t81_558_class_06_5_yolo.ipynb

{"cells": [{"cell_type": "markdown", "metadata": {"colab_type": "text", "id": "XKzF6dMaiLyP"}, "source": ["<a href=\"https://colab.research.google.com/github/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_06_5_yolo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"]}, {"cell_type": "markdown", "metadata": {"colab_type": "text", "id": "YDTXd8-Lmp8Q"}, "source": ["# T81-558: Applications of Deep Neural Networks\n", "**Module 6: Convolutional Neural Networks (CNN) for Computer Vision**\n", "* Instructor: [Jeff Heaton](https://sites.wustl.edu/jeffheaton/), McKelvey School of Engineering, [Washington University in St. Louis](https://engineering.wustl.edu/Programs/Pages/default.aspx)\n", "* For more information visit the [class website](https://sites.wustl.edu/jeffheaton/t81-558/)."]}, {"cell_type": "markdown", "metadata": {"colab_type": "text", "id": "ncNrAEpzmp8S"}, "source": ["# Module 6 Material\n", "\n", "* Part 6.1: Image Processing in Python [[Video]](https://www.youtube.com/watch?v=4Bh3gqHkIgc&list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN) [[Notebook]](t81_558_class_06_1_python_images.ipynb)\n", "* Part 6.2: Keras Neural Networks for Digits and Fashion MNIST [[Video]](https://www.youtube.com/watch?v=-SA8BmGvWYE&list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN) [[Notebook]](t81_558_class_06_2_cnn.ipynb)\n", "* Part 6.3: Implementing a ResNet in Keras [[Video]](https://www.youtube.com/watch?v=qMFKsMeE6fM&list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN) [[Notebook]](t81_558_class_06_3_resnet.ipynb)\n", "* Part 6.4: Using Your Own Images with Keras [[Video]](https://www.youtube.com/watch?v=VcFja1fUNSk&list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN) [[Notebook]](t81_558_class_06_4_keras_images.ipynb)\n", "* **Part 6.5: Recognizing Multiple Images with YOLO Darknet** [[Video]](https://www.youtube.com/watch?v=oQcAKvBFli8&list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN) [[Notebook]](t81_558_class_06_5_yolo.ipynb)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["# Google CoLab Instructions\n", "\n", "The following code ensures that Google CoLab is running the correct version of TensorFlow.\n", "  Running the following code will map your GDrive to ```/content/drive```."]}, {"cell_type": "code", "execution_count": 1, "metadata": {"colab": {"base_uri": "https://localhost:8080/", "height": 68}, "colab_type": "code", "id": "fU9UhAxTmp8S", "outputId": "3becf193-0a32-4bea-d087-95258f817148"}, "outputs": [], "source": ["try:\n", "    from google.colab import drive\n", "    drive.mount('/content/drive', force_remount=True)\n", "    COLAB = True\n", "    print(\"Note: using Google CoLab\")\n", "    %tensorflow_version 2.x\n", "except:\n", "    print(\"Note: not using Google CoLab\")\n", "    COLAB = False\n", "\n", "# Nicely formatted time string\n", "def hms_string(sec_elapsed):\n", "    h = int(sec_elapsed / (60 * 60))\n", "    m = int((sec_elapsed % (60 * 60)) / 60)\n", "    s = sec_elapsed % 60\n", "    return f\"{h}:{m:>02}:{s:>05.2f}\""]}, {"cell_type": "markdown", "metadata": {"colab_type": "text", "id": "QSKZqD1Mmp-C"}, "source": ["# Part 6.5: Recognizing Multiple Images with Darknet\n", "\n", "Programmers typically design convolutional neural networks to classify a single item centered in an image.  However, as humans, we can recognize many items in our field of view in real-time.  It is advantageous to be able to recognize multiple items in a single image.  One of the most advanced means of doing this is YOLO DarkNet (not to be confused with the Internet [Darknet](https://en.wikipedia.org/wiki/Darknet).  YOLO [[Cite:redmon2016you]](https://arxiv.org/abs/1506.02640) is an acronym for You Only Look Once.  The fact that YOLO must only look once speaks to the efficiency of the algorithm.  In this context, to \"look\" means to perform one scan over the image. Figure 6.YOLO shows YOLO tagging in action.\n", "\n", "**Figure 6.YOLO: YOLO Tagging**\n", "![DarkNet](https://raw.githubusercontent.com/jeffheaton/t81_558_deep_learning/master/images/darknet-predictions.jpg \"DarkNet\")\n", "\n", "It is also possible to run YOLO on live video streams.  