tl_classifier/TL_Classifier-Carla.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Trafic Light Classificator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import glob\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from keras.backend.tensorflow_backend import set_session\n",
    "config = tf.ConfigProto()\n",
    "config.gpu_options.per_process_gpu_memory_fraction = 0.3\n",
    "set_session(tf.Session(config=config))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Data Import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "X_train = []\n",
    "x_label = []\n",
    "for img_class, directory in enumerate(['carla_red', 'carla_yellow', 'carla_green', 'carla_none']):\n",
    "    for i, file_name in enumerate(glob.glob(\"{}/*.jpg\".format(directory))):\n",
    "        file = cv2.imread(file_name)\n",
    "        file = cv2.cvtColor(file, cv2.COLOR_BGR2RGB);\n",
    "        resized = cv2.resize(file, (32,64))\n",
    "\n",
    "\n",
    "        X_train.append(resized/255.)\n",
    "        x_label.append(img_class)\n",
    "        \n",
    "        #if (i < 3):\n",
    "        #    plt.imshow(rgb)\n",
    "        #    plt.show()\n",
    "        \n",
    "X_train = np.array(X_train)\n",
    "x_label = np.array(x_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_label"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. CNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D, Flatten, Dense, MaxPooling2D, Dropout\n",
    "from keras.utils.np_utils import to_categorical\n",
    "from keras import losses, optimizers, regularizers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_labels = to_categorical(x_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_classes = 4\n",
    "model = Sequential()\n",
    "model.add(Conv2D(32, (3, 3), input_shape=(64, 32, 3), padding='same', activation='relu', kernel_initializer='random_uniform', kernel_regularizer=regularizers.l2(0.01)))\n",
    "model.add(MaxPooling2D(2,2))\n",
    "#Dropout(0.8)\n",
    "#model.add(Conv2D(32, (3, 3), padding='same', activation='relu', kernel_initializer='random_uniform', kernel_regularizer=regularizers.l2(0.01)))\n",
    "#model.add(MaxPooling2D(2,2))\n",
    "Dropout(0.2)\n",
    "model.add(Flatten())\n",
    "\n",
    "#model.add(Dense(128, activation='relu', kernel_initializer='random_uniform', kernel_regularizer=regularizers.l2(0.01)))\n",
    "model.add(Dense(8, activation='relu', kernel_initializer='random_uniform', kernel_regularizer=regularizers.l2(0.01)))\n",
    "model.add(Dense(num_classes, activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "loss = losses.categorical_crossentropy\n",
    "optimizer = optimizers.Adam()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss=loss, optimizer=optimizer, metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.fit(X_train, categorical_labels, batch_size=64, epochs=10, verbose=True, validation_split=0.1, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "score = model.evaluate(X_train, categorical_labels, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = model.predict(X_train, verbose=1)\n",
    "for i in range(len(prediction)):\n",
    "    print (i, prediction[i], categorical_labels[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.imshow(X_train[46])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import h5py\n",
    "model.save('tl_classifier_carla.h5')"
   ]
  }
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