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Kelvinrr/cnet.ipynb

Created August 3, 2017 16:30
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cnet.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"source": [
"import os\n",
"import sys\n",
"sys.path.insert(0, os.path.abspath('..'))\n",
"\n",
"from autocnet import cuda \n",
"from autocnet.examples import get_path\n",
"from autocnet.graph.network import CandidateGraph\n",
"from autocnet.graph.edge import Edge\n",
"from autocnet.vis.graph_view import *\n",
"# from autocnet.matcher.feature import FlannMatcher\n",
"\n",
"import itertools\n",
"import networkx as nx\n",
"import pandas as pd\n",
"from IPython.display import display\n",
"\n",
"import json\n",
"from tinydb import TinyDB, Query\n",
"\n",
"from plio.io.io_autocnetgraph import load as load_graph\n",
"from plio.io.io_autocnetgraph import save as save_graph\n",
"\n",
"%pylab inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"#Point to the adjacency Graph\n",
"adjacency = get_path('three_image_adjacency.json')\n",
"basepath = get_path('Apollo15')\n",
"cg = CandidateGraph.from_adjacency(adjacency, basepath=basepath)\n",
"\n",
"#Apply SIFT to extract features\n",
"cg.extract_features(extractor_parameters={'nfeatures':800})"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"MATCHING COMPLETE ---------------------------------\n",
"updating CG...\n",
"update finished...\n"
]
},
{
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" point_id source_image destination_image source_idx match_idx \\\n",
"m_id \n",
"6 2.0 0.0 1.0 360.0 4.0 \n",
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"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"edge = cg.edge[0][1] \n",
"edge.match()\n",
"cg.cpoints.query('point_id == 2') "
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
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"edge.matches.head() "
]
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{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"updating CG...\n",
"update finished...\n"
]
},
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" point_id source_image destination_image source_idx match_idx \\\n",
"m_id \n",
"5 2.0 0.0 1.0 3.0 10.0 \n",
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"source": [
"edge.symmetry_check() \n",
"cg.cpoints.query('point_id == 2') "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Point to the adjacency Graph\n",
"adjacency = get_path('three_image_adjacency.json')\n",
"basepath = get_path('Apollo15')\n",
"cg = CandidateGraph.from_adjacency(adjacency, basepath=basepath)\n",
"\n",
"#Apply SIFT to extract features\n",
"cg.extract_features(extractor_parameters={'nfeatures':300})"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"MATCHING COMPLETE ---------------------------------\n",
"updating CG...\n",
"update finished...\n",
"MATCHING COMPLETE ---------------------------------\n",
"updating CG...\n",
"update finished...\n",
"MATCHING COMPLETE ---------------------------------\n",
"updating CG...\n",
"update finished...\n"
]
},
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],
"text/plain": [
" point_id source_image destination_image source_idx match_idx \\\n",
"m_id \n",
"6 2.0 0.0 1.0 711.0 4.0 \n",
"7 2.0 0.0 1.0 2.0 4.0 \n",
"8 2.0 0.0 1.0 130.0 5.0 \n",
"957 2.0 0.0 1.0 475.0 950.0 \n",
"958 2.0 0.0 1.0 647.0 951.0 \n",
"1213 2.0 0.0 1.0 654.0 1308.0 \n",
"1561 2.0 0.0 1.0 637.0 2874.0 \n",
"1838 2.0 0.0 2.0 238.0 476.0 \n",
"1883 2.0 0.0 2.0 283.0 566.0 \n",
"2089 2.0 0.0 2.0 489.0 978.0 \n",
"2108 2.0 0.0 2.0 508.0 1016.0 \n",
"2327 2.0 0.0 2.0 727.0 1454.0 \n",
"\n",
" x y \n",
"m_id \n",
"6 263.777069 386.623718 \n",
"7 716.104858 388.033691 \n",
"8 716.104858 388.033691 \n",
"957 992.194031 444.741760 \n",
"958 176.202057 490.933716 \n",
"1213 992.194031 444.741760 \n",
"1561 176.202057 490.933716 \n",
"1838 992.194031 444.741760 \n",
"1883 992.194031 444.741760 \n",
"2089 992.194031 444.741760 \n",
"2108 992.194031 444.741760 \n",
"2327 263.777069 386.623718 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Match\n",
"cg.match()\n",
"cg.cpoints.query('point_id == 2')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"#Apply outlier detection\n",
"# cg.symmetry_checks()\n",
"# cg.cpoints.query('point_id == 2')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# cg.apply_func_to_edges(Edge.ratio_check)\n",
"# cg.cpoints.query('point_id == 2')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"# cg.compute_fundamental_matrices(clean_keys=['symmetry', 'ratio'])\n",
