274 lines
6.5 KiB
Plaintext
274 lines
6.5 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Image Processing SS 18 - Assignment - 01\n",
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"\n",
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"### Deadline is 25.4.2018 at 8:00\n",
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"\n",
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"Please solve the assignments together with a partner.\n",
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"I will run every notebook. Make sure the code runs through, when clicked on `Kernel` -> `Restart & Run All`.\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Introduction to Python / Numpy\n",
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"\n",
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"* [Learn Python in 15 minutes](https://learnxinyminutes.com/docs/python3/): We will use Python 3.\n",
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"* [Numpy for Matlab Users](https://docs.scipy.org/doc/numpy-dev/user/numpy-for-matlab-users.html#general-purpose-equivalents)\n",
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"* [Numpy Quickstart](https://docs.scipy.org/doc/numpy-dev/user/quickstart.html)\n",
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"\n",
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"## Libraries\n",
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"\n",
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"We will use the following libraries:\n",
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"\n",
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"* matplotlib\n",
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"* numpy\n",
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"* scipy\n",
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"* skimage\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Exercise 0 - Setup Development Enviroment - [1 Point]\n",
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"\n",
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"Find a partner, follow the steps in the [README](https://github.com/BildverarbeitungSS18/Hausaufgaben/blob/master/README.md) and paste a link to your repository, names and matriculation numbers into the KVV assignment box.\n",
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"You do not need to upload any files to the KVV. I will clone your repository. "
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# display the plots inside the notebook\n",
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"%matplotlib inline"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import pylab\n",
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"pylab.rcParams['figure.figsize'] = (12, 12) # This makes the plot bigger"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The [skimage](http://scikit-image.org/) library comes with multiple useful test images. Let's start with an image of an astronaut. "
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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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"metadata": {},
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"outputs": [],
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"source": [
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"from skimage.data import astronaut"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"img = astronaut() # Get the image\n",
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"print(img.shape) # the dimension of the image\n",
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"print(img.dtype) # the image type"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We have a `(512, 512, 3)` array of unsigned bytes. At `img[x, y]` there are three values for R,G and B."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We will always work with floating point arrays between 0 and 1. "
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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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"metadata": {},
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"outputs": [],
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"source": [
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"img = img / 255."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Lets display the image."
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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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"metadata": {},
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"outputs": [],
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"source": [
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"plt.imshow(img)\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"This is [Eileen Collins](https://en.wikipedia.org/wiki/Eileen_Collins). She was the first astronaut \n",
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" to fly the Space Shuttle through a complete 360-degree pitch maneuver. What an inspiring woman."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Exercise 1 - Plot - [1 Point]\n",
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"\n",
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"Plot the R, G and B channels separately."
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Your code here"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Exercise 2 - RGB to HSV [6 Points]\n",
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"\n",
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"Implement the `rgb_to_hsv` and `hsv_to_rgb` functions. Don't use any color conversion functions from a library.\n"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"def rgb_to_hsv(x):\n",
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" \"\"\"\n",
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" Converts the numpy array `x` from RGB to the HSV. \n",
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" \"\"\"\n",
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" # Your code here\n",
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" return x"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"def hsv_to_rgb(x):\n",
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" \"\"\"\n",
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" Converts the numpy array `x` from HSV to the RGB. \n",
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" \"\"\"\n",
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" # Your code here\n",
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" return x"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Plot the saturation of the astronaut image"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"img_as_hsv = rgb_to_hsv(img)\n",
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"# your code"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Increase the saturation by a factor of 2, convert it back to RGB and plot the result."
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# your code"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Exercise 3 - Callculation [2 Points]\n",
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"\n",
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"In the figure below you can see the [CIE-XYZ](https://de.wikipedia.org/wiki/CIE-Normvalenzsystem) color space.\n",
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"\n",
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"\n",
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"What are the approximate x,y,z values for the following Adobe RGB colors:\n",
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"* `(0, 0.5, 0.5)`\n",
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"* `(0.33, 0.33, 0.33)`\n",
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"\n",
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"A sodium-vapor lamp shines with double the intensity of a mercury-vapor lamp\n",
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". The light from the sodium lamp only contains \n",
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"the spectral line at `589,00nm` and the light from the mercury lamp only the\n",
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"spectral line at `435,83 nm`.\n",
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"\n",
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"What color does a human experience? What are the approximate x,y,z values? \n"
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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": 3
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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": "ipython3",
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"version": "3.6.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 1
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}
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