Few_Shot_Learning_RetinaNet.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Few Shot Learning: RetinaNet.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/danielsoy/data/blob/main/Few_Shot_Learning_RetinaNet.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gUujHs9bBdyw"
      },
      "source": [
        "<img align=\"left\" 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\">\n",
        "\n",
        "<br>\n",
        "Wouldn’t it be frustrating if your smartphone needed to have thousands of pictures of you to recognize you and get unlocked? Thanks for the few-shot learning, this is not needed.\n",
        "\n",
        "This technique has drawn a lot of attention in the research community and many solutions have been developed. To predict something based on a few training examples, the solutions right now use meta-learning or in three words: learning to learn.\n",
        "\n",
        "If you want to learn how the few-shot detectors work, bring an object (or beer), join this Expert Class lecture, create your own object detector and run it in real-time.\n",
        "\n",
        "<br>\n",
        "Estimated time to run through this colab (with GPU): < 5 minutes.\n",
        "\n",
        "<br>\n",
        "<br>\n",
        "\n",
        "<table align=\"left\"><td>\n",
        "  <a target=\"_blank\"  href=\"https://colab.research.google.com/drive/11KAsKG6pTSUO-melYfCiohOVk4s6MwIB#scrollTo=PL8jmeQGKNT-\">\n",
        "    <img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab\n",
        "  </a>\n",
        "</td><td>\n",
        "  <a target=\"_blank\"  href=\"https://github.com/hugozanini/realtime-retinanet\">\n",
        "    <img width=32px src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
        "</td></table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N-DVF3dOcdXw"
      },
      "source": [
        "### Imports\n",
        "\n",
        "Importing the required packages and libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W9zHuDUAccr1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b046c728-a2dd-4787-9d5c-2e860bbb05fa"
      },
      "source": [
        "import os\n",
        "import pathlib\n",
        "\n",
        "# Get helper functions from utils.py\n",
        "if not pathlib.Path('utils.py').exists():\n",
        "  !wget https://github.com/raw/hugozanini/realtime-retinanet/main/utils.py\n",
        "\n",
        "# Clone the tensorflow models repository if it doesn't already exist\n",
        "if \"models\" in pathlib.Path.cwd().parts:\n",
        "  while \"models\" in pathlib.Path.cwd().parts:\n",
        "    os.chdir('..')\n",
        "elif not pathlib.Path('models').exists():\n",
        "  !git clone --depth 1 https://github.com/tensorflow/models"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2021-09-27 16:12:16--  https://github.com/raw/hugozanini/realtime-retinanet/main/utils.py\n",
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "seBnpIE8cfT2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2ed775e2-7a2c-40b0-ef33-50ce77f514c1"
      },
      "source": [
        "# Install the Object Detection API\n",
        "%%bash\n",
        "cd models/research/\n",
        "protoc object_detection/protos/*.proto --python_out=.\n",
        "cp object_detection/packages/tf2/setup.py .\n",
        "python -m pip install .\n",
        "\n",
        "#Install JupyterPlot\n",
        "pip install jupyterplot"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Building wheels for collected packages: object-detection, py-cpuinfo, avro-python3, dill, future, seqeval\n",
            "  Building wheel for object-detection (setup.py): started\n",
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            "Successfully built object-detection py-cpuinfo avro-python3 dill future seqeval\n",
            "Installing collected packages: requests, portalocker, future, dill, colorama, tf-slim, tensorflow-text, tensorflow-model-optimization, tensorflow-addons, seqeval, sentencepiece, sacrebleu, pyyaml, py-cpuinfo, orjson, opencv-python-headless, hdfs, fastavro, avro-python3, tf-models-official, lvis, apache-beam, object-detection\n",
            "  Attempting uninstall: requests\n",
            "    Found existing installation: requests 2.23.0\n",
            "    Uninstalling requests-2.23.0:\n",
            "      Successfully uninstalled requests-2.23.0\n",
