├── .gitignore
├── Active_contour_model.ipynb
├── Adapt_RGB_decorator.ipynb
├── Approximate_subdivide_polygon.ipynb
├── Block_view_mean_max_median_sampling.ipynb
├── Canny_edge_detector.ipynb
├── Circular_Elliptical_Hough_Transform.ipynb
├── Convex_Hull.ipynb
├── Denoise.ipynb
├── Edge_operators.ipynb
├── Entropy.ipynb
├── Filtering_regional_maxima.ipynb
├── Finding_contours.ipynb
├── Gamma_log_contrast_adjustment.ipynb
├── Histogram_matching.ipynb
├── Hough_transform_straight_line.ipynb
├── Hysteresis_thresholding.ipynb
├── Image_deconvolution.ipynb
├── Interpolation - Edge modes.ipynb
├── LICENSE
├── Local_Histogram_Equalization.ipynb
├── Marching_cubes.ipynb
├── README.md
├── RGB2Gray.ipynb
├── RGB_to_HSV.ipynb
├── Random_shapes.ipynb
├── Rescale_resize_downscale.ipynb
├── Ridge_operators.ipynb
├── Scipy_zooming.ipynb
├── Shapes_drawing.ipynb
├── Skeletonize.ipynb
├── Structural_similarity_index.ipynb
├── Swirl.ipynb
├── Tint_Grayscale.ipynb
├── Unsharp_mask.ipynb
├── Using_Numpy_image_manipulation.ipynb
├── _config.yml
├── images
    ├── Hysteresis_thresholding.PNG
    ├── Local_histogram_equalization.PNG
    ├── MRI1.jpg
    ├── Numpy_image_manipulation.png
    ├── Readme.md
    ├── Swirl.png
    ├── Tubes.jpg
    ├── block_view_pooling_sampling.PNG
    ├── convex_hull.PNG
    ├── edge_operators.png
    ├── filtering_regional_maxima.PNG
    ├── finding_contours.png
    ├── histogram_matching.PNG
    ├── interpolation_edge_mode.PNG
    ├── marching_cubes.PNG
    ├── rgb2gray.PNG
    ├── rgb2hsv.PNG
    ├── skeletonize.png
    ├── sobel_coins.PNG
    ├── tint_grayscale.PNG
    └── unsharp_mask.PNG
└── skimage_getting_started.ipynb


/.gitignore:
--------------------------------------------------------------------------------
  1 | # Byte-compiled / optimized / DLL files
  2 | __pycache__/
  3 | *.py[cod]
  4 | *$py.class
  5 | 
  6 | # C extensions
  7 | *.so
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 23 | *.egg-info/
 24 | .installed.cfg
 25 | *.egg
 26 | MANIFEST
 27 | 
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 30 | #  before PyInstaller builds the exe, so as to inject date/other infos into it.
 31 | *.manifest
 32 | *.spec
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 39 | htmlcov/
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 44 | nosetests.xml
 45 | coverage.xml
 46 | *.cover
 47 | .hypothesis/
 48 | .pytest_cache/
 49 | 
 50 | # Translations
 51 | *.mo
 52 | *.pot
 53 | 
 54 | # Django stuff:
 55 | *.log
 56 | local_settings.py
 57 | db.sqlite3
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101 | /site
102 | 
103 | # mypy
104 | .mypy_cache/
105 | 


--------------------------------------------------------------------------------
/Convex_Hull.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "# Convex Hull\n",
  8 |     "### Dr. Tirthajyoti Sarkar, Fremont CA 94536\n",
  9 |     "The convex hull of a binary image is the set of pixels included in the smallest convex polygon that surround all white pixels in the input.\n",
 10 |     "\n",
 11 |     "A good overview of the algorithm is given on [this blog](http://blogs.mathworks.com/steve/2011/10/04/binary-image-convex-hull-algorithm-notes/)."
 12 |    ]
 13 |   },
 14 |   {
 15 |    "cell_type": "code",
 16 |    "execution_count": 1,
 17 |    "metadata": {},
 18 |    "outputs": [],
 19 |    "source": [
 20 |     "import matplotlib.pyplot as plt\n",
 21 |     "\n",
 22 |     "from skimage.morphology import convex_hull_image\n",
 23 |     "from skimage import data, img_as_float\n",
 24 |     "from skimage.util import invert"
 25 |    ]
 26 |   },
 27 |   {
 28 |    "cell_type": "markdown",
 29 |    "metadata": {},
 30 |    "source": [
 31 |     "### The original image is inverted as the object must be white"
 32 |    ]
 33 |   },
 34 |   {
 35 |    "cell_type": "code",
 36 |    "execution_count": 2,
 37 |    "metadata": {},
 38 |    "outputs": [],
 39 |    "source": [
 40 |     "image = invert(data.horse())"
 41 |    ]
 42 |   },
 43 |   {
 44 |    "cell_type": "markdown",
 45 |    "metadata": {},
 46 |    "source": [
 47 |     "### Create a convex hull"
 48 |    ]
 49 |   },
 50 |   {
 51 |    "cell_type": "code",
 52 |    "execution_count": 8,
 53 |    "metadata": {},
 54 |    "outputs": [],
 55 |    "source": [
 56 |     "chull = convex_hull_image(image)"
 57 |    ]
 58 |   },
 59 |   {
 60 |    "cell_type": "markdown",
 61 |    "metadata": {},
 62 |    "source": [
 63 |     "### Show the result"
 64 |    ]
 65 |   },
 66 |   {
 67 |    "cell_type": "code",
 68 |    "execution_count": 10,
 69 |    "metadata": {},
 70 |    "outputs": [
 71 |     {
 72 |      "data": {
 73 |       "image/png": 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HVelSke8FnkW/Z6aqyteldg7MioCzBvMWJ04+O2prwV/EsVxG0CHklKWt7RsYZ5m1hoCzAsu4wu7hJETxR71o28DsCDgLtI5iNOw1uxB8KP4bLWvKCqvBcQMWi4Azh5ILUv+21RB4St7fJbHNvmoBjhGwXAScCdRQiNo8rVXD/h+kbccBi1FrewZK09mA0+UiU/K0VpePyyIxilMGjgGwPp0JOBSa8SbZR7OEIPb9eswTckoIu21Fewcmt8xa04mAQ8FZHPZluzCSs1rsa6Ac1QccCg5KVPIIScnbVirqDFCeav8WFX+HBUimCSyEm+lRZ4DZLLveVBlwKDgAVoFaA5SruoBDwUHpSh0lKXW7SkWtAcpW1RqcRRUcvhEWtWm2aYLM/KgNwHxWUYeqGcGh4ADjEW7mR60B5rOqOlTVCM68mjudIgYAQHtVEXAIIwCWjToDtEvrp6iWUXQoZFgWpogAdNkqa2DrA86icOIBAKAeBJw+jN4A6EddAOa36oGEVgecRRcdihgAAHVodcBZFKansAq0MwBdtY76R8ABVoBwAwCr1dqAw7cWA1gFagMwn3Vd4LU24AAAAAxDwAGWjOmp9mL0BmivVgYcig4AAOVb5wVeKwMOAADAKAQcAACwcOuenifgAACA6hBwgCVa9xUMZsdaP2B2JdQ+Ag4AAKhO6wLOIq+qSkiYAABg8VoXcAAAQLlKGTwg4AAAgIUoJdxIBBwAAFAhAg6wJCVdyQBA1xBwAADA3Eq7qCPgZKUdGAAAMDsCDgAAqA4BBwAAzKXEWRACDgAAmFmJ4UYi4AAAgAoRcBpKTaEAAJSo5PMmAQcAAFSHgNOn5DQKAAAmQ8ABAABTK31AgIADAACqQ8ABlqD0KxsAqB0BZwBOTgAADNeG8yQBBwAAVIeAAwAAJtaG0RtJ2rTuDSiVbUXEujdjrP6G1oZtBgBg2RjBabFBKdp2a9I1AKBd2nR+IeCMUPKBHLdtBJ31Yb8DwPoRcFpomhMoQQcA0EUEnI4g6KwG+xhArdpW3wg4LTNvAyPoLA/7FUCt2ljfCDhjtPGgToKgAwCoGR8Tb5FlBJLmc/IR89kRFgGgLIzg4OcY1ZkN+wxAzdpa4xjBaYlVNjC+PHBybe34AFA7Ag7GGnUS73L4IdwAqF2b6xwBpwVKbmDDtq3LwQcAsH4EHCzFskMZAQoAlqvki+tJEHDQSuueNmt7xweA2vEpqsJxIgUAYHoEHAAAsEENF9cEnDFY64F+NXR8AKgdAQcAAFSHgFMwRgoAAKtWy7mHgDPCOqenamlgAID2qOncQ8AZgrU3GKSmzg8ANSPgAACA6hBwBljE6A1/mRsA0Ca1nbM6G3CWeSDnfe7aGlktOC4A0B6dDTjDzDt60zwJso4HANAGNV7AEXAaFhlusD4cBwCYXK01s5MBZ9DBXHS4YfSmLrUWAACoVScDzqIt8uTHiXR+hEsAwKZ1b8C6MS0FAOiqms9hjODMoeaGgbtxnAGgfToXcEr+lBMnUgAAFqPzU1SzIIgAANqu9nNZ50ZwAABA/Qg4U6o98WIjjjeAGnWhthFwAABAdToVcPgbUZgGxxtAjbpS2zoVcOYxbYNY9v0BAMBwrQs4bQoCbdpWAABq0rqAM6t5wgZBBege+j1q1KV23ZmAsy5dakwAAJSitQFnVcGBgAIAqEHXzmetDDiLOEil/ZkGAACwOK0MOP3GBZ5ZA9Gi0m7XUjNQC/ouatHFtlxFwAEWrYvFAIPRFoB2IuAMQVEDANSgq+ez1gecthy4tmwnAAA1aHXAGRYamrfPEiwIIwCaqAlA+7Q64IxDUQIAdFmXz4PVBpwSD2qJ29Q1kxwDjhOAGnS9llUTcLp+IJHw/UZYFmoM0C6b1r0Bi0QBwii0DwDojmpGcBaFkyCAYagPaAvaKgEHHTFpZ6coAEAdCDgAMAVCMNAOBBwg48QFoAbUsoSAAwBAJQg3dyPgFIKPN8+PfYhV4SQClI+A07CKokVhXD32OZaBdgWUjYADAEAFCN0bEXAAAEB1OhlwSl2rUep2ARiMK2agXJ0MOCUj5AAApkXY3hEBBwCAFiPcDEbAAUSBwOxoO0CZCDhrMK4gMk21XJyQsGi0KawLbW+4zgUcwgMGoV0AQF06F3AAAED9CDhrwjTVYrG/sG5MFWDVaHOjEXAKxkkbAIDZEHAAAEB1OhVwxo2IrHrEZJLhRUZxgPZgygCrQlsbr1MBp0SEHADANAg3kyHg9FlHmKCxAgCwWAScAUoMORHBSM6MJt1v7F8sAhcsQBk6FXBsT1x8Sgw5EifhRRm2r9m/WARCDpaFtjW5TgWcnl7QKfG7aAg5synxWAIA1qeTAWcapZ4YS92uGrBvsQhcaQPrRcCZACe8dhg3KjfNceSYAygNoXk6BJwW4yQ8veY+Y1oLyzbNuj9gFNrR9Ag4E1rlyY6GvFyEHACoHwFnCqWFHILQ7JiuAoC6bVr3BmA4AsxyRcTPpxAm/TMeHBPMYpI2BgxD3ZlN50dwpm04FKlu4/hjVpykgNXqfMCRKDy1mfR4ctyxarQ5TIs2MzumqLJeIxp3hU5jaweOE0o1aa0BqGPzIeD0GVR8aGT1Y40EVo2gAywXAWcIQk33cMyxDrQ7YDlYgwMAAKpDwAEAANUh4AAAgOoQcAAAQHUIOAAAoDoEHAAAUB0CDgAAqA4BBwAAVMd8iyYAAKgNIzgAAKA6BBwAAFAdAg4AAKgOAQcAAFSHgAMAAKpDwAEAANUh4AAAgOoQcAAAQHUIOAAAoDoEHAAAUB0CDgAAqA4BBwAAVIeAAwAAqkPAAQAA1SHgAACA6hBwAABAdQg4AACgOgQcAABQHQIOAACoDgEHAABUh4ADAACqQ8ABAADVIeAAAIDq/D8FcI8K+8W2WAAAAABJRU5ErkJggg==\n",
 74 |       "text/plain": [
 75 |        "<Figure size 576x288 with 2 Axes>"
 76 |       ]
 77 |      },
 78 |      "metadata": {},
 79 |      "output_type": "display_data"
 80 |     }
 81 |    ],
 82 |    "source": [
 83 |     "fig, axes = plt.subplots(1, 2, figsize=(8, 4))\n",
 84 |     "ax = axes.ravel()\n",
 85 |     "\n",
 86 |     "ax[0].set_title('Original picture')\n",
 87 |     "ax[0].imshow(image, cmap=plt.cm.gray, interpolation='nearest')\n",
 88 |     "ax[0].set_axis_off()\n",
 89 |     "\n",
 90 |     "ax[1].set_title('Transformed picture - convex hull')\n",
 91 |     "ax[1].imshow(chull, cmap=plt.cm.gray, interpolation='nearest')\n",
 92 |     "ax[1].set_axis_off()\n",
 93 |     "\n",
 94 |     "plt.tight_layout()\n",
 95 |     "plt.show()"
 96 |    ]
 97 |   },
 98 |   {
 99 |    "cell_type": "markdown",
100 |    "metadata": {},
101 |    "source": [
102 |     "### Showing the difference by fixing the pixel intensity on the convex hull"
103 |    ]
104 |   },
105 |   {
106 |    "cell_type": "code",
107 |    "execution_count": 11,
108 |    "metadata": {},
109 |    "outputs": [],
110 |    "source": [
111 |     "chull_diff = img_as_float(chull.copy())\n",
112 |     "chull_diff[image] = 2"
113 |    ]
114 |   },
115 |   {
116 |    "cell_type": "code",
117 |    "execution_count": 12,
118 |    "metadata": {},
119 |    "outputs": [
120 |     {
121 |      "data": {
122 |       "image/png": 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\n",
123 |       "text/plain": [
124 |        "<Figure size 432x288 with 1 Axes>"
125 |       ]
126 |      },
127 |      "metadata": {},
128 |      "output_type": "display_data"
129 |     }
130 |    ],
131 |    "source": [
132 |     "fig, ax = plt.subplots()\n",
133 |     "ax.imshow(chull_diff, cmap=plt.cm.gray, interpolation='nearest')\n",
134 |     "ax.set_title('Difference')\n",
135 |     "plt.show()"
136 |    ]
137 |   }
138 |  ],
139 |  "metadata": {
140 |   "kernelspec": {
141 |    "display_name": "Python 3",
142 |    "language": "python",
143 |    "name": "python3"
144 |   },
145 |   "language_info": {
146 |    "codemirror_mode": {
147 |     "name": "ipython",
148 |     "version": 3
149 |    },
150 |    "file_extension": ".py",
151 |    "mimetype": "text/x-python",
152 |    "name": "python",
153 |    "nbconvert_exporter": "python",
154 |    "pygments_lexer": "ipython3",
155 |    "version": "3.6.2"
156 |   },
157 |   "latex_envs": {
158 |    "LaTeX_envs_menu_present": true,
159 |    "autoclose": false,
160 |    "autocomplete": true,
161 |    "bibliofile": "biblio.bib",
162 |    "cite_by": "apalike",
163 |    "current_citInitial": 1,
164 |    "eqLabelWithNumbers": true,
165 |    "eqNumInitial": 1,
166 |    "hotkeys": {
167 |     "equation": "Ctrl-E",
168 |     "itemize": "Ctrl-I"
169 |    },
170 |    "labels_anchors": false,
171 |    "latex_user_defs": false,
172 |    "report_style_numbering": false,
173 |    "user_envs_cfg": false
174 |   }
175 |  },
176 |  "nbformat": 4,
177 |  "nbformat_minor": 2
178 | }
179 | 


--------------------------------------------------------------------------------
/Finding_contours.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "# Finding contours\n",
  8 |     "### Dr. Tirthajyoti Sarkar, Fremont CA 94536\n",
  9 |     "We use a marching squares method to find constant valued contours in an image. In skimage.measure.find_contours, array values are linearly interpolated to provide better precision of the output contours. Contours which intersect the image edge are open; all others are closed."
