CV之FR:DIY脚本通过人脸图像得到人脸特征向量并输出多张人脸图片之两两图片之间的距离

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一个处女座的程序猿 发表于 2021/03/26 00:13:00 2021/03/26
【摘要】 CV之FR:DIY脚本通过人脸图像得到人脸特征向量并输出多张人脸图片之两两图片之间的距离           目录 输出结果 设计思路 实现代码 计算过程             输出结果   设计思路 实现代码 from scipy import miscimport tensorflow as tfimport nu...

CV之FR:DIY脚本通过人脸图像得到人脸特征向量并输出多张人脸图片之两两图片之间的距离

 

 

 

 

 

目录

输出结果

设计思路

实现代码

计算过程


 

 

 

 

 

 

输出结果

 

设计思路

实现代码


  
  1. from scipy import misc
  2. import tensorflow as tf
  3. import numpy as np
  4. import sys
  5. import os
  6. import argparse
  7. ……

 

计算过程


  
  1. prewhitened参数是 [[[ 0.12834621 0.69292342 0.78113861]
  2. [ 0.12834621 0.69292342 0.78113861]
  3. [ 0.12834621 0.67528038 0.78113861]
  4. ...
  5. [-1.77710188 -1.77710188 -1.61831454]
  6. [-1.63595757 -1.65360061 -1.44188416]
  7. [-1.33602593 -1.35366897 -1.12430948]]
  8. [[ 0.25184747 0.74585253 0.93992595]
  9. [ 0.25184747 0.74585253 0.93992595]
  10. [ 0.28713355 0.74585253 0.93992595]
  11. ...
  12. [-1.77710188 -1.79474492 -1.65360061]
  13. [-1.75945884 -1.79474492 -1.56538542]
  14. [-1.63595757 -1.67124365 -1.40659808]]
  15. [[ 0.28713355 0.78113861 0.97521202]
  16. [ 0.32241962 0.79878165 0.99285506]
  17. [ 0.3577057 0.81642468 1.0104981 ]
  18. ...
  19. [-1.77710188 -1.79474492 -1.65360061]
  20. [-1.77710188 -1.81238795 -1.6006715 ]
  21. [-1.79474492 -1.81238795 -1.6006715 ]]
  22. ...
  23. [[-0.66559049 -0.41858796 0.07541709]
  24. [-0.55973227 -0.31272974 0.16363228]
  25. [-0.5773753 -0.33037278 0.18127532]
  26. ...
  27. [-0.10101328 0.16363228 0.76349557]
  28. [-0.13629936 0.14598925 0.74585253]
  29. [-0.25980062 0.05777406 0.65763734]]
  30. [[-0.82437783 -0.5773753 -0.10101328]
  31. [-0.66559049 -0.41858796 0.05777406]
  32. [-0.59501834 -0.34801581 0.16363228]
  33. ...
  34. [-0.13629936 0.09306013 0.71056646]
  35. [-0.1539424 0.11070317 0.72820949]
  36. [-0.1539424 0.18127532 0.76349557]]
  37. [[-0.96552214 -0.71851961 -0.24215759]
  38. [-0.77144872 -0.52444619 -0.04808417]
  39. [-0.64794746 -0.40094493 0.11070317]
  40. ...
  41. [-0.18922847 0.05777406 0.67528038]
  42. [-0.22451455 0.04013102 0.65763734]
  43. [-0.1539424 0.18127532 0.76349557]]]
  44. prewhitened参数是 [[[-0.41843267 0.01911131 0.37196935]
  45. [-0.41843267 0.00499698 0.35785503]
  46. [-0.39020402 0.01911131 0.35785503]
  47. ...
  48. [-0.39020402 0.04733995 0.32962638]
  49. [-0.43254699 0.00499698 0.30139774]
  50. [-0.44666131 -0.00911734 0.28728342]]
  51. [[-0.40431834 0.00499698 0.3437407 ]
  52. [-0.39020402 0.01911131 0.35785503]
  53. [-0.36197538 0.01911131 0.3437407 ]
  54. ...
  55. [-0.41843267 0.01911131 0.30139774]
  56. [-0.44666131 -0.00911734 0.28728342]
  57. [-0.44666131 -0.00911734 0.28728342]]
  58. [[-0.40431834 0.00499698 0.32962638]