"# cg.cpoints.query('point_id == 2')"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [],
"source": [
"m01 = cg.edge[0][1].matches\n",
"cp = cg.cpoints"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" <th></th>\n",
" <th>point_id</th>\n",
" <th>source_image</th>\n",
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" <th>match_idx</th>\n",
" <th>x</th>\n",
" <th>y</th>\n",
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" <tr>\n",
" <th>m_id</th>\n",
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" <td>746.357971</td>\n",
" <td>156.659271</td>\n",
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" <td>2.0</td>\n",
" <td>985.959290</td>\n",
" <td>384.589508</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" point_id source_image destination_image source_idx match_idx \\\n",
"m_id \n",
"0 0.0 0.0 1.0 624.0 0.0 \n",
"1 0.0 0.0 1.0 0.0 0.0 \n",
"2 0.0 0.0 1.0 413.0 1.0 \n",
"3 1.0 0.0 1.0 50.0 2.0 \n",
"4 1.0 0.0 1.0 1.0 2.0 \n",
"\n",
" x y \n",
"m_id \n",
"0 483.393341 993.464905 \n",
"1 746.357971 156.659271 \n",
"2 746.357971 156.659271 \n",
"3 543.315247 380.489105 \n",
"4 985.959290 384.589508 "
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cp.head(5)"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>source_image</th>\n",
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" <tr>\n",
" <th>3</th>\n",
" <td>0.0</td>\n",
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" <td>195.338165</td>\n",
" </tr>\n",
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" <th>4</th>\n",
" <td>0.0</td>\n",
" <td>711.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>123.194969</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" source_image source_idx destination_image destination_idx distance\n",
"0 0.0 624.0 1.0 0.0 291.398346\n",
"1 0.0 413.0 1.0 0.0 302.978546\n",
"2 0.0 50.0 1.0 1.0 185.628662\n",
"3 0.0 152.0 1.0 1.0 195.338165\n",
"4 0.0 711.0 1.0 2.0 123.194969"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m01.head(5)"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
"data": {
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"<div>\n",
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],
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" source_image destination_image match_idx\n",
"0 0.0 1.0 0\n",
"1 0.0 1.0 1\n",
"2 0.0 1.0 2\n",
"3 0.0 1.0 3\n",
"4 0.0 1.0 4"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m01idx = m01.reset_index(drop=False)[['source_image', 'destination_image', 'index']]\n",
"m01idx.columns = ['source_image', 'destination_image', 'match_idx']\n",
"m01idx.head()"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
{
"data": {
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"</table>\n",
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],
"text/plain": [
" source_image destination_image match_idx\n",
"0 0.0 2.0 0\n",
"1 0.0 2.0 1\n",
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"3 0.0 2.0 3\n",
"4 0.0 2.0 4"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m12idx = cg.edge[0][2].matches.reset_index(drop=False)[['source_image', 'destination_image', 'index']]\n",
"m12idx.columns = ['source_image', 'destination_image', 'match_idx']\n",
"m12idx.head()"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {},
"outputs": [
{
"data": {
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" .dataframe thead tr:only-child th {\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" source_image destination_image match_idx\n",
"m_id \n",
"0 0.0 1.0 0.0\n",
"1 0.0 1.0 0.0\n",
"2 0.0 1.0 1.0\n",
"3 0.0 1.0 2.0\n",
"4 0.0 1.0 2.0"
]
},
"execution_count": 96,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cpidx = cp[['source_image', 'destination_image', 'match_idx']] \n",
"cpidx.head()"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {},
"outputs": [],
"source": [
"mask = pd.Series(np.zeros(cp.shape[0], dtype=bool)) "
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"mask[pd.merge(cpidx,m12idx, how='inner', left_index=True, on=['source_image', 'destination_image', 'match_idx']).index] = True "
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" point_id source_image destination_image source_idx match_idx \\\n",
"m_id \n",
"0 0.0 0.0 1.0 624.0 0.0 \n",
"2 0.0 0.0 1.0 413.0 1.0 \n",
"4 1.0 0.0 1.0 1.0 2.0 \n",
"6 2.0 0.0 1.0 711.0 4.0 \n",
"8 2.0 0.0 1.0 130.0 5.0 \n",
"\n",
" x y \n",
"m_id \n",
"0 483.393341 993.464905 \n",
"2 746.357971 156.659271 \n",
