这是我的代码。我正在使用face_recognition库。从数据库中获取face_encoding时。在for循环的第二次迭代中出现一些错误。
for each in rows:
face_data = pickle.loads(each[0])
for (top, right, bottom, left), face_encoding in zip(face_locations, face_encoding):
print(face_data)
matches = face_recognition.compare_faces([face_encoding], face_data)
if True in matches:
print(f'Face matches with {each[1]}')
break
else:
print('Face does not match')
这是我的执行顺序
[-0.07152465 0.10215988 0.01136428 -0.0308747 -0.16287753 -0.03153189
-0.06578728 -0.14805837 0.15640661 -0.07523477 0.14867496 0.04072035
-0.16808468 -0.00982519 -0.02270632 0.08025806 -0.20561409 -0.12184067
-0.11708203 -0.09774955 -0.06495257 0.02422624 0.00922573 0.01991012
-0.13370116 -0.20686181 -0.1488608 -0.12282808 0.18252769 -0.08956295
0.0171157 0.07873753 -0.1460952 -0.09000996 0.06861175 0.13834456
-0.07108913 -0.01959831 0.20239949 0.05026209 -0.15374154 -0.01580941
0.06390329 0.27492422 0.25704402 0.08602936 0.03448958 -0.04159172
0.14044689 -0.21021228 0.06922252 0.15272671 0.21329369 0.10292344
0.08227161 -0.22346584 0.09088185 0.14461374 -0.1744801 0.11717336
0.13315231 -0.16698143 0.00260702 0.07305491 0.18898451 0.10911863
-0.01746466 -0.16348183 0.23652273 -0.22252038 -0.06905969 0.12435962
-0.07139365 -0.08369306 -0.24058753 -0.06713018 0.39160824 0.16331878
-0.09675828 -0.01672895 -0.0439845 -0.05550656 0.00398231 0.04980896
-0.17432253 -0.07968439 -0.12069637 -0.02069626 0.22844598 -0.00178664
0.06633241 0.1762405 0.02624917 -0.00859033 0.03681781 -0.02436156
-0.13713233 -0.03320267 -0.03164122 -0.09198754 0.03632114 -0.15644331
0.04891206 0.11745825 -0.20992307 0.17861255 -0.07077605 0.00281386
-0.03633321 -0.04432689 -0.02048816 0.02499891 0.28510362 -0.21085122
0.21044926 0.21076941 -0.06554883 0.08499552 0.05917196 0.1140425
-0.05485246 -0.04627831 -0.15220977 -0.10018148 -0.06419093 -0.15043713
0.02276208 0.0441333 ]
Face does not match
[-0.09625021 0.14334008 0.04706059 -0.04014181 -0.08584346 -0.01887352
0.00490261 -0.1639722 0.09984879 -0.15265316 0.20287979 -0.04864441
-0.32042649 -0.09799721 -0.00439703 0.14766732 -0.14317626 -0.07932805
-0.15182969 -0.12430412 0.06176512 0.09200086 -0.01968544 -0.04486096
-0.10424317 -0.2421699 -0.08235801 -0.06906197 0.02620533 -0.09016494
0.03978066 0.03132563 -0.16744252 -0.04149911 -0.04274801 0.04262511
-0.06783581 -0.06297266 0.08989972 0.00187942 -0.13782793 -0.02583435
-0.02318384 0.22219715 0.21874472 -0.00792544 0.01723921 -0.13756718
0.12533604 -0.23840009 -0.01863693 0.0764866 0.05697979 0.10692431
0.03860043 -0.14166503 0.02846308 0.13315612 -0.17089525 0.05577727
0.06003947 -0.2250281 -0.06753878 -0.01277698 0.08528464 0.04355494
-0.0524184 -0.13549563 0.25745231 -0.17983361 -0.11317196 0.03724971
-0.05688772 -0.14536679 -0.33049241 0.00788163 0.37128115 0.1440357
-0.17786828 -0.03610786 -0.03851928 0.00788332 0.10840025 0.08781973
-0.0197401 -0.11310805 -0.12882231 0.05047181 0.22589801 -0.1145445
-0.02849352 0.24946831 0.02767072 0.01063228 0.03616182 0.05806186
-0.100531 0.00592576 -0.07722946 -0.0200903 0.09955086 -0.16360769
-0.02626547 0.12187837 -0.10974941 0.25797191 0.01019638 0.00796576
-0.01505023 -0.11661487 -0.02455078 0.05865026 0.19031769 -0.1405995
0.22529884 0.2284973 -0.00063039 0.16232219 0.03971991 0.09476222
-0.03303688 -0.08237147 -0.22452414 -0.15178023 0.02300116 0.01437929
-0.01491559 0.04098029]
Traceback (most recent call last):
File "set_faces.py", line 33, in <module>
matches = face_recognition.compare_faces([face_encoding], face_data)
File "/home/dipanshuchaubey/.local/share/virtualenvs/face_recog-byiVijNu/lib/python3.6/site-packages/face_recognition/api.py", line 222, in compare_faces
return list(face_distance(known_face_encodings, face_encoding_to_check) <= tolerance)
File "/home/dipanshuchaubey/.local/share/virtualenvs/face_recog-byiVijNu/lib/python3.6/site-packages/face_recognition/api.py", line 72, in face_distance
return np.linalg.norm(face_encodings - face_to_compare, axis=1)
File "/home/dipanshuchaubey/.local/share/virtualenvs/face_recog-byiVijNu/lib/python3.6/site-packages/numpy/linalg/linalg.py", line 2481, in norm
return sqrt(add.reduce(s, axis=axis, keepdims=keepdims))
numpy.AxisError: axis 1 is out of bounds for array of dimension 1
似乎一切正常，直到第一次迭代。为什么在第二次迭代中出现此错误？
谢谢。