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            "      Successfully uninstalled future-0.16.0\n",
            "  Attempting uninstall: dill\n",
            "    Found existing installation: dill 0.3.4\n",
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            "      Successfully uninstalled dill-0.3.4\n",
            "  Attempting uninstall: pyyaml\n",
            "    Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
            "Successfully installed apache-beam-2.32.0 avro-python3-1.9.2.1 colorama-0.4.4 dill-0.3.1.1 fastavro-1.4.5 future-0.18.2 hdfs-2.6.0 lvis-0.5.3 object-detection-0.1 opencv-python-headless-4.5.3.56 orjson-3.6.3 portalocker-2.3.2 py-cpuinfo-8.0.0 pyyaml-5.4.1 requests-2.26.0 sacrebleu-2.0.0 sentencepiece-0.1.96 seqeval-1.2.2 tensorflow-addons-0.14.0 tensorflow-model-optimization-0.6.0 tensorflow-text-2.6.0 tf-models-official-2.6.0 tf-slim-1.1.0\n",
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            "Successfully built lrcurve\n",
            "Installing collected packages: lrcurve, jupyterplot\n",
            "Successfully installed jupyterplot-0.0.3 lrcurve-1.1.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "  DEPRECATION: A future pip version will change local packages to be built in-place without first copying to a temporary directory. We recommend you use --use-feature=in-tree-build to test your packages with this new behavior before it becomes the default.\n",
            "   pip 21.3 will remove support for this functionality. You can find discussion regarding this at https://github.com/pypa/pip/issues/7555.\n",
            "ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "multiprocess 0.70.12.2 requires dill>=0.3.4, but you have dill 0.3.1.1 which is incompatible.\n",
            "google-colab 1.0.0 requires requests~=2.23.0, but you have requests 2.26.0 which is incompatible.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qwWj7bH0chm0"
      },
      "source": [
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "from jupyterplot import ProgressPlot\n",
        "%matplotlib inline\n",
        "\n",
        "import os\n",
        "import random\n",
        "import io\n",
        "import imageio\n",
        "import glob\n",
        "import scipy.misc\n",
        "import numpy as np\n",
        "from six import BytesIO\n",
        "from PIL import Image, ImageDraw, ImageFont\n",
        "from IPython.display import display, Javascript\n",
        "\n",
        "import tensorflow as tf\n",
        "\n",
        "from object_detection.utils import label_map_util\n",
        "from object_detection.utils import config_util\n",
        "from object_detection.utils import visualization_utils as viz_utils\n",
        "from object_detection.utils import colab_utils\n",
        "from object_detection.builders import model_builder\n",
        "\n",
        "from IPython.display import display, Javascript\n",
        "from google.colab.output import eval_js\n",
        "from google.colab.patches import cv2_imshow\n",
        "from base64 import b64decode, b64encode\n",
        "import cv2\n",
        "import PIL\n",
        "import html\n",
        "import time\n",
        "import utils"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nmYD6Y0lb2zC"
      },
      "source": [
        "### Creating the dataset\n",
        "\n",
        "Taking pictures from your webcam to train the model "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qkimNHJ-YsNP",
        "outputId": "63608ce8-c79a-4072-b8bf-a408fa224c38",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "num_labels = 10\n",
        "utils.create_dataset(num_labels)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:Directory already exists\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "        async function takePhoto(quality) {\n",
              "          const div = document.createElement('div');\n",
              "          const capture = document.createElement('button');\n",
              "          capture.textContent = 'Capture';\n",
              "          div.appendChild(capture);\n",
              "          const video = document.createElement('video');\n",
              "          video.style.display = 'block';\n",
              "          const stream = await navigator.mediaDevices.getUserMedia({video: true});\n",
              "          document.body.appendChild(div);\n",
