289 lines
6.8 KiB
Plaintext
289 lines
6.8 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"source": [
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"# Exploration of the traffic sign dataset"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%% md\n"
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}
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"# 1. Import libraries and datasets"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%% md\n"
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}
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}
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"import pickle\n",
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"import os"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"outputs": [],
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"source": [
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"# Get the training and testing dataset\n",
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"with open(os.path.join(\"dataset\", \"train.p\"), mode='rb') as training_data:\n",
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" train = pickle.load(training_data)\n",
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"with open(os.path.join(\"dataset\", \"valid.p\"), mode='rb') as validation_data:\n",
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" valid = pickle.load(validation_data)"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"outputs": [],
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"source": [
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"# Get the features and labels of the datasets\n",
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"# The features are the images of the signs\n",
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"X_train, y_train = train['features'], train['labels']\n",
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"X_valid, y_valid = valid['features'], valid['labels']"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"# 2. Visualize traffic sign dataset"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%% md\n"
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}
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [
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"print(\"Number of training examples: \", X_train.shape[0])\n",
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"print(\"Number of validation examples: \", X_valid.shape[0])\n",
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"print(\"Image data shape =\", X_train[0].shape)\n",
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"print(\"Number of classes =\", len(np.unique(y_train)))"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n",
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"is_executing": true
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}
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [
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"# Plot a random picture from the training dataset\n",
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"i = np.random.randint(1, len(X_train))\n",
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"plt.grid(False)\n",
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"plt.imshow(X_train[i])\n",
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"print(\"Label: \", y_train[i])"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n",
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"is_executing": true
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}
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [
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"# Plot (width x height) pictures from the training dataset\n",
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"grid_width = 5\n",
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"grid_height = 4\n",
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"\n",
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"fig, axes = plt.subplots(grid_height, grid_width, figsize = (10,10))\n",
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"axes = axes.ravel()\n",
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"\n",
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"for i in np.arange(0, grid_width * grid_height):\n",
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" index = np.random.randint(0, len(X_train))\n",
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" axes[i].imshow(X_train[index])\n",
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" axes[i].set_title(y_train[index], fontsize = 15)\n",
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" axes[i].axis('off')\n",
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"\n",
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"plt.subplots_adjust(hspace = 0.3)"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n",
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"is_executing": true
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}
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}
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"outputs": [],
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"source": [
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"# Plotting histograms of the count of each sign\n",
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"def histogram_plot(dataset: np.ndarray, label: str):\n",
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" \"\"\" Plots a histogram of the dataset\n",
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"\n",
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" Args:\n",
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" dataset: The input data to be plotted as a histogram.\n",
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" label: The label of the histogram.\n",
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" \"\"\"\n",
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" hist, bins = np.histogram(dataset, bins=43)\n",
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" width = 0.8 * (bins[1] - bins[0])\n",
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" center = (bins[:-1] + bins[1:]) / 2\n",
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" plt.bar(center, hist, align='center', width=width)\n",
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" plt.xlabel(label)\n",
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" plt.ylabel(\"Image count\")\n",
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" plt.show()"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [
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"histogram_plot(y_train, \"Training examples\")\n",
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"histogram_plot(y_valid, \"Validation examples\")"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n",
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"is_executing": true
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}
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"# A list of all classes:\n",
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"- 0 = Speed limit (20km/h)\n",
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"- 1 = Speed limit (30km/h)\n",
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"- 2 = Speed limit (50km/h)\n",
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"- 3 = Speed limit (60km/h)\n",
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"- 4 = Speed limit (70km/h)\n",
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"- 5 = Speed limit (80km/h)\n",
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"- 6 = End of speed limit (80km/h)\n",
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"- 7 = Speed limit (100km/h)\n",
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"- 8 = Speed limit (120km/h)\n",
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"- 9 = No passing\n",
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"- 10 = No passing for vehicles over 3.5 metric tons\n",
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"- 11 = Right-of-way at the next intersection\n",
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"- 12 = Priority road\n",
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"- 13 = Yield\n",
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"- 14 = Stop\n",
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"- 15 = No vehicles\n",
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"- 16 = Vehicles over 3.5 metric tons prohibited\n",
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"- 17 = No entry\n",
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"- 18 = General caution\n",
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"- 19 = Dangerous curve to the left\n",
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"- 20 = Dangerous curve to the right\n",
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"- 21 = Double curve\n",
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"- 22 = Bumpy road\n",
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"- 23 = Slippery road\n",
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"- 24 = Road narrows on the right\n",
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"- 25 = Road work\n",
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"- 26 = Traffic signals\n",
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"- 27 = Pedestrians\n",
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"- 28 = Children crossing\n",
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"- 29 = Bicycles crossing\n",
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"- 30 = Beware of ice/snow\n",
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"- 31 = Wild animals crossing\n",
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"- 32 = End of all speed and passing limits\n",
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"- 33 = Turn right ahead\n",
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"- 34 = Turn left ahead\n",
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"- 35 = Ahead only\n",
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"- 36 = Go straight or right\n",
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"- 37 = Go straight or left\n",
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"- 38 = Keep right\n",
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"- 39 = Keep left\n",
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"- 40 = Roundabout mandatory\n",
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"- 41 = End of no passing\n",
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"- 42 = End of no passing by vehicles over 3.5 metric tons"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%% md\n"
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}
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}
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"outputs": [],
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"source": [],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 2
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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"version": "2.7.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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} |