HOG特征检测

HOG（Histogram of Oriented Gradient）特征角点检测是一种在计算机视觉领域广泛应用的方法，用于检测图像中的角点。下面是一个简单的Python实现，具体如下： ```python import cv2 import numpy as np def hog_corner_detection(img, blockSize=2, ksize=3, k=0.04, threshold=0.1): # 计算x和y方向的Sobel导数 sobelx = cv2.Sobel(img, cv2.CV_64F, 1, 0, ksize=ksize) sobely = cv2.Sobel(img, cv2.CV_64F, 0, 1, ksize=ksize) # 计算Ix^2, Iy^2, Ix*Iy Ix2 = sobelx ** 2 Iy2 = sobely ** 2 Ixy = sobelx * sobely # 在每个像素周围计算积分和 height, width = img.shape Sx2 = np.zeros((height, width)) Sy2 = np.zeros((height, width)) Sxy = np.zeros((height, width)) for i in range(height): for j in range(width): if i == 0 or j == 0: Sx2[i][j] = Ix2[i][j] Sy2[i][j] = Iy2[i][j] Sxy[i][j] = Ixy[i][j] else: Sx2[i][j] = Sx2[i-1][j] + Ix2[i][j] Sy2[i][j] = Sy2[i][j-1] + Iy2[i][j] Sxy[i][j] = Sxy[i-1][j] + Sxy[i][j-1] + Ixy[i][j] - Sxy[i-1][j-1] # 计算每个像素的角点响应函数值 R = np.zeros((height, width)) for i in range(blockSize, height-blockSize): for j in range(blockSize, width-blockSize): # 计算每个块的Sx2, Sy2, Sxy Sx2_block = Sx2[i-blockSize:i+1+blockSize, j-blockSize:j+1+blockSize] Sy2_block = Sy2[i-blockSize:i+1+blockSize, j-blockSize:j+1+blockSize] Sxy_block = Sxy[i-blockSize:i+1+blockSize, j-blockSize:j+1+blockSize] # 计算块内角点响应函数值 det = Sx2_block * Sy2_block - Sxy_block ** 2 trace = Sx2_block + Sy2_block R[i][j] = np.min(det) / (np.max(trace) + 1e-12) # 选取响应函数值大于阈值的像素位置作为角点 corners = [] for i in range(height): for j in range(width): if R[i][j] > threshold: corners.append((i, j)) return corners ``` 其中，`img`是输入的灰度图像，`blockSize`是块的大小（默认为2），`ksize`是Sobel算子的大小（默认为3），`k`是常数因子（默认为0.04），`threshold`是响应函数值的阈值（默认为0.1）。函数返回一个列表，其中包含检测到的角点的位置。