Aggiornare 'README.md'

README.md

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

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')

src/LFBulkExtractionToDB.py

@@ -1,40 +0,0 @@
-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

@@ -1,55 +0,0 @@
-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
-