Commit 3ded32b4 authored by kberry's avatar kberry
Browse files

change structure of compute_homography to be part of CandidateGraph.

parent 7ae7189e

autocnet/graph/network.py

+35 −2
Original line number Diff line number Diff line
@@ -2,6 +2,8 @@ from collections import Hashable 


import networkx as nx

import pandas as pd

import cv2

import numpy as np



from autocnet.control.control import C, POINT_TYPE, MEASURE_TYPE

from autocnet.fileio import io_json
@@ -68,7 +70,6 @@ class CandidateGraph(nx.Graph): 
        #TODO: This really belongs in an outlier detection matcher class, not here.

        # Remove erroneous self neighbors

        matches = matches.loc[matches['matched_to'] != source_node]



        groups = matches.groupby('matched_to')

        for destination_node, group in groups:

            try:
@@ -82,6 +83,39 @@ class CandidateGraph(nx.Graph): 
            else:

                edge['matches'] = matches





    # deal with no edge case / fail better

    #document

    def compute_homography(self, source_key, destination_key, outlier_algorithm=cv2.RANSAC):

        if self.has_edge(source_key, destination_key):

            try:

                edge = self[source_key][destination_key]

            except:

                edge = self[destination_key][source_key]

            if 'matches' in edge.keys():

                source_keypoints = []

                destination_keypoints = []

                for i, row in edge['matches'].iterrows():

                    # Get the source and destination x,y coordinates for matches

                    source_idx = row['queryIdx_x']

                    src_keypoints = [self.node[source_key]['keypoints'][source_idx].pt[0],

                    self.node[source_key]['keypoints'][source_idx].pt[1]]



                    destination_idx = row['queryIdx_y']

                    #print(destination_idx)

                    #print(len(self.node[destination_key]['keypoints']))

                    dest_keypoints = [self.node[destination_key]['keypoints'][destination_idx-1].pt[0], self.node[destination_key]['keypoints'][destination_idx-1].pt[1]]



                    source_keypoints.append(src_keypoints)

                    destination_keypoints.append(dest_keypoints)



                return cv2.findHomography(np.array(source_keypoints), np.array(destination_keypoints), outlier_algorithm, 5.0)



            else:

                return (",")

        else:

            return ('','')



    def to_cnet(self):

        """

        Generate a control network (C) object from a graph
@@ -95,7 +129,6 @@ class CandidateGraph(nx.Graph): 
        point_ids = []

        serials = []

        for source, destination, attributes in self.edges_iter(data=True):



            for i, row in attributes['matches'].iterrows():



                # Get the source and destination x,y coordinates for matches

functional_tests/test_two_image.py

+6 −0
Original line number Diff line number Diff line
@@ -70,6 +70,11 @@ class TestTwoImageMatching(unittest.TestCase): 
            ignoreme, matches = fl.query(descriptors, k=2)

            cg.add_matches(node, matches)



        #test new

        M, n = cg.compute_homography('AS15-M-0297_SML.png', 'AS15-M-0298_SML.png')

        print(M) #for me

        print(n) # for me



        # Step: And create a C object

        cnet = cg.to_cnet()
@@ -83,6 +88,7 @@ class TestTwoImageMatching(unittest.TestCase): 


        cnet.index.set_levels(new_idx, inplace=True)



        self.assertTrue(False) #force test to fail



        # Step: Output a control network

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