{"id":296948,"date":"2020-01-10T09:00:32","date_gmt":"2020-01-10T09:00:32","guid":{"rendered":"http:\/\/savepearlharbor.com\/?p=296948"},"modified":"-0001-11-30T00:00:00","modified_gmt":"-0001-11-29T21:00:00","slug":"","status":"publish","type":"post","link":"https:\/\/savepearlharbor.com\/?p=296948","title":{"rendered":"\u0418\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0435 \u043a\u0430\u043c\u0435\u0440\u044b Fish eye \u043d\u0430 Raspberry Pi 3: \u0437\u0430\u043f\u0443\u0441\u043a \u043f\u0440\u0435\u0434\u043e\u0431\u0443\u0447\u0435\u043d\u043d\u044b\u0445 DL \u043c\u043e\u0434\u0435\u043b\u0435\u0439 \u0434\u043b\u044f \u043a\u043e\u043c\u043f\u044c\u044e\u0442\u0435\u0440\u043d\u043e\u0433\u043e \u0437\u0440\u0435\u043d\u0438\u044f"},"content":{"rendered":"\n<div class=\"post__text post__text-html js-mediator-article\" id=\"post-content-body\" data-io-article-url=\"https:\/\/habr.com\/ru\/post\/481066\/\">\u0414\u043e\u0431\u0440\u044b\u0439 \u0434\u0435\u043d\u044c,<br \/>  \u0432 \u043f\u0440\u043e\u0434\u043e\u043b\u0436\u0435\u043d\u0438\u0435 \u0441\u0435\u0440\u0438\u0438 \u0441\u0442\u0430\u0442\u0435\u0439: <a href=\"https:\/\/habr.com\/post\/417251\/\">\u043f\u0435\u0440\u0432\u0430\u044f<\/a> \u0438 <a href=\"https:\/\/habr.com\/ru\/post\/429894\/\">\u0432\u0442\u043e\u0440\u0430\u044f<\/a> \u043e\u0431 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0438 fish eye \u043a\u0430\u043c\u0435\u0440\u044b \u0441 Raspberry Pi 3 \u0438 ROS \u044f \u0431\u044b \u0445\u043e\u0442\u0435\u043b \u0440\u0430\u0441\u0441\u043a\u0430\u0437\u0430\u0442\u044c \u043e\u0431 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0438 \u043f\u0440\u0435\u0434\u043e\u0431\u0443\u0447\u0435\u043d\u043d\u044b\u0445 Deep Learning \u043c\u043e\u0434\u0435\u043b\u0435\u0439 \u0434\u043b\u044f \u043a\u043e\u043c\u043f\u044c\u044e\u0442\u0435\u0440\u043d\u043e\u0433\u043e \u0437\u0440\u0435\u043d\u0438\u044f \u0441 \u043a\u0430\u043c\u0435\u0440\u043e\u0439 Fish eye \u043d\u0430 Raspberry Pi 3. \u041a\u043e\u043c\u0443 \u0438\u043d\u0442\u0435\u0440\u0435\u0441\u043d\u043e, \u043f\u0440\u043e\u0448\u0443 \u043f\u043e\u0434 \u043a\u0430\u0442.<a name=\"habracut\"><\/a><\/p>\n<h2>\u041a\u043b\u0430\u0441\u0441\u0438\u0444\u0438\u043a\u0430\u0446\u0438\u044f \u0438\u0437\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u0438\u0439<\/h2>\n<p>  \u041a\u0430\u043a \u0438 \u0432 \u043f\u0440\u043e\u0448\u043b\u044b\u0445 \u0441\u0442\u0430\u0442\u044c\u044f\u0445 \u0431\u0443\u0434\u0435\u043c \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c Ubuntu 16.04. \u0414\u043b\u044f \u0440\u0430\u0431\u043e\u0442\u044b \u043d\u0430\u043c \u0431\u0443\u0434\u0435\u0442 \u043d\u0443\u0436\u043d\u0430 \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0430 Keras. \u0415\u0433\u043e \u043c\u043e\u0436\u043d\u043e \u0443\u0441\u0442\u0430\u043d\u043e\u0432\u0438\u0442\u044c \u043d\u0430 Ubuntu \u0441\u043b\u0435\u0434\u0443\u044f \u0440\u0443\u043a\u043e\u0432\u043e\u0434\u0441\u0442\u0432\u0443 <a href=\"https:\/\/medium.com\/@vivek.yadav\/deep-learning-setup-for-ubuntu-16-04-tensorflow-1-2-keras-opencv3-python3-cuda8-and-cudnn5-1-324438dd46f0\" rel=\"nofollow\">\u043e\u0442\u0441\u044e\u0434\u0430<\/a>.