Image restoration, Noise, Segmentation and Contours
So far, you have done some very cool things with your image processing skills! In this chapter, you will apply image restoration to remove objects, logos, text, or damaged areas in pictures! You will also learn how to apply noise, use segmentation to speed up processing, and find elements in images by their contours. This is the Summary of lecture "Image Processing in Python", via datacamp.
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
plt.rcParams['figure.figsize'] = (10, 8)
Let's restore a damaged image
In this exercise, we'll restore an image that has missing parts in it, using the inpaint_biharmonic()
function.
We'll work on an image with damaged. Some of the pixels have been replaced by 1s using a binary mask, on purpose, to simulate a damaged image. Replacing pixels with 1s turns them totally black.
The mask is a black and white image with patches that have the position of the image bits that have been corrupted. We can apply the restoration function on these areas.
Remember that inpainting is the process of reconstructing lost or deteriorated parts of images and videos.
def show_image(image, title='Image', cmap_type='gray'):
plt.imshow(image, cmap=cmap_type)
plt.title(title)
plt.axis('off')
def plot_comparison(img_original, img_filtered, img_title_filtered):
fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(10, 8), sharex=True, sharey=True)
ax1.imshow(img_original, cmap=plt.cm.gray)
ax1.set_title('Original')
ax1.axis('off')
ax2.imshow(img_filtered, cmap=plt.cm.gray)
ax2.set_title(img_title_filtered)
ax2.axis('off')
from skimage.restoration import inpaint
from skimage.transform import resize
from skimage import color
defect_image = plt.imread('./dataset/damaged_astronaut.png')
defect_image = resize(defect_image, (512, 512))
defect_image = color.rgba2rgb(defect_image)
mask = pd.read_csv('./dataset/astronaut_mask.csv').to_numpy()
# Apply the restoration function to the image using the mask
restored_image = inpaint.inpaint_biharmonic(defect_image, mask, multichannel=True)
# Show ther defective image
plot_comparison(defect_image, restored_image, 'Restored image')
Removing logos
As we saw in the video, another use of image restoration is removing objects from an scene. In this exercise, we'll remove the Datacamp logo from an image.
You will create and set the mask to be able to erase the logo by inpainting this area.
Remember that when you want to remove an object from an image you can either manually delineate that object or run some image analysis algorithm to find it.
image_with_logo = plt.imread('./dataset/4.2.06_w_logo_2_2.png')
# Initialize the mask
mask = np.zeros(image_with_logo.shape[:-1])
# Set the pixels where the logo is to 1
mask[210:272, 360:425] = 1
# Apply inpainting to remove the logo
image_logo_removed = inpaint.inpaint_biharmonic(image_with_logo,
mask,
multichannel=True)
# Show the original and logo removed images
plot_comparison(image_with_logo, image_logo_removed, 'Image with logo removed')
from skimage.util import random_noise
fruit_image = plt.imread('./dataset/fruits_square.jpg')
# Add noise to the image
noisy_image = random_noise(fruit_image)
# Show th original and resulting image
plot_comparison(fruit_image, noisy_image, 'Noisy image')
from skimage.restoration import denoise_tv_chambolle
noisy_image = plt.imread('./dataset/miny.jpeg')
# Apply total variation filter denoising
denoised_image = denoise_tv_chambolle(noisy_image, multichannel=True)
# Show the noisy and denoised image
plot_comparison(noisy_image, denoised_image, 'Denoised Image')
from skimage.restoration import denoise_bilateral
landscape_image = plt.imread('./dataset/noise-noisy-nature.jpg')
# Apply bilateral filter denoising
denoised_image = denoise_bilateral(landscape_image, multichannel=True)
# Show original and resulting images
plot_comparison(landscape_image, denoised_image, 'Denoised Image')