added 4 assignment
This commit is contained in:
Jakob Krause
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assignments/04_assignment.ipynb
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211
assignments/04_assignment.ipynb
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{
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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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"collapsed": true
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},
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"source": [
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"# Image Processing SS 18 - Assignment - 04\n",
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"\n",
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"### Deadline is 16.5.2016 at 8:00 o'clock\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. Select `Kernel` -> `Restart & Run All` to test it.\n",
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"Please strip the output from the cells, either select `Cell` -> `All Output` -> `Clear` or use the `nb_strip_output.py` script / git hook."
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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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"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": 2,
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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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"try:\n",
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" import urllib.request as urllib2\n",
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"except ImportError:\n",
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" import urllib2\n",
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"\n",
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"import random\n",
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"try:\n",
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" from StringIO import StringIO as BytesIO\n",
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"except ImportError:\n",
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" from io import BytesIO\n",
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" \n",
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"from PIL import Image\n",
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"\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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"# Exercise 1 - Qualify sharpness and noise - 5 Points\n",
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"\n",
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"Qualify the noise and sharpness in the images. Make a plot images, noise\n",
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"\n",
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"Please download sample picture from [here](http://www.imageprocessingplace.com/downloads_V3/root_downloads/image_databases/standard_test_images.zip)"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Load the pictures here\n",
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"sample_images = None"
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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 qualify_noise(img):\n",
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" \"\"\"Qualify the noise based on the std of a gaussian model.\n",
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" You may find a window that is contant in the images.\n",
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" \"\"\"\n",
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" # your code here\n",
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" return random.randint(0, 10)\n",
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"\n",
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"plt.bar(range(len(sample_images)), [qualify_noise(i) for i in sample_images])"
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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 qualify_sharpness(img):\n",
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" \"\"\"Qualify the sharpness based on the average pixel differences.\"\"\"\n",
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" # your code here\n",
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" return random.randint(0, 10)\n",
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"plt.bar(range(len(sample_images)), [qualify_sharpness(i) for i in sample_images])"
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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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"Does the result match the expectations? If not what processing step can be done?/"
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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 - SSIM JPEG Compression - 5 Points"
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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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"collapsed": true
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},
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"outputs": [],
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"source": [
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"def jpeg_enocde(img, quality):\n",
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" pil_img = Image.fromarray(img)\n",
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" buffer = BytesIO()\n",
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" im1.save(buffer, \"JPEG\", quality=quality)\n",
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" return buffer\n",
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"\n",
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"def jpeg_decode(buffer):\n",
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" img = Image.open(buffer)\n",
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" return np.array(img)\n",
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"\n",
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"def jpeg_quality_filter(img, quality):\n",
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" as_jpeg = jpeg_enocde(img, quality)\n",
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" return jpeg_decode(as_jpeg)"
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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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"len(images_for_jpeg)"
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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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"images10 = [jpeg_quality_filter(img, 10) for img in sample_images]\n",
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"images50 = [jpeg_quality_filter(img, 10) for img in sample_images]\n",
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"images80 = [jpeg_quality_filter(img, 10) for img in sample_images]"
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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 ssim(img, filtered_img):\n",
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" \"\"\"The SSIM similarity measure. Use the parameters from the paper \n",
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" as on the second to last slide from the lecture\"\"\"\n",
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" # your code\n",
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" return random.randint(0, 10)\n",
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"\n",
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"for i, img in enumerate(images_for_jpeg):\n",
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" print(i)\n",
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" compressed_images = [images10[i], images50[i], images80[i]]\n",
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" plt.bar(range(len(compressed_images)),\n",
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" [ssim(img, comp) for comp in compressed_images])\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": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": []
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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": "3.5.2"
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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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