              "          div.appendChild(video);\n",
              "          video.srcObject = stream;\n",
              "          await video.play();\n",
              "          // Resize the output to fit the video element.\n",
              "          google.colab.output.setIframeHeight(document.documentElement.scrollHeight, true);\n",
              "          // Wait for Capture to be clicked.\n",
              "          await new Promise((resolve) => capture.onclick = resolve);\n",
              "          const canvas = document.createElement('canvas');\n",
              "          canvas.width = video.videoWidth;\n",
              "          canvas.height = video.videoHeight;\n",
              "          canvas.getContext('2d').drawImage(video, 0, 0);\n",
              "          stream.getVideoTracks()[0].stop();\n",
              "          div.remove();\n",
              "          return canvas.toDataURL('image/jpeg', quality);\n",
              "        }\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:Saved to ./dataset/photo_0.png\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "        async function takePhoto(quality) {\n",
              "          const div = document.createElement('div');\n",
              "          const capture = document.createElement('button');\n",
              "          capture.textContent = 'Capture';\n",
              "          div.appendChild(capture);\n",
              "          const video = document.createElement('video');\n",
              "          video.style.display = 'block';\n",
              "          const stream = await navigator.mediaDevices.getUserMedia({video: true});\n",
              "          document.body.appendChild(div);\n",
              "          div.appendChild(video);\n",
              "          video.srcObject = stream;\n",
              "          await video.play();\n",
              "          // Resize the output to fit the video element.\n",
              "          google.colab.output.setIframeHeight(document.documentElement.scrollHeight, true);\n",
              "          // Wait for Capture to be clicked.\n",
              "          await new Promise((resolve) => capture.onclick = resolve);\n",
              "          const canvas = document.createElement('canvas');\n",
              "          canvas.width = video.videoWidth;\n",
              "          canvas.height = video.videoHeight;\n",
              "          canvas.getContext('2d').drawImage(video, 0, 0);\n",
              "          stream.getVideoTracks()[0].stop();\n",
              "          div.remove();\n",
              "          return canvas.toDataURL('image/jpeg', quality);\n",
              "        }\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:Saved to ./dataset/photo_1.png\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "        async function takePhoto(quality) {\n",
              "          const div = document.createElement('div');\n",
              "          const capture = document.createElement('button');\n",
              "          capture.textContent = 'Capture';\n",
              "          div.appendChild(capture);\n",
              "          const video = document.createElement('video');\n",
              "          video.style.display = 'block';\n",
              "          const stream = await navigator.mediaDevices.getUserMedia({video: true});\n",
              "          document.body.appendChild(div);\n",
              "          div.appendChild(video);\n",
              "          video.srcObject = stream;\n",
              "          await video.play();\n",
              "          // Resize the output to fit the video element.\n",
              "          google.colab.output.setIframeHeight(document.documentElement.scrollHeight, true);\n",
              "          // Wait for Capture to be clicked.\n",
              "          await new Promise((resolve) => capture.onclick = resolve);\n",
              "          const canvas = document.createElement('canvas');\n",
              "          canvas.width = video.videoWidth;\n",
              "          canvas.height = video.videoHeight;\n",
              "          canvas.getContext('2d').drawImage(video, 0, 0);\n",
              "          stream.getVideoTracks()[0].stop();\n",
              "          div.remove();\n",
              "          return canvas.toDataURL('image/jpeg', quality);\n",
