如何计算 Python OpenCV 中两个 ndarray 之间的欧氏距离
How to calculate euclidean distance between two ndarrays in Python OpenCV
我正在尝试计算两个图像之间的 euclidean distance。为此，我首先获取图像的 128d 数组，然后使用 cv2.norm() 获取距离。下面是代码：
embedder = cv2.dnn.readNetFromTorch(<model_path>)
embedder.setInput(faceBlob)
vec = embedder.forward()
print(vec)
vec_file = pickle.loads(open(args["recognizer"], "rb").read())
known_vec = vec_file.support_vectors_
for embedding in known_vec:
print(embedding)
distance = cv2.norm(vec, embedding)
在上面的代码中，我有来自图像文件的 vec 和来自 known_vec 的 embedding。下面是 vec 和 embedding 的样子：
vec:
[[ 1.50953727e-02 2.81099556e-03 -3.50600183e-02 -5.78538561e-03
2.31029615e-02 1.73964068e-01 3.79475281e-02 1.27083873e-02
-9.68848541e-02 -1.13846334e-02 1.92795545e-02 7.36472011e-02
7.79130757e-02 -2.11485863e-01 -6.82436973e-02 -1.64214987e-04
-2.01231852e-01 2.29396261e-02 -4.34093624e-02 9.49875787e-02
1.96524531e-01 -1.40022561e-01 1.00606538e-01 3.70812230e-02
-1.45635298e-02 3.85013111e-02 -8.84107649e-02 -3.15038770e-01
3.25521380e-02 4.29384746e-02 1.74971391e-02 3.27903479e-02
-4.76430990e-02 6.02841079e-02 3.60031053e-02 -4.40581292e-02
-8.15121531e-02 1.46739334e-01 3.19194235e-02 -5.45275658e-02
3.90344337e-02 -1.47340044e-01 -8.87186751e-02 9.13328975e-02
-1.33012265e-01 -6.64092153e-02 1.45769000e-01 -4.49066125e-02
-1.70968711e-01 1.84094254e-02 -1.43186841e-02 -3.82681675e-02
-9.34342016e-03 3.55955921e-02 6.70149326e-02 1.09950025e-02
1.09302737e-01 6.81546181e-02 -7.36390129e-02 -1.16702713e-01
-1.40488185e-02 -2.61708386e-02 2.10996747e-01 -6.54504001e-02
1.53530702e-01 -8.38626847e-02 -1.86689962e-02 -2.70418124e-03
-2.32851990e-02 5.15586026e-02 -8.13494101e-02 7.11051449e-02
-1.19156547e-01 1.64730344e-02 2.14404091e-02 -4.26124930e-02
-7.58614466e-02 3.41765210e-02 4.33261022e-02 1.71321735e-01
-1.44580662e-01 -4.46063727e-02 2.88061053e-02 4.15235199e-03
-1.05133533e-01 1.83968637e-02 1.12521172e-01 5.98449074e-02
2.27536708e-02 -3.94514054e-02 8.82636383e-02 -8.32060277e-02
-4.92165126e-02 7.84259290e-03 -1.18784890e-01 -9.60832909e-02
-4.92453715e-03 1.44542158e-01 3.30348462e-02 2.81231338e-03
6.14521280e-02 -7.35903298e-03 -7.54322633e-02 1.10058203e-01
5.87815009e-02 1.78886037e-02 -4.85782837e-03 1.84458613e-01
3.11982278e-02 -7.37933293e-02 -7.51596317e-02 1.04695961e-01
-9.72250253e-02 -9.44643840e-02 1.27530798e-01 1.23021275e-01
-9.76756811e-02 -8.43207240e-02 6.96085840e-02 1.64856598e-01
2.96653248e-02 -2.89077275e-02 -1.12501364e-02 2.36267108e-03
-3.10793705e-02 8.10181573e-02 3.76056321e-02 5.94174117e-02]]
embedding:
[ 0.03765839 0.09021743 -0.001356 0.04076054 0.04601533 0.25682124
0.03684118 0.04658685 -0.0683746 0.0922796 0.04687139 -0.00272194
0.01932732 -0.16777565 0.06045137 -0.03307288 -0.02232558 0.12863097
0.06122964 -0.09006073 0.20338912 -0.05094699 -0.05211756 0.07307947
0.14153366 -0.03110684 -0.11104943 -0.2103712 0.088107 0.09068976
0.10696387 0.05845631 -0.07577723 0.04438741 0.10031617 -0.02361435
-0.01955461 -0.08868567 0.11458483 -0.10992806 0.10672607 -0.12679504
0.01632918 0.07699546 -0.07913689 -0.12192447 0.11415054 -0.0351057
-0.14725251 -0.13427286 0.10578448 0.06842157 0.01293649 -0.02879749
