{"id":11056,"date":"2021-04-14T09:10:00","date_gmt":"2021-04-14T04:40:00","guid":{"rendered":"https:\/\/shahaab-co.com\/mag\/?p=11056"},"modified":"2024-11-29T18:37:01","modified_gmt":"2024-11-29T15:07:01","slug":"facemask-surveillance-system-with-drone-technology-part-2","status":"publish","type":"post","link":"https:\/\/shahaab-co.com\/mag\/edu\/robotics\/facemask-surveillance-system-with-drone-technology-part-2\/","title":{"rendered":"\u0633\u0627\u062e\u062a \u0633\u06cc\u0633\u062a\u0645 \u062a\u0634\u062e\u06cc\u0635 \u0645\u0627\u0633\u06a9 \u0628\u0627 \u0641\u0646\u0627\u0648\u0631\u06cc \u067e\u0647\u067e\u0627\u062f \u0648 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642 \u2013 \u0628\u062e\u0634 \u062f\u0648\u0645"},"content":{"rendered":"<p style=\"text-align: justify;\">\u0628\u0647 \u0628\u062e\u0634 \u062f\u0648\u0645 \u0633\u0627\u062e\u062a \u0633\u06cc\u0633\u062a\u0645 \u0646\u0638\u0627\u0631\u062a\u06cc \u062a\u0634\u062e\u06cc\u0635 \u0645\u0627\u0633\u06a9 \u0635\u0648\u0631\u062a \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u062a\u06a9\u0646\u0648\u0644\u0648\u0698\u06cc \u067e\u0647\u067e\u0627\u062f \u0648 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642 \u062e\u0648\u0634 \u0622\u0645\u062f\u06cc\u062f! \u062f\u0631 \u0628\u062e\u0634 \u0627\u0648\u0644 \u0627\u06cc\u0646 \u0622\u0645\u0648\u0632\u0634\u060c \u062f\u0631\u0628\u0627\u0631\u0647 \u00ad\u06cc \u062a\u06a9\u0646\u0648\u0644\u0648\u0698\u06cc \u067e\u0647\u067e\u0627\u062f \u060c \u0627\u0646\u0648\u0627\u0639 \u0637\u0628\u0642\u0647\u00ad \u0628\u0646\u062f\u06cc\u00ad \u0647\u0627 \u060c \u0645\u0639\u0645\u0627\u0631\u06cc \u067e\u0647\u067e\u0627\u062f\u06cc \u06a9\u0647 \u062f\u0631 \u0627\u06cc\u0646 \u067e\u0631\u0648\u0698\u0647 \u0627\u0632 \u0622\u0646 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc \u00ad\u0634\u0648\u062f \u0648 \u0647\u0645\u0686\u0646\u06cc\u0646 \u067e\u06cc\u06a9\u0631\u0628\u0646\u062f\u06cc \u0645\u062d\u06cc\u0637 \u0628\u0631\u0627\u06cc \u0628\u0631\u0646\u0627\u0645\u0647 \u00ad\u0646\u0648\u06cc\u0633\u06cc \u067e\u0647\u067e\u0627\u062f \u0628\u0627 \u067e\u0627\u06cc\u062a\u0648\u0646 \u060c \u0635\u062d\u0628\u062a \u0634\u062f. \u0627\u06cc\u0646 \u0628\u062e\u0634 \u0628\u0647 \u0686\u06af\u0648\u0646\u06af\u06cc \u0628\u0627\u0631\u06af\u0630\u0627\u0631\u06cc \u0648 \u067e\u06cc\u0634 \u00ad\u067e\u0631\u062f\u0627\u0632\u0634 \u0645\u062c\u0645\u0648\u0639\u0647 \u00ad\u062f\u0627\u062f\u0647 \u00ad\u0647\u0627\u06cc \u0645\u0627\u0633\u06a9 \u0635\u0648\u0631\u062a \u0645\u06cc\u00ad \u067e\u0631\u062f\u0627\u0632\u062f\u060c \u0633\u0627\u062e\u062a \u0645\u062f\u0644 \u062a\u0634\u062e\u06cc\u0635 \u062f\u0647\u0646\u062f\u0647 \u0645\u0627\u0633\u06a9 \u0635\u0648\u0631\u062a \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u062a\u0646\u0633\u0648\u0631\u0641\u0644\u0648 \/ Keras \u0631\u0627 \u0634\u0631\u062d \u0645\u06cc\u00ad \u062f\u0647\u062f \u0648 \u0686\u06af\u0648\u0646\u06af\u06cc \u0627\u062c\u0631\u0627\u06cc \u0622\u0645\u0648\u0632\u0634 \u06cc\u06a9 \u0645\u062f\u0644 \u0648 \u0630\u062e\u06cc\u0631\u0647\u00ad \u0633\u0627\u0632\u06cc \u0622\u0646 \u0645\u062f\u0644 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642 \u0628\u0631\u0627\u06cc \u067e\u06cc\u0627\u062f\u0647\u00ad \u0633\u0627\u0632\u06cc\u00ad \u0647\u0627\u06cc \u0622\u062a\u06cc \u0631\u0627 \u062a\u0648\u0635\u06cc\u0641 \u0645\u06cc\u00ad \u06a9\u0646\u062f.<\/p>\n\n\n<div class=\"wp-block-rank-math-toc-block\"><div id=\"ez-toc-container\" class=\"ez-toc-v2_0_76 counter-hierarchy ez-toc-counter-rtl ez-toc-custom ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">\u0622\u0646\u0686\u0647 \u062f\u0631 \u0627\u06cc\u0646 \u0645\u0637\u0644\u0628 \u062e\u0648\u0627\u0647\u06cc\u0645 \u062e\u0648\u0627\u0646\u062f :<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #0044bf;color:#0044bf\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #0044bf;color:#0044bf\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 eztoc-toggle-hide-by-default' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/shahaab-co.com\/mag\/edu\/robotics\/facemask-surveillance-system-with-drone-technology-part-2\/#%D9%81%D9%87%D8%B1%D8%B3%D8%AA_%D9%85%D8%B7%D8%A7%D9%84%D8%A8\" >\u0641\u0647\u0631\u0633\u062a \u0645\u0637\u0627\u0644\u0628<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/shahaab-co.com\/mag\/edu\/robotics\/facemask-surveillance-system-with-drone-technology-part-2\/#%D9%BE%DB%8C%D8%B4_%D9%86%DB%8C%D8%A7%D8%B2_%D9%87%D8%A7\" >\u067e\u06cc\u0634 \u0646\u06cc\u0627\u0632 \u0647\u0627<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/shahaab-co.com\/mag\/edu\/robotics\/facemask-surveillance-system-with-drone-technology-part-2\/#%D8%A8%D8%A7%D8%B1%DA%AF%D8%B0%D8%A7%D8%B1%DB%8C_%D9%88_%D9%BE%DB%8C%D8%B4_%D9%BE%D8%B1%D8%AF%D8%A7%D8%B2%D8%B4_%D9%85%D8%AC%D9%85%D9%88%D8%B9%D9%87_%D8%AF%D8%A7%D8%AF%D9%87_%D9%87%D8%A7\" >\u0628\u0627\u0631\u06af\u0630\u0627\u0631\u06cc \u0648 \u067e\u06cc\u0634\u00ad \u067e\u0631\u062f\u0627\u0632\u0634 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u00ad\u0647\u0627\u00a0<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/shahaab-co.com\/mag\/edu\/robotics\/facemask-surveillance-system-with-drone-technology-part-2\/#%D8%B3%D8%A7%D8%AE%D8%AA_%D9%85%D8%AF%D9%84\" >\u0633\u0627\u062e\u062a \u0645\u062f\u0644<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/shahaab-co.com\/mag\/edu\/robotics\/facemask-surveillance-system-with-drone-technology-part-2\/#%D9%BE%DB%8C%D8%A7%D8%AF%D9%87_%D8%B3%D8%A7%D8%B2%DB%8C_%D9%85%D8%AF%D9%84_%D8%B1%D9%88%DB%8C_%DB%8C%DA%A9_%D8%AC%D8%B1%DB%8C%D8%A7%D9%86_%D9%88%DB%8C%D8%AF%DB%8C%D9%88\" >\u067e\u06cc\u0627\u062f\u0647 \u00ad\u0633\u0627\u0632\u06cc \u0645\u062f\u0644 \u0631\u0648\u06cc \u06cc\u06a9 \u062c\u0631\u06cc\u0627\u0646 \u0648\u06cc\u062f\u06cc\u0648<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/shahaab-co.com\/mag\/edu\/robotics\/facemask-surveillance-system-with-drone-technology-part-2\/#%D9%86%D8%AA%DB%8C%D8%AC%D9%87_%DA%AF%DB%8C%D8%B1%DB%8C\" >\u0646\u062a\u06cc\u062c\u0647\u00ad \u06af\u06cc\u0631\u06cc<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"%D9%81%D9%87%D8%B1%D8%B3%D8%AA_%D9%85%D8%B7%D8%A7%D9%84%D8%A8\"><\/span>\u0641\u0647\u0631\u0633\u062a \u0645\u0637\u0627\u0644\u0628<span class=\"ez-toc-section-end\"><\/span><\/h2><nav><ul><li><a href=\"#h-\" >\u067e\u06cc\u0634 \u0646\u06cc\u0627\u0632 \u0647\u0627<\/a><\/li><li><a href=\"#h--1\" >\u0628\u0627\u0631\u06af\u0630\u0627\u0631\u06cc \u0648 \u067e\u06cc\u0634\u00ad \u067e\u0631\u062f\u0627\u0632\u0634 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u00ad\u0647\u0627\u00a0<\/a><\/li><li><a href=\"#h--2\" >\u0633\u0627\u062e\u062a \u0645\u062f\u0644<\/a><\/li><li><a href=\"#h--3\" >\u067e\u06cc\u0627\u062f\u0647 \u00ad\u0633\u0627\u0632\u06cc \u0645\u062f\u0644 \u0631\u0648\u06cc \u06cc\u06a9 \u062c\u0631\u06cc\u0627\u0646 \u0648\u06cc\u062f\u06cc\u0648<\/a><\/li><li><a href=\"#h--4\" >\u0646\u062a\u06cc\u062c\u0647\u00ad \u06af\u06cc\u0631\u06cc<\/a><\/li><\/nav><\/ul><\/div>\n\n\n\n<div class=\"wp-block-image\"><figure class=\"aligncenter size-large\"><img decoding=\"async\" width=\"1024\" height=\"576\" src=\"https:\/\/shahaab-co.com\/mag\/wp-content\/uploads\/2021\/04\/\u062a\u0634\u062e\u06cc\u0635-\u0645\u0627\u0633\u06a9.jpg\" alt=\"\u062a\u0634\u062e\u06cc\u0635 \u0645\u0627\u0633\u06a9\" class=\"wp-image-11076\" title=\"\" srcset=\"https:\/\/shahaab-co.com\/mag\/wp-content\/uploads\/2021\/04\/\u062a\u0634\u062e\u06cc\u0635-\u0645\u0627\u0633\u06a9.jpg 1024w, https:\/\/shahaab-co.com\/mag\/wp-content\/uploads\/2021\/04\/\u062a\u0634\u062e\u06cc\u0635-\u0645\u0627\u0633\u06a9-300x169.jpg 300w, https:\/\/shahaab-co.com\/mag\/wp-content\/uploads\/2021\/04\/\u062a\u0634\u062e\u06cc\u0635-\u0645\u0627\u0633\u06a9-768x432.jpg 768w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure><\/div>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-\"><span class=\"ez-toc-section\" id=\"%D9%BE%DB%8C%D8%B4_%D9%86%DB%8C%D8%A7%D8%B2_%D9%87%D8%A7\"><\/span><strong>\u067e\u06cc\u0634 \u0646\u06cc\u0627\u0632 \u0647\u0627<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n<p style=\"text-align: justify;\">\u062f\u0631\u06a9 \u06a9\u0627\u0645\u0644 \u0627\u06cc\u0646 \u0622\u0645\u0648\u0632\u0634 \u060c \u067e\u06cc\u0634 \u0646\u06cc\u0627\u0632 \u0647\u0627\u06cc\u06cc \u0644\u0627\u0632\u0645 \u062f\u0627\u0631\u062f \u06a9\u0647 \u0639\u0628\u0627\u0631\u062a\u0646\u062f \u0627\u0632:<\/p>\n<ul style=\"text-align: justify;\">\n<li>\u062f\u0627\u0634\u062a\u0646 \u0634\u0646\u0627\u062e\u062a \u0627\u0648\u0644\u06cc\u0647 \u0627\u0632 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u062a\u0646\u0633\u0648\u0631\u0641\u0644\u0648 \/ Keras<\/li>\n<li>\u0646\u0635\u0628 \u0628\u0648\u062f\u0646 \u067e\u0627\u06cc\u062a\u0648\u0646 ( \u0646\u0633\u062e\u0647\u00ad \u06cc \u06f3\u066b\u06f5 \u0628\u0647 \u0628\u0639\u062f ) \u0648 \u062a\u0646\u0633\u0648\u0631\u0641\u0644\u0648 ( \u0646\u0633\u062e\u0647 \u00ad\u06cc \u06f2\u066b\u06f0 \u0628\u0647 \u0628\u0639\u062f ) \u0628\u0631 \u0631\u0648\u06cc \u0633\u06cc\u0633\u062a\u0645 \u062e\u0648\u062f<\/li>\n<li>\u062e\u0648\u0627\u0646\u062f\u0646 \u0622\u0645\u0648\u0632\u0634 \u0642\u0628\u0644\u06cc<\/li>\n<\/ul>\n<p style=\"text-align: justify;\">\u062f\u0631 \u0632\u06cc\u0631 \u062a\u0639\u062f\u0627\u062f\u06cc \u0644\u06cc\u0646\u06a9 \u0645\u0639\u0631\u0641\u06cc \u0634\u062f\u0647 \u0627\u0633\u062a \u06a9\u0647 \u0628\u0647 \u0634\u0645\u0627 \u0628\u0631\u0627\u06cc \u062f\u0631\u06a9 \u0628\u0647\u062a\u0631 \u0627\u06cc\u0646 \u0622\u0645\u0648\u0632\u0634 \u06a9\u0645\u06a9 \u062e\u0648\u0627\u0647\u0646\u062f \u06a9\u0631\u062f:<\/p>\n<ul style=\"text-align: justify;\">\n<li><a href=\"https:\/\/wiki.python.org\/moin\/BeginnersGuide\/Download\" target=\"_blank\" rel=\"noopener\">https:\/\/wiki.python.org\/moin\/BeginnersGuide\/Download<\/a><\/li>\n<li><a href=\"https:\/\/www.tensorflow.org\/install\" target=\"_blank\" rel=\"noopener\">https:\/\/www.tensorflow.org\/install<\/a><\/li>\n<li><a href=\"https:\/\/www.tensorflow.org\/resources\/learn-ml\/basics-of-machine-learning\" target=\"_blank\" rel=\"noopener\">https:\/\/www.tensorflow.org\/resources\/learn-ml\/basics-of-machine-learning<\/a><\/li>\n<\/ul>\n<p style=\"text-align: justify;\">\u062d\u0627\u0644 \u0646\u0648\u0628\u062a \u0627\u06cc\u0646 \u0627\u0633\u062a \u06a9\u0647 \u0633\u0627\u062e\u062a \u0633\u06cc\u0633\u062a\u0645 \u0646\u0638\u0627\u0631\u062a\u06cc \u0631\u0627 \u0634\u0631\u0648\u0639 \u06a9\u0646\u06cc\u0645. \u062f\u0631 \u0627\u06cc\u0646 \u0628\u062e\u0634\u060c \u0645\u062f\u0644 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642 \u0628\u0631\u0627\u06cc \u062a\u0634\u062e\u06cc\u0635 \u062f\u0648 \u06a9\u0644\u0627\u0633 \u0627\u0641\u0631\u0627\u062f \u0628\u0627 \u0645\u0627\u0633\u06a9 \u0648 \u0627\u0641\u0631\u0627\u062f \u0628\u062f\u0648\u0646 \u0645\u0627\u0633\u06a9 \u0633\u0627\u062e\u062a\u0647 \u0645\u06cc \u00ad\u0634\u0648\u062f.<\/p>\n\n<p style=\"text-align: justify;\">\u0628\u0631\u0627\u06cc \u0627\u0646\u062c\u0627\u0645 \u0627\u06cc\u0646 \u0637\u0628\u0642\u0647 \u00ad\u0628\u0646\u062f\u06cc \u060c \u0627\u0632 \u06cc\u06a9 \u062f\u0633\u062a\u0647 \u0627\u0632 \u0634\u0628\u06a9\u0647 \u00ad\u0647\u0627\u06cc \u0639\u0635\u0628\u06cc \u0639\u0645\u06cc\u0642 \u0628\u0647 \u0646\u0627\u0645 \u0634\u0628\u06a9\u0647 \u00ad\u0647\u0627\u06cc \u0639\u0635\u0628\u06cc \u06a9\u0627\u0646\u0648\u0644\u0648\u0634\u0646\u06cc \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc\u00ad \u0634\u0648\u062f \u06a9\u0647 \u0645\u0639\u0645\u0648\u0644\u0627 \u0628\u0631\u0627\u06cc \u062a\u062d\u0644\u06cc\u0644 \u062a\u0635\u0627\u0648\u06cc\u0631 \u0628\u0647 \u06a9\u0627\u0631 \u0645\u06cc \u00ad\u0631\u0648\u062f.<\/p>\n\n\n<h2 class=\"wp-block-heading\" id=\"h--1\"><span class=\"ez-toc-section\" id=\"%D8%A8%D8%A7%D8%B1%DA%AF%D8%B0%D8%A7%D8%B1%DB%8C_%D9%88_%D9%BE%DB%8C%D8%B4_%D9%BE%D8%B1%D8%AF%D8%A7%D8%B2%D8%B4_%D9%85%D8%AC%D9%85%D9%88%D8%B9%D9%87_%D8%AF%D8%A7%D8%AF%D9%87_%D9%87%D8%A7\"><\/span><strong>\u0628\u0627\u0631\u06af\u0630\u0627\u0631\u06cc \u0648 \u067e\u06cc\u0634\u00ad \u067e\u0631\u062f\u0627\u0632\u0634 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u00ad\u0647\u0627\u00a0<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n<p style=\"text-align: justify;\">\u062f\u0627\u062f\u0647 \u060c \u0647\u0633\u062a\u0647\u00ad \u06cc \u0627\u0635\u0644\u06cc \u0647\u0631 \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645 \u0647\u0648\u0634 \u0645\u0635\u0646\u0648\u0639\u06cc \/ \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646 \u0627\u0633\u062a. \u062f\u0631 \u0627\u06cc\u0646 \u067e\u0631\u0648\u0698\u0647\u060c \u0627\u0632 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u00ad\u0647\u0627\u06cc \u06a9\u06af\u0644 \u0648 RMFD \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0634\u062f\u0647 \u0627\u0633\u062a. \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0634\u0627\u0645\u0644 \u06f3\u06f8\u06f3\u06f5 \u062a\u0635\u0648\u06cc\u0631 \u0627\u0633\u062a \u06a9\u0647 \u06f1\u06f9\u06f1\u06f6 \u062a\u0635\u0648\u06cc\u0631 \u0622\u0646 \u0628\u0647 \u06a9\u0644\u0627\u0633 \u0627\u0641\u0631\u0627\u062f \u0628\u062f\u0648\u0646 \u0645\u0627\u0633\u06a9 \u0648 \u06f1\u06f9\u06f1\u06f9 \u062a\u0635\u0648\u06cc\u0631 \u0622\u0646 \u0628\u0647 \u06a9\u0644\u0627\u0633 \u0627\u0641\u0631\u0627\u062f \u0628\u0627 \u0645\u0627\u0633\u06a9 \u062a\u0639\u0644\u0642 \u062f\u0627\u0631\u0646\u062f.<\/p>\n<p style=\"text-align: justify;\">\u062c\u0647\u062a \u0633\u0627\u062e\u062a \u0627\u06cc\u0646 \u0645\u062f\u0644\u060c \u0644\u0627\u0632\u0645 \u0627\u0633\u062a \u0627\u0628\u062a\u062f\u0627\u06cc \u06a9\u062a\u0627\u0628\u062e\u0627\u0646\u0647\u00ad \u0647\u0627\u06cc \u0636\u0631\u0648\u0631\u06cc \u0628\u0647 \u067e\u0631\u0648\u0698\u0647 \u0648\u0627\u0631\u062f \u0634\u0648\u0646\u062f. \u0627\u06cc\u0646 \u06a9\u062a\u0627\u0628\u062e\u0627\u0646\u0647 \u00ad\u0647\u0627 \u062f\u0631\u0628\u0631\u06af\u06cc\u0631\u0646\u062f\u0647 \u00ad\u06cc \u0645\u0627\u0698\u0648\u0644\u00ad \u0647\u0627\u06cc\u06cc \u0628\u0631\u0627\u06cc \u067e\u06cc\u0634 \u00ad\u067e\u0631\u062f\u0627\u0632\u0634 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u060c \u0645\u062f\u06cc\u0631\u06cc\u062a \u0641\u0627\u06cc\u0644 \u060c \u0633\u0627\u062e\u062a \u0645\u062f\u0644 \u0648 \u0627\u0631\u0632\u06cc\u0627\u0628\u06cc \u0648 \u0628\u0635\u0631\u06cc\u00ad \u0633\u0627\u0632\u06cc \u0622\u0646 \u0645\u062f\u0644 \u0647\u0633\u062a\u0646\u062f.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\">from tensorflow.keras.preprocessing.image import ImageDataGenerator\nfrom tensorflow.keras.applications import MobileNetV2\nfrom tensorflow.keras.layers import AveragePooling2D\nfrom tensorflow.keras.layers import Dropout\nfrom tensorflow.keras.layers import Flatten\nfrom tensorflow.keras.layers import Dense\nfrom tensorflow.keras.layers import Input\nfrom tensorflow.keras.models import Model\nfrom tensorflow.keras.optimizers import Adam\nfrom tensorflow.keras.applications.mobilenet_v2 import preprocess_input\nfrom tensorflow.keras.preprocessing.image import img_to_array\nfrom tensorflow.keras.preprocessing.image import load_img\nfrom tensorflow.keras.utils import to_categorical\nfrom sklearn.preprocessing import LabelBinarizer\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import classification_report\nfrom imutils import paths\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport argparse\nimport os<\/pre>\n<p style=\"text-align: justify;\">\u0647\u0648\u0634 \u0645\u0635\u0646\u0648\u0639\u06cc Neptune \u060c \u06cc\u06a9 \u062a\u062c\u0645\u06cc\u0639 \u0645\u0631\u062a\u0628 \u0627\u0632 \u0647\u0631 \u062f\u0648 \u0633\u06a9\u0648\u06cc \u0646\u067e\u062a\u06cc\u0648\u0646 \u0648 TensorBoard \u0633\u0627\u062e\u062a\u0647 \u0627\u0633\u062a \u06a9\u0647 \u0628\u0647 \u0628\u0631\u0646\u0627\u0645\u0647 \u0646\u0648\u06cc\u0633\u06cc TensorBoard \u06a9\u0645\u06a9 \u0645\u06cc \u06a9\u0646\u062f. \u0627\u06af\u0631 \u06a9\u062a\u0627\u0628\u062e\u0627\u0646\u0647 \u00ad\u0647\u0627\u06cc \u0630\u06cc\u0644 \u0628\u0647 \u067e\u0631\u0648\u0698\u0647 \u0648\u0627\u0631\u062f \u0645\u06cc\u00ad \u0634\u0648\u0646\u062f \u0645\u06cc\u00ad\u062a\u0648\u0627\u0646 \u0627\u0632 \u0627\u06cc\u0646 \u0633\u0631\u0648\u06cc\u0633 \u0628\u0647\u0631\u0647 \u0628\u0631\u062f.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\">import random\nimport psutil\nimport neptune\nimport neptune_tensorboard as neptune_tb<\/pre>\n<p style=\"text-align: justify;\">\u0633\u067e\u0633\u060c \u0627\u0639\u062a\u0628\u0627\u0631 \u0646\u0627\u0645\u0647 \u00ad\u0647\u0627\u06cc API \u0646\u067e\u062a\u06cc\u0648\u0646 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u0645\u0627\u0698\u0648\u0644 dotenv \u00a0\u0627\u0632 .env \u0628\u0627\u0631\u06af\u0630\u0627\u0631\u06cc \u0645\u06cc\u00ad \u0634\u0648\u0646\u062f. \u062a\u0648\u06a9\u0646\u0650 API\u060c \u0627\u0631\u062a\u0628\u0627\u0637 \u0628\u06cc\u0646 \u0627\u0633\u06a9\u0631\u06cc\u067e\u00ad \u0647\u0627\u06cc \u0622\u0645\u0648\u0632\u0634 \u0645\u062f\u0644 \u0648 \u0646\u067e\u062a\u06cc\u0648\u0646 \u0631\u0627 \u062a\u0635\u0648\u06cc\u0628 \/ \u0631\u062f \u0645\u06cc\u00ad \u06a9\u0646\u062f.