              "        }\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:Saved to ./dataset/photo_2.png\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "        async function takePhoto(quality) {\n",
              "          const div = document.createElement('div');\n",
              "          const capture = document.createElement('button');\n",
              "          capture.textContent = 'Capture';\n",
              "          div.appendChild(capture);\n",
              "          const video = document.createElement('video');\n",
              "          video.style.display = 'block';\n",
              "          const stream = await navigator.mediaDevices.getUserMedia({video: true});\n",
              "          document.body.appendChild(div);\n",
              "          div.appendChild(video);\n",
              "          video.srcObject = stream;\n",
              "          await video.play();\n",
              "          // Resize the output to fit the video element.\n",
              "          google.colab.output.setIframeHeight(document.documentElement.scrollHeight, true);\n",
              "          // Wait for Capture to be clicked.\n",
              "          await new Promise((resolve) => capture.onclick = resolve);\n",
              "          const canvas = document.createElement('canvas');\n",
              "          canvas.width = video.videoWidth;\n",
              "          canvas.height = video.videoHeight;\n",
              "          canvas.getContext('2d').drawImage(video, 0, 0);\n",
              "          stream.getVideoTracks()[0].stop();\n",
              "          div.remove();\n",
              "          return canvas.toDataURL('image/jpeg', quality);\n",
              "        }\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tqMTf2G0dRdx"
      },
      "source": [
        "### Utilities\n",
        "\n",
        "Creating auxiliary functions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vbS1kLoJdPtq"
      },
      "source": [
        "def load_image_into_numpy_array(path):\n",
        "  img_data = tf.io.gfile.GFile(path, 'rb').read()\n",
        "  image = Image.open(BytesIO(img_data))\n",
        "  (im_width, im_height) = image.size\n",
        "  return np.array(image.getdata()).reshape(\n",
        "      (im_height, im_width, 3)).astype(np.uint8)\n",
        "\n",
        "def plot_detections(image_np,\n",
        "                    boxes,\n",
        "                    classes,\n",
        "                    scores,\n",
        "                    category_index,\n",
        "                    figsize=(12, 16),\n",
        "                    image_name=None):\n",
        "  image_np_with_annotations = image_np.copy()\n",
        "  viz_utils.visualize_boxes_and_labels_on_image_array(\n",
        "      image_np_with_annotations,\n",
        "      boxes,\n",
        "      classes,\n",
        "      scores,\n",
        "      category_index,\n",
        "      use_normalized_coordinates=True,\n",
        "      min_score_thresh=0.8)\n",
        "  if image_name:\n",
        "    plt.imsave(image_name, image_np_with_annotations)\n",
        "  else:\n",
        "    plt.imshow(image_np_with_annotations)\n",
        "\n",
        "# Set up forward + backward pass for a single train step.\n",
        "def get_model_train_step_function(model, optimizer, vars_to_fine_tune):\n",
        "  @tf.function\n",
        "  def train_step_fn(image_tensors,\n",
        "                    groundtruth_boxes_list,\n",
        "                    groundtruth_classes_list):\n",
        "    shapes = tf.constant(batch_size * [[640, 480, 3]], dtype=tf.int32)\n",
        "    model.provide_groundtruth(\n",
        "        groundtruth_boxes_list=groundtruth_boxes_list,\n",
        "        groundtruth_classes_list=groundtruth_classes_list)\n",
        "    with tf.GradientTape() as tape:\n",
        "      preprocessed_images = tf.concat(\n",
        "          [detection_model.preprocess(image_tensor)[0]\n",
        "           for image_tensor in image_tensors], axis=0)\n",
        "      prediction_dict = model.predict(preprocessed_images, shapes)\n",
        "      losses_dict = model.loss(prediction_dict, shapes)\n",
        "      total_loss = losses_dict['Loss/localization_loss'] + losses_dict['Loss/classification_loss']\n",
