Created
February 1, 2013 21:40
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OpenCV IPython Notebook looking at contours and what you can do with the shape morphology functions.
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{ | |
"metadata": { | |
"name": "opencv-learning" | |
}, | |
"nbformat": 3, | |
"nbformat_minor": 0, | |
"worksheets": [ | |
{ | |
"cells": [ | |
{ | |
"cell_type": "raw", | |
"metadata": {}, | |
"source": [ | |
"Demonstration of OpenCV's cv2 API for manipulating images, creating contours, simplifying those contours and creating various bounding structures. I started with Abid Rahman's Contours 1 and 2 blog posts. Those posts do not directly give you all that you need to run them. By virtue of being an IPython Notebook, these are the actual command that worked for me. You will need wget, numpy, matplotlib, OpenCV and shapely.\n", | |
"\n", | |
"-kurt schwehr 2013-Jan-31" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"# BEGIN http://www.opencvpython.blogspot.com/2012/06/hi-this-article-is-tutorial-which-try.html" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"!wget http://3.bp.blogspot.com/-a5blM3JLkIU/T9OXg1YhN0I/AAAAAAAAASQ/MbdfSG2oaYg/s200/test.jpg" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"--2013-01-31 14:21:03-- http://3.bp.blogspot.com/-a5blM3JLkIU/T9OXg1YhN0I/AAAAAAAAASQ/MbdfSG2oaYg/s200/test.jpg\r\n", | |
"Resolving 3.bp.blogspot.com (3.bp.blogspot.com)... 2001:4860:4001:802::1008, 74.125.224.70, 74.125.224.72, ...\r\n", | |
"Connecting to 3.bp.blogspot.com (3.bp.blogspot.com)|2001:4860:4001:802::1008|:80... connected.\r\n", | |
"HTTP request sent, awaiting response... 200 OK\r\n", | |
"Length: 2605 (2.5K) [image/jpeg]\r\n", | |
"Saving to: `test.jpg'\r\n", | |
"\r\n", | |
"\r", | |
" 0% [ ] 0 --.-K/s \r", | |
"100%[======================================>] 2,605 --.-K/s in 0s \r\n", | |
"\r\n", | |
"2013-01-31 14:21:03 (292 MB/s) - `test.jpg' saved [2605/2605]\r\n", | |
"\r\n" | |
] | |
} | |
], | |
"prompt_number": 1 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"import numpy as np\n", | |
"import cv2\n", | |
"import cv\n", | |
"import shapely.geometry" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 92 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"im = cv2.imread('test.jpg')" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 3 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"whos" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"Variable Type Data/Info\n", | |
"--------------------------------\n", | |
"contours list n=1\n", | |
"cv module <module 'cv' from '/usr/l<...>odules/python2.7/cv.pyc'>\n", | |
"cv2 module <module 'cv2' from '/usr/<...>odules/python2.7/cv2.so'>\n", | |
"hierarchy ndarray 1x1x4: 4 elems, type `int32`, 16 bytes\n", | |
"im ndarray 200x200x3: 120000 elems, type `uint8`, 120000 bytes (117 kb)\n", | |
"imgray ndarray 200x200: 40000 elems, type `uint8`, 40000 bytes\n", | |
"ret float 127.0\n", | |
"thresh ndarray 200x200: 40000 elems, type `uint8`, 40000 bytes\n" | |
