Some warning and error fix for chapter 01 02 03

With python 3.9 and up-to-date libraries
Chapter 01
Perspective Projection and Homography
matplotlib 3.4 savefig bbox_in, pad_in is deprecated, updated to bbox_inches and pad_inches
plt.savefig('images/homography_out.png', bbox_inches='tight', pad_inches=0)
Pencil Sketches from images
plt.savefig(img_file.split('.')[0] + '_sketches_all.png', bbox_inches='tight')
Image need using tuple() instead array warning

#output = img - anisotropic_diffusion(img, niter=niter, kappa=kappa, gamma=gamma, voxelspacing=None, option=1) # change to
output = img - anisotropic_diffusion((img), niter=niter, kappa=kappa, gamma=gamma, voxelspacing=None, option=1)

Chapter 02
Edge Detection with Canny, LOG / Zero-Crossing and Wavelets

from skimage.io import imread # use io package as misc imread deprecrated
#img = rgb2gray(misc.imread('images/tiger.png')) # change to
img = rgb2gray(imread('images/tiger.png'))

Edge Detection with Anisotropic Diffusion
#diff_out = anisotropic_diffusion(img, niter=50, kappa=20, option=1)
diff_out = anisotropic_diffusion((img), niter=50, kappa=20, option=1)
Image Denoising with Denoising Autoencoder
import torchvision, matplotlib, sklearn, numpy as np, torch# added torch
Improving Image Contrast

#hist, bins = np.histogram(img[...,i].flatten(),256,[0,256], normed=True) # Changed to 
hist, bins = np.histogram(img[...,i].flatten(),256,[0,256], density=True)  
#plt.savefig('images/hist_out.png', bbox_in='tight', pad_in=0) # Changed to 
plt.savefig('images/hist_out.png', bbox_inches='tight', pad_inches=0)

Image Denoising with Anisotropic Diffusion

#diff_out = anisotropic_diffusion(noisy, niter=20, kappa=20, option=1) 
diff_out = anisotropic_diffusion((noisy), niter=20, kappa=20, option=1)
#diff_out = anisotropic_diffusion(noisy, niter=50, kappa=100, option=2)
diff_out = anisotropic_diffusion((noisy), niter=50, kappa=100, option=2)

Chapter 03
Wiener Filter
this import only support until < 0.16.2
from skimage.measure import compare_psnr
need update to
from skimage.metrics import peak_signal_noise_ratio as compare_psnr

#ax = fig.gca(projection='3d')
ax = fig.add_subplot(projection='3d')

Some warning updated on CLA section

from skimage.color import rgb2gray,rgba2rgb
im = rgb2gray(rgba2rgb(imread('images/book.png'))) # street

Noisy Image Restoration with Markov Random Field

#% matplotlib inline
%matplotlib inline

Image Completion with Inpainting (using Deep learning - pre-trained torch CompletionNet model)
Only with python 3.7 torch==0.4.1 torchvision==0.2.0

#from torch.legacy import nn
#from torch.legacy.nn.Sequential import Sequential
from torch import nn
from torch.nn import Sequential

Image Restoration with Dictionary Learning
Online Dictionary Learning

#lena = rgb2gray(imread('images/lena.png'))
lena = rgb2gray(rgba2rgb(imread('images/lena.png')))

Image Compression with Wavelets

#imw = soft_threshold(imw, 12)
imw = soft_threshold((imw), 12)

Untersuche die Notebooks oder Beispiele von Chapter 01, Chapter 02 und Chapter 03 und führe sie anschließend mit Python 3.9 und aktuellen Bibliotheken aus, um die aufgeführten Warnungen und Fehler zu reproduzieren. Wende die dokumentierten Kompatibilitätsaktualisierungen an und überprüfe, dass die betroffenen Beispiele zur Bildverarbeitung ohne Deprecation-Warnungen oder Fehler ausgeführt werden.