\u00a0 \u062a\u0648\u06a9\u0646 API \u0646\u067e\u062a\u06cc\u0648\u0646 \u0645\u0627\u0646\u0646\u062f \u06cc\u06a9 \u0631\u0645\u0632 \u0639\u0628\u0648\u0631 \u0628\u0631\u0627\u06cc \u06a9\u0627\u0631\u0628\u0631\u062f \u0646\u067e\u062a\u06cc\u0648\u0646 \u0639\u0645\u0644 \u0645\u06cc\u00ad \u06a9\u0646\u062f. \u0628\u0646\u0627\u0628\u0631\u0627\u06cc\u0646 \u062c\u0647\u062a \u0645\u062d\u0641\u0648\u0638 \u0648 \u0645\u062e\u0641\u06cc \u0645\u0627\u0646\u062f\u0646 \u0627\u06cc\u0646 \u062a\u0648\u06a9\u0646 \u0627\u0632 \u0645\u062a\u063a\u06cc\u0631 \u0647\u0627\u06cc \u0645\u062d\u06cc\u0637\u06cc \u0628\u0631\u0627\u06cc \u0628\u0627\u0631\u06af\u0630\u0627\u0631\u06cc \u0645\u0642\u062f\u0627\u0631 \u0622\u0646 \u0628\u0647\u0631\u0647 \u0628\u0631\u062f\u0647 \u0645\u06cc \u00ad\u0634\u0648\u062f. \u0645\u062a\u063a\u06cc\u0631\u0647\u0627\u06cc \u0645\u062d\u06cc\u0637\u06cc \u0627\u0632 \u06a9\u062a\u0627\u0628\u062e\u0627\u0646\u0647 dotenv\u00a0 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc\u00ad \u06a9\u0646\u0646\u062f.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\">from dotenv import load_dotenv\nload_dotenv()\n\nAPI_SECRET = os.getenv(\"NEPTUNE_API_TOKEN\")<\/pre>\n<p style=\"text-align: justify;\">\u0633\u067e\u0633\u060c \u067e\u0631\u0648\u0698\u0647 \u0631\u0627\u0647 \u00ad\u0627\u0646\u062f\u0627\u0632\u06cc \u0645\u06cc \u00ad\u0634\u0648\u062f \u0648 \u0628\u0647 \u0634\u06a9\u0644 \u062e\u0648\u062f\u06a9\u0627\u0631 \u0631\u0648\u06cc\u062f\u0627\u062f \u0647\u0627\u06cc \u0645\u0631\u062a\u0628\u0637 \u0628\u0627 \u0633\u0646\u062c\u0647\u00ad \u0647\u0627\u06cc TensorBoard \u0631\u0627 \u062b\u0628\u062a \u0645\u06cc\u00ad \u06a9\u0646\u062f.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\">&lt;pre class=\"hljs\" style=\"display: block; overflow-x: auto; padding: 0.5em; color: rgb(51, 51, 51); background: rgb(248, 248, 248);\"&gt;neptune.init(project_qualified_name=&lt;span class=\"hljs-string\" style=\"color: rgb(221, 17, 68);\"&gt;'codebrain\/Drone'&lt;\/span&gt;,\n         \tapi_token=API_SECRET,\n         \t)\nneptune_tb.integrate_with_tensorflow()\n&lt;\/pre&gt;\n<\/pre>\n<p style=\"text-align: justify;\">\u0628\u0639\u062f \u0627\u0632 \u0627\u0646\u062c\u0627\u0645 \u06a9\u0627\u0631\u0647\u0627\u06cc \u0641\u0648\u0642\u060c \u0645\u06cc\u00ad \u062a\u0648\u0627\u0646 \u062a\u0646\u0638\u06cc\u0645\u0627\u062a \u0645\u062d\u06cc\u0637 \u0628\u0631\u0646\u0627\u0645\u0647 \u0646\u0648\u06cc\u0633\u06cc \u0628\u0631\u0627\u06cc \u0622\u0645\u0648\u0632\u0634 \u0645\u062f\u0644 \u062a\u0634\u062e\u06cc\u0635 \u0645\u0627\u0633\u06a9 \u0635\u0648\u0631\u062a \u0631\u0627 \u0627\u0646\u062c\u0627\u0645 \u062f\u0627\u062f. \u0627\u0632 \u0637\u0631\u0641\u06cc \u0646\u0631\u062e \u0627\u0648\u0644\u06cc\u0647 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc\u060c \u062a\u0639\u062f\u0627\u062f Epoche \u0647\u0627 \u0628\u0631\u0627\u06cc \u0622\u0645\u0648\u0632\u0634 \u0645\u062f\u0644 \u0648 \u0627\u0646\u062f\u0627\u0632\u0647\u00ad \u06cc \u062f\u0633\u062a\u0647 \u0645\u0639\u06cc\u0646 \u0645\u06cc \u00ad\u0634\u0648\u0646\u062f \u0648 \u0645\u0633\u06cc\u0631 \u0631\u0648\u06cc\u062f\u0627\u062f \u0647\u0627\u06cc \u062a\u062c\u0631\u0628\u0647 \u0646\u06cc\u0632 \u062a\u0646\u0638\u06cc\u0645 \u0645\u06cc \u00ad\u0634\u0648\u062f.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\">PARAMS = {\n  'EPOCHS': 20,\n  'BS': 32,\n  'INIT_LR': 1e-4,\n}\nRUN_NAME = 'run_{}'.format(random.getrandbits(64))\nEXPERIMENT_LOG_DIR = 'logs\/{}'.format(RUN_NAME)\n<\/pre>\n<p style=\"text-align: justify;\">\u062f\u0631 \u0642\u062f\u0645 \u0628\u0639\u062f\u060c \u062a\u062c\u0632\u06cc\u0647 \u00ad\u06a9\u0646\u0646\u062f\u0647 \u00ad\u0647\u0627\u06cc \u0622\u0631\u06af\u0648\u0645\u0627\u0646\u06cc \u0645\u0639\u0631\u0641\u06cc \u0645\u06cc\u00ad \u0634\u0648\u0646\u062f. \u062a\u062c\u0632\u06cc\u0647\u00ad \u06a9\u0646\u0646\u062f\u0647\u00ad \u0647\u0627\u06cc \u0622\u0631\u06af\u0648\u0645\u0627\u0646\u06cc \u060c \u0646\u0648\u0634\u062a\u0646 \u0631\u0627\u0628\u0637\u00ad \u0647\u0627\u06cc \u062e\u0637 \u0641\u0631\u0645\u0627\u0646\u0650 \u06a9\u0627\u0631\u0628\u0631 \u067e\u0633\u0646\u062f \u0631\u0627 \u062a\u0633\u0647\u06cc\u0644 \u0645\u06cc \u00ad\u0628\u062e\u0634\u0646\u062f. \u0627\u06cc\u0646 \u0631\u0627\u0628\u0637\u00ad \u0647\u0627 \u0628\u0631\u0627\u06cc \u062a\u0639\u0627\u0645\u0644 \u0628\u0627 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647\u060c \u0637\u0631\u062d \u0648 \u0645\u062f\u0644 \u0627\u06cc\u062c\u0627\u062f \u0645\u06cc\u00ad \u0634\u0648\u0646\u062f.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\">ap = argparse.ArgumentParser()\nap.add_argument(\"-d\", \"--dataset\", required=True,\n            \thelp=\"path to input dataset\")\nap.add_argument(\"-p\", \"--plot\", type=str, default=\"plot.png\",\n            \thelp=\"path to output loss\/accuracy plot\")\nap.add_argument(\"-m\", \"--model\", type=str,\n            \tdefault=\"mask_detector.model\",\n            \thelp=\"path to output face mask detector model\")\nargs = vars(ap.parse_args())\n<\/pre>\n<p style=\"text-align: justify;\">\u062f\u0631 \u0642\u062f\u0645 \u0628\u0639\u062f\u060c \u0645\u0633\u06cc\u0631 \u0647\u0627\u06cc \u0647\u0631 \u06cc\u06a9 \u0627\u0632 \u062a\u0635\u0627\u0648\u06cc\u0631 \u0627\u0632 \u0645\u0633\u06cc\u0631 \u0630\u062e\u06cc\u0631\u0647 \u00ad\u0633\u0627\u0632\u06cc \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u062f\u0631\u06cc\u0627\u0641\u062a \u0645\u06cc \u00ad\u0634\u0648\u0646\u062f \u0648 \u062f\u0648 \u0644\u06cc\u0633\u062a \u0628\u0647 \u062a\u0631\u062a\u06cc\u0628 \u0628\u0631\u0627\u06cc \u0646\u06af\u0647\u062f\u0627\u0631\u06cc \u062f\u0627\u062f\u0647\u00ad \u0647\u0627 \u0648 \u06a9\u0644\u0627\u0633 \u0647\u0627 \/ \u0628\u0631\u0686\u0633\u0628 \u00ad\u0647\u0627\u00a0 \u0627\u06cc\u062c\u0627\u062f \u0645\u06cc\u00ad \u0634\u0648\u0646\u062f.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\">print(\"[INFO] loading images...\")\nimagePaths = list(paths.list_images(args[\"dataset\"]))\ndata = []\nlabels = []\n<\/pre>\n<p style=\"text-align: justify;\">\u062f\u0631 \u0642\u062f\u0645 \u0628\u0639\u062f\u06cc \u0631\u0648\u06cc \u0645\u0633\u06cc\u0631\u0647\u0627\u06cc \u062a\u0635\u0627\u0648\u06cc\u0631 \u062d\u0644\u0642\u0647 \u00ad\u0627\u06cc \u0627\u06cc\u062c\u0627\u062f \u0645\u06cc\u00ad \u0634\u0648\u062f \u06a9\u0647 \u062f\u0631 \u0647\u0631 \u062f\u0648\u0631 \u062d\u0644\u0642\u0647 \u0627\u0648\u0644\u0627 \u0628\u0631\u0686\u0633\u0628 \u06a9\u0644\u0627\u0633 \u0627\u0632 \u0646\u0627\u0645 \u0641\u0627\u06cc\u0644 \u0627\u0633\u062a\u062e\u0631\u0627\u062c \u0645\u06cc\u00ad \u0634\u0648\u062f\u060c \u062f\u0648\u0645\u0627 \u062a\u0635\u0648\u06cc\u0631 \u0628\u0627 \u062a\u0642\u0633\u06cc\u0645 \u0628\u0647 \u0642\u0637\u0639\u0627\u062a \u06f2\u06f2\u06f4 * \u06f2\u06f2\u06f4 \u067e\u06cc\u06a9\u0633\u0644\u06cc \u067e\u06cc\u0634 \u00ad\u067e\u0631\u062f\u0627\u0632\u0634 \u0645\u06cc\u00ad \u0634\u0648\u062f \u06a9\u0647 \u0634\u0628\u06a9\u0647 \u0639\u0635\u0628\u06cc \u0631\u0627 \u062a\u063a\u0630\u06cc\u0647 \u0645\u06cc\u00ad \u06a9\u0646\u0646\u062f. \u0633\u0648\u0645\u0627 \u062a\u0635\u0627\u0648\u06cc\u0631 \u0628\u0647 \u0622\u0631\u0627\u06cc\u0647 \u00ad\u0647\u0627 \u062a\u0628\u062f\u06cc\u0644 \u0645\u06cc\u00ad \u0634\u0648\u0646\u062f \u0648 \u062a\u0635\u0648\u06cc\u0631 \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0648\u0631\u0648\u062f\u06cc \u0628\u0647 \u062a\u0627\u0628\u0639 preprocess_input \u062f\u0627\u062f\u0647 \u0645\u06cc\u00ad \u0634\u0648\u062f \u06a9\u0647 \u0628\u0647 \u0645\u0639\u0646\u0627\u06cc \u0645\u062a\u0646\u0627\u0633\u0628 \u0628\u0648\u062f\u0646 \u062a\u0635\u0648\u06cc\u0631 \u0628\u0627 \u0642\u0627\u0644\u0628\u06cc \u0627\u0633\u062a \u06a9\u0647 \u0645\u062f\u0644 \u0646\u06cc\u0627\u0632 \u062f\u0627\u0631\u062f (\u0628\u0647 \u0628\u06cc\u0627\u0646 \u062f\u06cc\u06af\u0631 \u062a\u0636\u0645\u06cc\u0646 \u0645\u06cc\u00ad \u0634\u0648\u062f \u06a9\u0647 \u062a\u0635\u0627\u0648\u06cc\u0631 \u0628\u0627\u0631\u06af\u0630\u0627\u0631\u06cc \u0634\u062f\u0647 \u0628\u0627 preprocess_input \u0633\u0627\u0632\u06af\u0627\u0631 \u0647\u0633\u062a\u0646\u062f) . \u0628\u0639\u062f \u0627\u0632 \u0627\u062a\u0645\u0627\u0645 \u062d\u0644\u0642\u0647\u060c \u062f\u0627\u062f\u0647 \u0648 \u0628\u0631\u0686\u0633\u0628 \u00ad\u0647\u0627 \u0628\u0631\u0627\u06cc \u067e\u0631\u062f\u0627\u0632\u0634 \u00ad\u0647\u0627\u06cc \u0622\u062a\u06cc \u0628\u0647 \u0622\u0631\u0627\u06cc\u0647 \u00ad\u0647\u0627\u06cc NumPy\u00a0 \u062a\u0628\u062f\u06cc\u0644 \u0645\u06cc\u00ad \u0634\u0648\u0646\u062f \u0648 \u062f\u0631 \u0646\u0647\u0627\u06cc\u062a \u0628\u0631\u0686\u0633\u0628 \u00ad\u0647\u0627 \u0628\u0627 \u0631\u0648\u0634 \u06a9\u062f\u06af\u0630\u0627\u0631\u06cc \u0648\u0627\u0646 \u0647\u0627\u062a ( One Hot ) \u06a9\u062f\u0646\u0648\u06cc\u0633\u06cc \u0645\u06cc \u00ad\u0634\u0648\u0646\u062f \u062a\u0627 \u062f\u0627\u062f\u0647 \u00ad\u0647\u0627\u06cc \u0637\u0628\u0642\u0647 \u00ad\u0628\u0646\u062f\u06cc \u0631\u0627 \u0628\u0647 \u062f\u0627\u062f\u0647 \u00ad\u0647\u0627\u06cc \u0639\u062f\u062f\u06cc \u062a\u0628\u062f\u06cc\u0644 \u06a9\u0646\u062f.