Computer vision ORB后用PCA降维

我尝试使用ORB和PCA对提取的特征进行降维，然后提取PCA处理过的关键点

plt.rcParams['figure.figsize'] = [14.0, 7.0]

e1 = cv2.getTickCount()
#read image and convert to RGB
image_normal = cv2.imread(image)
image_query = cv2.cvtColor(image_normal, cv2.COLOR_BGR2RGB)
image_gray  = cv2.cvtColor(image_query, cv2.COLOR_RGB2GRAY)

#ORB function
orb = cv2.ORB_create()
keypoints, descriptor = orb.detectAndCompute(image_gray, None)

# Draw the keypoints 
image = cv2.drawKeypoints(image_query, keypoints,0,flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
mean, eigenvectors = cv2.PCACompute(image, np.mean(image, axis=0).reshape(1,-1))          

e2 = cv2.getTickCount()
t = (e2 - e1) / cv2.getTickFrequency() 

#Show Image
plt.imshow(image)
plt.title('ORB Keypoints')
plt.show()

# Print the number of keypoints detected and time
print("\nNumber of keypoints Detected: ", len(keypoints))
print('Time Cost for ORB: ', (t))

当我尝试时，我得到了一个错误 错误：OpenCV（3.4.2）C:\projects\OpenCV- python\opencv\modules\core\src\pca.cpp:72:错误：（-215:断言失败）

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函数“cv:：PCA:：operator（）”中的data.channels（）==1

看起来您试图在绘制关键点的图像上计算PCA，而不是在计算的描述符上。感谢您的回复，如何使用PCA计算descrptor，我对opencv非常陌生。看起来你试图在绘制关键点的图像上计算PCA，而不是在计算描述符上。谢谢你的回答，如何使用PCA计算descrptor，我对opencv非常陌生