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Created July 17, 2020 13:27
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hmwk3.ipynb
{
"nbformat": 4,
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"metadata": {
"colab": {
"name": "HMWK3",
"provenance": [],
"collapsed_sections": [],
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
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"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
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"source": [
"<a href=\"https://colab.research.google.com/gist/tuffacton/0a51c958936b91989fe619bc3250663d/hmwk3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "4ru12l9x2w83",
"colab_type": "text"
},
"source": [
"Write and test a program that will:\n",
"1. Extract the contour of the object using the Pavlidis algorithm. This should return a 2D numpy array of the XY points that make up the contour.\n",
"2. Compute the estimated area of the contour using the Gauss formula presented in the lecture.\n",
"3. Write a function that implements one pass of the Discrete Contour Evolution algorithm.\n",
"4. Use the starter code provided to test the code. This starter code does eight steps of successive contour evolution, each time reducing the number of points in the contour by half.\n",
"\n",
"**Do not use any OpenCV, Numpy, or other functions that perform contour extraction or similar.**"
]
},
{
"cell_type": "code",
"metadata": {
"id": "Uaey3TqklNeH",
"colab_type": "code",
"colab": {}
},
"source": [
"import numpy as np\n",
"import cv2\n",
"import math\n",
"import matplotlib.pyplot as plt\n",
"from google.colab.patches import cv2_imshow\n",
"\n",
"pathName = \"/content/\"\n",
"MAXCONTOUR = 50000\n",
"\n",
"def showImage(img,name):\n",
" cv2_imshow(img)\n",
" return\n",
"\n",
"######################################################\n",
"\n",
"def saveImage(img, name):\n",
" cv2.imwrite(pathName + name + \".png\", img)\n",
" return\n",
"\n",
"######################################################\n",
"\n",
"def outOfBounds(point,image):\n",
" if ( (point[0] < 0) or (point[0] >= image.shape[1]) ):\n",
" return True\n",
" if ( (point[1] < 0) or (point[1] >= image.shape[0]) ):\n",
" return True\n",
" return False\n",
"\n",
"######################################################\n",
"\n",
"def GaussArea(pts):\n",
" area = float(0)\n",
" for p in range(0, len(pts)-1):\n",
" # Come back around full cirlce\n",
" if p == len(pts):\n",
" area = area + pts[p][0]*pts[0][1] - pts[0][0]*pts[p][1]\n",
" else:\n",
" area = area + pts[p][0]*pts[p+1][1] - pts[p+1][0]*pts[p][1]\n",
" return abs(area)/2.0\n",
"\n",
"######################################################\n",
"'''\n",
"Derived using the slides 49-51 from Lecture 6a\n",
"'''\n",
"def theta(i, ctr):\n",
" '''\n",
" Given a point i return the relative theta measure between\n",
" i and its surrounding points\n",
" '''\n",
" index = ctr.shape[0]\n",
"\n",
" y_i = ctr[i][0]\n",
" x_i = ctr[i][1]\n",
"\n",
" y_imin1 = ctr[i-1][0]\n",
" x_imin1 = ctr[i-1][1]\n",
"\n",
" y_0 = ctr[0][0]\n",
" x_0 = ctr[0][1]\n",
" if i == index:\n",
" # return math.atan((y_i-y_imin1)/(x_i-x_imin1))\\\n",
" # - math.atan((y_0-y_i)/(x_0-x_i))\n",
" y_iplus1 = ctr[0][0]\n",
" x_iplus1 = ctr[0][1]\n",
" \n",
" else:\n",
" y_iplus1 = ctr[i+1][0]\n",
" x_iplus1 = ctr[i+1][1]\n",
"\n",
" return math.atan((y_i-y_imin1)/(x_i-x_imin1))\\\n",
" - math.atan((y_iplus1-y_i)/(x_iplus1-x_i)) \n",
"\n",
" # return (y_i-y_imin1)/x_i-x_imin1)\n",
