{"id":7467,"date":"2024-10-18T07:37:49","date_gmt":"2024-10-18T07:37:49","guid":{"rendered":"https:\/\/www.mixtile.com\/?post_type=ht_kb&#038;p=7467"},"modified":"2024-11-04T03:52:48","modified_gmt":"2024-11-04T03:52:48","slug":"monitoring-restaurant-table-status-with-yolo-on-hailo-8l","status":"publish","type":"ht_kb","link":"https:\/\/www.mixtile.com\/ja\/docs\/monitoring-restaurant-table-status-with-yolo-on-hailo-8l\/","title":{"rendered":"Hailo-8L\u306eYOLO\u3067\u30ec\u30b9\u30c8\u30e9\u30f3\u306e\u30c6\u30fc\u30d6\u30eb\u72b6\u6cc1\u3092\u76e3\u8996\u3059\u308b"},"content":{"rendered":"<div class=\"wp-block-jetpack-markdown\"><p><!-- # Monitoring Restaurant Table Status with YOLO on Hailo-8L --><\/p>\n<h2>\u30b9\u30c8\u30fc\u30ea\u30fc<\/h2>\n<p><img src=\"https:\/\/i0.wp.com\/downloads.mixtile.com\/doc-images\/hailo\/restaurant-table-status\/output-empty-table.jpeg?w=1020&#038;ssl=1\" alt=\"\" data-recalc-dims=\"1\"><\/p>\n<p>In today\u2019s fast-paced restaurant industry, delivering exceptional service and maximizing operational efficiency are key to success. Imagine a smart system that can detect whether customers are ready to order or to be served, and whether tableware is ready to be cleared \u2014 all without staff intervention. AI-powered object detection makes this possible. This technology not only speeds up table service but also streamlines operations to provide an elevated dining experience for customers.<\/p>\n<p>You Only Look Once (YOLO) is a state-of-the-art, real-time object detection algorithm. YOLO models have been popular for their performance and accuracy in object detection in images and videos.<\/p>\n<p>YOLO models can detect people and some tableware out of the box. Therefore, with the power of YOLO object detection, we can easily get a table&#8217;s status as follows:<\/p>\n<p><img src=\"https:\/\/i0.wp.com\/downloads.mixtile.com\/doc-images\/hailo\/restaurant-table-status\/restaurant-detection-flowchart.png?w=1020&#038;ssl=1\" alt=\"\" data-recalc-dims=\"1\"><\/p>\n<p><img src=\"https:\/\/i0.wp.com\/downloads.mixtile.com\/doc-images\/hailo\/restaurant-table-status\/output-single-table-detection.jpeg?w=1020&#038;ssl=1\" alt=\"\" data-recalc-dims=\"1\"><\/p>\n<h2>\u3053\u306e\u30d7\u30ed\u30b8\u30a7\u30af\u30c8\u3067\u4f7f\u7528\u3057\u305f\u3082\u306e<\/h2>\n<p><strong>Hardware:<\/strong><\/p>\n<ul>\n<li>Mixtile Edge 2 Kit<\/li>\n<li>Hailo-8L<\/li>\n<\/ul>\n<p><strong>Software:<\/strong><\/p>\n<ul>\n<li>AI Software Suite<\/li>\n<li>YOLOv7e6 model<\/li>\n<\/ul>\n<h2>How it works<\/h2>\n<p>Mixtile Edge 2 Kit (also known as Edge 2) is a high-performance, low-power ARM SBC (single-board computer) that comes with a Linux OS pre-installed. It&#8217;s capable of running AI tasks on the edge. Moreover, its M.2 interface makes it possible to integrate with a Hailo AI accelerator for higher AI performance.<\/p>\n<p>In this document, we run a YOLO model on Edge 2 powered by Hailo-8L to detect if customers and tableware are around a table to get the table&#8217;s status.<\/p>\n<p>For easier implementation, this document uses a ready-to-use YOLOv7e6 model pre-trained and compiled by Hailo. If you need more specific customizations and higher accuracy, you can train and compile your own model.