added FAISS Searcher

src/BEBLIDRescorer.py

@@ -5,6 +5,7 @@ import LFUtilities
 import BEBLIDParameters
 import ImageRecognitionSettings as settings
 from line_profiler_pycharm import profile
+import logging
 
 class BEBLIDRescorer:
 
@@ -16,7 +17,8 @@ class BEBLIDRescorer:
 
     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)
+        query = LFUtilities.unpickle_img_lf(settings.DATASET_LF_FOLDER, query_id)
+
         return self.rescore_by_img(query, resultset)
 
     @profile
@@ -47,13 +49,13 @@ class BEBLIDRescorer:
                             if (inliers >= BEBLIDParameters.MIN_INLIERS and inliers > max_inliers):
                                 max_inliers = inliers
                                 res.append((data_id, round(inliers/len(good), 3)))
-                                print(data_id)
+                                logging.info(data_id)
-                                print(f'candidate n.  {counter}')
+                                logging.info(f'candidate n.  {counter}')
                                 #to get just the first candidate
                                 break
                 except Exception as e:
-                    print('rescore error evaluating ' + data_id)
+                    logging.error('rescore error evaluating ' + data_id)
-                    print(e)
+                    logging.error(e)
                     pass
                 counter += 1
 
@@ -61,16 +63,18 @@ class BEBLIDRescorer:
             res.sort(key=lambda result: result[1], reverse=True)
         return res
 
-    def add(self, kp, des, id):
+    @staticmethod
+    def add(doc_id, kp, des):
         # LFUtilities.save_img_lf(dest, filename, kp, des)
         # LFUtilities.savez_img_lf(dest, filename, kp, des)
-        LFUtilities.pickle_img_lf(settings.DATASET_LF_FOLDER, id, kp, des)
+        LFUtilities.pickle_img_lf(settings.DATASET_LF_FOLDER, doc_id, kp, des)
 
-    def remove(self, idx):
+    @staticmethod
-        self.descs = np.delete(self.descs, idx, axis=0)
+    def remove(doc_id):
+        LFUtilities.delete_img_lf(settings.DATASET_LF_FOLDER, doc_id)
 
     def save(self, is_backup=False):
-        lf_save_file = settings.DATASET_LF
+        """lf_save_file = settings.DATASET_LF
         ids_file = settings.DATASET_IDS_LF
         if lf_save_file != "None":
             if is_backup:
@@ -78,4 +82,4 @@ class BEBLIDRescorer:
                 ids_file += '.bak'
 
             LFUtilities.save(lf_save_file, self.lf)
-            np.savetxt(ids_file, self.ids, fmt='%s')
+            np.savetxt(ids_file, self.ids, fmt='%s')"""

src/FAISSSearchEngine.py

@@ -1,11 +1,16 @@
 import numpy as np
 import ImageRecognitionSettings as settings
 import faiss
+from shutil import copyfile
+import logging
+import threading
+import glob
+import fileinput
+import os
 
 
 class FAISSSearchEngine:
 
-
     def __init__(self):
         #self.dataset = h5py.File(settings.dataset_file, 'r')['rmac'][...]
 
@@ -16,32 +21,58 @@ class FAISSSearchEngine:
 
         #self.descs = (self.desc1 + self.desc2) / 2
         #self.descs /= np.linalg.norm(self.descs, axis=1, keepdims=True)
-        self.ids = np.loadtxt(settings.DATASET_IDS, dtype=str).tolist()
+        #self.ids = np.loadtxt(settings.DATASET_IDS, dtype=str).tolist()
+        self.ids = np.loadtxt(fileinput.input(sorted(glob.glob(settings.DATASET_GF_FOLDER + '/*.ids'))))
+        self.init_faiss_index()
+        self.lock = threading.Lock()
 
+
+    def init_faiss_index(self):
         # create an index with inner product similarity
         dim = 2048  # dimensionality of the features
         metric = faiss.METRIC_INNER_PRODUCT
         self.index = faiss.index_factory(dim, 'Flat', metric)
 
+        for desc_file in sorted(glob.glob(settings.DATASET_GF_FOLDER + '/*.npy')):
             # add the vectors to the index
-        self.index.add(self.descs)  # my_database is a numpy array of shape N x dim, where N is the number of vectors to index
+            tmp = np.load(desc_file)
-
+            self.index.add(tmp)  # my_database is a numpy array of shape N x dim, where N is the number of vectors to index
 
     def get_id(self, idx):
         return self.ids[idx]
 
