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  1. {
  2.  "cells": [
  3.   {
  4.    "cell_type": "markdown",
  5.    "metadata": {},
  6.    "source": [
  7.     "Import stuff\n",
  8.     "------------"
  9.    ]
  10.   },
  11.   {
  12.    "cell_type": "code",
  13.    "execution_count": 2,
  14.    "metadata": {
  15.     "pycharm": {
  16.      "is_executing": false
  17.     }
  18.    },
  19.    "outputs": [],
  20.    "source": [
  21.     "import os\n",
  22.     "import sys\n",
  23.     "\n",
  24.     "import numpy as np\n",
  25.     "\n",
  26.     "import tensorflow as tf\n",
  27.     "from tensorflow import keras\n",
  28.     "\n",
  29.     "# Change this with the directory where you cloned the imgdetect-utils repo\n",
  30.     "basedir = os.path.join(os.path.expanduser('~'), 'git_tree', 'imgdetect-utils')\n",
  31.     "sys.path.append(os.path.join(basedir))\n",
  32.     "\n",
  33.     "from src.image_helpers import plot_images_grid, create_dataset_files\n",
  34.     "from src.train_helpers import load_data, plot_results, export_model\n",
  35.     "\n",
  36.     "# The Tensorflow model and properties file will be stored here\n",
  37.     "tf_model_dir = os.path.join(basedir, 'models', 'ir', 'tensorflow')\n",
  38.     "tf_model_file = os.path.join(tf_model_dir, 'ir.pb')\n",
  39.     "tf_properties_file = os.path.join(tf_model_dir, 'ir.json')\n",
  40.     "\n",
  41.     "# Base directory that contains your training images and dataset files\n",
  42.     "dataset_base_dir = os.path.join(basedir, 'datasets', 'ir')\n",
  43.     "dataset_dir = os.path.join(dataset_base_dir, 'dataset')\n",
  44.     "\n",
  45.     "# Store your thermal camera images here\n",
  46.     "img_dir = os.path.join(dataset_base_dir, 'images')\n",
  47.     "\n",
  48.     "# Size of the input images\n",
  49.     "input_size = (24, 32)"
  50.    ]
  51.   },
  52.   {
  53.    "cell_type": "markdown",
  54.    "metadata": {
  55.     "pycharm": {
  56.      "name": "#%% md\n"
  57.     }
  58.    },
  59.    "source": [
  60.     "Create model directories\n",
  61.     "------------------------"
  62.    ]
  63.   },
  64.   {
  65.    "cell_type": "code",
  66.    "execution_count": 3,
  67.    "metadata": {
  68.     "pycharm": {
  69.      "is_executing": false,
  70.      "name": "#%%\n"
  71.     }
  72.    },
  73.    "outputs": [],
  74.    "source": [
  75.     "os.makedirs(tf_model_dir, mode=0o775, exist_ok=True)"
  76.    ]
  77.   },
  78.   {
  79.    "cell_type": "markdown",
  80.    "source": [
  81.     "Create a dataset files from the available images\n",
  82.     "------------------------------------------------"
  83.    ],
  84.    "metadata": {
  85.     "collapsed": false
  86.    }
  87.   },
  88.   {
  89.    "cell_type": "code",
  90.    "execution_count": 4,
  91.    "outputs": [
  92.     {
  93.      "name": "stdout",
  94.      "text": [
  95.       "Processing 890 images to 1 dataset files. Format: /home/blacklight/git_tree/imgdetect-utils/datasets/ir/dataset/dataset{:01}.npz\n",
  96.       "Storing dataset vectors to /home/blacklight/git_tree/imgdetect-utils/datasets/ir/dataset/dataset0.npz\n"
  97.      ],
  98.      "output_type": "stream"
  99.     },
  100.     {
  101.      "data": {
  102.       "text/plain": "['/home/blacklight/git_tree/imgdetect-utils/datasets/ir/dataset/dataset0.npz']"
  103.      },
  104.      "metadata": {},
  105.      "output_type": "execute_result",
  106.      "execution_count": 4
  107.     }
  108.    ],
  109.    "source": [
  110.     "dataset_files = create_dataset_files(img_dir, dataset_dir,\n",
  111.     "                                     split_size=1000,\n",
  112.     "                                     num_threads=1,\n",
  113.     "                                     resize=input_size)\n",
  114.     "dataset_files"
  115.    ],
  116.    "metadata": {
  117.     "collapsed": false,
  118.     "pycharm": {
  119.      "name": "#%%\n",
  120.      "is_executing": false
  121.     }
  122.    }
  123.   },
  124.   {
  125.    "cell_type": "markdown",
  126.    "source": [
  127.     "Or load existing dataset files\n",
  128.     "------------------------------"
  129.    ],
  130.    "metadata": {
  131.     "collapsed": false
  132.    }
  133.   },
  134.   {
  135.    "cell_type": "code",
  136.    "execution_count": 8,
  137.    "outputs": [
  138.     {
  139.      "data": {
  140.       "text/plain": "['/home/blacklight/git_tree/imgdetect-utils/datasets/ir/dataset/dataset0.npz']"
  141.      },
  142.      "metadata": {},
  143.      "output_type": "execute_result",
  144.      "execution_count": 8
  145.     }
  146.    ],
  147.    "source": [
  148.     "dataset_files = [os.path.join(dataset_dir, f)\n",
