diff --git a/.travis.yml b/.travis.yml
index 5d4c074ddb90a7e1fbe45f24f16a30c3732054cf..027346f8311408e148fbbcf3870bb0ed67090061 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -44,7 +44,7 @@ install:
- conda config --add channels conda-forge
- conda config --add channels jlaura
- conda install -c conda-forge gdal h5py
- - conda install pandas sqlalchemy pyyaml
+ - conda install pandas sqlalchemy pyyaml networkx
- conda install -c jlaura pvl protobuf
# Development installation
diff --git a/appveyor.yml b/appveyor.yml
index ad091115a9002a4b2559a563cdb01681c284b9c2..47dedfaf6f75f9393f5a874551481630c1d5e354 100644
--- a/appveyor.yml
+++ b/appveyor.yml
@@ -53,7 +53,7 @@ install:
- cmd: conda config --add channels conda-forge
- cmd: conda config --add channels jlaura
- cmd: conda install --yes -c conda-forge gdal h5py
- - cmd: conda install --yes pandas sqlalchemy pyyaml
+ - cmd: conda install --yes pandas sqlalchemy pyyaml networkx
- cmd: conda install --yes -c jlaura protobuf pvl
# Development installation
diff --git a/environment.yml b/environment.yml
index c9752688e6a26f7550601269b160d255cc27df5c..f51c74af88f78d0d7468b559581c862cc1172c07 100644
--- a/environment.yml
+++ b/environment.yml
@@ -10,3 +10,4 @@ dependencies:
- pandas
- sqlalchemy
- pyyaml
+ - networkx
diff --git a/plio/io/io_autocnetgraph.py b/plio/io/io_autocnetgraph.py
new file mode 100644
index 0000000000000000000000000000000000000000..c1360aba6acd26275cfe29f6c1d0de71653a72e1
--- /dev/null
+++ b/plio/io/io_autocnetgraph.py
@@ -0,0 +1,132 @@
+from io import BytesIO
+import json
+import os
+import warnings
+from zipfile import ZipFile
+
+from networkx.readwrite import json_graph
+import numpy as np
+import pandas as pd
+
+
+try:
+ import autocnet
+ autocnet_avail = True
+except:
+ autocnet_avail = False
+
+class NumpyEncoder(json.JSONEncoder):
+ def default(self, obj):
+ """If input object is an ndarray it will be converted into a dict
+ holding dtype, shape and the data, base64 encoded.
+ """
+ if isinstance(obj, np.ndarray):
+ return dict(__ndarray__= obj.tolist(),
+ dtype=str(obj.dtype),
+ shape=obj.shape)
+ # Let the base class default method raise the TypeError
+ return json.JSONEncoder.default(self, obj)
+
+def save(network, projectname):
+ """
+ Save an AutoCNet candiate graph to disk in a compressed file. The
+ graph adjacency structure is stored as human readable JSON and all
+ potentially large numpy arrays are stored as compressed binary. The
+ project archive is a standard .zip file that can have any ending,
+ e.g., <projectname>.project, <projectname>.zip, <projectname>.myname.
+
+ TODO: This func. writes a intermediary .npz to disk when saving. Can
+ we write the .npz to memory?
+
+ Parameters
+ ----------
+ network : object
+ The AutoCNet Candidate Graph object
+
+ projectname : str
+ The PATH to the output file.
+ """
+ # Convert the graph into json format
+ js = json_graph.node_link_data(network)
+
+ with ZipFile(projectname, 'w') as pzip:
+ js_str = json.dumps(js, cls=NumpyEncoder, sort_keys=True, indent=4)
+ pzip.writestr('graph.json', js_str)
+
+ # Write the array node_attributes to hdf
+ for n, data in network.nodes_iter(data=True):
+ grp = data['node_id']
+ np.savez('{}.npz'.format(data['node_id']),
+ descriptors=data.descriptors,
+ _keypoints=data._keypoints,
+ _keypoints_idx=data._keypoints.index,
+ _keypoints_columns=data._keypoints.columns)
+ pzip.write('{}.npz'.format(data['node_id']))
+ os.remove('{}.npz'.format(data['node_id']))
+
+ # Write the array edge attributes to hdf
+ for s, d, data in network.edges_iter(data=True):
+ if s > d:
+ s, d = d, s
+ grp = str((s,d))
+ np.savez('{}_{}.npz'.format(s, d),
+ matches=data.matches,
+ matches_idx=data.matches.index,
+ matches_columns=data.matches.columns,
+ _masks=data._masks,
+ _masks_idx=data._masks.index,
+ _masks_columns=data._masks.columns)
+ pzip.write('{}_{}.npz'.format(s, d))
+ os.remove('{}_{}.npz'.format(s, d))
+
+def json_numpy_obj_hook(dct):
+ """Decodes a previously encoded numpy ndarray with proper shape and dtype.
+
+ :param dct: (dict) json encoded ndarray
+ :return: (ndarray) if input was an encoded ndarray
+ """
+ if isinstance(dct, dict) and '__ndarray__' in dct:
+ data = np.asarray(dct['__ndarray__'])
+ return np.frombuffer(data, dct['dtype']).reshape(dct['shape'])
+ return dct
+
+def load(projectname):
+ if autocnet_avail is False:
+ warning.warn('AutoCNet Library is not available. Unable to load an AutoCNet CandidateGraph')
+ return
+
+ with ZipFile(projectname, 'r') as pzip:
+ # Read the graph object
+ with pzip.open('graph.json', 'r') as g:
+ data = json.loads(g.read().decode(),object_hook=json_numpy_obj_hook)
+
+ cg = autocnet.graph.network.CandidateGraph()
+ Edge = autocnet.graph.edge.Edge
+ Node = autocnet.graph.node.Node
+ # Reload the graph attributes
+ cg.graph = data['graph']
+ # Handle nodes
+ for d in data['nodes']:
+ n = Node(image_name=d['image_name'], image_path=d['image_path'], node_id=d['id'])
+ n['hash'] = d['hash']
+ # Load the byte stream for the nested npz file into memory and then unpack
+ nzf = np.load(BytesIO(pzip.read('{}.npz'.format(d['id']))))
+ n._keypoints = pd.DataFrame(nzf['_keypoints'], index=nzf['_keypoints_idx'], columns=nzf['_keypoints_columns'])
+ n.descriptors = nzf['descriptors']
+ cg.add_node(d['node_id'])
+ cg.node[d['node_id']] = n
+ for e in data['links']:
+ cg.add_edge(e['source'], e['target'])
+ edge = Edge()
+ edge.source = cg.node[e['source']]
+ edge.destination = cg.node[e['target']]
+ edge['fundamental_matrix'] = e['fundamental_matrix']
+ edge['weight'] = e['weight']
+ nzf = np.load(BytesIO(pzip.read('{}_{}.npz'.format(e['source'], e['target']))))
+
+ edge._masks = pd.DataFrame(nzf['_masks'], index=nzf['_masks_idx'], columns=nzf['_masks_columns'])
+ edge.matches = pd.DataFrame(nzf['matches'], index=nzf['matches_idx'], columns=nzf['matches_columns'])
+ # Add a mock edge
+ cg.edge[e['source']][e['target']] = edge
+
+ return cg