Chapter05/ch5_nb1_yolo_inference.ipynb

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   "source": [
    "<p style=\"border: 1px solid #e7692c; border-left: 15px solid #e7692c; padding: 10px; text-align:justify;\">\n",
    "    <strong style=\"color: #e7692c\">Tip.</strong> <a style=\"color: #000000;\" href=\"https://nbviewer.jupyter.org/github/PacktPublishing/Hands-On-Computer-Vision-with-TensorFlow-2/blob/master/Chapter05/ch5_nb1_yolo_inference.ipynb\" title=\"View with Jupyter Online\">Click here to view this notebook on <code>nbviewer.jupyter.org</code></a>. \n",
    "    <br/>These notebooks are better read there, as Github default viewer ignores some of the formatting and interactive content.\n",
    "    </p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table style=\"font-size: 1em; padding: 0; margin: 0;\">\n",
    "    <tr style=\"vertical-align: top; padding: 0; margin: 0;background-color: #ffffff\">\n",
    "        <td style=\"vertical-align: top; padding: 0; margin: 0; padding-right: 15px;\">\n",
    "    <p style=\"background: #363636; color:#ffffff; text-align:justify; padding: 10px 25px;\">\n",
    "        <strong style=\"font-size: 1.0em;\"><span style=\"font-size: 1.2em;\"><span style=\"color: #e7692c;\">Hands-on</span> Computer Vision with TensorFlow 2</span><br/>by <em>Eliot Andres</em> & <em>Benjamin Planche</em> (Packt Pub.)</strong><br/><br/>\n",
    "        <strong>> Chapter 5: Object Detection Models</strong><br/>\n",
    "    </p>\n",
    "\n",
    "<h1 style=\"width: 100%; text-align: left; padding: 0px 25px;\"><small style=\"color: #e7692c;\">Notebook 1:</small><br/>Inference with YOLO</h1>\n",
    "<br/>\n",
    "<p style=\"border-left: 15px solid #363636; text-align:justify; padding: 0 10px;\">\n",
    "    We introduced the inner workings of You Only Look Once (YOLO), a fast object detection model.\n",
    "    <br/>In this first notebook, we will use the model for inference.\n",
    "</p>\n",
    "<br/>\n",
    "<p style=\"border-left: 15px solid #363636; text-align:justify; padding: 0 10px;\">\n",
    "    <strong> Requirements </strong>\n",
    "<br/><br/>\n",
    "    To run this notebook, download the YOLO V3 weights and convert them using the command:\n",
    "<pre>\n",
    "wget https://pjreddie.com/media/files/yolov3.weights -O weights/yolov3.weights\n",
    "<br>\n",
    "python convert.py --weights weights/yolov3.weights --output weights/yolov3.tf\n",
    "</pre>\n",
    "</p>\n",
    "<p style=\"border-left: 15px solid #363636; text-align:justify; padding: 0 10px;\">\n",
    "    <strong> Acknowledgements </strong><br/><br/>\n",
    "    This implementation is based on Zihao Zhang's <a href=\"https://github.com/zzh8829/yolov3-tf2\">implementation</a>. Thanks to him!\n",
    "\n",
    "</p>\n",
    "<p style=\"border-left: 15px solid #e7692c; padding: 0 10px; text-align:justify;\">\n",
    "    <strong style=\"color: #e7692c;\">Tip.</strong> The notebooks shared on this git repository illustrate some notions from the book \"<em><strong>Hands-on Computer Vision with TensorFlow 2</strong></em>\" written by Eliot Andres and Benjamin Planche, published by Packt. If you enjoyed the insights shared here, <a href=\"https://www.amazon.com/Hands-Computer-Vision-TensorFlow-processing/dp/1788830644\" title=\"Learn more about the book!\"><strong>please consider acquiring the book!</strong></a>\n",
    "<br/><br/>\n",
    "The book provides further guidance for those eager to learn about computer vision and to harness the power of TensorFlow 2 and Keras to build efficient recognition systems for object detection, segmentation, video processing, smartphone applications, and more.</p>\n",
    "        </td>\n",
    "        <td style=\"vertical-align: top; padding: 0; margin: 0; width: 280px;\">\n",
    "    <a href=\"https://www.amazon.com/Hands-Computer-Vision-TensorFlow-processing/dp/1788830644\" title=\"Learn more about the book!\" target=\"_blank\">\n",