<\/p>\n<p>  \u041f\u0435\u0440\u0435\u0439\u0434\u0435\u043c \u043d\u0430 <a href=\"https:\/\/www.pyimagesearch.com\/2017\/10\/02\/deep-learning-on-the-raspberry-pi-with-opencv\/\" rel=\"nofollow\">\u0441\u0442\u0440\u0430\u043d\u0438\u0446\u0443<\/a> \u0438 \u043d\u0430\u0436\u043c\u0435\u043c \u043a\u043d\u043e\u043f\u043a\u0443 DOWNLOAD THE CODE! \u0441\u043d\u0438\u0437\u0443 \u0441\u0442\u0440\u0430\u043d\u0438\u0446\u044b. \u0412\u044b \u043f\u043e\u043b\u0443\u0447\u0438\u0442\u0435 email \u0441 \u0438\u0441\u0445\u043e\u0434\u043d\u0438\u043a\u0430\u043c\u0438.<\/p>\n<p>  \u0421\u043a\u0430\u0447\u0430\u0435\u043c \u0430\u0440\u0445\u0438\u0432, \u0440\u0430\u0437\u0430\u0440\u0445\u0438\u0432\u0438\u0440\u0443\u0435\u043c \u0435\u0433\u043e \u0438 \u043f\u0435\u0440\u0435\u0439\u0434\u0435\u043c \u0432 \u043f\u0430\u043f\u043a\u0443. \u0414\u043b\u044f \u043d\u0430\u0447\u0430\u043b\u0430 \u0437\u0430\u043f\u0443\u0441\u0442\u0438\u043c \u0441\u043a\u0440\u0438\u043f\u0442 \u043a\u043b\u0430\u0441\u0441\u0438\u0444\u0438\u043a\u0430\u0446\u0438\u0438 \u043e\u0431\u044a\u0435\u043a\u0442\u043e\u0432 \u043d\u0430 \u043e\u0441\u043d\u043e\u0432\u0435 \u043f\u0440\u0435\u0434\u043e\u0431\u0443\u0447\u0435\u043d\u043d\u043e\u0439 \u043c\u043e\u0434\u0435\u043b\u0438 GoogleNet:<\/p>\n<pre><code class=\"bash\">cd pi-deep-learning\/ python pi_deep_learning.py --prototxt models\/bvlc_googlenet.prototxt \\     --model models\/bvlc_googlenet.caffemodel --labels synset_words.txt \\     --image images\/barbershop.png <\/code><\/pre>\n<p>  \u041f\u043e\u043b\u0443\u0447\u0438\u043c \u0442\u0430\u043a\u043e\u0439 \u0432\u044b\u0432\u043e\u0434 \u0432 \u0442\u0435\u0440\u043c\u0438\u043d\u0430\u043b\u0435<\/p>\n<pre><code class=\"bash\">[INFO] loading model... [ INFO:0] Initialize OpenCL runtime... [INFO] classification took 1.7103 seconds [INFO] 1. label: barbershop, probability: 0.78055 [INFO] 2. label: barber chair, probability: 0.2194 [INFO] 3. label: rocking chair, probability: 3.4663e-05 [INFO] 4. label: restaurant, probability: 3.7257e-06 [INFO] 5. label: hair spray, probability: 1.4715e-06 <\/code><\/pre>\n<p>  <img decoding=\"async\" src=\"https:\/\/habrastorage.org\/webt\/bg\/pl\/a-\/bgpla-qokdubmjxjzvrpv40nko8.png\" alt=\"image\"><\/p>\n<p>  \u0417\u0434\u0435\u0441\u044c \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0435\u0442\u0441\u044f \u043c\u043e\u0434\u0443\u043b\u044c Deep Neural Network (DNN) \u0438\u0437 OpenCV 3.3. \u041e \u043d\u0435\u043c \u043c\u043e\u0436\u043d\u043e \u043f\u0440\u043e\u0447\u0438\u0442\u0430\u0442\u044c <a href=\"https:\/\/docs.opencv.org\/3.4.2\/d2\/d58\/tutorial_table_of_content_dnn.html\" rel=\"nofollow\">\u0437\u0434\u0435\u0441\u044c<\/a>.