" # - math.atan((y_iplus1-y_i)/(x_iplus1-x_i)) \n",
"\n",
"def length(i, ctr):\n",
" '''\n",
" Given a point, find the length from the previous connected point\n",
" '''\n",
" return math.sqrt((ctr[i][0] - ctr[i-1][0])**2\\\n",
" + (ctr[i][1] - ctr[i-1][1])**2)\n",
"\n",
"\n",
"def onePassDCE(ctr):\n",
" index = ctr.shape[0]\n",
"\n",
" K = []\n",
" ref = []\n",
"\n",
" for i in range(index-1):\n",
" k = abs(theta(i, ctr))*length(i, ctr)*length(i+1, ctr)/\\\n",
" (length(i, ctr) + length(i+1, ctr))\n",
" K.append(k)\n",
" ref.append([int(ctr[i][0]), int(ctr[i][1]), k])\n",
"\n",
" # Playing with various thresholds for K\n",
" # k_thresh = np.min(K)\n",
" #k_thresh = 1.1\n",
" k_thresh = np.percentile(np.unique(K), 50)\n",
"\n",
" new_ref = []\n",
" for i in ref:\n",
" if i[2] > k_thresh:\n",
" new_ref = np.append(new_ref, np.asarray([int(i[0]), int(i[1])]))\n",
" new_contours = np.reshape(new_ref, (-1,2))\n",
" new_contours = new_contours.astype(int)\n",
" \n",
" # Return the contour AND return the unique values of\n",
" # K for exploration and threshold tuning\n",
" return new_contours, np.unique(K)\n",
"\n",
"######################################################\n",
"\n",
"def showContour(ctr, img):\n",
" length = ctr.shape[0]\n",
" contourImage = np.zeros(binary.shape)\n",
" for i in range(length):\n",
" if i == length-1:\n",
" cv2.line(contourImage, (ctr[i,1], ctr[i,0]),\\\n",
" (ctr[0,1], ctr[0,0]), 255,1)\n",
" else:\n",
" cv2.line(contourImage, (ctr[i,1], ctr[i,0]),\\\n",
" (ctr[i+1%length,1], ctr[i+1%length,0]), 255,1)\n",
" cv2_imshow(contourImage)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "kDRAkwVxp4zL",
"colab_type": "code",
"colab": {}
},
"source": [
"'''\n",
"Derived with the immense help of this demonstration:\n",
"http://www.imageprocessingplace.com/downloads_V3/root_downloads/tutorials/contour_tracing_Abeer_George_Ghuneim/theo.html\n",
"'''\n",
"\n",
"def pathCheck(point, img, direction):\n",
" point_y = point[0]\n",
" point_x = point[1]\n",
"\n",
" # Define surrounding points\n",
" # by compass configuration\n",
" w = (point[0], point[1]-1)\n",
" nw = (point[0]-1, point[1]-1)\n",
" n = (point[0]-1, point[1])\n",
" ne = (point[0]-1, point[1]+1)\n",
" e = (point[0], point[1]+1)\n",
" se = (point[0]+1, point[1]+1)\n",
" s = (point[0]+1, point[1])\n",
" sw = (point[0]+1, point[1]-1)\n",
"\n",
" # Check North\n",
" if direction == 'north':\n",
" # P1 check\n",
" if img[nw] > 2:\n",
" new_point = nw\n",
" direction = 'west'\n",
" # P2 check\n",
" elif img[n] > 0:\n",
" new_point = n\n",
" direction = direction\n",
" # P3 Check\n",
" elif img[ne] > 0:\n",
" new_point = ne\n",
" direction = direction\n",
" # No P1, P2, P3\n",
" # Rotate 90 deg\n",
" else:\n",
" new_point = point\n",
" direction = 'east'\n",
"\n",
" # Check East\n",
" elif direction == 'east':\n",
" # P1 check\n",
" if img[ne] > 0:\n",
" new_point = ne\n",
" direction = 'north'\n",
" # P2 check\n",
" elif img[e] > 0:\n",
" new_point = e\n",
" direction = direction\n",
" # P3 check\n",
" elif img[se] > 0:\n",
" new_point = se\n",
" direction = direction\n",
" # No P1, P2, P3\n",
" # Rotate 90 deg\n",
" else:\n",
" new_point = point\n",
" direction = 'south'\n",
"\n",
"\n",
" # Check South\n",
" elif direction == 'south':\n",
" # P1 check\n",
" if img[se] > 0:\n",
" new_point = se\n",
" direction = 'east'\n",
" # P2 check\n",
" elif img[s] > 0:\n",
" new_point = s\n",
" direction = direction\n",
" # P3 check\n",
" elif img[sw] > 0:\n",