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\">for imagePath in imagePaths:\n  label = imagePath.split(os.path.sep)[-2]\n\n    image = load_img(imagePath, target_size=(224, 224))\n  image = img_to_array(image)\n  image = preprocess_input(image)\n\n  # update the data and labels lists, respectively\n  data.append(image)\n  labels.append(label)\n\n# convert the data and labels to NumPy arrays\ndata = np.array(data, dtype=\"float32\")\nlabels = np.array(labels)\n\n# perform one-hot encoding on the labels\nlb = LabelBinarizer()\nlabels = lb.fit_transform(labels)\nlabels = to_categorical(labels)\n<\/pre>\n<p style=\"text-align: justify;\">\u062d\u0627\u0644 \u06cc\u06a9 \u0622\u0632\u0645\u0627\u06cc\u0634 \u062f\u0631 \u0646\u067e\u062a\u06cc\u0648\u0646 \u0627\u06cc\u062c\u0627\u062f \u0634\u062f\u0647 \u0648 \u06cc\u06a9 \u0646\u0627\u0645\u00a0 \u0628\u0647 \u0627\u06cc\u0646 \u0622\u0632\u0645\u0627\u06cc\u0634 \u062a\u062e\u0635\u06cc\u0635 \u062f\u0627\u062f\u0647 \u0645\u06cc\u00ad \u0634\u0648\u062f \u0648 \u0627\u0628\u0631 \u067e\u0627\u0631\u0627\u0645\u062a\u0631 \u0647\u0627 \u06cc\u0627\u062f \u062f\u0627\u0634\u062a \u0645\u06cc\u00ad \u0634\u0648\u0646\u062f. \u062c\u0647\u062a \u067e\u0627\u06a9\u0633\u0627\u0632\u06cc \u0628\u0647 \u0635\u0648\u0631\u062a \u062e\u0648\u062f\u06a9\u0627\u0631 \u0628\u0639\u062f \u0627\u0632 \u0627\u062a\u0645\u0627\u0645 \u0622\u0632\u0645\u0627\u06cc\u0634 \u060c \u062a\u0648\u0635\u06cc\u0647 \u0645\u06cc \u0634\u0648\u062f \u062f\u0631\u0635\u0648\u0631\u062a \u0627\u0645\u06a9\u0627\u0646 \u0647\u0631 \u0686\u06cc\u0632\u06cc \u062f\u0631 \u0639\u0628\u0627\u0631\u062a with \u0642\u0631\u0627\u0631 \u06af\u06cc\u0631\u062f. \u0642\u0628\u0644 \u0627\u0632 \u0622\u0645\u0648\u0632\u0634 \u0645\u062f\u0644 \u0644\u0627\u0632\u0645 \u0627\u0633\u062a \u06a9\u0647 \u062f\u0627\u062f\u0647 \u0628\u0647 \u062f\u0627\u062f\u0647\u00ad \u0647\u0627\u06cc \u0622\u0645\u0648\u0632\u0634\u06cc \u0648 \u062f\u0627\u062f\u0647 \u00ad\u0647\u0627\u06cc \u0622\u0632\u0645\u0627\u06cc\u0634\u06cc \u062a\u0642\u0633\u06cc\u0645 \u0634\u0648\u062f. \u062f\u0631 \u0627\u06cc\u0646 \u067e\u0631\u0648\u0698\u0647 \u0646\u0632\u062f\u06cc\u06a9 \u0628\u0647 \u06f8\u06f0 \u062f\u0631\u0635\u062f \u062f\u0627\u062f\u0647 \u00ad\u0647\u0627 \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u062f\u0627\u062f\u0647 \u00ad\u0647\u0627\u06cc \u0622\u0645\u0648\u0632\u0634\u06cc \u0648 \u06f2\u06f0 \u062f\u0631\u0635\u062f \u062f\u0627\u062f\u0647 \u00ad\u0647\u0627 \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u062f\u0627\u062f\u0647 \u00ad\u0647\u0627\u06cc \u0622\u0632\u0645\u0627\u06cc\u0634\u06cc \u062f\u0631 \u0646\u0638\u0631 \u06af\u0631\u0641\u062a\u0647 \u0645\u06cc \u00ad\u0634\u0648\u0646\u062f. \u00a0<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\">with neptune.create_experiment(name=RUN_NAME, params=PARAMS):\n\n  # partition the data into training and testing splits using 75% of\n  # the data for training and the remaining 25% for testing\n  (trainX, testX, trainY, testY) = train_test_split(data, labels,\n                                                  \ttest_size=0.20, stratify=labels, random_state=42)\n<\/pre>\n<p style=\"text-align: justify;\">\u062c\u0647\u062a \u0627\u0641\u0632\u0627\u06cc\u0634 \u062f\u0627\u062f\u0647\u060c \u0645\u0648\u0644\u062f \u0628\u0631\u0627\u06cc \u062a\u0635\u0648\u06cc\u0631 \u0622\u0645\u0648\u0632\u0634\u06cc\u00ad \u0633\u0627\u062e\u062a\u0647 \u0645\u06cc\u00ad \u0634\u0648\u062f. \u0627\u06cc\u0646 \u0645\u0648\u0644\u062f \u0628\u0647 \u0637\u0648\u0631 \u0645\u0635\u0646\u0648\u0639\u06cc \u0627\u0646\u062f\u0627\u0632\u0647\u00ad \u06cc \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u00ad\u06cc \u0622\u0645\u0648\u0632\u0634\u06cc \u0631\u0627 \u0627\u0632 \u0637\u0631\u06cc\u0642 \u0627\u06cc\u062c\u0627\u062f \u0646\u0633\u062e\u0647\u00ad \u0647\u0627\u06cc \u0645\u062e\u062a\u0644\u0641 \u0628\u0631\u0627\u06cc \u062a\u0635\u0648\u06cc\u0631 \u060c \u0627\u0641\u0632\u0627\u06cc\u0634 \u0645\u06cc \u00ad\u062f\u0647\u062f. \u0627\u06cc\u0646 \u0627\u0641\u0632\u0627\u06cc\u0634 \u062f\u0627\u062f\u0647 \u0628\u0647 \u0645\u062f\u0644 \u06a9\u0645\u06a9 \u0645\u06cc \u00ad\u06a9\u0646\u062f \u062a\u0627 \u0628\u0647\u062a\u0631 \u062a\u0639\u0645\u06cc\u0645 \u062f\u0627\u062f\u0647 \u0634\u0648\u062f.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\">aug = ImageDataGenerator(\n  rotation_range=20,\n  zoom_range=0.15,\n  width_shift_range=0.2,\n  height_shift_range=0.2,\n  shear_range=0.15,\n  horizontal_flip=True,\n  fill_mode=\"nearest\")\n<\/pre>\n\n\n<h2 class=\"wp-block-heading\" id=\"h--2\"><span class=\"ez-toc-section\" id=\"%D8%B3%D8%A7%D8%AE%D8%AA_%D9%85%D8%AF%D9%84\"><\/span><strong>\u0633\u0627\u062e\u062a \u0645\u062f\u0644<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n<p style=\"text-align: justify;\">\u0628\u0639\u062f \u0627\u0632 \u067e\u06cc\u0634 \u00ad\u067e\u0631\u062f\u0627\u0634 \u062f\u0627\u062f\u0647 \u0648 \u0628\u0631\u0686\u0633\u0628\u00ad \u06af\u0630\u0627\u0631\u06cc \u062f\u0631\u0633\u062a \u0622\u0646\u060c \u06af\u0627\u0645 \u0628\u0639\u062f\u06cc \u0622\u0645\u0648\u0632\u0634 \u06cc\u06a9 \u0645\u062f\u0644 \u0627\u0633\u062a \u062a\u0627 \u062a\u0635\u0627\u0648\u06cc\u0631 \u0631\u0627 \u0628\u0627 \u062f\u0642\u062a \u0637\u0628\u0642\u0647 \u00ad\u0628\u0646\u062f\u06cc \u06a9\u0646\u062f. \u062f\u0648 \u0631\u0627\u0647 \u0628\u0631\u0627\u06cc \u0627\u06cc\u0646 \u0645\u0647\u0645 \u0648\u062c\u0648\u062f \u062f\u0627\u0631\u062f. \u0647\u0645 \u0645\u06cc\u00ad\u062a\u0648\u0627\u0646 \u06cc\u06a9 \u0645\u062f\u0644 \u0637\u0628\u0642\u0647 \u00ad\u0628\u0646\u062f\u06cc \u00ad\u06a9\u0646\u0646\u062f\u0647 \u0631\u0627 \u0627\u0632 \u0635\u0641\u0631 \u0627\u06cc\u062c\u0627\u062f \u06a9\u0631\u062f \u0648 \u00a0\u0647\u0645 \u0645\u06cc\u00ad \u062a\u0648\u0627\u0646 \u0627\u0632 \u0645\u062f\u0644 \u00ad\u0647\u0627\u06cc \u0627\u0632 \u067e\u06cc\u0634 \u0622\u0645\u0648\u0632\u0634 \u062f\u0627\u062f\u0647 \u0634\u062f\u0647 \u0628\u0647\u0631\u0647 \u0628\u0631\u062f. \u062f\u0631 \u0627\u06cc\u0646 \u0622\u0645\u0648\u0632\u0634 \u0631\u0648\u0634 \u062f\u0648\u0645\u00a0 \u0627\u0646\u062a\u062e\u0627\u0628 \u0645\u06cc\u00ad \u0634\u0648\u062f \u0648 \u0645\u062f\u0644 mobilinet_v2 \u06a9\u0647 \u06cc\u06a9 \u0634\u0628\u06a9\u0647 \u0639\u0635\u0628\u06cc \u06a9\u0627\u0646\u0648\u0644\u0648\u0634\u0646\u06cc \u0628\u0627 \u0639\u0645\u0642 \u06f5\u06f3 \u0644\u0627\u06cc\u0647 \u0627\u0633\u062a\u060c \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc\u00ad \u0634\u0648\u062f.<\/p>\n\n\n<div class=\"wp-block-image\"><figure class=\"aligncenter size-large\"><img decoding=\"async\" width=\"504\" height=\"560\" src=\"https:\/\/shahaab-co.com\/mag\/wp-content\/uploads\/2021\/04\/\u0645\u0639\u0645\u0627\u0631\u06cc-\u0645\u062f\u0644-Mobilenet-V2.png\" alt=\"\u0645\u0639\u0645\u0627\u0631\u06cc \u0645\u062f\u0644 Mobilenet V2\" class=\"wp-image-11078\" title=\"\" srcset=\"https:\/\/shahaab-co.com\/mag\/wp-content\/uploads\/2021\/04\/\u0645\u0639\u0645\u0627\u0631\u06cc-\u0645\u062f\u0644-Mobilenet-V2.png 504w, https:\/\/shahaab-co.com\/mag\/wp-content\/uploads\/2021\/04\/\u0645\u0639\u0645\u0627\u0631\u06cc-\u0645\u062f\u0644-Mobilenet-V2-270x300.png 270w\" sizes=\"(max-width: 504px) 100vw, 504px\" \/><figcaption>\u0645\u0639\u0645\u0627\u0631\u06cc \u0645\u062f\u0644 Mobilenet V2<\/figcaption><\/figure><\/div>\n\n\n<p style=\"text-align: justify;\"><strong>\u0646\u06a9\u062a\u0647:<\/strong> \u0648\u0642\u062a\u06cc \u0627\u0632 \u06cc\u06a9 \u0645\u062f\u0644 \u0627\u0632 \u067e\u06cc\u0634 \u0622\u0645\u0648\u0632\u0634\u00ad \u062f\u0627\u062f\u0647 \u00ad\u0634\u062f\u0647 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc\u00ad \u0634\u0648\u062f\u060c \u0627\u0646\u062c\u0627\u0645 \u0645\u0637\u0627\u0644\u0639\u0647 \u00ad\u06cc \u06a9\u0627\u0645\u0644 \u0631\u0648\u06cc \u0645\u062f\u0644 \u0645\u0648\u0631\u062f \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0628\u0633\u06cc\u0627\u0631 \u0645\u0647\u0645 \u0627\u0633\u062a. \u0645\u062f\u0644 \u0628\u0631\u0627\u06cc \u062d\u0644 \u0645\u0633\u0626\u0644\u0647\u00ad \u06cc \u062f\u0631 \u062f\u0633\u062a \u0628\u0631\u0631\u0633\u06cc \u0628\u0627\u06cc\u062f \u0642\u0627\u0628\u0644 \u062a\u0637\u0627\u0628\u0642 \u0628\u0627 \u0645\u0633\u0626\u0644\u0647 \u0628\u0627\u0634\u062f \u0648 \u0628\u0627\u06cc\u062f \u0642\u0627\u062f\u0631 \u0628\u0647 \u06a9\u0627\u0631 \u06a9\u0631\u062f\u0646 \u0628\u0627 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647\u00ad \u06cc \u0627\u0632 \u067e\u06cc\u0634 \u00ad\u067e\u0631\u062f\u0627\u0632\u0634 \u0634\u062f\u0647 \u0628\u0627\u0634\u062f. \u062f\u0631 \u0627\u06cc\u0646 \u067e\u0631\u0648\u0698\u0647\u060c \u00a0mobilinet_v2 \u0627\u0642\u062a\u0628\u0627\u0633 \u0634\u062f\u0647 \u0627\u0633\u062a \u0632\u06cc\u0631\u0627 \u062f\u0631 \u0632\u0645\u06cc\u0646\u0647 \u00ad\u06cc \u06a9\u0627\u0631\u0627\u06cc\u06cc \u00ad\u0647\u0627\u06cc \u0645\u0631\u062a\u0628\u0637 \u0628\u0627 \u062a\u0634\u062e\u06cc\u0635 \u0627\u0634\u06cc\u0627 \u060c \u06a9\u0627\u0647\u0634 \u067e\u06cc\u0686\u06cc\u062f\u06af\u06cc \u0648 \u06a9\u0627\u0647\u0634 \u0645\u062d\u062f\u0648\u062f\u06cc\u062a\u00ad \u0647\u0627\u06cc \u0645\u062d\u0627\u0633\u0628\u0627\u062a\u06cc\u060c \u067e\u0631\u062f\u0627\u0632\u0634 \u06af\u0631\u0627\u0641\u06cc\u06a9\u06cc \u0648 \u0630\u062e\u06cc\u0631\u0647\u00ad \u0633\u0627\u0632\u06cc \u0627\u0632 \u0641\u0646\u0627\u0648\u0631\u06cc \u0647\u0627\u06cc \u067e\u06cc\u0634 \u0631\u0641\u062a\u0647 \u0648 \u0641\u0648\u0642 \u0627\u0644\u0639\u0627\u062f\u0647 \u0631\u0648\u0632 \u0628\u0647 \u0634\u0645\u0627\u0631 \u0645\u06cc \u0622\u06cc\u062f.<\/p>\n<p style=\"text-align: justify;\">\u0648\u0642\u062a\u06cc \u0627\u0632 \u06cc\u06a9 \u0645\u062f\u0644 \u0627\u0642\u062a\u0628\u0627\u0633\u06cc \u062f\u0631 \u067e\u0631\u0648\u0698\u0647 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0634\u0648\u062f \u0628\u0627\u06cc\u062f \u0645\u062f\u0644 \u0628\u0627 \u0648\u0632\u0646 \u0647\u0627\u06cc \u0627\u0632 \u067e\u06cc\u0634 \u00ad\u0622\u0645\u0648\u0632\u0634 \u062f\u0627\u062f\u0647 \u0634\u062f\u0647 \u0628\u0627\u0631\u06af\u0630\u0627\u0631\u06cc \u0634\u0648\u062f \u0648 \u0633\u0627\u062e\u062a\u0627\u0631 \u0647\u0627\u06cc \u0628\u06cc\u0634\u062a\u0631\u06cc \u0628\u0647 \u0645\u062f\u0644 \u0627\u0636\u0627\u0641\u0647 \u0634\u0648\u062f. \u0627\u0632 \u062c\u0645\u0644\u0647 \u0633\u0627\u062e\u062a\u0627\u0631 \u0647\u0627\u06cc\u06cc \u06a9\u0647 \u0628\u0647 \u062f\u0646\u0628\u0627\u0644 \u0644\u0627\u06cc\u0647\u00ad \u0647\u0627\u06cc \u06a9\u0627\u0646\u0648\u0644\u0648\u0634\u0646\u06cc \u0628\u0647 \u0645\u062f\u0644 \u0627\u0636\u0627\u0641\u0647 \u0645\u06cc\u00ad \u0634\u0648\u0646\u062f\u060c \u062a\u0648\u0627\u0628\u0639 \u0641\u0639\u0627\u0644\u00ad\u0633\u0627\u0632\u06cc ReLU ( \u0628\u0631\u0627\u06cc \u0627\u0636\u0627\u0641\u0647 \u06a9\u0631\u062f\u0646 \u0648\u06cc\u0698\u06af\u06cc \u063a\u06cc\u0631\u00ad\u062e\u0637\u06cc \u0628\u0648\u062f\u0646 ) \u0648 Max Pooling ( \u0628\u0631\u0627\u06cc \u06a9\u0627\u0647\u0634 \u0646\u06af\u0627\u0634\u062a \u0648\u06cc\u0698\u06af\u06cc ) \u0647\u0633\u062a\u0646\u062f. \u0628\u0647 \u0645\u0646\u0638\u0648\u0631 \u0627\u062c\u062a\u0646\u0627\u0628 \u0627\u0632\u00a0 \u0628\u06cc\u0634\u00ad \u0628\u0631\u0627\u0632\u0634 \u062f\u0631 \u0634\u0628\u06a9\u0647 \u00ad\u0647\u0627\u06cc \u0639\u0635\u0628\u06cc \u060c Dropout \u0646\u06cc\u0632 \u0627\u0636\u0627\u0641\u0647 \u0645\u06cc \u00ad\u0634\u0648\u062f. \u0633\u067e\u0633 \u0644\u0627\u06cc\u0647\u00ad \u0647\u0627\u06cc \u062a\u0645\u0627\u0645\u0627 \u0645\u062a\u0635\u0644 \u062f\u0631 \u0627\u0646\u062a\u0647\u0627 \u0627\u0636\u0627\u0641\u0647 \u0645\u06cc \u00ad\u0634\u0648\u0646\u062f. \u062f\u0631 \u0622\u062e\u0631 \u062a\u0627\u0628\u0639 \u00a0\u0647\u0632\u06cc\u0646\u0647 \u060c \u0628\u0647\u06cc\u0646\u0647 \u00ad\u0633\u0627\u0632 \u0648 \u0633\u0646\u062c\u0647 \u00ad\u0647\u0627 \u0628\u0647 \u0645\u062f\u0644 \u0645\u0630\u06a9\u0648\u0631 \u0627\u0636\u0627\u0641\u0647 \u0645\u06cc \u00ad\u0634\u0648\u0646\u062f. <strong>\u062a\u0627\u0628\u0639 \u0647\u0632\u06cc\u0646\u0647<\/strong> \u062c\u0647\u062a \u06cc\u0627\u0641\u062a\u0646 \u062e\u0637\u0627 \u0647\u0627 \u0648 \u0627\u0646\u062d\u0631\u0627\u0641\u0627\u062a \u062f\u0631 \u0641\u0631\u0622\u06cc\u0646\u062f \u0622\u0645\u0648\u0632\u0634 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc \u00ad\u0634\u0648\u062f. Keras \u0646\u06cc\u0627\u0632\u0645\u0646\u062f \u062a\u0627\u0628\u0639 \u0647\u0632\u06cc\u0646\u0647 \u0637\u06cc \u0641\u0631\u0622\u06cc\u0646\u062f \u06a9\u0627\u0645\u067e\u0627\u06cc\u0644 \u06a9\u0631\u062f\u0646 \u0645\u062f\u0644 \u0627\u0633\u062a. <strong>\u0628\u0647\u06cc\u0646\u0647\u00ad \u0633\u0627\u0632\u06cc<\/strong> \u0641\u0631\u0622\u06cc\u0646\u062f \u0628\u0633\u06cc\u0627\u0631 \u0645\u0647\u0645\u06cc \u0627\u0633\u062a \u06a9\u0647 \u00a0\u0648\u0632\u0646\u00ad \u0647\u0627\u06cc \u0648\u0631\u0648\u062f\u06cc \u0631\u0627 \u0627\u0632 \u0637\u0631\u06cc\u0642 \u0645\u0642\u0627\u0633\u0647\u00ad \u06cc \u062a\u0627\u0628\u0639 \u0647\u0632\u06cc\u0646\u0647 \u0648 \u067e\u06cc\u0634\u00ad \u0628\u06cc\u0646\u06cc \u0648 \u0633\u0646\u062c\u0647 \u00ad\u0647\u0627\u06cc\u06cc \u06a9\u0647 \u0628\u0631\u0627\u06cc \u0627\u0631\u0632\u06cc\u0627\u0628\u06cc \u06a9\u0627\u0631\u0627\u06cc\u06cc \u0645\u062f\u0644 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc \u0634\u0648\u0646\u062f \u060c \u0628\u0647\u0628\u0648\u062f \u0645\u06cc \u00ad\u0628\u062e\u0634\u062f. \u0645\u062f\u0644 \u0633\u0631\u06cc\u0627\u0644\u0627\u06cc\u0632 \u0634\u062f\u0647 \u0648 \u0631\u0648\u06cc \u062f\u06cc\u0633\u06a9 \u0645\u062d\u0644\u06cc \u0630\u062e\u06cc\u0631\u0647 \u0645\u06cc\u00ad \u0634\u0648\u062f.<\/p>\n<p style=\"text-align: justify;\"><strong>\u0646\u06a9\u062a\u0647:<\/strong> \u0645\u062f\u0644 \u0648\u0627\u0642\u0639\u06cc \u06a9\u0647 \u0622\u0645\u0648\u0632\u0634 \u062f\u0627\u062f\u0647 \u0645\u06cc\u00ad \u0634\u0648\u062f\u060c \u0645\u062f\u0644 \u0627\u0635\u0644\u06cc ( headModel ) \u0627\u0633\u062a \u06a9\u0647 \u0631\u0648\u06cc \u0645\u062f\u0644 \u067e\u0627\u06cc\u0647 ( baseModel ) \u0642\u0631\u0627\u0631 \u06af\u0631\u0641\u062a\u0647 \u0627\u0633\u062a.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\">baseModel = MobileNetV2(weights=\"imagenet\", include_top=False,\n                    \tinput_tensor=Input(shape=(224, 224, 3)))\n\nheadModel = baseModel.output\nheadModel = AveragePooling2D(pool_size=(7, 7))(headModel)\nheadModel = Flatten(name=\"flatten\")(headModel)\nheadModel = Dense(128, activation=\"relu\")(headModel)\nheadModel = Dropout(0.5)(headModel)\nheadModel = Dense(2, activation=\"softmax\")(headModel)\nmodel = Model(inputs=baseModel.input, outputs=headModel)\n\n# loop over all layers in the base model and freeze them so they will\n# *not* be updated during the first training process\nfor layer in baseModel.layers:\n  layer.trainable = False\n\n# compile our model\nprint(\"[INFO] compiling model...\")\nopt = Adam(lr=PARAMS['INIT_LR'],\n           decay=PARAMS['INIT_LR'] \/ PARAMS['EPOCHS'])\nmodel.compile(loss=\"binary_crossentropy\", optimizer=opt,\n              metrics=[\"accuracy\"])\n\n# train the head of the network\nprint(\"[INFO] training head...\")\ntensorboard=tf.keras.callbacks.TensorBoard(log_dir=EXPERIMENT_LOG_DIR)\nH = model.fit(\n    \taug.flow(trainX, trainY, batch_size=PARAMS['BS']),\n    \tsteps_per_epoch=len(trainX) \/\/ PARAMS['BS'],\n    \tvalidation_data=(testX, testY),\n    \tvalidation_steps=len(testX) \/\/ PARAMS['BS'],\n    \tepochs=PARAMS['EPOCHS'],\n    \tcallbacks=[tensorboard]\n  )\n<\/pre>\n<p style=\"text-align: justify;\">\u00a0\u0642\u062f\u0645 \u0628\u0639\u062f\u06cc \u0627\u0631\u0632\u06cc\u0627\u0628\u06cc \u0645\u062f\u0644 \u062a\u0648\u0633\u0637 \u067e\u06cc\u0634 \u00ad\u0628\u06cc\u0646\u06cc \u0628\u0631\u0686\u0633\u0628 \u062f\u0627\u062f\u0647 \u00ad\u0647\u0627\u06cc \u0622\u0632\u0645\u0627\u06cc\u0634\u06cc \u0627\u0633\u062a.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\">print(\"[INFO] evaluating network...\")\npredIdxs = model.predict(testX, batch_size=PARAMS['BS'])\n\n# for each image in the testing set we need to find the index of the\n# label with corresponding largest predicted probability\npredIdxs = np.argmax(predIdxs, axis=1)\n\n# show a nicely formatted classification report\nprint(classification_report(testY.argmax(axis=1), predIdxs,\n                            target_names=lb.classes_))\n\n# serialize the model to disk\nprint(\"[INFO] saving mask detector model...