<\/p>\n<p>  \u0422\u0435\u043f\u0435\u0440\u044c \u043f\u043e\u043f\u0440\u043e\u0431\u0443\u0435\u043c \u043a\u043b\u0430\u0441\u0441\u0438\u0444\u0438\u043a\u0430\u0446\u0438\u044e \u043e\u0431\u044a\u0435\u043a\u0442\u043e\u0432 \u0441 \u043f\u0440\u0435\u0434\u043e\u0431\u0443\u0447\u0435\u043d\u043d\u043e\u0439 \u043c\u043e\u0434\u0435\u043b\u044c\u044e Squeezenet:<\/p>\n<pre><code class=\"bash\">python pi_deep_learning.py --prototxt models\/squeezenet_v1.0.prototxt \\     --model models\/squeezenet_v1.0.caffemodel --labels synset_words.txt \\     --image images\/barbershop.png <\/code><\/pre>\n<pre><code class=\"bash\">[INFO] loading model... [ INFO:0] Initialize OpenCL runtime... [INFO] classification took 0.86275 seconds [INFO] 1. label: barbershop, probability: 0.80578 [INFO] 2. label: barber chair, probability: 0.15124 [INFO] 3. label: half track, probability: 0.0052873 [INFO] 4. label: restaurant, probability: 0.0040124 [INFO] 5. label: desktop computer, probability: 0.0033352 <\/code><\/pre>\n<p>  <img decoding=\"async\" src=\"https:\/\/habrastorage.org\/webt\/zk\/nu\/yh\/zknuyhxuenawkprhai6ndiylnau.png\" alt=\"image\"><\/p>\n<p>  \u0423 \u043c\u0435\u043d\u044f \u043a\u043b\u0430\u0441\u0441\u0438\u0444\u0438\u043a\u0430\u0446\u0438\u044f \u0441\u0440\u0430\u0431\u043e\u0442\u0430\u043b\u0430 \u0437\u0430 0.86 sec.<br \/>  \u041f\u043e\u043f\u0440\u043e\u0431\u0443\u0435\u043c \u0442\u0435\u043f\u0435\u0440\u044c \u043d\u0430 \u0438\u0437\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u0438\u0438 cobra.png:<\/p>\n<pre><code class=\"bash\">python pi_deep_learning.py --prototxt models\/squeezenet_v1.0.prototxt \\     --model models\/squeezenet_v1.0.caffemodel --labels synset_words.txt \\     --image images\/cobra.png <\/code><\/pre>\n<p>  \u0412\u044b\u0432\u043e\u0434: <\/p>\n<pre><code class=\"bash\">[INFO] classification took 0.87402 seconds [INFO] 1. label: Indian cobra, probability: 0.47972 [INFO] 2. label: leatherback turtle, probability: 0.16858 [INFO] 3. label: water snake, probability: 0.10558 [INFO] 4. label: common iguana, probability: 0.059227 [INFO] 5. label: sea snake, probability: 0.046393 <\/code><\/pre>\n<p>  <img decoding=\"async\" src=\"https:\/\/habrastorage.org\/webt\/qy\/kx\/_d\/qykx_dt99zzihlpffhharqpyox4.png\" alt=\"image\"><\/p>\n<h2>\u0414\u0435\u0442\u0435\u043a\u0446\u0438\u044f \u043e\u0431\u044a\u0435\u043a\u0442\u043e\u0432<\/h2>\n<p>  \u0422\u0435\u043f\u0435\u0440\u044c \u043f\u043e\u043f\u0440\u043e\u0431\u0443\u0435\u043c \u0434\u0435\u0442\u0435\u043a\u0446\u0438\u044e \u043e\u0431\u044a\u0435\u043a\u0442\u043e\u0432 \u0441 Raspberry Pi \u0441 fish eye \u043a\u0430\u043c\u0435\u0440\u043e\u0439. \u041f\u0435\u0440\u0435\u0439\u0434\u0435\u043c \u043d\u0430 <a href=\"https:\/\/www.pyimagesearch.com\/2017\/10\/16\/raspberry-pi-deep-learning-object-detection-with-opencv\" rel=\"nofollow\">\u0441\u0442\u0440\u0430\u043d\u0438\u0446\u0443<\/a> \u0438 \u043d\u0430\u0436\u043c\u0435\u043c \u043a\u043d\u043e\u043f\u043a\u0443 DOWNLOAD THE CODE! \u0441\u043d\u0438\u0437\u0443 \u0441\u0442\u0440\u0430\u043d\u0438\u0446\u044b. \u0412\u044b \u043f\u043e\u043b\u0443\u0447\u0438\u0442\u0435 email \u0441 \u0438\u0441\u0445\u043e\u0434\u043d\u0438\u043a\u0430\u043c\u0438.