 10 |    ]
 11 |   },
 12 |   {
 13 |    "cell_type": "code",
 14 |    "execution_count": 1,
 15 |    "metadata": {},
 16 |    "outputs": [],
 17 |    "source": [
 18 |     "import numpy as np\n",
 19 |     "import matplotlib.pyplot as plt\n",
 20 |     "from skimage import measure"
 21 |    ]
 22 |   },
 23 |   {
 24 |    "cell_type": "markdown",
 25 |    "metadata": {},
 26 |    "source": [
 27 |     "### Construct test data"
 28 |    ]
 29 |   },
 30 |   {
 31 |    "cell_type": "code",
 32 |    "execution_count": 2,
 33 |    "metadata": {},
 34 |    "outputs": [],
 35 |    "source": [
 36 |     "x, y = np.ogrid[-np.pi:np.pi:100j, -np.pi:np.pi:100j]\n",
 37 |     "r = np.sin(np.exp((np.sin(x)**3 + np.cos(y)**2)))"
 38 |    ]
 39 |   },
 40 |   {
 41 |    "cell_type": "code",
 42 |    "execution_count": 4,
 43 |    "metadata": {},
 44 |    "outputs": [
 45 |     {
 46 |      "data": {
 47 |       "image/png": 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\n",
 48 |       "text/plain": [
 49 |        "<Figure size 432x288 with 1 Axes>"
 50 |       ]
 51 |      },
 52 |      "metadata": {},
 53 |      "output_type": "display_data"
 54 |     }
 55 |    ],
 56 |    "source": [
 57 |     "fig, ax = plt.subplots()\n",
 58 |     "ax.imshow(r, interpolation='nearest', cmap=plt.cm.gray)\n",
 59 |     "plt.show()"
 60 |    ]
 61 |   },
 62 |   {
 63 |    "cell_type": "markdown",
 64 |    "metadata": {},
 65 |    "source": [
 66 |     "### Find contours at a fixed threshold level"
 67 |    ]
 68 |   },
 69 |   {
 70 |    "cell_type": "code",
 71 |    "execution_count": 5,
 72 |    "metadata": {},
 73 |    "outputs": [],
 74 |    "source": [
 75 |     "contours = measure.find_contours(r, 0.8)"
 76 |    ]
 77 |   },
 78 |   {
 79 |    "cell_type": "code",
 80 |    "execution_count": 6,
 81 |    "metadata": {},
 82 |    "outputs": [
 83 |     {
 84 |      "data": {
 85 |       "image/png": 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\n",
 86 |       "text/plain": [
 87 |        "<Figure size 432x288 with 1 Axes>"
 88 |       ]
 89 |      },
 90 |      "metadata": {},
 91 |      "output_type": "display_data"
 92 |     }
 93 |    ],
 94 |    "source": [
 95 |     "fig, ax = plt.subplots()\n",
 96 |     "ax.imshow(r, interpolation='nearest', cmap=plt.cm.gray)\n",
 97 |     "for n, contour in enumerate(contours):\n",
 98 |     "    ax.plot(contour[:, 1], contour[:, 0], linewidth=2)\n",
 99 |     "\n",
100 |     "ax.axis('image')\n",
101 |     "ax.set_xticks([])\n",
102 |     "ax.set_yticks([])\n",
103 |     "plt.show()"
104 |    ]
105 |   }
106 |  ],
107 |  "metadata": {
108 |   "kernelspec": {
109 |    "display_name": "Python 3",
110 |    "language": "python",
111 |    "name": "python3"
112 |   },
113 |   "language_info": {
114 |    "codemirror_mode": {
115 |     "name": "ipython",
116 |     "version": 3
117 |    },
118 |    "file_extension": ".py",
119 |    "mimetype": "text/x-python",
120 |    "name": "python",
121 |    "nbconvert_exporter": "python",
122 |    "pygments_lexer": "ipython3",
123 |    "version": "3.6.2"
124 |   },
125 |   "latex_envs": {
126 |    "LaTeX_envs_menu_present": true,
127 |    "autoclose": false,
128 |    "autocomplete": true,
129 |    "bibliofile": "biblio.bib",
130 |    "cite_by": "apalike",
131 |    "current_citInitial": 1,
132 |    "eqLabelWithNumbers": true,
133 |    "eqNumInitial": 1,
134 |    "hotkeys": {
135 |     "equation": "Ctrl-E",
136 |     "itemize": "Ctrl-I"
137 |    },
138 |    "labels_anchors": false,
139 |    "latex_user_defs": false,
140 |    "report_style_numbering": false,
141 |    "user_envs_cfg": false
142 |   }
143 |  },
144 |  "nbformat": 4,
145 |  "nbformat_minor": 2
146 | }
147 | 


--------------------------------------------------------------------------------
/Interpolation - Edge modes.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "# Interpolation: Edge modes\n",
  8 |     "### Dr. Tirthajyoti Sarkar, Fremont, CA 94536\n",
  9 |     "\n",
 10 |     "This example illustrates the different edge modes available during interpolation in routines such as `skimage.transform.rescale()` and `skimage.transform.resize()`."
 11 |    ]
 12 |   },
 13 |   {
 14 |    "cell_type": "code",
 15 |    "execution_count": 1,
 16 |    "metadata": {},
 17 |    "outputs": [],
 18 |    "source": [
 19 |     "import numpy as np\n",
 20 |     "import matplotlib.pyplot as plt\n",
 21 |     "\n",
 22 |     "from skimage.util import pad"
 23 |    ]
 24 |   },
 25 |   {
 26 |    "cell_type": "markdown",
 27 |    "metadata": {},
 28 |    "source": [
 29 |     "### Prepare data"
 30 |    ]
 31 |   },
 32 |   {
 33 |    "cell_type": "code",
 34 |    "execution_count": 2,
 35 |    "metadata": {},
 36 |    "outputs": [],
 37 |    "source": [
 38 |     "img = np.zeros((16, 16))\n",
 39 |     "img[:8, :8] += 1\n",
 40 |     "img[:4, :4] += 1\n",
 41 |     "img[:2, :2] += 1\n",
 42 |     "img[:1, :1] += 2\n",
 43 |     "img[8, 8] = 4"
 44 |    ]
 45 |   },
 46 |   {
 47 |    "cell_type": "markdown",
 48 |    "metadata": {},
 49 |    "source": [
 50 |     "### Show"
 51 |    ]
 52 |   },
 53 |   {
 54 |    "cell_type": "code",
 55 |    "execution_count": 4,
 56 |    "metadata": {},
 57 |    "outputs": [
 58 |     {
 59 |      "data": {
 60 |       "image/png": 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\n",
 61 |       "text/plain": [
 62 |        "<Figure size 720x432 with 6 Axes>"
 63 |       ]
 64 |      },
 65 |      "metadata": {},
 66 |      "output_type": "display_data"
 67 |     }
 68 |    ],
 69 |    "source": [
 70 |     "modes = ['constant', 'edge', 'wrap', 'reflect', 'symmetric']\n",
 71 |     "fig, axes = plt.subplots(nrows=2, ncols=3,figsize=(10,6))\n",
 72 |     "ax = axes.flatten()\n",
 73 |     "\n",
 74 |     "for n, mode in enumerate(modes):\n",
 75 |     "    img_padded = pad(img, pad_width=img.shape[0], mode=mode)\n",
 76 |     "    ax[n].imshow(img_padded, cmap=plt.cm.gray, interpolation='nearest')\n",
 77 |     "    ax[n].plot([15.5, 15.5, 31.5, 31.5, 15.5],\n",
 78 |     "               [15.5, 31.5, 31.5, 15.5, 15.5], 'y--', linewidth=0.5)\n",
 79 |     "    ax[n].set_title(mode)\n",
 80 |     "\n",
 81 |     "for a in ax:\n",
 82 |     "    a.set_axis_off()\n",
 83 |     "    a.set_aspect('equal')\n",
 84 |     "\n",
 85 |     "plt.tight_layout()\n",
 86 |     "plt.show()"
 87 |    ]
 88 |   }
 89 |  ],
 90 |  "metadata": {
 91 |   "kernelspec": {
 92 |    "display_name": "Python 3",
 93 |    "language": "python",
 94 |    "name": "python3"
 95 |   },
 96 |   "language_info": {
 97 |    "codemirror_mode": {
 98 |     "name": "ipython",
 99 |     "version": 3
100 |    },
101 |    "file_extension": ".py",
102 |    "mimetype": "text/x-python",
103 |    "name": "python",
104 |    "nbconvert_exporter": "python",
105 |    "pygments_lexer": "ipython3",
106 |    "version": "3.6.2"
107 |   },
108 |   "latex_envs": {
109 |    "LaTeX_envs_menu_present": true,
110 |    "autoclose": false,
111 |    "autocomplete": true,
112 |    "bibliofile": "biblio.bib",
113 |    "cite_by": "apalike",
114 |    "current_citInitial": 1,
115 |    "eqLabelWithNumbers": true,
116 |    "eqNumInitial": 1,
117 |    "hotkeys": {
118 |     "equation": "Ctrl-E",
119 |     "itemize": "Ctrl-I"
120 |    },
121 |    "labels_anchors": false,
122 |    "latex_user_defs": false,
123 |    "report_style_numbering": false,
124 |    "user_envs_cfg": false
125 |   }
126 |  },
127 |  "nbformat": 4,
128 |  "nbformat_minor": 2
129 | }
130 | 


--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
 1 | MIT License
 2 | 
 3 | Copyright (c) 2019 Tirthajyoti Sarkar
 4 | 
 5 | Permission is hereby granted, free of charge, to any person obtaining a copy
 6 | of this software and associated documentation files (the "Software"), to deal
 7 | in the Software without restriction, including without limitation the rights
 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 | 
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 | 
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
  1 | ### Please feel free to [connect with me here on LinkedIn](https://www.linkedin.com/in/tirthajyoti-sarkar-2127aa7/) if you are interested in data science and machine learning.
  2 | 
  3 | ---
  4 | 
  5 | # Image processing examples with Numpy, Scipy, and Scikit-image
  6 | 
  7 | ### Requirements
  8 | 
  9 | * **Python 3.4+**
 10 | * **NumPy (`$ pip install numpy`)**
 11 | * **SciPy (`$ pip install scipy`)**
 12 | * **MatplotLib (`$ pip install matplotlib`)**
 13 | * **Scikit-image (`$ pip install scikit-image`)**
 14 | 
 15 | ---
 16 | 
 17 | ### Testing after install
 18 | 
 19 | Open a Jupyter notebook and execute the following code,
 20 | ```
 21 | import numpy as np
 22 | import matplotlib.pyplot as plt
 23 | from skimage import data, io, filters
 24 | 
 25 | image = data.coins()  # or any NumPy array!
 26 | edges = filters.sobel(image)
 27 | io.imshow(edges)
 28 | ```
 29 | 
 30 | You should see the following output. If you see this, you are all set to go!
