{
"metadata": {
"name": "",
"signature": "sha256:5963a0b03438d79b8bddee9411be0e3bf52b93fe4ab00c5912be8e9036084951"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"\u3053\u306e\u30ce\u30fc\u30c8\u306b\u3064\u3044\u3066"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u30ed\u30dc\u30c3\u30c8\u30a2\u30fc\u30e0\u3092\u4f7f\u3063\u305f\u30d4\u30c3\u30af\u30a2\u30f3\u30c9\u30d7\u30ec\u30fc\u30b9\u4f5c\u696d\u306b\u5fc5\u8981\u306a\u753b\u50cf\u4e2d\u306e\u7269\u4f53\u306e\u4f4d\u7f6e\u59ff\u52e2\u63a8\u5b9a\u3092\u624b\u3092\u52d5\u304b\u3057\u306a\u304c\u3089\u5b66\u7fd2\u3057\u307e\u3057\u3087\u3046\u3002\u753b\u50cf\u51e6\u7406\u3067\u3088\u304f\u4f7f\u3046\u8868\u8272\u7cfb\u5909\u63db\u3001\u9818\u57df\u5206\u5272\u3001\u30cf\u30d5\u5909\u63db\u306a\u3069\u306e\u30c6\u30af\u30cb\u30c3\u30af\u306b\u3064\u3044\u3066\u89e3\u8aac\u3057\u307e\u3059\u3002"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Written by Yosuke Matsusaka (MID Academic Promotions, Inc. [@yosuke](https://twitter.com/yosuke))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u3053\u306e\u30ce\u30fc\u30c8\u306fCreative Commons\u306eBY-SA\u30e9\u30a4\u30bb\u30f3\u30b9\u3067\u516c\u958b\u3057\u307e\u3059\u3002"
]
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"\u6e96\u5099"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u3053\u306e\u30ce\u30fc\u30c8\u306fipython notebook\u3092\u4f7f\u3063\u305f\u624b\u3092\u52d5\u304b\u3057\u306a\u304c\u3089\u5b66\u3079\u308b\u6559\u6750\u306b\u306a\u3063\u3066\u3044\u307e\u3059\u3002Ubuntu\u4e0a\u3067\u306f\n",
"```\n",
"sudo apt-get install ipython-notebook python-matplotlib python-opencv\n",
"ipython notebook --pylab inline\n",
"```\n",
"\u3068\u5165\u529b\u3059\u308b\u3053\u3068\u3067\u74b0\u5883\u3092\u7acb\u3061\u4e0a\u3052\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"ipython notebook\u3092\u7acb\u3061\u4e0a\u3052\u3066\u65b0\u898f\u30ce\u30fc\u30c8\u3092\u4f5c\u6210\u3057\u305f\u3089\u3001\u307e\u305a\u30b0\u30e9\u30d5\u63cf\u753b\u3068\u6570\u5f0f\u51e6\u7406\u306e\u30e9\u30a4\u30d6\u30e9\u30ea\u3092\u8aad\u307f\u8fbc\u307f\u307e\u3059\u3002"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import cv2\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.animation as animation"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u4ee5\u4e0b\u3082\u5165\u529b\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u3053\u306e\u90e8\u5206\u306f\u30a2\u30cb\u30e1\u30fc\u30b7\u30e7\u30f3GIF\u3092\u8868\u793a\u3059\u308b\u305f\u3081\u306e\u304a\u307e\u3058\u306a\u3044\u3067\u3059\u3002"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Part of code is taken from: http://nbviewer.ipython.org/url/jakevdp.github.io/downloads/notebooks/AnimationEmbedding.ipynb\n",
"from tempfile import NamedTemporaryFile\n",
"\n",
"GIF_TAG = \"\"\"![](\"data:image/gif;bogus=ABCDEF;base64,{0}\")
\"\"\"\n",
"\n",
"def anim_to_html(anim):\n",
" if not hasattr(anim, '_encoded_video'):\n",
" with NamedTemporaryFile(suffix='.gif') as f:\n",
" anim.save(f.name, writer='imagemagick', fps=4)\n",
" video = open(f.name, \"rb\").read()\n",
" anim._encoded_video = video.encode(\"base64\")\n",
" return GIF_TAG.format(anim._encoded_video)\n",
"\n",
"animation.Animation._repr_html_ = anim_to_html"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u4eca\u56de\u4f7f\u3046\u753b\u50cf\u306f\u4ee5\u4e0b\u306e\u30b7\u30df\u30e5\u30ec\u30fc\u30b7\u30e7\u30f3\u306b\u3088\u3063\u3066\u751f\u6210\u3057\u305f\u3082\u306e\u3067\u3059\u3002"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from IPython.display import Image\n",