Figure 6.VIDEO is from the YouTube Video for this module.\n", "\n", "**Figure 6.VIDEO: YOLO Video Tagging**\n", "![DarkNet Video](https://raw.githubusercontent.com/jeffheaton/t81_558_deep_learning/master/images/yolo_video.png \"DarkNet Video\")\n", "\n", "As you can see, it is classifying many things in this video.  My collection of books behind me is adding considerable \"noise,\" as DarkNet tries to classify every book behind me.  If you watch the video, you can see that it is less than perfect.  The coffee mug that I pick up gets classified as a cell phone and, at times, a remote.  The small yellow object behind me on the desk is a small toolbox (not a remote).  However, it gets classified as a book at times and a remote at other times.  Currently, this algorithm classifies each frame on its own.  The program could achieve greater accuracy if it analyzed multiple images from a video stream. Consider when you see an object coming towards you, if it changes angles, you might form a better opinion of what it was.  If that same object now changes to an unfavorable angle, you still know what it is, based on previous information.\n", "\n", "### How Does DarkNet/YOLO Work?\n", "\n", "YOLO begins by resizing the image to a $S \\times S$ grid.  YOLO runs a  single convolutional neural network against this grid that predicts bounding boxes and what might be contained by those boxes.  Each bounding box also has a confidence in which item it believes the box contains.  YOLO is a regular convolution network, just like we've seen previously.  The only difference is that a YOLO CNN outputs multiple prediction bounding boxes. At a high level, Figure 6.YOLO-DET illustrates this.\n", "\n", "**Figure 6.YOLO-DET: The YOLO Detection System**\n", "![The YOLO Detection System](https://raw.githubusercontent.com/jeffheaton/t81_558_deep_learning/master/images/yolo-1.png \"The YOLO Detection System\")\n", "\n", "The output of the YOLO convolutional neural networks is essentially a multiple regression.  YOLO generated the following values for each of the bounding rectangles.\n", "\n", "* **x** - The x-coordinate of the center of a bounding rectangle.\n", "* **y** - The y-coordinate of the center of a bounding rectangle.\n", "* **w** - The width of each bounding rectangle.\n", "* **h** - The height of each bounding rectangle.\n", "* **labels** - The relative probabilities of each of the labels (1 value for each label)\n", "* **confidence** - The confidence in this rectangle.\n", "\n", "The output layer of a Keras neural network is a Tensor.  In the case of YOLO, this output tensor is 3D and is of the following dimensions.\n", "\n", "$ S \\times S \\times (B \\cdot 5 + C) $\n", "\n", "The constants in the above expression are:\n", "\n", "* *S* - The dimensions that YOLO overlays across the source image.\n", "* *B* - The number of potential bounding rectangles generated for each grid cell.\n", "* *C* - The number of class labels that here are.\n", "\n", "The value 5 in the above expression is simply the count of non-label components of each bounding rectangle ($x$, $y$, $h$, $w$, $confidence$.\n", "\n", "Because there are $S^2 \\cdot B$ total potential bounding rectangles, the image is nearly full.  Because of this, it is essential to drop all rectangles below some threshold of confidence.  The image below demonstrates this.\n", "\n", "The actual structure of the convolutional neural network behind YOLO is relatively simple, as the following figure illustrates.  Because there is only one convolutional neural network, and it \"only looks once,\" the performance is not impacted by how many objects are detected.  Figure 6.YOLO-STRUCT shows the YOLO structure.\n", "\n", "**Figure 6.YOLO-STRUCT: YOLO Structure**\n", "![YOLO Structure](https://raw.githubusercontent.com/jeffheaton/t81_558_deep_learning/master/images/yolo-3.png \"YOLO Structure\")\n", "\n", "Figure 6.YOLO-DEMO shows some additional recognitions performed by a YOLO.\n", "\n", "**Figure 6.YOLO-DEMO: YOLO Structure**\n", "![YOLO Structure](https://raw.githubusercontent.com/jeffheaton/t81_558_deep_learning/master/images/yolo-4.png \"YOLO Structure\")\n", "\n", "\n", "\n", "### Using YOLO in Python\n", "\n", "To make use of YOLO in Python, you have several options:\n", "\n", "* **[DarkNet](https://pjreddie.com/darknet/yolo/)** - The original implementation of YOLO, written in C.