added FAISS Searcher

.gitignore

@@ -0,0 +1 @@
+/venv/

Dockerfile

@@ -9,7 +9,7 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
         nano \
         unzip
 
-RUN pip install numpy tornado flask-restful pillow numpy matplotlib tqdm scikit-learn h5py requests
+RUN pip install numpy tornado flask-restful pillow numpy matplotlib tqdm scikit-learn h5py requests faiss-cpu==1.7.2
 ADD . /workspace
 
 WORKDIR /workspace                                                                               

src/BeniCulturaliSearchEngine.py

@@ -1,4 +1,3 @@
-import h5py
 import numpy as np
 import beniculturaliSettings as settings
 

src/BeniCulturaliSearcher.py

@@ -7,7 +7,7 @@ import beniculturaliSettings as settings
 from BeniCulturaliRescorer import BeniCulturaliRescorer
 from BeniCulturaliSearchEngine import BeniCulturaliSearchEngine
 import FeatureExtractor as fe
-import ORBExtractor as lf
+#import ORBExtractor as lf
 
 
 class BeniCulturaliSearcher:

src/FAISSSearchEngine.py

@@ -0,0 +1,72 @@
+import numpy as np
+import beniculturaliSettings as settings
+import faiss
+
+
+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()
+
+        # 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)
+
+        # 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
+
+
+    def get_id(self, idx):
+        return self.ids[idx]
+
+
+    def add(self, desc, id):
+        self.ids.append(id)
+        self.descs = np.vstack((self.descs, desc))
+        self.save()
+
+
+    def remove(self, id):
+        idx = self.ids.index(id)
+        del self.ids[idx]
+        self.descs = np.delete(self.descs, idx, axis=0)
+
+
+    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-------')
+        print(query)
+        queries = np.reshape(query, (-1, 2048))
+        print(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
+
+        if is_backup:
+            descs_file += '.bak'
+            ids_file += '.bak'
+
+        np.save(descs_file, self.descs)
+        np.savetxt(ids_file, self.ids, fmt='%s')

src/Searcher.py

@@ -0,0 +1,68 @@
+import cv2
+import numpy as np
+import pickle as pickle
+
+import LFUtilities
+import beniculturaliSettings as settings
+from BeniCulturaliRescorer import BeniCulturaliRescorer
+from FAISSSearchEngine import FAISSSearchEngine
+import FeatureExtractor as fe
+import ORBExtractor as lf
+
+
+class Searcher:
+    K_REORDERING = 15
+
+    def __init__(self):
+        # self.dataset = h5py.File(settings.dataset_file, 'r')['rmac'][...]
+
+        # np.save('/media/Data/data/beni_culturali/deploy/dataset', self.dataset)
+        self.search_engine = FAISSSearchEngine()
+        #self.rescorer = BeniCulturaliRescorer()
+
+    def get_id(self, idx):
+        return self.search_engine.get_id(idx)
+
+    def add(self, img_file, id):
+        self.save(True)
+
+        desc = fe.extract(img_file)
+        #orb = lf.extract(img_file)
+        self.search_engine.add(desc, id)
+        #self.rescorer.add(orb)
+
+        self.save()
+        print('added ' + id)
+
+    def remove(self, id):
+        self.save(True)
+        self.search_engine.remove(id)
+        #self.rescorer.remove(idx)
+        self.save()
+        print('removed ' + id)
+
+    def search_by_id(self, query_id, k=10, rescorer=False):
+        kq = k
+        if rescorer:
+            kq = self.K_REORDERING
+        res = self.search_engine.search_by_id(query_id, kq)
+       # if rescorer:
+       #     res_lf = self.rescorer.rescore_by_id(query_id, res)
+       #     res = res_lf if res_lf else res[:k]
+        return res
+
+    def search_by_img(self, query_img, k=10, rescorer=False):
+        kq = k
+        if rescorer:
+            kq = self.K_REORDERING
+        query_desc = fe.extract(query_img)
+        res = self.search_engine.search_by_img(query_desc, kq)
+        #if rescorer:
+        #    query_lf = lf.extract(query_img)
+        #    res_lf = self.rescorer.rescore_by_img(query_lf, res)
+        #    res = res_lf if res_lf else res[:k]
+        return res
+
+    def save(self, is_backup=False):
+        self.search_engine.save(is_backup)
+        #self.rescorer.save(is_backup)

src/beniculturali.py

@@ -1,5 +1,3 @@
-from re import split
-
 from flask import Flask, request, redirect, url_for, flash, render_template, send_from_directory, abort
 from random import randint
 import cv2
@@ -9,13 +7,12 @@ import json
 
 import urllib
 
-from BeniCulturaliSearcher import BeniCulturaliSearcher
+#from BeniCulturaliSearcher import BeniCulturaliSearcher
+from Searcher import Searcher
 from BeniCulturaliSearchEngine import BeniCulturaliSearchEngine
 import beniculturaliSettings as settings
 import uuid
-import requests
 import os, os.path
-from PIL import Image
 import tornado.wsgi
 import tornado.httpserver
 import argparse
@@ -29,7 +26,7 @@ def api_root():
     print('index_with_randoms.html')
     random_ids = []
     for i in range(0, 15):
-        random_ids.append(searcher.get_id(randint(0, 3000)))
+        random_ids.append(searcher.get_id(randint(0, 30)))
     return render_template('index_with_randoms.html', random_ids=random_ids)
 
 
@@ -188,7 +185,7 @@ def start_from_terminal(app):
     args = parser.parse_args()
     settings.load_setting(args.conf)
     global searcher
-    searcher = BeniCulturaliSearcher()
+    searcher = Searcher()
 
     #if args.debug:
     #    app.run(debug=True, host='0.0.0.0', port=settings.port)