btp_cnn_model1.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "btp_cnn_model1.ipynb",
      "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/KD-3/DeepLearningMovidiusNCS/blob/master/btp_cnn_model1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EhmQBpPwNo_x",
        "colab_type": "code",
        "outputId": "3d7e2992-ba6b-4d66-f192-bd49c198ed59",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/gdrive')\n",
        "%cd /gdrive"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /gdrive; to attempt to forcibly remount, call drive.mount(\"/gdrive\", force_remount=True).\n",
            "/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N86e16hvPOwC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!unzip \"/gdrive/My Drive/Colab Notebooks/NEU-DET.zip\" -d /content"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MLYLa-5sPqWN",
        "colab_type": "code",
        "outputId": "19f393a1-9a58-4a2a-f6cf-5eba4693d2da",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "import cv2\n",
        "import tensorflow as tf\n",
        "\n",
        "\n",
        "DATADIR_IMAGES = \"/content/NEU-DET/IMAGES\"\n",
        "DATADIR_ANNOTATIONS = \"/content/NEU-DET/ANNOTATIONS\"\n",
        "print(DATADIR_ANNOTATIONS)\n",
        "for img in os.listdir(\"/content/NEU-DET\"):\n",
        "    print(os.path.join(\"/content/NEU-DET\", img))\n",
        "    print(os.path.join(DATADIR_ANNOTATIONS, img))\n",
        "    break"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/NEU-DET/ANNOTATIONS\n",
            "/content/NEU-DET/IMAGES\n",
            "/content/NEU-DET/ANNOTATIONS/IMAGES\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ua-LdlflUmkQ",
        "colab_type": "code",
        "outputId": "8c63c165-3bc5-4f45-868d-c3aab971f82c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        }
      },
      "source": [
        "  for img in os.listdir(DATADIR_IMAGES):\n",
        "    print(os.path.join(DATADIR_IMAGES, img))\n",
        "    img_array = cv2.imread(os.path.join(DATADIR_IMAGES, img), cv2.IMREAD_GRAYSCALE)\n",
        "    plt.imshow(img_array, cmap=\"gray\")\n",
        "    plt.show()\n",
        "    break"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/NEU-DET/IMAGES/inclusion_11.jpg\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oF0-D135bg_i",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from xml.etree import ElementTree\n",
        "\n",
        "# function to extract bounding boxes from an annotation file\n",
        "def extract_boxes(filename):\n",
        "  tree = ElementTree.parse(filename)\n",
        "  root = tree.getroot()\n",
        "  # extract each bounding box\n",
        "  boxes = list()\n",
        "  for box in root.findall('.//bndbox'):\n",
        "    xmin = int(box.find('xmin').text)\n",
        "    ymin = int(box.find('ymin').text)\n",
        "    xmax = int(box.find('xmax').text)\n",
        "    ymax = int(box.find('ymax').text)\n",
        "    coors = [xmin, ymin, xmax, ymax]\n",
        "    boxes.append(coors)\n",
        "  # extract image dimensions\n",
        "  width = int(root.find('.//size/width').text)\n",
        "  height = int(root.find('.//size/height').text)\n",
        "  return boxes, width, height"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rtv0r8eww7VL",
        "colab_type": "code",
        "outputId": "c19076ab-9378-45e8-b35f-144d885508ee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "  for img in os.listdir(DATADIR_ANNOTATIONS):\n",
        "    print(os.path.join(DATADIR_ANNOTATIONS, img))\n",
        "    boxes, w, h = extract_boxes(os.path.join(DATADIR_ANNOTATIONS, img))\n",
        "    print(boxes, w, h)\n",
        "    break\n",
        "\n",
        "  training_data = []"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/NEU-DET/ANNOTATIONS/crazing_158.xml\n",
            "[[51, 57, 125, 135]] 200 200\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cTesscveu-57",
        "colab_type": "code",
        "outputId": "e37b63fb-26d2-4fda-d92f-8bb68070180b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "!pip install opencv-python==4.1.2.30\n",
        "!pip install keras==2.3.1\n",
        "%tensorflow_version 1.x\n",
        "!pip install imageai --upgrade #necessary"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: opencv-python==4.1.2.30 in /usr/local/lib/python3.6/dist-packages (4.1.2.30)\n",
            "Requirement already satisfied: numpy>=1.11.3 in /usr/local/lib/python3.6/dist-packages (from opencv-python==4.1.2.30) (1.18.4)\n",
            "Requirement already satisfied: keras==2.3.1 in /usr/local/lib/python3.6/dist-packages (2.3.1)\n",
            "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from keras==2.3.1) (1.1.2)\n",