] | |
} | |
], | |
"prompt_number": 11 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"imgray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 7 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"ret, thresh = cv2.threshold(imgray, 127, 255, 0)\n", | |
"imshow(thresh)" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "pyout", | |
"prompt_number": 14, | |
"text": [ | |
"<matplotlib.image.AxesImage at 0x42cfe50>" | |
] | |
}, | |
{ | |
"output_type": "display_data", | |
"png": 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padbvGzZs4P777wfA4/EQCASs1xobG/F4Brst9l0jbVJERsXX9/OTyrBbjnhE0NzcbP2+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\nTkSuQmCC\n" | |
} | |
], | |
"prompt_number": 14 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n", | |
"imshow(thresh)" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "pyout", | |
"prompt_number": 15, | |
"text": [ | |
"<matplotlib.image.AxesImage at 0x4467dd0>" | |
] | |
}, | |
{ | |
"output_type": "display_data", | |
"png": 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| |
} | |
], | |
"prompt_number": 15 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"len(contours), hierarchy" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "pyout", | |
"prompt_number": 12, | |
"text": [ | |
"(1, array([[[-1, -1, -1, -1]]], dtype=int32))" | |
] | |
} | |
], | |
"prompt_number": 12 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"len(contours[0])" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "pyout", | |
"prompt_number": 17, | |
"text": [ | |
"170" | |
] | |
} | |
], | |
"prompt_number": 17 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"cv2.drawContours(im, contours, -1, (0,255,0), 3)\n", | |
"imshow(im)" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "pyout", | |
"prompt_number": 18, | |
"text": [ | |
"<matplotlib.image.AxesImage at 0x4855350>" | |
] | |
}, | |
{ | |
"output_type": "display_data", | |
"png": 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5K7FLcftGY8KCYM+ePcyaNSt8XFNTw549eyZq8ePGOceiRYtYsGABDzzwAAC9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\nRERBICIoCEQEBYGIoCAQERQEIgL8fyegsySiv9qVAAAAAElFTkSuQmCC\n" | |
} | |
], | |
"prompt_number": 18 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"cv2.drawContours(im, contours, -1, (0,255,0), -1)\n", | |
"imshow(im)" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "pyout", | |
"prompt_number": 19, | |
"text": [ | |
"<matplotlib.image.AxesImage at 0x4a61850>" | |
] | |
}, | |
{ | |
"output_type": "display_data", | |
"png": 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JngEv58ELdXyNEFw4O3W7tDYFgdRF6kspXnb3y/B97RrEgWY6kroIfhRg3yt2ALpaj0Bp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\ngkBEUBCICAoCEUFBICIoCEQE+H+r2TE6nnXuIgAAAABJRU5ErkJggg==\n" | |
} | |
], | |
"prompt_number": 19 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"cv2.drawContours(im, contours, 0, (0,255,0), 1)\n", | |
"imshow(im)" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "pyout", | |
