README.md

@@ -1,4 +1,3 @@
 # image-recognition
 
-SWOADS Project
-Release 1.1
+SWOADS Project

src/BEBLIDRescorerDB.py

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+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')

src/LFBulkExtractionToDB.py

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+from pathlib import Path
+import tqdm
+
+import LFUtilities
+import BEBLIDExtractorQ as lf
+import argparse
+import os
+from LFDB import LFDB
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='LF bulk extraction')
+    parser.add_argument('src', type=str, help='img src folder path')
+    parser.add_argument('dest', type=str, help='LF DB file')
+
+    args = parser.parse_args()
+    src = args.src
+    dest = args.dest
+
+    lf_db = LFDB(dest)
+
+    paths = Path(src).rglob('*.*')
+    paths_list = list(paths)
+
+    print('Extracting lf...')
+    for path in tqdm.tqdm(paths_list):
+        try:
+            kp, des = lf.extract(os.path.join(path.parent, path.name))
+            features = LFUtilities.pickle_keypoints(kp, des)
+            blob = LFUtilities.serialize_object(features)
+            filename = os.path.splitext(path.name)[0]
+            lf_db.put(filename, blob)
+        except Exception as e:
+            print("cannot process '%s'" % path)
+            print(e)
+            pass
+
+    lf_db.commit()
+    lf_db.close()
+    print('lf extracted.')

src/LFDB.py

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+import os
+import sqlite3
+from sqlite3 import Error
+from werkzeug.datastructures import FileStorage
+
+
+class LFDB:
+
+    def __init__(self, db_path):
+        # 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.conn = sqlite3.connect(db_path, check_same_thread=False)
+
+    def close(self):
+        if self.conn:
+            self.conn.close()
+
+    def put(self, docId, features):
+        try:
+            self.conn.text_factory = str
+            #print("[INFO] : Successful connection!")
+            cur = self.conn.cursor()
+            insert_file = '''INSERT INTO lf(docId, features) VALUES(?, ?)'''
+            cur = self.conn.cursor()
+            cur.execute(insert_file, (docId, features,))
+            #print("[INFO] : The blob for ", docId, " is in the database.")
+        except Error as e:
+            print(e)
+
+    def commit(self):
+        try:
+            if self.conn:
+                self.conn.commit()
+                print("committing...")
+        except Error as e:
+            print(e)
+
+    def get(self, docId):
+        try:
+            self.conn.text_factory = str
+            cur = self.conn.cursor()
+           # print("[INFO] : Connected to SQLite to read_blob_data")
+            sql_fetch_blob_query = """SELECT * from lf where docId = ?"""
+            cur.execute(sql_fetch_blob_query, (docId,))
+            record = cur.fetchall()
+            for row in record:
+                converted_file_name = row[1]
+                blob = row[2]
+                # parse out the file name from converted_file_name
+            cur.close()
+        except sqlite3.Error as error:
+            print("[INFO] : Failed to read blob data from sqlite table", error)
+        return blob
+