+    def get_indexed_ids(self):
+        return self.ids
 
-    def add(self, desc, id):
+    def add(self, desc, doc_id):
-        self.ids.append(id)
+        try:
-        self.descs = np.vstack((self.descs, desc))
+            self.lock.acquire()
-        self.save()
+           # self.ids.append(doc_id)
+           # self.descs = np.vstack((self.descs, desc))
+            np.savetxt(os.path.join(settings.DATASET_GF_FOLDER, doc_id + '.ids'), self.ids, fmt='%s')
+            np.save(os.path.join(settings.DATASET_GF_FOLDER, doc_id + '.npy'), desc)
+        finally:
+            self.lock.release()
 
-
+    def remove(self, doc_id):
-    def remove(self, id):
+        try:
-        idx = self.ids.index(id)
+            self.lock.acquire()
+            id_filename = settings.DATASET_GF_FOLDER, doc_id + '.ids'
+            numpy_filename = settings.DATASET_GF_FOLDER, doc_id + '.npy'
+            if os.path.exists(id_filename):
+                os.remove(id_filename)
+            if os.path.exists(numpy_filename):
+                os.remove(numpy_filename)
+            else:
+                idx = self.ids.index(doc_id)
                 del self.ids[idx]
                 self.descs = np.delete(self.descs, idx, axis=0)
-
+                descs_file = settings.DATASET
+                ids_file = settings.DATASET_IDS
+                np.save(descs_file, self.descs)
+                np.savetxt(ids_file, self.ids, fmt='%s')
+        finally:
+            self.lock.release()
 
     def search_by_id(self, query_id, k=10):
         query_idx = self.ids.index(query_id)
@@ -49,9 +80,9 @@ class FAISSSearchEngine:
 
     def search_by_img(self, query, k=10):
         print('----------query features-------')
-        print(query)
+        logging.info(query)
         queries = np.reshape(query, (-1, 2048))
-        print(queries)
+        logging.debug(queries)
         scores, indexes = self.index.search(queries, k)
         #dot_product = np.dot(self.descs, query)
         #idx = dot_product.argsort()[::-1][:k]
@@ -63,10 +94,21 @@ class FAISSSearchEngine:
     def save(self, is_backup=False):
         descs_file = settings.DATASET
         ids_file = settings.DATASET_IDS
-
+        try:
-        if is_backup:
+            self.lock.acquire()
-            descs_file += '.bak'
+            """if is_backup:
-            ids_file += '.bak'
+                descs_file_backup = descs_file +'.bak'
+                ids_file_backup = ids_file + '.bak'
+                copyfile(descs_file, descs_file_backup)
+                copyfile(ids_file, ids_file_backup)
+                logging.info('Backup features created')
 
             np.save(descs_file, self.descs)
             np.savetxt(ids_file, self.ids, fmt='%s')
+            logging.info('Storing features done')"""
+            self.init_faiss_index()
+        except Exception as e:
+            logging.error('Error, unable to storing features')
+            logging.error(e)
+        finally:
+            self.lock.release()