"prompt_number": 22, | |
"text": [ | |
"<matplotlib.image.AxesImage at 0x4a96650>" | |
] | |
}, | |
{ | |
"output_type": "display_data", | |
"png": 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| |
} | |
], | |
"prompt_number": 22 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"!wget http://3.bp.blogspot.com/-1UtLXb7c73U/T9QZT3tpVjI/AAAAAAAAATE/Nyo7SFg8T1o/s1600/balls.png" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"--2013-01-31 15:06:48-- http://3.bp.blogspot.com/-1UtLXb7c73U/T9QZT3tpVjI/AAAAAAAAATE/Nyo7SFg8T1o/s1600/balls.png\r\n", | |
"Resolving 3.bp.blogspot.com (3.bp.blogspot.com)... 2001:4860:4001:800::1005, 74.125.224.97, 74.125.224.101, ...\r\n", | |
"Connecting to 3.bp.blogspot.com (3.bp.blogspot.com)|2001:4860:4001:800::1005|:80... connected.\r\n", | |
"HTTP request sent, awaiting response... " | |
] | |
}, | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"200 OK\r\n", | |
"Length: 3499 (3.4K) [image/png]\r\n", | |
"Saving to: `balls.png'\r\n", | |
"\r\n", | |
"\r", | |
" 0% [ ] 0 --.-K/s \r", | |
"100%[======================================>] 3,499 --.-K/s in 0s \r\n", | |
"\r\n", | |
"2013-01-31 15:06:48 (337 MB/s) - `balls.png' saved [3499/3499]\r\n", | |
"\r\n" | |
] | |
} | |
], | |
"prompt_number": 23 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"im = cv2.imread('balls.png')\n", | |
"imgray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 24 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"ret, thresh = cv2.threshold(imgray, 100, 255, 0)\n", | |
"imshow(thresh)" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "pyout", | |
"prompt_number": 28, | |
"text": [ | |
"<matplotlib.image.AxesImage at 0x57eb390>" | |
] | |
}, | |
{ | |
"output_type": "display_data", | |
"png": 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| |
} | |
], | |
"prompt_number": 28 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 30 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"for h, cnt in enumerate(contours):\n", | |
" mask = np.zeros(imgray.shape, np.uint8)\n", | |
" cv2.drawContours(mask, [cnt], 0, 255, -1)\n", | |
" mean = cv2.mean(im, mask=mask)\n", | |
" print h, mean" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"0" | |
] | |
}, | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
" (0.0, 242.0, 255.0, 0.0)\n", | |
"1 (232.0, 162.0, 0.0, 0.0)\n", | |
"2 (76.0, 177.0, 34.0, 0.0)\n", | |
"3 (39.0, 127.0, 255.0, 0.0)\n" | |
] | |
} | |
], | |
"prompt_number": 31 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"# END http://www.opencvpython.blogspot.com/2012/06/hi-this-article-is-tutorial-which-try.html" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 32 | |
}, | |
{ | |
"cell_type": "heading", | |
"level": 1, | |
"metadata": {}, | |
"source": [ | |
"BEGIN Contours - 2" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"moments = cv2.moments(contours[0])\n", | |
"area = moments['m00']\n", | |
"moments" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "pyout", | |