\n", "* **[yolov3-tf2](https://github.com/zzh8829/yolov3-tf2)** - An unofficial Python package that implements YOLO in Python, using TensorFlow 2.0.\n", "\n", "The code provided in this notebook works equally well when run either locally or from Google CoLab.  In either case, the programmer should use TensorFlow 2.0.\n"]}, {"cell_type": "markdown", "metadata": {"colab_type": "text", "id": "Ym5_juokofQl"}, "source": ["### Installing YoloV3-TF2\n", "\n", "YoloV3-TF2 is not available directly through either PIP or CONDA.  Additionally, YoloV3-TF2 is not installed in Google CoLab by default. Therefore, whether you wish to use YoloV3-TF2 through CoLab or run it locally, you need to go through several steps to install it.  This section describes the process of installing YoloV3-TF2.  The same steps apply to either CoLab or a local install.  For CoLab, you must repeat these steps each time the system restarts your virtual environment.  For a local install, you must perform these steps only once for your virtual Python environment.  If you are installing locally, make sure to install to the same virtual environment that you created for this course.  The following command installs YoloV3-TF2 directly from it's GitHub repository."]}, {"cell_type": "code", "execution_count": 2, "metadata": {"colab": {"base_uri": "https://localhost:8080/", "height": 170}, "colab_type": "code", "id": "Fd_Dgjm4iLyh", "outputId": "baf1adfa-ca47-4e4b-efb9-b640a5e2d2e5"}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Collecting git+https://github.com/zzh8829/yolov3-tf2.git@master\n", "  Cloning https://github.com/zzh8829/yolov3-tf2.git (to revision master) to /private/var/folders/nz/jw1_lq4s10389lbx_j_vjyg4qy7h5_/T/pip-req-build-plnt65lr\n", "  Running command git clone -q https://github.com/zzh8829/yolov3-tf2.git /private/var/folders/nz/jw1_lq4s10389lbx_j_vjyg4qy7h5_/T/pip-req-build-plnt65lr\n", "Requirement already satisfied (use --upgrade to upgrade): yolov3-tf2==0.1 from git+https://github.com/zzh8829/yolov3-tf2.git@master in /Users/jheaton/miniconda3/envs/tensorflow/lib/python3.7/site-packages\n", "Building wheels for collected packages: yolov3-tf2\n", "  Building wheel for yolov3-tf2 (setup.py) ... \u001b[?25ldone\n", "\u001b[?25h  Created wheel for yolov3-tf2: filename=yolov3_tf2-0.1-cp37-none-any.whl size=9193 sha256=f8aa39a7acb8d7a766475aa5779bb7ae5831ef15eff8442eace8ccf04f97f74c\n", "  Stored in directory: /private/var/folders/nz/jw1_lq4s10389lbx_j_vjyg4qy7h5_/T/pip-ephem-wheel-cache-6ks2ceq_/wheels/59/1b/97/905ab51e9c0330efe8c3c518aff17de4ee91100412cd6dd553\n", "Successfully built yolov3-tf2\n"]}], "source": ["import sys\n", "\n", "!{sys.executable} -m pip install git+https://github.com/\n", "        zzh8829/yolov3-tf2.git@master"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Before you can make use of YoloV3-TF2 there are several files you must obtain:\n", "\n", "* **yolov3.weights** - These are the pre-trained weights provided by the author of YOLO.\n", "* **convert.py** - This is a Python script that converts **yolov3.weights** into a TensorFlow compatible weight format.\n", "* **coco.names** - The names of the 80 items that the **yolov3.weights** neural network was trained to recognize.\n", "* **yolov3.tf** - The YOLO weights converted to a format that TensorFlow can use directly.\n", "\n", "The code provided below obtains these files.  The script stores these files to either your GDrive, if you are using CoLab, or a local folder named \"data\" if you are running locally.\n", "\n", "Researchers have trained YOLO on a variety of different computer image datasets.  The version of YOLO weights used in this course is from the dataset Common Objects in Context (COCO). [[Cite: lin2014microsoft]](https://arxiv.org/abs/1405.0312) This dataset contains images labeled into 80 different classes.  COCO is the source of the file coco.txt that used in this module.  \n", "\n", "Developers have also adapted YOLO for mobile devices by creating the YOLO Tiny pre-trained weights that use a much smaller convolutional neural network and still achieve acceptable levels of quality.  