training, nearest_neighbor, inference_threshold)\u001b[0m\n\u001b[0;32m     23\u001b[0m         \u001b[1;32mraise\u001b[0m \u001b[0mNameError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"beta should be in range [0,1]\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     24\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 25\u001b[1;33m     \u001b[1;32mif\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m<\u001b[0m\u001b[1;36m0.1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     26\u001b[0m         \u001b[1;32mraise\u001b[0m \u001b[0mNameError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"k should be in range [0.1,infinite]\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     27\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mmtrand.pyx\u001b[0m in \u001b[0;36mmtrand.RandomState.uniform\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mmtrand.pyx\u001b[0m in \u001b[0;36mmtrand.cont2_array\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: negative dimensions are not allowed"
     ]
    }
   ],
   "source": [
    "points = getpoint(my_mask,1,-0.95,nearest_neighbor=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = [196,1960,19600,196000]\n",
    "t1 = [6.11,61.8,609,6280]\n",
    "v1 = [0.122,2.92,14.3,99.9]\n",
    "t2 = [1.3,3.93,28.2,267]\n",
    "v2 = [0.0084,0.383,0.643,12.7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": "<Figure size 432x288 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax2 = plt.subplots(1, 1)\n",
    "\n",
    "\n",
    "ax2.set_xscale(\"log\")\n",
    "ax2.set_yscale(\"log\")\n",
    "ax2.set_adjustable(\"datalim\")\n",
    "ax2.plot(n, t1, \"o-\", label = 'original algorithm')\n",
    "ax2.plot(n, t2, \"go-\", label = 'improved algorithm')\n",
    "#ax2.set_xlim(1e-1, 1e2)\n",
    "#ax2.set_ylim(1e-1, 1e3)\n",
    "plt.ylabel('Time (ms)')\n",
    "plt.xlabel('Number of points')\n",
    "ax2.set_aspect(1)\n",
    "ax2.set_title(\"Performance improvement\")\n",
    "plt.legend()\n",
    "plt.savefig('performance improvement.png',dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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