"Image(url=\"http://devrt.tk/build/repo/54237a608ec2f07bded7cf2d.gif\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"![](\"http://devrt.tk/build/repo/54237a608ec2f07bded7cf2d.gif\"/)
"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 3,
"text": [
""
]
}
],
"prompt_number": 3
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u30b7\u30df\u30e5\u30ec\u30fc\u30b7\u30e7\u30f3\u5b9f\u884c\u306e\u305f\u3081\u306b\u5fc5\u8981\u306a\u30c7\u30fc\u30bf\u3068\u3001\u30d9\u30eb\u30c8\u4e0a\u3092\u6d41\u308c\u308b\u7269\u4f53\u3092\u4eee\u60f3\u30ab\u30e1\u30e9\u304b\u3089\u64ae\u5f71\u3057\u305f\u9023\u7d9a\u753b\u50cf\u306e\u30c7\u30fc\u30bf\u3092\u4ee5\u4e0b\u306e\u30ec\u30dd\u30b8\u30c8\u30ea\u3067\u516c\u958b\u3057\u3066\u3044\u307e\u3059\u3002\u624b\u3092\u52d5\u304b\u3057\u305f\u3044\u65b9\u306fgit\u3067\u30af\u30ed\u30fc\u30f3\u3057\u3066\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3057\u3066\u304f\u3060\u3055\u3044\u3002\n",
"```\n",
"git clone https://github.com/devrt/world-robot-picking.git\n",
"cd world-robot-picking\n",
"git submodule init\n",
"git submodule update\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u7269\u4f53\u3092\u64ae\u5f71\u3057\u305f\u9023\u7d9a\u753b\u50cf\u3092\u8aad\u307f\u8fbc\u307f\u307e\u3059\u3002\u6700\u7d42\u7684\u306b\u306f\u9023\u7d9a\u753b\u50cf\u3092\u51e6\u7406\u3059\u308b\u306e\u3067\u3059\u304c\u3001\u89e3\u8aac\u3068\u691c\u8a0e\u306b\u3064\u3044\u3066\u306f\u9014\u4e2d\uff08\uff14\u30d5\u30ec\u30fc\u30e0\u76ee\uff09\u306e\u9759\u6b62\u753b\u3092\u4f7f\u3063\u3066\u884c\u3044\u307e\u3059\u3002"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"imgs = []\n",
"for i in range(1, 10):\n",
" imgs.append(cv2.imread('../cap%i.ppm' % i))\n",
"img = imgs[4]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u30b0\u30e9\u30d5\u9818\u57df\u3092\u521d\u671f\u5316\u3057\u307e\u3059\u3002\u3053\u306e\u30b0\u30e9\u30d5\u4e0a\u3067\u753b\u50cf\u51e6\u7406\u306e\u7d50\u679c\u3092\u78ba\u304b\u3081\u3066\u3044\u304d\u307e\u3059\u3002"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig = plt.figure()\n",
"im = plt.imshow(imgs[0])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
""
]
}
],
"prompt_number": 5
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u9023\u7d9a\u753b\u50cf\u3092\u30a2\u30cb\u30e1\u30fc\u30b7\u30e7\u30f3\u3067\u8868\u793a\u3059\u308b\u3068\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u306a\u308a\u307e\u3059\u3002\u30d9\u30eb\u30c8\u306e\u4e0a\u3092\u8907\u6570\u306e\u7269\u4f53\u304c\u53f3\u304b\u3089\u5de6\u306b\u6d41\u308c\u3066\u3044\u304f\u69d8\u5b50\u304c\u308f\u304b\u308b\u3068\u601d\u3044\u307e\u3059\u3002"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"i = 0\n",
"def animate(*args):\n",
" global i, im, imgs\n",
" im.set_array(imgs[i])\n",
" i = i + 1\n",
" return im,\n",
"animation.FuncAnimation(fig, animate, frames=8, blit=True)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"![](\"data:image/gif;bogus=ABCDEF;base64,R0lGODlhsAEgAfcAAAAAAAwMDBERERoXFxsbGzUNDToODiUlJS0tLTMzMz09PUwTFVUUFGcaHm8c\n",)
"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 6,
"text": [
""
]
}
],
"prompt_number": 6
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"\u8868\u8272\u7cfb\u306e\u5909\u63db"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u3053\u308c\u304b\u3089\u30d9\u30eb\u30c8\u4e0a\u3092\u6d41\u308c\u308b\u7269\u4f53\u306e\u4f4d\u7f6e\u59ff\u52e2\u3092\u753b\u50cf\u51e6\u7406\u306b\u3088\u3063\u3066\u8a8d\u8b58\u3057\u3066\u3044\u304f\u308f\u3051\u3067\u3059\u304c\u3001\u307e\u305a\u306f\u30d9\u30eb\u30c8\u3068\u7269\u4f53\u3092\u533a\u5225\u3059\u308b\u3053\u3068\u3092\u8003\u3048\u307e\u3059\u3002\n",
