add all L1 notebooks

1_Image_Representation/2. Visualizing RGB Channels.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# RGB colorspace"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Import resources"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.image as mpimg\n",
+    "\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Read in an image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Read in the image\n",
+    "image = mpimg.imread('images/wa_state_highway.jpg')\n",
+    "\n",
+    "plt.imshow(image)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### RGB channels \n",
+    "\n",
+    "Visualize the levels of each color channel. Pay close attention to the traffic signs!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Isolate RGB channels\n",
+    "r = image[:,:,0]\n",
+    "g = image[:,:,1]\n",
+    "b = image[:,:,2]\n",
+    "\n",
+    "# Visualize the individual color channels\n",
+    "f, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(20,10))\n",
+    "ax1.set_title('R channel')\n",
+    "ax1.imshow(r, cmap='gray')\n",
+    "ax2.set_title('G channel')\n",
+    "ax2.imshow(g, cmap='gray')\n",
+    "ax3.set_title('B channel')\n",
+    "ax3.imshow(b, cmap='gray')\n",
+    "\n",
+    "## Which area has the lowest value for red? What about for blue?"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

1_Image_Representation/3. Blue Screen.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Color Threshold, Blue Screen"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Import resources"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import cv2\n",
+    "\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Read in and display the image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# Read in the image\n",
+    "image = cv2.imread('images/pizza_bluescreen.jpg')\n",
+    "\n",
+    "# Print out the type of image data and its dimensions (height, width, and color)\n",
+    "print('This image is:', type(image), \n",
+    "      ' with dimensions:', image.shape)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Make a copy of the image\n",
+    "image_copy = np.copy(image)\n",
+    "\n",
+    "# Change color to RGB (from BGR)\n",
+    "image_copy = cv2.cvtColor(image_copy, cv2.COLOR_BGR2RGB)\n",
+    "\n",
+    "# Display the image copy\n",
+    "plt.imshow(image_copy)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Define the color threshold"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## TODO: Define the color selection boundaries in RGB values\n",
+    "# play around with these values until you isolate the blue background\n",
+    "lower_blue = np.array([0,0,200]) \n",
+    "upper_blue = np.array([250,250,255])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create a mask"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define the masked area\n",
+    "mask = cv2.inRange(image_copy, lower_blue, upper_blue)\n",
+    "\n",
+    "# Vizualize the mask\n",
+    "plt.imshow(mask, cmap='gray')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Mask the image to let the pizza show through\n",
+    "masked_image = np.copy(image_copy)\n",
+    "\n",
+    "masked_image[mask != 0] = [0, 0, 0]\n",
+    "\n",
+    "# Display it!\n",
+    "plt.imshow(masked_image)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Mask and add a background image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load in a background image, and convert it to RGB \n",
+    "background_image = cv2.imread('images/space_background.jpg')\n",
+    "background_image = cv2.cvtColor(background_image, cv2.COLOR_BGR2RGB)\n",
+    "\n",
+    "# Crop it to the right size (514x816)\n",
+    "crop_background = background_image[0:514, 0:816]\n",
+    "\n",
+    "# Mask the cropped background so that the pizza area is blocked\n",
+    "crop_background[mask == 0] = [0, 0, 0]\n",
+    "\n",
+    "# Display the background\n",
+    "plt.imshow(crop_background)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create a complete image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add the two images together to create a complete image!\n",
+    "complete_image = masked_image + crop_background\n",
+    "\n",
+    "# Display the result\n",
+    "plt.imshow(complete_image)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}