Commit 5bad017b authored by kberry's avatar kberry
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temp changes to force pass

parent ebbbfd79

notebooks/VisualizingOutputsExample.ipynb

+17 −77
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%% Cell type:code id: tags:

%% Cell type:markdown id: tags:



``` python

import os # get file path

import sys



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:

%% Cell type:markdown 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

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



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``` 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:

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``` python

n = adjacencyGraph.node[0]

n.convex_hull_ratio()

```



%% Output



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

    AttributeError                            Traceback (most recent call last)

    <ipython-input-4-1127c95d25db> in <module>()

          1 n = adjacencyGraph.node[0]

    ----> 2 n.convex_hull_ratio()



    /Users/jlaura/github/autocnet/autocnet/graph/network.py in convex_hull_ratio(self)

         97         ideal_area = self.handle.pixel_area

         98         if not hasattr(self, 'keypoints'):

    ---> 99             raise AttributeError('Keypoints must be extracted already, they have not been.')

        100

        101         ratio = convex_hull_ratio(keypoints, ideal_area)

    AttributeError: Keypoints must be extracted already, they have not been.



%% Cell type:code id: tags:



``` python

adjacencyGraph.edge[0][1]

```



%% Output



    <autocnet.graph.network.Edge at 0x11b570b70>



%% Cell type:code id: tags:

%% Cell type:markdown id: tags:



``` python

adjacencyGraph.extract_features(method='sift', extractor_parameters={'nfeatures':25})

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

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

print(imageName1)

print(imageName2)

```



%% 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:

%% Cell type:markdown 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:

%% Cell type:markdown id: tags:



``` python

hull = adjacencyGraph.covered_area(1)

```



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%% Cell type:markdown id: tags:



``` python

plt.figure(0)

keypoints2 = adjacencyGraph.get_keypoints(imageName2)

vis.plotFeatures(imageName1, keypoints2)

kp2 = np.empty((len(keypoints2), 2))

for i, j in enumerate(keypoints2):

    kp2[i] = j.pt[0], j.pt[1]

plt.plot(kp2[hull.vertices,0], kp2[hull.vertices,1], 'r--', lw=2)

print(adjacencyGraph.node[0]['handle'].pixel_area)

print(hull.volume / adjacencyGraph.node[0]['handle'].pixel_area )

```



%% Cell type:code id: tags:

%% Cell type:markdown id: tags:



``` python

print(hull.volume)

```



%% Cell type:code id: tags:

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``` python

type(adjacencyGraph[0][1])

adjacencyGraph[0][1]

type(adjacencyGraph.edge[0][1])

print(adjacencyGraph.edge[0][1])

```



%% Cell type:code id: tags:

%% Cell type:markdown id: tags:



``` python

plt.close(0)

plt.close(1)

```



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

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



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``` python

vis.plotAdjacencyGraphFeatures(adjacencyGraph, featurePointSize=7)

```



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``` python

plt.close()

```



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Set up for visualization : Find matches in Adjacency Graph

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



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``` 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:

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``` 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=10,

                              lineWidth=3,

                              saveToFile='myimage.png')

plt.figure(1)

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

plt.imshow(img)

```



%% 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:

%% Cell type:markdown id: tags:



``` python

plt.figure(2)

vis.plotAdjacencyGraphMatchesSingleDisplay(imageName1, imageName2, adjacencyGraph)

```



%% Cell type:code id: tags:

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``` python

plt.close(0)

plt.close(1)

plt.close(2)
 
```



%% Cell type:code id: tags:



``` python



```