Jump into filtering

Traitement d’images en Python

Rebeca Gonzalez

Data Engineer

Filters

Enhancing an image
Emphasize or remove features
Smoothing
Sharpening
Edge detection

Neighborhoods

Filter neighborhood

Edge detection

Coins in where edges are detected

Edge detection

Original image of chocolate kisses

Chocolate kisses image in where the edges are detected

Edge detection

Sobel

# Import module and function 
from skimage.filters import sobel


# Apply edge detection filter
edge_sobel = sobel(image_coins)


# Show original and resulting image to compare
plot_comparison(image_coins, edge_sobel, "Edge with Sobel")

Edge detection

Sobel

Coins image in where edges are detected showing the shape

Comparing plots

def plot_comparison(original, filtered, title_filtered):

    fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(8, 6), sharex=True,
                                   sharey=True)
    ax1.imshow(original, cmap=plt.cm.gray)
    ax1.set_title('original')
    ax1.axis('off')
    ax2.imshow(filtered, cmap=plt.cm.gray)
    ax2.set_title(title_filtered)
    ax2.axis('off')

Gaussian smoothing

Image of a dog next to the same image with Gaussian filter applied

Gaussian smoothing

Image of Rebeca in Amsterdam holding a bike in a bridge

Gaussian smoothing

# Import the module and function
from skimage.filters import gaussian


# Apply edge detection filter
gaussian_image = gaussian(amsterdam_pic, multichannel=True)


# Show original and resulting image to compare
plot_comparison(amsterdam_pic, gaussian_image, "Blurred with Gaussian filter")

Gaussian smoothing

Resulted image

Gaussian smoothing

Resulted image zoomed in

Let's practice!

Traitement d’images en Python