        "      gradients = tape.gradient(total_loss, vars_to_fine_tune)\n",
        "      optimizer.apply_gradients(zip(gradients, vars_to_fine_tune))\n",
        "    return total_loss\n",
        "  return train_step_fn\n",
        "\n",
        "# Again, uncomment this decorator if you want to run inference eagerly\n",
        "@tf.function\n",
        "def detect(input_tensor):\n",
        "  preprocessed_image, shapes = detection_model.preprocess(input_tensor)\n",
        "  prediction_dict = detection_model.predict(preprocessed_image, shapes)\n",
        "  return detection_model.postprocess(prediction_dict, shapes)\n",
        "\n",
        "# function to convert OpenCV Rectangle bounding box image into base64 byte string to be overlayed on video stream\n",
        "def bbox_to_bytes(bbox_array):\n",
        "  bbox_PIL = PIL.Image.fromarray(bbox_array, 'RGBA')\n",
        "  iobuf = io.BytesIO()\n",
        "  bbox_PIL.save(iobuf, format='png')\n",
        "  bbox_bytes = 'data:image/png;base64,{}'.format((str(b64encode(iobuf.getvalue()), 'utf-8')))\n",
        "\n",
        "  return bbox_bytes"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PbjrUz5sdfLj"
      },
      "source": [
        "### Annotate images with bounding boxes\n",
        "\n",
        "In this cell you will annotate the objects --- draw a box around the object in each image; click next image to go to the next image, and submit when there are no more images."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kyAqunFJdkf6",
        "outputId": "9d6d9ef9-c379-42af-c81b-f3e5b73e7057",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        }
      },
      "source": [
        "train_image_dir = './dataset'\n",
        "train_images_np = []\n",
        "for i in range(num_labels):\n",
        "  image_path = os.path.join(train_image_dir, 'photo_' + str(i) + '.png')\n",
        "  train_images_np.append(load_image_into_numpy_array(image_path))"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNotFoundError\u001b[0m                             Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-6-e6c28d54a7c7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_labels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m   \u001b[0mimage_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_image_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'photo_'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'.png'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m   \u001b[0mtrain_images_np\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mload_image_into_numpy_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-5-5ee02ddae23e>\u001b[0m in \u001b[0;36mload_image_into_numpy_array\u001b[0;34m(path)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mload_image_into_numpy_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m   \u001b[0mimg_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGFile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rb'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m   \u001b[0mimage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m   \u001b[0;34m(\u001b[0m\u001b[0mim_width\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mim_height\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m   return np.array(image.getdata()).reshape(\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/lib/io/file_io.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, n)\u001b[0m\n\u001b[1;32m    115\u001b[0m       \u001b[0mstring\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mstring\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mregular\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    116\u001b[0m     \"\"\"\n\u001b[0;32m--> 117\u001b[0;31m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_preread_check\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    118\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    119\u001b[0m       \u001b[0mlength\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtell\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/lib/io/file_io.py\u001b[0m in \u001b[0;36m_preread_check\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     78\u001b[0m                                            \"File isn't open for reading\")\n\u001b[1;32m     79\u001b[0m       self._read_buf = _pywrap_file_io.BufferedInputStream(\n\u001b[0;32m---> 80\u001b[0;31m           compat.path_to_str(self.__name), 1024 * 512)\n\u001b[0m\u001b[1;32m     81\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     82\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_prewrite_check\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNotFoundError\u001b[0m: ./dataset/photo_1.png; No such file or directory"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LGc7jeWEdaFD"
