Eliminare 'src/BEBLIDRescorerDB.py'

src/BEBLIDRescorerDB.py

@@ -1,81 +0,0 @@
-import cv2
-import numpy as np
-
-import LFUtilities
-import BEBLIDParameters
-import ImageRecognitionSettings as settings
-from LFDB import LFDB
-
-
-class BEBLIDRescorerDB:
-
-    def __init__(self):
-        #self.lf = LFUtilities.load(settings.DATASET_BEBLID)
-        #self.ids = np.loadtxt(settings.DATASET_IDS, dtype=str).tolist()
-        #self.bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
-        self.bf = cv2.DescriptorMatcher_create(cv2.DescriptorMatcher_BRUTEFORCE_HAMMING)
-        self.lf_db = LFDB(settings.DB_LF)
-
-    def rescore_by_id(self, query_id, resultset):
-        #query_idx = self.ids.index(query_id)
-        query = LFUtilities.load_img_lf(settings.DATASET_LF_FOLDER, query_id)
-        return self.rescore_by_img(query, resultset)
-
-    def rescore_by_img(self, query, resultset):
-        max_inliers = -1
-        res = []
-        counter = 0
-        if len(query[0]) > 0:
-            for data_id, _ in resultset:
-                try:
-                    blob = self.lf_db.get(data_id)
-                    serialized_obj = LFUtilities.deserialize_object(blob)
-                    data_el = LFUtilities.unpickle_keypoints(serialized_obj)
-
-
-                    if len(data_el[1]) > 0:
-                        nn_matches = self.bf.knnMatch(query[1], data_el[1], 2)
-                        good = [m for m, n in nn_matches if m.distance < BEBLIDParameters.NN_MATCH_RATIO * n.distance]
-
-                        if len(good) > BEBLIDParameters.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, 3.0)
-                            matches_mask = mask.ravel().tolist()
-                            # print(len(good))
-                            inliers = np.count_nonzero(matches_mask)
-                            # print(inliers)
-                            if (inliers >= BEBLIDParameters.MIN_INLIERS and inliers > max_inliers):
-                                max_inliers = inliers
-                                res.append((data_id, round(inliers/len(good), 3)))
-                                print(data_id)
-                                print(f'candidate n.  {counter}')
-                                #to get just the first candidate
-                                break
-                except Exception as e:
-                    print('rescore error evaluating ' + data_id)
-                    print(e)
-                    pass
-                counter += 1
-
-        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')