<\/p>\n<p><strong>Model performance:<\/strong><\/p>\n<table>\n<thead>\n<tr>\n<th>Network Name<\/th>\n<th>mAP<\/th>\n<th>Quantized<\/th>\n<th>FPS (Batch Size=1)<\/th>\n<th>FPS (Batch Size=8)<\/th>\n<th>Input Resolution (HxWxC)<\/th>\n<th>Params (M)<\/th>\n<th>OPS (G)<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>yolov7e6<\/td>\n<td>55.37<\/td>\n<td>2.19<\/td>\n<td>4<\/td>\n<td>5<\/td>\n<td>1280x1280x3<\/td>\n<td>97.20<\/td>\n<td>515.12<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<blockquote>\n<p>\u6ce8\uff1a<\/p>\n<p>For other ready-to-use models, go to <a href=\"https:\/\/github.com\/hailo-ai\/hailo_model_zoo\/tree\/master\/docs\/public_models\">hailo_model_zoo<\/a>.\nThis guide uses Hailo-8L. If you use another Hailo AI accelerator, use a model compatible with your product.<\/p>\n<\/blockquote>\n<h2>\u30b9\u30bf\u30fc\u30c8<\/h2>\n<h3>\u6e96\u5099<\/h3>\n<ul>\n<li>\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb <a href=\"https:\/\/downloads.mixtile.com\/edge2\/ubuntu_image\/image-release-raw-format-mixtile-edge2-ubuntu-22.04-desktop.img.zip\">Ubuntu 22.04 Desktop<\/a> on Edge 2 (see <a href=\"https:\/\/www.mixtile.com\/ja\/docs\/installing-an-operating-system-on-mixtile-edge2-kit\/\">Mixtile Edge 2\u30ad\u30c3\u30c8\u3078\u306e\u30aa\u30da\u30ec\u30fc\u30c6\u30a3\u30f3\u30b0\u30b7\u30b9\u30c6\u30e0\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb<\/a>).<\/li>\n<li>Connect Edge 2 to the Internet.<\/li>\n<li>Connect Edge 2 to a monitor.<\/li>\n<li>Install Hailo-8L to Edge 2 as follows:<\/li>\n<\/ul>\n<p><img src=\"https:\/\/i0.wp.com\/downloads.mixtile.com\/doc-images\/hailo\/installed-hailo-8lm2-to-edge2.jpg?w=1020&#038;ssl=1\" alt=\"\" data-recalc-dims=\"1\"><\/p>\n<h3>Setting up Hailo and YOLO environments<\/h3>\n<h4>Setting up Hailo environments<\/h4>\n<p>To integrate a Hailo AI accelerator with Edge 2, install HailoRT, PCIe Driver, and TAPPAS.<\/p>\n<h5>Installing HailoRT and PCIe Driver<\/h5>\n<ol>\n<li>\n<p>Log in to Edge 2 as a standard user.<\/p>\n<\/li>\n<li>\n<p>\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb <code>dkms<\/code>:<\/p>\n<pre><code class=\"language-sh\">sudo apt-get update -y &amp;&amp; sudo apt-get install -y dkms\n<\/code><\/pre>\n<\/li>\n<li>\n<p>Download HailoRT and PCIe Driver to a desired directory:<\/p>\n<pre><code class=\"language-sh\">wget https:\/\/downloads.mixtile.com\/doc-files\/hailo\/hailort-pcie-driver_4.19.0_all.deb \\\nhttps:\/\/downloads.mixtile.com\/doc-files\/hailo\/hailort_4.19.0_arm64.deb\n<\/code><\/pre>\n<\/li>\n<li>\n<p>Install HailoRT and PCIe Driver:<\/p>\n<pre><code class=\"language-sh\">sudo apt install .\/hailort-pcie-driver_4.19.0_all.deb .\/hailort_4.19.0_arm64.deb\n<\/code><\/pre>\n<p>Note: If messages below are prompted, input <code>y<\/code>:<\/p>\n<pre><code class=\"language-sh\">Do you wish to activate hailort service? (required for most pyHailoRT use cases) [y\/N]:\nDo you wish to use DKMS? [Y\/n]:\n<\/code><\/pre>\n<\/li>\n<li>\n<p>Reboot Edge 2.<\/p>\n<\/li>\n<li>\n<p>Verify if the Hailo AI accelerator is recognized by the system:<\/p>\n<pre><code class=\"language-sh\">hailortcli fw-control identify\n<\/code><\/pre>\n<p>If successfully recognized, it returns device details such as the board name and serial number.