"prompt_number": 37, | |
"text": [ | |
"{'m00': 10033.5,\n", | |
" 'm01': 1850134.5,\n", | |
" 'm02': 352044973.5833333,\n", | |
" 'm03': 68983799276.15001,\n", | |
" 'm10': 2010061.8333333333,\n", | |
" 'm11': 368666693.125,\n", | |
" 'm12': 69763579350.98334,\n", | |
" 'm20': 409360323.5833333,\n", | |
" 'm21': 74691021944.88333,\n", | |
" 'm30': 84692116672.95001,\n", | |
" 'mu02': 10888082.359906793,\n", | |
" 'mu03': 0.005234025965704581,\n", | |
" 'mu11': -1980114.5675549507,\n", | |
" 'mu12': -33122544.260385513,\n", | |
" 'mu20': 6674463.831166983,\n", | |
" 'mu21': 101313.30416250229,\n", | |
" 'mu30': 8633090.369003296,\n", | |
" 'nu02': 0.10815497152071127,\n", | |
" 'nu11': -0.019669141689288665,\n", | |
" 'nu12': -0.0032846761082870463,\n", | |
" 'nu20': 0.06629968636479547,\n", | |
" 'nu21': 1.0046975468372928e-05,\n", | |
" 'nu30': 0.0008561209988226333}" | |
] | |
} | |
], | |
"prompt_number": 37 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"cv2.contourArea(contours[0])" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "pyout", | |
"prompt_number": 38, | |
"text": [ | |
"10033.5" | |
] | |
} | |
], | |
"prompt_number": 38 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"perimeter = cv2.arcLength(contours[0], True) # True says that curve is closed\n", | |
"perimeter" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "pyout", | |
"prompt_number": 39, | |
"text": [ | |
"473.34523379802704" | |
] | |
} | |
], | |
"prompt_number": 39 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"cnt = contours[0]\n", | |
"len(cnt)" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "pyout", | |
"prompt_number": 42, | |
"text": [ | |
"231" | |
] | |
} | |
], | |
"prompt_number": 42 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"x = cnt[:,0, 0]; y = cnt[:,0,1]\n", | |
"plot(x,y)\n" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "pyout", | |
"prompt_number": 57, | |
"text": [ | |
"[<matplotlib.lines.Line2D at 0x5ac8f50>]" | |
] | |
}, | |
{ | |
"output_type": "display_data", | |
"png": 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g88/h0CHo109CXwjRMsXFUFnpXGt3nKhU2woIgFdegc8+axj6//uf0ZUJIRzV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\nYII=\n" | |
} | |
], | |
"prompt_number": 57 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"simpler = cv2.approxPolyDP(cnt, 2, True)\n", | |
"plot(simpler[:,0,0], simpler[:,0,1])" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "pyout", | |
"prompt_number": 61, | |
"text": [ | |
"[<matplotlib.lines.Line2D at 0x60c09d0>]" | |
] | |
}, | |
{ | |
"output_type": "display_data", | |
"png": 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fAPn5SldDRJ6GPXs3csMNwIYNwNNPAzt3Kl0NEXkSjtm7mUGDgL/+FZg2jRds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\nF2DYExF5AYY9EZEXYNgTEXmB/wfRy+mVCbKdMgAAAABJRU5ErkJggg==\n" | |
} | |
], | |
"prompt_number": 61 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"hull = cv2.convexHull(cnt)\n", | |
