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@mprymek
Created March 6, 2016 08:27
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[[ 36, 28, 237],\n",
" [ 29, 230, 181],\n",
" [ 0, 0, 0]],\n",
"\n",
" [[ 36, 28, 237],\n",
" [ 29, 230, 181],\n",
" [ 29, 230, 181]]], dtype=uint8)"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"img = np.array([\n",
" [[36,28,237],[29,230,181],[0,0,0]],\n",
" [[36,28,237],[29,230,181],[29,230,181]]\n",
"], np.uint8)\n",
"img"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[1, 0, 0],\n",
" [1, 0, 0]], dtype=uint8)"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"alpha = np.array([[1,0,0],[1,0,0]],np.uint8)\n",
"alpha"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>a</th>\n",
" <th>b</th>\n",
" <th>g</th>\n",
" <th>r</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>237</td>\n",
" <td>28</td>\n",
" <td>36</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>181</td>\n",
" <td>230</td>\n",
" <td>29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>237</td>\n",
" <td>28</td>\n",
" <td>36</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>181</td>\n",
" <td>230</td>\n",
" <td>29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>0</td>\n",
" <td>181</td>\n",
" <td>230</td>\n",
" <td>29</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b g r\n",
"0 1 237 28 36\n",
"1 0 181 230 29\n",
"2 0 0 0 0\n",
"3 1 237 28 36\n",
"4 0 181 230 29\n",
"5 0 181 230 29"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# tohle asi neni uplne efektivni, nejspis se da 3. dimenze vyselektovat nejak lip...\n",
"img_f = np.ndarray.flatten(img)\n",
"alpha_f = np.ndarray.flatten(alpha)\n",
"rgba = pd.DataFrame({\"r\": img_f[0::3],\"g\": img_f[1::3], \"b\": img_f[2::3], \"a\": alpha_f})\n",
"rgba"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>a</th>\n",
" <th>b</th>\n",
" <th>g</th>\n",
" <th>r</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>237</td>\n",
" <td>28</td>\n",
" <td>36</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>237</td>\n",
" <td>28</td>\n",
" <td>36</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b g r\n",
"0 1 237 28 36\n",
"3 1 237 28 36"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rgba.loc[rgba[\"a\"]==1]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.9"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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