" new_point = sw\n",
" direction = direction\n",
" # No P1, P2, P3\n",
" # Rotate 90 deg\n",
" else:\n",
" new_point = point\n",
" direction = 'west'\n",
"\n",
" # Check West\n",
" elif direction == 'west':\n",
" # P1 check\n",
" if img[sw] > 0:\n",
" new_point = sw\n",
" direction = 'south'\n",
" # P2 check\n",
" elif img[w] > 0:\n",
" new_point = w\n",
" direction = direction\n",
" # P3 check\n",
" elif img[nw] > 0:\n",
" new_point = nw\n",
" direction = direction\n",
" # No P1, P2, P3\n",
" # Rotate 90 deg\n",
" else:\n",
" new_point = point\n",
" direction = 'north'\n",
"\n",
" return new_point, direction\n",
"\n",
"def Pavlidis(img):\n",
" # Find a start point\n",
" ## Since this image is a continuous binary image,\n",
" ## this start point can be probably one of the first\n",
" ## points with a 255 intensity.\n",
" start = (ystt, xstt)\n",
" first_direction = 'north'\n",
"\n",
" first_direction = 'north'\n",
" first_start_point = ([start_y, start_x], first_direction)\n",
"\n",
"\n",
" # Initialize Points\n",
" points = []\n",
" points.append(first_start_point[0])\n",
"\n",
" start_point = first_start_point\n",
" next_point = pathCheck(start_point[0], img, start_point[1])\n",
"\n",
" # Travel along the points, collecting contours as you go\n",
" # 5000 is our max contours\n",
" i = 0\n",
" while i < 5000:\n",
" next_point = pathCheck(start_point[0], img, start_point[1])\n",
" points.append(next_point[0])\n",
" start_point = next_point\n",
" i = i + 1\n",
"\n",
" # We actually don't need any contour lines that are already considered\n",
" # So we can pull only our unique ones and retain order\n",
" points_array = np.array(points)\n",
" u, ind = np.unique(points_array, axis=0, return_index=True)\n",
" points_array = u[np.argsort(ind)]\n",
" return points_array"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "-uk79H6t8p5T",
"colab_type": "code",
"colab": {}
},
"source": [
"####### Testing Area #########\n",
"%%capture\n",
"!wget https://github.com/tuffacton/ece5554/raw/master/hmwk3/USAsmall.png -O usa.png\n",
"\n",
"inputImage = cv2.imread(pathName+'usa.png')\n",
"grayImage = cv2.cvtColor(inputImage, cv2.COLOR_BGR2GRAY)\n",
"thresh = 70\n",
"binary = cv2.threshold(grayImage, thresh, 255, cv2.THRESH_BINARY)[1]\n",
"(height, width) = binary.shape\n",
"\n",
"# Initialize starts and base images\n",
"ystt = np.uint8(height/2)\n",
"for xstt in range(width):\n",
" if (binary[ystt, xstt] > 0):\n",
" break\n",
"\n",
"img = binary.copy()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "4jrvVRnMzyA_",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"outputId": "032035a9-7b7c-424a-c13f-d5aec8a2d2dd"
},
"source": [
"Steps = 9\n",
"for s in range(Steps):\n",
" if s == 0:\n",
" ctr = Pavlidis(img)\n",
" else:\n",
" ctr = onePassDCE(ctr)[0]\n",
" print(\"Gaussian Area for Step %d is: %d\" % (s, GaussArea(ctr)))\n",
" # Print the K for threshold exploration and tuning\n",
" print(onePassDCE(ctr)[1])\n",
" showContour(ctr,img)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Gaussian Area for Step 0 is: 198224\n",
"[0. 0.14183169 0.15642817 0.27873592 0.32034502 0.32037262\n",
" 0.37891879 0.46007559 0.46185949 0.65064514 0.78539816 1.11072073\n",
" 1.38022678 1.40576619 1.85041452 2.08174295 2.0943951 2.51409215]\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<PIL.Image.Image image mode=L size=743x468 at 0x7FE31EE9EE10>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Gaussian Area for Step 1 is: 201288\n",