Though YoloV3-TF2 can work with either YOLO Tiny or regular YOLO we are not using the tiny weights for this course."]}, {"cell_type": "code", "execution_count": 3, "metadata": {"colab": {"base_uri": "https://localhost:8080/", "height": 71}, "colab_type": "code", "id": "ThxRyzT1iLyj", "outputId": "a9efc33a-8e09-4d1d-f449-70a23e499a68"}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Downloading data from https://raw.githubusercontent.com/zzh8829/yolov3-tf2/master/convert.py\n", "8192/1067 [======================================================================================================================================================================================================================================] - 0s 0us/step\n"]}], "source": ["import tensorflow as tf\n", "import os\n", "\n", "if COLAB:\n", "  ROOT = '/content/drive/My Drive/projects/t81_558_dlearning/yolo'\n", "else:\n", "  ROOT = os.path.join(os.getcwd(),'data')\n", "\n", "filename_darknet_weights = tf.keras.utils.get_file(\n", "    os.path.join(ROOT,'yolov3.weights'),\n", "    origin='https://pjreddie.com/media/files/yolov3.weights')\n", "TINY = False\n", "\n", "filename_convert_script = tf.keras.utils.get_file(\n", "    os.path.join(os.getcwd(),'convert.py'),\n", "    origin='https://raw.githubusercontent.com/zzh8829/'\\\n", "        \"yolov3-tf2/master/convert.py')\n", "\n", "filename_classes = tf.keras.utils.get_file(\n", "    os.path.join(ROOT,'coco.names'),\n", "    origin='https://raw.githubusercontent.com/zzh8829/'\\\n", "       'yolov3-tf2/master/data/coco.names')\n", "filename_converted_weights = os.path.join(ROOT,'yolov3.tf')"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### Transfering Weights\n", "\n", "In the course, we transfer already trained weights into our YOLO networks.  It can take considerable time to train a YOLO network from scratch.  If you would like to train a YOLO network to recognize images other than the COLO provided images, then you may need to train your own YOLO information.  If training from scratch is something you need to do, there is further information on this at the YoloV3-TF2 GitHub repository.\n", "\n", "The weights provided by the original authors of YOLO is not directly compatible with TensorFlow.  Because of this, it is necessary first to convert the YOLO provided weights into a TensorFlow compatible format. The following code does this conversion.  This process does not need to be repeated by the program. Once the conversion script processes YOLO weights the saved to the yolov3.tf YOLO can reuse these converted wights. The following code performs this conversion."]}, {"cell_type": "code", "execution_count": 4, "metadata": {"colab": {"base_uri": "https://localhost:8080/", "height": 1000}, "colab_type": "code", "id": "m-9PTn-uiLyl", "outputId": "1449dbe0-9f23-4a08-925b-99fc7878fc69"}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["2019-12-24 20:08:13.233259: I tensorflow/core/platform/cpu_feature_guard.cc:145] This TensorFlow binary is optimized with Intel(R) MKL-DNN to use the following CPU instructions in performance critical operations:  SSE4.1 SSE4.2 AVX AVX2 FMA\n", "To enable them in non-MKL-DNN operations, rebuild TensorFlow with the appropriate compiler flags.\n", "2019-12-24 20:08:13.233578: I tensorflow/core/common_runtime/process_util.cc:115] Creating new thread pool with default inter op setting: 8. Tune using inter_op_parallelism_threads for best performance.\n", "Model: \"yolov3\"\n", "__________________________________________________________________________________________________\n", "Layer (type)                    Output Shape         Param #     Connected to                     \n", "==================================================================================================\n", "input_1 (InputLayer)            [(None, None, None,  0                                            \n", "__________________________________________________________________________________________________\n", "yolo_darknet (Model)            ((None, None, None,  40620640    input_1[0][0]                    \n", "__________________________________________________________________________________________________\n", "yolo_conv_0 (Model)             (None, None, None, 5 11024384    yolo_darknet[1][2]               \n", "__________________________________________________________________________________________________\n", "yolo_conv_1 (Model)             (None, None, None, 2 2957312     yolo_conv_0[1][0]                \n", "                                                                 yolo_darknet[1][1]               \n", "__________________________________________________________________________________________________\n", "yolo_conv_2 (Model)             (None, None, None, 1 741376      yolo_conv_1[1][0]                \n", "                                                                 yolo_darknet[1][0]               \n", "__________________________________________________________________________________________________\n", "yolo_output_0 (Model)           (None, None, None, 3 4984063     yolo_conv_0[1][0]                \n", "__________________________________________________________________________________________________\n", "yolo_output_1 (Model)           (None, None, None, 3 1312511     yolo_conv_1[1][0]                \n", "__________________________________________________________________________________________________\n", "yolo_output_2 (Model)           (None, None, None, 3 361471      yolo_conv_2[1][0]                \n", "__________________________________________________________________________________________________\n", "yolo_boxes_0 (Lambda)           ((None, None, None,  0           yolo_output_0[1][0]              \n", "__________________________________________________________________________________________________\n", "yolo_boxes_1 (Lambda)           ((None, None, None,  0           yolo_output_1[1][0]              \n", "__________________________________________________________________________________________________\n", "yolo_boxes_2 (Lambda)           ((None, None, None,  0           yolo_output_2[1][0]              \n", "__________________________________________________________________________________________________\n", "yolo_nms (Lambda)               ((None, 100, 4), (No 0           yolo_boxes_0[0][0]               \n", "                                                                 yolo_boxes_0[0][1]               \n", "                                                                 yolo_boxes_0[0][2]               \n", "                                                                 yolo_boxes_1[0][0]               \n", "                                                                 yolo_boxes_1[0][1]               \n", "                                                                 yolo_boxes_1[0][2]               \n", "                                                                 yolo_boxes_2[0][0]               \n", "                                                                 yolo_boxes_2[0][1]               \n", "                                                                 yolo_boxes_2[0][2]               \n", "==================================================================================================\n", "Total params: 62,001,757\n", "Trainable params: 61,949,149\n", "Non-trainable params: 52,608\n", "__________________________________________________________________________________________________\n", "I1224 20:08:20.524322 140735710913408 convert.py:19] model created\n", "I1224 20:08:20.526310 140735710913408 utils.py:45] yolo_darknet/conv2d bn\n", "I1224 20:08:20.528602 140735710913408 utils.py:45] yolo_darknet/conv2d_1 bn\n", "I1224 20:08:20.530750 140735710913408 utils.py:45] yolo_darknet/conv2d_2 bn\n", "I1224 20:08:20.532802 140735710913408 utils.py:45] yolo_darknet/conv2d_3 bn\n", "I1224 20:08:20.535872 140735710913408 utils.py:45] yolo_darknet/conv2d_4 bn\n", "I1224 20:08:20.539076 140735710913408 utils.py:45] yolo_darknet/conv2d_5 bn\n", "I1224 20:08:20.541434 140735710913408 utils.py:45] yolo_darknet/conv2d_6 bn\n", "I1224 20:08:20.544061 140735710913408 utils.py:45] yolo_darknet/conv2d_7 bn\n", "I1224 20:08:20.546896 140735710913408 utils.py:45] yolo_darknet/conv2d_8 