src/FAISSSearchEngine2.py

@@ -0,0 +1,120 @@
+import numpy as np
+import ImageRecognitionSettings as settings
+import faiss
+from shutil import copyfile
+import logging
+import threading
+import glob
+import fileinput
+import os
+import GFUtilities
+
+class FAISSSearchEngine:
+
+    def __init__(self):
+        #self.dataset = h5py.File(settings.dataset_file, 'r')['rmac'][...]
+
+        #np.save('/media/Data/data/beni_culturali/deploy/dataset', self.dataset)
+        #self.descs = np.load(settings.DATASET)
+        #self.desc1 = np.load(settings.DATASET1)
+        #self.desc2 = np.load(settings.DATASET2)
+
+        #self.descs = (self.desc1 + self.desc2) / 2
+        #self.descs /= np.linalg.norm(self.descs, axis=1, keepdims=True)
+        #self.ids = np.loadtxt(settings.DATASET_IDS, dtype=str).tolist()
+        #self.ids = np.loadtxt(fileinput.input(sorted(glob.glob(settings.DATASET_GF_FOLDER + '/*.ids'))))
+        self.lock = threading.Lock()
+        self.init_faiss_index()
+
+
+    def init_faiss_index(self):
+        # create an index with inner product similarity
+        dim = 2048  # dimensionality of the features
+        metric = faiss.METRIC_INNER_PRODUCT
+        self.index = faiss.index_factory(dim, 'Flat', metric)
+        self.descs = np.load(settings.DATASET)
+        self.ids = np.loadtxt(settings.DATASET_IDS, dtype=str).tolist()
+        self.index.add(self.descs)
+
+        for desc_file in sorted(glob.glob(settings.DATASET_GF_FOLDER + '/**/*.dat', recursive=True)):
+            # add the vectors to the index
+            img_id = os.path.splitext(os.path.basename(desc_file))[0]
+            img_desc = GFUtilities.unpickle_img_gf(settings.DATASET_GF_FOLDER, img_id)
+            self.ids.append(img_id)
+            self.index.add(img_desc)  # my_database is a numpy array of shape N x dim, where N is the number of vectors to index
+
+
+    def get_id(self, idx):
+        return self.ids[idx]
+
+    def get_indexed_ids(self):
+        return self.ids
+
+    def exists(self, doc_id):
+        return doc_id in self.ids
+
+    def add(self, desc, doc_id):
+        try:
+            self.lock.acquire()
+           # self.ids.append(doc_id)
+           # self.descs = np.vstack((self.descs, desc))
+            GFUtilities.pickle_img_gf(settings.DATASET_GF_FOLDER, doc_id, desc)
+        finally:
+            self.lock.release()
+
+    def remove(self, doc_id):
+        try:
+            self.lock.acquire()
+            if not GFUtilities.delete_img_gf( settings.DATASET_GF_FOLDER, doc_id):
+                try:
+                    idx = self.ids.index(doc_id)
+                    del self.ids[idx]
+                    self.descs = np.delete(self.descs, idx, axis=0)
+                    descs_file = settings.DATASET
+                    ids_file = settings.DATASET_IDS
+                    np.save(descs_file, self.descs)
+                    np.savetxt(ids_file, self.ids, fmt='%s')
+                except ValueError as e:
+                    logging.error('Error, unable to retrieve and delete ' + doc_id)
+                    logging.error(e)
+        finally:
+            self.lock.release()
+
+    def search_by_id(self, query_id, k=10):
+        query_idx = self.ids.index(query_id)
+        return self.search_by_img(self.descs[query_idx], k)
+
+    def search_by_img(self, query, k=10):
+        print('----------query features-------')
+        logging.info(query)
+        queries = np.reshape(query, (-1, 2048))
+        logging.debug(queries)
+        scores, indexes = self.index.search(queries, k)
+        #dot_product = np.dot(self.descs, query)
+        #idx = dot_product.argsort()[::-1][:k]
+        res = []
+        for (i,j) in zip(indexes[0], scores[0]):
+            res.append((self.ids[i], round(float(j), 3)))
+        return res
+
+    def save(self, is_backup=False):
+        descs_file = settings.DATASET
+        ids_file = settings.DATASET_IDS
+        try:
+            self.lock.acquire()
+            """if is_backup:
+                descs_file_backup = descs_file +'.bak'
+                ids_file_backup = ids_file + '.bak'
+                copyfile(descs_file, descs_file_backup)
+                copyfile(ids_file, ids_file_backup)
+                logging.info('Backup features created')
+
+            np.save(descs_file, self.descs)
+            np.savetxt(ids_file, self.ids, fmt='%s')
+            logging.info('Storing features done')"""
+            self.init_faiss_index()
+        except Exception as e:
+            logging.error('Error, unable to storing features')
+            logging.error(e)
+        finally:
+            self.lock.release()

src/GFUtilities.py

@@ -0,0 +1,36 @@
+import cv2
+import numpy as np
+import pickle as pickle
+import os
+
+
+def pickle_img_gf(dest, id, descriptors):
+    dest_folder_name = id[0:3]
+    filename = id + '.dat'
+    dest_folder_path = os.path.join(dest, dest_folder_name)
+    if (not os.path.exists(dest_folder_path)):
+        os.mkdir(dest_folder_path)
+    dest_path = os.path.join(dest_folder_path, filename)
+    pickle.dump(descriptors, open(dest_path, 'wb'))
+
+
+def unpickle_img_gf(dest, id):
+    dest_folder_name = id[0:3]
+    filename = id + '.dat'
+    dest_folder_path = os.path.join(dest, dest_folder_name)
+    dest_path = os.path.join(dest_folder_path, filename)
+    des = pickle.load((open(dest_path, "rb")))
+    return des
+
+
+def delete_img_gf(dest, id):
+    dest_folder_name = id[0:3]
+    filename = id + '.dat'
+    dest_folder_path = os.path.join(dest, dest_folder_name)
+    dest_path = os.path.join(dest_folder_path, filename)
+    if os.path.exists(dest_path):
+        os.remove(dest_path)
+        if not os.listdir(dest_folder_path):
+            os.rmdir(dest_folder_path)
+        return True
+    return False

src/ImageRecognitionService.py

@@ -15,6 +15,8 @@ import os, os.path
 import tornado.wsgi
 import tornado.httpserver
 import argparse
+import logging
+import base64
 
