def find_tfl(self, frame_path):
can, aux = self.find_lights(frame_path)
lights_candidates = Candidates(frame_path, can, aux)
can, aux = self.recognize_tfl(lights_candidates)
tfl_candidates = Candidates(frame_path, can, aux)
return lights_candidates, tfl_candidates
time.time()).strftime('%Y-%m-%d %H:%M:%S'))
print_timestamp()
model = KeyedVectors.load_word2vec_format("Resource/glove_s300.txt",
unicode_errors="ignore")
print("model loaded")
print_timestamp()
similar_candidates = []
path = "candidates/D1_C26_Folha_20-08-2007_13h16.txt"
with open(path, "r") as file:
for line in file:
line = line.replace("\n", "")
candidate = Candidates(line)
similarity = calculate_similarity(candidate, model)
if (similarity > 0.8):
ca = SimilarCandidates(candidate.sn_id, candidate.sn_id_antecedent,
similarity)
similar_candidates.append(ca)
for c in similar_candidates:
print(c.sn_id + c.sn_id_antecedent + c.similarity)
print_timestamp()
print("done")
def __init__(self, pp, focal, egomotion):
self.principle_point = pp
self.focal = focal
self.em = egomotion
self.my_model = load_model("model.h5")
self.prev_candidates = Candidates("", [], [])
def _candidatestab_default(self):
return Candidates(self)