            "Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.6/dist-packages (from keras==2.3.1) (1.12.0)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.6/dist-packages (from keras==2.3.1) (3.13)\n",
            "Requirement already satisfied: keras-applications>=1.0.6 in /usr/local/lib/python3.6/dist-packages (from keras==2.3.1) (1.0.8)\n",
            "Requirement already satisfied: scipy>=0.14 in /usr/local/lib/python3.6/dist-packages (from keras==2.3.1) (1.4.1)\n",
            "Requirement already satisfied: numpy>=1.9.1 in /usr/local/lib/python3.6/dist-packages (from keras==2.3.1) (1.18.4)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras==2.3.1) (2.10.0)\n",
            "TensorFlow is already loaded. Please restart the runtime to change versions.\n",
            "Requirement already up-to-date: imageai in /usr/local/lib/python3.6/dist-packages (2.1.5)\n",
            "Requirement already satisfied, skipping upgrade: pillow in /usr/local/lib/python3.6/dist-packages (from imageai) (7.0.0)\n",
            "Requirement already satisfied, skipping upgrade: numpy in /usr/local/lib/python3.6/dist-packages (from imageai) (1.18.4)\n",
            "Requirement already satisfied, skipping upgrade: matplotlib in /usr/local/lib/python3.6/dist-packages (from imageai) (3.2.1)\n",
            "Requirement already satisfied, skipping upgrade: h5py in /usr/local/lib/python3.6/dist-packages (from imageai) (2.10.0)\n",
            "Requirement already satisfied, skipping upgrade: scipy in /usr/local/lib/python3.6/dist-packages (from imageai) (1.4.1)\n",
            "Requirement already satisfied, skipping upgrade: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->imageai) (2.8.1)\n",
            "Requirement already satisfied, skipping upgrade: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->imageai) (2.4.7)\n",
            "Requirement already satisfied, skipping upgrade: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib->imageai) (0.10.0)\n",
            "Requirement already satisfied, skipping upgrade: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->imageai) (1.2.0)\n",
            "Requirement already satisfied, skipping upgrade: six in /usr/local/lib/python3.6/dist-packages (from h5py->imageai) (1.12.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H-RCBXsoxRUb",
        "colab_type": "code",
        "outputId": "f0b831d3-165e-40c8-d30b-091647946f7d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 471
        }
      },
      "source": [
        "import tensorflow\n",
        "print(tensorflow.__version__)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1.14.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CYl0HauDOWB7",
        "colab_type": "code",
        "outputId": "16c247d8-7bc9-44ec-c4d0-4b9668fa1492",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "from imageai.Detection.Custom import DetectionModelTrainer"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5574-_UOwhmk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "trainer = DetectionModelTrainer()\n",
        "trainer.setModelTypeAsYOLOv3()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IZQvsMx5wpc4",
        "colab_type": "code",
        "outputId": "b153e340-5387-4311-808b-ab6884b093eb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        }
      },
      "source": [
        "trainer.setDataDirectory(data_directory=\"/gdrive/My Drive/NEU-DET/NEU-DET\")\n",
        "trainer.setTrainConfig(object_names_array=[\"crazing\", \"inclusion\", \"patches\", \"pitted_surface\", \"rolled-in_scale\", \"scratches\"], batch_size=4, num_experiments=100, train_from_pretrained_model=\"/gdrive/My Drive/NEU-DET/NEU-DET/models/pretrained-yolov3.h5\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Generating anchor boxes for training images and annotation...\n",
            "Average IOU for 9 anchors: 0.68\n",
            "Anchor Boxes generated.\n",
            "Detection configuration saved in  /gdrive/My Drive/NEU-DET/NEU-DET/json/detection_config.json\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2sIIBLr8KU2-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "trainer.trainModel()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KAFKOC3ESYy6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from imageai.Detection.Custom import DetectionModelTrainer\n",
        "\n",
        "trainer = DetectionModelTrainer()\n",
        "trainer.setModelTypeAsYOLOv3()\n",
        "trainer.setDataDirectory(data_directory=\"NEU-DET\")\n",
        "trainer.evaluateModel(model_path=\"NEU-DET/models\", json_path=\"NEU-DET/json/detection_config.json\", iou_threshold=0.5, object_threshold=0.3, nms_threshold=0.5)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OgLsm7EBJ2Zf",
        "colab_type": "text"
      },
      "source": [
        "## Image Generator Approach"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5niU309sJ9e-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}