Loading autocnet/graph/network.py +7 −12 Original line number Diff line number Diff line Loading @@ -180,10 +180,8 @@ class CandidateGraph(nx.Graph): """ array = self.node[nodeindex]['handle'].read_array() newx_size = int(array.shape[0]) newy_size = int(array.shape[1]) return bytescale(resized_array) return bytescale(array) def extract_features(self, method='orb', extractor_parameters={}, downsampling=1): """ Loading Loading @@ -435,9 +433,6 @@ class CandidateGraph(nx.Graph): for source, destination, attributes in self.edges_iter(data=True): matches = attributes['matches'] s_downsampling = self.node[source]['downsampling'] d_downsampling = self.node[destination]['downsampling'] # Merge all of the masks if clean_keys: mask = np.prod([attributes[i] for i in clean_keys], axis=0, dtype=np.bool) Loading @@ -455,18 +450,18 @@ class CandidateGraph(nx.Graph): m1 = (source, int(row['source_idx'])) m2 = (destination, int(row['destination_idx'])) values.append([kp1[m1[1]].pt[0] * s_downsampling, kp1[m1[1]].pt[1] * s_downsampling, values.append([kp1[m1[1]].pt[0], kp1[m1[1]].pt[1], m1, pt_idx, source]) kp2x = kp2[m2[1]].pt[0] * d_downsampling kp2y = kp2[m2[1]].pt[1] * d_downsampling kp2x = kp2[m2[1]].pt[0] kp2y = kp2[m2[1]].pt[1] if 'subpixel' in clean_keys: kp2x += (offsets['x_offset'].values[i] * d_downsampling) kp2y += (offsets['y_offset'].values[i] * d_downsampling) kp2x += (offsets['x_offset'].values[i]) kp2y += (offsets['y_offset'].values[i]) values.append([kp2x, kp2y, m2, Loading Loading
autocnet/graph/network.py +7 −12 Original line number Diff line number Diff line Loading @@ -180,10 +180,8 @@ class CandidateGraph(nx.Graph): """ array = self.node[nodeindex]['handle'].read_array() newx_size = int(array.shape[0]) newy_size = int(array.shape[1]) return bytescale(resized_array) return bytescale(array) def extract_features(self, method='orb', extractor_parameters={}, downsampling=1): """ Loading Loading @@ -435,9 +433,6 @@ class CandidateGraph(nx.Graph): for source, destination, attributes in self.edges_iter(data=True): matches = attributes['matches'] s_downsampling = self.node[source]['downsampling'] d_downsampling = self.node[destination]['downsampling'] # Merge all of the masks if clean_keys: mask = np.prod([attributes[i] for i in clean_keys], axis=0, dtype=np.bool) Loading @@ -455,18 +450,18 @@ class CandidateGraph(nx.Graph): m1 = (source, int(row['source_idx'])) m2 = (destination, int(row['destination_idx'])) values.append([kp1[m1[1]].pt[0] * s_downsampling, kp1[m1[1]].pt[1] * s_downsampling, values.append([kp1[m1[1]].pt[0], kp1[m1[1]].pt[1], m1, pt_idx, source]) kp2x = kp2[m2[1]].pt[0] * d_downsampling kp2y = kp2[m2[1]].pt[1] * d_downsampling kp2x = kp2[m2[1]].pt[0] kp2y = kp2[m2[1]].pt[1] if 'subpixel' in clean_keys: kp2x += (offsets['x_offset'].values[i] * d_downsampling) kp2y += (offsets['y_offset'].values[i] * d_downsampling) kp2x += (offsets['x_offset'].values[i]) kp2y += (offsets['y_offset'].values[i]) values.append([kp2x, kp2y, m2, Loading