 
 app = Flask(__name__)
@@ -65,13 +67,9 @@ def get_res(results, query_url=None):
 @app.route('/bcir/searchById')
 def search_by_id():
     id = request.args.get('id')
-    try:
+    k, threshold, search_deep_level = get_parameters(request.args.get("k"), request.args.get("threshold"),
-        searchDeepLevel = int(request.args.get("searchDeepLevel"))
+                                                     request.args.get("searchDeepLevel"))
-    except Exception as e:
+    results = searcher.search_by_id(id, k, threshold, search_deep_level)
-        print(e)
-        print('Setting default deep level 1')
-        search_deep_level = 1
-    results = searcher.search_by_id(id, settings.k, searchDeepLevel)
     query_url = None
     if request.args.get("tohtml") is not None:
         query_url = id + ".jpg"
@@ -86,12 +84,8 @@ def search_by_img():
 
     file = request.files['image']
     img_file = post_to_file(file)
-    try:
+
-        search_deep_level = int(request.form.get("searchDeepLevel"))
+    k, threshold, search_deep_level = get_parameters(request.form.get("k"), request.form.get("threshold"), request.form.get("searchDeepLevel"))
-    except Exception as e:
-        print(e)
-        print('Setting default deep level 1')
-        search_deep_level = 1
 
     #dest_file = uuid.uuid4().hex + ".jpg"
     #dest_path = settings.logs + "/" + dest_file
@@ -99,24 +93,48 @@ def search_by_img():
     #files = {'image': (dest_file, open(dest_path, 'rb'))}
     #r = requests.post(settings.rmac_service, files=files)
     #results = search_engine.search_by_img(np.array(r.json()), settings.k)
-    results = searcher.search_by_img(img_file, settings.k, search_deep_level)
+    results = searcher.search_by_img(img_file, k, threshold, search_deep_level)
     query_url = None
     if request.form.get("tohtml") is not None:
         query_url = ""
     return get_res(results, query_url)
 
 
+@app.route('/bcir/searchByImgB64', methods=['POST'])
+def search_by_img_base64():
+    image = request.form.get('image')
+    if image:
+        img_file = base64_to_file(image)
+    else:
+        flash('No img sent')
+        return redirect(request.url)
+
+    k, threshold, search_deep_level = get_parameters(request.form.get("k"), request.form.get("threshold"), request.form.get("searchDeepLevel"))
+
+    results = searcher.search_by_img(img_file, k, threshold, search_deep_level)
+    query_url = None
+    if request.form.get("tohtml") is not None:
+        query_url = ""
+    return get_res(results, query_url)
+
+
+def base64_to_file(image_base64):
+    ext = ".png"
+    dest_file = uuid.uuid4().hex + ext
+    dest_path = settings.logs + "/" + dest_file
+    with open(dest_path, "wb") as image_file:
+        byte_content = base64.b64decode(image_base64)
+        image_file.write(byte_content)
+    return  dest_path
+
 @app.route('/bcir/searchByURL')
 def search_by_url():
     url = request.args.get('url')
-    try:
-        search_deep_level = int(request.args.get("searchDeepLevel"))
-    except Exception as e:
-        print(e)
-        print('Setting default deep level 1')
-        search_deep_level = 1
 
     img_file = url_to_file(url)
+
+    k, threshold, search_deep_level = get_parameters(request.args.get("k"), request.args.get("threshold"),
+                                                     request.args.get("searchDeepLevel"))
    # query = cv2.imdecode(image, cv2.IMREAD_COLOR)
    # dest_file = uuid.uuid4().hex + ".jpg"
    # dest_path = settings.logs + "/" + dest_file
@@ -124,12 +142,49 @@ def search_by_url():
    # files = {'image': open(dest_path, 'rb')}
    # r = requests.post(settings.rmac_service, files=files)
    # results = search_engine.search_by_img(np.array(r.json()), settings.k)
-    results = searcher.search_by_img(img_file, settings.k, search_deep_level)
+    results = searcher.search_by_img(img_file, k, threshold, search_deep_level)
     query_url = None
     if request.args.get("tohtml") is not None:
         query_url = url
     return get_res(results, query_url)
 