 31 | 
 32 | ![sobel_coins](https://github.com/tirthajyoti/Scikit-image-processing/blob/master/images/sobel_coins.PNG)
 33 | 
 34 | ---
 35 | 
 36 | ### Simple NumPy array based operations
 37 | * [Demo of NumPy based image manipulation](https://github.com/tirthajyoti/Scikit-image-processing/blob/master/Using_Numpy_image_manipulation.ipynb)<br>
 38 | <img src="https://raw.githubusercontent.com/tirthajyoti/Scikit-image-processing/master/images/Numpy_image_manipulation.png" width="700" height="200" />
 39 | 
 40 | * [Block view function and pooling/sampling](https://github.com/tirthajyoti/Scikit-image-processing/blob/master/Block_view_mean_max_median_sampling.ipynb)<br>
 41 | <img src="https://github.com/tirthajyoti/Scikit-image-processing/blob/master/images/block_view_pooling_sampling.PNG" width="350" height="350" />
 42 | 
 43 | * [Zooming (interpolation) based on SciPy](https://github.com/tirthajyoti/Scikit-image-processing/blob/master/Scipy_zooming.ipynb)
 44 | 
 45 | ---
 46 | 
 47 | ### Exposure and color channel manipulations
 48 | * [RGB to gray conversion](https://github.com/tirthajyoti/Scikit-image-processing/blob/master/RGB2Gray.ipynb)<br>
 49 | <img src="https://raw.githubusercontent.com/tirthajyoti/Scikit-image-processing/master/images/rgb2gray.PNG" width="360" height="200" />
 50 | 
 51 | * [RGB to HSV (Hue-Saturation-Value) conversion](https://github.com/tirthajyoti/Scikit-image-processing/blob/master/RGB_to_HSV.ipynb)<br>
 52 | <img src="https://raw.githubusercontent.com/tirthajyoti/Scikit-image-processing/master/images/rgb2hsv.PNG" width="640" height="180" />
 53 | 
 54 | * [Adapting gray-scale filters to RGB images using special decorator](https://github.com/tirthajyoti/Scikit-image-processing/blob/master/Adapt_RGB_decorator.ipynb)
 55 | 
 56 | * [Adjusting contrast by filtering regional maxima](https://github.com/tirthajyoti/Scikit-image-processing/blob/master/Filtering_regional_maxima.ipynb)<br>
 57 | <img src="https://github.com/tirthajyoti/Scikit-image-processing/blob/master/images/filtering_regional_maxima.PNG" width="600" height="170" />
 58 | 
 59 | * [Local Histogram equalization](https://github.com/tirthajyoti/Scikit-image-processing/blob/master/Local_Histogram_Equalization.ipynb)<br>
 60 | <img src="https://github.com/tirthajyoti/Scikit-image-processing/blob/master/images/Local_histogram_equalization.PNG" width="400" height="280" />
 61 | 
 62 | * [Gamma and log contrast](https://github.com/tirthajyoti/Scikit-image-processing/blob/master/Gamma_log_contrast_adjustment.ipynb)
 63 | 
 64 | * [Tinting grayscale images](https://github.com/tirthajyoti/Scikit-image-processing/blob/master/Tint_Grayscale.ipynb)<br>
 65 | <img src="https://github.com/tirthajyoti/Scikit-image-processing/blob/master/images/tint_grayscale.PNG" width="400" height="280" />
 66 | 
 67 | ---
 68 | 
 69 | ### Edges, lines, and contours
 70 | 
 71 | * [Finding contours](https://github.com/tirthajyoti/Scikit-image-processing/blob/master/Finding_contours.ipynb)<br>
 72 | <img src="https://github.com/tirthajyoti/Scikit-image-processing/blob/master/images/finding_contours.png" width="600" height="240" />
 73 | 
 74 | * [Convex Hull of an image](https://github.com/tirthajyoti/Scikit-image-processing/blob/master/Convex_Hull.ipynb)<br>
 75 | <img src="https://github.com/tirthajyoti/Scikit-image-processing/blob/master/images/convex_hull.PNG" width="600" height="240" />
 76 | 
 77 | * [Skeletonize 2-D and 3-D images](https://github.com/tirthajyoti/Scikit-image-processing/blob/master/Skeletonize.ipynb)<br>
 78 | <img src="https://github.com/tirthajyoti/Scikit-image-processing/blob/master/images/skeletonize.png" width="360" height="280" />
 79 | 
 80 | * [Marching cubes](https://github.com/tirthajyoti/Scikit-image-processing/blob/master/Marching_cubes.ipynb)<br>
 81 | <img src="https://github.com/tirthajyoti/Scikit-image-processing/blob/master/images/marching_cubes.PNG" width="400" height="400" />
 82 | 
 83 | * [Edge operators](https://github.com/tirthajyoti/Scikit-image-processing/blob/master/Edge_operators.ipynb)<br>
 84 | <img src="https://github.com/tirthajyoti/Scikit-image-processing/blob/master/images/edge_operators.png" width="600" height="400" />
 85 | 
 86 | ---
 87 | 
 88 | ### Geometrical transformations and registration
 89 | 
 90 | * [Swirl](https://github.com/tirthajyoti/Scikit-image-processing/blob/master/Swirl.ipynb)<br>
 91 | <img src="https://github.com/tirthajyoti/Scikit-image-processing/blob/master/images/Swirl.png" width="400" height="200" />
 92 | 
 93 | * [Interpolation - edge modes](https://github.com/tirthajyoti/Scikit-image-processing/blob/master/Interpolation%20-%20Edge%20modes.ipynb)<br>
 94 | <img src="https://github.com/tirthajyoti/Scikit-image-processing/blob/master/images/interpolation_edge_mode.PNG" width="400" height="200" />
 95 | 
 96 | * [Rescale, resize, downscale](https://github.com/tirthajyoti/Scikit-image-processing/blob/master/Rescale_resize_downscale.ipynb)
 97 | 
 98 | * [Histogram matching](https://github.com/tirthajyoti/Scikit-image-processing/blob/master/Histogram_matching.ipynb)<br>
 99 | <img src="https://github.com/tirthajyoti/Scikit-image-processing/blob/master/images/histogram_matching.PNG" width="400" height="130" />
100 | 
101 | * [Structural similarity index](https://github.com/tirthajyoti/Scikit-image-processing/blob/master/Structural_similarity_index.ipynb)
102 | 
103 | ### Filtering and restoration
104 | 
105 | [Hysteresis thresholding](https://github.com/tirthajyoti/Scikit-image-processing/blob/master/Hysteresis_thresholding.ipynb)<br>
106 | <img src="https://github.com/tirthajyoti/Scikit-image-processing/blob/master/images/Hysteresis_thresholding.PNG" width="400" height="400" />
107 | 
108 | [Image deconvolution](https://github.com/tirthajyoti/Scikit-image-processing/blob/master/Image_deconvolution.ipynb)
109 | 
110 | [Unsharp mask](https://github.com/tirthajyoti/Scikit-image-processing/blob/master/Unsharp_mask.ipynb)<br>
111 | <img src="https://github.com/tirthajyoti/Scikit-image-processing/blob/master/images/unsharp_mask.PNG" width="400" height="400" />
112 | 


--------------------------------------------------------------------------------
/Random_shapes.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "# Generating random shapes\n",
  8 |     "### Dr. Tirthajyoti Sarkar, Fremont, CA 94536\n",
  9 |     "\n",
 10 |     "This notebook demonstrates generation of random shapes with various properties.<br>\n",
 11 |     "It uses the `random_shapes()` function from `skimage.draw`."
 12 |    ]
 13 |   },
 14 |   {
 15 |    "cell_type": "code",
 16 |    "execution_count": 1,
 17 |    "metadata": {},
 18 |    "outputs": [],
 19 |    "source": [
 20 |     "import matplotlib.pyplot as plt\n",
 21 |     "\n",
 22 |     "from skimage.draw import random_shapes"
 23 |    ]
 24 |   },
 25 |   {
 26 |    "cell_type": "markdown",
 27 |    "metadata": {},
 28 |    "source": [
 29 |     "### Let's start simple and generate a 128x128 image with a single grayscale rectangle.\n",
 30 |     "We get back a tuple consisting of \n",
 31 |     "1. The image with the generated shapes and \n",
 32 |     "2. A list of label tuples with the kind of shape (e.g. circle, rectangle) and ((r0, r1), (c0, c1)) coordinates."