0.04028381 0.08853597 0.04816869 -0.01133396 -0.0159949 -0.16353707
-0.02181644 -0.07351912 0.09002206 -0.15716557 0.09319755 -0.02052106
0.03212938 -0.03629737 -0.03515568 0.13036096 -0.03792502 0.10754489
-0.15451996 -0.11948325 -0.04193863 -0.02881463 -0.07436965 0.11885778
0.0090537 0.10868978 -0.15199617 0.11014692 0.12235526 0.03885943
0.03852987 -0.01098366 0.10460863 0.01727468 0.04457604 0.01060722
0.00488355 -0.04175444 -0.10867393 0.00945349 -0.09279638 -0.11769478
0.03810817 0.09189356 -0.06156022 -0.0081004 0.08123636 0.08515859
0.0019427 0.05686275 -0.00857953 0.03230546 0.03530128 0.04284313
0.0120915 -0.00855714 -0.06190326 -0.03082059 -0.13773248 -0.13991699
0.18191327 0.00246803 -0.08906183 -0.16354702 0.04687581 0.09188556
0.11612693 -0.06407943 0.01638488 -0.01842222 0.03551267 0.05930701
0.13821986 0.0852181 ]
当我尝试在这两者之间执行 cv2.norm 时，出现以下错误：
OpenCV(4.2.0) C:\projects\opencv-python\opencv\modules\core\src\norm.cpp:1081: error: (-2:Unspecified error) in function 'double __cdecl cv::norm(const class cv::_InputArray &,const class cv::_InputArray &,int,const class cv::_InputArray &)'
> Input type mismatch (expected: '_src1.type() == _src2.type()'), where
> '_src1.type()' is 5 (CV_32FC1)
> must be equal to
> '_src2.type()' is 6 (CV_64FC1)
我在cv2.norm和ndarray方面不是很有经验。任何人都可以帮助并提出一些好的解决方案来计算 euclidean 距离。请帮忙。谢谢
cv2.norm 期望两个参数的形状相同。当您调用该函数时，您的两个输入具有不同的形状。要克服这个问题，您需要将其中一个重塑为与第二个相同的形状。
# vec.shape (1,128) This means vec is a 2d array, with 128 values in ist row
# embedding.shape (128,) This mean embedding is a 1d array of 128 values
embedding = np.reshape(embedding, (1,128))
# embedding.shape (1,128) same as vec
distance = cv2.norm(vec, embedding)
我正在尝试计算两个图像之间的 euclidean distance。为此，我首先获取图像的 128d 数组，然后使用 cv2.norm() 获取距离。下面是代码：
embedder = cv2.dnn.readNetFromTorch(<model_path>)
embedder.setInput(faceBlob)
vec = embedder.forward()
print(vec)
vec_file = pickle.loads(open(args["recognizer"], "rb").read())
known_vec = vec_file.support_vectors_
for embedding in known_vec:
print(embedding)
distance = cv2.norm(vec, embedding)
在上面的代码中，我有来自图像文件的 vec 和来自 known_vec 的 embedding。下面是 vec 和 embedding 的样子：
vec:
[[ 1.50953727e-02 2.81099556e-03 -3.50600183e-02 -5.78538561e-03
2.31029615e-02 1.73964068e-01 3.79475281e-02 1.27083873e-02
-9.68848541e-02 -1.13846334e-02 1.92795545e-02 7.36472011e-02
7.79130757e-02 -2.11485863e-01 -6.82436973e-02 -1.64214987e-04
-2.01231852e-01 2.29396261e-02 -4.34093624e-02 9.49875787e-02
1.96524531e-01 -1.40022561e-01 1.00606538e-01 3.70812230e-02
-1.45635298e-02 3.85013111e-02 -8.84107649e-02 -3.15038770e-01
3.25521380e-02 4.29384746e-02 1.74971391e-02 3.27903479e-02
-4.76430990e-02 6.02841079e-02 3.60031053e-02 -4.40581292e-02
-8.15121531e-02 1.46739334e-01 3.19194235e-02 -5.45275658e-02
3.90344337e-02 -1.47340044e-01 -8.87186751e-02 9.13328975e-02
-1.33012265e-01 -6.64092153e-02 1.45769000e-01 -4.49066125e-02
-1.70968711e-01 1.84094254e-02 -1.43186841e-02 -3.82681675e-02
-9.34342016e-03 3.55955921e-02 6.70149326e-02 1.09950025e-02
1.09302737e-01 6.81546181e-02 -7.36390129e-02 -1.16702713e-01
-1.40488185e-02 -2.61708386e-02 2.10996747e-01 -6.54504001e-02
1.53530702e-01 -8.38626847e-02 -1.86689962e-02 -2.70418124e-03
-2.32851990e-02 5.15586026e-02 -8.13494101e-02 7.11051449e-02