  149.     "                 for f in os.listdir(dataset_dir)\n",
  150.     "                 if os.path.isfile(os.path.join(dataset_dir, f))\n",
  151.     "                 and f.endswith('.npz')]\n",
  152.     "\n",
  153.     "dataset_files"
  154.    ],
  155.    "metadata": {
  156.     "collapsed": false,
  157.     "pycharm": {
  158.      "name": "#%%\n",
  159.      "is_executing": false
  160.     }
  161.    }
  162.   },
  163.   {
  164.    "cell_type": "markdown",
  165.    "metadata": {},
  166.    "source": [
  167.     "Get the training and test set randomly out of the dataset with a split of 70/30\n",
  168.     "-------------------------------------------------------------------------------"
  169.    ]
  170.   },
  171.   {
  172.    "cell_type": "code",
  173.    "execution_count": 5,
  174.    "metadata": {
  175.     "pycharm": {
  176.      "is_executing": false
  177.     }
  178.    },
  179.    "outputs": [
  180.     {
  181.      "name": "stdout",
  182.      "text": [
  183.       "Loaded 623 training images and 267 test images. Classes: ['negative' 'positive']\n"
  184.      ],
  185.      "output_type": "stream"
  186.     }
  187.    ],
  188.    "source": [
  189.     "train_set, test_set, classes = load_data(*dataset_files, split_percentage=0.7)\n",
  190.     "print('Loaded {} training images and {} test images. Classes: {}'.format(\n",
  191.     "    train_set.shape[0], test_set.shape[0], classes))"
  192.    ]
  193.   },
  194.   {
  195.    "cell_type": "code",
  196.    "execution_count": 6,
  197.    "metadata": {
  198.     "pycharm": {
  199.      "is_executing": false,
  200.      "name": "#%%\n"
  201.     }
  202.    },
  203.    "outputs": [],
  204.    "source": [
  205.     "train_images = np.asarray([item[0] for item in train_set])\n",
  206.     "train_labels = np.asarray([item[1] for item in train_set])\n",
  207.     "test_images = np.asarray([item[0] for item in test_set])\n",
  208.     "test_labels = np.asarray([item[1] for item in test_set])"
  209.    ]
  210.   },
  211.   {
  212.    "cell_type": "markdown",
  213.    "metadata": {},
  214.    "source": [
  215.     "Inspect the first 25 images in the training set\n",
  216.     "-----------------------------------------------\n"
  217.    ]
  218.   },
  219.   {
  220.    "cell_type": "code",
  221.    "execution_count": 7,
  222.    "metadata": {
  223.     "pycharm": {
  224.      "is_executing": false
  225.     }
  226.    },
  227.    "outputs": [
  228.     {
  229.      "data": {
  230.       "text/plain": "<Figure size 720x720 with 25 Axes>",
  231.       "image/png": 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\n"
  232.      },
  233.      "metadata": {},
  234.      "output_type": "display_data"
  235.     }
  236.    ],
  237.    "source": [
  238.     "plot_images_grid(images=train_images, labels=train_labels, classes=classes, rows=5, cols=5)\n"
  239.    ]
  240.   },
  241.   {
  242.    "cell_type": "markdown",
  243.    "metadata": {},
  244.    "source": [
  245.     "Declare the model\n",
  246.     "-----------------\n",
  247.     "\n",
  248.     "* Flatten input\n",
  249.     "* Layer 1: 50% the number of pixels per image\n",
  250.     "* Layer 2: 20% the number of pixels per image\n",
  251.     "* Layer 3: as many neurons as the output labels (in this case 2: negative, positive)"
  252.    ]
  253.   },
  254.   {
  255.    "cell_type": "code",
  256.    "execution_count": 12,
  257.    "metadata": {
  258.     "pycharm": {
  259.      "is_executing": false
  260.     }
  261.    },
  262.    "outputs": [],
  263.    "source": [
  264.     "model = keras.Sequential([\n",
  265.     "    keras.layers.Flatten(input_shape=train_images[0].shape),\n",
  266.     "    keras.layers.Dense(int(0.5 * train_images.shape[1] * train_images.shape[2]), activation=tf.nn.relu),\n",
  267.     "    keras.layers.Dense(int(0.2 * train_images.shape[1] * train_images.shape[2]), activation=tf.nn.relu),\n",
  268.     "    keras.layers.Dense(len(classes), activation=tf.nn.softmax)\n",
  269.     "])"
  270.    ]
  271.   },
  272.   {
  273.    "cell_type": "markdown",
  274.    "metadata": {},
  275.    "source": [
  276.     "Compile the model\n",
  277.     "-----------------\n",
  278.     "\n",
  279.     "- *Loss function*:This measures how accurate the model is during training. We want to minimize this function to \"steer\" the model in the right direction.\n",
  280.     "- *Optimizer*: This is how the model is updated based on the data it sees and its loss function.\n",
  281.     "- *Metrics*: Used to monitor the training and testing steps. The following example uses accuracy, the fraction of the images that are correctly classified."