<\/p>\n<p>  \u0410\u043d\u0430\u043b\u043e\u0433\u0438\u0447\u043d\u043e \u043a\u043b\u0430\u0441\u0441\u0438\u0444\u0438\u043a\u0430\u0446\u0438\u0438 \u0441\u043a\u0430\u0447\u0430\u0435\u043c, \u0440\u0430\u0437\u0430\u0440\u0445\u0438\u0432\u0438\u0440\u0443\u0435\u043c, \u043f\u0435\u0440\u0435\u0439\u0434\u0435\u043c \u0432 \u043f\u0430\u043f\u043a\u0443. \u0417\u0430\u043f\u0443\u0441\u0442\u0438\u043c \u0441\u043a\u0440\u0438\u043f\u0442 \u0434\u0435\u0442\u0435\u043a\u0446\u0438\u0438 \u043e\u0431\u044a\u0435\u043a\u0442\u043e\u0432:<\/p>\n<pre><code class=\"bash\">python pi_object_detection.py --prototxt MobileNetSSD_deploy.prototxt.txt --model MobileNetSSD_deploy.caffemodel<\/code><\/pre>\n<p>  \u0412\u044b\u0432\u043e\u0434: <\/p>\n<pre><code class=\"bash\">[INFO] loading model... [INFO] starting process... [INFO] starting video stream... [ INFO:0] Initialize OpenCL runtime... libEGL warning: DRI3: failed to query the version libEGL warning: DRI2: failed to authenticate  <\/code><\/pre>\n<p>  \u041f\u0440\u0438 \u0437\u0430\u043f\u0443\u0441\u043a\u0435 \u0441\u043a\u0440\u0438\u043f\u0442\u0430 \u043c\u044b \u043c\u043e\u0436\u0435\u043c \u043f\u043e\u043b\u0443\u0447\u0438\u0442\u044c \u0442\u0430\u043a\u0443\u044e \u043e\u0448\u0438\u0431\u043a\u0443:<\/p>\n<pre><code class=\"bash\">Error: AttributeError: \u2018NoneType\u2019 object has no attribute \u2018shape\u2019 (comment to post) <\/code><\/pre>\n<p>  \u0427\u0442\u043e\u0431\u044b \u0440\u0435\u0448\u0438\u0442\u044c \u043f\u0440\u043e\u0431\u043b\u0435\u043c\u0443 \u043e\u0442\u043a\u0440\u043e\u0435\u043c \u0444\u0430\u0439\u043b pi_object_detection.py \u0438 \u0437\u0430\u043a\u043e\u043c\u043c\u0435\u043d\u0442\u0438\u0440\u0443\u0435\u043c \u0441\u0442\u0440\u043e\u043a\u0443 74:<\/p>\n<pre><code class=\"python\">vs = VideoStream(src=0).start() <\/code><\/pre>\n<p>  \u0438 \u0440\u0430\u0441\u043a\u043e\u043c\u043c\u0435\u043d\u0442\u0438\u0440\u0443\u0435\u043c \u0441\u0442\u0440\u043e\u043a\u0443 75<\/p>\n<pre><code class=\"python\"> vs = VideoStream(usePiCamera=True).start() <\/code><\/pre>\n<p>  <img decoding=\"async\" src=\"https:\/\/habrastorage.org\/webt\/ac\/6g\/ui\/ac6gui3b3u5gifzpwvhnmxngauu.png\" alt=\"image\"><\/p>\n<p>  <img decoding=\"async\" src=\"https:\/\/habrastorage.org\/webt\/pw\/7o\/km\/pw7okmss6z2hxmvh46ab7dw9ygq.png\" alt=\"image\"><\/p>\n<p>  \u041c\u044b \u0432\u0438\u0434\u0438\u043c, \u0447\u0442\u043e \u0441\u043a\u0440\u0438\u043f\u0442 \u043e\u0431\u043d\u0430\u0440\u0443\u0436\u0438\u0432\u0430\u0435\u0442 \u043d\u0435\u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u043e\u0431\u044a\u0435\u043a\u0442\u044b, \u0445\u043e\u0442\u044f \u043d\u0435 \u0432\u0441\u0435 \u043e\u0431\u044a\u0435\u043a\u0442\u044b \u043f\u0440\u0430\u0432\u0438\u043b\u044c\u043d\u043e \u0440\u0430\u0441\u043f\u043e\u0437\u043d\u0430\u0435\u0442. \u041f\u043e \u043c\u043e\u0438\u043c \u043d\u0430\u0431\u043b\u044e\u0434\u0435\u043d\u0438\u044f\u043c \u0441\u043a\u0440\u0438\u043f\u0442 \u0432\u044b\u043f\u043e\u043b\u043d\u044f\u0435\u0442 \u0434\u0435\u0442\u0435\u043a\u0446\u0438\u044e \u043e\u0431\u044a\u0435\u043a\u0442\u043e\u0432 \u0434\u043e\u0432\u043e\u043b\u044c\u043d\u043e \u0431\u044b\u0441\u0442\u0440\u043e (\u043a \u0441\u043e\u0436\u0430\u043b\u0435\u043d\u0438\u044e, fps \u043d\u0435 \u0443\u0434\u0430\u043b\u043e\u0441\u044c \u0437\u0430\u0444\u0438\u043a\u0441\u0438\u0440\u043e\u0432\u0430\u0442\u044c).