"\n",
"\u4eca\u56de\u306f\u6df1\u5ea6\u60c5\u5831\u306e\u5f97\u3089\u308c\u306a\u3044\u901a\u5e38\u306e\u30ab\u30e9\u30fc\u30ab\u30e1\u30e9\u3092\u4f7f\u3046\u305f\u3081\u3001\u8272\u3092\u4f7f\u3063\u3066\u7269\u4f53\u3092\u533a\u5225\u3057\u307e\u3059\u3002\n",
"\n",
"\u4e00\u8a00\u306b\u300c\u8272\u300d\u3068\u8a00\u3063\u3066\u3082\u3001\u305d\u306e\u6271\u3044\u306f\u601d\u3063\u305f\u3088\u308a\u96e3\u3057\u3044\u3053\u3068\u304c\u591a\u304f\u3042\u308a\u307e\u3059\u3002\u4e0a\u306e\u30a2\u30cb\u30e1\u30fc\u30b7\u30e7\u30f3\u3092\u898b\u3066\u3082\u305d\u308c\u305e\u308c\u306e\u7269\u4f53\u306b\u3088\u3063\u3066\u8272\u306f\u7570\u306a\u308a\u307e\u3059\u3057\u3001\u7269\u4f53\u306e\u5074\u9762\u306b\u5f71\u304c\u3067\u304d\u3066\u8272\u304c\u5909\u5316\u3057\u3066\u3044\u307e\u3059\u3002\u4eca\u56de\u306f\u30b7\u30df\u30e5\u30ec\u30fc\u30bf\u3067\u751f\u6210\u3057\u305f\u753b\u50cf\u3092\u4f7f\u3044\u307e\u3059\u304c\u3001\u5b9f\u969b\u306e\u753b\u50cf\u8a8d\u8b58\u3067\u306f\u7167\u660e\u306e\u30e0\u30e9\u304b\u3089\u3067\u304d\u308b\u30d9\u30eb\u30c8\u9762\u306e\u8272\u5909\u5316\u306b\u3082\u5bfe\u5fdc\u3057\u306a\u3051\u308c\u3070\u306a\u308a\u307e\u305b\u3093\u3002\n",
"\n",
"\u305d\u306e\u3088\u3046\u306a\u3068\u304d\u306b\u4fbf\u5229\u306b\u4f7f\u3048\u308b\u306e\u304c\u300c\u8868\u8272\u7cfb\u300d\u3067\u3059\u3002\u901a\u5e38\u306e\u30ab\u30e9\u30fc\u753b\u50cf\u306f\u5149\u306e\u4e09\u539f\u8272\u3067\u3042\u308b\u8d64\u30fb\u7dd1\u30fb\u9752\u306e\u6df7\u5408\u3067\u8272\u3092\u8868\u73fe\u3059\u308b\u300cRGB\u8868\u8272\u7cfb\u300d\u304c\u4f7f\u308f\u308c\u307e\u3059\u304c\u3001\u753b\u50cf\u51e6\u7406\u306e\u5834\u5408\u3001\u8272\u76f8\u30fb\u5f69\u5ea6\u30fb\u660e\u5ea6\u3067\u8272\u3092\u8868\u73fe\u3059\u308b\u300cHSV\u8868\u8272\u7cfb\u300d\u304c\u3088\u304f\u4f7f\u308f\u308c\u307e\u3059\u3002\n",
"\n",
"\u4eca\u56de\u306f\u753b\u50cf\u51e6\u7406\u306bOpenCV\u3092\u4f7f\u3044\u307e\u3059\u304c\u3001OpenCV\u3067\u306f\u4ee5\u4e0b\u306e\u95a2\u6570\u3092\u4f7f\u3063\u3066\u8868\u8272\u7cfb\u3092\u5909\u63db\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"hsv = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 7
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u5909\u63db\u5f8c\u306eHSV\u753b\u50cf\u304b\u3089\u300c\u8272\u76f8\u300d\u3092\u898b\u3066\u307f\u307e\u3059\u3002\u5404\u7269\u4f53\u306e\u8272\u306e\u7279\u5fb4\u304c\u51fa\u3066\u3044\u307e\u3059\u3002\u5143\u306e\u753b\u50cf\u304b\u3089\u306f\u6c17\u3065\u304d\u307e\u305b\u3093\u3067\u3057\u305f\u304c\u30d9\u30eb\u30c8\u9762\u304c\u5c11\u3057\u8d64\u307f\u304c\u304b\u3063\u3066\u3044\u305f\uff08\u8d64\u3044\u7269\u4f53\u3068\u540c\u8272\u3060\u3063\u305f\uff09\u3053\u3068\u304c\u5206\u304b\u308a\u307e\u3059\u3002"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.imshow(hsv[:, :, 0])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 8,
"text": [
""
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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UWEd43v36uAwwGzg7vs4DVmY8jtT2/vqMrZS3dVHe1lWzEAU4ZFyZi/7xJzXbn4aqNuDe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\n+n9SRAntGQoStgAAAABJRU5ErkJggg==\n",
"text": [
""
]
}
],
"prompt_number": 8
