Object_Detection_version2_0_ipynb.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "“Object-Detection_version1.0.ipynb”的副本",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/wyhpolyu2020/ObjectDetection/blob/main/Object_Detection_version2_0_ipynb.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l7EOtpvlLeS0"
      },
      "source": [
        "# Install TensorFlow2 Object Detection Dependencies"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uhItym94lJRL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "90700f6a-3bb7-4410-ff93-14e958ece4d3"
      },
      "source": [
        "\n",
        "from google.colab import drive\n",
        "\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BdO0G7rwwrUj"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ypWGYdPlLRUN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e60ae6cc-b16c-4579-a58a-6e735ff2a0e3"
      },
      "source": [
        "import os\n",
        "import pathlib\n",
        "\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": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'models'...\n",
            "remote: Enumerating objects: 2697, done.\u001b[K\n",
            "remote: Counting objects: 100% (2697/2697), done.\u001b[K\n",
            "remote: Compressing objects: 100% (2238/2238), done.\u001b[K\n",
            "remote: Total 2697 (delta 684), reused 1231 (delta 425), pack-reused 0\u001b[K\n",
            "Receiving objects: 100% (2697/2697), 32.68 MiB | 33.84 MiB/s, done.\n",
            "Resolving deltas: 100% (684/684), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6QPmVBSlLTzM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2d0ed606-bbee-4834-f2d8-7beafb5fb087"
      },
      "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 ."
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Processing /content/models/research\n",
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            "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.5->tensorflow>=2.5.0->tf-models-official->object-detection==0.1) (1.8.0)\n",
            "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.5->tensorflow>=2.5.0->tf-models-official->object-detection==0.1) (0.6.1)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.5->tensorflow>=2.5.0->tf-models-official->object-detection==0.1) (1.0.1)\n",
            "Requirement already satisfied: cached-property; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from h5py~=3.1.0->tensorflow>=2.5.0->tf-models-official->object-detection==0.1) (1.5.2)\n",
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            "Requirement already satisfied: zipp>=3.1.0; python_version < \"3.10\" in /usr/local/lib/python3.7/dist-packages (from importlib-resources; python_version < \"3.9\"->tensorflow-datasets->tf-models-official->object-detection==0.1) (3.4.1)\n",
            "Requirement already satisfied: packaging>=14.3 in /usr/local/lib/python3.7/dist-packages (from google-api-core<2dev,>=1.21.0->google-api-python-client>=1.6.7->tf-models-official->object-detection==0.1) (20.9)\n",
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            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.5->tensorflow>=2.5.0->tf-models-official->object-detection==0.1) (1.3.0)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.5->tensorflow>=2.5.0->tf-models-official->object-detection==0.1) (3.1.1)\n",
            "Building wheels for collected packages: object-detection, avro-python3, dill, future, seqeval, py-cpuinfo\n",
            "  Building wheel for object-detection (setup.py): started\n",
            "  Building wheel for object-detection (setup.py): finished with status 'done'\n",
            "  Created wheel for object-detection: filename=object_detection-0.1-cp37-none-any.whl size=1654815 sha256=06670c139c2a8d4974324ae035d71baed39c2f8a76f73036628d933764ae24cc\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-i327stqg/wheels/94/49/4b/39b051683087a22ef7e80ec52152a27249d1a644ccf4e442ea\n",
            "  Building wheel for avro-python3 (setup.py): started\n",
            "  Building wheel for avro-python3 (setup.py): finished with status 'done'\n",
            "  Created wheel for avro-python3: filename=avro_python3-1.10.2-cp37-none-any.whl size=44011 sha256=a8476a18547bd1edc9e1b7c8652b5817d2717e99c66da410e5d98d8e036c4093\n",
            "  Stored in directory: /root/.cache/pip/wheels/ee/ee/18/c466221ca6900e3efce2f4ea9c329288808679aecdcb2838d3\n",
            "  Building wheel for dill (setup.py): started\n",
            "  Building wheel for dill (setup.py): finished with status 'done'\n",
            "  Created wheel for dill: filename=dill-0.3.1.1-cp37-none-any.whl size=78545 sha256=3cccb7711c508237931abaa2d82dea85add57e86400100f839f144762c49e16f\n",