"[ 0.22306549 0.24924424 0.2779284 0.28889335 0.329189 0.34570308\n",
" 0.46942141 0.48165652 0.6004951 0.63565973 0.6401409 0.65209272\n",
" 0.66330625 0.66971557 0.70225267 0.70634181 0.86582768 0.86848826\n",
" 0.87098951 0.90344 0.9212794 0.93310719 1.04186843 1.05125289\n",
" 1.08206162 1.09830911 1.109907 1.11072073 1.14950276 1.16049824\n",
" 1.20446699 1.21231895 1.30070896 1.30526649 1.30899694 1.34203025\n",
" 1.35725623 1.36692998 1.38022678 1.40576619 1.41644842 1.44549939\n",
" 1.46134642 1.47827776 1.48096098 1.48595386 1.50581519 1.52352293\n",
" 1.57200631 1.6047998 1.62577818 1.63953346 1.63953346 1.64570328\n",
" 1.65450862 1.66266152 1.6660811 1.6835728 1.69006049 1.69138424\n",
" 1.70722063 1.74074736 1.74637751 1.77715318 1.78552122 1.7869888\n",
" 1.80206302 1.8021497 1.83036892 1.84475113 1.84524786 1.85120122\n",
" 1.87579884 1.89441507 1.90676554 1.91713872 1.92995673 1.9337665\n",
" 1.93813713 1.94221435 1.95193543 1.96843739 1.98020588 1.98805361\n",
" 2.00224435 2.00775007 2.01197374 2.01649835 2.03343641 2.04534368\n",
" 2.05637718 2.06426354 2.10778916 2.15967009 2.16412323 2.17378931\n",
" 2.17529714 2.1845593 2.20608879 2.21027147 2.21027147 2.21290706\n",
" 2.22316928 2.26461327 2.26461327 2.32737589 2.36560252 2.39010646\n",
" 2.39010646 2.42362293 2.4429 2.44537087 2.45446486 2.46178852\n",
" 2.50073194 2.50388158 2.51409215 2.51409215 2.55522831 2.59369439\n",
" 2.59748305 2.60957455 2.61112026 2.62837566 2.62837566 2.64092655\n",
" 2.66572976 2.67332465 2.68527014 2.69657369 2.69657369 2.71858181\n",
" 2.76045355 2.77211117 2.79754087 2.81153237 2.83241713 2.84028813\n",
" 2.85153182 2.86455037 2.8796663 2.90179355 2.96440846 2.99993766\n",
" 3.0242672 3.0734351 3.13514752 3.15310448 3.17348781 3.2026468\n",
" 3.24477884 3.35918511 3.36445494 3.39204759 3.42216864 3.4302519\n",
" 3.43085719 3.46262716 3.48149472 3.5218188 3.54209214 3.6633012\n",
" 3.75455329 3.77616708 3.85834756 3.90387085 4.08439008 4.09367314\n",
" 4.25649003 4.2584514 4.44633857 4.47355638 4.49510196 4.52226256\n",
" 4.52922654 4.52922654 4.61899268 4.63577069 4.65191306 4.74646352\n",
" 4.78021293 4.78021293 4.80041576 4.84678139 4.85110876 4.91446976\n",
" 4.92066097 5.10153124 5.2534329 5.45059461 5.47622839 5.55496425\n",
" 5.68297138 5.84584616 5.96332554 6.03420692 6.42204566 6.47596319\n",
" 6.49843308 6.54498469 6.70050821 6.70859089 6.90113388 6.93810212\n",
" 7.06546764 7.06969395 7.30673211 8.55398277 8.60714718 10.18914784\n",
" 10.63844004 10.85805061 11.98882127 12.22675663 12.249542 12.80516567\n",
" 15.69428594 17.30793898]\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<PIL.Image.Image image mode=L size=743x468 at 0x7FE31EE9EBE0>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Gaussian Area for Step 2 is: 203773\n",
"[ 0. 0.0335204 0.39852577 0.74722533 0.76719871 0.8068194\n",
" 0.85253251 0.89322627 0.9061506 1.00204112 1.30006856 1.4746641\n",
" 1.58513369 1.64666691 1.65499816 1.72337415 1.76827572 2.21343002\n",
" 2.23014668 2.42362293 2.4429 2.46178852 2.51409215 2.51409215\n",
" 2.5338631 2.59369439 2.59748305 2.60957455 2.61112026 2.62837566\n",
" 2.64092655 2.65461758 2.77211117 2.79754087 2.84028813 2.85153182\n",
" 2.8796663 2.94246084 3.03334352 3.08347202 3.21866837 3.62065131\n",
" 3.64424754 3.75455329 3.91161515 4.09367314 4.16560263 4.25649003\n",
" 4.47355638 4.49510196 4.52226256 4.52922654 4.61899268 4.62156839\n",
" 4.63864819 4.6637626 4.78021293 4.78021293 4.87312524 4.91446976\n",