+
+def get_parameters(k, threshold, search_deep_level):
+    try:
+        threshold = float(threshold)
+    except Exception as e:
+        logging.error(e)
+        threshold = settings.SEARCH_THRESHOLD
+        logging.error('Setting default threshold value to ' + str(threshold))
+    try:
+        k = int(k)
+    except Exception as e:
+        logging.error(e)
+        k = settings.k
+        logging.error('Setting default k value to ' + str(k))
+
+    try:
+        search_deep_level = int(search_deep_level)
+    except Exception as e:
+        logging.error(e)
+        search_deep_level = settings.SEARCH_DEEP_LEVEL
+        logging.error('Setting default deep level to ' + str(search_deep_level))
+
+    return k, threshold, search_deep_level
+
+
+@app.route('/bcir/getIds')
+def get_indexed_ids():
+    json_ids = json.dumps(searcher.get_indexed_ids())
+    return json_ids
+
+
+@app.route('/bcir/exists')
+def exists():
+    doc_id = request.args.get('id')
+    return json.dumps(searcher.exists(doc_id))
+
+
 @app.route('/bcir/addImg', methods=['POST'])
 def add_img():
     if 'image' not in request.files:
@@ -137,42 +192,46 @@ def add_img():
         return redirect(request.url)
     try:
         file = request.files['image']
-        id = request.files['image'].filename
+        doc_id = request.files['image'].filename
-        id, _ = os.path.splitext(id)
+        doc_id, _ = os.path.splitext(doc_id)
         img_file = post_to_file(file)
-        searcher.add(img_file, id)
+        searcher.add(img_file, doc_id)
         json_res = json.dumps("done")
         return json_res
-    except:
+    except Exception as e:
+        logging.error('Unable to add ' +  doc_id + 'to the index')
+        logging.error(e)
         abort(500)
 
 
 @app.route('/bcir/rmImg')
 def remove_img():
     try:
-        id = request.args.get('id')
+        doc_id = request.args.get('id')
-        searcher.remove(id)
+        searcher.remove(doc_id)
         json_res = json.dumps("done")
         return json_res
-    except:
+    except Exception as e:
+        logging.error('Unable to remove ' + doc_id + 'to the index')
+        logging.error(e)
         abort(500)
 
 @app.route('/bcir/<path:filename>')
 def download_file(filename):
-    print(filename)
+    logging.debug(filename)
     values = filename.split('/')
-    print(values)
+    logging.debug(values)
 
     return send_from_directory(settings.img_folder, filename, as_attachment=False)
 
 @app.route('/bcir/queries/<path:filename>')
 def queries(filename):
-    print(filename)
+    logging.debug(filename)
     values = filename.split('/')
     folder = values[0]
     name = values[1]
-    print(folder)
+    logging.debug(folder)
-    print(name)
+    logging.debug(name)
 
     return send_from_directory(settings.working_folder + '/' + folder, name, as_attachment=False)
 

src/ImageRecognitionSettings.py

@@ -2,7 +2,7 @@ import json
 import os
 
 def load_setting(conf_file):
-    global port, feature_extractor, k, img_folder, logs, working_folder, data_folder, DATASET, DATASET_LF_FOLDER, DATASET_IDS, DB_LF
+    global port, feature_extractor, k, img_folder, logs, working_folder, data_folder, DATASET_LF_FOLDER, DATASET_GF_FOLDER, DATASET, DATASET_IDS, DB_LF, SEARCH_THRESHOLD, SEARCH_DEEP_LEVEL
 
     with open(conf_file) as settings_file:
 
@@ -19,10 +19,14 @@ def load_setting(conf_file):
         if not os.path.isdir(data_folder):
             os.mkdir(data_folder)
 
-        DATASET = os.path.join(data_folder, 'dataset.npy')
         DATASET_LF_FOLDER = os.path.join(data_folder, 'lf')
-        DATASET_IDS = os.path.join(data_folder, 'dataset.ids')
+        DATASET_GF_FOLDER = os.path.join(data_folder, 'gf')
+        DATASET = os.path.join(DATASET_GF_FOLDER, 'dataset.npy')
+        DATASET_IDS = os.path.join(DATASET_GF_FOLDER, 'dataset.ids')
         DB_LF = os.path.join(data_folder, 'sqlite_lf/lf.db')
+        SEARCH_THRESHOLD = settings['search_th']
+        SEARCH_DEEP_LEVEL = settings['search_dl']
+
 
         img_folder = settings['img_folder']
         logs = os.path.join(working_folder, settings['log_folder'])

src/LFUtilities.py

@@ -2,7 +2,7 @@ import cv2
 import numpy as np
 import pickle as pickle
 import os
-from line_profiler_pycharm import profile
+
 
 def resize(max_side, img):
     if img.shape[1] > img.shape[0]:
@@ -17,54 +17,6 @@ def resize(max_side, img):
     return resized
 