-1.19156547e-01 1.64730344e-02 2.14404091e-02 -4.26124930e-02
-7.58614466e-02 3.41765210e-02 4.33261022e-02 1.71321735e-01
-1.44580662e-01 -4.46063727e-02 2.88061053e-02 4.15235199e-03
-1.05133533e-01 1.83968637e-02 1.12521172e-01 5.98449074e-02
2.27536708e-02 -3.94514054e-02 8.82636383e-02 -8.32060277e-02
-4.92165126e-02 7.84259290e-03 -1.18784890e-01 -9.60832909e-02
-4.92453715e-03 1.44542158e-01 3.30348462e-02 2.81231338e-03
6.14521280e-02 -7.35903298e-03 -7.54322633e-02 1.10058203e-01
5.87815009e-02 1.78886037e-02 -4.85782837e-03 1.84458613e-01
3.11982278e-02 -7.37933293e-02 -7.51596317e-02 1.04695961e-01
-9.72250253e-02 -9.44643840e-02 1.27530798e-01 1.23021275e-01
-9.76756811e-02 -8.43207240e-02 6.96085840e-02 1.64856598e-01
2.96653248e-02 -2.89077275e-02 -1.12501364e-02 2.36267108e-03
-3.10793705e-02 8.10181573e-02 3.76056321e-02 5.94174117e-02]]
embedding:
[ 0.03765839 0.09021743 -0.001356 0.04076054 0.04601533 0.25682124
0.03684118 0.04658685 -0.0683746 0.0922796 0.04687139 -0.00272194
0.01932732 -0.16777565 0.06045137 -0.03307288 -0.02232558 0.12863097
0.06122964 -0.09006073 0.20338912 -0.05094699 -0.05211756 0.07307947
0.14153366 -0.03110684 -0.11104943 -0.2103712 0.088107 0.09068976
0.10696387 0.05845631 -0.07577723 0.04438741 0.10031617 -0.02361435
-0.01955461 -0.08868567 0.11458483 -0.10992806 0.10672607 -0.12679504
0.01632918 0.07699546 -0.07913689 -0.12192447 0.11415054 -0.0351057
-0.14725251 -0.13427286 0.10578448 0.06842157 0.01293649 -0.02879749
0.04028381 0.08853597 0.04816869 -0.01133396 -0.0159949 -0.16353707
-0.02181644 -0.07351912 0.09002206 -0.15716557 0.09319755 -0.02052106
0.03212938 -0.03629737 -0.03515568 0.13036096 -0.03792502 0.10754489
-0.15451996 -0.11948325 -0.04193863 -0.02881463 -0.07436965 0.11885778
0.0090537 0.10868978 -0.15199617 0.11014692 0.12235526 0.03885943
0.03852987 -0.01098366 0.10460863 0.01727468 0.04457604 0.01060722
0.00488355 -0.04175444 -0.10867393 0.00945349 -0.09279638 -0.11769478
0.03810817 0.09189356 -0.06156022 -0.0081004 0.08123636 0.08515859
0.0019427 0.05686275 -0.00857953 0.03230546 0.03530128 0.04284313
0.0120915 -0.00855714 -0.06190326 -0.03082059 -0.13773248 -0.13991699
0.18191327 0.00246803 -0.08906183 -0.16354702 0.04687581 0.09188556
0.11612693 -0.06407943 0.01638488 -0.01842222 0.03551267 0.05930701
0.13821986 0.0852181 ]
当我尝试在这两者之间执行 cv2.norm 时，出现以下错误：
OpenCV(4.2.0) C:\projects\opencv-python\opencv\modules\core\src\norm.cpp:1081: error: (-2:Unspecified error) in function 'double __cdecl cv::norm(const class cv::_InputArray &,const class cv::_InputArray &,int,const class cv::_InputArray &)'
> Input type mismatch (expected: '_src1.type() == _src2.type()'), where
> '_src1.type()' is 5 (CV_32FC1)
> must be equal to
> '_src2.type()' is 6 (CV_64FC1)
我在cv2.norm和ndarray方面不是很有经验。任何人都可以帮助并提出一些好的解决方案来计算 euclidean 距离。请帮忙。谢谢
cv2.norm 期望两个参数的形状相同。当您调用该函数时，您的两个输入具有不同的形状。要克服这个问题，您需要将其中一个重塑为与第二个相同的形状。
# vec.shape (1,128) This means vec is a 2d array, with 128 values in ist row
# embedding.shape (128,) This mean embedding is a 1d array of 128 values
embedding = np.reshape(embedding, (1,128))
# embedding.shape (1,128) same as vec
distance = cv2.norm(vec, embedding)