" 4.92066097 5.05688828 5.15291218 5.19479072 5.40678284 5.44878484\n",
" 5.46239913 5.47622839 5.55131454 5.56508139 5.59080445 5.84584616\n",
" 5.92474431 5.98826318 6.1288554 6.14231059 6.19852914 6.30674774\n",
" 6.42204566 6.47596319 6.65798059 6.70859089 6.94368418 7.06713848\n",
" 7.06969395 7.13740441 7.30673211 7.36620993 7.39180304 7.43412808\n",
" 7.47555305 7.49931096 7.70574802 7.80241398 7.82246008 7.88082147\n",
" 7.88871014 8.14502608 8.28618058 8.30276529 8.38732774 8.43025245\n",
" 8.66754584 8.67879557 8.78569687 9.0116936 9.17893841 9.47642947\n",
" 9.61585068 9.96726254 10.00489961 10.09997118 10.18914784 10.26650591\n",
" 10.4149184 10.4510979 10.52032262 10.59082712 10.63844004 10.81723468\n",
" 10.85805061 10.94100258 11.34970819 11.56227423 11.68391792 11.75117088\n",
" 12.0264716 12.09980628 12.90037637 12.91947989 13.3767007 13.63126312\n",
" 14.22419977 14.35457046 15.27901833 15.32489139 15.39882726 16.16121369\n",
" 17.30793898 21.69165143 21.81313286 31.03400647]\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"image/png": 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nvcUslihgkXA1Wq6Wp8S8ZQmS47TelOXHrI+3YN5iEVMWGCZVkBat5SnxcUVrEBmpVacsKaWU7j5TlHlLIItPWTaex76C7pK0ci3/cP5Z0UEufgV9Q7X0lOXbMevcJwph8SnLt4uPmYELz2tSpFr+6a+uR+0Blx4OVqgpy8FP1pm3LC/4ozTfH6kENx6MV+xiDp9aQ0vK4VFjbEk5fGpJLsV8CF6HThVsyRxrqa3R1PJRqOfzUMzHIeezkHJ4N/1xgvhCPZ+CYj4YOZ+AlGMJPF/Nmv9mNwAYgJyroGQjhNvb+fwUYBmTHuAADHaZdHKOpVwknZxjMadJJ+dYDg/kQQzHpJNzrGjgg6eAifZJJ+dY1S7pBB3L+kg6OcfC3kkn51jaK+nkHIvLOZFzBKD8aFic4A1cM2Q6HiFQzwEAAAAAAAAAAAB9/wN1peTsd1LMAAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<PIL.Image.Image image mode=L size=743x468 at 0x7FE31EE9EBE0>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Gaussian Area for Step 3 is: 207386\n",
"[ 1.31187308 1.69565039 1.93711696 3.04775459 3.82802655 4.05624457\n",
" 4.13771907 4.28710948 4.41362149 4.73877028 4.86998535 5.39113319\n",
" 5.65974224 5.85500216 6.47596319 6.65798059 7.23693271 7.26837412\n",
" 7.39180304 7.43412808 7.50041509 7.86998881 7.88082147 8.06280119\n",
" 8.14502608 8.23198277 8.29606264 8.78569687 8.96939343 9.0116936\n",
" 9.17893841 9.749966 9.96726254 10.00489961 10.09997118 10.19232159\n",
" 10.26650591 10.4149184 10.63844004 10.99942339 11.27455235 11.34970819\n",
" 11.56227423 11.75117088 11.86457628 12.0264716 12.91947989 12.96946907\n",
" 14.34304446 15.06874478 15.27901833 15.42476241 15.92713761 16.62715833\n",
" 16.82601556 17.30793898 17.4113438 19.52266905 20.19355315 21.17569908\n",
" 21.69165143 21.81858186 22.53354295 22.95129688 26.22215396 30.4278365\n",
" 30.76198877 31.03400647 33.96105846 34.90300626 40.84085278 45.2086072 ]\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"image/png": 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0go6wuAcycuAeyMiBeyAjh1oLOUcC3AN5Pi7g0lAZeKRAnwMAAAAAAAAAAADj/QcHqna51NuIJgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<PIL.Image.Image image mode=L size=743x468 at 0x7FE31EE9E240>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Gaussian Area for Step 4 is: 203419\n",
"[ 0.35894253 0.49844599 4.23598414 5.57995211 7.38238524\n",
" 8.1934339 10.99942339 11.56227423 12.0264716 13.24941664\n",
" 13.84233202 14.34304446 14.80406415 15.42476241 15.92713761\n",
" 17.30793898 18.570455 19.54013429 21.41424202 21.81858186\n",
" 22.53354295 23.04318564 26.93104909 27.92768334 29.97727337\n",
" 30.4278365 31.03400647 31.72892296 35.46052899 36.29527326\n",