 33 |    ]
 34 |   },
 35 |   {
 36 |    "cell_type": "code",
 37 |    "execution_count": 2,
 38 |    "metadata": {},
 39 |    "outputs": [],
 40 |    "source": [
 41 |     "rect = random_shapes((128, 128), max_shapes=1, shape='rectangle',\n",
 42 |     "                       multichannel=False)"
 43 |    ]
 44 |   },
 45 |   {
 46 |    "cell_type": "code",
 47 |    "execution_count": 17,
 48 |    "metadata": {},
 49 |    "outputs": [
 50 |     {
 51 |      "name": "stdout",
 52 |      "output_type": "stream",
 53 |      "text": [
 54 |       "Image shape: (128, 128)\n",
 55 |       "Labels: [('rectangle', ((45, 69), (20, 52)))]\n"
 56 |      ]
 57 |     }
 58 |    ],
 59 |    "source": [
 60 |     "image, labels = rect\n",
 61 |     "print('Image shape: {}\\nLabels: {}'.format(image.shape, labels))"
 62 |    ]
 63 |   },
 64 |   {
 65 |    "cell_type": "code",
 66 |    "execution_count": 18,
 67 |    "metadata": {},
 68 |    "outputs": [
 69 |     {
 70 |      "data": {
 71 |       "image/png": 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h7bim8CXg5+n1RGBN3bKBNO8tJM2V1C+pv1artaEMM2uHUqEg6ZvAJuCawVlNmkWzdSNifkT0RURfb29vmTLMrI2GHYp+KJJmA8cBM9IQ9FD0DCbXNZsEPN16eWZWtZZ6CpKOBs4CPh0RL9ctWgKcIGmMpKnANOC35cs0s6oM21OQdC1wBDBe0gBwDsW3DWOAWyUB3BkRJ0fEg5JuAB6iOK04NSJe61TxZtZ+erPn3z19fX3R39/f7TLMtmmSlkdE33DtfEejmWUcCmaWcSiYWcahYGYZh4KZZRwKZpZxKJhZZkTcpyCpBrwEPNvtWoDxuI56riO3NdexT0QM+x+NRkQoAEjq35IbK1yH63Adna3Dpw9mlnEomFlmJIXC/G4XkLiOnOvIbfN1jJhrCmY2MoyknoKZjQAOBTPLjIhQkHR0GidilaSzK9rnZEm3SVoh6UFJp6f5u0u6VdLK9DyuonpGS7pH0k1peqqkZamO6yVtX0ENYyUtSmN6rJB0aDeOh6Svpp/JA5KulbRDVcdjiHFOmh4DFS5Jn9v7JB3c4ToqGW+l66GQxoX4IXAM8AHgxDR+RKdtAs6IiPcD04FT037PBpZGxDRgaZquwunAirrp84ALUx3PA3MqqOFi4BcR8T7ggFRPpcdD0kTgNKAvIvYHRlOMJVLV8VjAW8c5GeoYHEPxJwenAXOByzpcRzXjrUREVx/AocAtddPzgHldqGMx8EngEWBCmjcBeKSCfU+i+LAdCdxE8VexnwV6mh2jDtWwK7CadPG5bn6lx4M3hwnYneLPBd4EHFXl8QCmAA8MdwyAHwMnNmvXiToalv0lcE16nf3OALcAh7a63673FHgbY0V0iqQpwEHAMmCviFgLkJ73rKCEi4AzgdfT9B7AhnhzwJ0qjsm+QA24Kp3GXC5pJyo+HhHxFHA+8CSwFtgILKf641FvqGPQzc9uS+OtbImREApbPFZER3Yu7Qz8DPhKRLxQ1X7r9n8csD4iltfPbtK008ekBzgYuCwiDqL4vyhVnTq9IZ2vzwSmAnsDO1F00xuNhO/Su/LZLTPeypYYCaHQtbEiJG1HEQjXRMSNafY6SRPS8gnA+g6XcTjwaUm/A66jOIW4CBgrafCvbVdxTAaAgYhYlqYXUYRE1cfjE8DqiKhFxKvAjcBhVH886g11DCr/7NaNt3JSpHOFdtcxEkLhLmBaurq8PcUFkyWd3qmKv01/BbAiIi6oW7QEmJ1ez6a41tAxETEvIiZFxBSK9/7LiDgJuA04vsI6ngHWSHpvmjWD4k/1V3o8KE4bpkvaMf2MBuuo9Hg0GOoYLAG+kL6FmA5sHDzN6ITKxlvp5EWjt3FB5ViKq6mPAd+saJ8fpehi3Qfcmx7HUpzPLwVWpufdKzwORwA3pdf7ph/sKuCnwJgK9n8g0J+OyX8B47pxPIDvAA8DDwA/oRhjpJLjAVxLcS3jVYp/gecMdQwouu0/TJ/b+ym+MelkHasorh0Mfl5/VNf+m6mOR4BjyuzbtzmbWWYknD6Y2QjiUDCzjEPBzDIOBTPLOBTMLONQMLOMQ8HMMv8PMSiC222YdvIAAAAASUVORK5CYII=\n",
 72 |       "text/plain": [
 73 |        "<Figure size 288x288 with 1 Axes>"
 74 |       ]
 75 |      },
 76 |      "metadata": {},
 77 |      "output_type": "display_data"
 78 |     }
 79 |    ],
 80 |    "source": [
 81 |     "fig, ax = plt.subplots(nrows=1, ncols=1,figsize=(4,4))\n",
 82 |     "ax.imshow(image, cmap='gray')\n",
 83 |     "ax.set_title('Grayscale shape')\n",
 84 |     "plt.show()"
 85 |    ]
 86 |   },
 87 |   {
 88 |    "cell_type": "markdown",
 89 |    "metadata": {},
 90 |    "source": [
 91 |     "### The generated images can be much more complex \n",
 92 |     "For example, let's try many shapes of any color. If we want the colors to be particularly light, we can set the `intensity_range` to an upper subrange of (0,255)."
 93 |    ]
 94 |   },
 95 |   {
 96 |    "cell_type": "code",
 97 |    "execution_count": 19,
 98 |    "metadata": {},
 99 |    "outputs": [],
100 |    "source": [
101 |     "image1, _ = random_shapes((128, 128), max_shapes=10,\n",
102 |     "                          intensity_range=((100, 255),))\n",
103 |     "image2, _ = random_shapes((128, 128), max_shapes=10,\n",
104 |     "                          intensity_range=((200, 255),))\n",
105 |     "image3, _ = random_shapes((128, 128), max_shapes=10,\n",
106 |     "                          intensity_range=((50, 255),))\n",
107 |     "image4, _ = random_shapes((128, 128), max_shapes=10,\n",
108 |     "                          intensity_range=((0, 255),))"
109 |    ]
110 |   },
111 |   {
112 |    "cell_type": "code",
113 |    "execution_count": 23,
114 |    "metadata": {},
115 |    "outputs": [
116 |     {
117 |      "data": {
118 |       "image/png": 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\n",
119 |       "text/plain": [
120 |        "<Figure size 576x576 with 4 Axes>"
121 |       ]
122 |      },
123 |      "metadata": {},
124 |      "output_type": "display_data"
125 |     }
126 |    ],
127 |    "source": [
128 |     "fig, axes = plt.subplots(nrows=2, ncols=2,figsize=(8,8))\n",
129 |     "ax = axes.ravel()\n",
130 |     "for i, image in enumerate([image1, image2, image3, image4], 0):\n",
131 |     "    ax[i].imshow(image)\n",
132 |     "    ax[i].set_title('Colored shapes, #{}'.format(i-1))\n",
133 |     "plt.show()"
134 |    ]
135 |   },
136 |   {
137 |    "cell_type": "markdown",
138 |    "metadata": {},
139 |    "source": [
140 |     "### Overlapping shapes\n",
141 |     "These shapes are well suited to test segmentation algorithms. Often, we want shapes to overlap to test the algorithm."
142 |    ]
143 |   },
144 |   {
145 |    "cell_type": "code",
146 |    "execution_count": 32,
147 |    "metadata": {},
148 |    "outputs": [],
149 |    "source": [
150 |     "image, _ = random_shapes((128, 128), min_shapes=5, max_shapes=6,\n",
151 |     "                         min_size=20, allow_overlap=True)"
152 |    ]
153 |   },
154 |   {
155 |    "cell_type": "code",
156 |    "execution_count": 33,
157 |    "metadata": {},
158 |    "outputs": [
159 |     {
160 |      "data": {
161 |       "image/png": 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ebzqrA4ARaEqYW003qEVtRKMwUwLQSrxPCXOLptJONCUAqRBKAFIhlACkQigBSIVQApAKoQQgFUIJQCqEEoBUCCUAqRBKAFIhlACkQigBSGVVv7rEdk/S/0g6VNuKpmej2rFOqT1rbcs6pfastS3rlNa+1q0RsTRup1WFkiTZ7k7yO1Ga1pZ1Su1Za1vWKbVnrW1ZpzS7tXL6BiAVQglAKicSSitTX0U92rJOqT1rbcs6pfastS3rlGa01lXPlACgTpy+AUiFUAKQCqEEIBVCCUAqhBKAVP4PTkriMhBInxEAAAAASUVORK5CYII=\n",