    "        <img src=\"../banner_images/book_cover.png\" width=280>\n",
    "    </a>\n",
    "    <p style=\"background: #e7692c; color:#ffffff; padding: 10px; text-align:justify;\"><strong>Leverage deep learning to create powerful image processing apps with TensorFlow 2 and Keras. <br/></strong>Get the book for more insights!</p>\n",
    "    <ul style=\"height: 32px; white-space: nowrap; text-align: center; margin: 0px; padding: 0px; padding-top: 10px;\">\n",
    "    <li style=\"display: block;height: 100%;float: left;vertical-align: middle;margin: 0 25px 10px;padding: 0px;\">\n",
    "        <a href=\"https://www.amazon.com/Hands-Computer-Vision-TensorFlow-processing/dp/1788830644\" title=\"Get the book on Amazon (paperback or Kindle version)!\" target=\"_blank\">\n",
    "        <img style=\"vertical-align: middle; max-width: 72px; max-height: 32px;\" src=\"../banner_images/logo_amazon.png\" width=\"75px\">\n",
    "        </a>\n",
    "    </li>\n",
    "    <li style=\"display: inline-block;height: 100%;vertical-align: middle;float: right;margin: -5px 25px 10px;padding: 0px;\">\n",
    "        <a href=\"https://www.packtpub.com/application-development/hands-computer-vision-tensorflow-2\" title=\"Get your Packt book (paperback, PDF, ePUB, or MOBI version)!\" target=\"_blank\">\n",
    "        <img style=\"vertical-align: middle; max-width: 72px; max-height: 32px;\" src=\"../banner_images/logo_packt.png\" width=\"75px\">\n",
    "        </a>\n",
    "    </li>\n",
    "    </ul>\n",
    "        </td>\n",
    "        </tr>\n",
    "        </table>"
   ]
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   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "import time\n",
    "from absl import app, flags, logging\n",
    "from absl.flags import FLAGS\n",
    "import cv2\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from models import YoloV3\n",
    "from utils import draw_outputs, transform_images\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "CLASSES_PATH = './coco.names'\n",
    "WEIGHTS_PATH = './weights/yolov3.tf'\n",
    "IMAGE_SIZE = 416"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "end_time": "2019-05-28T16:19:10.078797Z",
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   },
   "source": [
    "## Load the model, the classes and the example image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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     "start_time": "2019-05-28T16:19:57.263Z"
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   "outputs": [],
   "source": [
    "yolo = YoloV3()\n",
    "yolo.load_weights(WEIGHTS_PATH)\n",
    "class_names = [c.strip() for c in open(CLASSES_PATH).readlines()]\n",
    "\n",
    "img = tf.image.decode_image(open('./dog_example.jpg', 'rb').read(), channels=3)\n",
    "plt.imshow(img)\n",
    "plt.show()\n",
    "\n",
    "input_img = tf.expand_dims(img, 0)\n",
    "input_img = transform_images(input_img, IMAGE_SIZE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run inference and display result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2019-05-28T16:19:58.150Z"
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   },
   "outputs": [],
   "source": [
    "boxes, scores, classes, nums = yolo(input_img)\n",
    "\n",
    "\n",
    "logging.info('detections:')\n",
    "for i in range(nums[0]):\n",
    "    print('\\t{}, {}, {}'.format(class_names[int(classes[0][i])],\n",
    "                                       np.array(scores[0][i]),\n",
    "                                       np.array(boxes[0][i])))\n",
    "\n",
    "prediction_img = draw_outputs(img.numpy(), (boxes, scores, classes, nums), class_names)\n",
    "plt.figure(figsize=(10, 20))\n",
    "plt.imshow(prediction_img)"
   ]
  }
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