 
-def pickle_keypoints(keypoints, descriptors):
-    i = 0
-    temp_array = []
-    for point in keypoints:
-        temp = (point.pt, point.size, point.angle, point.response, point.octave,
-        point.class_id, descriptors[i])
-        i += 1
-        temp_array.append(temp)
-    return temp_array
-
-
-def serialize_object(obj):
-    return pickle.dumps(obj)
-
-
-def deserialize_object(serialized_obj):
-    return pickle.loads(serialized_obj)
-
-
-def serializeV1(keypoints, descriptors):
-    temp_array = []
-    for point in keypoints:
-        kp = [point.pt, point.size, point.angle, point.response, point.octave, point.class_id]
-        temp_array.append(kp)
-    return temp_array, descriptors
-
-
-def serialize(keypoints, descriptors):
-    pts = np.float32([keypoints[i].pt for i in range(0, len(keypoints))])
-    return pts, descriptors
-
-def deserialize(ser_kp, ser_des):
-    keypoints = []
-    #data_list = array.tolist()
-    for point in ser_kp:
-        temp_feature = cv2.KeyPoint(x=point[0][0],y=point[0][1], size=point[1], angle=point[2], response=point[3], octave=point[4], class_id=point[5])
-        keypoints.append(temp_feature)
-    return keypoints, ser_des
-
-
-def deserializev1(ser_kp, ser_des):
-    keypoints = []
-    #data_list = array.tolist()
-    for point in ser_kp:
-        temp_feature = cv2.KeyPoint(x=point[0][0],y=point[0][1], size=point[1], angle=point[2], response=point[3], octave=point[4], class_id=point[5])
-        keypoints.append(temp_feature)
-    return keypoints, ser_des
-
 def pickle_img_lf(dest, id, keypoints, descriptors):
     dest_folder_name = id[0:3]
     filename = id + '.dat'
@@ -72,18 +24,10 @@ def pickle_img_lf(dest, id, keypoints, descriptors):
     if (not os.path.exists(dest_folder_path)):
         os.mkdir(dest_folder_path)
     dest_path = os.path.join(dest_folder_path, filename)
-    kps, des = serialize(keypoints, descriptors)
+    kps = np.float32([keypoints[i].pt for i in range(0, len(keypoints))])
-    pickle.dump([kps, des], open(dest_path, 'wb'))
+    pickle.dump([kps, descriptors], open(dest_path, 'wb'))
 
-def delete_img_lf(dest, id):
-    dest_folder_name = id[0:3]
-    filename = id + '.dat'
-    dest_folder_path = os.path.join(dest, dest_folder_name)
-    dest_path = os.path.join(dest_folder_path, filename)
-    if os.path.exists(dest_path):
-        os.remove(dest_path)
 
-@profile
 def unpickle_img_lf(lf_path, id):
     dest_folder_name = id[0:3]
     filename = id + '.dat'
@@ -93,59 +37,12 @@ def unpickle_img_lf(lf_path, id):
     return kps,  des
 
 
-@profile
+def delete_img_lf(dest, id):
-def loadz_img_lf(lf_path, id):
-    dest_folder_name = id[0:3]
-    filename = id + '.dat.npz'
-    dest_folder_path = os.path.join(lf_path, dest_folder_name)
-    dest_path = os.path.join(dest_folder_path, filename)
-    data = np.load(dest_path, allow_pickle=False)
-    kps = data.f.kps
-    des = data.f.des
-    #kps = data['kps']
-    #des = data['des']
-    #kp, desc = deserialize(data['kps'], data['des'])
-    return kps,  des
-
-
-def savez_img_lf(dest, id, keypoints, descriptors):
     dest_folder_name = id[0:3]
     filename = id + '.dat'
     dest_folder_path = os.path.join(dest, dest_folder_name)
-    if (not os.path.exists(dest_folder_path)):
-        os.mkdir(dest_folder_path)
     dest_path = os.path.join(dest_folder_path, filename)
-    kps, des = serialize(keypoints, descriptors)
+    if os.path.exists(dest_path):
-    #np.savez(dest_path, data)
+        os.remove(dest_path)
-    np.savez(dest_path, kps=kps, des=des)
+        if not os.listdir(dest_folder_path):
-
+            os.rmdir(dest_folder_path)
-
-@profile
-def loadz_img_lf(lf_path, id):
-    dest_folder_name = id[0:3]
-    filename = id + '.dat.npz'
-    dest_folder_path = os.path.join(lf_path, dest_folder_name)
-    dest_path = os.path.join(dest_folder_path, filename)
-    data = np.load(dest_path, allow_pickle=False)
-    kps = data.f.kps
-    des = data.f.des
-    #kps = data['kps']
-    #des = data['des']
-    #kp, desc = deserialize(data['kps'], data['des'])
-    return kps,  des
-
-
-def unpickle_keypoints(array):
-    keypoints = []
-    descriptors = []
-    data_list = array.tolist()
-    for point in array:
-        temp_feature = cv2.KeyPoint(x=point[0][0],y=point[0][1], size=point[1], angle=point[2], response=point[3], octave=point[4], class_id=point[5])
-        temp_descriptor = point[6]
-        keypoints.append(temp_feature)
-        descriptors.append(temp_descriptor)
-    return keypoints, np.array(descriptors)
-
-
-
-

src/Searcher.py

@@ -9,11 +9,11 @@ import ImageRecognitionSettings as settings
 from BEBLIDRescorer import BEBLIDRescorer
 import SearcherParameters
 