bn\n", "I1224 20:08:20.550781 140735710913408 utils.py:45] yolo_darknet/conv2d_9 bn\n", "I1224 20:08:20.555396 140735710913408 utils.py:45] yolo_darknet/conv2d_10 bn\n", "I1224 20:08:20.558006 140735710913408 utils.py:45] yolo_darknet/conv2d_11 bn\n", "I1224 20:08:20.562070 140735710913408 utils.py:45] yolo_darknet/conv2d_12 bn\n", "I1224 20:08:20.564539 140735710913408 utils.py:45] yolo_darknet/conv2d_13 bn\n", "I1224 20:08:20.568817 140735710913408 utils.py:45] yolo_darknet/conv2d_14 bn\n", "I1224 20:08:20.571511 140735710913408 utils.py:45] yolo_darknet/conv2d_15 bn\n", "I1224 20:08:20.575397 140735710913408 utils.py:45] yolo_darknet/conv2d_16 bn\n", "I1224 20:08:20.578016 140735710913408 utils.py:45] yolo_darknet/conv2d_17 bn\n", "I1224 20:08:20.581743 140735710913408 utils.py:45] yolo_darknet/conv2d_18 bn\n", "I1224 20:08:20.584420 140735710913408 utils.py:45] yolo_darknet/conv2d_19 bn\n", "I1224 20:08:20.589673 140735710913408 utils.py:45] yolo_darknet/conv2d_20 bn\n", "I1224 20:08:20.592389 140735710913408 utils.py:45] yolo_darknet/conv2d_21 bn\n", "I1224 20:08:20.596343 140735710913408 utils.py:45] yolo_darknet/conv2d_22 bn\n", "I1224 20:08:20.598808 140735710913408 utils.py:45] yolo_darknet/conv2d_23 bn\n", "I1224 20:08:20.602786 140735710913408 utils.py:45] yolo_darknet/conv2d_24 bn\n", "I1224 20:08:20.606070 140735710913408 utils.py:45] yolo_darknet/conv2d_25 bn\n", "I1224 20:08:20.610819 140735710913408 utils.py:45] yolo_darknet/conv2d_26 bn\n", "I1224 20:08:20.626549 140735710913408 utils.py:45] yolo_darknet/conv2d_27 bn\n", "I1224 20:08:20.630322 140735710913408 utils.py:45] yolo_darknet/conv2d_28 bn\n", "I1224 20:08:20.645246 140735710913408 utils.py:45] yolo_darknet/conv2d_29 bn\n", "I1224 20:08:20.649082 140735710913408 utils.py:45] yolo_darknet/conv2d_30 bn\n", "I1224 20:08:20.663831 140735710913408 utils.py:45] 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140735710913408 utils.py:45] yolo_conv_2/conv2d_67 bn\n", "I1224 20:08:21.423701 140735710913408 utils.py:45] yolo_conv_2/conv2d_68 bn\n", "I1224 20:08:21.425631 140735710913408 utils.py:45] yolo_conv_2/conv2d_69 bn\n", "I1224 20:08:21.428262 140735710913408 utils.py:45] yolo_conv_2/conv2d_70 bn\n", "I1224 20:08:21.429989 140735710913408 utils.py:45] yolo_conv_2/conv2d_71 bn\n", "I1224 20:08:21.433197 140735710913408 utils.py:45] yolo_conv_2/conv2d_72 bn\n", "I1224 20:08:21.434813 140735710913408 utils.py:45] yolo_output_2/conv2d_73 bn\n", "I1224 20:08:21.437976 140735710913408 utils.py:45] yolo_output_2/conv2d_74 bias\n", "I1224 20:08:21.439261 140735710913408 convert.py:22] weights loaded\n", "I1224 20:08:22.945503 140735710913408 convert.py:26] sanity check passed\n", "I1224 20:08:23.474303 140735710913408 convert.py:29] weights saved\n"]}], "source": ["import sys\n", "!{sys.executable} \"{filename_convert_script}\" --weights \n", "    \"{filename_darknet_weights}\" --output \"{filename_converted_weights}\""]}, {"cell_type": "markdown", "metadata": {}, "source": ["The conversion script is no longer needed once this script converts the YOLO weights have to a TensorFlow format.  Because this executable file resides in the same directory as the course files, we delete it at this point."]}, {"cell_type": "code", "execution_count": 5, "metadata": {"colab": {}, "colab_type": "code", "id": "_Ql4pFiBkuWg"}, "outputs": [], "source": ["import os\n", "os.remove(filename_convert_script)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Now that we have all of the files needed for YOLO, we are ready to use it to recognize components of an image."]}, {"cell_type": "markdown", "metadata": {"colab_type": "text", "id": "qYOvD3M7ofQl"}, "source": ["### Running DarkFlow (YOLO)\n", "\n", "The YoloV3-TF2 library can easily integrate with Python applications.  The initialization of the library consists of three steps.  First, it is essential to import all of the needed packages for the library.  Next, the Python program must define all of the YOLO configurations through the Keras flags architecture. The Keras flag system primarily works from the command line; however, it also allows configuration programmatically in an application.  For this example, we configure the package programmatically.  