def generate_basic_graph_with_mapping(
VISJS=False) -> (GraphDataStruct, dict):
mapping = {}
# Create a graphe structure
if VISJS:
tmp_meta = Metadata(Source.VISJS)
else:
tmp_meta = Metadata(Source.DBDUMP)
tmp_graph = GraphDataStruct(tmp_meta)
# For each cluster, fetch all pictures and store it
for cluster_id in range(0, 2):
tmp_graph.add_cluster(
Cluster(label="", tmp_id=cluster_id, image=""))
for id in range(0, 3):
pic_id = str(cluster_id) + "_" + str(id) + "OLD"
pic_image = str(cluster_id) + "_" + str(id) + "IMAGE"
# Prepare mapping
mapping[pic_image] = str(cluster_id) + "_" + str(id) + "NEW"
# Label = picture score, here
tmp_graph.add_node(
Node(label="picture name +" + pic_id,
tmp_id=pic_id,
image=pic_image))
tmp_graph.add_edge(Edge(_from=cluster_id, _to=pic_id))
return tmp_graph, mapping
def get_storage_graph(self) -> GraphDataStruct:
"""
# TODO : Move to API ?
Export the current state of the database as a graph datastructure. This represents the storage graph of the server.
:return: The storage graph of the server, as is in the database
"""
# Create a graphe structure
tmp_meta = Metadata(Source.DBDUMP)
tmp_graph = GraphDataStruct(tmp_meta)
# Get all clusters
cluster_list = self.get_cluster_list()
# For each cluster, fetch all pictures and store it
for cluster_id in cluster_list:
tmp_graph.add_cluster(Cluster(label="", tmp_id=cluster_id, image=""))
picture_list = self.get_pictures_of_cluster(cluster_id, with_score=True)
self.logger.info(f"Picture list : {picture_list}")
for picture in picture_list:
# Label = picture score, here
tmp_graph.add_node(Node(label=picture[1], tmp_id=picture[0], image=""))
tmp_graph.add_edge(Edge(_from=cluster_id, _to=picture[0]))
return tmp_graph
def generate_basic_graph() -> GraphDataStruct:
# Create a graphe structure
tmp_meta = Metadata(Source.DBDUMP)
tmp_graph = GraphDataStruct(tmp_meta)
# For each cluster, fetch all pictures and store it
for cluster_id in range(0, 2):
tmp_graph.add_cluster(
Cluster(label="", tmp_id=cluster_id, image=""))
for id in range(0, 3):
pic_id = str(cluster_id) + "_" + str(id)
# Label = picture score, here
tmp_graph.add_node(
Node(label="picture name +" + pic_id,
tmp_id=pic_id,
image=""))
tmp_graph.add_edge(Edge(_from=cluster_id, _to=pic_id))
return tmp_graph
def test_compute_score_for_one_threshold(self):
# Graph example. Please check documentation for more information
cal_conf = Default_calibrator_conf()
quality_evaluator = similarity_graph_quality_evaluator.similarity_graph_quality_evaluator(
cal_conf)
requests_results = [
# 1 to 2 and 3
{
"list_pictures": [{
"cluster_id": "XXX",
"decision": "YES",
"distance": 0.1,
"image_id": "2"
}, {
"cluster_id": "XXX",
"decision": "YES",
"distance": 0.6,
"image_id": "3"
}],
"request_id":
"1",
"status":
"matches_found",
"request_time":
0
},
{
"list_pictures": [{
"cluster_id": "XXX",
"decision": "YES",
"distance": 0.3,
"image_id": "6"
}],
"request_id":
"2",
"status":
"matches_found",
"request_time":
0
},
{
"list_pictures": [{
"cluster_id": "XXX",
"decision": "YES",
"distance": 0.2,
"image_id": "1"
}],
"request_id":
"3",
"status":
"matches_found",
"request_time":
0
},
{
"list_pictures": [{
"cluster_id": "XXX",
"decision": "YES",
"distance": 0.4,
"image_id": "2"
}],
"request_id":
"4",
"status":
"matches_found",
"request_time":
0
},
{
"list_pictures": [{
"cluster_id": "XXX",
"decision": "YES",
"distance": 0.6,
"image_id": "4"
}],
"request_id":
