SURF descriptors with opencv 2.4.6.1 and Python
我正在使用Python和opencv进行SURF功能检测。 我在stackoverflow OpenCV 2.4.1上找到了这些示例-在Python中计算SURF描述符，但是不幸的是，它们无法与最新版本的opencv(即2.4.6.1)一起使用。 cv2.SURF.detect()命令必须已更改，因为它现在仅允许使用两个参数：
1 | cv2.SURF.detect(image[, mask]) → keypoints? |
这样我就可以获取关键点，但是如何获取描述符呢？ 找不到解决方案。 希望你能在这里帮助我。 谢谢
根据Abid Rahman K在评论中发布的教程，我修改了此示例代码OpenCV 2.4.1-在Python中计算SURF描述符，因此它可以与opencv 2.4.6.1一起使用
获取SURF关键点和描述符的功能已更改为：
1 | cv2.SURF.detectAndCompute(image, mask[, descriptors[, useProvidedKeypoints]]) → keypoints, descriptors |
因此，这是链接的修改示例：
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 | import cv2 import numpy opencv_haystack =cv2.imread('haystack.jpg') opencv_needle =cv2.imread('needle.jpg') ngrey = cv2.cvtColor(opencv_needle, cv2.COLOR_BGR2GRAY) hgrey = cv2.cvtColor(opencv_haystack, cv2.COLOR_BGR2GRAY) # build feature detector and descriptor extractor hessian_threshold = 5000 detector = cv2.SURF(hessian_threshold) hkeypoints,hdescriptors = detector.detectAndCompute(hgrey,None) nkeypoints,ndescriptors = detector.detectAndCompute(ngrey,None) # extract vectors of size 64 from raw descriptors numpy arrays rowsize = len(hdescriptors) / len(hkeypoints) if rowsize > 1: hrows = numpy.array(hdescriptors, dtype = numpy.float32).reshape((-1, rowsize)) nrows = numpy.array(ndescriptors, dtype = numpy.float32).reshape((-1, rowsize)) #print hrows.shape, nrows.shape else: hrows = numpy.array(hdescriptors, dtype = numpy.float32) nrows = numpy.array(ndescriptors, dtype = numpy.float32) rowsize = len(hrows[0]) # kNN training - learn mapping from hrow to hkeypoints index samples = hrows responses = numpy.arange(len(hkeypoints), dtype = numpy.float32) #print len(samples), len(responses) knn = cv2.KNearest() knn.train(samples,responses) # retrieve index and value through enumeration for i, descriptor in enumerate(nrows): descriptor = numpy.array(descriptor, dtype = numpy.float32).reshape((1, rowsize)) #print i, descriptor.shape, samples[0].shape retval, results, neigh_resp, dists = knn.find_nearest(descriptor, 1) res, dist = int(results[0][0]), dists[0][0] #print res, dist if dist < 0.1: # draw matched keypoints in red color color = (0, 0, 255) else: # draw unmatched in blue color color = (255, 0, 0) # draw matched key points on haystack image x,y = hkeypoints[res].pt center = (int(x),int(y)) cv2.circle(opencv_haystack,center,2,color,-1) # draw matched key points on needle image x,y = nkeypoints[i].pt center = (int(x),int(y)) cv2.circle(opencv_needle,center,2,color,-1) cv2.imshow('haystack',opencv_haystack) cv2.imshow('needle',opencv_needle) cv2.waitKey(0) cv2.destroyAllWindows() |