dholecenter.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# mmdholecenter\n",
    "Hole center misalignment in PCB."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Description\n",
    "\n",
    "The input image is a binary image of a gear. The opening top-hat is used to detect the gear teeth. Finally, the teeth detected are labeled.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-06T03:37:57.757663Z",
     "start_time": "2019-01-06T03:37:56.900819Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from PIL import Image\n",
    "import ia870 as ia\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reading\n",
    "\n",
    "The image of the PCB is read. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-06T03:37:58.302404Z",
     "start_time": "2019-01-06T03:37:57.761047Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.5, 255.5, 199.5, -0.5)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a_pil = Image.open('data/pcbholes.tif').convert('L')\n",
    "a = np.array (a_pil)\n",
    "a = a.astype('bool')\n",
    "(fig, axes) = plt.subplots(nrows=1, ncols=1,figsize=(5, 5))\n",
    "axes.set_title('a')\n",
    "axes.imshow(a, cmap='gray')\n",
    "axes.axis('off')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# First, find center of the pads.\n",
    "\n",
    "Use the close hole function to remove the holes. Note that one hole is open. This is not considered in this experiment. The regional maxima of the distance transform gives the radius of the largest disk inside the pad. We are interested only in radius larger than 20 pixels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-06T03:37:59.642518Z",
     "start_time": "2019-01-06T03:37:58.306463Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.5, 255.5, 199.5, -0.5)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "b = ia.iaclohole(a)\n",
    "d = ia.iadist(b,ia.iasecross(),'EUCLIDEAN')\n",
    "e = ia.iaregmax(d,ia.iasebox())\n",
    "f = ia.iathreshad(d, np.uint16(20))  # radius larger than 20 pixels\n",
    "g = ia.iaintersec(e,f)\n",
    "h = ia.iablob(ia.ialabel(g,ia.iasebox()),'CENTROID') # pad center\n",
    "\n",
    "(fig, axes) = plt.subplots(nrows=1, ncols=2,figsize=(10, 5))\n",
    "axes[0].set_title('b')\n",
    "axes[0].imshow(b, cmap='gray')\n",
    "axes[0].axis('off')\n",
    "axes[1].set_title('b, h')\n",
    "axes[1].imshow(ia.iagshow(b, ia.iadil(h)).transpose(1, 2, 0))\n",
    "axes[1].axis('off')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Find the center of the holes\n",
    "\n",
    "The holes are given by the difference of the pad image from the original image. Repeat the same procedure to find the center of the pads to find now the center of the holes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-06T03:38:00.112529Z",
     "start_time": "2019-01-06T03:37:59.646313Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.5, 255.5, 199.5, -0.5)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = ia.iasubm(b,a)\n",
    "j = ia.iadist(i,ia.iasecross(),'EUCLIDEAN')\n",
    "k = ia.iaregmax(j,ia.iasebox())\n",
    "l = ia.iablob(ia.ialabel(k,ia.iasebox()),'CENTROID') # hole center\n",
    "\n",
    "(fig, axes) = plt.subplots(nrows=2, ncols=2,figsize=(10, 5))\n",
    "axes[0][0].set_title('i')\n",
    "axes[0][0].imshow(ia.iagshow(i).transpose(1, 2, 0))\n",
    "axes[0][0].axis('off')\n",
    "axes[0][1].set_title('j')\n",
    "axes[0][1].imshow(d, cmap='gray')\n",
    "axes[0][1].axis('off')\n",
    "axes[1][0].set_title('k')\n",
    "axes[1][0].imshow(ia.iagshow(ia.iadil(k)).transpose(1, 2, 0))\n",
    "axes[1][0].axis('off')\n",
    "axes[1][1].set_title('i, l')\n",
    "axes[1][1].imshow(ia.iagshow(i, ia.iadil(l)).transpose(1, 2, 0))\n",
    "axes[1][1].axis('off')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Show the eccentricity\n",
    "\n",
    "First both centers (pads and holes) are displayed together. Then the actual misalignment is computed using the distance from one point to the other. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-06T03:38:01.119124Z",
     "start_time": "2019-01-06T03:38:00.116230Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 43  44 153] [ 88 191 187] [3 8 6]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(-0.5, 255.5, 199.5, -0.5)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m = ia.iadist(ia.ianeg(l),ia.iasecross(),'EUCLIDEAN');\n",
    "n = ia.iaintersec(ia.iagray(h),np.uint8(m));\n",
    "[x,y]=np.nonzero(n);\n",
    "v = n[np.nonzero(n)]\n",
    "print (x, y, v)\n",
    "\n",
    "#fprintf('displacement of %d at (%d,%d)\\n',[double(v)';x';y']);\n",
    "\n",
    "#displacement of 3 at (44,89)\n",
    "#displacement of 6 at (154,188)\n",
    "#displacement of 8 at (45,192)\n",
    "\n",
    "(fig, axes) = plt.subplots(nrows=1, ncols=2,figsize=(10, 5))\n",
    "axes[0].set_title('a, h, l')\n",
    "axes[0].imshow(ia.iagshow(a, h, l).transpose(1, 2, 0))\n",
    "axes[0].axis('off')\n",
    "axes[1].set_title('n, a')\n",
    "axes[1].imshow(ia.iagshow(n, a).transpose(1, 2, 0))\n",
    "axes[1].axis('off')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Find the narrowest region around the holes\n",
    "\n",
    "First, the thinning to compute the skeleton of the PCB image, then remove iteratively all the end points of the skeleton so just the skeleton loop around the holes remains. Find the minimum distance of these loops to the border and display their location. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-06T03:38:02.904700Z",
     "start_time": "2019-01-06T03:38:01.123579Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7. 7. 3.]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(-0.5, 255.5, 199.5, -0.5)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "o=ia.iathin(a)\n",
    "p=ia.iathin(o,ia.iaendpoints())\n",
    "q = ia.iadist(a,ia.iasecross(),'EUCLIDEAN')\n",
    "r = ia.iagrain(ia.ialabel(p,ia.iasebox()),q,'min') # minimum\n",
    "s = ia.iagrain(ia.ialabel(p,ia.iasebox()),q,'min','data'); # minimum\n",
    "t = ia.iaintersec(ia.iacmp(r,'==',q),a);\n",
    "\n",
    "print (2*s+1)\n",
    "#fprintf('Minimum distance: %d pixels\\n',2*double(s)+1);\n",
    "#Minimum distance: 7 pixels\n",
    "#Minimum distance: 3 pixels\n",
    "#Minimum distance: 7 pixels\n",
    "\n",
    "(fig, axes) = plt.subplots(nrows=1, ncols=2,figsize=(10, 5))\n",
    "axes[0].set_title('a, p')\n",
    "axes[0].imshow(ia.iagshow(a, p).transpose(1, 2, 0))\n",
    "axes[0].axis('off')\n",
    "axes[1].set_title('a, t')\n",
    "axes[1].imshow(ia.iagshow(a, ia.iadil(t)).transpose(1, 2, 0))\n",
    "axes[1].axis('off')"
   ]
  }
 ],
 "metadata": {
  "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": "3.6.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}