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u6b21\u306f\u300c\u5f69\u5ea6\u300d\u3067\u3059\u3002\u5730\u5473\u306a\u8272\u306e\u30d9\u30eb\u30c8\u9762\u3068\u305d\u308c\u306b\u6bd4\u3079\u3066\u9bae\u3084\u304b\u306a\u7269\u4f53\u306e\u8272\u306e\u7279\u5fb4\u304c\u304d\u308c\u3044\u306b\u51fa\u3066\u3044\u307e\u3059\u3002\u3053\u306e\u7279\u5fb4\u3092\u4f7f\u3048\u3070\u7269\u4f53\u3092\u3046\u307e\u304f\u5207\u308a\u51fa\u3059\u3053\u3068\u304c\u3067\u304d\u305d\u3046\u3067\u3059\u3002"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.imshow(hsv[:, :, 1])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 9,
"text": [
""
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
""
]
}
],
"prompt_number": 9
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u6700\u5f8c\u306b\u300c\u660e\u5ea6\u300d\u3082\u898b\u3066\u307f\u307e\u3057\u3087\u3046\u3002\u4eca\u56de\u306f\u3053\u306e\u60c5\u5831\u306f\u4f7f\u3044\u307e\u305b\u3093\u304c\u3001\u7269\u4f53\u306e\u9762\u306b\u5fdc\u3058\u305f\u7167\u660e\u306e\u53cd\u5c04\u306e\u69d8\u5b50\u3092\u304d\u308c\u3044\u306b\u898b\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.imshow(hsv[:, :, 2])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 10,
"text": [
""
]
},
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAVIAAAEACAYAAADhk/4TAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSZIkSZIkqRr9P8fBpD6xYdzsAAAAAElFTkSuQmCC\n",
"text": [
""
]
}
],
"prompt_number": 10
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"\uff12\u5024\u5316\u3068\u9818\u57df\u5206\u5272"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"HSV\u8868\u8272\u7cfb\u306e\u300c\u5f69\u5ea6\u300d\u3092\u4f7f\u3046\u3068\u7269\u4f53\u3092\u3046\u307e\u304f\u5207\u308a\u51fa\u305b\u305d\u3046\u306a\u3053\u3068\u304c\u308f\u304b\u3063\u305f\u306e\u3067\u65e9\u901f\u4f5c\u696d\u3057\u3066\u307f\u307e\u3057\u3087\u3046\u3002\n",
"\n",
"\u7269\u4f53\u3092\u5207\u308a\u51fa\u3059\u306b\u306f\u307e\u305a\u3042\u308b\u95be\u5024\uff08\u4eca\u56de\u306f100\uff09\u3067\u753b\u50cf\u30922\u5024\u5316\u3057\u307e\u3059\u3002\u5143\u306e\u753b\u50cf\u306f8bit\u8272\uff08256\u968e\u8abf\uff09\u306a\u306e\u3067\u3059\u304c\u30012\u5024\u5316\u51e6\u7406\u3092\u884c\u3046\u3068\u6587\u5b57\u901a\u308a2\u8272\u306b\u306a\u308a\u307e\u3059\u3002"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ret, mask = cv2.threshold(hsv[:, :, 1], 100, 255, cv2.THRESH_BINARY)\n",
"plt.imshow(mask)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 11,
"text": [
""
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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DESbl0KrGY5BKY9A7uIdzeHTYfa7hBkemNWKQSmPEKfyGNdxU0TEttBqmNWCQSgV3IbtY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\n",
"text": [
""
]
}
],
"prompt_number": 11
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"2\u5024\u5316\u3057\u305f\u753b\u50cf\u30921\uff08\u8d64\uff09\u306b\u306a\u3063\u3066\u3044\u308b\u5cf6\u3054\u3068\u306b\u5207\u308a\u5206\u3051\u307e\u3059\u3002\u3053\u306e\u3088\u3046\u306a\u51e6\u7406\u306e\u3053\u3068\u3092\u300c\u9818\u57df\u5206\u5272\u300d\u3068\u8a00\u3044\u307e\u3059\u3002OpenCV\u3067\u306f\u4ee5\u4e0b\u306e\u95a2\u6570\u3092\u4f7f\u3044\u307e\u3059\u3002"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"help(cv2.findContours)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Help on built-in function findContours in module cv2:\n",
"\n",
"findContours(...)\n",
" findContours(image, mode, method[, contours[, hierarchy[, offset]]]) -> contours, hierarchy\n",
"\n"
]
}
],
"prompt_number": 12
