{"id":14450,"date":"2024-01-05T06:05:14","date_gmt":"2024-01-05T02:35:14","guid":{"rendered":"https:\/\/rasanegar.com\/blog\/%d8%b7%d8%a8%d9%82%d9%87-%d8%a8%d9%86%d8%af%db%8c-%d8%aa%d8%b5%d8%a7%d9%88%db%8c%d8%b1-%d8%a8%d8%a7-%d9%85%d8%af%d9%84-%d8%a7%d8%b2-%d9%be%db%8c%d8%b4-%d8%a2%d9%85%d9%88%d8%b2%d8%b4-%d8%af%db%8c%d8%af\/"},"modified":"2024-01-05T06:05:14","modified_gmt":"2024-01-05T02:35:14","slug":"%d8%b7%d8%a8%d9%82%d9%87-%d8%a8%d9%86%d8%af%db%8c-%d8%aa%d8%b5%d8%a7%d9%88%db%8c%d8%b1-%d8%a8%d8%a7-%d9%85%d8%af%d9%84-%d8%a7%d8%b2-%d9%be%db%8c%d8%b4-%d8%a2%d9%85%d9%88%d8%b2%d8%b4-%d8%af%db%8c%d8%af","status":"publish","type":"post","link":"https:\/\/rasanegaar.com\/blog\/%d8%b7%d8%a8%d9%82%d9%87-%d8%a8%d9%86%d8%af%db%8c-%d8%aa%d8%b5%d8%a7%d9%88%db%8c%d8%b1-%d8%a8%d8%a7-%d9%85%d8%af%d9%84-%d8%a7%d8%b2-%d9%be%db%8c%d8%b4-%d8%a2%d9%85%d9%88%d8%b2%d8%b4-%d8%af%db%8c%d8%af\/","title":{"rendered":"\u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u062a\u0635\u0627\u0648\u06cc\u0631 \u0628\u0627 \u0645\u062f\u0644 \u0627\u0632 \u067e\u06cc\u0634 \u0622\u0645\u0648\u0632\u0634 \u062f\u06cc\u062f\u0647 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 Keras"},"content":{"rendered":"<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_85 counter-hierarchy ez-toc-counter ez-toc-custom ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\"><p class=\"ez-toc-title\" style=\"cursor:inherit\">\u0633\u0631\u0641\u0635\u0644\u0647\u0627\u06cc \u0645\u0637\u0644\u0628<\/p>\n<\/div><nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/rasanegaar.com\/blog\/%d8%b7%d8%a8%d9%82%d9%87-%d8%a8%d9%86%d8%af%db%8c-%d8%aa%d8%b5%d8%a7%d9%88%db%8c%d8%b1-%d8%a8%d8%a7-%d9%85%d8%af%d9%84-%d8%a7%d8%b2-%d9%be%db%8c%d8%b4-%d8%a2%d9%85%d9%88%d8%b2%d8%b4-%d8%af%db%8c%d8%af\/#opencv\" >OpenCV<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/rasanegaar.com\/blog\/%d8%b7%d8%a8%d9%82%d9%87-%d8%a8%d9%86%d8%af%db%8c-%d8%aa%d8%b5%d8%a7%d9%88%db%8c%d8%b1-%d8%a8%d8%a7-%d9%85%d8%af%d9%84-%d8%a7%d8%b2-%d9%be%db%8c%d8%b4-%d8%a2%d9%85%d9%88%d8%b2%d8%b4-%d8%af%db%8c%d8%af\/#%d9%be%db%8c%d8%b4_%d9%be%d8%b1%d8%af%d8%a7%d8%b2%d8%b4_keras\" >\u067e\u06cc\u0634 \u067e\u0631\u062f\u0627\u0632\u0634 Keras<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/rasanegaar.com\/blog\/%d8%b7%d8%a8%d9%82%d9%87-%d8%a8%d9%86%d8%af%db%8c-%d8%aa%d8%b5%d8%a7%d9%88%db%8c%d8%b1-%d8%a8%d8%a7-%d9%85%d8%af%d9%84-%d8%a7%d8%b2-%d9%be%db%8c%d8%b4-%d8%a2%d9%85%d9%88%d8%b2%d8%b4-%d8%af%db%8c%d8%af\/#pil\" >PIL<\/a><\/li><\/ul><\/nav><\/div>\n<span class=\"span-reading-time rt-reading-time\" style=\"display: block;\"><span class=\"rt-label rt-prefix\">\u0632\u0645\u0627\u0646 \u0644\u0627\u0632\u0645 \u0628\u0631\u0627\u06cc \u0645\u0637\u0627\u0644\u0639\u0647: <\/span> <span class=\"rt-time\"> 2<\/span> <span class=\"rt-label rt-postfix\">\u062f\u0642\u06cc\u0642\u0647<\/span><\/span><p> <br \/>\n<\/p>\n<div><noscript><\/noscript><\/p>\n<p>\u0645\u062f\u0644\u200c\u0647\u0627\u06cc Computer Vision \u0631\u0627\u0647 \u062f\u0631\u0627\u0632\u06cc \u0631\u0627 \u067e\u06cc\u0645\u0648\u062f\u0647\u200c\u0627\u0646\u062f &#8211; \u0648 \u0634\u0645\u0627 \u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u06cc\u062f \u0627\u0632 \u0645\u062f\u0644\u200c\u0647\u0627\u06cc \u0645\u0648\u062c\u0648\u062f\u060c \u0627\u0632 \u0642\u0628\u0644 \u0622\u0645\u0648\u0632\u0634 \u062f\u06cc\u062f\u0647\u060c \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0646\u06cc\u062f \u0631\u0648\u06cc \u0645\u062c\u0645\u0648\u0639\u0647 \u0628\u0632\u0631\u06af\u06cc \u0627\u0632 \u062f\u0627\u062f\u0647 \u0647\u0627\u060c \u0648 \u0641\u0642\u0637 \u0622\u0646\u0647\u0627 \u0631\u0627 \u0628\u0647 \u062e\u0637 \u0644\u0648\u0644\u0647 \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u062e\u0648\u062f \u0648\u0635\u0644 \u06a9\u0646\u06cc\u062f.<\/p>\n<blockquote>\n<p>\u062f\u0631 \u062d\u0627\u0644\u06cc \u06a9\u0647 \u062a\u0646\u0638\u06cc\u0645 \u062f\u0642\u06cc\u0642 \u06cc\u06a9 \u0634\u0628\u06a9\u0647 \u0628\u0647\u062a\u0631\u06cc\u0646 \u0631\u0627\u0647 \u0627\u0633\u062a &#8211; \u0648\u0627\u0631\u062f \u06a9\u0631\u062f\u0646 \u06cc\u06a9 \u0645\u062f\u0644 \u0645\u0648\u062c\u0648\u062f \u0648 \u0627\u062c\u0631\u0627\u06cc \u067e\u06cc\u0634\u200c\u0628\u06cc\u0646\u06cc\u200c\u0647\u0627 \u0627\u0632 \u0647\u0645\u0627\u0646 \u0627\u0628\u062a\u062f\u0627 \u06cc\u06a9 \u0645\u0648\u0631\u062f \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u0639\u062a\u0628\u0631 \u062f\u0631 \u0645\u0631\u0627\u062d\u0644 \u0627\u0648\u0644\u06cc\u0647 \u0646\u0645\u0648\u0646\u0647\u200c\u0633\u0627\u0632\u06cc \u06cc\u0627 \u0635\u0631\u0641\u0627\u064b \u0628\u0647 \u062e\u0627\u0637\u0631 \u0622\u0632\u0645\u0627\u06cc\u0634 \u06cc\u06a9 \u0645\u062f\u0644 \u0627\u0633\u062a.  \u0628\u0631\u0627\u06cc \u06cc\u06a9 \u0631\u0627\u0647\u0646\u0645\u0627\u06cc \u0639\u0645\u06cc\u0642 \u0648 \u062f\u0642\u06cc\u0642 \u0631\u0648\u06cc \u062a\u0646\u0638\u06cc\u0645 \u062f\u0642\u06cc\u0642 \u0627\u0632 \u0637\u0631\u06cc\u0642 \u0622\u0645\u0648\u0632\u0634 \u0627\u0646\u062a\u0642\u0627\u0644 &#8211; \u062f\u0631\u0633 \u0631\u0627\u06cc\u06af\u0627\u0646 \u0645\u0627 \u0631\u0627 \u0628\u062e\u0648\u0627\u0646\u06cc\u062f \u0631\u0648\u06cc &#8220;\u0627\u0646\u062a\u0642\u0627\u0644 \u0622\u0645\u0648\u0632\u0634 \u0628\u0631\u0627\u06cc \u06a9\u0627\u0645\u067e\u06cc\u0648\u062a\u0631 \u0648\u06cc\u0698\u0646 \u0628\u0627 \u06a9\u0631\u0627\u0633&#8221;!