Updated Visualization to be compliant with full extractor parameters

%% Cell type:code id: tags:

``` python

import os # get file path

import sys

root = '/scratch/autocnet'

sys.path.insert(0, root)

sys.path.insert(0, os.path.abspath('../..'))

from scipy.misc import bytescale # store image array

import autocnet

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

from autocnet.utils import visualization as vis

```

%% 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:

Set up for visualization : Construct an adjacency graph with features extracted

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

%% Cell type:code id: tags:

``` python

adjacency_dict = {"../examples/Apollo15/AS15-M-0297_SML.png"

                  : ["../examples/Apollo15/AS15-M-0298_SML.png"],

                  "../examples/Apollo15/AS15-M-0298_SML.png"

                  : ["../examples/Apollo15/AS15-M-0297_SML.png"]}

adjacencyGraph = CandidateGraph.from_adjacency(adjacency_dict)

```

%% Cell type:code id: tags:

``` python

adjacencyGraph.extract_features(25)

adjacencyGraph.extract_features({'nfeatures':25})

imageName1 = adjacencyGraph.node[0]['image_name']

imageName2 = adjacencyGraph.node[1]['image_name']

print(imageName1)

print(imageName2)

```

%% Output

    AS15-M-0297_SML.png

    AS15-M-0298_SML.png

%% Cell type:markdown id: tags:

Use visualization utility plotFeatures() to plot the features of a single image

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

In this example, we plot both images to open in separate windows

1. Features found in AS15-M-0298_SML.png

2. Features found in AS15-M-0297_SML.png

%% Cell type:code id: tags:

``` python

plt.figure(0)

keypoints1 = adjacencyGraph.get_keypoints(imageName1)

vis.plotFeatures(imageName1, keypoints1)

plt.figure(1)

keypoints2 = adjacencyGraph.get_keypoints(imageName2)

vis.plotFeatures(imageName2, keypoints2)

plt.show()

```

%% Cell type:code id: tags:

``` python

plt.close(0)

plt.close(1)

```

%% Cell type:markdown id: tags:

Use visualization utility plotAdjacencyGraphFeatures() to plot the features on all images of the graph in a single figure.

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

%% Cell type:code id: tags:

``` python

vis.plotAdjacencyGraphFeatures(adjacencyGraph, featurePointSize=7)

```

%% Cell type:code id: tags:

``` python

plt.close()

```

%% Cell type:markdown id: tags:

Set up for visualization : Find matches in Adjacency Graph

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

%% 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()

# 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:markdown id: tags:

Use visualization utility plotAdjacencyGraphMatches() to plot the matches between two images of the graph in a single figure.

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

%% Cell type:code id: tags:

``` python

vis.plotAdjacencyGraphMatches(imageName1,

                              imageName2,

                              adjacencyGraph,

                              aspectRatio=0.44,

                              featurePointSize=3,

                              lineWidth=1,

                              saveToFile='myimage.png')

plt.figure(0)

img = plt.imread('myimage.png')

plt.imshow(img)

vis.plotAdjacencyGraphMatches(imageName1,

                              imageName2,

                              adjacencyGraph,

                              aspectRatio=0.44,

                              featurePointSize=5,

                              featurePointSize=10,

                              lineWidth=3,

                              saveToFile='myimage.png')

plt.figure(1)

img = plt.imread('myimage.png')

plt.imshow(img)

```

%% Output

    <matplotlib.image.AxesImage at 0x120ad6b38>

%% Cell type:markdown id: tags:

Below is an earlier attempt at plotting images within the same display box.<br>

Features are plotted.<br>

Lines are not drawn.

%% Cell type:code id: tags:

``` python

plt.figure(2)

vis.plotAdjacencyGraphMatchesSingleDisplay(imageName1, imageName2, adjacencyGraph)

```

%% Cell type:code id: tags:

``` python

plt.close(0)

plt.close(1)

plt.close(2)

```

%% Cell type:code id: tags:

``` python

```