  59. [-0.41843267 -0.02323166 0.30139774]
  60. [-0.33374674 0.04733995 0.37196935]
  61. ...
  62. [-0.41843267 0.01911131 0.30139774]
  63. [-0.44666131 -0.00911734 0.28728342]
  64. [-0.46077563 -0.02323166 0.2731691 ]]
  65. ...
  66. [[-1.67460729 -1.67460729 -1.67460729]
  67. [-1.67460729 -1.67460729 -1.67460729]
  68. [-1.67460729 -1.67460729 -1.67460729]
  69. ...
  70. [-1.60403568 -1.44877815 -1.19472036]
  71. [-1.64637865 -1.47700679 -1.25117764]
  72. [-1.67460729 -1.50523543 -1.27940629]]
  73. [[-1.67460729 -1.67460729 -1.67460729]
  74. [-1.67460729 -1.67460729 -1.67460729]
  75. [-1.67460729 -1.67460729 -1.67460729]
  76. ...
  77. [-1.60403568 -1.44877815 -1.19472036]
  78. [-1.61815001 -1.44877815 -1.222949 ]
  79. [-1.66049297 -1.49112111 -1.26529196]]
  80. [[-1.67460729 -1.67460729 -1.67460729]
  81. [-1.67460729 -1.67460729 -1.67460729]
  82. [-1.67460729 -1.67460729 -1.67460729]
  83. ...
  84. [-1.60403568 -1.44877815 -1.19472036]
  85. [-1.58992136 -1.4205495 -1.19472036]
  86. [-1.64637865 -1.47700679 -1.25117764]]]
  87. prewhitened参数是 [[[-0.68576058 -0.65851282 -0.6721367 ]
  88. [-1.21709198 -1.18984422 -1.2034681 ]
  89. [-0.94461434 -0.91736658 -0.93099046]
  90. ...
  91. [-1.5849368 -1.5031935 -1.5849368 ]
  92. [-1.14897257 -1.06722928 -1.18984422]
  93. [-1.0536054 -0.95823822 -1.09447704]]
  94. [[-1.08085316 -1.06722928 -1.08085316]
  95. [-1.36695468 -1.3533308 -1.36695468]
  96. [-1.5031935 -1.48956962 -1.5031935 ]
  97. ...
  98. [-1.39420245 -1.33970692 -1.39420245]
  99. [-1.42145021 -1.3533308 -1.44869798]
  100. [-1.38057857 -1.28521139 -1.40782633]]
  101. [[-0.98548599 -0.98548599 -1.01273375]
  102. [-1.06722928 -1.06722928 -1.09447704]
  103. [-1.25796363 -1.25796363 -1.28521139]
  104. ...
  105. [-1.18984422 -1.18984422 -1.21709198]
  106. [-1.02635763 -1.02635763 -1.06722928]
  107. [-1.31245916 -1.29883527 -1.36695468]]
  108. ...
  109. [[ 1.2215829 1.24883066 1.23520678]
  110. [ 1.23520678 1.26245454 1.24883066]
  111. [ 1.23520678 1.26245454 1.24883066]
  112. ...
  113. [ 0.43139774 0.44502162 0.49951715]
  114. [ 0.41777386 0.43139774 0.48589327]
  115. [ 0.43139774 0.44502162 0.49951715]]
  116. [[ 1.23520678 1.26245454 1.24883066]
  117. [ 1.2215829 1.24883066 1.23520678]
  118. [ 1.20795901 1.23520678 1.2215829 ]
  119. ...
  120. [ 0.48589327 0.49951715 0.55401268]
  121. [ 0.47226939 0.48589327 0.5403888 ]
  122. [ 0.47226939 0.48589327 0.5403888 ]]
  123. [[ 1.19433513 1.2215829 1.20795901]
  124. [ 1.16708737 1.19433513 1.18071125]
  125. [ 1.15346349 1.18071125 1.16708737]
  126. ...
  127. [ 0.67662762 0.6902515 0.74474703]
  128. [ 0.70387538 0.71749926 0.77199479]
  129. [ 0.73112314 0.74474703 0.79924255]]]
  130. prewhitened参数是 [[[-1.35183598 -1.45036667 -1.43805033]
  131. [-1.32720331 -1.38878499 -1.38878499]