162 |       "text/plain": [
163 |        "<Figure size 360x360 with 1 Axes>"
164 |       ]
165 |      },
166 |      "metadata": {},
167 |      "output_type": "display_data"
168 |     }
169 |    ],
170 |    "source": [
171 |     "fig, ax = plt.subplots(nrows=1, ncols=1,figsize=(5,5))\n",
172 |     "ax.imshow(image)\n",
173 |     "ax.set_title('Overlapping shapes')\n",
174 |     "ax.set_xticklabels([])\n",
175 |     "ax.set_yticklabels([])\n",
176 |     "\n",
177 |     "plt.show()"
178 |    ]
179 |   }
180 |  ],
181 |  "metadata": {
182 |   "kernelspec": {
183 |    "display_name": "Python 3",
184 |    "language": "python",
185 |    "name": "python3"
186 |   },
187 |   "language_info": {
188 |    "codemirror_mode": {
189 |     "name": "ipython",
190 |     "version": 3
191 |    },
192 |    "file_extension": ".py",
193 |    "mimetype": "text/x-python",
194 |    "name": "python",
195 |    "nbconvert_exporter": "python",
196 |    "pygments_lexer": "ipython3",
197 |    "version": "3.6.2"
198 |   },
199 |   "latex_envs": {
200 |    "LaTeX_envs_menu_present": true,
201 |    "autoclose": false,
202 |    "autocomplete": true,
203 |    "bibliofile": "biblio.bib",
204 |    "cite_by": "apalike",
205 |    "current_citInitial": 1,
206 |    "eqLabelWithNumbers": true,
207 |    "eqNumInitial": 1,
208 |    "hotkeys": {
209 |     "equation": "Ctrl-E",
210 |     "itemize": "Ctrl-I"
211 |    },
212 |    "labels_anchors": false,
213 |    "latex_user_defs": false,
214 |    "report_style_numbering": false,
215 |    "user_envs_cfg": false
216 |   }
217 |  },
218 |  "nbformat": 4,
219 |  "nbformat_minor": 2
220 | }
221 | 


--------------------------------------------------------------------------------
/Shapes_drawing.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "# Drawing shapes\n",
  8 |     "### Dr. Tirthajyoti Sarkar, Fremont CA 94536\n",
  9 |     "This example shows how to draw several different shapes:\n",
 10 |     "\n",
 11 |     "* line\n",
 12 |     "* Bezier curve\n",
 13 |     "* polygon\n",
 14 |     "* circle\n",
 15 |     "* ellipse\n",
 16 |     "\n",
 17 |     "Anti-aliased drawing for:\n",
 18 |     "\n",
 19 |     "* line\n",
 20 |     "* circle"
 21 |    ]
 22 |   },
 23 |   {
 24 |    "cell_type": "code",
 25 |    "execution_count": 1,
 26 |    "metadata": {},
 27 |    "outputs": [],
 28 |    "source": [
 29 |     "import math\n",
 30 |     "import numpy as np\n",
 31 |     "import matplotlib.pyplot as plt\n",
 32 |     "\n",
 33 |     "from skimage.draw import (line, polygon, circle,\n",
 34 |     "                          circle_perimeter,\n",
 35 |     "                          ellipse, ellipse_perimeter,\n",
 36 |     "                          bezier_curve)\n",
 37 |     "from skimage.draw import line_aa, circle_perimeter_aa"
 38 |    ]
 39 |   },
 40 |   {
 41 |    "cell_type": "markdown",
 42 |    "metadata": {},
 43 |    "source": [
 44 |     "### Blank canvas"
 45 |    ]
 46 |   },
 47 |   {
 48 |    "cell_type": "code",
 49 |    "execution_count": 2,
 50 |    "metadata": {},
 51 |    "outputs": [],
 52 |    "source": [
 53 |     "img = np.zeros((500, 500, 3), dtype=np.double)\n",
 54 |     "img = np.zeros((500, 500, 3))"
 55 |    ]
 56 |   },
 57 |   {
 58 |    "cell_type": "markdown",
 59 |    "metadata": {},
 60 |    "source": [
 61 |     "### Draw line"
 62 |    ]
 63 |   },
 64 |   {
 65 |    "cell_type": "code",
 66 |    "execution_count": 3,
 67 |    "metadata": {},
 68 |    "outputs": [],
 69 |    "source": [
 70 |     "rr, cc = line(120, 123, 20, 400)\n",
 71 |     "img[rr, cc, 0] = 255"
 72 |    ]
 73 |   },
 74 |   {
 75 |    "cell_type": "markdown",
 76 |    "metadata": {},
 77 |    "source": [
 78 |     "### Fill polygon"
 79 |    ]
 80 |   },
 81 |   {
 82 |    "cell_type": "code",
 83 |    "execution_count": 4,
 84 |    "metadata": {},
 85 |    "outputs": [],
 86 |    "source": [
 87 |     "poly = np.array((\n",
 88 |     "    (300, 300),\n",
 89 |     "    (480, 320),\n",
 90 |     "    (380, 430),\n",
 91 |     "    (220, 590),\n",
 92 |     "    (300, 300),\n",
 93 |     "))\n",
 94 |     "\n",
 95 |     "rr, cc = polygon(poly[:, 0], poly[:, 1], img.shape)\n",
 96 |     "img[rr, cc, 1] = 1"
 97 |    ]
 98 |   },
 99 |   {
100 |    "cell_type": "markdown",
101 |    "metadata": {},
102 |    "source": [
103 |     "### Fill circle"
104 |    ]
105 |   },
106 |   {
107 |    "cell_type": "code",
108 |    "execution_count": 5,
109 |    "metadata": {},
110 |    "outputs": [],
111 |    "source": [
112 |     "rr, cc = circle(200, 200, 100, img.shape)\n",
113 |     "img[rr, cc, :] = (1, 1, 0)"
114 |    ]
115 |   },
116 |   {
117 |    "cell_type": "markdown",
118 |    "metadata": {},
119 |    "source": [
120 |     "### Fill ellipse"
121 |    ]
122 |   },
123 |   {
124 |    "cell_type": "code",
125 |    "execution_count": 6,
126 |    "metadata": {},
127 |    "outputs": [],
128 |    "source": [
129 |     "rr, cc = ellipse(300, 300, 100, 200, img.shape)\n",
130 |     "img[rr, cc, 2] = 1"
131 |    ]
132 |   },
133 |   {
134 |    "cell_type": "markdown",
135 |    "metadata": {},
136 |    "source": [
137 |     "### Circle"
138 |    ]
139 |   },
140 |   {
141 |    "cell_type": "code",
142 |    "execution_count": 7,
143 |    "metadata": {},
144 |    "outputs": [],
145 |    "source": [
146 |     "rr, cc = circle_perimeter(120, 400, 15)\n",
147 |     "img[rr, cc, :] = (1, 0, 0)"
148 |    ]
149 |   },
150 |   {
151 |    "cell_type": "markdown",
152 |    "metadata": {},
153 |    "source": [
154 |     "### Bezier curve"
155 |    ]
156 |   },
157 |   {
158 |    "cell_type": "code",
159 |    "execution_count": 8,
160 |    "metadata": {},
161 |    "outputs": [],
162 |    "source": [
163 |     "rr, cc = bezier_curve(70, 100, 10, 10, 150, 100, 1)\n",