" 52.27112191 67.60384081 67.99323679 72.69967315 101.01292079]\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"image/png": "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\n",
"text/plain": [
"<PIL.Image.Image image mode=L size=743x468 at 0x7FE31EE9EE10>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Gaussian Area for Step 5 is: 173654\n",
"[ 21.41424202 21.81858186 22.53354295 26.93104909 27.92768334\n",
" 30.4278365 52.27112191 53.11429712 67.60384081 67.99323679\n",
" 68.86815253 88.63359529 101.31523473 117.19918832 175.71548277\n",
" 204.59624585]\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
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"text/plain": [
"<PIL.Image.Image image mode=L size=743x468 at 0x7FE31EE9E588>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Gaussian Area for Step 6 is: 147621\n",
"[ 22.33698966 48.28922356 67.60384081 90.08383554 137.10348131\n",
" 194.1940237 235.21995421]\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
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"text/plain": [
"<PIL.Image.Image image mode=L size=743x468 at 0x7FE31EE9E748>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Gaussian Area for Step 7 is: 5596\n",
"[153.85635155 194.1940237 ]\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"image/png": "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\n",
"text/plain": [
"<PIL.Image.Image image mode=L size=743x468 at 0x7FE31EE9E588>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Gaussian Area for Step 8 is: 0\n"
],
"name": "stdout"
},
{
"output_type": "error",
"ename": "IndexError",
"evalue": "ignored",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-732-9d423a0a5e5a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Gaussian Area for Step %d is: %d\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mGaussArea\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mctr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;31m# Print the K for threshold exploration and tuning\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0monePassDCE\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mctr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 10\u001b[0m \u001b[0mshowContour\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mctr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-731-1ff748ce93fa>\u001b[0m in \u001b[0;36monePassDCE\u001b[0;34m(ctr)\u001b[0m\n\u001b[1;32m 94\u001b[0m \u001b[0;31m# k_thresh = np.min(K)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 95\u001b[0m \u001b[0;31m#k_thresh = 1.1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 96\u001b[0;31m \u001b[0mk_thresh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpercentile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mK\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m50\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 97\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 98\u001b[0m \u001b[0mnew_ref\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mpercentile\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36mpercentile\u001b[0;34m(a, q, axis, out, overwrite_input, interpolation, keepdims)\u001b[0m\n\u001b[1;32m 3704\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Percentiles must be in the range [0, 100]\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3705\u001b[0m return _quantile_unchecked(\n\u001b[0;32m-> 3706\u001b[0;31m a, q, axis, out, overwrite_input, interpolation, keepdims)\n\u001b[0m\u001b[1;32m 