  132. [-1.35183598 -1.425734 -1.425734 ]
  133. ...
  134. [ 1.43165578 1.41933944 1.46860478]
  135. [ 1.44397211 1.41933944 1.46860478]
  136. [ 1.45628845 1.40702311 1.45628845]]
  137. [[-1.52426468 -1.56121368 -1.54889735]
  138. [-1.56121368 -1.59816269 -1.58584635]
  139. [-1.51194834 -1.54889735 -1.53658101]
  140. ...
  141. [ 1.40702311 1.38239044 1.43165578]
  142. [ 1.41933944 1.39470677 1.44397211]
  143. [ 1.43165578 1.39470677 1.45628845]]
  144. [[-1.57353002 -1.59816269 -1.58584635]
  145. [-1.57353002 -1.59816269 -1.58584635]
  146. [-1.52426468 -1.54889735 -1.53658101]
  147. ...
  148. [ 1.41933944 1.39470677 1.45628845]
  149. [ 1.40702311 1.3700741 1.44397211]
  150. [ 1.44397211 1.41933944 1.46860478]]
  151. ...
  152. [[ 1.48092112 1.48092112 1.48092112]
  153. [ 1.48092112 1.48092112 1.48092112]
  154. [ 1.48092112 1.48092112 1.48092112]
  155. ...
  156. [-0.71138655 -0.77296823 -0.69907022]
  157. [-0.77296823 -0.8345499 -0.76065189]
  158. [-0.8345499 -0.89613158 -0.82223357]]
  159. [[ 1.48092112 1.48092112 1.48092112]
  160. [ 1.48092112 1.48092112 1.48092112]
  161. [ 1.48092112 1.48092112 1.48092112]
  162. ...
  163. [-0.88381524 -0.94539692 -0.87149891]
  164. [-0.95771326 -1.01929493 -0.94539692]
  165. [-0.36652917 -0.42811084 -0.35421283]]
  166. [[ 1.48092112 1.48092112 1.48092112]
  167. [ 1.48092112 1.48092112 1.48092112]
  168. [ 1.48092112 1.48092112 1.48092112]
  169. ...
  170. [-0.8345499 -0.92076425 -0.8345499 ]
  171. [-0.48969252 -0.57590686 -0.48969252]
  172. [-0.00935544 -0.09556979 -0.00935544]]]
  173. images参数 [[[[ 0.12834621 0.69292342 0.78113861]
  174. [ 0.12834621 0.69292342 0.78113861]
  175. [ 0.12834621 0.67528038 0.78113861]
  176. ...
  177. [-1.77710188 -1.77710188 -1.61831454]
  178. [-1.63595757 -1.65360061 -1.44188416]
  179. [-1.33602593 -1.35366897 -1.12430948]]
  180. [[ 0.25184747 0.74585253 0.93992595]
  181. [ 0.25184747 0.74585253 0.93992595]
  182. [ 0.28713355 0.74585253 0.93992595]
  183. ...
  184. [-1.77710188 -1.79474492 -1.65360061]
  185. [-1.75945884 -1.79474492 -1.56538542]
  186. [-1.63595757 -1.67124365 -1.40659808]]
  187. [[ 0.28713355 0.78113861 0.97521202]
  188. [ 0.32241962 0.79878165 0.99285506]
  189. [ 0.3577057 0.81642468 1.0104981 ]
  190. ...
  191. [-1.77710188 -1.79474492 -1.65360061]
  192. [-1.77710188 -1.81238795 -1.6006715 ]
  193. [-1.79474492 -1.81238795 -1.6006715 ]]
  194. ...
  195. [[-0.66559049 -0.41858796 0.07541709]
  196. [-0.55973227 -0.31272974 0.16363228]
  197. [-0.5773753 -0.33037278 0.18127532]
  198. ...
  199. [-0.10101328 0.16363228 0.76349557]
  200. [-0.13629936 0.14598925 0.74585253]
  201. [-0.25980062 0.05777406 0.65763734]]
  202. [[-0.82437783 -0.5773753 -0.10101328]
  203. [-0.66559049 -0.41858796 0.05777406]
  204. [-0.59501834 -0.34801581 0.16363228]
