houghlines.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "IMAGE_PATH = \"images/gauge-1.jpg\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "def avg_circles(circles, b):\n",
    "    avg_x=0\n",
    "    avg_y=0\n",
    "    avg_r=0\n",
    "    for i in range(b):\n",
    "        #optional - average for multiple circles (can happen when a gauge is at a slight angle)\n",
    "        avg_x = avg_x + circles[0][i][0]\n",
    "        avg_y = avg_y + circles[0][i][1]\n",
    "        avg_r = avg_r + circles[0][i][2]\n",
    "    avg_x = int(avg_x/(b))\n",
    "    avg_y = int(avg_y/(b))\n",
    "    avg_r = int(avg_r/(b))\n",
    "    return avg_x, avg_y, avg_r\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dist_2_pts(x1, y1, x2, y2):\n",
    "    #print np.sqrt((x2-x1)^2+(y2-y1)^2)\n",
    "    return np.sqrt((x2 - x1)**2 + (y2 - y1)**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calibrate_gauge(gauge_number, file_type):\n",
    "#     img = cv2.imread('image/gauge-%s.%s' %(gauge_number, file_type))\n",
    "    img = cv2.imread('image/gauge-%s.%s' %(gauge_number, file_type))\n",
    "    height, width = img.shape[:2]\n",
    "    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)  #convert to gray\n",
    "    cv2.imwrite('gauge-%s-bw.%s' %(gauge_number, file_type),gray)\n",
    "    circles = cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, 1, 20, np.array([]), 100, 50, int(height*0.35), int(height*0.48))\n",
    "    # average found circles, found it to be more accurate than trying to tune HoughCircles parameters to get just the right one\n",
    "    a, b, c = circles.shape\n",
    "    x,y,r = avg_circles(circles, b)\n",
    "    #draw center and circle\n",
    "    cv2.circle(img, (x, y), r, (0, 0, 255), 3, cv2.LINE_AA)  # draw circle\n",
    "    cv2.circle(img, (x, y), 2, (0, 255, 0), 3, cv2.LINE_AA)  # draw center of circle\n",
    "    #for testing, output circles on image\n",
    "    cv2.imwrite('gauge-%s-circles.%s' % (gauge_number, file_type), img)\n",
    "\n",
    "    #for calibration, plot lines from center going out at every 10 degrees and add marker\n",
    "    \n",
    "    separation = 10.0 #in degrees\n",
    "    interval = int(360 / separation)\n",
    "    p1 = np.zeros((interval,2))  #set empty arrays\n",
    "    p2 = np.zeros((interval,2))\n",
    "    p_text = np.zeros((interval,2))\n",
    "    for i in range(0,interval):\n",
    "        for j in range(0,2):\n",
    "            if (j%2==0):\n",
    "                p1[i][j] = x + 0.9 * r * np.cos(separation * i * 3.14 / 180) #point for lines\n",
    "            else:\n",
    "                p1[i][j] = y + 0.9 * r * np.sin(separation * i * 3.14 / 180)\n",
    "    text_offset_x = 10\n",
    "    text_offset_y = 5\n",
    "    for i in range(0, interval):\n",
    "        for j in range(0, 2):\n",
    "            if (j % 2 == 0):\n",
    "                p2[i][j] = x + r * np.cos(separation * i * 3.14 / 180)\n",
    "                p_text[i][j] = x - text_offset_x + 1.2 * r * np.cos((separation) * (i+9) * 3.14 / 180) #point for text labels, i+9 rotates the labels by 90 degrees\n",
    "            else:\n",
    "                p2[i][j] = y + r * np.sin(separation * i * 3.14 / 180)\n",
    "                p_text[i][j] = y + text_offset_y + 1.2* r * np.sin((separation) * (i+9) * 3.14 / 180)  # point for text labels, i+9 rotates the labels by 90 degrees\n",
    "\n",
    "    #add the lines and labels to the image\n",
    "    for i in range(0,interval):\n",
    "        cv2.line(img, (int(p1[i][0]), int(p1[i][1])), (int(p2[i][0]), int(p2[i][1])),(0, 255, 0), 2)\n",
    "        cv2.putText(img, '%s' %(int(i*separation)), (int(p_text[i][0]), int(p_text[i][1])), cv2.FONT_HERSHEY_SIMPLEX, 0.3,(0,0,0),1,cv2.LINE_AA)\n",
    "\n",
    "    cv2.imwrite('gauge-%s-calibration.%s' % (gauge_number, file_type), img)\n",
    "\n",
    "    #get user input on min, max, values, and units\n",
    "    print('gauge number: %s' %gauge_number)\n",
    "#     min_angle = input('Min angle (lowest possible angle of dial) - in degrees: ') #the lowest possible angle\n",
    "#     max_angle = input('Max angle (highest possible angle) - in degrees: ') #highest possible angle\n",
    "#     min_value = input('Min value: ') #usually zero\n",
    "#     max_value = input('Max value: ') #maximum reading of the gauge\n",
    "#     units = input('Enter units: ')\n",
    "    #for testing purposes: hardcode and comment out raw_inputs above\n",
    "    min_angle = 45\n",
    "    max_angle = 320\n",
    "    min_value = 0\n",
    "    max_value = 200\n",
    "    units = \"PSI\"\n",
    "\n",