\")\nmodel.save(args[\"model\"], save_format=\"h5\")\n<\/pre>\n<p style=\"text-align: justify;\">\u062f\u0631 \u0627\u0646\u062a\u0647\u0627 \u0644\u0627\u0632\u0645 \u0627\u0633\u062a \u062f\u0642\u062a \u0648 \u0647\u0632\u06cc\u0646\u0647 \u00ad\u06cc \u0622\u0645\u0648\u0632\u0634 \u0627\u0632 \u0637\u0631\u06cc\u0642 \u062f\u0627\u0634\u0628\u0648\u0631\u062f \u0622\u0632\u0645\u0627\u06cc\u0634 \u0646\u067e\u062a\u06cc\u0648\u0646 \u0628\u0635\u0631\u06cc \u00ad\u0633\u0627\u0632\u06cc \u0634\u0648\u0646\u062f. \u0628\u0631\u0627\u06cc \u0645\u0634\u0627\u0647\u062f\u0647 \u062f\u0627\u0634\u0628\u0648\u0631\u062f \u0628\u0647 \u0622\u062f\u0631\u0633 <a href=\"https:\/\/ui.neptune.ai\/codebrain\/Drone\/e\/DRONE-13\/charts\" target=\"_blank\" rel=\"noopener\">https:\/\/ui.neptune.ai\/codebrain\/Drone\/e\/DRONE-13\/charts<\/a> \u0631\u062c\u0648\u0639 \u0634\u0648\u062f.<\/p>\n<p style=\"text-align: justify;\"><img decoding=\"async\" class=\"aligncenter wp-image-11080 size-full\" src=\"https:\/\/shahaab-co.com\/mag\/wp-content\/uploads\/2021\/04\/\u062f\u0627\u0634\u0628\u0648\u0631\u062f-\u0622\u0632\u0645\u0627\u06cc\u0634-\u0647\u0648\u0634-\u0645\u0635\u0646\u0648\u0639\u06cc-\u0646\u067e\u062a\u06cc\u0648\u0646.png\" alt=\"\u062f\u0627\u0634\u0628\u0648\u0631\u062f \u0622\u0632\u0645\u0627\u06cc\u0634 \u0647\u0648\u0634 \u0645\u0635\u0646\u0648\u0639\u06cc \u0646\u067e\u062a\u06cc\u0648\u0646\" width=\"1024\" height=\"589\" title=\"\" srcset=\"https:\/\/shahaab-co.com\/mag\/wp-content\/uploads\/2021\/04\/\u062f\u0627\u0634\u0628\u0648\u0631\u062f-\u0622\u0632\u0645\u0627\u06cc\u0634-\u0647\u0648\u0634-\u0645\u0635\u0646\u0648\u0639\u06cc-\u0646\u067e\u062a\u06cc\u0648\u0646.png 1024w, https:\/\/shahaab-co.com\/mag\/wp-content\/uploads\/2021\/04\/\u062f\u0627\u0634\u0628\u0648\u0631\u062f-\u0622\u0632\u0645\u0627\u06cc\u0634-\u0647\u0648\u0634-\u0645\u0635\u0646\u0648\u0639\u06cc-\u0646\u067e\u062a\u06cc\u0648\u0646-300x173.png 300w, https:\/\/shahaab-co.com\/mag\/wp-content\/uploads\/2021\/04\/\u062f\u0627\u0634\u0628\u0648\u0631\u062f-\u0622\u0632\u0645\u0627\u06cc\u0634-\u0647\u0648\u0634-\u0645\u0635\u0646\u0648\u0639\u06cc-\u0646\u067e\u062a\u06cc\u0648\u0646-768x442.png 768w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/p>\n\n<p style=\"text-align: justify;\">\u0647\u0645\u0686\u0646\u06cc\u0646 \u0645\u06cc \u00ad\u062a\u0648\u0627\u0646 \u0645\u06cc\u0632\u0627\u0646 \u0645\u0635\u0631\u0641 \u0633\u062e\u062a \u00ad\u0627\u0641\u0632\u0627\u0631 \u0631\u0627 \u0627\u0632 \u0637\u0631\u06cc\u0642 \u062f\u0627\u0634\u0628\u0648\u0631\u062f \u0622\u0632\u0645\u0627\u06cc\u0634 \u0646\u067e\u062a\u06cc\u0648\u0646 \u0646\u0638\u0627\u0631\u062a \u06a9\u0631\u062f. \u0628\u0631\u0627\u06cc \u0645\u0634\u0627\u0647\u062f\u0647 \u0627\u06cc\u0646 \u0627\u0645\u0631 \u0628\u0647 \u0622\u062f\u0631\u0633 <a href=\"https:\/\/ui.neptune.ai\/codebrain\/Drone\/e\/DRONE-13\/monitoring\" target=\"_blank\" rel=\"noopener\">https:\/\/ui.neptune.ai\/codebrain\/Drone\/e\/DRONE-13\/monitoring<\/a>\u00a0 \u0631\u062c\u0648\u0639 \u0634\u0648\u062f.<\/p>\n\n\n<div class=\"wp-block-image\"><figure class=\"aligncenter size-large\"><img decoding=\"async\" width=\"1024\" height=\"589\" src=\"https:\/\/shahaab-co.com\/mag\/wp-content\/uploads\/2021\/04\/\u0633\u062e\u062a-\u0627\u0641\u0632\u0627\u0631-\u0622\u0632\u0645\u0627\u06cc\u0634-\u0647\u0648\u0634-\u0645\u0635\u0646\u0648\u0639\u06cc-\u0646\u067e\u062a\u06cc\u0648\u0646.png\" alt=\"\u0633\u062e\u062a \u0627\u0641\u0632\u0627\u0631 \u0622\u0632\u0645\u0627\u06cc\u0634 \u0647\u0648\u0634 \u0645\u0635\u0646\u0648\u0639\u06cc \u0646\u067e\u062a\u06cc\u0648\u0646\" class=\"wp-image-11082\" title=\"\" srcset=\"https:\/\/shahaab-co.com\/mag\/wp-content\/uploads\/2021\/04\/\u0633\u062e\u062a-\u0627\u0641\u0632\u0627\u0631-\u0622\u0632\u0645\u0627\u06cc\u0634-\u0647\u0648\u0634-\u0645\u0635\u0646\u0648\u0639\u06cc-\u0646\u067e\u062a\u06cc\u0648\u0646.png 1024w, https:\/\/shahaab-co.com\/mag\/wp-content\/uploads\/2021\/04\/\u0633\u062e\u062a-\u0627\u0641\u0632\u0627\u0631-\u0622\u0632\u0645\u0627\u06cc\u0634-\u0647\u0648\u0634-\u0645\u0635\u0646\u0648\u0639\u06cc-\u0646\u067e\u062a\u06cc\u0648\u0646-300x173.png 300w, https:\/\/shahaab-co.com\/mag\/wp-content\/uploads\/2021\/04\/\u0633\u062e\u062a-\u0627\u0641\u0632\u0627\u0631-\u0622\u0632\u0645\u0627\u06cc\u0634-\u0647\u0648\u0634-\u0645\u0635\u0646\u0648\u0639\u06cc-\u0646\u067e\u062a\u06cc\u0648\u0646-768x442.png 768w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure><\/div>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h--3\"><span class=\"ez-toc-section\" id=\"%D9%BE%DB%8C%D8%A7%D8%AF%D9%87_%D8%B3%D8%A7%D8%B2%DB%8C_%D9%85%D8%AF%D9%84_%D8%B1%D9%88%DB%8C_%DB%8C%DA%A9_%D8%AC%D8%B1%DB%8C%D8%A7%D9%86_%D9%88%DB%8C%D8%AF%DB%8C%D9%88\"><\/span><strong>\u067e\u06cc\u0627\u062f\u0647 \u00ad\u0633\u0627\u0632\u06cc \u0645\u062f\u0644 \u0631\u0648\u06cc \u06cc\u06a9 \u062c\u0631\u06cc\u0627\u0646 \u0648\u06cc\u062f\u06cc\u0648<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n<p style=\"text-align: justify;\">\u0628\u0631\u0627\u06cc \u067e\u06cc\u0627\u062f\u0647\u00ad \u0633\u0627\u0632\u06cc \u0645\u062f\u0644 \u0631\u0648\u06cc \u06cc\u06a9 \u062c\u0631\u06cc\u0627\u0646 \u0648\u06cc\u062f\u06cc\u0648\u060c \u0645\u062f\u0644 \u0630\u062e\u06cc\u0631\u0647 \u00ad\u0634\u062f\u0647 \u0627\u0632 \u0642\u0633\u0645\u062a \u0642\u0628\u0644\u06cc \u0628\u0627\u0631\u06af\u0630\u0627\u0631\u06cc \u062e\u0648\u0627\u0647\u062f \u0634\u062f. \u062c\u0647\u062a \u0634\u0631\u0648\u0639 \u067e\u06cc\u0627\u062f\u0647 \u00ad\u0633\u0627\u0632\u06cc\u060c \u0644\u0627\u0632\u0645 \u0627\u0633\u062a \u062a\u0639\u062f\u0627\u062f\u06cc \u06a9\u062a\u0627\u0628\u062e\u0627\u0646\u0647 \u00ad\u06cc \u0636\u0631\u0648\u0631\u06cc \u0648\u0627\u0631\u062f \u067e\u0631\u0648\u0698\u0647 \u0634\u0648\u0646\u062f. \u0627\u06cc\u0646 \u06a9\u062a\u0627\u0628\u062e\u0627\u0646\u0647\u00ad \u0647\u0627 \u0648 \u062f\u0633\u062a\u0648\u0631\u06cc \u06a9\u0647 \u0622\u0646 \u0647\u0627 \u0631\u0627 \u0628\u0647 \u067e\u0631\u0648\u0698\u0647 \u0645\u06cc\u00ad \u0627\u0641\u0632\u0627\u06cc\u062f\u060c \u062f\u0631 \u0632\u06cc\u0631 \u0646\u0634\u0627\u0646 \u062f\u0627\u062f\u0647 \u0634\u062f\u0647 \u00ad\u0627\u0646\u062f.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\">from tensorflow.keras.applications.mobilenet_v2 import preprocess_input\nfrom tensorflow.keras.preprocessing.image import img_to_array\nfrom tensorflow.keras.models import load_model\nfrom imutils.video import VideoStream\nimport numpy as np\nimport argparse\nimport imutils\nimport time\nimport cv2\nimport os\nimport time<\/pre>\n<p style=\"text-align: justify;\">\u0633\u067e\u0633 \u062f\u0648 \u062a\u0627\u0628\u0639 \u0628\u0647 \u0646\u0627\u0645\u00ad \u0647\u0627\u06cc\u00a0 get_facenet_masknet\u00a0\u00a0 \u0648\u00a0 \u00a0detect_and_predict_mask \u0627\u06cc\u062c\u0627\u062f \u0645\u06cc \u00ad\u0634\u0648\u062f. \u062a\u0627\u0628\u0639 \u0627\u0648\u0644 \u0645\u062f\u0644 \u0622\u0645\u0648\u0632\u0634 \u00ad\u062f\u06cc\u062f\u0647 \u00ad\u06cc \u0633\u0631\u06cc\u0627\u0644\u0627\u06cc\u0632 \u0634\u062f\u0647 \u0642\u0628\u0644\u06cc \u0648 \u0648\u0632\u0646\u00ad \u0647\u0627\u06cc \u0645\u062a\u0646\u0627\u0638\u0631\u0634 \u0631\u0627 \u0645\u06cc \u00ad\u062e\u0648\u0627\u0646\u062f.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\">def get_facenet_masknet():\n  # construct the argument parser and parse the arguments\n  ap = argparse.ArgumentParser()\n  ap.add_argument(\"-f\", \"--face\", type=str,\n                \tdefault=\"face_detector\",\n                \thelp=\"path to face detector model directory\")\n  ap.add_argument(\"-m\", \"--model\", type=str,\n                \tdefault=\"mask_detector.model\",\n                \thelp=\"path to trained face mask detector model\")\n  ap.add_argument(\"-c\", \"--confidence\", type=float, default=0.5,\n                \thelp=\"minimum probability to filter weak detections\")\n  args = vars(ap.parse_args())\n\n  # load our serialized face detector model from disk\n  print(\"[INFO] loading face detector model...\")\n  # prototxtPath = os.path.sep.join([args[\"face\"],  \"deploy.prototxt\"])\n  prototxtPath = (\n    \t'\/Users\/USER\/Documents\/DroneProjects\/facemaskdetection\/face_detector\/deploy.prototxt')\n  # weightsPath = os.path.sep.join([args[\"face\"],\n  #                             \t\"res10_300x300_ssd_iter_140000.caffemodel\"])\n  weightsPath = (\n    \t'\/Users\/USER\/Documents\/DroneProjects\/facemaskdetection\/face_detector\/res10_300x300_ssd_iter_140000.caffemodel')\n  faceNet = cv2.dnn.readNet(prototxtPath, weightsPath)\n\n  # load the face mask detector model from disk\n  print(\"[INFO] loading face mask detector model...