  205. ...
  206. [-0.13629936 0.09306013 0.71056646]
  207. [-0.1539424 0.11070317 0.72820949]
  208. [-0.1539424 0.18127532 0.76349557]]
  209. [[-0.96552214 -0.71851961 -0.24215759]
  210. [-0.77144872 -0.52444619 -0.04808417]
  211. [-0.64794746 -0.40094493 0.11070317]
  212. ...
  213. [-0.18922847 0.05777406 0.67528038]
  214. [-0.22451455 0.04013102 0.65763734]
  215. [-0.1539424 0.18127532 0.76349557]]]
  216. [[[-0.41843267 0.01911131 0.37196935]
  217. [-0.41843267 0.00499698 0.35785503]
  218. [-0.39020402 0.01911131 0.35785503]
  219. ...
  220. [-0.39020402 0.04733995 0.32962638]
  221. [-0.43254699 0.00499698 0.30139774]
  222. [-0.44666131 -0.00911734 0.28728342]]
  223. [[-0.40431834 0.00499698 0.3437407 ]
  224. [-0.39020402 0.01911131 0.35785503]
  225. [-0.36197538 0.01911131 0.3437407 ]
  226. ...
  227. [-0.41843267 0.01911131 0.30139774]
  228. [-0.44666131 -0.00911734 0.28728342]
  229. [-0.44666131 -0.00911734 0.28728342]]
  230. [[-0.40431834 0.00499698 0.32962638]
  231. [-0.41843267 -0.02323166 0.30139774]
  232. [-0.33374674 0.04733995 0.37196935]
  233. ...
  234. [-0.41843267 0.01911131 0.30139774]
  235. [-0.44666131 -0.00911734 0.28728342]
  236. [-0.46077563 -0.02323166 0.2731691 ]]
  237. ...
  238. [[-1.67460729 -1.67460729 -1.67460729]
  239. [-1.67460729 -1.67460729 -1.67460729]
  240. [-1.67460729 -1.67460729 -1.67460729]
  241. ...
  242. [-1.60403568 -1.44877815 -1.19472036]
  243. [-1.64637865 -1.47700679 -1.25117764]
  244. [-1.67460729 -1.50523543 -1.27940629]]
  245. [[-1.67460729 -1.67460729 -1.67460729]
  246. [-1.67460729 -1.67460729 -1.67460729]
  247. [-1.67460729 -1.67460729 -1.67460729]
  248. ...
  249. [-1.60403568 -1.44877815 -1.19472036]
  250. [-1.61815001 -1.44877815 -1.222949 ]
  251. [-1.66049297 -1.49112111 -1.26529196]]
  252. [[-1.67460729 -1.67460729 -1.67460729]
  253. [-1.67460729 -1.67460729 -1.67460729]
  254. [-1.67460729 -1.67460729 -1.67460729]
  255. ...
  256. [-1.60403568 -1.44877815 -1.19472036]
  257. [-1.58992136 -1.4205495 -1.19472036]
  258. [-1.64637865 -1.47700679 -1.25117764]]]
  259. [[[-0.68576058 -0.65851282 -0.6721367 ]
  260. [-1.21709198 -1.18984422 -1.2034681 ]
  261. [-0.94461434 -0.91736658 -0.93099046]
  262. ...
  263. [-1.5849368 -1.5031935 -1.5849368 ]
  264. [-1.14897257 -1.06722928 -1.18984422]
  265. [-1.0536054 -0.95823822 -1.09447704]]
  266. [[-1.08085316 -1.06722928 -1.08085316]
  267. [-1.36695468 -1.3533308 -1.36695468]
  268. [-1.5031935 -1.48956962 -1.5031935 ]
  269. ...
  270. [-1.39420245 -1.33970692 -1.39420245]
  271. [-1.42145021 -1.3533308 -1.44869798]
  272. [-1.38057857 -1.28521139 -1.40782633]]
  273. [[-0.98548599 -0.98548599 -1.01273375]
  274. [-1.06722928 -1.06722928 -1.09447704]
  275. [-1.25796363 -1.25796363 -1.28521139]
  276. ...