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"OpenCV\u306efindContours\u95a2\u6570\u306b\u306f\u9069\u7528\u3059\u308b\u9818\u57df\u5206\u5272\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306b\u3088\u308a\u69d8\u3005\u306a\u30aa\u30d7\u30b7\u30e7\u30f3\u304c\u3042\u308b\u306e\u3067\u3059\u304c\u3001\u4eca\u56de\u306f\u4ee5\u4e0b\u306e\u6a19\u6e96\u7684\u306a\u8a2d\u5b9a\u3092\u4f7f\u3044\u307e\u3059\u3002"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"blob, idx = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 13
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u9818\u57df\u5206\u5272\u3092\u884c\u3063\u305f\u7d50\u679c\u3092\u78ba\u8a8d\u3057\u307e\u3059\u3002\u7121\u4e8b\u306b3\u3064\u306e\u9818\u57df\u306b\u5206\u5272\u3067\u304d\u3066\u3044\u308b\u3088\u3046\u3067\u3059\u3002"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"shape(blob)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 14,
"text": [
"(3,)"
]
}
],
"prompt_number": 14
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u8a66\u3057\u306b2\u756a\u76ee\u306e\u9818\u57df\u3092\u753b\u50cf\u3068\u3057\u3066\u8868\u793a\u3057\u3066\u307f\u307e\u3057\u3087\u3046\u3002"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"mask = np.zeros(img.shape[:-1], np.uint8)\n",
"cv2.drawContours(mask, blob, 1, 255, -1)\n",
"plt.imshow(mask)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 15,
"text": [
""
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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Pg/hOJEmSJEmSVBudQD9wHLiz4LakWgo8DbwAPA/cFusXAPuBY8A+YH7mmO2EvvcDa+rW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\nzVstSZIkSZIkSZIkSZIkSZIkSdKF6P8BLVRF7zuiQWIAAAAASUVORK5CYII=\n",
"text": [
""
]
}
],
"prompt_number": 15
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u4e0a\u8a18\u306e\u5207\u308a\u51fa\u3057\u305f\u753b\u50cf\u3092\u5143\u306e\u5165\u529b\u753b\u50cf\u306b\u30de\u30b9\u30af\u3068\u3057\u3066\u639b\u3051\u3066\u3042\u3052\u308b\u3068\u3001\u7dd1\u306e\u7269\u4f53\u3060\u3051\u3092\u629c\u304d\u51fa\u3057\u3066\u8868\u793a\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.imshow(cv2.bitwise_and(img, img, mask=mask))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 16,
"text": [
""
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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+YHk1G5SoFbgmli8HDhL6sw34SqzfBNwZyysIfZ5H+DcYor7unPgScD9hyW3IZz97CSvg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\nSZIkSZIkSZJmo/8HdDuIjsJJ4VEAAAAASUVORK5CYII=\n",
"text": [
""
]
}
],
"prompt_number": 16
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1\u756a\u76ee\u306e\u9818\u57df\u306f\u9752\u306e\u7269\u4f53\u306b\u5bfe\u5fdc\u3057\u3066\u3044\u308b\u307f\u305f\u3044\u3067\u3059\u306d\u3002"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"mask2 = np.zeros(img.shape[:-1], np.uint8)\n",
"cv2.drawContours(mask2, blob, 0, 255, -1)\n",
"plt.imshow(cv2.bitwise_and(img, img, mask=mask2))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 17,
"text": [
""
]
},
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAVIAAAEACAYAAADhk/4TAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/w9kQmn6ywX8hAAAAABJRU5ErkJggg==\n",
"text": [
""
]
}
],
"prompt_number": 17
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u9818\u57df\u5206\u5272\u304c\u3067\u304d\u308b\u3068\u7269\u4f53\u306e\u69d8\u3005\u306a\u60c5\u5831\u3092\u5f97\u308b\u3053\u3068\u304c\u3067\u304d\u308b\u3088\u3046\u306b\u306a\u308a\u307e\u3059\u3002\n",
"\n",
"\u9818\u57df\u306e\u91cd\u5fc3\uff08\uff1d\u7269\u4f53\u306e\u753b\u50cf\u5185\u3067\u306e\u5ea7\u6a19\uff09\u306f\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u6c42\u307e\u308a\u307e\u3059\u3002"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"m = cv2.moments(blob[1])\n",
