我们从Python开源项目中，提取了以下11个代码示例，用于说明如何使用skimage.feature.local_binary_pattern()。
def extract_RGB_LBP_features(image, labels, size=5, P=8, R=2): n_sp = np.max(labels)+1 hs = size//2 img_superpixel = np.zeros_like(labels, dtype='int') # calculate lbp for entire region lbp_img = np.empty((3, ), dtype='object') for d in range(3): lbp_img[d] = local_binary_pattern(image[..., d], P=P, R=R, method='uniform') feat_desc_size = P+1 feat_descs = np.zeros((n_sp, feat_desc_size*3)) for i in range(n_sp): # get centroid of i'th superpixel img_superpixel[:] = labels == i cy, cx = [np.rint(x).astype('int') for x in regionprops(img_superpixel)[0].centroid] # extract lbp values in sizeXsize region centred on the centroid x0, y0, x1, y1 = cx-hs, cy-hs, cx+hs+1, cy+hs+1 # clip to boundaries of image x0 = 0 if x0 < 0 else x0 y0 = 0 if y0 < 0 else y0 x1 = image.shape[1]-1 if x1 > image.shape[1]-2 else x1 y1 = image.shape[0]-1 if y1 > image.shape[0]-2 else y1 # fill in the feature vector for each image channel for d in range(3): j, k = d*feat_desc_size, (1+d)*feat_desc_size patch = lbp_img[d][y0:y1, x0:x1].flat fv = np.histogram(patch, bins=np.arange(0, feat_desc_size+1), range=(0, feat_desc_size+1))[0] feat_descs[i, j:k] = fv return feat_descs
def LBP(train_image,radius, no_points): im = cv2.imread(train_image) im_gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY) lbp = local_binary_pattern(im_gray, no_points, radius, method='default') #(hist, _) = np.histogram(lbp.ravel(), bins=np.arange(0,(no_points*(no_points-1)+4))) #hist = hist.astype("float") #hist /= (hist.sum() + eps) #print hist.sum() print lbp return lbp
def LBP(train_image,radius, no_points): im = cv2.imread(train_image) im_gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY) lbp = local_binary_pattern(im_gray, no_points, radius, method='default') (hist, _) = np.histogram(lbp[0:63][0:63].ravel(), bins=np.arange(0,(no_points*(no_points-1)+4))) hist = hist.astype("float") hist /= (hist.sum() + eps) #print hist.sum() #print lbp return hist
def calculatehistogram(self, image, eps=1e-7): lbp = feature.local_binary_pattern(image, self.numberOfPoints, self.radius, method="uniform") (histogram, _) = np.histogram(lbp.ravel(), bins=np.arange(0, self.numberOfPoints + 3), range=(0, self.numberOfPoints + 2)) #now we need to normalise the histogram so that the total sum=1 histogram = histogram.astype("float") histogram /= (histogram.sum() + eps) return histogram
def calculatehistogram(self, image, eps=1e-7): lbp = feature.local_binary_pattern(image, self.numberOfPoints, self.radius, method="uniform") (histogram, _) = np.histogram(lbp.ravel(), bins=np.arange(0, self.numberOfPoints + 3), range=(0, self.numberOfPoints + 2)) # now we need to normalise the histogram so that the total sum=1 histogram = histogram.astype("float") histogram /= (histogram.sum() + eps) return histogram
def getLBP(img): img2 = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) radius = 1 n_points = 8 * radius lbpImage = (local_binary_pattern(img2, n_points, radius)).astype(int)**(1.0/radius) # block processing: lbpImages = block_view(lbpImage, ( int(lbpImage.shape[0] / 2), int(lbpImage.shape[1] / 4))) count = 0 LBP = np.array([]); for i in range(lbpImages.shape[0]): # for each block: for j in range(lbpImages.shape[1]): count += 1 # plt.subplot(4,2,count) # plt.imshow(lbpImages[i,j,:,:],cmap = cm.Greys_r) # plt.subplot(4,2,count+4*2/2) # print count*2+1 LBPt = cv2.calcHist([lbpImages[i,j,:,:].astype('uint8')], [0], None, [8], [0, 256]) LBP = np.append(LBP, LBPt[:,0]); # plt.plot(LBPt) # plt.show() Fnames = ["LBP"+str(i).zfill(2) for i in range(len(LBP))] return normalize(LBP).tolist(), Fnames
def generate_lbp_features(filename): radius = 3 n_points = 10 * radius image_arr = io.imread(filename, as_grey=True) return local_binary_pattern(image_arr, P=n_points, R=radius)
