try get index

.gitignore

@@ -104,3 +104,5 @@ venv.bak/
 .mypy_cache/
 
 _index/
+diary_after_cover_vec.txt
+diary_before_cover_vec.txt
\ No newline at end of file

src/diary_cover_similarity.py

@@ -90,37 +90,43 @@ def main():
     face_to_vec_f = lambda img: face_to_vec(img, face_rec, face_detector, shape_predictor)
 
     # save_diary_image_info(diary_after_cover_vec_file, face_to_vec_f)
-    save_faiss_index(diary_after_cover_vec_file, faiss_index_path)
-
-    # img_url = "https://pic.igengmei.com/2020/07/03/1437/1b9975bb0b81-w"
-    # img = url_to_ndarray(img_url)
-    # if img.any():
-    #     faces = face_to_vec_f(img)
-
-    #     if len(faces) > 0:
-    #         for face in faces:
-    #             print(face)
-    #             print("\n")
-    #             print("-------------")
-    #             face_feature = np.array(json.loads(face["feature"]))
-
-    #         ids = [17418645]
-    #         ids_np = np.array(ids).astype("int")
-    #         faces_np = np.array([face_feature]).astype("float32")
-    #         print(ids_np)
-    #         print(faces_np)
-
-    #         index = faiss.IndexHNSWFlat(128, 32)
-    #         print("index: ")
-    #         print(index)
-    #         index2 = faiss.IndexIDMap(index)
-    #         print("index2: ")
-    #         print(index2)
-    #         index2.add_with_ids(faces_np, ids_np)
-    #         print("index2: ")
-    #         print(index2)
-    #     else:
-    #         print("no faces")
+    # save_faiss_index(diary_after_cover_vec_file, faiss_index_path)
+
+    a = [
+        -0.08361373096704483, 0.06760436296463013, 0.10752949863672256, -0.020746365189552307, -0.07035162299871445,
+        -0.014547230675816536, -0.043201886117458344, -0.12196271121501923, 0.13929598033428192, -0.1360183209180832,
+        0.23247791826725006, -0.08867999166250229, -0.24177594482898712, -0.05600903555750847, -0.05371646583080292,
+        0.22015368938446045, -0.12883149087429047, -0.0822330191731453, -0.0413128100335598, 0.08704500645399094,
+        0.10081718862056732, -0.03764188289642334, 0.036720920354127884, 0.04766431450843811, -0.0685625970363617,
+        -0.38336044549942017, -0.10978807508945465, -0.07328074425458908, -0.023904308676719666, -0.007438751868903637,
+        -0.09545779973268509, 0.027364756911993027, -0.1537190079689026, -0.04008519649505615, -0.03581209108233452,
+        0.04322449117898941, -0.05686069279909134, -0.11610691249370575, 0.1640746295452118, -0.004643512889742851,
+        -0.34821364283561707, 0.03711444139480591, -0.0026186704635620117, 0.1917344480752945, 0.14298999309539795,
+        0.04084448516368866, 0.06119539216160774, -0.12611950933933258, 0.10941470414400101, -0.20786598324775696,
+        0.03435457497835159, 0.11412393301725388, 0.0602775476872921, 0.054409340023994446, -0.002967053558677435,
+        -0.12524624168872833, 0.026284342631697655, 0.08236880600452423, -0.10654348134994507, 0.00403654295951128,
+        0.10716681182384491, -0.08270247280597687, 0.018992319703102112, -0.11595900356769562, 0.18344789743423462,
+        0.0895184576511383, -0.1307670772075653, -0.15750591456890106, 0.11103398352861404, -0.13521818816661835,
+        -0.03199139982461929, 0.11129119992256165, -0.17407448589801788, -0.20658859610557556, -0.3114454746246338,
+        0.01914297416806221, 0.39955294132232666, 0.12365783005952835, -0.14545315504074097, -0.03254598751664162,
+        -0.10342024266719818, 0.03375910595059395, 0.11272192746400833, 0.21788232028484344, 0.08588762581348419,
+        0.012640122324228287, -0.07646650820970535, -0.043292030692100525, 0.21306097507476807, -0.12407292425632477,
+        -0.025112995877861977, 0.2634827196598053, 0.005047444254159927, 0.06562616676092148, -0.07397496700286865,
+        0.06206338107585907, -0.0634055882692337, 0.05882266163825989, -0.05909111723303795, 0.027562778443098068,
+        0.043835900723934174, 0.00407575536519289, -0.007656056433916092, 0.1048622876405716, -0.17822585999965668,
+        0.1303984671831131, -0.021631652489304543, 0.0836174339056015, 0.11956407874822617, 0.007379574701189995,
+        -0.07777556777000427, -0.08474794030189514, 0.09585978090763092, -0.21120299398899078, 0.1435444951057434,
+        0.19884724915027618, 0.07154559344053268, 0.06259742379188538, 0.10118959099054337, 0.10188969224691391,
+        -0.015351934358477592, -0.04335442930459976, -0.26258283853530884, -0.021509556099772453, 0.12185295671224594,
+        -0.011788002215325832, 0.01337978895753622, -0.008025042712688446
+    ]
+    feature = np.array(a).astype("float32")
+    index = faiss.read_index(faiss_index_path)
+    D, I  = index.search(np.array[feature], 5)
+    print(D)
+    print(I)
+    ids = list(I.flat)
+    print(ids)
 
 
 if __name__ == "__main__":