Finally, we must scan available devices so that our application takes advantage of any GPUs.   The following code performs all three of these steps."]}, {"cell_type": "code", "execution_count": 6, "metadata": {"colab": {}, "colab_type": "code", "id": "MY3gVyidmp-K"}, "outputs": [], "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 yolov3_tf2.models import (YoloV3, YoloV3Tiny)\n", "from yolov3_tf2.dataset import transform_images, load_tfrecord_dataset\n", "from yolov3_tf2.utils import draw_outputs\n", "import sys\n", "from PIL import Image, ImageFile\n", "import requests\n", "\n", "# Flags are used to define several options for YOLO.\n", "flags.DEFINE_string('classes', filename_classes, 'path to classes file')\n", "flags.DEFINE_string('weights', filename_converted_weights, 'path to weights file')\n", "flags.DEFINE_boolean('tiny', False, 'yolov3 or yolov3-tiny')\n", "flags.DEFINE_integer('size', 416, 'resize images to')\n", "flags.DEFINE_string('tfrecord', None, 'tfrecord instead of image')\n", "flags.DEFINE_integer('num_classes', 80, 'number of classes in the model')\n", "FLAGS([sys.argv[0]])\n", "\n", "# Locate devices to run YOLO on (e.g. GPU)\n", "physical_devices = tf.config.experimental.list_physical_devices('GPU')\n", "if len(physical_devices) > 0:\n", "    tf.config.experimental.set_memory_growth(physical_devices[0], True)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["It is important to understand that Keras flags can only be defined once. If you are going to classify more than one image, make sure that you do not define the flags additional times.\n", "\n", "The following code initializes a YoloV3-TF2 classification object.  The weights are loaded, and the object is ready for use as the **yolo** variable.  It is not necessary to reload the weights and obtain a new **yolo** variable for each classification.  "]}, {"cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["weights loaded\n", "classes loaded\n"]}], "source": ["# This example does not use the \"Tiny version\"\n", "if FLAGS.tiny:\n", "    yolo = YoloV3Tiny(classes=FLAGS.num_classes)\n", "else:\n", "    yolo = YoloV3(classes=FLAGS.num_classes)\n", "\n", "# Load weights and classes\n", "yolo.load_weights(FLAGS.weights).expect_partial()\n", "print('weights loaded')\n", "\n", "class_names = [c.strip() for c in open(FLAGS.classes).readlines()]\n", "print('classes loaded')"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Next, we obtain an image to classify. For this example, the program loads the image from a URL.  YoloV3-TF2 expects that the image is in the format of a Numpy array.  An image file, such as JPEG or PNG, is converted into this raw Numpy format by calling the TensorFlow **decode_image** function.  YoloV3-TF2 can obtain images from other sources, so long as the program first decodes them to raw Numpy format.  The following code obtains the image in this format."]}, {"cell_type": "code", "execution_count": 8, "metadata": {"colab": {}, "colab_type": "code", "id": "J5ilnhhNiLyq"}, "outputs": [], "source": ["# Read image to classify\n", "url = \"https://raw.githubusercontent.com/jeffheaton/\"\\\n", "    \"t81_558_deep_learning/master/images/cook.jpg\"\n", "response = requests.get(url)\n", "img_raw = tf.image.decode_image(response.content, channels=3)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["At this point, we can classify the image that was just loaded.  The program should preprocess the image so that it is the size expected by YoloV3-TF2.  Your program also sets the confidence threshold at this point.  Any sub-image recognized with confidence below this value is not returned by YOLO."]}, {"cell_type": "code", "execution_count": 9, "metadata": {"colab": {"base_uri": "https://localhost:8080/", "height": 68}, "colab_type": "code", "id": "gfortIjsiLyr", "outputId": "b8f48006-0537-4561-f46b-3900b14c3bd1"}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Prediction time: 0:00:02.18\n"]}], "source": ["# Preprocess image\n", "img = tf.expand_dims(img_raw, 0)\n", "img = transform_images(img, FLAGS.size)\n", "\n", "# Desired threshold (any sub-image below this confidence \n", "# level will be ignored.)