      },
      "source": [
        "gt_boxes = []\n",
        "colab_utils.annotate(train_images_np, box_storage_pointer=gt_boxes)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9y1GcwqJeMy1"
      },
      "source": [
        "### Prepare date for training\n",
        "\n",
        "Below we add the class annotations (for simplicity, we assume a single class in this colab; though it should be straightforward to extend this to handle multiple classes). We also convert everything to the format that the training loop below expects (e.g., everything converted to tensors, classes converted to one-hot representations, etc.)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s-HnLiYYd7xv"
      },
      "source": [
        "'''\n",
        "By convention, our non-background classes start counting at 1.  Given\n",
        "that we will be predicting just one class, we will therefore assign it a\n",
        "`class id` of 1.\n",
        "'''\n",
        "\n",
        "class_id = 1\n",
        "num_classes = 1\n",
        "class_name = '<YOUR OBJECT>'\n",
        "category_index = {class_id: {'id': class_id, 'name': class_name}}\n",
        "class_colors = {}\n",
        "class_colors[class_name] = (102, 255, 51)\n",
        "\n",
        "'''\n",
        "Convert class labels to one-hot; convert everything to tensors.\n",
        "The `label_id_offset` here shifts all classes by a certain number of indices;\n",
        "we do this here so that the model receives one-hot labels where non-background\n",
        "classes start counting at the zeroth index.  This is ordinarily just handled\n",
        "automatically in our training binaries, but we need to reproduce it here.\n",
        "'''\n",
        "\n",
        "label_id_offset = 1\n",
        "train_image_tensors = []\n",
        "gt_classes_one_hot_tensors = []\n",
        "gt_box_tensors = []\n",
        "for (train_image_np, gt_box_np) in zip(\n",
        "    train_images_np, gt_boxes):\n",
        "  train_image_tensors.append(tf.expand_dims(tf.convert_to_tensor(\n",
        "      train_image_np, dtype=tf.float32), axis=0))\n",
        "  gt_box_tensors.append(tf.convert_to_tensor(gt_box_np, dtype=tf.float32))\n",
        "  zero_indexed_groundtruth_classes = tf.convert_to_tensor(\n",
        "      np.ones(shape=[gt_box_np.shape[0]], dtype=np.int32) - label_id_offset)\n",
        "  gt_classes_one_hot_tensors.append(tf.one_hot(\n",
        "      zero_indexed_groundtruth_classes, num_classes))\n",
        "print('Done prepping data.')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6J0OsL5ifuD_"
      },
      "source": [
        "## Training the model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wH2O-uocfx7b"
      },
      "source": [
        "### Create model and restore weights for all but last layer\n",
        "\n",
        "In this cell we build a single stage detection architecture (RetinaNet) and restore all but the classification layer at the top (which will be automatically randomly initialized).\n",
        "\n",
        "For simplicity, we have hardcoded a number of things in this colab for the specific RetinaNet architecture (including assuming that the image size will always be 640x640), however it is not difficult to generalize to other model configurations."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zJoRr7dmfvfp"
      },
      "source": [
        "# Download the checkpoint and put it into models/research/object_detection/test_data/\n",
        "\n",
        "!wget http://download.tensorflow.org/models/object_detection/tf2/20200711/ssd_resnet50_v1_fpn_640x640_coco17_tpu-8.tar.gz\n",
        "!tar -xf ssd_resnet50_v1_fpn_640x640_coco17_tpu-8.tar.gz\n",
        "!mv ssd_resnet50_v1_fpn_640x640_coco17_tpu-8/checkpoint models/research/object_detection/test_data/"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BmqUc_9Rf5yH"
      },
      "source": [
        "tf.keras.backend.clear_session()\n",
        "\n",
        "print('Building model and restoring weights for fine-tuning...', flush=True)\n",
