68 lines
2.3 KiB
Python
Executable File
68 lines
2.3 KiB
Python
Executable File
import cv2
|
|
import numpy as np
|
|
|
|
import LFUtilities
|
|
import ORBParameters
|
|
import WebAppSettings as settings
|
|
|
|
|
|
class ORBRescorer:
|
|
|
|
def __init__(self):
|
|
self.lf = LFUtilities.load(settings.DATASET_LF)
|
|
self.ids = np.loadtxt(settings.DATASET_IDS, dtype=str).tolist()
|
|
|
|
#self.orb = cv2.ORB_create()
|
|
self.bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
|
|
|
|
def rescore_by_id(self, query_id, resultset):
|
|
query_idx = self.ids.index(query_id)
|
|
return self.rescore_by_img(self.lf[query_idx], resultset)
|
|
|
|
def rescore_by_img(self, query, resultset):
|
|
max_inliers = -1
|
|
res = []
|
|
|
|
for data_id, _ in resultset:
|
|
data_idx = self.ids.index(data_id)
|
|
try:
|
|
data_el = self.lf[data_idx]
|
|
matches = self.bf.match(query[1], data_el[1])
|
|
good = [m for m in matches if m.distance <= ORBParameters.THRESHOLD]
|
|
if len(good) > ORBParameters.MIN_GOOD_MATCHES:
|
|
src_pts = np.float32([query[0][m.queryIdx].pt for m in good]).reshape(-1, 1, 2)
|
|
dst_pts = np.float32([data_el[0][m.trainIdx].pt for m in good]).reshape(-1, 1, 2)
|
|
|
|
M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 1.0)
|
|
matches_mask = mask.ravel().tolist()
|
|
# print(len(good))
|
|
inliers = np.count_nonzero(matches_mask)
|
|
# print(inliers)
|
|
if (inliers >= ORBParameters.MIN_INLIERS and inliers > max_inliers):
|
|
max_inliers = inliers
|
|
res.append((data_id, inliers))
|
|
except:
|
|
print('rescore error evaluating ' + data_id)
|
|
pass
|
|
|
|
if res:
|
|
res.sort(key=lambda result: result[1], reverse=True)
|
|
return res
|
|
|
|
def add(self, lf):
|
|
self.lf.append(lf)
|
|
|
|
def remove(self, idx):
|
|
self.descs = np.delete(self.descs, idx, axis=0)
|
|
|
|
def save(self, is_backup=False):
|
|
lf_save_file = settings.DATASET_LF
|
|
ids_file = settings.DATASET_IDS_LF
|
|
if lf_save_file != "None":
|
|
if is_backup:
|
|
lf_save_file += '.bak'
|
|
ids_file += '.bak'
|
|
|
|
LFUtilities.save(lf_save_file, self.lf)
|
|
np.savetxt(ids_file, self.ids, fmt='%s')
|