<\/p>\n<\/blockquote>\n<p>\u0647\u0645\u0627\u0646\u0637\u0648\u0631 \u06a9\u0647 \u06af\u0641\u062a\u0647 \u0634\u062f &#8211; \u0645\u062a\u063a\u06cc\u0631 \u0627\u0635\u0644\u06cc \u0627\u0633\u062a <em>\u0686\u06af\u0648\u0646\u0647<\/em> \u0634\u0645\u0627 \u06cc\u06a9 \u062a\u0635\u0648\u06cc\u0631 \u0631\u0627 \u0628\u0627\u0631\u06af\u0630\u0627\u0631\u06cc \u0645\u06cc \u06a9\u0646\u06cc\u062f\u060c \u0642\u0627\u0628\u0644 \u062a\u0648\u062c\u0647 \u062a\u0631\u06cc\u0646 \u0631\u0648\u0634 \u0647\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 OpenCV\u060c PIL \u0648 \u0631\u0648\u0634 \u0647\u0627\u06cc \u06a9\u0645\u06a9\u06cc \u067e\u06cc\u0634 \u067e\u0631\u062f\u0627\u0632\u0634 Keras \u0627\u0633\u062a.<\/p>\n<h2 id=\"opencv\"><span class=\"ez-toc-section\" id=\"opencv\"><\/span>OpenCV<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>OpenCV \u0628\u0631\u0627\u06cc Computer Vision \u0627\u0633\u062a\u060c \u0647\u0645\u0627\u0646 \u0686\u06cc\u0632\u06cc \u06a9\u0647 Scikit-Learn \u0628\u0631\u0627\u06cc \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646 \u0627\u0633\u062a.  \u0627\u06cc\u0646 \u06cc\u06a9 \u06a9\u062a\u0627\u0628\u062e\u0627\u0646\u0647 \u0645\u062d\u0628\u0648\u0628 \u0648 \u06a9\u0627\u0645\u0644 \u0627\u0633\u062a \u06a9\u0647 \u0628\u0647 \u0637\u0648\u0631 \u06af\u0633\u062a\u0631\u062f\u0647 \u062f\u0631 \u0635\u0646\u0639\u062a \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc \u0634\u0648\u062f\u060c \u0628\u0627 \u0645\u0646\u062d\u0646\u06cc \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u06a9\u0645\u06cc \u062a\u0646\u062f\u062a\u0631 \u0627\u0632 \u06a9\u062a\u0627\u0628\u062e\u0627\u0646\u0647 \u0647\u0627\u06cc \u067e\u0631\u062f\u0627\u0632\u0634 \u062a\u0635\u0648\u06cc\u0631 \u0645\u0627\u0646\u0646\u062f PIL.<\/p>\n<p>\u0645\u0627 \u06cc\u06a9 \u062a\u0627\u0628\u0639 \u06a9\u0645\u06a9\u06cc \u0627\u062e\u062a\u0635\u0627\u0635\u06cc \u0627\u06cc\u062c\u0627\u062f \u0645\u06cc \u06a9\u0646\u06cc\u0645 \u062a\u0627 \u062a\u0635\u0648\u06cc\u0631\u06cc \u0631\u0627 \u0627\u0632 \u0637\u0631\u06cc\u0642 URL \u0628\u0647 \u0622\u0631\u0627\u06cc\u0647 NumPy \u0628\u0647 \u062f\u0633\u062a \u0622\u0648\u0631\u06cc\u0645 \u0648 \u062a\u0628\u062f\u06cc\u0644 \u06a9\u0646\u06cc\u0645 \u0648 \u0633\u067e\u0633 \u0622\u0646 \u0631\u0627 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u06cc\u06a9 \u0645\u062f\u0644 \u0627\u0632 \u067e\u06cc\u0634 \u0622\u0645\u0648\u0632\u0634 \u062f\u06cc\u062f\u0647 \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u06a9\u0646\u06cc\u0645. <code>keras.applications<\/code>:<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-keyword\">from<\/span> tensorflow <span class=\"hljs-keyword\">import<\/span> keras\n\n<span class=\"hljs-keyword\">from<\/span> keras.applications.efficientnet <span class=\"hljs-keyword\">import<\/span> preprocess_input, decode_predictions\n\n<span class=\"hljs-keyword\">import<\/span> urllib\n<span class=\"hljs-keyword\">import<\/span> cv2\n<span class=\"hljs-keyword\">import<\/span> numpy <span class=\"hljs-keyword\">as<\/span> np\n\n<span class=\"hljs-function\"><span class=\"hljs-keyword\">def<\/span> <span