Scikit Image − Scharr Filter

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The Scharr filter is a type of edge detection filter used in image processing and computer vision. It is designed to compute gradients and edges in images, and it offers better rotation invariance compared to other commonly used edge filters such as the Sobel or Prewitt operators.

This filter is used to enhance the edges and boundaries within an image, which is often an important step in various image processing tasks, including object detection, image segmentation, and feature extraction.

The scikit-image (skimage) library offers three key functions within its filter module for applying the Scharr filter to images, which include scharr(), scharr_h(), and scharr_v().

Using the skimage.filters.scharr() function

The scharr() function is used to compute the edge magnitude using the Scharr transform on an input image.

Syntax

Following is the syntax of this function −

skimage.filters.scharr(image, mask=None, *, axis=None, mode='reflect', cval=0.0)

Parameters

Here are the details of the parameters −

• image (array): The input image on which the Scharr edge transform will be computed.
• mask (array of bool, optional): this boolean array can be used to clip the output image. Wherever mask is set to 0, the corresponding values in the output image will be set to 0 as well.
• axis (int or sequence of int, optional): This parameter specifying the axis or axes along which to compute the edge filter. If not provided, the edge magnitude is computed. Which is defined as:

sch_mag = np.sqrt(sum([scharr(image, axis=i)**2 for i in range(image.ndim)]) / image.ndim)

mode (str or sequence of str, optional): This parameter specifies the boundary mode for the convolution. It determines how the filter behaves near the edges of the image. It can be a single boundary mode or one boundary mode per axis. Possible modes include 'reflect' (default), 'constant', 'nearest', etc. Refer to the documentation of scipy.ndimage.convolve for a description of these modes.

cval (float, optional): This parameter is only relevant when the mode is set to 'constant'. It defines the constant value used for values outside the boundary of the image data.

Return value

The function returns an array of floats, which represents the Scharr edge map.

Example

Here's a simple example of applying the Scharr filter to an image using the scharr() filter −

import matplotlib.pyplot as plt
from skimage.filters import scharr
from skimage import io

# Load the input image 
image = io.imread('Images/lines.jpg')

# Apply the Scharr filter 
filtered_image = scharr(image)

# Plot the original and filtered images
fig, axes = plt.subplots(1, 2, figsize=(10, 5))
ax = axes.ravel()

# Display the Original Image
ax[0].imshow(image, cmap='gray')
ax[0].set_title('Original Image')
ax[0].axis('off')

# Display the Scharr Filter Result
ax[1].imshow(filtered_image, cmap='gray')
ax[1].axis('off')
ax[1].set_title('Scharr Filter Result')

plt.tight_layout()
plt.show()

Output

Horizontal and Vertical edge detection using the scharr_h() and scharr_v() functions

The skimage.filters.scharr_h() and skimage.filters.scharr_v() functions are used to detect horizontal and vertical edges, respectively, in a 2-D image using the Scharr transform. These functions compute the gradients of the image in the horizontal and vertical directions. Both functions return a 2-D array, which represents the Scharr edge map.

Syntax

Syntax for scharr_h() function −

skimage.filters.scharr_h(image, mask=None)

Syntax

Syntax for scharr_v() function −

skimage.filters.scharr_v(image, mask=None)

Parameters

Here is an explanation of the parameters for both functions −

• image (2-D array): This parameter represents the input image on which the horizontal and vertical edge detection using the Scharr transform will be processed.
• mask (optional, 2-D array): mask (optional, 2-D array): An optional mask to limit the application of the Scharr transform to a specific area of the image. Pixels surrounding the masked regions are also masked to ensure they do not influence the result.

Example

This example applies the scharr horizontal and vertical edge detection filters on an input image using the scharr_h() and scharr_v() function −

import matplotlib.pyplot as plt
from skimage.filters import scharr_h, scharr_v
from skimage import io, color

# Load the input image 
image = io.imread('Images/Lines.jpg')

# Convert the image to grayscale 
gray_image = color.rgb2gray(image)

# Apply the Scharr horizontal filter
filtered_image_h = scharr_h(gray_image)

# Apply the Scharr vertical filter
filtered_image_v = scharr_v(gray_image)

# Plot the original, Scharr horizontal, and Scharr vertical filtered images
fig, axes = plt.subplots(1, 3, figsize=(15, 5))
ax = axes.ravel()

# Display the Original Image
ax[0].imshow(gray_image, cmap='gray')
ax[0].set_title('Original Image')
ax[0].axis('off')

# Display the Scharr Horizontal Filter Result
ax[1].imshow(filtered_image_h, cmap='gray')
ax[1].set_title('Scharr Horizontal Filter Result')
ax[1].axis('off')

# Display the Scharr Vertical Filter Result
ax[2].imshow(filtered_image_v, cmap='gray')
ax[2].set_title('Scharr Vertical Filter Result')
ax[2].axis('off')

plt.tight_layout()
plt.show()

Output