\n", "FLAGS.yolo_score_threshold = 0.5\n", "\n", "# Recognize and report results\n", "t1 = time.time()\n", "boxes, scores, classes, nums = yolo(img)\n", "t2 = time.time()\n", "print(f\"Prediction time: {hms_string(t2 - t1)}\")"]}, {"cell_type": "markdown", "metadata": {}, "source": ["It is important to note that the **yolo** class instantiated here is a callable object, which means that it can fill the role of both an object and a function. Acting as a function, *yolo* returns three arrays named **boxes**, **scores**, and **classes** that are of the same length.  The function returns all sub-images found with a score above the minimum threshold.  Additionally, the **yolo** function returns an array named called **nums**. The first element of the **nums** array specifies how many sub-images YOLO found to be above the score threshold.\n", "\n", "* **boxes** - The bounding boxes for each of the sub-images detected in the image sent to YOLO.\n", "* **scores** - The confidence for each of the sub-images detected.\n", "* **classes** - The string class names for each of the items.  These are COCO names such as \"person\" or \"dog.\" \n", "* **nums** - The number of images above the threshold.\n", "\n", "Your program should use these values to perform whatever actions you wish as a result of the input image.  The following code simply displays the images detected above the threshold."]}, {"cell_type": "code", "execution_count": 10, "metadata": {"colab": {"base_uri": "https://localhost:8080/", "height": 136}, "colab_type": "code", "id": "tzXAESCLiLyt", "outputId": "5e5ad983-482f-436a-8f8b-4c3353f392fc"}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["detections:\n", "\tperson, 0.9995919466018677, [0.31659657 0.10725152 0.68426734 0.74258995]\n", "\tdog, 0.9896982312202454, [0.51111007 0.55769485 0.933974   0.8187981 ]\n", "\tmicrowave, 0.9839581847190857, [0.0069517  0.08101548 0.27909756 0.28820014]\n", "\toven, 0.9383126497268677, [0.00773552 0.32521245 0.42321444 0.83368266]\n", "\tbottle, 0.8538918495178223, [0.73093545 0.23399045 0.76463544 0.32874537]\n", "\tbottle, 0.5538208484649658, [0.790116   0.26327905 0.8189085  0.3274593 ]\n"]}], "source": ["print('detections:')\n", "for i in range(nums[0]):\n", "    cls = class_names[int(classes[0][i])]\n", "    score = np.array(scores[0][i])\n", "    box = np.array(boxes[0][i])\n", "    print(f\"\\t{cls}, {score}, {box}\")"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Your program should use these values to perform whatever actions you wish as a result of the input image.  The following code simply displays the images detected above the threshold.\n", "\n", "YoloV3-TF2 includes a function named **draw_outputs** that allows the sub-image detections to visualized.  The following image shows the output of the draw_outputs function.  You might have first seen YOLO demonstrated as an image with boxes and labels around the sub-images. A program can produce this output with the arrays returned by the **yolo** function."]}, {"cell_type": "code", "execution_count": 11, "metadata": {"colab": {"base_uri": "https://localhost:8080/", "height": 337}, "colab_type": "code", "id": "vmOhAGktiLyv", "outputId": "139b441a-2d26-451c-d211-65693e6dcf7b"}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<PIL.Image.Image image mode=RGB size=240x320 at 0x10743E590>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["# Display image using YOLO library's built in function\n", "img = img_raw.numpy()\n", "img = draw_outputs(img, (boxes, scores, classes, nums), class_names)\n", "#cv2.imwrite(FLAGS.output, img) # Save the image\n", "display(Image.fromarray(img, 'RGB')) # Display the image"]}, {"cell_type": "markdown", "metadata": {"colab_type": "text", "id": "0eBtaFbimp-M"}, "source": ["# Module 6 Assignment\n", "\n", "You can find the first assignment here: [assignment 6](https://github.com/jeffheaton/t81_558_deep_learning/blob/master/assignments/assignment_yourname_class6.ipynb)"]}], "metadata": {"anaconda-cloud": {}, "kernelspec": {"display_name": "Python 3.6 (tensorflow)", "language": "python", "name": "tensorflow"}, "language_info": {"codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.5"}}, "nbformat": 4, "nbformat_minor": 1}