"5",
"status":
"matches_found",
"request_time":
0
},
{
"list_pictures": [{
"cluster_id": "XXX",
"decision": "YES",
"distance": 0.7,
"image_id": "2"
}],
"request_id":
"6",
"status":
"matches_found",
"request_time":
0
}
]
# Create the reference graph
graph_data_struct = GraphDataStruct(Metadata(Source.DBDUMP))
graph_data_struct.add_cluster(Cluster(label="A", tmp_id="A",
image="A"))
graph_data_struct.add_cluster(Cluster(label="B", tmp_id="B",
image="B"))
graph_data_struct.add_cluster(Cluster(label="C", tmp_id="C",
image="C"))
graph_data_struct.add_node(Node(label="1", tmp_id="1", image="1"))
graph_data_struct.add_node(Node(label="2", tmp_id="2", image="2"))
graph_data_struct.add_node(Node(label="3", tmp_id="3", image="3"))
graph_data_struct.add_node(Node(label="4", tmp_id="4", image="4"))
graph_data_struct.add_node(Node(label="5", tmp_id="5", image="5"))
graph_data_struct.add_node(Node(label="6", tmp_id="6", image="6"))
graph_data_struct.add_edge(Edge(_from="1", _to="A"))
graph_data_struct.add_edge(Edge(_from="2", _to="A"))
graph_data_struct.add_edge(Edge(_from="3", _to="A"))
graph_data_struct.add_edge(Edge(_from="4", _to="B"))
graph_data_struct.add_edge(Edge(_from="5", _to="B"))
graph_data_struct.add_edge(Edge(_from="6", _to="C"))
pprint.pprint(requests_results)
pprint.pprint(graph_data_struct.export_as_dict())
quality_evaluator.cal_conf.NB_TO_CHECK = 1
dist_threshold = 0
stats_datastruct = quality_evaluator.compute_score_for_one_threshold(
requests_results, graph_data_struct, dist_threshold)
print(stats_datastruct)
self.assertEqual(stats_datastruct.P, 3)
self.assertEqual(stats_datastruct.N, 3)
self.assertAlmostEqual(stats_datastruct.TPR, 0.0, delta=0.05)
self.assertAlmostEqual(stats_datastruct.TNR, 1.0, delta=0.05)
self.assertAlmostEqual(stats_datastruct.FPR, 0.0, delta=0.05)
self.assertAlmostEqual(stats_datastruct.FNR, 1.0, delta=0.05)
dist_threshold = 0.5
stats_datastruct = quality_evaluator.compute_score_for_one_threshold(
requests_results, graph_data_struct, dist_threshold)
print(stats_datastruct)
self.assertEqual(stats_datastruct.P, 3)
self.assertEqual(stats_datastruct.N, 3)
self.assertAlmostEqual(stats_datastruct.TPR, 0.66, delta=0.05)
self.assertAlmostEqual(stats_datastruct.TNR, 0.33, delta=0.05)
self.assertAlmostEqual(stats_datastruct.FPR, 0.66, delta=0.05)
self.assertAlmostEqual(stats_datastruct.FNR, 0.33, delta=0.05)
dist_threshold = 1
stats_datastruct = quality_evaluator.compute_score_for_one_threshold(
requests_results, graph_data_struct, dist_threshold)
print(stats_datastruct)
self.assertEqual(stats_datastruct.P, 3)
self.assertEqual(stats_datastruct.N, 3)
self.assertAlmostEqual(stats_datastruct.TPR, 1.0, delta=0.05)
self.assertAlmostEqual(stats_datastruct.TNR, 0.0, delta=0.05)
self.assertAlmostEqual(stats_datastruct.FPR, 1.0, delta=0.05)
self.assertAlmostEqual(stats_datastruct.FNR, 0.0, delta=0.05)
quality_evaluator.cal_conf.NB_TO_CHECK = 3
dist_threshold = 0.5
stats_datastruct = quality_evaluator.compute_score_for_one_threshold(
requests_results, graph_data_struct, dist_threshold)
print(stats_datastruct)
self.assertEqual(stats_datastruct.P, 4)
self.assertEqual(stats_datastruct.N, 3)
self.assertAlmostEqual(stats_datastruct.TPR, 0.5, delta=0.05)
self.assertAlmostEqual(stats_datastruct.TNR, 0.33, delta=0.05)
self.assertAlmostEqual(stats_datastruct.FPR, 0.66, delta=0.05)
self.assertAlmostEqual(stats_datastruct.FNR, 0.5, delta=0.05)