<\/p>\n<\/li>\n<\/ol>\n<h5>Installing TAPPAS<\/h5>\n<ol>\n<li>\n<p>Install dependencies, which might take about several minutes:<\/p>\n<pre><code class=\"language-sh\">sudo apt-get install -y rsync ffmpeg x11-utils python3-dev python3-pip python3-setuptools python3-virtualenv python-gi-dev \\\nlibgirepository1.0-dev gcc-12 g++-12 cmake git libzmq3-dev librga-dev libopencv-dev python3-opencv libcairo2-dev libgirepository1.0-dev \\\nlibgstreamer1.0-dev libgstreamer-plugins-base1.0-dev libgstreamer-plugins-bad1.0-dev gstreamer1.0-plugins-base gstreamer1.0-plugins-good \\\ngstreamer1.0-plugins-bad gstreamer1.0-plugins-ugly gstreamer1.0-libav gstreamer1.0-tools gstreamer1.0-x gstreamer1.0-alsa gstreamer1.0-gl \\\ngstreamer1.0-gtk3 gstreamer1.0-qt5 gstreamer1.0-pulseaudio python-gi-dev python3-gi python3-gi-cairo gir1.2-gtk-3.0\n<\/code><\/pre>\n<p><!-- Time: several minutes --><\/p>\n<\/li>\n<li>\n<p>Install TAPPAS:<\/p>\n<pre><code class=\"language-sh\">git clone https:\/\/github.com\/hailo-ai\/tappas -b v3.29.0\ncd tappas\n.\/install.sh --skip-hailort\n<\/code><\/pre>\n<blockquote>\n<p>\u6ce8\uff1a<\/p>\n<ol>\n<li>The installation may take about an hour to complete.<\/li>\n<li>Enter the password when prompted.<\/li>\n<\/ol>\n<\/blockquote>\n<\/li>\n<li>\n<p>Verify TAPPAS installation:<\/p>\n<pre><code class=\"language-sh\">gst-inspect-1.0 hailotools\n<\/code><\/pre>\n<p>If the installation is successful, it returns information about hailotools, including its filename and version.<\/p>\n<\/li>\n<\/ol>\n<h4>Setting up YOLO environments<\/h4>\n<ol>\n<li>\n<p>Clone the repository:<\/p>\n<pre><code class=\"language-sh\">cd ~\ngit clone https:\/\/github.com\/hailo-ai\/Hailo-Application-Code-Examples\/\ncd Hailo-Application-Code-Examples\/runtime\/python\/object_detection\n<\/code><\/pre>\n<\/li>\n<li>\n<p>Download the YOLOv7e6 model:<\/p>\n<pre><code class=\"language-sh\">wget https:\/\/hailo-model-zoo.s3.eu-west-2.amazonaws.com\/ModelZoo\/Compiled\/v2.13.0\/hailo8l\/yolov7e6.hef\n<\/code><\/pre>\n<\/li>\n<li>\n<p>Set up environments:<\/p>\n<pre><code class=\"language-sh\">wget https:\/\/github.com\/hailo-ai\/hailo-rpi5-examples\/raw\/refs\/heads\/main\/setup_env.sh\nsource setup_env.sh\n<\/code><\/pre>\n<\/li>\n<li>\n<p>\u4f9d\u5b58\u95a2\u4fc2\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u307e\u3059\uff1a<\/p>\n<pre><code class=\"language-sh\">pip install -r requirements.txt\n<\/code><\/pre>\n<\/li>\n<li>\n<p>Copy <code>utils.py<\/code> to the <code>object_detection<\/code> directory:<\/p>\n<pre><code class=\"language-sh\">cp ..\/utils.py .\n<\/code><\/pre>\n<\/li>\n<li>\n<p>Install PyHailoRT:<\/p>\n<pre><code class=\"language-sh\">wget https:\/\/downloads.mixtile.com\/doc-files\/hailo\/hailort-4.19.0-cp310-cp310-linux_aarch64.whl\npip install hailort-4.19.0-cp310-cp310-linux_aarch64.whl\n<\/code><\/pre>\n<\/li>\n<\/ol>\n<h3>Detecting customers and tableware<\/h3>\n<p>After all the setup, now let&#8217;s get into the most exciting part: detecting customers and tableware in the restaurant images!<\/p>\n<ol>\n<li>\n<p>Let&#8217;s say your camera has taken pictures of your restaurants. You can put them in the <code>input-images<\/code> folder or other folders as you like (supported format: <code>.jpg<\/code>, <code>.jpeg<\/code>, <code>.png<\/code>, <code>.bmp<\/code>). To quickly try out the object detection feature, you can also download input images used in this document with:<\/p>\n<pre><code class=\"language-sh\">wget https:\/\/downloads.mixtile.com\/doc-files\/hailo\/input-images.zip\nunzip input-images.zip\n<\/code><\/pre>\n<p>Necessary files in the <code>object_detection<\/code> directory will be:<\/p>\n<pre><code class=\"language-bash\">\u251c\u2500\u2500 coco.txt\n\u251c\u2500\u2500 