"x = m['m10']/m['m00']\n",
"y = m['m01']/m['m00']\n",
"x, y"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 18,
"text": [
"(475.51380117999344, 499.64567582792256)"
]
}
],
"prompt_number": 18
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u4eca\u56de\u306f\u4f7f\u3044\u307e\u305b\u3093\u304c\u3001\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u3059\u308b\u3068\u7269\u4f53\u306e\u8272\u3092\u6c42\u3081\u308b\u3053\u3068\u3082\u3067\u304d\u307e\u3059\u3002\u300c\u7dd1\u306e\u7bb1\u3060\u3051\u3092\u30d4\u30c3\u30af\u30a2\u30c3\u30d7\u300d\u306e\u3088\u3046\u306a\u30bf\u30b9\u30af\u306f\u3053\u306e\u60c5\u5831\u3092\u4f7f\u3046\u3053\u3068\u3067\u5b9f\u73fe\u3067\u304d\u307e\u3059\u3002"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"mean = cv2.mean(hsv, mask=mask)\n",
"mean"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 19,
"text": [
"(59.01709998255104, 191.94870005234688, 157.47077298900717, 0.0)"
]
}
],
"prompt_number": 19
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"\u30cf\u30d5\u5909\u63db\u3092\u7528\u3044\u305f\u7269\u4f53\u306e\u59ff\u52e2\u63a8\u5b9a"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u7269\u4f53\u3092\u5207\u308a\u51fa\u3057\u3066\u4f4d\u7f6e\u3092\u63a8\u5b9a\u3059\u308b\u3068\u3053\u308d\u307e\u3067\u306f\u3067\u304d\u307e\u3057\u305f\u304c\u30ed\u30dc\u30c3\u30c8\u30a2\u30fc\u30e0\u3067\u7269\u4f53\u3092\u5b89\u5b9a\u7684\u306b\u628a\u6301\u3059\u308b\u3053\u3068\u3092\u8003\u3048\u308b\u3068\u3001\u7269\u4f53\u306e\u59ff\u52e2\u306b\u5408\u308f\u305b\u3066\u624b\u3092\u4f38\u3070\u3057\u3066\u3042\u3052\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002\n",
"\n",
"\u7269\u4f53\u306e\u59ff\u52e2\u63a8\u5b9a\u3092\u884c\u3046\u65b9\u6cd5\u306f\u3044\u304f\u3064\u304b\u3042\u308a\u307e\u3059\u304c\u3001\u3053\u3053\u3067\u306f\u30cf\u30d5\u5909\u63db\u3092\u4f7f\u3063\u305f\u65b9\u6cd5\u3092\u7d39\u4ecb\u3057\u305f\u3044\u3068\u601d\u3044\u307e\u3059\u3002\n",
"\n",
"\u30cf\u30d5\u5909\u63db\u3092\u884c\u3046\u305f\u3081\u306b\u306f\u307e\u305a\u7269\u4f53\u306e\u30a8\u30c3\u30b8\u753b\u50cf\u3092\u7528\u610f\u3057\u307e\u3059\u3002"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"im_edge = np.zeros(img.shape[:-1], np.uint8)\n",
"cv2.drawContours(im_edge, blob, 1, 255, 2)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 20
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u7d30\u3081\u306b\u30a8\u30c3\u30b8\u3092\u51fa\u3057\u305f\u307b\u3046\u304c\u7cbe\u5ea6\u3088\u304f\u30cf\u30d5\u5909\u63db\u3067\u304d\u308b\u305f\u3081\u3084\u3084\u898b\u3065\u3089\u3044\u3068\u601d\u3044\u307e\u3059\u304c\u3001\u30a8\u30c3\u30b8\u753b\u50cf\u306f\u4ee5\u4e0b\u306e\u3088\u3046\u306a\u753b\u50cf\u3067\u3059\u3002"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.imshow(im_edge)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 21,
"text": [
""
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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kYB8wGdgf6/cC08uOmxbrBplXUUMlqbYKHD6g25r5yEqm9p9nYFoPsAFYHsvLgfVl9csI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\n",
"text": [
""
]
}
],
"prompt_number": 21
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u300c\u30cf\u30d5\u5909\u63db\u300d\u306f\u753b\u50cf\u4e0a\u306e\u5404\u70b9\u3092\u60f3\u5b9a\u3057\u3066\u3044\u308b\u56f3\u5f62\u3092\u69cb\u6210\u3059\u308b\u30d1\u30e9\u30e1\u30fc\u30bf\u7a7a\u9593\uff08\u30cf\u30d5\u7a7a\u9593\uff09\u306b\u5c04\u5f71\u3057\u3066\u3001\u305d\u306e\u5c04\u5f71\u3055\u308c\u305f\u70b9\u306e\u5bc6\u5ea6\uff08\u300c\u6295\u7968\u300d\u3068\u3082\u8a00\u3046\uff09\u304b\u3089\u5143\u306e\u56f3\u5f62\u3092\u63a8\u5b9a\u3059\u308b\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3067\u3059\u3002\n",