    "    return min_angle, max_angle, min_value, max_value, units, x, y, r\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_current_value(img, min_angle, max_angle, min_value, max_value, x, y, r, gauge_number, file_type):\n",
    "\n",
    "    #for testing purposes\n",
    "    img = cv2.imread('image/gauge-%s.%s' % (gauge_number, file_type))\n",
    "\n",
    "    gray2 = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "    # Set threshold and maxValue\n",
    "    thresh = 175\n",
    "    maxValue = 255\n",
    "\n",
    "    th, dst2 = cv2.threshold(gray2, thresh, maxValue, cv2.THRESH_BINARY_INV);\n",
    "\n",
    "    # found Hough Lines generally performs better without Canny / blurring, though there were a couple exceptions where it would only work with Canny / blurring\n",
    "    #dst2 = cv2.medianBlur(dst2, 5)\n",
    "    #dst2 = cv2.Canny(dst2, 50, 150)\n",
    "    #dst2 = cv2.GaussianBlur(dst2, (5, 5), 0)\n",
    "\n",
    "    # for testing, show image after thresholding\n",
    "    cv2.imwrite('gauge-%s-tempdst2.%s' % (gauge_number, file_type), dst2)\n",
    "\n",
    "    # find lines\n",
    "    minLineLength = 10\n",
    "    maxLineGap = 0\n",
    "    lines = cv2.HoughLinesP(image=dst2, rho=3, theta=np.pi / 180, threshold=100,minLineLength=minLineLength, maxLineGap=0)  # rho is set to 3 to detect more lines, easier to get more then filter them out later\n",
    "\n",
    "    #for testing purposes, show all found lines\n",
    "    # for i in range(0, len(lines)):\n",
    "    #   for x1, y1, x2, y2 in lines[i]:\n",
    "    #      cv2.line(img, (x1, y1), (x2, y2), (0, 255, 0), 2)\n",
    "    #      cv2.imwrite('gauge-%s-lines-test.%s' %(gauge_number, file_type), img)\n",
    "\n",
    "    # remove all lines outside a given radius\n",
    "    final_line_list = []\n",
    "    #print \"radius: %s\" %r\n",
    "\n",
    "    diff1LowerBound = 0.15 #diff1LowerBound and diff1UpperBound determine how close the line should be from the center\n",
    "    diff1UpperBound = 0.25\n",
    "    diff2LowerBound = 0.5 #diff2LowerBound and diff2UpperBound determine how close the other point of the line should be to the outside of the gauge\n",
    "    diff2UpperBound = 1.0\n",
    "    for i in range(0, len(lines)):\n",
    "        for x1, y1, x2, y2 in lines[i]:\n",
    "            diff1 = dist_2_pts(x, y, x1, y1)  # x, y is center of circle\n",
    "            diff2 = dist_2_pts(x, y, x2, y2)  # x, y is center of circle\n",
    "            #set diff1 to be the smaller (closest to the center) of the two), makes the math easier\n",
    "            if (diff1 > diff2):\n",
    "                temp = diff1\n",
    "                diff1 = diff2\n",
    "                diff2 = temp\n",
    "            # check if line is within an acceptable range\n",
    "            if (((diff1<diff1UpperBound*r) and (diff1>diff1LowerBound*r) and (diff2<diff2UpperBound*r)) and (diff2>diff2LowerBound*r)):\n",
    "                line_length = dist_2_pts(x1, y1, x2, y2)\n",
    "                # add to final list\n",
    "                final_line_list.append([x1, y1, x2, y2])\n",
    "\n",
    "    #testing only, show all lines after filtering\n",
    "    # for i in range(0,len(final_line_list)):\n",
    "    #     x1 = final_line_list[i][0]\n",
    "    #     y1 = final_line_list[i][1]\n",
    "    #     x2 = final_line_list[i][2]\n",
    "    #     y2 = final_line_list[i][3]\n",
    "    #     cv2.line(img, (x1, y1), (x2, y2), (0, 255, 0), 2)\n",
    "\n",
    "    # assumes the first line is the best one\n",
    "    x1 = final_line_list[0][0]\n",
    "    y1 = final_line_list[0][1]\n",
    "    x2 = final_line_list[0][2]\n",
    "    y2 = final_line_list[0][3]\n",
    "    cv2.line(img, (x1, y1), (x2, y2), (0, 255, 0), 2)\n",
    "\n",
    "    #for testing purposes, show the line overlayed on the original image\n",
    "    #cv2.imwrite('gauge-1-test.jpg', img)\n",
    "    cv2.imwrite('gauge-%s-lines-2.%s' % (gauge_number, file_type), img)\n",
    "\n",
    "    #find the farthest point from the center to be what is used to determine the angle\n",
    "    dist_pt_0 = dist_2_pts(x, y, x1, y1)\n",
    "    dist_pt_1 = dist_2_pts(x, y, x2, y2)\n",
    "    if (dist_pt_0 > dist_pt_1):\n",
    "        x_angle = x1 - x\n",
    "        y_angle = y - y1\n",
    "    else:\n",
    "        x_angle = x2 - x\n",
    "        y_angle = y - y2\n",
    "    # take the arc tan of y/x to find the angle\n",
    "    res = np.arctan(np.divide(float(y_angle), float(x_angle)))\n",
    "    #np.rad2deg(res) #coverts to degrees\n",
    "\n",
    "    # print x_angle\n",
    "    # print y_angle\n",
    "    # print res\n",
    "    # print np.rad2deg(res)\n",
    "\n",
    "    #these were determined by trial and error\n",
    "    res = np.rad2deg(res)\n",
    "    if x_angle > 0 and y_angle > 0:  #in quadrant I\n",