            "  Stored in directory: /root/.cache/pip/wheels/59/b1/91/f02e76c732915c4015ab4010f3015469866c1eb9b14058d8e7\n",
            "  Building wheel for future (setup.py): started\n",
            "  Building wheel for future (setup.py): finished with status 'done'\n",
            "  Created wheel for future: filename=future-0.18.2-cp37-none-any.whl size=491070 sha256=70d37e768f83b63477e4d8fe12fa113fa6e771d195ce81accf1e8078366023ed\n",
            "  Stored in directory: /root/.cache/pip/wheels/8b/99/a0/81daf51dcd359a9377b110a8a886b3895921802d2fc1b2397e\n",
            "  Building wheel for seqeval (setup.py): started\n",
            "  Building wheel for seqeval (setup.py): finished with status 'done'\n",
            "  Created wheel for seqeval: filename=seqeval-1.2.2-cp37-none-any.whl size=16184 sha256=33f61211e076347519bb5ec2726ca14ffb400fe545382a20fafc3d3ad24cc2cb\n",
            "  Stored in directory: /root/.cache/pip/wheels/52/df/1b/45d75646c37428f7e626214704a0e35bd3cfc32eda37e59e5f\n",
            "  Building wheel for py-cpuinfo (setup.py): started\n",
            "  Building wheel for py-cpuinfo (setup.py): finished with status 'done'\n",
            "  Created wheel for py-cpuinfo: filename=py_cpuinfo-8.0.0-cp37-none-any.whl size=22258 sha256=faa266e63a78b1b2f46ff440ffa5764677fd55cf681d489c3517259f20a93046\n",
            "  Stored in directory: /root/.cache/pip/wheels/2e/15/f5/aa2a056d223903b52cf4870134e3a01df0c723816835dd08db\n",
            "Successfully built object-detection avro-python3 dill future seqeval py-cpuinfo\n",
            "Installing collected packages: avro-python3, dill, fastavro, future, requests, hdfs, apache-beam, tf-slim, lvis, seqeval, tensorflow-addons, opencv-python-headless, sentencepiece, tensorflow-model-optimization, py-cpuinfo, pyyaml, portalocker, sacrebleu, tf-models-official, object-detection\n",
            "  Found existing installation: dill 0.3.3\n",
            "    Uninstalling dill-0.3.3:\n",
            "      Successfully uninstalled dill-0.3.3\n",
            "  Found existing installation: future 0.16.0\n",
            "    Uninstalling future-0.16.0:\n",
            "      Successfully uninstalled future-0.16.0\n",
            "  Found existing installation: requests 2.23.0\n",
            "    Uninstalling requests-2.23.0:\n",
            "      Successfully uninstalled requests-2.23.0\n",
            "  Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
            "Successfully installed apache-beam-2.30.0 avro-python3-1.10.2 dill-0.3.1.1 fastavro-1.4.1 future-0.18.2 hdfs-2.6.0 lvis-0.5.3 object-detection-0.1 opencv-python-headless-4.5.2.54 portalocker-2.0.0 py-cpuinfo-8.0.0 pyyaml-5.4.1 requests-2.25.1 sacrebleu-1.5.1 sentencepiece-0.1.95 seqeval-1.2.2 tensorflow-addons-0.13.0 tensorflow-model-optimization-0.5.0 tf-models-official-2.5.0 tf-slim-1.1.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "ERROR: multiprocess 0.70.11.1 has requirement dill>=0.3.3, but you'll have dill 0.3.1.1 which is incompatible.\n",
            "ERROR: google-colab 1.0.0 has requirement requests~=2.23.0, but you'll have requests 2.25.1 which is incompatible.\n",
            "ERROR: datascience 0.10.6 has requirement folium==0.2.1, but you'll have folium 0.8.3 which is incompatible.\n",
            "ERROR: apache-beam 2.30.0 has requirement avro-python3!=1.9.2,<1.10.0,>=1.8.1, but you'll have avro-python3 1.10.2 which is incompatible.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wHfsJ5nWLWh9"
      },
      "source": [
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\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",
        "from IPython.display import Image as IPyImage\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",
        "%matplotlib inline"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7Vz2vJeCCyZR"
      },
      "source": [
        "# Run Inference on Test Images with Custom TensorFlow2 Object Detector"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xxtm1NutE5vK"
      },
      "source": [
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import io\n",
        "import scipy.misc\n",
        "import numpy as np\n",
        "from six import BytesIO\n",
        "from PIL import Image, ImageDraw, ImageFont\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.builders import model_builder\n",
        "\n",
        "%matplotlib inline"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qs1HJnEhyevJ"
      },
      "source": [
        "def load_image_into_numpy_array(path):\n",
        "  \"\"\"Load an image from file into a numpy array.\n",
        "\n",
        "  Puts image into numpy array to feed into tensorflow graph.\n",
        "  Note that by convention we put it into a numpy array with shape\n",
        "  (height, width, channels), where channels=3 for RGB.\n",