"plot(hull[:,0,0], hull[:,0,1])" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "pyout", | |
"prompt_number": 63, | |
"text": [ | |
"[<matplotlib.lines.Line2D at 0x6381fd0>]" | |
] | |
}, | |
{ | |
"output_type": "display_data", | |
"png": 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oR8HXqwcN4/iFLlwuEnoKCyEyEu6+2+5Kzo+GcfxESzJFQkd1Ndx4I+zcaW2E\naAcN4wQoLckUCQ0nT8LcufDjH9sX9L6gsPcjz5LMrVu1JFMkGBljrbyJjITvf9/uanpGgwt+dtll\n8OqrWpIpEmw8QV9aCq+/bgV+MNOYfS/RkkyR4OEJ+pISK+ivvNLuijRmHzS0JFMkOARi0PuChnF6\nkZZkigS2UA16OEfPPjc3l9jYWEaOHHnWfY8//jiRkZF88skn3mP5+fkkJSWRnJxMaWmp76sNAQsX\nWku47roLTpywuxoR8TAG7rsvNIMezhH2c+fOpaSk5KzjdXV1bNq0iWuuucZ7zOVysW7dOlwuFyUl\nJcybN4+WlhbfVxzktCRTJPC43dbyys2bQzPo4RxhP378ePr163fW8QULFvDII4+0OVZcXExOTg7R\n0dEkJCSQmJhIeXm5b6sNEZ4lmdu3W79gR47YXZFI+Dp6FL7yFfjkEygrC82gh/MYsy8uLiY+Pp5R\nZ1yPq7GxkaysLO+f4+PjaWhoaPc1li5d6v3Z6XTidDq7W0bQu+wy+POfYckSGDkSfvELa7WOiPSe\n+nrr39348dZ2CF/4gt0VnVZWVkZZWZnPXq9bYf/ZZ5+xfPlyNm3a5D3W2VKgiIiIdo+3DvtwFhMD\nTz8N06dbe2M7nfDEE3D55XZXJhL6/vIXa7HED35gzaV1EFe2ObMjvGzZsh69XreWXn700UfU1taS\nlpbG0KFDqa+vZ/To0Rw8eJC4uDjqWq0prK+vJy4urkfFhYsvfxl274aLLrJ6+Rs32l2RSGgrLYVJ\nk+Dxx2HRosALer8w51BTU2NSU1PbvS8hIcEcPnzYGGPMnj17TFpamvn888/N3r17zbXXXmtaWlrO\nek4X3jKsvf66MQkJxnzzm8Y0NdldjUjoWbXKmNhYY7Zts7uS7ulpdnbas8/JyeGLX/wiVVVVDB48\nmOeff77N/a2HaRwOB9nZ2TgcDu644w6Kioo6HMaRjp3Zy//d77RiR8QXjIGlS+Ghh6z9qsaPt7ui\n3qXtEgLYli0wf761PfLSpdb4oj4/RbrP7YZvfxtcLvj97yE21u6Kuq+n2amwD3AtLVBcbIW9Ql+k\n+44ehRkz4MIL4Ve/gosvtrui86OwDxMKfZHu8yytvOkma5vxQFpa2V0K+zBzZuj/6Edw++1Wr0VE\nTgv0pZXdpbAPU57Qf/xxePdda9tkp9O6ZWUp/CW8lZbCrFnw1FOQnW13Nb6hsBeam2HHDutU77Iy\nazXPqFHeSndxAAAJiklEQVSQkXH6NmIE9O1rd6Ui/vf889bOlb/5DYwbZ3c1vqOwl7N8+im88w5U\nVp6+ffihdf1MT/inp1s3na0rocKztHL1auvExOuus7si31LYS5ccOwZ79rT9ANi1CwYMaPsBkJEB\nV18d/OObEl5CYWnluSjs5bydPGn1+Ft/AFRWWkF/5gdAUlLwX4NTQtPRo9b+UhddFNxLK89FYS8+\nZQw0Np4O/nfftf778cfWPIAn/DMyIDUVLrjA7oolnNXVWUsrb745+JdWnovCXnrFkSNW8HvCv715\ngIwMSEvTPID0Ds/SyvnzYcGC0B96VNiLbY4dg/fea/sBcOY8gGcoSPMA4kuhuLTyXBT2ElBOnoTq\n6rYfAJoHEF8xxrqs5/Llobe08lwU9hLwzpwH8MwFeOYBWn8AaB5AOnLkCOTmwr591mU9r73W7op6\nl8JegpZnHqD1RPCHH1o9/tYTwenp1mUcJXxVVMBdd1lj9I8+Gp4dAoW9hBTPPEDrD4D25gEyMmDQ\nIM0DhDrPsM2//zs884y1xDJcKewl5J1rHmDYMLjmGhgy5PRt0CBrozgJXi4X3HefNQS4bp319xzO\nFPYSljzzAO++CzU11jjuvn3wt79Z//34YyvwPeF/5ofBkCFw6aV2t0La43JZV5PavBl++EPrFo7D\nNmdS2Iu0w+2GhobTHwKtb54PhKiotuGvbwf2ah3yCxbAPfdATIzdVQUOhX2AKSsrw+l02l2GX4RS\n24yBpqa2HwJvvFEGODv8dtDeB0IwfTsItL+/f/wDdu60dmrdsgU++KBnIR9o7fO1nmZnp/2W3Nxc\nXn31VQYMGMDu3bsBWLx4MX/4wx/o06cPw4YN4/nnn+eyU0sl8vPzWbVqFV/4whcoLCzk1ltvPe/C\nglUo/8KFUtsiIuCKK6xberp17NChMpYudXof0963g8pK6zoCwfjtwO6/v9bhXlYGb78NI0da12D4\nf//PugD4RRed/+vb3b5A1+mv4dy5c/n+97/PN77xDe+xW2+9lZ/97GdERkZy3333kZ+fz4oVK3C5\nXKxbtw6Xy0VDQwMTJ06kqqqKSJ01I0GqTx8YOtS6tae9bweeD4TOvh20/lC48sreW1HU3AwHDvTO\ne4H1/+evf20/3B94AL70JQ3T9KZOw378+PHU1ta2OTZp0iTvz2PHjuW3v/0tAMXFxeTk5BAdHU1C\nQgKJiYmUl5eTlZXl+6pFAkB73w7O1N63g3ffhQ0brA+ETz7pvXqbm6293nvT0KEK94BhzqGmpsak\npqa2e99Xv/pVs2bNGmOMMd/73vfM6tWrvffl5eWZ3/zmN2c9B9BNN9100+08bj1x3qOJDz/8MH36\n9GHmzJkdPiaine+nJoQnZ0VEAtV5hf0LL7zAxo0bef31173H4uLiqKur8/65vr6euLi4nlcoIiI9\n1u3Z05KSEh599FGKi4vp2+oK1pMnT2bt2rW43W5qamqorq4mMzPTp8WKiMj56bRnn5OTw9atWzl0\n6BCDBw9m2bJl5Ofn43a7vRO1N954I0VFRTgcDrKzs3E4HERFRVFUVNTuMI6IiNigRyP+7Zg7d64Z\nMGBAu5O6jz32mImIiDCHDx/2Hlu+fLlJTEw01113nfnjH//o63J8qqO2FRYWmuTkZDNixAhz7733\neo8HU9uMab99b731lhkzZoxJT083N9xwgykvL/feF2zt27dvn3E6ncbhcJgRI0aYgoICY4wxhw8f\nNhMnTjRJSUlm0qRJpqmpyfucYGpjR+1btGiRSU5ONqNGjTJTp041R44c8T4nFNrnEcz50lnbfJUv\nPg/7bdu2mXfeeeesQNy3b5+57bbbTEJCgvcvY8+ePSYtLc243W5TU1Njhg0bZk6ePOnrknymvbZt\n3rzZTJw40bjdbmOMMf/7v/9rjAm+thnTfvtuvvlmU1JSYowxZuPGjcbpdBpjgrN9+/fvN5WVlcYY\nYz799FMzfPhw43K5zOLFi83PfvYzY4wxK1asMEuWLDHGBF8bO2pfaWmpt+4lS5aEXPuMCf586aht\nvswXn5/xNH78ePr163fW8QULFvDII4+0OdbR2vxA1V7bnnnmGe6//36io6MB6N+/PxB8bYP22zdo\n0CCOHj0KwJEjR7yT7sHYvoEDB5J+akF8TEwMKSkpNDQ0sGHDBubMmQPAnDlzWL9+PRB8bWyvfY2N\njUyaNMl7cuPYsWOpr68HQqd9EPz50tHv5rPPPuuzfOmV01uLi4uJj49n1KhRbY43NjYSHx/v/XN8\nfDwNDQ29UZLPVFdXs23bNrKysnA6nfzP//wPEBptA1ixYgULFy5kyJAhLF68mPz8fCD421dbW0tl\nZSVjx47l4MGDxMbGAhAbG8vBgweB4G5j6/a1tmrVKu68804gdNoXavnSum1VVVU+yxe/79rx2Wef\nsXz5cjZt2uQ9ZjpZax9sk7onTpygqamJnTt3UlFRQXZ2Nnv37m33scHWNoC8vDwKCwuZOnUqr7zy\nCrm5uW3+LlsLlvY1Nzczffp0CgoKuOSSS9rcFxER0Wk7gqGNzc3NzJgxg4KCAmJanbJ6vufGBJrW\n7YuMjAypfGndtksuucSn+eL3nv1HH31EbW0taWlpDB06lPr6ekaPHs3BgwdDYm1+fHw806ZNA2DM\nmDFERkZy6NChkGgbQHl5OVOnTgVgxowZ3q+Kwdq+48ePM336dGbPns2UKVMAqzd/4NSmMfv372fA\ngAFAcLbR075Zs2Z52wenz41Zs2aN91gotC+U8qW9vzuf5os/Jhs622KhvQmUzz//3Ozdu9dce+21\npqWlxR8l+cyZbXv22WfNT37yE2OMMR988IEZPHiwMSY422bM2e3LyMgwZWVlxhhj/vSnP5kbbrjB\nGBOc7WtpaTGzZ8828+fPb3N88eLFZsWKFcYYY/Lz88+awAyWNnbUvtdee804HA7z8ccftzkeKu1r\nLVjzpaO2+TJffB72X//6182gQYNMnz59THx8vFm1alWb+4cOHdpmadTDDz9shg0bZq677jrvqo9A\n1V7b3G63mTVrlklNTTXXX3+92bJli/fxwdQ2Y063Lzo62tu+iooKk5mZadLS0kxWVpZ55513vI8P\ntvZt377dREREmLS0NJOenm7S09PNa6+9Zg4fPmxuueWWdpdeBlMb22vfxo0bTWJiohkyZIj32He/\n+13vc0Khfa0Fa7509Lvpy3zp9YuXiIhI79Nm8yIiYUBhLyISBhT2IiJhQGEvIhIGFPYiImFAYS8i\nEgb+P2oXxlNCKI3PAAAAAElFTkSuQmCC\n" | |
} | |
], | |
"prompt_number": 63 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"simpler_hull = cv2.approxPolyDP(hull, 2.5, True)\n", | |
"plot(simpler_hull[:,0,0], simpler_hull[:,0,1])\n", | |
"plot(x,y)" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "pyout", | |
"prompt_number": 69, | |
"text": [ | |
"[<matplotlib.lines.Line2D at 0x6957e10>]" | |
] | |
}, | |
{ | |
"output_type": "display_data", | |
"png": 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dCXohyhAfex8mtp3IY00u8dZb8MQT8Mcfxq7KNEjYU/QwTln79iGEKOzVV2HV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\nYQYk7IUQwgz8P+PKuCe6d+TuAAAAAElFTkSuQmCC\n" | |
} | |
], | |
"prompt_number": 69 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"r_x, r_y, r_w, r_h = cv2.boundingRect(cnt)\n", | |
"r_x, r_y, r_w, r_h # (150, 121, 103, 146)\n", | |
"plot((r_x, r_x, r_x+r_w, r_x+r_w, r_x), (r_y, r_y+r_h, r_y+r_h, r_y, r_y))\n", | |
"plot(x,y)" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "pyout", | |
"prompt_number": 73, | |
"text": [ | |
