Commit 7e2c2567 authored by kberry's avatar kberry
Browse files

First draft of slope-filtering

parent c98858a5

autocnet/fileio/utils.py

+33 −0
Original line number Diff line number Diff line
@@ -3,6 +3,7 @@ import tempfile 
import os

import fnmatch

import numpy as np

import pandas as pd



def create_dir(basedir=''):

    """
@@ -34,3 +35,35 @@ def file_search(searchdir,searchstring): 
            filelist.append(os.path.join(root, filename))

    filelist=np.array(filelist)

    return filelist    



# TODO: FIXME

def calculate_slope(x1, x2, y1, y2):

    """



    Parameters

    ----------

    x1

    x2

    y1

    y2



    Returns

    -------



    """

    return (x2-x1)/(y2-y1)



def calculate_slope(x1, x2):

    """



    Parameters

    ----------

    x1

    x2



    Returns

    -------



    """

    slopes = (x2.y.values - x1.y.values)/(x2.x.values-x1.x.values)

    return pd.DataFrame(slopes, columns=['slope'])

autocnet/transformation/tests/test_transformations.py

+11 −1
Original line number Diff line number Diff line
@@ -8,7 +8,7 @@ import numpy as np 
import numpy.testing

import pandas as pd

from autocnet.transformation import transformations



from autocnet.fileio import utils



class TestHomography(unittest.TestCase):
@@ -64,6 +64,16 @@ class TestFundamentalMatrix(unittest.TestCase): 


        self.assertIsInstance(F.error, pd.DataFrame)



        # TODO: FIXME



        df1 = pd.DataFrame(fp, columns=['x', 'y'])

        df2 = pd.DataFrame(tp.T[:, :2], columns=['x', 'y'])

        slopes = utils.calculate_slope(df1, df2)



        F.refine(arr=slopes)



        self.assertTrue(False)



        # This should raise an error.

        F.refine()

        self.assertIsInstance(F.error, pd.DataFrame)

autocnet/transformation/transformations.py

+7 −4
Original line number Diff line number Diff line
@@ -180,7 +180,7 @@ class FundamentalMatrix(TransformationMatrix): 
            compute this homography

    """



    def refine(self, method=ps.esda.mapclassify.Fisher_Jenks, bin_id=0, **kwargs):

    def refine(self, method=ps.esda.mapclassify.Fisher_Jenks, df=None, bin_id=0, **kwargs):

        """

        Refine the fundamental matrix by accepting some data classification

        method that accepts an ndarray and returns an object with a bins
@@ -214,10 +214,12 @@ class FundamentalMatrix(TransformationMatrix): 
               data in the new fundamental matrix.

        """

        # Perform the data classification

        fj = method(self.error.values.ravel(), **kwargs)

        if df is None:

            df = self.error

        fj = method(df.values.ravel(), **kwargs)

        bins = fj.bins

        # Mask the data that falls outside the provided bins

        mask = self.error['Reprojection Error'] <= bins[bin_id]

        mask = df <= bins[bin_id]

        new_x1 = self.x1.iloc[mask[mask == True].index]

        new_x2 = self.x2.iloc[mask[mask == True].index]

        fmatrix, new_mask = compute_fundamental_matrix(new_x1.values, new_x2.values)
@@ -225,7 +227,8 @@ class FundamentalMatrix(TransformationMatrix): 


        # Update the current state

        self[:] = fmatrix

        self.mask[self.mask == True] = mask

    #    self.mask[self.mask == True] = mask

        self.mask = mask



        # Update the action stack

        try:

notebooks/Testing Slopes.ipynb

0 → 100644
+167 −0
Original line number Diff line number Diff line 
%% Cell type:markdown id: tags:



This notebook demonstrates a test of autocnet on the New Horizons LORRI data



%% Cell type:code id: tags:



``` python

import os

import sys

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



import unittest



from autocnet.examples import get_path

from autocnet.fileio.io_controlnetwork import to_isis

from autocnet.fileio.io_controlnetwork import write_filelist

from autocnet.graph.network import CandidateGraph

from autocnet.fileio import utils



#%pylab inline

%pylab qt4

