浅谈cv2.imread()和keras.preprocessing中的image.load_img()区别
1、image.load_img()
fromkeras.preprocessingimportimage img_keras=image.load_img('./original/dog/880.jpg') print(img_keras) img_keras=image.img_to_array(img_keras) print(img_keras[:,1,1])
效果如下:
#image.load_img()只是加载了一个文件,没有形成numpy数组, #下面的numpy数组是通过image.img_to_array()的函数形成的 [108.108.110.115.119.120.122.125.127.127.129.131.132.134. 1.135.138.138.139.143.141.136.132.131.135.121.103.97. 2.85.69.65.69.67.74.80.77.82.92.99.105.113. 3.126.128.129.132.134.135.135.135.135.134.133.131.130. 4.124.122.120.119.119.121.122.123.121.120.120.122.124. 5.124.123.121.120.119.119.118.116.114.121.120.117.115. 6.112.111.111.114.105.104.107.104.103.106.105.101.71. 7.99.99.77.71.80.69.71.69.65.63.65.64.61. 8.67.74.77.79.81.79.76.78.78.77.75.77.79. 9.72.68.68.67.66.64.63.61.61.57.57.56.56. 10.51.45.42.34.31.28.26.27.28.28.28.29.29. 11.28.27.26.25.26.24.23.22.21.21.21.22.22. 12.21.21.20.20.20.19.19.19.18.18.18.18.18. 13.18.18.18.17.16.14.13.12.12.10.10.10.10. 14.9.9.8.10.10.10.10.12.15.18.20.23.20. 15.175.229.231.230.221.219.220.227.223.213.220.227.221. 16.216.219.214.197.187.179.165.175.160.175.201.206.207. 17.196.178.189.207.195.190.188.152.124.97.113.179.214. 18.122.172.178.204.196.200.184.167.147.112.106.131.193. 19.202.188.187.199.206.207.208.172.139.147.128.130.215. 20.224.221.219.217.218.206.185.158.180.174.173.142.139. 21.200.202.205.174.122.119.123.120.155.206.160.191.191. 22.182.158.116.66.29.6.22.47.54.53.55.61.64. 23.75.80.84.86.88.87.88.89.89.88.87.86.86. 24.71.174.136.13.7.38.68.77.79.80.81.81.80. 25.78.77.77.77.77.76.76.76.75.74.75.75.75. 26.73.71.70.68.65.62.59.57.55.52.49.46.43. 27.34.31.28.25.23.]
2、cv2.imread()
importcv2 img_cv2=cv2.imread('./original/dog/880.jpg') print(img_cv2[:,1,1])
效果如下:
[108108110115119120122125127127129131132134134135138138 139143141136132131135121103979785696569677480 77829299105113120126128129132134135135135135134133 131130126124122120119119121122123121120120122124124124 123121120119119118116114121120117115113112111111114105 1041071041031061051017172999977718069716965 636564616267747779817976787877757779 767268686766646361615757565654514542 343128262728282829292828272625262423 222121212222212121202020191919181818 1818181818181716141312121010101099 981010101012151820232027175229231230221 219220227223213220227221220216219214197187179165175160 17520120620720719617818920719519018815212497113179214 147122172178204196200184167147112106131193218202188187 199206207208172139147128130215228224221219217218206185 158180174173142139151200202205174122119123120155206160 19119119218215811666296224754535561646975 8084868887888989888786869271174136137 386877798081818079787777777776767675 747575757473717068656259575552494643 383431282523]
补充知识:keras报错:load_weights()gotanunexpectedkeywordarguement'skip_mmismatch'
网上下载了一个Yolo(keras+tensorflow)网络的训练代码,在运行的时候,报了以下错误:
load_weights()gotanunexpectedkeywordarguement'skip_mmismatch'。
在网上搜索了半天,也没有发现具体原因,最后,仔细看了看这句话的报错,因为我调用的是一个keras的内置函数,却报了没有这个参数的错,就想到了版本问题。最后将keras进行升级(我的升级到了2.1.5版本),这个问题就解决了。
总结:
在跑keras和tensorflow程序的时候遇到了好多次这种版本导致的问题。因为深度学习现在发展比较迅速,所以很多框架的API更新比较快,以后debug的时候一定要注意排查版本问题。
以上这篇浅谈cv2.imread()和keras.preprocessing中的image.load_img()区别就是小编分享给大家的全部内容了,希望能给大家一个参考,也希望大家多多支持毛票票。
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