cv2.SIFT()
cv2.SURF()
cv2.HOGDescriptor()
使用cv2.SIFT的一个样例:(cv2.SURF使用与之类似)
#coding=utf-8 import cv2 import scipy as sp img1 = cv2.imread('x1.jpg',0) # queryImage img2 = cv2.imread('x2.jpg',0) # trainImage # Initiate SIFT detector sift = cv2.SIFT() # find the keypoints and descriptors with SIFT kp1, des1 = sift.detectAndCompute(img1,None) kp2, des2 = sift.detectAndCompute(img2,None) # FLANN parameters FLANN_INDEX_KDTREE = 0 index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5) search_params = dict(checks=50) # or pass empty dictionary flann = cv2.FlannBasedMatcher(index_params,search_params) matches = flann.knnMatch(des1,des2,k=2) print 'matches...',len(matches) # Apply ratio test good = [] for m,n in matches: if m.distance < 0.75*n.distance: good.append(m) print 'good',len(good) # ##################################### # visualization h1, w1 = img1.shape[:2] h2, w2 = img2.shape[:2] view = sp.zeros((max(h1, h2), w1 + w2, 3), sp.uint8) view[:h1, :w1, 0] = img1 view[:h2, w1:, 0] = img2 view[:, :, 1] = view[:, :, 0] view[:, :, 2] = view[:, :, 0] for m in good: # draw the keypoints # print m.queryIdx, m.trainIdx, m.distance color = tuple([sp.random.randint(0, 255) for _ in xrange(3)]) #print 'kp1,kp2',kp1,kp2 cv2.line(view, (int(kp1[m.queryIdx].pt[0]), int(kp1[m.queryIdx].pt[1])) , (int(kp2[m.trainIdx].pt[0] + w1), int(kp2[m.trainIdx].pt[1])), color) cv2.imshow("view", view) cv2.waitKey()
cv2.HOGDescriptor()的例子:还可以参考:https://www.programcreek.com/python/example/84776/cv2.HOGDescriptor
def createTrainingInstances(self, images): start = time.time() hog = cv2.HOGDescriptor() instances = [] for img, label in images: # print img img = read_color_image(img) img = cv2.resize(img, (128, 128), interpolation = cv2.INTER_AREA) descriptor = hog.compute(img) if descriptor is None: descriptor = [] else: descriptor = descriptor.ravel() pairing = Instance(descriptor, label) instances.append(pairing) end = time.time() - start self.training_instances = instances print "HOG TRAIN SERIAL: %d images -> %f" % (len(images), end)
def createTestingInstances(self, images): start = time.time() hog = cv2.HOGDescriptor() instances = [] for img, label in images: # print img img = read_color_image(img) img = cv2.resize(img, (128, 128), interpolation = cv2.INTER_AREA) descriptor = hog.compute(img) if descriptor is None: descriptor = [] else: descriptor = descriptor.ravel() pairing = Instance(descriptor, label) instances.append(pairing) end = time.time() - start self.testing_instances = instances print "HOG TEST SERIAL: %d images -> %f" % (len(images), end)
还有:
def get_hog(image): # winSize = (64,64) winSize = (image.shape[1], image.shape[0]) blockSize = (8,8) # blockSize = (16,16) blockStride = (8,8) cellSize = (8,8) nbins = 9 derivAperture = 1 winSigma = 4. histogramNormType = 0 L2HysThreshold = 2.0000000000000001e-01 gammaCorrection = 0 nlevels = 64 hog = cv2.HOGDescriptor(winSize,blockSize,blockStride,cellSize,nbins,derivAperture,winSigma, histogramNormType,L2HysThreshold,gammaCorrection,nlevels) #compute(img[, winStride[, padding[, locations]]]) -> descriptors winStride = (8,8) padding = (8,8) locations = [] # (10, 10)# ((10,20),) hist = hog.compute(image,winStride,padding,locations) return hist