        "\n",
        "  Args:\n",
        "    path: the file path to the image\n",
        "\n",
        "  Returns:\n",
        "    uint8 numpy array with shape (img_height, img_width, 3)\n",
        "  \"\"\"\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)"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0f6DTolSDfXs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "670e61d3-aaf6-4f4f-b90d-3139d4b12e3b"
      },
      "source": [
        "%ls '/content/drive/MyDrive/content/training/'"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "checkpoint                  ckpt-2.data-00000-of-00001  ckpt-3.index\n",
            "ckpt-1.data-00000-of-00001  ckpt-2.index                \u001b[0m\u001b[01;34mtrain\u001b[0m/\n",
            "ckpt-1.index                ckpt-3.data-00000-of-00001\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gFY75DfTDHaU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "887a3fb0-4258-47fe-e4f7-ced54409ae06"
      },
      "source": [
        "import pathlib\n",
        "pipeline_file = '/content/drive/MyDrive/content/models/research/deploy/pipeline_file.config'\n",
        "filenames = list(pathlib.Path('/content/drive/MyDrive/content/training/').glob('*.index'))\n",
        "\n",
        "filenames.sort()\n",
        "print(filenames)\n",
        "\n",
        "#recover our saved model\n",
        "pipeline_config = pipeline_file\n",
        "#generally you want to put the last ckpt from training in here\n",
        "model_dir = str(filenames[-1]).replace('.index','')\n",
        "configs = config_util.get_configs_from_pipeline_file(pipeline_config)\n",
        "model_config = configs['model']\n",
        "detection_model = model_builder.build(\n",
        "      model_config=model_config, is_training=False)\n",
        "\n",
        "# Restore checkpoint\n",
        "ckpt = tf.compat.v2.train.Checkpoint(\n",
        "      model=detection_model)\n",
        "ckpt.restore(os.path.join(str(filenames[-1]).replace('.index','')))\n",
        "\n",
        "\n",
        "def get_model_detection_function(model):\n",
        "  \"\"\"Get a tf.function for detection.\"\"\"\n",
        "\n",
        "  @tf.function\n",
        "  def detect_fn(image):\n",
        "    \"\"\"Detect objects in image.\"\"\"\n",
        "\n",
        "    image, shapes = model.preprocess(image)\n",
        "    prediction_dict = model.predict(image, shapes)\n",
        "    detections = model.postprocess(prediction_dict, shapes)\n",
        "\n",
        "    return detections, prediction_dict, tf.reshape(shapes, [-1])\n",
        "\n",
        "  return detect_fn\n",
        "\n",
        "detect_fn = get_model_detection_function(detection_model)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[PosixPath('/content/drive/MyDrive/content/training/ckpt-1.index'), PosixPath('/content/drive/MyDrive/content/training/ckpt-2.index'), PosixPath('/content/drive/MyDrive/content/training/ckpt-3.index')]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wiqZAJRTrdqB",
        "outputId": "e4eb2e19-c34b-474d-83fa-ca4017d195bd"
      },
      "source": [
        "%ls '/content/drive/MyDrive/content/training/'"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "checkpoint                  ckpt-2.data-00000-of-00001  ckpt-3.index\n",
            "ckpt-1.data-00000-of-00001  ckpt-2.index                \u001b[0m\u001b[01;34mtrain\u001b[0m/\n",
            "ckpt-1.index                ckpt-3.data-00000-of-00001\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Ycfl7rnDT1D"
      },
      "source": [
        "#map labels for inference decoding\n",
        "#label_map_path = configs['eval_input_config'].label_map_path\n",
        "label_map_path = '/content/drive/MyDrive/content/train/1_label_map.pbtxt'\n",
        "label_map = label_map_util.load_labelmap(label_map_path)\n",
        "categories = label_map_util.convert_label_map_to_categories(\n",
        "    label_map,\n",
        "    max_num_classes=label_map_util.get_max_label_map_index(label_map),\n",
        "    use_display_name=True)\n",
        "category_index = label_map_util.create_category_index(categories)\n",
        "label_map_dict = label_map_util.get_label_map_dict(label_map, use_display_name=True)"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dDe0TCUDyVrr"
      },
      "source": [
        ""
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dk_7dM9PyYWk"
      },
      "source": [
        "from IPython.display import display, Javascript\n",
        "from google.colab.output import eval_js\n",
        "from base64 import b64decode\n",
        "\n",
        "def take_photo(filename='photo.jpg', quality=0.8):\n",
        "  js = 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",
        "\n",
        "      const video = document.createElement('video');\n",
