带词袋的 ORB FeatureDetector
ORB FeatureDetector with Bag Of Words
BOWImgDescriptorExtractor 必须接收 32F，所以 SURF 或 SIFT 必须用于 DescriptorExtractor，但对于 FeatureDetector 肯定可以任何你想要的，对吗？
我只是需要在这里澄清一下，我只见过有人说 "You can't use ORB with Bow" 但是在检测特征时，为什么使用哪个很重要？
我认为这无关紧要。您可以使用任意方法进行特征点检测（即 ORB、FAST、SIFT、SURF 等）。
问题可能出在下一步，从特征点描述，原因在their answer here关大说：
The link you posted, gives one possibilty to solve the issue of binary descriptors by simple conversion to float (CV_32F) and relies on the fact that OpenCV's k-means algorithm can only deal with CV_32F and uses L2-distance for comparison. Thus, the binary descriptors may however also cluster in a wrong way (since actually you want to have a Hamming distance measure)!
这就是为什么建议使用 SIFT/SURF 描述符的原因。但除此之外，您可以将不同类型的特征点检测器与不同类型的描述符混合使用。
粗略地说，与您的标题问题相关的 pseudo-code 是：
# Construct vocabulary
bow_trainer = cv2.BOWKMeansTrainer(50)
bow_trainer.add(np.float32(descriptors))
vocab = bow_trainer.cluster().astype(descriptor.dtype)
# Create an object for computing global image BoW descriptors
bow_descr = cv2.BOWImgDescriptorExtractor(ORBdetector, CV2.BFMatcher(CV.NORM_HAMMING))
bow_descr.setVocabulary(vocab)
# Load an image, find keypoints, compute global image descriptor
img = cv2.imread("PathtoImage", ...)
keypoints = detector.detect(img,None)
description = bow_descr.compute(img, kps)
# Visualization
plt.figure( ...)
# Distance calculation (assuming you have two histograms, stored each in the "description" variable)
dist = 1 - cv2.compareHist(pic1.description, pic2.description, cv2.HISTCMP_CORREL)
BOWImgDescriptorExtractor 必须接收 32F，所以 SURF 或 SIFT 必须用于 DescriptorExtractor，但对于 FeatureDetector 肯定可以任何你想要的，对吗？
我只是需要在这里澄清一下，我只见过有人说 "You can't use ORB with Bow" 但是在检测特征时，为什么使用哪个很重要？
我认为这无关紧要。您可以使用任意方法进行特征点检测（即 ORB、FAST、SIFT、SURF 等）。
问题可能出在下一步，从特征点描述，原因在their answer here关大说：
The link you posted, gives one possibilty to solve the issue of binary descriptors by simple conversion to float (CV_32F) and relies on the fact that OpenCV's k-means algorithm can only deal with CV_32F and uses L2-distance for comparison. Thus, the binary descriptors may however also cluster in a wrong way (since actually you want to have a Hamming distance measure)!
这就是为什么建议使用 SIFT/SURF 描述符的原因。但除此之外，您可以将不同类型的特征点检测器与不同类型的描述符混合使用。
粗略地说，与您的标题问题相关的 pseudo-code 是：
# Construct vocabulary
bow_trainer = cv2.BOWKMeansTrainer(50)
bow_trainer.add(np.float32(descriptors))
vocab = bow_trainer.cluster().astype(descriptor.dtype)
# Create an object for computing global image BoW descriptors
bow_descr = cv2.BOWImgDescriptorExtractor(ORBdetector, CV2.BFMatcher(CV.NORM_HAMMING))
bow_descr.setVocabulary(vocab)
# Load an image, find keypoints, compute global image descriptor
img = cv2.imread("PathtoImage", ...)
keypoints = detector.detect(img,None)
description = bow_descr.compute(img, kps)
# Visualization
plt.figure( ...)
# Distance calculation (assuming you have two histograms, stored each in the "description" variable)
dist = 1 - cv2.compareHist(pic1.description, pic2.description, cv2.HISTCMP_CORREL)