3707\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3708\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36m_quantile_unchecked\u001b[0;34m(a, q, axis, out, overwrite_input, interpolation, keepdims)\u001b[0m\n\u001b[1;32m 3824\u001b[0m r, k = _ureduce(a, func=_quantile_ureduce_func, q=q, axis=axis, out=out,\n\u001b[1;32m 3825\u001b[0m \u001b[0moverwrite_input\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moverwrite_input\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3826\u001b[0;31m interpolation=interpolation)\n\u001b[0m\u001b[1;32m 3827\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3828\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36m_ureduce\u001b[0;34m(a, func, **kwargs)\u001b[0m\n\u001b[1;32m 3401\u001b[0m \u001b[0mkeepdim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3402\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3403\u001b[0;31m \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3404\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdim\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3405\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36m_quantile_ureduce_func\u001b[0;34m(a, q, axis, out, overwrite_input, interpolation, keepdims)\u001b[0m\n\u001b[1;32m 3939\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misnan\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0map\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m...\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3940\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3941\u001b[0;31m \u001b[0mx1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0map\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindices_below\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mweights_below\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3942\u001b[0m \u001b[0mx2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0map\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindices_above\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mweights_above\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3943\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mtake\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36mtake\u001b[0;34m(a, indices, axis, out, mode)\u001b[0m\n\u001b[1;32m 192\u001b[0m [5, 7]])\n\u001b[1;32m 193\u001b[0m \"\"\"\n\u001b[0;32m--> 194\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_wrapfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'take'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindices\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 195\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 196\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36m_wrapfunc\u001b[0;34m(obj, method, *args, **kwds)\u001b[0m\n\u001b[1;32m 59\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 60\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 61\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mbound\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 62\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[0;31m# A TypeError occurs if the object does have such a method in its\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mIndexError\u001b[0m: cannot do a non-empty take from an empty axes."
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "vmE4ySc33PyR",
"colab_type": "code",
"colab": {}
},
"source": [
""
],
"execution_count": null,
"outputs": []
}
]
}
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