"\n",
"\u4eca\u56de\u306f\u7269\u4f53\u306e4\u8fba\u3092\u69cb\u6210\u3059\u308b\u76f4\u7dda\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u63a8\u5b9a\u3057\u307e\u3059\u3002\n",
"OpenCV\u3067\u76f4\u7dda\u3092\u63a8\u5b9a\u3059\u308b\u30cf\u30d5\u5909\u63db\uff08\u6295\u7968\u6570100\u4ee5\u4e0b\u3067\u8db3\u5207\u308a\uff09\u306f\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u3057\u3066\u5b9f\u884c\u3067\u304d\u307e\u3059\u3002"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"lines = cv2.HoughLines(im_edge, 2, np.pi/180, 100)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 22
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u30d1\u30e9\u30e1\u30fc\u30bf\u304b\u3089\u753b\u50cf\u306b\u623b\u3059\u3068\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u306a\u308a\u307e\u3059\u3002\u5404\u8fba\u306e\u76f4\u7dda\u304c\u304d\u308c\u3044\u306b\u63a8\u5b9a\u3067\u304d\u3066\u3044\u307e\u3059\u3002"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Part of code is taken from: http://opencv-python-tutroals.readthedocs.org/en/latest/py_tutorials/py_imgproc/py_houghlines/py_houghlines.html\n",
"im_out = np.zeros(img.shape, np.uint8)\n",
"for rho,theta in lines[0]:\n",
" a = np.cos(theta)\n",
" b = np.sin(theta)\n",
" x0 = a*rho\n",
" y0 = b*rho\n",
" x1 = int(x0 + 1000*(-b))\n",
" y1 = int(y0 + 1000*(a))\n",
" x2 = int(x0 - 1000*(-b))\n",
" y2 = int(y0 - 1000*(a))\n",
" cv2.line(im_out, (x1,y1), (x2,y2), (255,255,0), 2)\n",
"plt.imshow(im_out)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 23,
"text": [
""
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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fdS/1iGcmHNeoeP31YJ8VkYyGG69ep/9ci7iINJlmjgv9+0vFaDXAXZs2Mq36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\nRK5CYII=\n",
"text": [
""
]
}
],
"prompt_number": 23
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u63a8\u5b9a\u3055\u308c\u305f\u5404\u76f4\u7dda\u306e\u89d2\u5ea6\u3092\u898b\u308b\u3068\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u306a\u3063\u3066\u3044\u307e\u3059\u3002\u5de6\u65b9\u5411\u3068\u53f3\u65b9\u5411\u306e\u76f4\u7dda\u306e\u89d2\u5ea6\u304c\u305d\u308c\u305e\u308c\u7b97\u51fa\u3055\u308c\u3066\u3044\u308b\u306e\u3067\u30010.7\u524d\u5f8c\u306e\u5024\u30682.3\u524d\u5f8c\u306e\u5024\u306e2\u7fa4\u304c\u89b3\u5bdf\u3067\u304d\u307e\u3059\u3002"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"thetas = lines[:, :, 1]\n",
"thetas"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 24,
"text": [
"array([[ 2.3561945 , 0.73303831, 0.78539819, 0.73303831, 0.78539819,\n",
" 2.33874106, 2.32128787, 2.30383468, 2.3561945 , 2.37364769,\n",
" 2.33874106, 2.30383468, 0.71558499, 2.32128787, 2.28638124,\n",
" 2.28638124, 2.37364769, 0.71558499, 2.39110112, 2.26892805,\n",
" 2.39110112, 2.26892805, 0.68067843]], dtype=float32)"
]
}
],
"prompt_number": 24
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
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},
{
"cell_type": "code",
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"input": [
"theta = np.median(thetas[thetas < np.pi/2])\n",
"theta"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 25,
"text": [
"0.73303831"
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}
],
"prompt_number": 25