\")\n  maskNet = load_model(\n    \t'\/Users\/USER\/Documents\/DroneProjects\/facemaskdetection\/mask_detector.model')\n  return(faceNet, maskNet, args)\n<\/pre>\n<p style=\"text-align: justify;\">\u00a0\u062a\u0627\u0628\u0639 \u062f\u0648\u0645 \u0627\u0628\u0639\u0627\u062f\u00a0 \u0641\u0631\u06cc\u0645 \u0631\u0627 \u0645\u06cc\u00ad \u06af\u06cc\u0631\u062f \u0648 \u06cc\u06a9 \u062a\u0648\u062f\u0647 \u0627\u0632 \u0622\u0646 \u0645\u06cc\u00ad \u0633\u0627\u0632\u062f. \u0627\u06cc\u0646 \u062a\u0648\u062f\u0647 \u0628\u0647 \u062f\u0631\u0648\u0646 \u0634\u0628\u06a9\u0647 \u0639\u0635\u0628\u06cc \u0641\u0631\u0633\u062a\u0627\u062f\u0647 \u0645\u06cc\u00ad \u0634\u0648\u062f \u062a\u0627 \u0686\u0647\u0631\u0647 \u0631\u0627 \u062a\u0634\u062e\u06cc\u0635 \u062f\u0647\u062f. \u0642\u0637\u0639\u0647 \u06a9\u062f \u0632\u06cc\u0631 \u0686\u06af\u0648\u0646\u06af\u06cc \u06af\u0631\u0641\u062a\u0646 \u0627\u0628\u0639\u0627\u062f \u0631\u0627 \u0646\u0634\u0627\u0646 \u0645\u06cc\u00ad \u062f\u0647\u062f.\u00a0<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\">def detect_and_predict_mask(frame, faceNet, maskNet, args):\n(h, w) = frame.shape[:2]\n  blob = cv2.dnn.blobFromImage(frame, 1.0, (300, 300),\n                             \t(\u06f1\u06f0\u06f4\u066b\u06f0, \u06f1\u06f7\u06f7\u066b\u06f0, \u06f1\u06f2\u06f3\u066b\u06f0))\n\n  faceNet.setInput(blob)\n  detections = faceNet.forward()\n<\/pre>\n<p style=\"text-align: justify;\">\u0628\u0647 \u062f\u0646\u0628\u0627\u0644 \u062a\u0639\u0631\u06cc\u0641 \u0627\u06cc\u0646 \u062a\u0627\u0628\u0639\u060c \u0644\u06cc\u0633\u062a \u0686\u0647\u0631\u0647\u00ad \u0647\u0627 \u0648 \u0645\u06a9\u0627\u0646\u00ad \u0647\u0627\u06cc \u0645\u062a\u0646\u0627\u0638\u0631 \u0622\u0646 \u0647\u0627\u00a0 \u0648 \u0647\u0645\u0686\u0646\u06cc\u0646 \u0644\u06cc\u0633\u062a \u067e\u06cc\u0634\u00ad \u0628\u06cc\u0646\u06cc \u00ad\u0647\u0627 \u0627\u0632 \u0634\u0628\u06a9\u0647 \u00ad\u06cc \u0645\u0627\u0633\u06a9 \u0635\u0648\u0631\u062a \u0631\u0627\u0647 \u00ad\u0627\u0646\u062f\u0627\u0632\u06cc \u0627\u0648\u0644\u06cc\u0647 \u0645\u06cc\u00ad \u0634\u0648\u0646\u062f.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\">faces = []\nlocs = []\npreds = []\n<\/pre>\n<p style=\"text-align: justify;\">\u00a0\u0633\u067e\u0633 \u0631\u0648\u06cc \u062a\u0634\u062e\u06cc\u0635 \u00ad\u0647\u0627\u060c \u062d\u0644\u0642\u0647 \u00ad\u0627\u06cc \u0627\u06cc\u062c\u0627\u062f \u0645\u06cc \u00ad\u0634\u0648\u062f \u0648 \u00a0\u0627\u062d\u062a\u0645\u0627\u0644 \u0645\u0631\u062a\u0628\u0637 \u0628\u0627 \u0647\u0631 \u062a\u0634\u062e\u06cc\u0635 \u0645\u0639\u06cc\u0646 \u0645\u06cc\u00ad \u0634\u0648\u062f.\u00a0<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\">for i in range(0, detections.shape[2]):\n  \t\tconfidence = detections[0, 0, i, 2]\n<\/pre>\n<p style=\"text-align: justify;\">\u062f\u0631 \u06af\u0627\u0645 \u0628\u0639\u062f \u0627\u062d\u062a\u0645\u0627\u0644 \u0628\u0647 \u00ad\u062f\u0633\u062a \u00ad\u0622\u0645\u062f\u0647 \u0627\u0632 \u0647\u0631 \u062a\u0634\u062e\u06cc\u0635 \u0628\u0627 \u062d\u062f\u0627\u0642\u0644 \u0627\u062d\u062a\u0645\u0627\u0644 \u0645\u0642\u0627\u06cc\u0633\u0647 \u0645\u06cc\u00ad \u0634\u0648\u0646\u062f \u0648 \u062a\u0634\u062e\u06cc\u0635\u00ad \u0647\u0627\u06cc\u06cc \u06a9\u0647 \u0627\u062d\u062a\u0645\u0627\u0644 \u0622\u0646 \u0647\u0627 \u0627\u0632 \u062d\u062f\u0627\u0642\u0644 \u0627\u062d\u062a\u0645\u0627\u0644 \u06a9\u0648\u0686\u06a9 \u062a\u0631 \u0628\u0627\u0634\u062f \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u062a\u0634\u062e\u06cc\u0635 \u0636\u0639\u06cc\u0641 \u0634\u0646\u0627\u062e\u062a\u0647 \u0634\u062f\u0647 \u0648 \u06a9\u0646\u0627\u0631 \u06af\u0630\u0627\u0634\u062a\u0647 \u0645\u06cc\u00ad \u0634\u0648\u0646\u062f.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\">\tif confidence &gt; args[\"confidence\"]:\n     \t# compute the (x, y)-coordinates of the bounding box for\n     \t# the object\n     \tbox = detections[0, 0, i, 3:7] * np.array([w, h, w, h])\n     \t(startX, startY, endX, endY) = box.astype(\"int\")\n<\/pre>\n<p style=\"text-align: justify;\">\u0647\u0645\u0686\u0646\u06cc\u0646 \u0628\u0627\u06cc\u062f \u0627\u0632 \u0642\u0631\u0627\u0631 \u06af\u0631\u0641\u062a\u0646 \u06a9\u0627\u062f\u0631 \u0645\u062d\u0635\u0648\u0631 \u06a9\u0646\u0646\u062f\u0647 \u062f\u0631\u0648\u0646 \u0627\u0628\u0639\u0627\u062f \u0641\u0631\u06cc\u0645 \u0627\u0637\u0645\u06cc\u0646\u0627\u0646 \u062d\u0627\u0635\u0644 \u06a9\u0631\u062f.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\">\t(startX, startY) = (max(0, startX), max(0, startY))\n\t(endX, endY) = (min(w - 1, endX), min(h - 1, endY))\n<\/pre>\n<p style=\"text-align: justify;\">\u0647\u0645\u0686\u0646\u06cc\u0646 \u062a\u0639\u062f\u0627\u062f\u06cc \u06af\u0627\u0645\u00ad \u067e\u06cc\u0634\u00ad \u067e\u0631\u062f\u0627\u0632\u0634 \u0627\u062c\u0631\u0627 \u0645\u06cc \u00ad\u0634\u0648\u0646\u062f \u062a\u0627 \u0645\u0646\u0637\u0642\u0647 \u00ad\u06cc \u0645\u0648\u0631\u062f \u0646\u0638\u0631 \u0686\u0647\u0631\u0647 \u0631\u0627 \u0627\u0633\u062a\u062e\u0631\u0627\u062c \u06a9\u0646\u0646\u062f \u0648 \u0633\u067e\u0633 \u0622\u0646 \u0631\u0627 \u0627\u0632 \u062a\u0631\u062a\u06cc\u0628 \u06a9\u0627\u0646\u0627\u0644 BGR \u0628\u0647 RGB \u062a\u0628\u062f\u06cc\u0644 \u06a9\u0646\u0646\u062f. \u0633\u067e\u0633\u00a0 \u0628\u0627 \u062a\u063a\u06cc\u06cc\u0631 \u0627\u0646\u062f\u0627\u0632\u0647 \u0642\u0627\u0628 \u0628\u0647 \u0627\u0628\u0639\u0627\u062f \u06f2\u06f2\u06f4 *\u06f2\u06f2\u06f4\u00a0 \u0627\u062f\u0645\u0647 \u0645\u06cc\u00ad \u062f\u0647\u062f \u0648 \u0622\u0646 \u0631\u0627 \u0628\u0647 \u0622\u0631\u0627\u06cc\u0647\u00ad \u0647\u0627 \u062a\u0628\u062f\u06cc\u0644 \u0645\u06cc \u06a9\u0646\u062f.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\">\tface = frame[startY:endY, startX:endX]\n\tface = cv2.cvtColor(face, cv2.COLOR_BGR2RGB)\n\tface = cv2.resize(face, (224, 224))\n\tface = img_to_array(face)\n\tface = preprocess_input(face)\n<\/pre>\n<p style=\"text-align: justify;\">\u00a0\u0647\u0645\u0686\u0646\u06cc\u0646 \u0628\u0627\u06cc\u062f \u0627\u0632 \u0627\u0636\u0627\u0641\u0647 \u0634\u062f\u0646 \u0635\u0648\u0631\u062a \u0648 \u062c\u0639\u0628\u0647 \u00ad\u0647\u0627\u06cc \u0644\u0628\u0647 \u00ad\u0627\u06cc \u0628\u0647 \u0644\u06cc\u0633\u062a \u0645\u062a\u0646\u0627\u0638\u0631\u00ad\u0634\u0627\u0646 \u0627\u0637\u0645\u06cc\u0646\u0627\u0646 \u062d\u0627\u0635\u0644 \u0634\u0648\u062f.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\"> \tfaces.append(face)\n\tlocs.append((startX, startY, endX, endY))\n<\/pre>\n<p style=\"text-align: justify;\">\u067e\u06cc\u0634 \u00ad\u0628\u06cc\u0646\u06cc \u0632\u0645\u0627\u0646\u06cc \u0627\u06cc\u062c\u0627\u062f \u0645\u06cc\u00ad \u0634\u0648\u062f \u06a9\u0647 \u062d\u062f\u0627\u0642\u0644 \u06cc\u06a9 \u0635\u0648\u0631\u062a \u062a\u0634\u062e\u06cc\u0635 \u062f\u0627\u062f\u0647 \u0634\u0648\u062f \u0633\u067e\u0633 \u0628\u0647 \u062c\u0627\u06cc \u067e\u06cc\u0634 \u00ad\u0628\u06cc\u0646\u06cc \u06cc\u06a9 \u0628\u0647 \u06cc\u06a9 \u0686\u0647\u0631\u0647 \u00ad\u0647\u0627\u060c\u00a0 \u06cc\u06a9 \u067e\u06cc\u0634\u00ad \u0628\u06cc\u0646\u06cc \u062f\u0633\u062a\u0647 \u00ad\u0627\u06cc \u0631\u0648\u06cc \u062a\u0645\u0627\u0645 \u0635\u0648\u0631\u062a \u00ad\u0647\u0627 \u062f\u0631 \u06cc\u06a9 \u0644\u062d\u0638\u0647 \u0627\u0632 \u0632\u0645\u0627\u0646 \u0627\u0646\u062c\u0627\u0645 \u0645\u06cc\u00ad \u06af\u06cc\u0631\u062f.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\">if len(faces) &gt; 0:\n  \tfaces = np.array(faces, dtype=\"float32\")\n  \tpreds = maskNet.predict(faces, batch_size=32)\n<\/pre>\n<p style=\"text-align: justify;\">\u062f\u0631 \u0627\u0646\u062a\u0647\u0627 \u06cc\u06a9 \u062e\u0631\u0648\u062c\u06cc \u062f\u0648 \u0628\u0639\u062f\u06cc \u0627\u0632 \u0645\u06a9\u0627\u0646 \u00ad\u0686\u0647\u0631\u0647 \u0647\u0627 \u0648 \u0645\u06a9\u0627\u0646 \u0645\u062a\u0646\u0627\u0638\u0631 \u0622\u0646 \u0647\u0627 \u0628\u0631\u06af\u0631\u062f\u0627\u0646\u062f\u0647 \u0645\u06cc \u00ad\u0634\u0648\u062f.