        "num_classes = 1\n",
        "pipeline_config = 'models/research/object_detection/configs/tf2/ssd_resnet50_v1_fpn_640x640_coco17_tpu-8.config'\n",
        "checkpoint_path = 'models/research/object_detection/test_data/checkpoint/ckpt-0'\n",
        "\n",
        "'''\n",
        "Load pipeline config and build a detection model.\n",
        "\n",
        "Since we are working off of a COCO architecture which predicts 90\n",
        "class slots by default, we override the `num_classes` field here to be just\n",
        "one (for our new object).\n",
        "'''\n",
        "\n",
        "configs = config_util.get_configs_from_pipeline_file(pipeline_config)\n",
        "model_config = configs['model']\n",
        "model_config.ssd.num_classes = num_classes\n",
        "model_config.ssd.freeze_batchnorm = True\n",
        "detection_model = model_builder.build(\n",
        "      model_config=model_config, is_training=True)\n",
        "\n",
        "'''\n",
        "Set up object-based checkpoint restore --- RetinaNet has two prediction\n",
        "`heads` --- one for classification, the other for box regression.  We will\n",
        "restore the box regression head but initialize the classification head\n",
        "from scratch.\n",
        "'''\n",
        "\n",
        "fake_box_predictor = tf.compat.v2.train.Checkpoint(\n",
        "    _base_tower_layers_for_heads=detection_model._box_predictor._base_tower_layers_for_heads,\n",
        "    _box_prediction_head=detection_model._box_predictor._box_prediction_head,\n",
        "    )\n",
        "fake_model = tf.compat.v2.train.Checkpoint(\n",
        "          _feature_extractor=detection_model._feature_extractor,\n",
        "          _box_predictor=fake_box_predictor)\n",
        "ckpt = tf.compat.v2.train.Checkpoint(model=fake_model)\n",
        "ckpt.restore(checkpoint_path).expect_partial()\n",
        "\n",
        "# Run model through a dummy image so that variables are created\n",
        "image, shapes = detection_model.preprocess(tf.zeros([1, 640, 640, 3]))\n",
        "prediction_dict = detection_model.predict(image, shapes)\n",
        "_ = detection_model.postprocess(prediction_dict, shapes)\n",
        "print('Weights restored!')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PLgFpy07gL9n"
      },
      "source": [
        "### Eager mode custom training loop"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i1rkFhieNalz"
      },
      "source": [
        "tf.keras.backend.set_learning_phase(True)\n",
        "\n",
        "'''\n",
        "These parameters can be tuned; since our training set has 10 images\n",
        "it doesn't make sense to have a much larger batch size, though we could\n",
        "fit more examples in memory if we wanted to.\n",
        "'''\n",
        "batch_size = 4\n",
        "learning_rate = 0.01\n",
        "num_iterations = 120\n",
        "\n",
        "# Select variables in top layers to fine-tune.\n",
        "trainable_variables = detection_model.trainable_variables\n",
        "to_fine_tune = []\n",
        "prefixes_to_train = [\n",
        "  'WeightSharedConvolutionalBoxPredictor/WeightSharedConvolutionalBoxHead',\n",
        "  'WeightSharedConvolutionalBoxPredictor/WeightSharedConvolutionalClassHead']\n",
        "for var in trainable_variables:\n",
        "  if any([var.name.startswith(prefix) for prefix in prefixes_to_train]):\n",
        "    to_fine_tune.append(var)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-it000x7f8wj"
      },
      "source": [
        "optimizer = tf.keras.optimizers.SGD(learning_rate=learning_rate, momentum=0.9)\n",
        "train_step_fn = get_model_train_step_function(\n",
        "    detection_model, optimizer, to_fine_tune)\n",
        "\n",
        "print('Starting the training...', flush=True)\n",
        "pp = ProgressPlot(plot_names=['loss'], line_names=['loss'], x_label=\"iteration\")\n",
        "\n",
        "\n",
        "for idx in range(num_iterations):\n",
        "  # Grab keys for a random subset of examples\n",
        "  all_keys = list(range(len(train_images_np)))\n",
        "  random.shuffle(all_keys)\n",
        "  example_keys = all_keys[:batch_size]\n",
        "  \n",
        "  '''\n",
        "  Note that we do not do data augmentation in this demo.  If you want a\n",
        "  a fun exercise, we recommend experimenting with random horizontal flipping\n",