  277. [-1.18984422 -1.18984422 -1.21709198]
  278. [-1.02635763 -1.02635763 -1.06722928]
  279. [-1.31245916 -1.29883527 -1.36695468]]
  280. ...
  281. [[ 1.2215829 1.24883066 1.23520678]
  282. [ 1.23520678 1.26245454 1.24883066]
  283. [ 1.23520678 1.26245454 1.24883066]
  284. ...
  285. [ 0.43139774 0.44502162 0.49951715]
  286. [ 0.41777386 0.43139774 0.48589327]
  287. [ 0.43139774 0.44502162 0.49951715]]
  288. [[ 1.23520678 1.26245454 1.24883066]
  289. [ 1.2215829 1.24883066 1.23520678]
  290. [ 1.20795901 1.23520678 1.2215829 ]
  291. ...
  292. [ 0.48589327 0.49951715 0.55401268]
  293. [ 0.47226939 0.48589327 0.5403888 ]
  294. [ 0.47226939 0.48589327 0.5403888 ]]
  295. [[ 1.19433513 1.2215829 1.20795901]
  296. [ 1.16708737 1.19433513 1.18071125]
  297. [ 1.15346349 1.18071125 1.16708737]
  298. ...
  299. [ 0.67662762 0.6902515 0.74474703]
  300. [ 0.70387538 0.71749926 0.77199479]
  301. [ 0.73112314 0.74474703 0.79924255]]]
  302. [[[-1.35183598 -1.45036667 -1.43805033]
  303. [-1.32720331 -1.38878499 -1.38878499]
  304. [-1.35183598 -1.425734 -1.425734 ]
  305. ...
  306. [ 1.43165578 1.41933944 1.46860478]
  307. [ 1.44397211 1.41933944 1.46860478]
  308. [ 1.45628845 1.40702311 1.45628845]]
  309. [[-1.52426468 -1.56121368 -1.54889735]
  310. [-1.56121368 -1.59816269 -1.58584635]
  311. [-1.51194834 -1.54889735 -1.53658101]
  312. ...
  313. [ 1.40702311 1.38239044 1.43165578]
  314. [ 1.41933944 1.39470677 1.44397211]
  315. [ 1.43165578 1.39470677 1.45628845]]
  316. [[-1.57353002 -1.59816269 -1.58584635]
  317. [-1.57353002 -1.59816269 -1.58584635]
  318. [-1.52426468 -1.54889735 -1.53658101]
  319. ...
  320. [ 1.41933944 1.39470677 1.45628845]
  321. [ 1.40702311 1.3700741 1.44397211]
  322. [ 1.44397211 1.41933944 1.46860478]]
  323. ...
  324. [[ 1.48092112 1.48092112 1.48092112]
  325. [ 1.48092112 1.48092112 1.48092112]
  326. [ 1.48092112 1.48092112 1.48092112]
  327. ...
  328. [-0.71138655 -0.77296823 -0.69907022]
  329. [-0.77296823 -0.8345499 -0.76065189]
  330. [-0.8345499 -0.89613158 -0.82223357]]
  331. [[ 1.48092112 1.48092112 1.48092112]
  332. [ 1.48092112 1.48092112 1.48092112]
  333. [ 1.48092112 1.48092112 1.48092112]
  334. ...
  335. [-0.88381524 -0.94539692 -0.87149891]
  336. [-0.95771326 -1.01929493 -0.94539692]
  337. [-0.36652917 -0.42811084 -0.35421283]]
  338. [[ 1.48092112 1.48092112 1.48092112]
  339. [ 1.48092112 1.48092112 1.48092112]
  340. [ 1.48092112 1.48092112 1.48092112]
  341. ...
  342. [-0.8345499 -0.92076425 -0.8345499 ]
  343. [-0.48969252 -0.57590686 -0.48969252]
  344. [-0.00935544 -0.09556979 -0.00935544]]]]
  345. (4, 160, 160, 3)

 

 

 

 

文章来源: yunyaniu.blog.csdn.net,作者:一个处女座的程序猿,版权归原作者所有,如需转载,请联系作者。

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*长度不超过10个汉字或20个英文字符,设置后3个月内不可修改。

*长度不超过10个汉字或20个英文字符,设置后3个月内不可修改。