  282.    ]
  283.   },
  284.   {
  285.    "cell_type": "code",
  286.    "execution_count": 13,
  287.    "metadata": {
  288.     "pycharm": {
  289.      "is_executing": false
  290.     }
  291.    },
  292.    "outputs": [],
  293.    "source": [
  294.     "model.compile(optimizer='adam',\n",
  295.     "              loss='sparse_categorical_crossentropy',\n",
  296.     "              metrics=['accuracy'])"
  297.    ]
  298.   },
  299.   {
  300.    "cell_type": "markdown",
  301.    "metadata": {},
  302.    "source": [
  303.     "Train the model\n",
  304.     "---------------"
  305.    ]
  306.   },
  307.   {
  308.    "cell_type": "code",
  309.    "execution_count": 14,
  310.    "metadata": {
  311.     "pycharm": {
  312.      "is_executing": false
  313.     }
  314.    },
  315.    "outputs": [
  316.     {
  317.      "name": "stdout",
  318.      "text": [
  319.       "Epoch 1/3\n\r 32/623 [>.............................] - ETA: 2s - loss: 0.7070 - acc: 0.4688",
  320.       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r224/623 [=========>....................] - ETA: 0s - loss: 0.5165 - acc: 0.7455",
  321.       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r384/623 [=================>............] - ETA: 0s - loss: 0.3831 - acc: 0.8307",
  322.       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r608/623 [============================>.] - ETA: 0s - loss: 0.2703 - acc: 0.8849",
  323.       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r623/623 [==============================] - 0s 487us/sample - loss: 0.2672 - acc: 0.8860\n",
  324.       "Epoch 2/3\n\r 32/623 [>.............................] - ETA: 0s - loss: 0.0745 - acc: 0.9375",
  325.       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r160/623 [======>.......................] - ETA: 0s - loss: 0.0428 - acc: 0.9875",
  326.       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r352/623 [===============>..............] - ETA: 0s - loss: 0.0354 - acc: 0.9886",
  327.       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r544/623 [=========================>....] - ETA: 0s - loss: 0.0276 - acc: 0.9926",
  328.       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r623/623 [==============================] - 0s 362us/sample - loss: 0.0247 - acc: 0.9936\n",
  329.       "Epoch 3/3\n\r 32/623 [>.............................] - ETA: 0s - loss: 0.0036 - acc: 1.0000",
  330.       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r160/623 [======>.......................] - ETA: 0s - loss: 0.0032 - acc: 1.0000",
  331.       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r320/623 [==============>...............] - ETA: 0s - loss: 0.0083 - acc: 0.9969",
  332.       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r448/623 [====================>.........] - ETA: 0s - loss: 0.0078 - acc: 0.9978",
  333.       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r623/623 [==============================] - 0s 373us/sample - loss: 0.0083 - acc: 0.9984\n"
  334.      ],
  335.      "output_type": "stream"
  336.     },
  337.     {
  338.      "data": {
  339.       "text/plain": "<tensorflow.python.keras.callbacks.History at 0x7f9efb151ad0>"
  340.      },
  341.      "metadata": {},
  342.      "output_type": "execute_result",
  343.      "execution_count": 14
  344.     }
  345.    ],
  346.    "source": [
  347.     "model.fit(train_images, train_labels, epochs=3)"
  348.    ]
  349.   },
  350.   {
  351.    "cell_type": "markdown",
  352.    "metadata": {},
  353.    "source": [
  354.     "Evaluate accuracy against the test set\n",
  355.     "--------------------------------------"
  356.    ]
  357.   },
  358.   {
  359.    "cell_type": "code",
  360.    "execution_count": 15,
  361.    "metadata": {
  362.     "pycharm": {
  363.      "is_executing": false
  364.     }
  365.    },
