def __init__(self, graph_obj: Graph):
# some_query_img_objects = (query_video_distinct_frames.get_objects(0, 2))
# img_objects_list contains 3 elements
# nodes_matched = self.match_node_with_frames(some_query_img_objects, graph_obj)
print("atleast started")
# nodes_matched = []
# self.nodes_matched.append(graph_obj.get_node(2))
self.nodes_matched.append(graph_obj.get_node(0))
# self.find_edge_with_nodes(0)
return
def match_node_with_frames(some_query_img_objects: list, graph_obj: Graph):
search_list = graph_obj.Nodes
node_confidence = []
# node_confidence is list of (node.identity:int , confidence:int , total_fraction_matched:float)
for node in search_list:
for img_obj in some_query_img_objects:
node_images: vo2.DistinctFrames = node.node_images
if node_images is not None:
for data_obj in node_images.get_objects():
image_fraction_matched, min_good_matches = mt.SURF_returns(
img_obj.get_elements(), data_obj.get_elements(),
2500, 0.7)
if min_good_matches > 100 and image_fraction_matched != -1:
if image_fraction_matched > 0.05 or min_good_matches > 225:
print("Match found btw" +
str(img_obj.get_time()) +
" of query video and " +
str(data_obj.get_time()) +
" of node data")
if len(node_confidence
) > 0 and node_confidence[-1][
0] == node.identity:
entry = node_confidence[-1]
node_confidence[-1] = (
node.identity, entry[1] + 1,
entry[2] + image_fraction_matched)
# print(str(node.identity) + " matched by " + str(image_fraction_matched))
else:
node_confidence.append(
(node.identity, 1,
image_fraction_matched))
node_confidence = sorted(node_confidence,
key=lambda x: (x[1], x[2]),
reverse=True)
print(node_confidence)
final_node_list = []
for entry in node_confidence:
final_node_list.append(graph_obj.get_node(entry[0]))
return final_node_list