164 |     "img[rr, cc, :] = (1, 0, 0)"
165 |    ]
166 |   },
167 |   {
168 |    "cell_type": "markdown",
169 |    "metadata": {},
170 |    "source": [
171 |     "### Ellipses"
172 |    ]
173 |   },
174 |   {
175 |    "cell_type": "code",
176 |    "execution_count": 9,
177 |    "metadata": {},
178 |    "outputs": [],
179 |    "source": [
180 |     "rr, cc = ellipse_perimeter(120, 400, 60, 20, orientation=math.pi / 4.)\n",
181 |     "img[rr, cc, :] = (1, 0, 1)\n",
182 |     "rr, cc = ellipse_perimeter(120, 400, 60, 20, orientation=-math.pi / 4.)\n",
183 |     "img[rr, cc, :] = (0, 0, 1)\n",
184 |     "rr, cc = ellipse_perimeter(120, 400, 60, 20, orientation=math.pi / 2.)\n",
185 |     "img[rr, cc, :] = (1, 1, 1)"
186 |    ]
187 |   },
188 |   {
189 |    "cell_type": "code",
190 |    "execution_count": 10,
191 |    "metadata": {},
192 |    "outputs": [
193 |     {
194 |      "name": "stderr",
195 |      "output_type": "stream",
196 |      "text": [
197 |       "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
198 |      ]
199 |     },
200 |     {
201 |      "data": {
202 |       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAF3CAYAAACvy1BzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAFYRJREFUeJzt3WuofGtdB/DfYwcTg8KSiqSIyC7UiwwDA6uTWcjpQioVkZjUmyCyyxuLDDM7gkGWvQuEI5RYdpUuaGjWKQqspMwXdbpLHUw0K4LUk65ezMx/z5491zXr8jy/9fnA9jh7z39mzVrP853f/NZlStd1AUA+j5t7AQAYh4AHSErAAyQl4AGSEvAASQl4gKQEPNUrpXxzKeXhUsr7Sin/W0r5l1LKb5ZSnrN1nxeVUrpSyufOuaxQEwFP1UopL46I34iIv4uI746Ir4+In1z/+VlzLRe0oDjRiZqVUt4TEX/Rdd1z9/ztcV3XfWz9/18UEQ9FxFO7rvv7aZcS6qSCp3afHBHv3feHTbjveHIp5fWllP8upTxaSvm5UsoTtu9QSnl5KeWdpZT/KqW8v5Ty+6WUZ+zc5/51y+f5pZTXlVI+uH7M15dSPmXnvveVUn6klPI3pZQPr5/3p7efd32fV5RS/qGU8qH18/5xKeWZ/VcNHHff3AsAJ7wjIr6zlPKPEfGmruseOXH/X4iIN0TE8yLiyyPixyPigxHxsq37PCUifiYi/jUiPiEiXhARD5dSnt513bt2Hu9nI+KtEfHtEfHUiHhlRHxGRHz11n1+MSK+MSJeFRF/EhFfGBGviIjPjojnr+/zkoj4wYj40Yj4y4j4xIh4eqzewGAcXdf58VPtT0R8XkS8KyK69c/7YxXgX7dzvxet//7ynd//dkQ8cuTxPy5Whc7fRsRrtn5///rx3rxz/+9Y//5r1re/Yn37hQfu9yVby/Hrc69PP8v60aKhat2qYn9aRHxVRDwYq+r3uRHxllLKS/f8k9/Zuf3XEfFZ278opTy7lPL2UsoHIuL/IuKxWL2RfP6ex3vjzu1fiYiPxerTQUTEcyLiIxHxa+s2zH2llPsi4vfWf//K9X//LCIeKKU8WEp5Zinl8cdeNwxBwFO9rus+2nXdw13XvbTrumdHxOfEKrhfVkp50s7d/2Pn9ocj4uM3N0opXxoRvxsR/xOro3KeERFfFhF/FRFPiLv+fWdZPhKrls9T1r/61Ih4/PrxHtv6ed/675t+/Stj1Sb6poj4o4j4QCnloVLKk0+uAOhJD57mdF33aCnltRHxmlj1xd9xwT9/fqyq9ud1XffY5pfrN4r/3HP/T9u+sa68nxQR/7b+1Qci4kOxatXs8+h6mR+LVY/+VaWUT4+Ib4iIV0fEEyPi2y5YfjibCp6qlVI+88CfvmD9371H2BzxxIj4aKz645vneFbstHG2fOvO7W+J1bz50/XtN8eq8v+kruv+fM/Po7sP2HXde7uue22sdt5+8YXLD2dTwVO7d5dS3h6rk53+KVZHnzwQEd8TEW/suu49Fz7emyPiByLidaWUh2LVe/+xuKnId33R+n6/tL7vgxHxh13XvS0iouu6PyilvCEifrWU8upYfZr4WKyOoHkgIl7Sdd0jpZQ3xaoN9M5YtXieFqv+/c9fuPxwNgFP7V4Sq6D8iVi1Sz4aEY9ExA/H6hDGi3Rd95b12bE/FKt2zbsj4oURsW+HbUTE98eqb/7LsTri5rci4sU793lBRHxfRHxXrA6D/HBE/HNEvCVuevgPx6r6/95YfYp4T0T8VKzeMGAUzmSFPUop90fE2yPia7uue+vMiwO96MEDJCXgAZLSogFISgUPkJSAB0iqisMkSyn6RAA7uq4r1/x7FTxAUgIeICkBD5CUgAdISsADJCXgAZIS8ABJCXiApAQ8QFICHiApAQ+QlIAHquPiVMMQ8EAVtkP9qitscU8VV5MElmsT7EJ9eAIemNRuoAv28Qh4YBIq9ekJeGA026Eu2Kcn4IHBqdbrIOCBq+mr10nAA72p1Osm4IGzqdTbIuCBs3Qh0FvjTFZgry6cXdo6FTxwi756Hip44E6lLtxzEPCwQNovy6BFAwui/bIsAh6Sc7mA5RLwkJRqHT14SMTOUrap4CEB1Tr7CHhokEsGcA4Bf8K+L/81mZiLSp1LCPgDNtfdODaRTDbGplLnGgL+gHMm0uY+LsLEGIwrruUomj32tWWO2Q76S/8tbDi7lKGp4AekoqcPrT7GIuBHsB3027chQl+d6Qj4Eano2WUsMCU9+AnsVvQsh746c1LBT6iECm4ptOeogQp+jzEnpWo+L9eBoTYCfiaCvn3aL9ROwB8wVfBu2ja0YxPsqnRqpwdfASFRP1+aQYsEPBxhZyktE/AHOOJlmZyEdI42r7Ha7Vnu0sByX0PAw5o39FOOfZ6pc3fzdqjvC/PN37MGvYA/IucmZ0O1fqlja+hQ6E+/Vrvo7gX2qeDOGuwbjqI5wREu+TgK5lJ9Z8Gm0TndLNoO9z7/NhsBT3r7jlcX7FPZXtvjBOipNsySCfgTHKfeLpX6UIZae8PPpmsq9l0lSroqXsCfQTi0Q6Veu2Gq+ew7R4diJyspOF69NdvV/PlbTbBfRsCfySF0dXEETAaXfTXOkO2YpRDwZ3LiUz1sh2xOb03h3o8e/AUMr3m4auPcptzx2O3cmq4lk/FNRAVPtfTVl+jms3LGwJ2aCv5CuQ6iqo+jYBDuwxHwFzLkhqX90oLptsqcR8lkfEMR8D2o4q/nJCR2Ha7azbi+BHwPzm7tZ7taF+ytGXfEH2/JTLODNSMBz+hU6xmMV9ac32+f+/nb4yiannIOh2E4CSmry7dk1+0P5VJudqSeH67bJ0aVk49/rqzhHiHgr+KEm9tOHdZ4YC72csH8ZUK7gXssaPsH682/O/T4lyxHZuXQO+CkC1HK/AvR09JDfvf1zz2cFjqPJ7Q+Rn1rQ18ansO1RPrNvs2yb3+KqFXXdVctnAqeXnar9bmDfWOzHIJ+HKv1211VEQ8XqP0eZ7Psm6Dvrnw9NRPwV8o5LO66037pbv++NvvecJLO4dF13U0AXhOEp6rlQy2+02c096vk972W7deagYAfQOY2Ta2Veh9dJ+Qvsd3K2PPXuHTUHwr3UwF+d9fqoXuc79CbTSnlxOtuix78QDKFfG199bEkmL8zu37U93mEw//mvEerve++7doevOPgB9LGcDlu+3j1e79LGu4Rq9eW+fVdquu6g4cdXvW4Bxp5fd8ejh2XM4ax1ssUtGgG1FoVf+h49UbHcm92zPbtPZ/+loRD1fIQc2X/Y/RbnmO2d8q21rZRwS/UoTNLlxbu25b62scMrulbIeOdcbvdn2+FgB9QzdeoOXXVRu2KlaWth+vD/VA3/PBKHOqT7vFWzd3nH6L33lrIa9EMrLYPcFOeXZrJEto2w1but2N7/p2Yt9tHLe1YHZIKfgRzZ+a5X5oh3E/LXNEP25a5qZpPXZlxyGc995mGDPeW+vACfgRTt2ou/dKMzKE1FuvrHKuRX1Ol3N2ZHcsi4Ecy5RC/5DK8gqo/6+647l5LpJ4VVRZ+kWoBP6J6hvmKgLpepk8/Q+4svN3jriNQb7eKBnytDQ0AAT+immqZhsYkDbrbljl+FM1wz3voOXZbRcPNRj147pl7KGSqOGuSYb0OEVSHd6jON/IPHzFzXci3VLlvCPgJzDUsGhyPzcmwjscNrruPPVQtve8xxjwcsqXKfUPAT2COVk2G4GlF6+t69/ro5zovTPeP/nEuU3DO4ZCXz8YWK/cNAZ9Qw+OxWRnW+XhnaR6/TPBQj3jq+Ps+Wrz+zDYBP5GpqvgMQdOqDOt+u5o/FvaDnPbf498cW8WXfXn3kdd2xdcR1kbAJ5IhYFqXZRuUUo5W9P3C/fBlg89ZbYfaMv0q9/3f5tR6xb7LF34kUsGmZC1RRtwzVWW7O4zHeqYWvrnJl24TEcKd8ZVS7rVmrgvH49eTPPus7B5tortvUrknjgp+BkN/MUgFm5A9Ki4MR7EvS+aojneXo+YK/RQVfIOE+zIs7Uu+9wXp8dDvV+rsfoo4ZzmWSgU/k0G+smxxa61NWfJmvJOIzpsNcz//HHzpdqPyd//IZNwvzDhvNsz9/C0S8DO6Zriq3tuRYVuNf433wyE7xglM+58/HwE/sz5DN0NgLI1tdo7t3vwq2Jf6VXtDEfAzy/vhkCymqaC33Xwz1LThnm8mCvgKXDKEVYLtynCJ4SnU+M1QrRLwlTCUYd83Q5kZ1xDwlTinilf9MYcp2yR3n+t2X37sZ8/2hiLgGyHc87Atb9vsTD0uX/hOwZmsMIOlneW6zybU+13md+Er70wCHphc/8Mfr7vMwdJo0TTAR3qyuLxqP2TK3ny7BHzlhHterWzboY6DH+ekpSF78/k+FQh4mFErId/X9g7Uca8lc/OM3NCDB47qG8zzXGZgU9Ff09/PQ8BXLHt1R1uOVeLbbZzN3+e7hsyhin7/N7oe/lv7BDxwlk1g7+vJ13tBsFPtm1qXexgCvlKq9+Vo7Zj4esP8lFaXuz87WQGSEvBQAZ/YGIOAB0hKwFdINQcMQcADJCXgK6N6Xy7bnqEJeICkBDxAUgIeKqJNw5AEPEBSAh4gKQEPkJSAr4j+KzAkAQ+QlIAHSErAAyQl4AGSEvAASQl4gKQEPEBSAh4gqfvmXgDIo8Yz1crcC8CMBDxcpMYQP+bY8gr/7AQ8NBfaQ7nkdXszaJGAZ4GWGujX2F1nAr8FAp6EBPj4Tq1jbwA1EPAkIdTrsr09hP1cBDyNEeTtObTNBP/YBDyNEOz5bLapoB+LgKdCwnxZ9m1voT8EAU8lhDrb9PCHIOArUsrSvrZvUS+W3oR9XwKeGQh2+tK3v4SAZwICnaE58eocAp4RCfZLFTnVk8p+HwHPgAQ6c1PZbxPwDECwU6tlV/a+8KMybX1E70K4D6etbd+aZY5VFTwXWt4kIZNlHXKpgq9QvZWccCeT/ONZBc8J+ScBS5a7olfBc4RwZ0nyjXcVPDvyDfIW1NuWW5pcFb0Kni3CHW60Px8EfKWmreiWeQhZLVTvNWt7bgj4ik0z8dsdvDCdNueJHvxitTlgYT7tnRWrgl8k4V4L7ZkWtTN/BHzlhg2AtvuJ2Qj3lrUxlwT8YtQ/GKE9dc8rAd+A6yq9NioNaFe9c8xO1tTqHHRoz+TUxSA7YAectir4RlweCMK9VsI9sx7zrtv5GZAKviGlRHQnB4Bgh3kdOZxy4umpgk9FuEM9utv/d4bpqYJvzOEqXrjXTmtmIbrdG/NteAGfgnCHWR2dgvOFvBZNg25XgsK9Bar3ZC7eMTrPPFXBN024w2Sunm7TV/Iq+EaVItxboXpv1CiHL047bwV8wwRH/Wyjxox0PPrdJ5mGFk2TbgbIecfGMwfhXrkFzBsVfHMWMCphTFVcOmaaBVDBN2X/oNhUiir5eqjeK1H1nBh/p6sKvhlVj1SoSxVV+jnGXUgVfCL68fNTuc/AmD9IBd+E80ewgGExmqnSTxnvRajgq3f5xlfJT88b6wRSj+lx+vEq+KQEDilMclx6Xir4ql03qlXy4/NGOqDFj9Xhq3gVfHICiOqp0Eejgq/WcCPecfLD88bZkzF4wrBVvIBfEC2b6wn2Cxlvs9KiWRgB1Z91dyY7Rquhgq/SuDNDy+Zywv0AY2gEw7VpVPDVmW7GCK3TSrGe9lKhj2yYlauCXzjV/H5CfYux0SwBD+wn2Jsn4ImI2xXrUqv5RVftC93m2enBc8cSg26Jr9nRLvmp4KtSz0xbQkW/uFBPuh3zun6DqeA5KWMQZnxNe6nSF00Fz1l2A7HFqn4Rod7gdmE8Ap5eWjq8UrCzVAKeq9RW2acPc0HOBQQ8g5pj52z6UI8Q7PQi4BnNIoJ3DMKcgTiKBmrjjZGBCHiApAQ8QFICHiApAQ+QlIAHSErAV8XhE6wZCgxwnLGAB0jKiU4ANRnw05sKHiApFTxADUbY76KCr469a8AwOSDgqyTkYTFK7Ez54ea/gAdISg8eYA4TfFBXwQMkJeCrpQ8PaR2c3sPOewFfNSEP6UwU7hECHurl/T2fibepgK+eWQ7Nu3Mo5L47DE/AN0HIQ17jzW+HSQKMZebaTAXfDFU85DPuvBbwTRHy0ISTPfc45w5XE/DNEfLQvmnmsYBvkpCHKp1VuU9HwANMarp3AAHfrIrKBFi6syv3aeetgG+akId2TD9fBXzzhHxqNm/dKq3cNwR8ClIA6jXf/BTwaQh5mEzllfuGgE9FyEM95p+PrkWTzmZQdbMuBaRVwRmq51LBp1XPIIM0Ggr3CAGfXF2DjZ5sxjo0Fu4RWjQLoGUDV2kw2DdU8ItR7yCEdtU9r1Twi6Kah7PN8BV7Q1PBL1IbgxPq1M78UcEvlmoe9jqY3+0E+4YKfvHaG7QwvTbniQqeUM1DHMjwNoN9QwXPlrYHMwyr/fmggmfH9qBW0bMA5eCN5qngOSLXYIfj8o13FTwnqOhJqtz7n7RU8Fwg92SomlU/gvwrVQXPhVT0tKwsIdfvEfBcweGVtGJBqb5FwDMAQU+tytGb2Ql4BiToqcWeJF9YuEcIeEaxO5MEPmM7kd4LDPcIAc8kBP4gSlh195yZ2AsN9g0Bzwy0cuhr4Yl9IQHPjBxyyTl6hLr3gYgQ8FRD2LNNQg9BwFOhfZNb6Oc1YJh7X7hFwNMIfft8Bk5j4X6HgKcxh2ax4K/XBMkr3PcS8CShh1+XCRNXuB8k4Eno1Iz3BnC9SlK1ksWolYBngZx4dbkKk7TCRaqNgIeLkiLTm0HDCdnwok9JwMNFjiVLjeGfMAkTvqSxCHgYzERHi9T4PjIFwX4xX9kHkJQKHqibyr03FTxAUip4oE4q96up4KE1Swi+JbzGCQh4oC7CfTBaNEAdBPvgVPAASanggXmp3EejggdISgUPzEPlPjoVPEBSKnhgWir3yajggekI90kJeGAawn1yAh4Yn3CfhYCHFrUUmC0tazICHhiPcJ+Vo2iA4Qn2KqjgAZJSwQPDUblXRQUPDEO4V0fAQ6tqCtSaloV7BDxwHeFeLQEP9Cfcq2YnK3A5wd4EFTxAUip44Hwq96ao4AGSUsEDp6ncm6SCB0hKBQ8cpnJvmgoe2E+4N0/AA3cJ9xQEPLRsjCAW7mkIeOCGcE9FwAMrwj0dAQ8I96QcJglLJthTU8HDUgn39AQ8tK5PUAv3RRDwsDTCfTEEPCyJcF8UAQ9LIdwXx1E0kJ1gXywVPEBSAh4yU70vmoCHrIT74gl4yEi4EwIe8hHurAl4yES4s8VhkpCBYGcPFTxAUgIeICkBD5CUgAdISsADJCXgAZIS8ABJCXiApAQ8QFICHiApAQ+QlIAHSKp0XTf3MgAwAhU8QFICHiApAQ+QlIAHSErAAyQl4AGSEvAASQl4gKQEPEBSAh4gKQEPkJSAB0hKwAMkJeABkhLwAEkJeICkBDxAUgIeICkBD5CUgAdISsADJCXgAZIS8ABJ/T+IpMt+TV25KAAAAABJRU5ErkJggg==\n",
203 |       "text/plain": [
204 |        "<Figure size 432x432 with 1 Axes>"
205 |       ]
206 |      },
207 |      "metadata": {},
208 |      "output_type": "display_data"
209 |     }
210 |    ],
211 |    "source": [
212 |     "fig, ax1 = plt.subplots(ncols=1, nrows=1, figsize=(6, 6))\n",
213 |     "ax1.imshow(img)\n",
214 |     "ax1.set_title('Shapes',fontsize=16)\n",
215 |     "ax1.axis('off')\n",
216 |     "plt.show()"
217 |    ]
218 |   },
219 |   {
220 |    "cell_type": "markdown",
221 |    "metadata": {},
222 |    "source": [
223 |     "### Anti-aliasing drwaing"
224 |    ]
225 |   },
226 |   {
227 |    "cell_type": "code",
228 |    "execution_count": 11,
229 |    "metadata": {},
230 |    "outputs": [
231 |     {
232 |      "data": {
233 |       "image/png": 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234 |       "text/plain": [
235 |        "<Figure size 432x432 with 1 Axes>"
236 |       ]
237 |      },
238 |      "metadata": {},
239 |      "output_type": "display_data"
240 |     }
241 |    ],
242 |    "source": [
243 |     "img = np.zeros((100, 100), dtype=np.double)\n",
244 |     "\n",
245 |     "# anti-aliased line\n",
246 |     "rr, cc, val = line_aa(12, 12, 20, 50)\n",
247 |     "img[rr, cc] = val\n",
248 |     "\n",
249 |     "# anti-aliased circle\n",
250 |     "rr, cc, val = circle_perimeter_aa(60, 40, 30)\n",
251 |     "img[rr, cc] = val\n",
252 |     "\n",
253 |     "fig, ax2 = plt.subplots(ncols=1, nrows=1, figsize=(6, 6))\n",
254 |     "ax2.imshow(img, cmap=plt.cm.gray, interpolation='nearest')\n",
255 |     "ax2.set_title('Anti-aliasing')\n",
256 |     "ax2.axis('off')\n",
257 |     "\n",
258 |     "plt.show()"
259 |    ]
260 |   }
261 |  ],
262 |  "metadata": {
263 |   "kernelspec": {
264 |    "display_name": "Python 3",
265 |    "language": "python",
266 |    "name": "python3"
267 |   },
268 |   "language_info": {
269 |    "codemirror_mode": {
270 |     "name": "ipython",
271 |     "version": 3
272 |    },
273 |    "file_extension": ".py",
274 |    "mimetype": "text/x-python",
275 |    "name": "python",
276 |    "nbconvert_exporter": "python",
277 |    "pygments_lexer": "ipython3",
278 |    "version": "3.6.2"
279 |   },
280 |   "latex_envs": {
281 |    "LaTeX_envs_menu_present": true,
282 |    "autoclose": false,
283 |    "autocomplete": true,
284 |    "bibliofile": "biblio.bib",
285 |    "cite_by": "apalike",
286 |    "current_citInitial": 1,
287 |    "eqLabelWithNumbers": true,
288 |    "eqNumInitial": 1,
289 |    "hotkeys": {
290 |     "equation": "Ctrl-E",
291 |     "itemize": "Ctrl-I"
292 |    },
293 |    "labels_anchors": false,
294 |    "latex_user_defs": false,
295 |    "report_style_numbering": false,
296 |    "user_envs_cfg": false
297 |   }
298 |  },
299 |  "nbformat": 4,
300 |  "nbformat_minor": 2
301 | }
302 | 


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1 | theme: jekyll-theme-slate


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/images/Local_histogram_equalization.PNG:
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/images/Readme.md:
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1 | ## Image files
2 | 


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/images/skeletonize.png:
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