"[<matplotlib.lines.Line2D at 0x6eab610>]" | |
] | |
}, | |
{ | |
"output_type": "display_data", | |
"png": 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a9Gyz+gghmq24ohjd13SHbKms3ix8dRuNo7PdOE+G/G6/3fAWej//jYQQUkfX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\nQnQAhT0hhOiA/weJAQ9tXsppNAAAAABJRU5ErkJggg==\n" | |
} | |
], | |
"prompt_number": 73 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"rect = cv2.minAreaRect(cnt)\n", | |
"rect # ((202.134521484375, 192.14178466796875), (102.39618682861328, 140.3079376220703), -5.128190994262695)\n", | |
"box = cv2.cv.BoxPoints(rect)\n", | |
"box # ((157.41201782226562, 266.59124755859375), (144.87069702148438, 126.84494018554688), (246.85702514648438, 117.69232177734375), (259.3983459472656, 257.4386291503906))\n", | |
"# plot( [p[0] for p in box] + [box[0][0]], [p[1] for p in box] + [box[0][1]] )\n", | |
"box_list = list(box)\n", | |
"box_list.append(box[0])\n", | |
"ba = np.array(box_list) # Box array\n", | |
"plot(ba[:,0], ba[:,1])\n", | |
"plot(x,y)" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "pyout", | |
"prompt_number": 88, | |
"text": [ | |
"[<matplotlib.lines.Line2D at 0x7746a10>]" | |
] | |
}, | |
{ | |
"output_type": "display_data", | |
"png": 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PxAEzX+I4NRWeeMLQUQgh6qtzq878X9D/4WjvyIEzBwwdTr2lpMDrr+v3mmZ/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\nBiTZCyGEGZBkL4QQZkCSvRBCmIH/B5DdcKXZ82UEAAAAAElFTkSuQmCC\n" | |
} | |
], | |
"prompt_number": 88 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"(c_x, c_y), radius = cv2.minEnclosingCircle(cnt)\n", | |
"c_x, c_y, radius # (197.0, 194.5, 82.92139434814453)\n", | |
"center = shapely.geometry.Point(c_x, c_y)\n", | |
"circle = np.array(center.buffer(radius).boundary.coords)\n", | |
"len(circle) # 66 points\n", | |
"plot(circle[:,0], circle[:,1])\n", | |
"plot(x,y)" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "pyout", | |
"prompt_number": 97, | |
"text": [ | |
"[<matplotlib.lines.Line2D at 0x7ba81d0>]" | |
] | |
}, | |
{ | |
"output_type": "display_data", | |
"png": 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5Ql66GiEQCCAQCJoYtvQWLgQWLJDPLvCqjhCCLSlbMDd+Lqa5ToOzqTPXITEq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\n" | |
} | |
], | |
"prompt_number": 97 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"ellipse = cv2.fitEllipse(cnt)\n", | |
"ellipse # ((199.31251525878906, 185.9192352294922), (93.7149658203125, 138.58531188964844), 202.948486328125)\n", | |
"# TODO: what is a convenient way to get the coords for an ellipse?" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "pyout", | |
"prompt_number": 100, | |
"text": [ | |
"((199.31251525878906, 185.9192352294922),\n", | |
" (93.7149658203125, 138.58531188964844),\n", | |
" 202.948486328125)" | |
] | |
} | |
], | |
"prompt_number": 100 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [] | |
} | |
], | |
"metadata": {} | |
} | |
] | |
} |
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