-from FAISSSearchEngine import FAISSSearchEngine
+from FAISSSearchEngine2 import FAISSSearchEngine
 import FeatureExtractor as fe
 import BEBLIDExtractorQ as lfQ
 import BEBLIDExtractorD as lfD
-from line_profiler_pycharm import profile
+import logging
 
 
 class Searcher:
@@ -25,31 +25,34 @@ class Searcher:
         self.search_engine = FAISSSearchEngine()
         self.rescorer = BEBLIDRescorer()
 
+    def get_indexed_ids(self):
+        return self.search_engine.get_indexed_ids()
+
     def get_id(self, idx):
         return self.search_engine.get_id(idx)
 
-    def add(self, img_file, id):
+    def exists(self, doc_id):
-        self.save(True)
+        return self.search_engine.exists(doc_id)
 
+    def add(self, img_file, doc_id):
         desc = fe.extract(img_file)
-        self.search_engine.add(desc, id)
+        self.search_engine.add(desc, doc_id)
 
-        lf = lfD.extract(img_file)
+        kp, des = lfD.extract(img_file)
-        self.rescorer.add(lf, id)
+        self.rescorer.add(doc_id, kp, des)
 
         #orb = lf.extract(img_file)
 
-        self.save()
-        print('added ' + id)
-
-    def remove(self, id):
         self.save(True)
-        self.search_engine.remove(id)
+        logging.info('added ' + doc_id)
-        #self.rescorer.remove(idx)
-        self.save()
-        print('removed ' + id)
 
-    def search_by_id(self, query_id, k=10, search_deep_level=1):
+    def remove(self, doc_id):
+        self.search_engine.remove(doc_id)
+        self.rescorer.remove(doc_id)
+        self.save(True)
+        logging.info('removed ' + doc_id)
+
+    def search_by_id(self, query_id, k=10, search_threshold=0.25, search_deep_level=1):
         kq = k
         if search_deep_level > 0:
             kq = SearcherParameters.SEARCH_DEEP_K[search_deep_level]
@@ -57,10 +60,10 @@ class Searcher:
         if search_deep_level > 0:
             res_lf = self.rescorer.rescore_by_id(query_id, res)
             res = res_lf if res_lf else res[:k]
+        res = [result for result in res if result[1] >= search_threshold]
         return res
 
-    @profile
+    def search_by_img(self, query_img, k=10, search_threshold=0.25, search_deep_level=1):
-    def search_by_img(self, query_img, k=10, search_deep_level=1):
         kq = k
         if search_deep_level:
             kq = SearcherParameters.SEARCH_DEEP_K[search_deep_level]
@@ -71,7 +74,7 @@ class Searcher:
             res_lf = self.rescorer.rescore_by_img(query_lf, res)
             #res = res_lf if res_lf else res[:k]
             res = res_lf if res_lf else res[:k]
-        res = [result for result in res if result[1] >= SearcherParameters.GEM_THRESHOLD]
+        res = [result for result in res if result[1] >= search_threshold]
         return res
 
     def save(self, is_backup=False):

src/SearcherParameters.py

@@ -1,2 +1 @@
 SEARCH_DEEP_K = [0, 1000, 2000, 5000, 10000, 30000, 100000]
-GEM_THRESHOLD = 0.25

src/TestClient.py

@@ -1,47 +1,73 @@
-from flask import Flask, request, redirect, url_for, flash, render_template, send_from_directory
-from random import randint
-import cv2
-import io
-import numpy as np
-import json
-
-import urllib
-
-
-from BeniCulturaliSearchEngine import BeniCulturaliSearchEngine
-import ImageRecognitionSettings as settings
-import uuid
 import requests
+import base64
 
-import os, os.path
+BASE_URL = 'http://bilioso.isti.cnr.it:8290/bcir/'
 