        "      video.style.display = 'block';\n",
        "      const stream = await navigator.mediaDevices.getUserMedia({video: true});\n",
        "\n",
        "      document.body.appendChild(div);\n",
        "      div.appendChild(video);\n",
        "      video.srcObject = stream;\n",
        "      await video.play();\n",
        "\n",
        "      // Resize the output to fit the video element.\n",
        "      google.colab.output.setIframeHeight(document.documentElement.scrollHeight, true);\n",
        "\n",
        "      // Wait for Capture to be clicked.\n",
        "      await new Promise((resolve) => capture.onclick = resolve);\n",
        "\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",
        "    ''')\n",
        "  display(js)\n",
        "  data = eval_js('takePhoto({})'.format(quality))\n",
        "  binary = b64decode(data.split(',')[1])\n",
        "  with open(filename, 'wb') as f:\n",
        "    f.write(binary)\n",
        "  return filename"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "PZUzlS71yYWl",
        "outputId": "81c08e92-fc20-4327-8cca-124e3c9d326f"
      },
      "source": [
        "from IPython.display import Image\n",
        "try:\n",
        "  filename = take_photo()\n",
        "  print('Saved to {}'.format(filename))\n",
        "  \n",
        "  # Show the image which was just taken.\n",
        "  display(Image(filename))\n",
        "except Exception as err:\n",
        "  # Errors will be thrown if the user does not have a webcam or if they do not\n",
        "  # grant the page permission to access it.\n",
        "  print(str(err))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "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",
              "\n",
              "      const video = document.createElement('video');\n",
              "      video.style.display = 'block';\n",
              "      const stream = await navigator.mediaDevices.getUserMedia({video: true});\n",
              "\n",
              "      document.body.appendChild(div);\n",
              "      div.appendChild(video);\n",
              "      video.srcObject = stream;\n",
              "      await video.play();\n",
              "\n",
              "      // Resize the output to fit the video element.\n",
              "      google.colab.output.setIframeHeight(document.documentElement.scrollHeight, true);\n",
              "\n",
              "      // Wait for Capture to be clicked.\n",
              "      await new Promise((resolve) => capture.onclick = resolve);\n",
              "\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": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wN1BzORoIzV4"
      },
      "source": [
        "#run detector on test image\n",
        "#it takes a little longer on the first run and then runs at normal speed. \n",
        "import random\n",
        "\n",
        "TEST_IMAGE_PATHS = glob.glob('/content/drive/MyDrive/content/images/*.jpg')\n",
        "image_path = random.choice(TEST_IMAGE_PATHS)\n",
        "image_np = load_image_into_numpy_array(filename)\n",
        "\n",
        "# Things to try:\n",
        "# Flip horizontally\n",
        "#image_np = np.fliplr(image_np).copy()\n",
        "\n",
        "# Convert image to grayscale\n",
        "# image_np = np.tile(\n",
        "#     np.mean(image_np, 2, keepdims=True), (1, 1, 3)).astype(np.uint8)\n",
        "\n",
        "input_tensor = tf.convert_to_tensor(\n",
        "    np.expand_dims(image_np, 0), dtype=tf.float32)\n",
        "detections, predictions_dict, shapes = detect_fn(input_tensor)\n",
        "\n",
        "label_id_offset = 1\n",
        "image_np_with_detections = image_np.copy()\n",
        "\n",
        "viz_utils.visualize_boxes_and_labels_on_image_array(\n",
        "      image_np_with_detections,\n",
        "      detections['detection_boxes'][0].numpy(),\n",
        "      (detections['detection_classes'][0].numpy() + label_id_offset).astype(int),\n",
        "      detections['detection_scores'][0].numpy(),\n",
        "      category_index,\n",
        "      use_normalized_coordinates=True,\n",
        "      max_boxes_to_draw=200,\n",
        "      min_score_thresh=.5,\n",
        "      agnostic_mode=False,\n",
        "      line_thickness=5,\n",
        "\n",
        ")\n",
        "\n",
        "plt.figure(figsize=(12,16))\n",
        "plt.imshow(image_np_with_detections)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zQ-N94cKB82o"
      },
      "source": [
        "## Congrats!\n",
        "\n",
        "Hope you enjoyed this!\n",
        "\n",
        "--Team [Roboflow](https://roboflow.ai)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ny2d3qp_sXn9"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}