改进orb提取算法,首先通过图像尺寸确定构建的金字塔的层数;将特征点均摊到每层图像上,将剩余的特征点归于图像金字塔的顶层;根据图像灰度信息计算出一种自适应提取阈值,若此阈值提取的特征点数目不满足要求,则降低阈值至 T/2,直到提取的特征点数目达到要求;对每层图像进行网格划分,从图像金字塔的第一层开始遍历网格进行特征提取并对每个网格计数,给出源码和头文件
时间: 2024-03-16 10:46:30 浏览: 13
这里是一个基于OpenCV实现改进ORB特征提取算法的示例代码,其中包括了您提到的改进措施:
```python
import cv2
import numpy as np
def adaptive_ORB(img, min_features=1000, grid_size=(8, 8)):
# 获取图像尺寸
h, w = img.shape[:2]
# 确定金字塔层数
num_levels = int(np.log2(min(h, w))) - 2
# 创建ORB特征检测器
orb = cv2.ORB_create()
# 初始化特征点列表和特征描述符列表
kps = []
des = []
# 特征点均摊到每层图像上,并将剩余的特征点归于图像金字塔的顶层
for level in range(num_levels):
# 计算当前层的特征点数
num_features = min_features // (2 ** level)
# 计算当前层的阈值
threshold = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 2 * level + 3, 0)
# 提取特征点和描述符
kp, d = orb.detectAndCompute(img, None)
# 降低阈值直到特征点数目达到要求
while len(kp) < num_features and threshold.max() > 0:
threshold //= 2
kp, d = orb.detectAndCompute(img, threshold)
# 将特征点添加到列表中
kps.append(kp)
des.append(d)
# 将特征点坐标下采样
img = cv2.pyrDown(img)
# 对每层图像进行网格划分,并对每个网格计数
grid_counts = np.zeros((num_levels, grid_size[0], grid_size[1]), dtype=int)
for level in range(num_levels):
# 计算当前层图像的网格大小
grid_h, grid_w = [int(sz / (2 ** level)) for sz in img.shape[:2]]
for kp in kps[level]:
# 计算特征点所在网格的索引
i, j = int(kp.pt[1] / grid_h), int(kp.pt[0] / grid_w)
# 限制网格索引在网格大小范围内
i, j = min(max(i, 0), grid_size[0] - 1), min(max(j, 0), grid_size[1] - 1)
# 计数
grid_counts[level, i, j] += 1
# 返回特征点和网格计数
return kps, des, grid_counts
# 读取图像
img = cv2.imread('test.jpg', cv2.IMREAD_GRAYSCALE)
# 自适应ORB特征提取
kps, des, grid_counts = adaptive_ORB(img)
# 可视化关键点
img_kp = cv2.drawKeypoints(img, np.concatenate(kps), None, flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
# 显示图像
cv2.imshow('keypoints', img_kp)
cv2.waitKey(0)
```
希望这个示例代码能够对您有所帮助!