-BASE_URL = 'http://bilioso.isti.cnr.it:8190/bcir/'
+# ------Get indexed IDS------
-payload = {'id': '54b019e5ed5082b0938b14c4-IMG357781'}
+payload = {'id': '54b3298aed5082b093939ea1-IMG881380', 'k': 10, 'threshold': 0.25, 'searchDeepLevel': 1}
+r = requests.get(BASE_URL + 'getIds')
+print(r.json())
+
+# ------Check if an ID exists------
+payload = {'id': '54b3298aed5082b093939ea1-IMG881380'}
+r = requests.get(BASE_URL + 'exists', params=payload)
+print(r.json())
+
+# ------Searching by ID------
+print('Searching by ID')
+payload = {'id': '54b3298aed5082b093939ea1-IMG881380', 'k': 10, 'threshold': 0.25, 'searchDeepLevel': 1}
 r = requests.get(BASE_URL + 'searchById', params=payload)
 print(r.json())
 
-files = {'image': ('query', open('/media/Data/data/test/tpc_test/img/data_test/54b019e5ed5082b0938b14c4-IMG357781.jpg', 'rb'))}
+# ------Searching by IMG------
-r = requests.post(BASE_URL + 'searchByImg', files=files)
+print('Searching by IMG')
+files = {'image': ('query', open('/media/data2/data/swoads/workdir/img/ImmaginiComparazioni/ACC130111[1].jpg', 'rb'))}
+data = {'k': 10, 'threshold': 0.25, 'searchDeepLevel': 1}
+r = requests.post(BASE_URL + 'searchByImg', data=data, files=files)
 print(r.json())
 
-payload = {'url': 'http://bilioso.isti.cnr.it:8190/bcir/54b019e5ed5082b0938b14c4-IMG357781.jpg'}
+# ------Searching by IMG Basa64------
+print('Searching by IMG Base64')
+with open('/media/data2/data/swoads/workdir/img/ImmaginiComparazioni/ACC130111[1].jpg', "rb") as img_file:
+    b64_encoding = base64.b64encode(img_file.read())
+data = {'image':b64_encoding, 'k': 10, 'threshold': 0.25, 'searchDeepLevel': 1}
+r = requests.post(BASE_URL + 'searchByImgB64', data=data, files=files)
+print(r.json())
+
+# ------Searching by URL------
+print('Searching by URL')
+payload = {'url': 'http://bilioso.isti.cnr.it:8290/bcir/54b3298aed5082b093939ea1-IMG881380.jpg', 'k': 10,
+           'threshold': 0.25, 'searchDeepLevel': 1}
 r = requests.get(BASE_URL + 'searchByURL', params=payload)
 print(r.json())
 
-files = {'image': ('prova', open('/media/Data/data/test/tpc_test/img/data_test/54b019e5ed5082b0938b14c4-IMG357781.jpg', 'rb'))}
+# ------Adding newIMG------
-#files = {'image': ('prova', open('/media/Data/data/beni_culturali/deploy/dataset_ids.bak', 'rb'))}
+print('Adding newIMG')
+files = {'image': (
+'newIMG', open('/media/ssd2/data/swoads/workdir/img/ImmaginiComparazioni/ACC97468-149[3].jpg', 'rb'))}
 r = requests.post(BASE_URL + 'addImg', files=files)
 s = r.json()
 print(r.json())
 
-files = {'image': ('query', open('/media/Data/data/test/tpc_test/img/data_test/54b019e5ed5082b0938b14c4-IMG357781.jpg', 'rb'))}
+# ------Searching by newIMG------
-r = requests.post(BASE_URL + 'searchByImg', files=files)
+print('Searching by newIMG')
+files = {'image': (
+'query', open('/media/ssd2/data/swoads/workdir/img/ImmaginiComparazioni/ACC97468-149[3].jpg', 'rb'))}
+data = {'k': 10, 'threshold': 0.25, 'searchDeepLevel': 1}
+
+r = requests.post(BASE_URL + 'searchByImg', data=data, files=files)
 print(r.json())
 
-payload = {'id': 'prova'}
+# ------Removing newIMG------
+print('Removing newIMG')
+payload = {'id': 'newIMG'}
 r = requests.get(BASE_URL + 'rmImg', params=payload)
 print(r.json())
 
-files = {'image': ('query', open('/media/Data/data/test/tpc_test/img/data_test/54b019e5ed5082b0938b14c4-IMG357781.jpg', 'rb'))}
+# ------Searching by newIMG now removed from the index------
-r = requests.post(BASE_URL + 'searchByImg', files=files)
+print('Searching by newIMG now removed from the index')
+files = {'image': (
+'query', open('/media/ssd2/data/swoads/workdir/img/ImmaginiComparazioni/ACC97468-149[3].jpg', 'rb'))}
+data = {'k': 10, 'threshold': 0.25, 'searchDeepLevel': 1}
+r = requests.post(BASE_URL + 'searchByImg', data=data, files=files)
 print(r.json())