如何通过compute可视化hog特征
时间: 2023-11-16 19:05:58 浏览: 35
要可视化HOG特征,可以使用Python中的OpenCV库。以下是一个简单的代码示例:
```python
import cv2
import numpy as np
# 读取图像
img = cv2.imread('path/to/image.jpg')
# 计算HOG特征
winSize = (64, 128)
blockSize = (16, 16)
blockStride = (8, 8)
cellSize = (8, 8)
nbins = 9
hog = cv2.HOGDescriptor(winSize, blockSize, blockStride, cellSize, nbins)
features = hog.compute(img)
# 可视化HOG特征
hog_image = np.zeros((img.shape[0], img.shape[1]), dtype=np.float32)
gradient_strengths = features[:, 0]
gradient_orientations = features[:, 1]
cell_size = cellSize[0]
angle_unit = 360 / nbins
# 遍历每个cell
cell_count = 0
for i in range(int(img.shape[0] / cell_size)):
for j in range(int(img.shape[1] / cell_size)):
cell_grad_strengths = gradient_strengths[cell_count:cell_count + nbins]
cell_grad_orientations = gradient_orientations[cell_count:cell_count + nbins]
# 遍历每个bin
for k in range(nbins):
angle = cell_grad_orientations[k] * angle_unit
magnitude = cell_grad_strengths[k]
x = i * cell_size + cell_size / 2
y = j * cell_size + cell_size / 2
x1 = int(x + magnitude * np.cos(np.deg2rad(angle)) * cell_size / 2)
y1 = int(y + magnitude * np.sin(np.deg2rad(angle)) * cell_size / 2)
x2 = int(x - magnitude * np.cos(np.deg2rad(angle)) * cell_size / 2)
y2 = int(y - magnitude * np.sin(np.deg2rad(angle)) * cell_size / 2)
cv2.line(hog_image, (y1, x1), (y2, x2), int(255 / nbins) * (k + 1), 1)
cell_count += nbins
# 显示图像
cv2.imshow('HOG Features', hog_image)
cv2.waitKey(0)
cv2.destroyAllWindows()
```
在计算HOG特征后,我们遍历每个cell和bin,根据梯度方向和强度绘制线条。绘制的结果将显示为灰度图像,其中不同的颜色表示不同的方向。