class=\"hljs-title\">url_to_array<\/span>(<span class=\"hljs-params\">url<\/span>):<\/span>\n    req = urllib.request.urlopen(url)\n    arr = np.array(<span class=\"hljs-built_in\">bytearray<\/span>(req.read()), dtype=np.int8)\n    arr = cv2.imdecode(arr, -<span class=\"hljs-number\">1<\/span>)\n    arr = cv2.cvtColor(arr, cv2.COLOR_BGR2RGB)\n    arr = cv2.resize(arr, (<span class=\"hljs-number\">224<\/span>, <span class=\"hljs-number\">224<\/span>))\n    <span class=\"hljs-keyword\">return<\/span> arr\n\nurl = <span class=\"hljs-string\">'https:\/\/upload.wikimedia.org\/wikipedia\/commons\/0\/02\/Black_bear_large.jpg'<\/span>\nimg = url_to_array(url)\nimg_batch = np.expand_dims(img, <span class=\"hljs-number\">0<\/span>)\n\neffnet = keras.applications.EfficientNetV2B0(weights=<span class=\"hljs-string\">'imagenet'<\/span>, include_top=<span class=\"hljs-literal\">True<\/span>)\npred = effnet.predict(img_batch)\n<span class=\"hljs-built_in\">print<\/span>(decode_predictions(pred))\n\n<\/code><\/pre>\n<p>\u0628\u06cc\u0627\u06cc\u06cc\u062f \u062a\u0635\u0648\u06cc\u0631 \u0631\u0627 \u0628\u0627 \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u062a\u062c\u0633\u0645 \u06a9\u0646\u06cc\u0645:<\/p>\n<pre><code class=\"hljs\">plt.imshow(img)\nplt.title(<span class=\"hljs-string\">f'Class: <span class=\"hljs-subst\">{decode_predictions(pred)(<span class=\"hljs-number\">0<\/span>)(<span class=\"hljs-number\">0<\/span>)(<span class=\"hljs-number\">1<\/span>)}<\/span>\\nConfidence: <span class=\"hljs-subst\">{decode_predictions(pred)(<span class=\"hljs-number\">0<\/span>)(<span class=\"hljs-number\">0<\/span>)(<span class=\"hljs-number\">2<\/span>)*<span class=\"hljs-number\">100<\/span>}<\/span>%'<\/span>)\nplt.show()\n<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/rasanegar.com\/blog\/wp-content\/uploads\/2024\/01\/classify-images-with-pre-trained-model-using-keras-1.png\" alt=\"\u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u062a\u0635\u0627\u0648\u06cc\u0631 \u0628\u0627 cnn keras \u0627\u0632 \u0642\u0628\u0644 \u0622\u0645\u0648\u0632\u0634 \u062f\u06cc\u062f\u0647\" title=\"\"><\/p>\n<h2 id=\"keraspreprocessing\"><span class=\"ez-toc-section\" id=\"%d9%be%db%8c%d8%b4_%d9%be%d8%b1%d8%af%d8%a7%d8%b2%d8%b4_keras\"><\/span>\u067e\u06cc\u0634 \u067e\u0631\u062f\u0627\u0632\u0634 Keras<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Keras \u062a\u0648\u0627\u0628\u0639 \u0648 \u06a9\u0644\u0627\u0633 \u0647\u0627\u06cc \u06a9\u0645\u06a9\u06cc \u067e\u06cc\u0634 \u067e\u0631\u062f\u0627\u0632\u0634 \u0631\u0627 \u0627\u0631\u0627\u0626\u0647 \u0645\u06cc \u062f\u0647\u062f \u06a9\u0647 \u0628\u0647 \u0645\u0627 \u0627\u0645\u06a9\u0627\u0646 \u0645\u06cc \u062f\u0647\u062f \u062a\u0635\u0627\u0648\u06cc\u0631 \u0631\u0627 \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0622\u0631\u0627\u06cc\u0647 \u0647\u0627\u06cc NumPy \u0628\u0627\u0631\u06af\u06cc\u0631\u06cc \u0648 \u0622\u0645\u0627\u062f\u0647 \u06a9\u0646\u06cc\u0645:<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-keyword\">from<\/span> tensorflow <span class=\"hljs-keyword\">import<\/span> keras\n\n<span class=\"hljs-keyword\">from<\/span> keras.applications.efficientnet <span class=\"hljs-keyword\">import<\/span> preprocess_input, decode_predictions\n<span class=\"hljs-keyword\">from<\/span> tensorflow.keras.preprocessing <span class=\"hljs-keyword\">import<\/span> image\n\n<span class=\"hljs-keyword\">import<\/span> urllib.request\n<span class=\"hljs-keyword\">import<\/span> matplotlib.pyplot <span class=\"hljs-keyword\">as<\/span> plt\n<span class=\"hljs-keyword\">import<\/span> numpy <span class=\"hljs-keyword\">as<\/span> np\n\n\nurl = <span class=\"hljs-string\">'https:\/\/upload.wikimedia.org\/wikipedia\/commons\/0\/02\/Black_bear_large.jpg'<\/span>\nurllib.request.urlretrieve(url, <span class=\"hljs-string\">'bear.jpg'<\/span>)\n\n\nimg = image.load_img(<span class=\"hljs-string\">'bear.jpg'<\/span>, target_size=(<span class=\"hljs-number\">224<\/span>, <span class=\"hljs-number\">224<\/span>))\n\nimg = image.img_to_array(img)\n\nimg_batch = np.expand_dims(img, <span class=\"hljs-number\">0<\/span>)\n\n\n\n\neffnet = keras.applications.EfficientNetV2B0(weights=<span class=\"hljs-string\">'imagenet'<\/span>, include_top=<span class=\"hljs-literal\">True<\/span>)\npred = effnet.predict(img_batch)\n<span class=\"hljs-built_in\">print<\/span>(decode_predictions(pred))\n\n<\/code><\/pre>\n<p>\u0628\u06cc\u0627\u06cc\u06cc\u062f \u0622\u0646 \u0631\u0627 \u0637\u0631\u062d \u06a9\u0646\u06cc\u0645:<\/p>\n<pre><code class=\"hljs\">plt.imshow(img.astype(<span class=\"hljs-string\">'int'<\/span>))\nplt.title(<span class=\"hljs-string\">f'Class: <span class=\"hljs-subst\">{decode_predictions(pred)(<span class=\"hljs-number\">0<\/span>)(<span class=\"hljs-number\">0<\/span>)(<span class=\"hljs-number\">1<\/span>)}<\/span>\\nConfidence: <span class=\"hljs-subst\">{decode_predictions(pred)(<span class=\"hljs-number\">0<\/span>)(<span class=\"hljs-number\">0<\/span>)(<span class=\"hljs-number\">2<\/span>)*<span class=\"hljs-number\">100<\/span>}<\/span>%'<\/span>)\nplt.show()\n<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/rasanegar.com\/blog\/wp-content\/uploads\/2024\/01\/classify-images-with-pre-trained-model-using-keras-2.png\" alt=\"\u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u062a\u0635\u0627\u0648\u06cc\u0631 \u0628\u0627 \u06a9\u0631\u0627\u0633 \u0645\u062f\u0644 \u0627\u0632 \u067e\u06cc\u0634 \u0622\u0645\u0648\u0632\u0634 \u062f\u06cc\u062f\u0647\" title=\"\"><\/p>\n<h2 id=\"pil\"><span class=\"ez-toc-section\" id=\"pil\"><\/span>PIL<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u062f\u0631 \u0646\u0647\u0627\u06cc\u062a\u060c PIL \u06cc\u06a9 \u06a9\u062a\u0627\u0628\u062e\u0627\u0646\u0647 \u067e\u0631\u062f\u0627\u0632\u0634 \u062a\u0635\u0648\u06cc\u0631 \u0639\u0645\u0648\u0645\u0627\u064b \u0645\u062d\u0628\u0648\u0628 \u0627\u0633\u062a \u0648 \u0628\u0647 \u0637\u0648\u0631 \u0637\u0628\u06cc\u0639\u06cc \u0645\u06cc \u062a\u0648\u0627\u0646\u062f \u0628\u0631\u0627\u06cc \u0628\u0627\u0631\u06af\u0630\u0627\u0631\u06cc \u062a\u0635\u0627\u0648\u06cc\u0631 \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0622\u0631\u0627\u06cc\u0647 \u0647\u0627\u06cc NumPy \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0634\u0648\u062f:<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-keyword\">from<\/span> tensorflow <span class=\"hljs-keyword\">import<\/span> keras\n\n<span class=\"hljs-keyword\">from<\/span> keras.applications.efficientnet <span class=\"hljs-keyword\">import<\/span> preprocess_input, decode_predictions\n<span class=\"hljs-keyword\">from<\/span> tensorflow.keras.preprocessing <span class=\"hljs-keyword\">import<\/span> image\n\n<span class=\"hljs-keyword\">import<\/span> PIL\n\n<span class=\"hljs-keyword\">import<\/span> urllib.request\n<span class=\"hljs-keyword\">import<\/span> matplotlib.pyplot <span class=\"hljs-keyword\">as<\/span> plt\n<span class=\"hljs-keyword\">import<\/span> numpy <span class=\"hljs-keyword\">as<\/span> np\n\n\nurl = <span class=\"hljs-string\">'https:\/\/upload.wikimedia.org\/wikipedia\/commons\/0\/02\/Black_bear_large.jpg'<\/span>\nimg = PIL.Image.<span class=\"hljs-built_in\">open<\/span>(urllib.request.urlopen(url))\nimg = img.resize((<span class=\"hljs-number\">224<\/span>, <span class=\"hljs-number\">224<\/span>))\nimg_batch = np.expand_dims(img, <span class=\"hljs-number\">0<\/span>)\n\n\neffnet = keras.applications.EfficientNetV2B0(weights=<span class=\"hljs-string\">'imagenet'<\/span>, include_top=<span class=\"hljs-literal\">True<\/span>)\npred = effnet.predict(img_batch)\n<span class=\"hljs-built_in\">print<\/span>(decode_predictions(pred))\n\n<\/code><\/pre>\n<p>\u0628\u06cc\u0627\u06cc\u06cc\u062f \u0622\u0646 \u0631\u0627 \u0637\u0631\u062d \u06a9\u0646\u06cc\u0645:<\/p>\n<pre><code class=\"hljs\">plt.imshow(img)\nplt.title(<span class=\"hljs-string\">f'Class: <span class=\"hljs-subst\">{decode_predictions(pred)(<span class=\"hljs-number\">0<\/span>)(<span class=\"hljs-number\">0<\/span>)(<span class=\"hljs-number\">1<\/span>)}<\/span>\\nConfidence: <span class=\"hljs-subst\">{decode_predictions(pred)(<span class=\"hljs-number\">0<\/span>)(<span class=\"hljs-number\">0<\/span>)(<span class=\"hljs-number\">2<\/span>)*<span class=\"hljs-number\">100<\/span>}<\/span>%'<\/span>)\nplt.show()\n<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/rasanegar.com\/blog\/wp-content\/uploads\/2024\/01\/classify-images-with-pre-trained-model-using-keras-3.png\" alt=\"\u06a9\u0644\u0627\u0633 \u062a\u0635\u0648\u06cc\u0631 \u0631\u0627 \u0628\u0627 \u06a9\u0631\u0627\u0633 \u0648 \u0645\u062f\u0644 \u0627\u0632 \u067e\u06cc\u0634 \u0622\u0645\u0648\u0632\u0634 \u062f\u06cc\u062f\u0647 \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u06a9\u0646\u06cc\u062f\" title=\"\">\n<\/div>\n<p><script>\n                        !function(f,b,e,v,n,t,s)\n                        {if(f.fbq)return;n=f.fbq=function(){n.callMethod?