Untitled.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from skimage import io\n",
"\n",
"skin = [[241, 211, 193], [232, 197, 176], [222, 179, 158], [236, 210, 194], [237, 204, 186], [237, 195, 177], [217, 187, 186], [214, 181, 179], [209, 176, 172], [243, 202, 202], [238, 193, 188], [226, 179, 175], [214, 192, 165], [211, 187, 157], [213, 184, 145], [247, 210, 194], [239, 199, 185], [207, 171, 163], [238, 212, 210], [236, 205, 202], [233, 197, 193], [240, 188, 176], [247, 195, 190], [225, 175, 166], [232, 206, 198]]\n",
"eyebr = [[0, 0, 255], [150, 103, 76], [174, 125, 99], [0, 0, 255], [157, 119, 106], [184, 146, 131], [0, 0, 254], [82, 52, 37], [59, 34, 21], [0, 0, 255], [161, 122, 110], [146, 108, 97], [0, 0, 255], [218, 193, 166], [169, 127, 80], [0, 0, 254], [209, 168, 146], [199, 151, 126], [0, 0, 254], [119, 87, 95], [151, 111, 114], [0, 0, 254], [80, 54, 61], [113, 80, 84], [0, 0, 254]]\n",
"eye = [[48, 32, 27], [187, 159, 149], [109, 90, 84], [41, 25, 36], [150, 117, 112], [182, 177, 177], [35, 23, 22], [134, 105, 102], [84, 67, 69], [51, 19, 15], [85, 53, 47], [144, 108, 101], [52, 35, 26], [87, 67, 52], [130, 105, 83], [206, 185, 179], [71, 58, 57], [113, 93, 93], [54, 47, 68], [198, 185, 200], [103, 85, 105], [42, 22, 29], [95, 72, 80], [145, 117, 126], [121, 98, 91]]\n",
"mouth = [[0, 0, 254], [206, 77, 107], [233, 111, 139], [0, 0, 254], [239, 133, 126], [225, 107, 101], [0, 0, 255], [190, 119, 129], [200, 189, 197], [0, 0, 254], [185, 90, 98], [214, 120, 130], [0, 0, 255], [189, 107, 67], [215, 140, 107], [0, 0, 254], [239, 123, 135], [213, 94, 114], [0, 0, 255], [222, 105, 146], [223, 161, 174], [0, 0, 255], [237, 113, 142], [219, 90, 120], [0, 0, 254]]\n",
"palette_skin = np.array(skin, dtype=np.uint8)\n",
"palette_eyebr = np.array(eyebr, dtype=np.uint8)\n",
"palette_eye = np.array(eye, dtype=np.uint8)\n",
"palette_mouth = np.array(mouth, dtype=np.uint8)\n",
"\n",
"m, n = 4, 6\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,\n",
" 17, 18, 19, 20, 21, 22, 23, 24])]"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"indices = [np.arange(25)]\n",
"indices"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Program Files (x86)\\Microsoft Visual Studio\\Shared\\Anaconda3_64\\lib\\site-packages\\skimage\\io\\_plugins\\matplotlib_plugin.py:51: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
" out_of_range_float = (np.issubdtype(image.dtype, np.float) and\n",
"C:\\Program Files (x86)\\Microsoft Visual Studio\\Shared\\Anaconda3_64\\lib\\site-packages\\matplotlib\\axes\\_base.py:1400: MatplotlibDeprecationWarning: The 'box-forced' keyword argument is deprecated since 2.2.\n",
" \" since 2.2.\", cbook.mplDeprecation)\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x24e2589a1d0>"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"io.imshow(palette_skin[indices])"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[[1, 1, 253],\n",
" [226, 192, 201],\n",
" [210, 177, 183],\n",
" [253, 222, 204],\n",
" [249, 216, 200]]"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"leb_colors = [[1, 1, 253], [226, 192, 201], [210, 177, 183], [253, 222, 204], [249, 216, 200]]\n",
"leb_colors"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[[226, 192, 201], [210, 177, 183], [253, 222, 204], [249, 216, 200]]"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from itertools import compress\n",
"fil = [leb_colors[i][2] < 250 and leb_colors[i][0] > 10 for i in range(5)]\n",
"list(compress(leb_colors, fil))"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[True, False, False, False, False]"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"[leb_colors[i][2] > 250 and leb_colors[i][0] < 10 for i in range(5)]"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Program Files (x86)\\Microsoft Visual Studio\\Shared\\Anaconda3_64\\lib\\site-packages\\skimage\\io\\_plugins\\matplotlib_plugin.py:51: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
" out_of_range_float = (np.issubdtype(image.dtype, np.float) and\n"
]
},
{
"ename": "TypeError",
"evalue": "Invalid dimensions for image data",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-33-c2d582182efe>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 7\u001b[0m ], dtype=np.uint8)\n\u001b[0;32m 8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m \u001b[0mio\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpalette\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32mC:\\Program Files (x86)\\Microsoft Visual Studio\\Shared\\Anaconda3_64\\lib\\site-packages\\skimage\\io\\_io.py\u001b[0m in \u001b[0;36mimshow\u001b[1;34m(arr, plugin, **plugin_args)\u001b[0m\n\u001b[0;32m 154\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstring_types\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 155\u001b[0m \u001b[0marr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcall_plugin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'imread'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mplugin\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mplugin\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 156\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mcall_plugin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'imshow'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mplugin\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mplugin\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mplugin_args\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 157\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 158\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Program Files (x86)\\Microsoft Visual Studio\\Shared\\Anaconda3_64\\lib\\site-packages\\skimage\\io\\manage_plugins.py\u001b[0m in \u001b[0;36mcall_plugin\u001b[1;34m(kind, *args, **kwargs)\u001b[0m\n\u001b[0;32m 209\u001b[0m (plugin, kind))\n\u001b[0;32m 210\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 211\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 212\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 213\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Program Files (x86)\\Microsoft Visual Studio\\Shared\\Anaconda3_64\\lib\\site-packages\\skimage\\io\\_plugins\\matplotlib_plugin.py\u001b[0m in \u001b[0;36mimshow\u001b[1;34m(im, ax, show_cbar, **kwargs)\u001b[0m\n\u001b[0;32m 155\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 156\u001b[0m \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0max\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgca\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 157\u001b[1;33m \u001b[0max_im\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mim\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 158\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mcmap\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0m_default_colormap\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mshow_cbar\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mshow_cbar\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 159\u001b[0m \u001b[0mdivider\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmake_axes_locatable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Program Files (x86)\\Microsoft Visual Studio\\Shared\\Anaconda3_64\\lib\\site-packages\\matplotlib\\__init__.py\u001b[0m in \u001b[0;36minner\u001b[1;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1853\u001b[0m \u001b[1;34m\"the Matplotlib list!)\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mlabel_namer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1854\u001b[0m RuntimeWarning, stacklevel=2)\n\u001b[1;32m-> 1855\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1856\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1857\u001b[0m inner.__doc__ = _add_data_doc(inner.__doc__,\n",
"\u001b[1;32mC:\\Program Files (x86)\\Microsoft Visual Studio\\Shared\\Anaconda3_64\\lib\\site-packages\\matplotlib\\axes\\_axes.py\u001b[0m in \u001b[0;36mimshow\u001b[1;34m(self, X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, shape, filternorm, filterrad, imlim, resample, url, **kwargs)\u001b[0m\n\u001b[0;32m 5485\u001b[0m resample=resample, **kwargs)\n\u001b[0;32m 5486\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5487\u001b[1;33m \u001b[0mim\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 5488\u001b[0m \u001b[0mim\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_alpha\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0malpha\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5489\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mim\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_clip_path\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Program Files (x86)\\Microsoft Visual Studio\\Shared\\Anaconda3_64\\lib\\site-packages\\matplotlib\\image.py\u001b[0m in \u001b[0;36mset_data\u001b[1;34m(self, A)\u001b[0m\n\u001b[0;32m 651\u001b[0m if not (self._A.ndim == 2\n\u001b[0;32m 652\u001b[0m or self._A.ndim == 3 and self._A.shape[-1] in [3, 4]):\n\u001b[1;32m--> 653\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Invalid dimensions for image data\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 654\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 655\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_A\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mTypeError\u001b[0m: Invalid dimensions for image data"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"palette = np.array([[255, 0, 0], # index 0: red\n",
" [ 0, 255, 0], # index 1: green\n",
" [ 0, 0, 255], # index 2: blue\n",
" [255, 255, 255], # index 3: white\n",
" [ 0, 0, 0], # index 4: black\n",
" [255, 255, 0], # index 5: yellow\n",
" ], dtype=np.uint8)\n",
"\n",
"io.imshow(palette[3])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}