```



%% Cell type:code id: tags:



``` python

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

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

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

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

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

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



for k, v in serial_numbers.items():

    serial_numbers[k] = 'APOLLO15/METRIC/{}'.format(v)





```



%% Cell type:code id: tags:



``` python

adjacency = get_path('two_image_adjacency.json')

basepath = get_path('Apollo15')

cg = CandidateGraph.from_adjacency(adjacency, basepath=basepath)



cg.extract_features(method='sift', extractor_parameters={"nfeatures":500})

cg.match_features(k=2)

```



%% Cell type:code id: tags:



``` python

for source, destination, edge in cg.edges_iter(data=True):

    # Perform the symmetry check

    edge.symmetry_check()

    # Perform the ratio test

    edge.ratio_check(clean_keys=['symmetry'])

```



%% Cell type:code id: tags:



``` python

cg.apply_func_to_edges("compute_fundamental_matrix", clean_keys=['symmetry', 'ratio'])

```



%% Cell type:code id: tags:



``` python

f = cg.edge[0][1].fundamental_matrix

f

```



%% Cell type:code id: tags:



``` python

len((f.x2.y.values - f.x1.y.values)/(f.x2.x.values - f.x1.x.values))

```



%% Cell type:code id: tags:



``` python

df=utils.calculate_slope(f.x1, f.x2)

df.values.ravel()

```



%% Cell type:code id: tags:



``` python

f.mask[f.mask==True]

```



%% Cell type:code id: tags:



``` python

f.refine(df=utils.calculate_slope(f.x1, f.x2))

```



%% Cell type:code id: tags:



``` python

f.rollback()

```



%% Cell type:code id: tags:



``` python

f.mask

```



%% Cell type:code id: tags:



``` python

figsize(18,18)

fig, ax = plt.subplots(1,1)

ax = cg.edge[0][1].plot(clean_keys=['ratio', 'fundamental'], ax=ax)

```



%% Cell type:code id: tags:



``` python

figsize(18,18)

fig, ax = plt.subplots(1,1)

ax = cg.edge[0][2].plot(clean_keys=['ratio', 'symmetry', 'fundamental', 'subpixel'], ax=ax)

```



%% Cell type:code id: tags:



``` python



        for source, destination, edge in cg.edges_iter(data=True):



            # Perform the symmetry check

            edge.symmetry_check()

            self.assertIn(edge.masks['symmetry'].sum(), range(400, 600))



            # Perform the ratio test

            edge.ratio_check(clean_keys=['symmetry'])

            self.assertIn(edge.masks['ratio'].sum(), range(40, 100))



        # Step: Compute the homographies and apply RANSAC

        cg.apply_func_to_edges("compute_homography", clean_keys=['symmetry', 'ratio'])



        # Step: Compute the overlap ratio and coverage ratio

        for s, d, edge in cg.edges_iter(data=True):

            edge.coverage_ratio(clean_keys=['symmetry', 'ratio'])



        # Step: Compute subpixel offsets for candidate points

        cg.apply_func_to_edges("subpixel_register", clean_keys=['ransac'])



        # Step: And create a C object

        cnet = cg.to_cnet(clean_keys=['symmetry', 'ratio', 'ransac', 'subpixel'])



        # Step: Create a fromlist to go with the cnet and write it to a file

        filelist = cg.to_filelist()

        write_filelist(filelist, path="fromlis.lis")



        # Step update the serial numbers

        nid_to_serial = {}

        for i, node in cg.nodes_iter(data=True):

            nid_to_serial[i] = self.serial_numbers[node.image_name]



        cnet.replace({'nid': nid_to_serial}, inplace=True)

        # Step: Output a control network

        to_isis('TestTwoImageMatching.net', cnet, mode='wb',

                networkid='TestTwoImageMatching', targetname='Moon')

```