        "  and random cropping :)\n",
        "  '''\n",
        "\n",
        "  gt_boxes_list = [gt_box_tensors[key] for key in example_keys]\n",
        "  gt_classes_list = [gt_classes_one_hot_tensors[key] for key in example_keys]\n",
        "  image_tensors = [train_image_tensors[key] for key in example_keys]\n",
        "\n",
        "  # Training step (forward pass + backwards pass)\n",
        "  total_loss = train_step_fn(image_tensors, gt_boxes_list, gt_classes_list)\n",
        "\n",
        "  pp.update(float(total_loss.numpy()))\n",
        "\n",
        "print('The training is done!')\n",
        "pp.finalize()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eIuMdOo_kI5M"
      },
      "source": [
        "## RetinaNet on Webcam Videos"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uYDg-XEBs4gm"
      },
      "source": [
        "Running RetinaNet on webcam video is a little more complex than images. We need to start a video stream using our webcam as input. Then we run each frame through our RetinaNet model and create an overlay image that contains bounding box of detection(s). We then overlay the bounding box image back onto the next frame of our video stream. \n",
        "\n",
        "\n",
        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)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I0LJN1QKMAwm"
      },
      "source": [
        "# start streaming video from webcam\n",
        "utils.video_stream()\n",
        "# label for video\n",
        "label_html = 'Capturing...'\n",
        "# initialze bounding box to empty\n",
        "bbox = ''\n",
        "count = 0 \n",
        "while True:\n",
        "    js_reply = utils.video_frame(label_html, bbox)\n",
        "    if not js_reply:\n",
        "        break\n",
        "\n",
        "    # convert JS response to OpenCV Image\n",
        "    frame = utils.js_to_image(js_reply[\"img\"])\n",
        "    \n",
        "    input_tensor = tf.convert_to_tensor(np.expand_dims(frame, axis=0), dtype=tf.float32)\n",
        "    detections = detect(input_tensor)\n",
        "\n",
        "    image_np_with_annotations = np.zeros([480,640,3], dtype=np.uint8).copy()\n",
        "    viz_utils.visualize_boxes_and_labels_on_image_array(\n",
        "      image_np_with_annotations,\n",
        "      detections['detection_boxes'][0].numpy(),\n",
        "      detections['detection_classes'][0].numpy().astype(np.uint32) \n",
        "        + label_id_offset,\n",
        "      detections['detection_scores'][0].numpy(),\n",
        "      category_index,\n",
        "      use_normalized_coordinates=True,\n",
        "      min_score_thresh=0.8)\n",
        "    bbox_array = np.zeros([480,640,4], dtype=np.uint8)\n",
        "    bbox_array[:,:,3] = \\\n",
        "              (image_np_with_annotations.max(axis = 2) > 0 ).astype(int) * 255\n",
        "    # convert overlay of bbox into bytes\n",
        "    bbox_bytes = bbox_to_bytes(bbox_array)\n",
        "    # update bbox so next frame gets new overlay\n",
        "    bbox = bbox_bytes"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "trEyRLsC7Fox"
      },
      "source": [
        "## References\n",
        "\n",
        "\n",
        "[0] Lin, Tsung-Yi, et al. \"Focal loss for dense object detection.\" Proceedings of the IEEE international conference on computer vision. 2017.\n",
        "\n",
        "---\n",
        "\n",
        "[1] Rathod, Vivek, and Kmindspark. “Eager Few Shot Object Detection Colab.” GitHub, Tensorflow, 11 July 2020, [github.com/tensorflow/models/blob/master/research/object_detection/colab_tutorials/eager_few_shot_od_training_tf2_colab.ipynb](https://github.com/tensorflow/models/blob/master/research/object_detection/colab_tutorials/eager_few_shot_od_training_tf2_colab.ipynb). \n",
        "\n",
        "---\n",
        "\n",
        "[2] Tsang, Sik-Ho. “Review: RetinaNet - Focal Loss (Object Detection).” Medium, Towards Data Science, 26 Mar. 2019, towardsdatascience.com/review-retinanet-focal-loss-object-detection-38fba6afabe4. \n",
        "\n",
        "---\n",
        "\n",
        "[3] Humbarwadi, rihari. “Keras Documentation: Object Detection with RetinaNet.” Keras, 17 May 2020, [keras.io/examples/vision/retinanet/](https://keras.io/examples/vision/retinanet/). \n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    }
  ]
}