{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cec39ca8",
   "metadata": {},
   "source": [
    "# Lab 16: Image Processing\n",
    "In this lab, you are going to practice working with the Python Imaging Library and using nested loops to manipulate images."
   ]
  },
  {
   "attachments": {
    "pillowlibrary.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "0de880ab",
   "metadata": {},
   "source": [
    "## Installing the pillow module\n",
    "\n",
    "In Thonny, install the `Pillow` package by selecting __Tools__$\\rightarrow$__Manage packages...__ then search for and install `Pillow`.\n",
    "\n",
    "<center>\n",
    "<div>\n",
    "<img src=\"attachment:pillowlibrary.png\" width=\"600\"/>\n",
    "</div>\n",
    "</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8424ef1b",
   "metadata": {},
   "source": [
    "## Displying an image with PIL in Python\n",
    "\n",
    "Download the image <a href=\"http://analytics.drake.edu/~manley/CS65/data/griff.jpg\">griff.jpg</a>, and move it to a directory where you keep your Python code. Then, run the following code to make sure you can open and display the file.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c75577f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "\n",
    "with Image.open(\"griff.jpg\") as griff_image:\n",
    "    griff_image.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dc648dc",
   "metadata": {},
   "source": [
    "## Exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d91b7a5",
   "metadata": {},
   "source": [
    "__Exercise 1:__ Get Python to display some other image (a photograph you've taken, another image you found on the Internet, etc.)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5ac580b",
   "metadata": {},
   "source": [
    "__Exercise 2:__ In the lecture, we were able to find the size of the image and look at the tuple for the color in the upper-left corner of the image with code like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b0c1a292",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(griff_image.size)\n",
    "\n",
    "pixels = griff_image.load()\n",
    "    \n",
    "print(pixels[0,0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca5ba6fc",
   "metadata": {},
   "source": [
    "Note that `griff_image.size[0]` gives the width of the image in pixels, and `griff_image.size[1]` gives the height. Print out the tuple for the colors of _all four corners_ of your image (note that you'll need to `griff_image.size[0]` and `griff_image.size[1]` for this - but remember that the pixel indices start counting at 0)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06653e8a",
   "metadata": {},
   "source": [
    "__Exercise 3:__ In the lecture, we used a nested `for` loop to make a thick, black, horizontal line accross the Griff image. Write a nested `for` loop that makes a thick, green, vertical line somewhere down the middlel of your image. Adjust the width so it has a similar thickness in proportion to the size of your image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b6c8e7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "\n",
    "with Image.open(\"griff.jpg\") as griff_image:\n",
    "    pixels = griff_image.load()\n",
    "    \n",
    "for p in range(griff_image.size[0]):\n",
    "    for r in range(50,60):\n",
    "        pixels[p,r] = (0,0,0)\n",
    "    \n",
    "    \n",
    "    \n",
    "griff_image.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db35ec57",
   "metadata": {},
   "source": [
    "__Exercise 4:__ In the lecture, we wrote the following code to flip the Griff image horizontally. Write a nested `for` loop that will flip your image _vertically_."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c2e8f10c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "\n",
    "with Image.open(\"griff.jpg\") as griff_image:\n",
    "    pixels = griff_image.load()\n",
    "    \n",
    "for c in range(griff_image.size[0]//2):\n",
    "    for r in range(griff_image.size[1]):\n",
    "\n",
    "    \n",
    "        leftside = pixels[griff_image.size[0]-1-c,r]\n",
    "        pixels[griff_image.size[0]-1-c,r] = pixels[c,r]\n",
    "        pixels[c,r] = leftside\n",
    "    \n",
    "    \n",
    "griff_image.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "845c8938",
   "metadata": {},
   "source": [
    "__Exercise 5:__ In the lecture, we converted the Griff image to grayscale using the following code. Write a nested `for` loop that converts your image to grayscale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "96d45d68",
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "\n",
    "with Image.open(\"griff.jpg\") as griff_image:\n",
    "    pixels = griff_image.load()\n",
    "    \n",
    "for c in range(griff_image.size[0]):\n",
    "    for r in range(griff_image.size[1]):\n",
    "        red = pixels[c,r][0]\n",
    "        green = pixels[c,r][1]\n",
    "        blue = pixels[c,r][2]\n",
    "        \n",
    "        average_pixel_color = (red+green+blue)//3\n",
    "        \n",
    "        pixels[c,r] = (average_pixel_color,average_pixel_color,average_pixel_color)\n",
    "    \n",
    "    \n",
    "    \n",
    "griff_image.show()"
   ]
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    "__Exercise 6:__ Use nested `for` loops to apply some new kind of filter or effect to an image.  You only have to do one thing, and the difficulty level doesn't matter as long as you're using nested `for` loops. Here are some ideas:\n",
    "* (Easy) Custom filter: Adjust the colors of your image to emphasize or de-emphasize each of the different color components to produce a neat effect.\n",
    "* (Easy) Color-blindness simulation: Some individuals have trouble distinguishing some shades of red and green (these types of color blindess are called Protanopia and Deuteranopia). One way to crudely simulate what a person with one of these conditions would see is to average the red and green values for each pixel, but leave the blue value as it was originally.\n",
    "* (Easy) Inverse filter: Change all color values to 255-original_value to see the inverse colors of the image.\n",
    "* (Medium) Classic filter: Look up the algorithm for a specific kind of color filter, and implement that (e.g., you can find a description of the Sepia algorithm here: https://dyclassroom.com/image-processing-project/how-to-convert-a-color-image-into-sepia-image )\n",
    "* (Medium) Red-eye remover: find any red pixel (say, where the red color value is greater than the sum of the blue and green color values), and change these pixels to black.\n",
    "* (Medium) Blurr effect: Make each pixel the average of the pixels around it (you can play around with how many pixels away you want to use in the average based on how blurry you want it to be)\n",
    "* (Hard) Green-screen effect: Replace all green pixels of one photograph with the corresponding pixel from a different image. (For images, try searching http://images.google.com for something like \"green screen movie set\")\n",
    "* (Hard) Pixel transformations: Pick a spot in the middle of the image, and move around the pixels to create a stretch, twist, bubble, etc. effect."
   ]
  }
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