scikit-imageModule in Python
- Convert Image Format in Python
- Supervised Segmentation in Python
- Unsupervised Segmentation in Python
In this tutorial, we will learn how we can perform image segmentation in Python using the
Image segmentation divides images into many layers, each represented by a smart, pixel-wise mask. Combining, blocking, and splitting images based on their integration level.
scikit-image Module in Python
pip install scikit-image
After installation, we will convert the image format to perform segmentation.
Convert Image Format in Python
The required input for applying filters and other processing techniques is a two-dimensional vector, i.e., a monochromatic image.
We will use the
skimage.color.rgb2gray() function to convert an RGB image to grayscale format.
from skimage import data, io, filters from skimage.color import rgb2gray import matplotlib.pyplot as plt image = data.coffee() plt.figure(figsize=(15, 15)) gray_coffee = rgb2gray(image) plt.subplot(1, 2, 2) plt.imshow(gray_coffee, cmap="gray") plt.show()
We convert the sample coffee image in the
scikit library to grayscale in the above code.
Let us take a look at the grayscale version of the image.
We will perform the segmentation using two techniques, i.e., Supervised and Unsupervised segmentation.
Supervised Segmentation in Python
External input is required for this form of segmentation to work. We will perform this type using two approaches.
Segmentation by Thresholding – Manual Input in Python
An external pixel value from 0-255 is employed to distinguish the image from the background. As a result, the image is altered to be larger or smaller than the given threshold.
We will perform this method using the below code.
from skimage import data from skimage import filters from skimage.color import rgb2gray import matplotlib.pyplot as plt coffee = data.coffee() gray_coffee = rgb2gray(coffee) plt.figure(figsize=(15, 15)) for i in range(10): binarized_gray = (gray_coffee > i * 0.1) * 1 plt.subplot(5, 2, i + 1) plt.title("Threshold: >" + str(round(i * 0.1, 1))) plt.imshow(binarized_gray, cmap="gray") plt.show() plt.tight_layout()
Let us now see the output of the above code, where we can see the image segmented using various threshold values.
Let us learn another approach to supervised segmentation called Active Contour Segmentation.
Active Contour Segmentation in Python
An active contour is a segmentation method that separates the pixels of interest from the rest of the image for further processing and analysis using energy forces and limitations. By fitting snakes to image features, the
skimage.segmentation.active_contour() function creates active contours.
We will use the below code to apply this method.
import numpy as np import matplotlib.pyplot as plt from skimage.color import rgb2gray from skimage import data from skimage.filters import gaussian from skimage.segmentation import active_contour astronaut = data.astronaut() gray_astronaut = rgb2gray(astronaut) gray_astronaut_noiseless = gaussian(gray_astronaut, 1) x1 = 220 + 100 * np.cos(np.linspace(0, 2 * np.pi, 500)) x2 = 100 + 100 * np.sin(np.linspace(0, 2 * np.pi, 500)) snake = np.array([x1, x2]).T astronaut_snake = active_contour(gray_astronaut_noiseless, snake) fig = plt.figure(figsize=(10, 10)) ax = fig.add_subplot(111) ax.imshow(gray_astronaut_noiseless) ax.plot(astronaut_snake[:, 0], astronaut_snake[:, 1], "-b", lw=5) ax.plot(snake[:, 0], snake[:, 1], "--r", lw=5) plt.show()
We use the sample astronaut image in
skimage to perform active contour segmentation in the above code. We circle the part where we see the segmentation occurring.
Let us see the output of this technique.
Now let us explore the Unsupervised Segmentation technique.
Unsupervised Segmentation in Python
The first method we employ in this type of segmentation is marking the boundaries method.
Mark Boundaries Method in Python
This methodology provides an image with highlighted borders between labeled sections. The
skimage.segmentation.mark_boundaries() function returns an image with labelled region borders.
We will use the below code to employ this technique.
from skimage.segmentation import slic, mark_boundaries from skimage.data import astronaut import matplotlib.pyplot as plt plt.figure(figsize=(15, 15)) astronaut = astronaut() astronaut_segments = slic(astronaut, n_segments=100, compactness=1) plt.subplot(1, 2, 1) plt.imshow(astronaut) plt.subplot(1, 2, 2) plt.imshow(mark_boundaries(astronaut, astronaut_segments)) plt.show()
The segmented image was obtained using the above code for the following technique.
The second technique in this type of approach is Felzenszwalb’s Segmentation.
Felzenszwalb’s Segmentation in Python
Felzenszwalb’s Segmentation uses a rapid, minimum spanning tree-based clustering to over-segment an RGB picture on the image grid. The Euclidean distance between pixels is used in this approach.
Felzenszwalb’s efficient graph-based image segmentation is computed using the
Let us see the below code for performing this type of segmentation.
from skimage.segmentation import felzenszwalb, mark_boundaries from skimage.color import label2rgb from skimage.data import astronaut import matplotlib.pyplot as plt plt.figure(figsize=(15, 15)) astronaut = astronaut() astronaut_segments = felzenszwalb(astronaut, scale=2, sigma=5, min_size=100) plt.subplot(1, 2, 1) plt.imshow(astronaut) # Marking the boundaries of # Felzenszwalb's segmentations plt.subplot(1, 2, 2) plt.imshow(mark_boundaries(astronaut, astronaut_segments)) plt.show()
The above code’s output would be below.
Thus we have successfully performed image segmentation in Python using the
scikit-image module by employing multiple techniques in supervised and unsupervised segmentation approaches.