input-images\n\u2502   \u251c\u2500\u2500 input_image0.jpeg\n\u2502   \u251c\u2500\u2500 input_image1.jpg\n\u2502   \u2514\u2500\u2500 input_image2.jpg\n\u251c\u2500\u2500 object_detection.py\n\u251c\u2500\u2500 object_detection_utils.py\n\u251c\u2500\u2500 README.md\n\u251c\u2500\u2500 requirements.txt\n\u251c\u2500\u2500 setup_env.sh\n\u251c\u2500\u2500 utils.py\n\u2514\u2500\u2500 yolov7e6.hef\n<\/code><\/pre>\n<\/li>\n<li>\n<p>(Optional) Switch to the virtual environment (this step is required if you have restarted Edge 2 or opened a new terminal):<\/p>\n<pre><code class=\"language-sh\">source setup_env.sh\n<\/code><\/pre>\n<\/li>\n<li>\n<p>Perform inference:<\/p>\n<pre><code class=\"language-sh\">.\/object_detection.py -n yolov7e6.hef -i input-images\/\n<\/code><\/pre>\n<ul>\n<li><code>-n<\/code>: path to the pre-trained hef model.<\/li>\n<li><code>-i<\/code>: path to input images to perform inference on.<\/li>\n<\/ul>\n<p>Successful inference returns:<\/p>\n<pre><code class=\"language-sh\">2024-10-17 07:37:09.335 | INFO     | __main__:infer:181 - Inference was successful! Results have been saved in output_images\n<\/code><\/pre>\n<p>The results will be output to <code>output_images<\/code>:<\/p>\n<p><img src=\"https:\/\/i0.wp.com\/downloads.mixtile.com\/doc-images\/hailo\/restaurant-table-status\/output-restaurant-tables.jpeg?w=1020&#038;ssl=1\" alt=\"\" data-recalc-dims=\"1\"><\/p>\n<p><img src=\"https:\/\/i0.wp.com\/downloads.mixtile.com\/doc-images\/hailo\/restaurant-table-status\/output-empty-and-occupied-tables.jpeg?w=1020&#038;ssl=1\" alt=\"\" data-recalc-dims=\"1\"><\/p>\n<p><img src=\"https:\/\/i0.wp.com\/downloads.mixtile.com\/doc-images\/hailo\/restaurant-table-status\/output_seashore_tables.jpeg?w=1020&#038;ssl=1\" alt=\"\" data-recalc-dims=\"1\"><\/p>\n<p>From the output image, you can see persons and tableware are detected successfully. You can easily determine a table&#8217;s status:<\/p>\n<ul>\n<li>Person: yes; tableware: no: table ready to take order or serve food<\/li>\n<li>Person: yes; tableware: yes: table in use<\/li>\n<li>Person: no; tableware: yes: table ready to be cleared up<\/li>\n<li>Person: no; tableware: no: table available<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n<h2>Next steps<\/h2>\n<p>This document only uses a pre-trained and compiled model provided by Hailo, meaning that it still has a lot of limitations. You can further train your own model to detect specific items, configure alerts for the results, and do so much more based on your needs. There are a lot of possibilities for you to find out, and here are some:<\/p>\n<ul>\n<li>Detect and manage inventory.<\/li>\n<li>Count customers.<\/li>\n<li>Count tableware usage.<\/li>\n<li>Analyze customers&#8217; order preferences.<\/li>\n<li>&#8230;<\/li>\n<\/ul>\n<\/div>","protected":false},"excerpt":{"rendered":"","protected":false},"author":110,"comment_status":"open","ping_status":"closed","template":"","format":"standard","meta":{"spay_email":""},"ht-kb-category":[211],"ht-kb-tag":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v17.8 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Monitoring Restaurant Table Status with YOLO on Hailo-8L | Mixtile<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.mixtile.com\/ja\/docs\/monitoring-restaurant-table-status-with-yolo-on-hailo-8l\/\" \/>\n<meta property=\"og:locale\" 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