<\/p>\n<p>  \u0422\u0430\u043a\u0436\u0435 \u043c\u043e\u0436\u0435\u043c \u0437\u0430\u043f\u0443\u0441\u0442\u0438\u0442\u044c \u0441\u043a\u0440\u0438\u043f\u0442 real_time_object_detection.py. \u0412 \u0441\u043b\u0443\u0447\u0430\u0435 \u043e\u0448\u0438\u0431\u043a\u0438 \u043f\u043e\u0432\u0442\u043e\u0440\u0438\u043c \u043f\u0440\u043e\u0446\u0435\u0434\u0443\u0440\u0443 \u0434\u043b\u044f \u0441\u043a\u0440\u0438\u043f\u0442\u0430 pi_object_detection.py: \u0437\u0430\u043a\u043e\u043c\u043c\u0435\u043d\u0442\u0438\u0440\u0443\u0435\u043c \u0441\u0442\u0440\u043e\u043a\u0443 38 \u0438 \u0440\u0430\u0441\u043a\u043e\u043c\u043c\u0435\u043d\u0442\u0438\u0440\u0443\u0435\u043c \u0441\u0442\u0440\u043e\u043a\u0443 39. \u0420\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442<\/p>\n<p>  <img decoding=\"async\" src=\"https:\/\/habrastorage.org\/webt\/sh\/g-\/br\/shg-brs2geeon8u8t3cgmmblrto.png\" alt=\"image\"><\/p>\n<p>  \u041d\u0430 \u044d\u0442\u043e\u043c \u043f\u043e\u043a\u0430 \u0432\u0441\u0435. \u0412\u0441\u0435\u043c \u0443\u0434\u0430\u0447\u0438 \u0438 \u0434\u043e \u043d\u043e\u0432\u044b\u0445 \u0432\u0441\u0442\u0440\u0435\u0447!<\/p><\/div>\n<p>               <script class=\"js-mediator-script\">!function(e){function t(t,n){if(!(n in e)){for(var 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\u0437\u0440\u0435\u043d\u0438\u044f \u0441 \u043a\u0430\u043c\u0435\u0440\u043e\u0439 Fish eye \u043d\u0430 Raspberry Pi 3. \u041a\u043e\u043c\u0443 \u0438\u043d\u0442\u0435\u0440\u0435\u0441\u043d\u043e, \u043f\u0440\u043e\u0448\u0443 \u043f\u043e\u0434 \u043a\u0430\u0442.<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[],"tags":[],"class_list":["post-296948","post","type-post","status-publish","format-standard","hentry"],"_links":{"self":[{"href":"https:\/\/savepearlharbor.com\/index.php?rest_route=\/wp\/v2\/posts\/296948","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/savepearlharbor.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/savepearlharbor.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/savepearlharbor.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/savepearlharbor.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=296948"}],"version-history":[{"count":0,"href":"https:\/\/savepearlharbor.com\/index.php?rest_route=\/wp\/v2\/posts\/296948\/revisions"}],"wp:attachment":[{"href":"https:\/\/savepearlharbor.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=296948"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/savepearlharbor.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=296948"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/savepearlharbor.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=296948"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}