  366.    "outputs": [
  367.     {
  368.      "name": "stdout",
  369.      "text": [
  370.       "\r 32/267 [==>...........................] - ETA: 0s - loss: 9.6843e-04 - acc: 1.0000",
  371.       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r267/267 [==============================] - 0s 243us/sample - loss: 0.0096 - acc: 0.9963\n",
  372.       "Test accuracy: 0.9962547\n"
  373.      ],
  374.      "output_type": "stream"
  375.     }
  376.    ],
  377.    "source": [
  378.     "test_loss, test_acc = model.evaluate(test_images, test_labels)\n",
  379.     "print('Test accuracy:', test_acc)"
  380.    ]
  381.   },
  382.   {
  383.    "cell_type": "markdown",
  384.    "metadata": {},
  385.    "source": [
  386.     "Make predictions on the test set\n",
  387.     "--------------------------------"
  388.    ]
  389.   },
  390.   {
  391.    "cell_type": "code",
  392.    "execution_count": 16,
  393.    "metadata": {
  394.     "pycharm": {
  395.      "is_executing": false
  396.     }
  397.    },
  398.    "outputs": [
  399.     {
  400.      "data": {
  401.       "text/plain": "<Figure size 1152x1296 with 72 Axes>",
  402.       "image/png": 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\n"
  403.      },
  404.      "metadata": {},
  405.      "output_type": "display_data"
  406.     }
  407.    ],
  408.    "source": [
  409.     "predictions = model.predict(test_images)\n",
  410.     "plot_results(images=test_images, labels=test_labels, classes=classes, predictions=predictions, rows=9, cols=4)"
  411.    ]
  412.   },
  413.   {
  414.    "cell_type": "markdown",
  415.    "metadata": {},
  416.    "source": [
  417.     "Export as a Tensorflow model\n",
  418.     "----------------------------"
  419.    ]
  420.   },
  421.   {
  422.    "cell_type": "code",
  423.    "execution_count": 27,
  424.    "metadata": {
  425.     "pycharm": {
  426.      "is_executing": false,
  427.      "name": "#%%\n"
  428.     }
  429.    },
  430.    "outputs": [
  431.     {
  432.      "name": "stdout",
  433.      "output_type": "stream",
  434.      "text": [
  435.       "INFO:tensorflow:Froze 29 variables.\n"
  436.      ]
  437.     },
  438.     {
  439.      "name": "stderr",
  440.      "output_type": "stream",
  441.      "text": [
  442.       "INFO:tensorflow:Froze 29 variables.\n"
  443.      ]
  444.     },
  445.     {
  446.      "name": "stdout",
  447.      "output_type": "stream",
  448.      "text": [
  449.       "INFO:tensorflow:Converted 29 variables to const ops.\n"
  450.      ]
  451.     },
  452.     {
  453.      "name": "stderr",
  454.      "output_type": "stream",
  455.      "text": [
  456.       "INFO:tensorflow:Converted 29 variables to const ops.\n"
  457.      ]
  458.     }
  459.    ],
  460.    "source": [
  461.     "export_model(model, tf_model_file,\n",
  462.     "             properties_file=tf_properties_file,\n",
  463.     "             classes=classes,\n",
  464.     "             input_size=input_size)"
  465.    ]
  466.   }
  467.  ],
  468.  "metadata": {
  469.   "kernelspec": {
  470.    "display_name": "Python 3",
  471.    "language": "python",
  472.    "name": "python3"
  473.   },
  474.   "language_info": {
  475.    "codemirror_mode": {
  476.     "name": "ipython",
  477.     "version": 3
  478.    },
  479.    "file_extension": ".py",
  480.    "mimetype": "text/x-python",
  481.    "name": "python",
  482.    "nbconvert_exporter": "python",
  483.    "pygments_lexer": "ipython3",
  484.    "version": "3.7.4"
  485.   },
  486.   "pycharm": {
  487.    "stem_cell": {
  488.     "cell_type": "raw",
  489.     "source": [],
  490.     "metadata": {
  491.      "collapsed": false
  492.     }
  493.    }
  494.   }
  495.  },
  496.  "nbformat": 4,
  497.  "nbformat_minor": 1
  498. }
RAW Paste Data
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