\n                        n.callMethod.apply(n,arguments):n.queue.push(arguments)};\n                        if(!f._fbq)f._fbq=n;n.push=n;n.loaded=!0;n.version='2.0';\n                        n.queue=();t=b.createElement(e);t.async=!0;\n                        t.src=v;s=b.getElementsByTagName(e)(0);\n                        s.parentNode.insertBefore(t,s)}(window, document,'script',\n                        'https:\/\/connect.facebook.net\/en_US\/fbevents.js');\n                        fbq('init', '525232124909042');\n                        fbq('track', 'PageView');\n                    <\/script>    (\u0628\u0631\u0686\u0633\u0628\u200c\u0647\u0627 \u0628\u0647 \u062a\u0631\u062c\u0645\u0647)# python<br \/>\n<br \/><br \/>\n<br \/>\u0645\u0646\u062a\u0634\u0631 \u0634\u062f\u0647 \u062f\u0631 1403-01-05 06:05:08<br \/>\n<\/p>\n\n\n<div class=\"kk-star-ratings kksr-auto kksr-align-center kksr-valign-bottom\"\n    data-payload='{&quot;align&quot;:&quot;center&quot;,&quot;id&quot;:&quot;14450&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 \u0634\u0645\u0627 \u0628\u0647 \u0627\u06cc\u0646 \u0645\u0637\u0644\u0628&quot;,&quot;legend&quot;:&quot;0\\\/5 (0 \u0631\u0627\u06cc)&quot;,&quot;size&quot;:&quot;30&quot;,&quot;title&quot;:&quot;\u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u062a\u0635\u0627\u0648\u06cc\u0631 \u0628\u0627 \u0645\u062f\u0644 \u0627\u0632 \u067e\u06cc\u0634 \u0622\u0645\u0648\u0632\u0634 \u062f\u06cc\u062f\u0647 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 Keras&quot;,&quot;width&quot;:&quot;0&quot;,&quot;_legend&quot;:&quot;{score}\\\/{best} ({count} \u0631\u0627\u06cc)&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: 30px; height: 30px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" data-star=\"2\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 30px; height: 30px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" data-star=\"3\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 30px; height: 30px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" data-star=\"4\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 30px; height: 30px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" data-star=\"5\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 30px; height: 30px;\"><\/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: 30px; height: 30px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 30px; height: 30px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 30px; height: 30px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 30px; height: 