Deleted more notebooks

%% Cell type:code id: tags:

``` python

import sys

sys.path.insert(0, '/scratch/autocnet')

import networkx as nx # draw adjacency graph

import os # get file path

from scipy.misc import bytescale # store image array

import autocnet

import autocnet.fileio.io_json as io_json # read json file

from autocnet.examples import get_path # get file path

from autocnet.fileio.io_gdal import GeoDataset # set handle, get image as array

from autocnet.graph.network import CandidateGraph #construct adjacency graph

from autocnet.matcher import feature_extractor as fe # extract features from image

from autocnet.matcher.matcher import FlannMatcher # match features between images

```

%% Cell type:code id: tags:

``` python

# display graphs in separate window to be able to change size

# %pylab qt4

# displays graphs in noteboook

%pylab inline

```

%% Output

    Populating the interactive namespace from numpy and matplotlib

%% Cell type:markdown id: tags:

View the input json adjacency file

--------------------------------------

%% Cell type:code id: tags:

``` python

adjacency_file = get_path('two_image_adjacency.json')

# This file is a dictionary of images with a corresponding list of known adjacent images

print(type(io_json.read_json(adjacency_file)))

print( io_json.read_json(adjacency_file))

```

%% Output

    <class 'dict'>

    {'AS15-M-0297_SML.png': ['AS15-M-0298_SML.png'], 'AS15-M-0298_SML.png': ['AS15-M-0297_SML.png']}

%% Cell type:markdown id: tags:

View the Adjacency Graph

---------------------------

%% Cell type:code id: tags:

``` python

# Create a dictionary of images and their serial numbers.

serial_numbers = {'AS15-M-0295_SML.png': 'APOLLO15/METRIC/1971-07-31T01:24:11.754',

                  'AS15-M-0296_SML.png': 'APOLLO15/METRIC/1971-07-31T01:24:36.970',

                  'AS15-M-0297_SML.png': 'APOLLO15/METRIC/1971-07-31T01:25:02.243',

                  'AS15-M-0298_SML.png': 'APOLLO15/METRIC/1971-07-31T01:25:27.457',

                  'AS15-M-0299_SML.png': 'APOLLO15/METRIC/1971-07-31T01:25:52.669',

                  'AS15-M-0300_SML.png': 'APOLLO15/METRIC/1971-07-31T01:26:17.923'}

# Construct an adjacency graph from a json file and draw it.

adjacencyGraph = CandidateGraph.from_adjacency(adjacency_file)

nx.draw(adjacencyGraph)

```

%% Output

%% Cell type:markdown id: tags:

View the Adjacency Graph Attributes

---------------------------------------

* 'handle' = The GeoDataset from the image file

* 'image'  = The NumPy array of the image data

* 'keypoints' = The list of OpenCV Keypoints (features)

* 'descriptors' = The list of descriptor arrays that correspond with the Keypoints

%% Cell type:code id: tags:

``` python

# Extracting image data and attribute nodes

# Get the path without file name

#basepath = os.path.dirname(adjacency_file)

# Loop through the nodes (images) on the graph and fill in their attributes dictionaries.

#adjacencyGraph.extract_features(5)

#for node, attributes in adjacencyGraph.nodes_iter(data=True):

#    print('NODE HANDLE = ', attributes['handle'])

#    print('NODE IMAGE ARRAY = ')

#    print(attributes['image'])

#    print('NODE KEYPOINTS = ')

#    for kp in attributes['keypoints']:

#        print(kp.pt)

#    print('NODE KEYPOINT DESCRIPTORS = ')

#    print(attributes['descriptors'])

#|    print('-------------------------------------End Node Attribute-------------------------------------')

```

%% Cell type:markdown id: tags:

View the KD-Tree

----------------

%% Cell type:code id: tags:

``` python

# Apply a FLANN matcher

#matcher = FlannMatcher()

# Loop through the nodes on the graph and feature descriptors to the matcher

#for node, attributes in adjacencyGraph.nodes_iter(data=True):

#    matcher.add(attributes['descriptors'], key=node)

# build KD-Tree using the feature descriptors

#matcher.train()

```

%% Cell type:markdown id: tags:

TODO: KD-Tree visualization???

see http://cgi.di.uoa.gr/~std04139/Kd_tree_Project/PythonImplementation/kdtree_visualization_2.py

%% Cell type:markdown id: tags:

View the matches attribute of the edge

-------------------------------------------

%% Cell type:code id: tags:

``` python

# Loop through the nodes on the graph to find all features that match at 1 neighbor

# These matches are returned as PANDAS dataframes and added to the adjacency graph

#for node, attributes in adjacencyGraph.nodes_iter(data=True):

#    descriptors = attributes['descriptors']

#matches = matcher.query(descriptors, node, k=2)

#adjacencyGraph.add_matches(matches)

```

%% Cell type:code id: tags:

``` python

#for image1, image2, attributes in adjacencyGraph.edges_iter(data=True):

#    print(attributes['matches'])

# there is one edge, display matches between 2 images:

```

%% Cell type:code id: tags:

``` python

# TEMP

# TO DO ADD __str__() to graph class

#for image1, image2, attributes in adjacencyGraph.edges_iter(data=True):

#    image_name_src = attributes['matches']['source_image'].values[0]

#    print(adjacencyGraph.node[image_name_src]['image_name'])

# there is one edge, display matches between 2 images:

```

%% Cell type:code id: tags:

``` python

```