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\">return (locs, preds)\n<\/pre>\n\n\n<h2 class=\"wp-block-heading\" id=\"h--4\"><span class=\"ez-toc-section\" id=\"%D9%86%D8%AA%DB%8C%D8%AC%D9%87_%DA%AF%DB%8C%D8%B1%DB%8C\"><\/span><strong>\u0646\u062a\u06cc\u062c\u0647\u00ad \u06af\u06cc\u0631\u06cc<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n<p>\u00a0\u062f\u0631 \u0627\u06cc\u0646 \u0622\u0645\u0648\u0632\u0634 \u060c \u0686\u06af\u0648\u0646\u06af\u06cc \u067e\u06cc\u0634 \u00ad\u067e\u0631\u062f\u0627\u0632\u0634 \u0648 \u0628\u0627\u0631\u06af\u0630\u0627\u0631\u06cc \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647\u00ad \u06cc \u0645\u0627\u0633\u06a9 \u0635\u0648\u0631\u062a \u0648 \u0647\u0645\u0686\u0646\u06cc\u0646 \u0622\u0645\u0648\u0632\u0634 \u0645\u062f\u0644 \u062a\u0634\u062e\u06cc\u0635 \u0645\u0627\u0633\u06a9 \u0635\u0648\u0631\u062a \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u062a\u0646\u0633\u0648\u0631\u0641\u0644\u0648<strong>\u00a0 <\/strong>Mobilenet V2 \u0628\u0627 \u067e\u0627\u06cc\u062a\u0648\u0646 \u0634\u0631\u062d \u062f\u0627\u062f\u0647 \u0634\u062f. \u0645\u062f\u0644 \u0622\u0645\u0648\u0632\u0634 \u062f\u0627\u062f\u0647 \u0634\u062f\u0647 \u0628\u0639\u062f\u0627 \u0628\u0631\u0627\u06cc \u067e\u06cc\u0627\u062f\u0647 \u00ad\u0633\u0627\u0632\u06cc \u062c\u0631\u06cc\u0627\u0646 \u0648\u06cc\u062f\u06cc\u0648 \u0627\u0642\u062a\u0628\u0627\u0633 \u0634\u062f \u0648 \u062f\u0631 \u0627\u0646\u062a\u0647\u0627 \u0645\u062f\u0644 \u0628\u0631\u0627\u06cc \u0641\u0627\u0632 \u0628\u0639\u062f\u06cc \u067e\u0631\u0648\u0698\u0647 \u0633\u0631\u06cc\u0627\u0644\u0627\u06cc\u0632 \u0634\u062f \u06a9\u0647 \u0646\u06cc\u0627\u0632\u0645\u0646\u062f \u0627\u0633\u062a\u0642\u0631\u0627\u0631 \u0627\u06cc\u0646 \u0633\u06cc\u0633\u062a\u0645 \u0646\u0638\u0627\u0631\u062a\u06cc \u0631\u0648\u06cc \u06cc\u06a9 \u0627\u067e\u0644\u06cc\u06a9\u06cc\u0634\u0646 \u0627\u0633\u062a. \u0628\u0627 \u062f\u0627\u0646\u0634 \u06a9\u0633\u0628\u00ad \u0634\u062f\u0647 \u0627\u0632 \u0628\u062e\u0634 \u06cc\u06a9 \u0648 \u062f\u0648 \u060c \u0627\u06a9\u0646\u0648\u0646 \u0686\u0627\u0631\u0686\u0648\u0628\u06cc \u0628\u0631\u0627\u06cc \u0633\u06cc\u0633\u062a\u0645 \u0646\u0638\u0627\u0631\u062a\u06cc \u0645\u062f \u0646\u0638\u0631 \u0627\u06cc\u062c\u0627\u062f \u06a9\u0631\u062f\u0647 \u0627\u06cc\u0645. \u0628\u062e\u0634 \u0628\u0639\u062f\u06cc \u0646\u062d\u0648\u0647\u00ad \u06cc \u0627\u0633\u062a\u0642\u0631\u0627\u0631 \u0627\u06cc\u0646 \u0633\u06cc\u0633\u062a\u0645 \u0631\u0627 \u0628\u0631\u0631\u0633\u06cc \u0645\u06cc \u06a9\u0646\u06cc\u0645.<\/p>\n<h4><strong>\u0628\u06cc\u0634\u062a\u0631 \u0628\u062e\u0648\u0627\u0646\u06cc\u062f :<\/strong><\/h4>\n\n\n<div class=\"wp-block-group has-vivid-green-cyan-color has-text-color\"><div class=\"wp-block-group__inner-container is-layout-flow wp-block-group-is-layout-flow\">\n<ul class=\"wp-block-yoast-seo-related-links\"><li><a href=\"https:\/\/shahaab-co.com\/mag\/tech\/amazing-uses-for-face-recognition\/\">\u06f2\u06f0 \u06a9\u0627\u0631\u0628\u0631\u062f \u0634\u06af\u0641\u062a \u0627\u0646\u06af\u06cc\u0632 \u062a\u0634\u062e\u06cc\u0635 \u0686\u0647\u0631\u0647<\/a><\/li><li><a href=\"https:\/\/shahaab-co.com\/mag\/tech\/convolutional-neural-networks-and-machine-vision\/\">\u0634\u0628\u06a9\u0647 \u0647\u0627\u06cc \u0639\u0635\u0628\u06cc \u06a9\u0627\u0646\u0648\u0644\u0648\u0634\u0646\u06cc ( CNN ) \u0648 \u0628\u06cc\u0646\u0627\u06cc\u06cc \u0645\u0627\u0634\u06cc\u0646<\/a><\/li><li><a href=\"https:\/\/shahaab-co.com\/mag\/edu\/machine-vision\/image-segmentation-part-one\/\">\u0646\u0627\u062d\u06cc\u0647 \u0628\u0646\u062f\u06cc \u062a\u0635\u0648\u06cc\u0631 &#8211; \u0642\u0633\u0645\u062a \u0627\u0648\u0644<\/a><\/li><li><a href=\"https:\/\/shahaab-co.com\/mag\/edu\/using-google-colab-for-deep-learning-tutorial\/\">\u0686\u06af\u0648\u0646\u0647 \u0627\u0632 \u06af\u0648\u06af\u0644 Colab \u0628\u0631\u0627\u06cc  \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0646\u06cc\u0645\u061f<\/a><\/li><\/ul>\n\n\n\n<a href=\"#\" class=\"shortc-button small blue \">\u0645\u0646\u0628\u0639<\/a> <a href=\"https:\/\/neptune.ai\/blog\/applications-of-ai-in-drone-technology-machine-learning-models-with-tensorflow-keras\" target=\"_blank\" class=\"shortc-button small gray \" rel=\"noopener\">Neptune AI<\/a>\n<\/div><\/div>\n\n\n<div class=\"kk-star-ratings kksr-auto kksr-align-right kksr-valign-bottom\"\n    data-payload='{&quot;align&quot;:&quot;right&quot;,&quot;id&quot;:&quot;11056&quot;,&quot;slug&quot;:&quot;default&quot;,&quot;valign&quot;:&quot;bottom&quot;,&quot;ignore&quot;:&quot;&quot;,&quot;reference&quot;:&quot;auto&quot;,&quot;class&quot;:&quot;&quot;,&quot;count&quot;:&quot;0&quot;,&quot;legendonly&quot;:&quot;&quot;,&quot;readonly&quot;:&quot;&quot;,&quot;score&quot;:&quot;0&quot;,&quot;starsonly&quot;:&quot;&quot;,&quot;best&quot;:&quot;5&quot;,&quot;gap&quot;:&quot;5&quot;,&quot;greet&quot;:&quot;\u0627\u0645\u062a\u06cc\u0627\u0632 \u062f\u0647\u06cc\u062f!&quot;,&quot;legend&quot;:&quot;0\\\/5 - (0 \u0627\u0645\u062a\u06cc\u0627\u0632)&quot;,&quot;size&quot;:&quot;24&quot;,&quot;title&quot;:&quot;\u0633\u0627\u062e\u062a \u0633\u06cc\u0633\u062a\u0645 \u062a\u0634\u062e\u06cc\u0635 \u0645\u0627\u0633\u06a9 \u0628\u0627 \u0641\u0646\u0627\u0648\u0631\u06cc \u067e\u0647\u067e\u0627\u062f \u0648 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642 \u2013 \u0628\u062e\u0634 \u062f\u0648\u0645&quot;,&quot;width&quot;:&quot;0&quot;,&quot;_legend&quot;:&quot;{score}\\\/{best} - ({count} \u0627\u0645\u062a\u06cc\u0627\u0632)&quot;,&quot;font_factor&quot;:&quot;1.25&quot;}'>\n            \n<div class=\"kksr-stars\">\n    \n<div class=\"kksr-stars-inactive\">\n            <div class=\"kksr-star\" data-star=\"1\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" data-star=\"2\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" data-star=\"3\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" data-star=\"4\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" data-star=\"5\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n    <\/div>\n    \n<div class=\"kksr-stars-active\" style=\"width: 0px;\">\n            <div class=\"kksr-star\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n    <\/div>\n<\/div>\n                \n\n<div class=\"kksr-legend\" style=\"font-size: 19.2px;\">\n            <span class=\"kksr-muted\">\u0627\u0645\u062a\u06cc\u0627\u0632 \u062f\u0647\u06cc\u062f!<\/span>\n    <\/div>\n    <\/div>\n","protected":false},"excerpt":{"rendered":"<p>\u0628\u0647 \u0628\u062e\u0634 \u062f\u0648\u0645 \u0633\u0627\u062e\u062a \u0633\u06cc\u0633\u062a\u0645 \u0646\u0638\u0627\u0631\u062a\u06cc \u062a\u0634\u062e\u06cc\u0635 \u0645\u0627\u0633\u06a9 \u0635\u0648\u0631\u062a \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u062a\u06a9\u0646\u0648\u0644\u0648\u0698\u06cc \u067e\u0647\u067e\u0627\u062f \u0648 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642 \u062e\u0648\u0634 \u0622\u0645\u062f\u06cc\u062f! \u062f\u0631 \u0628\u062e\u0634 \u0627\u0648\u0644 \u0627\u06cc\u0646 \u0622\u0645\u0648\u0632\u0634\u060c \u062f\u0631\u0628\u0627\u0631\u0647 \u00ad\u06cc \u062a\u06a9\u0646\u0648\u0644\u0648\u0698\u06cc \u067e\u0647\u067e\u0627\u062f \u060c \u0627\u0646\u0648\u0627\u0639 \u0637\u0628\u0642\u0647\u00ad \u0628\u0646\u062f\u06cc\u00ad \u0647\u0627 \u060c \u0645\u0639\u0645\u0627\u0631\u06cc \u067e\u0647\u067e\u0627\u062f\u06cc \u06a9\u0647 \u062f\u0631 \u0627\u06cc\u0646 \u067e\u0631\u0648\u0698\u0647 \u0627\u0632 \u0622\u0646 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc \u00ad\u0634\u0648\u062f \u0648 \u0647\u0645\u0686\u0646\u06cc\u0646 \u067e\u06cc\u06a9\u0631\u0628\u0646\u062f\u06cc \u0645\u062d\u06cc\u0637 \u0628\u0631\u0627\u06cc \u0628\u0631\u0646\u0627\u0645\u0647 \u00ad\u0646\u0648\u06cc\u0633\u06cc \u067e\u0647\u067e\u0627\u062f \u0628\u0627 \u067e\u0627\u06cc\u062a\u0648\u0646 &hellip;<\/p>\n","protected":false},"author":7,"featured_media":11096,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_editorskit_title_hidden":false,"_editorskit_reading_time":0,"_editorskit_is_block_options_detached":false,"_editorskit_block_options_position":"{}","footnotes":""},"categories":[198,22,18],"tags":[84,147,100,157,97,101,86],"class_list":["post-11056","post","type-post","status-publish","format-standard","has-post-thumbnail","","category-robotics","category-machine-vision","category-edu","tag-84","tag-147","tag-100","tag-157","tag-97","tag-101","tag-86"],"_links":{"self":[{"href":"https:\/\/shahaab-co.com\/mag\/wp-json\/wp\/v2\/posts\/11056","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/shahaab-co.com\/mag\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/shahaab-co.com\/mag\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/shahaab-co.com\/mag\/wp-json\/wp\/v2\/users\/7"}],"replies":[{"embeddable":true,"href":"https:\/\/shahaab-co.com\/mag\/wp-json\/wp\/v2\/comments?post=11056"}],"version-history":[{"count":1,"href":"https:\/\/shahaab-co.com\/mag\/wp-json\/wp\/v2\/posts\/11056\/revisions"}],"predecessor-version":[{"id":16202,"href":"https:\/\/shahaab-co.com\/mag\/wp-json\/wp\/v2\/posts\/11056\/revisions\/16202"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/shahaab-co.com\/mag\/wp-json\/wp\/v2\/media\/11096"}],"wp:attachment":[{"href":"https:\/\/shahaab-co.com\/mag\/wp-json\/wp\/v2\/media?parent=11056"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/shahaab-co.com\/mag\/wp-json\/wp\/v2\/categories?post=11056"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/shahaab-co.com\/mag\/wp-json\/wp\/v2\/tags?post=11056"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}