python 提取图像特征点 并 均匀化特征点
时间: 2023-07-27 09:25:16 浏览: 37
要提取图像特征点,可以使用常见的特征点检测算法,如SIFT、SURF、ORB等。以下以SIFT为例:
```python
import cv2
# 读取图像
img = cv2.imread('image.jpg')
# 创建SIFT对象
sift = cv2.xfeatures2d.SIFT_create()
# 检测特征点并计算特征描述子
keypoints, descriptors = sift.detectAndCompute(img, None)
```
要均匀化特征点,可以将图像分成若干个区域,对每个区域内的特征点进行均匀化处理。以下代码实现了将图像分成4个区域,对每个区域内的特征点进行均匀化处理:
```python
import numpy as np
# 将图像分成4个区域
rows, cols = img.shape[:2]
cell_size = 100
n_cells_row = int(rows / cell_size)
n_cells_col = int(cols / cell_size)
# 计算每个区域内的特征点数量
cell_size = 100
n_cells_row = int(rows / cell_size)
n_cells_col = int(cols / cell_size)
n_keypoints = [0] * (n_cells_row * n_cells_col)
for kp in keypoints:
row = int(kp.pt[1] / cell_size)
col = int(kp.pt[0] / cell_size)
n_keypoints[row * n_cells_col + col] += 1
# 计算每个区域内的特征点数目平均值
avg_n_keypoints = np.mean(n_keypoints)
# 均匀化处理
for i in range(n_cells_row):
for j in range(n_cells_col):
if n_keypoints[i * n_cells_col + j] > avg_n_keypoints:
continue
x_min = j * cell_size
y_min = i * cell_size
x_max = min((j+1)*cell_size, cols)
y_max = min((i+1)*cell_size, rows)
cell_keypoints = [kp for kp in keypoints if x_min <= kp.pt[0] < x_max and y_min <= kp.pt[1] < y_max]
if not cell_keypoints:
continue
cell_descriptors = descriptors[[keypoints.index(kp) for kp in cell_keypoints]]
n_descriptors = len(cell_descriptors)
n_descriptors_to_add = int(avg_n_keypoints - n_descriptors)
if n_descriptors_to_add <= 0:
continue
new_descriptors = np.zeros((n_descriptors_to_add, 128))
for k in range(n_descriptors_to_add):
idx = np.random.randint(0, n_descriptors)
new_descriptors[k] = cell_descriptors[idx]
descriptors = np.vstack((descriptors, new_descriptors))
new_keypoints = sift.detect(img[y_min:y_max, x_min:x_max], None)
for kp in new_keypoints:
kp.pt = (kp.pt[0]+x_min, kp.pt[1]+y_min)
keypoints.append(kp)
```
以上代码将图像分成4个区域,对每个区域内的特征点进行均匀化处理,使每个区域内的特征点数目接近于平均值。如果某个区域内的特征点数目已经大于平均值,则不进行处理。