"4 985.959290 384.589508 \n",
"6 263.777069 386.623718 \n",
"8 716.104858 388.033691 "
]
},
"execution_count": 99,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cp[mask].head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"# def somefunc(x):\n",
"# x[2].matches.index.name = 'match_idx'\n",
"# return x[2].matches.reset_index(drop=False)[['source_image','destination_image', 'match_idx']]\n",
"\n",
"# df = pd.concat(cg.apply(somefunc))\n",
"# df.head()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"scrolled": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'df' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-14-f410de8656f3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcpoints\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmerge\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mon\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'source_image'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'destination_image'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'match_idx'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'left'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'point_id == 2'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"
]
}
],
"source": [
"cg.cpoints.merge(df.reset_index(drop=False), on=['source_image','destination_image', 'match_idx'], how='left').query('point_id == 2')"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
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" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Empty DataFrame\n",
"Columns: []\n",
"Index: []"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cg.cpoints"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {
"collapsed": true,
"scrolled": false
},
"outputs": [],
"source": [
"def subgraph(self, nbunch):\n",
" s = super(CandidateGraph, self)\n",
" sg = s.subgraph(nbunch)\n",
" self.nodemask = sg.nodes()\n",
" \n",
"def nodes_iter(self, data=False):\n",
" s = super(CandidateGraph, self)\n",
" return (node for node in s.nodes_iter(data) if node in self.nodemask)\n",
"\n",
"def edges_iter(self, nbunch=[], data=False, key=False):\n",
" s = super(CandidateGraph, self)\n",
" nbunch.extend(self.nodemask)\n",
" print(nbunch)\n",
" return s.edges_iter(nbunch, data,key)\n",
" \n",
" "
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"cg.subgraph = subgraph\n",
"cg.nodes_iter = nodes_iter\n",
"cg.edges_iter = edges_iter"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"dict_keys([])\n",
"None\n",
"[None]\n",
"try\n"
]
}
],
"source": [
"cg.subgraph([0])"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"[0]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cg.nodemask"
]
},
{
"cell_type": "code",
"execution_count": 266,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"[(0,\n",
" \n",
" NodeID: 0\n",
" Image Name: AS15-M-0297_SML.png\n",
" Image PATH: /Users/krodriguez/repos/autocnet/autocnet/examples/Apollo15/AS15-M-0297_SML.png\n",
" Number Keypoints: 800\n",
" Available Masks : Empty DataFrame\n",
"Columns: []\n",
"Index: []\n",
" Type: <class 'autocnet.graph.node.Node'>\n",
" ),\n",
" (1,\n",
" \n",
" NodeID: 1\n",
" Image Name: AS15-M-0299_SML.png\n",
" Image PATH: /Users/krodriguez/repos/autocnet/autocnet/examples/Apollo15/AS15-M-0299_SML.png\n",
" Number Keypoints: 800\n",
" Available Masks : Empty DataFrame\n",
"Columns: []\n",
"Index: []\n",
" Type: <class 'autocnet.graph.node.Node'>\n",
" ),\n",
" (2,\n",
" \n",
" NodeID: 2\n",
" Image Name: AS15-M-0298_SML.png\n",
" Image PATH: /Users/krodriguez/repos/autocnet/autocnet/examples/Apollo15/AS15-M-0298_SML.png\n",
" Number Keypoints: 800\n",
" Available Masks : Empty DataFrame\n",
"Columns: []\n",
"Index: []\n",
" Type: <class 'autocnet.graph.node.Node'>\n",
" )]"
]
},
"execution_count": 266,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(cg.nodes_iter(data=True))"
]
},
{
"cell_type": "code",
"execution_count": 267,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"# globals()['test'] = 'hello'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 272,
"metadata": {},
"outputs": [],
"source": [
"func.__kwdefaults__ = {'hello':'test'}"
]
},
{
"cell_type": "code",
"execution_count": 273,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"test\n"
]
}
],
"source": [
"func()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"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": "3.5.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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