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{
"cell_type": "heading",
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{
"cell_type": "markdown",
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"source": [
"\u4e0a\u8a18\u3057\u305f\u51e6\u7406\u3092\u9023\u7d9a\u753b\u50cf\u306b\u9069\u7528\u3057\u3066\u307f\u307e\u3057\u3087\u3046\u3002"
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},
{
"cell_type": "code",
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"input": [
"import copy\n",
"\n",
"i = 0\n",
"def animate(*args):\n",
" global i, im, imgs\n",
" \n",
" # convert to HSV color space and binarize\n",
" hsv = cv2.cvtColor(imgs[i], cv2.COLOR_RGB2HSV)\n",
" ret, mask = cv2.threshold(hsv[:, :, 1], 100, 255, cv2.THRESH_BINARY)\n",
" \n",
" # split into blobs\n",
" blob, idx = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n",
" \n",
" im_out = copy.deepcopy(imgs[i])\n",
" for j in range(0, shape(blob)[0]):\n",
" try:\n",
" # calc moment of the blob\n",
" m = cv2.moments(blob[j])\n",
" x = int(m['m10']/m['m00'])\n",
" y = int(m['m01']/m['m00'])\n",
"\n",
" # calc attitude of the object by using hough transform\n",
" im_edge = np.zeros(img.shape[:-1], np.uint8)\n",
" cv2.drawContours(im_edge, blob, j, 255, 1)\n",
" lines = cv2.HoughLines(im_edge, 2, np.pi/180, 30)\n",
" thetas = lines[:, :, 1]\n",
" theta = np.median(thetas[thetas < np.pi/2])\n",
"\n",
" # create and show annotated image\n",
" cv2.circle(im_out, (x, y), 20, (255,255,0), 10)\n",
" cv2.line(im_out, (x, y), (x + int(150*np.sin(theta)), y - int(150*np.cos(theta))), (255,255,0), 10)\n",
" except:\n",
" pass\n",
" im.set_array(im_out)\n",
" i = i + 1\n",
" return im,\n",
"\n",
"animation.FuncAnimation(fig, animate, frames=8, blit=True)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
""
],
"metadata": {},
"output_type": "pyout",
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""
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}
],
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{
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{
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{
"cell_type": "heading",
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"\u304a\u307e\u3051"
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},
{
"cell_type": "markdown",
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},
{
"cell_type": "code",
"collapsed": false,
"input": [
"img2 = np.ones(img.shape, np.uint8) * 255\n",
"cv2.circle(img2, (500, 400), 200, (100,100,100), 100)\n",
"\n",
"i = 0\n",
"def animate(*args):\n",
" global i, im, imgs, img2\n",
" hsv = cv2.cvtColor(imgs[i], cv2.COLOR_RGB2HSV)\n",
" ret, mask = cv2.threshold(hsv[:, :, 1], 100, 255, cv2.THRESH_BINARY)\n",
" blob, idx = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n",
" mask = np.zeros(img.shape[:-1], np.uint8)\n",
" for j in range(0, shape(blob)[0]):\n",
" cv2.drawContours(mask, blob, j, 255, -1)\n",
" im.set_array(np.where(cv2.merge((mask,mask,mask)) == 255, imgs[i], img2))\n",
" i = i + 1\n",
" return im,\n",
"\n",
"animation.FuncAnimation(fig, animate, frames=8, blit=True)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"![](\"data:image/gif;bogus=ABCDEF;base64,R0lGODlhsAEgAfcAAAAAAAwMDBERERsbGzUNDToODiUlJS0tLTMzMz09PUwTFVUUFGcaHm8cH3wc\n",)
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],
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""
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{
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}
],
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}
]
}