Visualizing HOG Features with Python and skimage
To understand how we can implement and visualize Histogram of Oriented Gradients (HOG) features using Python’s skimage library. Let’s start by importing necessary modules: color conversion utilities from skimage, HOG feature extraction, image data, exposure utilities, input-output functions, and plotting functionalities from matplotlib.
- An example image of an astronaut is loaded using
data.astronaut()
. The image is converted to grayscale usingcolor.rgb2gray()
. converting the image to grayscale simplifies the feature extraction process and improves the performance and interpretability of the HOG algorithm for tasks such as object detection and recognition. - Next, HOG features are extracted from the grayscale image using the
hog()
function, specifying parameters like orientations, pixels per cell, and cells per block. Thevisualize=True
argument ensures the computation of the HOG image.
from skimage import color
from skimage.feature import hog
from skimage import data, exposure, io
import matplotlib.pyplot as plt
# Loading an example image
image = data.astronaut()
image_gray = color.rgb2gray(image) # Converting image to grayscale
# Extract HOG features
features, hog_image = hog(image_gray, orientations=9, pixels_per_cell=(8, 8),
cells_per_block=(2, 2), visualize=True)
plt.figure(figsize=(8, 4))
plt.subplot(1, 2, 1)
plt.imshow(image_gray, cmap='gray')
plt.title('Input image')
plt.subplot(1, 2, 2)
plt.imshow(hog_image, cmap='gray')
plt.title('HOG features')
plt.show()
Output:
By computing the distribution of local intensity gradients or edge directions in an image, HOG features capture the presence of specific shapes and edges.
HOG Feature Visualization in Python Using skimage
Object detection is a fundamental task in computer vision, where the goal is to identify and locate objects within images or videos. However, this task can be challenging due to the complexity of real-world images, which often contain varying lighting conditions, occlusions, and cluttered backgrounds. Traditional approaches to object detection rely on handcrafted features, which can be time-consuming and may not generalize well to new scenarios. One popular method for feature extraction is the Histogram of Oriented Gradients (HOG) technique.
In this article, we will understand and implement examples of visualizing HOG feature arrays using skimage.
Table of Content
- Understanding HOG Features
- Advantages of HOG Feature
- Visualizing HOG Features with Python and skimage
- Customizing HOG Feature Visualization with skimage