HOG (Histogram of Oriented Gradients) Method
Introduction
The Histogram of Oriented Gradients (HOG) is a feature descriptor used in computer vision and image processing for object detection. It is particularly effective for detecting faces, pedestrians, and other objects due to its ability to capture the structure and shape of objects. HOG works by counting occurrences of gradient orientation in localized portions of an image.How HOG Works
The HOG method involves several key steps:1. Gradient Computation: Calculate the gradient of the image to capture the edge information. This is usually done using the Sobel operator, which computes the intensity gradient of each pixel.
`python
import cv2
import numpy as np
Load the image
image = cv2.imread('image.jpg')Convert to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)Compute gradients using Sobel operator
gradient_x = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=5) gradient_y = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=5)`2. Magnitude and Angle Calculation: For each pixel, compute the magnitude and direction (angle) of the gradient. The magnitude indicates how strong the gradient is, while the angle gives the direction of the edge.
`python
magnitude = np.sqrt(gradient_x2 + gradient_y2)
angle = np.arctan2(gradient_y, gradient_x)
`
3. Cell Division: The image is divided into small connected regions, called cells (e.g., 8x8 pixels). For each cell, a histogram of gradient directions is created.
4. Histogram Normalization: The histograms are normalized across overlapping blocks to improve robustness to changes in illumination and contrast.
5. Feature Vector Creation: The final HOG descriptor is formed by concatenating the normalized histograms of all cells.
Practical Example
Consider detecting faces using HOG. The following code snippet demonstrates how to use the HOG method with OpenCV's pre-trained models.`python
import cv2
Load the HOG face detector
hog_face_detector = cv2.HOGDescriptor()Set the SVM detector to the pre-trained face detection model
hog_face_detector.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())Read the image
image = cv2.imread('face.jpg')Detect faces in the image
boxes, weights = hog_face_detector.detectMultiScale(image, winStride=(8, 8))Draw rectangles around the detected faces
for (x, y, w, h) in boxes: cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)Show the output image
cv2.imshow('Detected Faces', image) cv2.waitKey(0) cv2.destroyAllWindows()`