    "        final_angle = 270 - res\n",
    "    if x_angle < 0 and y_angle > 0:  #in quadrant II\n",
    "        final_angle = 90 - res\n",
    "    if x_angle < 0 and y_angle < 0:  #in quadrant III\n",
    "        final_angle = 90 - res\n",
    "    if x_angle > 0 and y_angle < 0:  #in quadrant IV\n",
    "        final_angle = 270 - res\n",
    "\n",
    "    #print final_angle\n",
    "\n",
    "    old_min = float(min_angle)\n",
    "    old_max = float(max_angle)\n",
    "\n",
    "    new_min = float(min_value)\n",
    "    new_max = float(max_value)\n",
    "\n",
    "    old_value = final_angle\n",
    "\n",
    "    old_range = (old_max - old_min)\n",
    "    new_range = (new_max - new_min)\n",
    "    new_value = (((old_value - old_min) * new_range) / old_range) + new_min\n",
    "\n",
    "    return new_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'NoneType' object has no attribute 'shape'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-65-3a3d45af3e97>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0m__name__\u001b[0m\u001b[1;33m==\u001b[0m\u001b[1;34m'__main__'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 13\u001b[1;33m     \u001b[0mmain\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<ipython-input-65-3a3d45af3e97>\u001b[0m in \u001b[0;36mmain\u001b[1;34m()\u001b[0m\n\u001b[0;32m      3\u001b[0m     \u001b[0mfile_type\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'jpg'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[1;31m# name the calibration image of your gauge 'gauge-#.jpg', for example 'gauge-5.jpg'.  It's written this way so you can easily try multiple images\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m     \u001b[0mmin_angle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmax_angle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmin_value\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmax_value\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0munits\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcalibrate_gauge\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgauge_number\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfile_type\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[1;31m#feed an image (or frame) to get the current value, based on the calibration, by default uses same image as calibration\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-63-167b99549da9>\u001b[0m in \u001b[0;36mcalibrate_gauge\u001b[1;34m(gauge_number, file_type)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mcalibrate_gauge\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgauge_number\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfile_type\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m     \u001b[0mimg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'image/gauge-%s.%s'\u001b[0m \u001b[1;33m%\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgauge_number\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfile_type\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m     \u001b[0mheight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwidth\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mimg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m     \u001b[0mgray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcvtColor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mCOLOR_BGR2GRAY\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m#convert to gray\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'gauge-%s-bw.%s'\u001b[0m \u001b[1;33m%\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgauge_number\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfile_type\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mgray\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'shape'"
     ]
    }
   ],
   "source": [
    "def main():\n",
    "    gauge_number = 1\n",
    "    file_type='jpg'\n",
    "    # name the calibration image of your gauge 'gauge-#.jpg', for example 'gauge-5.jpg'.  It's written this way so you can easily try multiple images\n",
    "    min_angle, max_angle, min_value, max_value, units, x, y, r = calibrate_gauge(gauge_number, file_type)\n",
    "\n",
    "    #feed an image (or frame) to get the current value, based on the calibration, by default uses same image as calibration\n",
    "    img = cv2.imread('image/gauge-%s.%s' % (gauge_number, file_type))\n",
    "    val = get_current_value(img, min_angle, max_angle, min_value, max_value, x, y, r, gauge_number, file_type)\n",
    "    print(\"Current reading: %s %s\" %(val, units))\n",
    "\n",
    "if __name__=='__main__':\n",
    "    main()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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