30px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 30px; height: 30px;\"><\/div>\n        <\/div>\n    <\/div>\n<\/div>\n                \n\n<div class=\"kksr-legend\" style=\"font-size: 24px;\">\n            <span class=\"kksr-muted\">\u0627\u0645\u062a\u06cc\u0627\u0632 \u0634\u0645\u0627 \u0628\u0647 \u0627\u06cc\u0646 \u0645\u0637\u0644\u0628<\/span>\n    <\/div>\n    <\/div>\n","protected":false},"excerpt":{"rendered":"<p><span class=\"span-reading-time rt-reading-time\" style=\"display: block;\"><span class=\"rt-label rt-prefix\">\u0632\u0645\u0627\u0646 \u0644\u0627\u0632\u0645 \u0628\u0631\u0627\u06cc \u0645\u0637\u0627\u0644\u0639\u0647: <\/span> <span class=\"rt-time\"> 2<\/span> <span class=\"rt-label rt-postfix\">\u062f\u0642\u06cc\u0642\u0647<\/span><\/span>\u0645\u062f\u0644\u200c\u0647\u0627\u06cc Computer Vision \u0631\u0627\u0647 \u062f\u0631\u0627\u0632\u06cc \u0631\u0627 \u067e\u06cc\u0645\u0648\u062f\u0647\u200c\u0627\u0646\u062f &#8211; \u0648 \u0634\u0645\u0627 \u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u06cc\u062f \u0627\u0632 \u0645\u062f\u0644\u200c\u0647\u0627\u06cc \u0645\u0648\u062c\u0648\u062f\u060c \u0627\u0632 \u0642\u0628\u0644 \u0622\u0645\u0648\u0632\u0634 \u062f\u06cc\u062f\u0647\u060c \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0646\u06cc\u062f \u0631\u0648\u06cc \u0645\u062c\u0645\u0648\u0639\u0647 \u0628\u0632\u0631\u06af\u06cc \u0627\u0632 \u062f\u0627\u062f\u0647 \u0647\u0627\u060c \u0648 \u0641\u0642\u0637 \u0622\u0646\u0647\u0627 \u0631\u0627 \u0628\u0647 \u062e\u0637 \u0644\u0648\u0644\u0647 \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u062e\u0648\u062f \u0648\u0635\u0644 \u06a9\u0646\u06cc\u062f. \u062f\u0631 \u062d\u0627\u0644\u06cc \u06a9\u0647 \u062a\u0646\u0638\u06cc\u0645 \u062f\u0642\u06cc\u0642 \u06cc\u06a9 \u0634\u0628\u06a9\u0647 \u0628\u0647\u062a\u0631\u06cc\u0646 \u0631\u0627\u0647 \u0627\u0633\u062a &#8211; \u0648\u0627\u0631\u062f \u06a9\u0631\u062f\u0646 \u06cc\u06a9 \u0645\u062f\u0644 \u0645\u0648\u062c\u0648\u062f \u0648 [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":14451,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1743,620],"tags":[],"class_list":["post-14450","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-python","category-programming"],"acf":[],"_links":{"self":[{"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/posts\/14450","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/comments?post=14450"}],"version-history":[{"count":0,"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/posts\/14450\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/media\/14451"}],"wp:attachment":[{"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/media?parent=14450"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/categories?post=14450"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/tags?post=14450"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}