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专门做辅助的网站,自适应网站开发,外贸的整个详细流程,长安镇网站建设公司1. SIFT#xff0c;SuperPoint 都具有提取图片特征点#xff0c;并且输出特征描述子的特性#xff0c;本篇文章从特征点的提取数量#xff0c;特征点的正确匹配数量来探索一下二者的优劣。 SuperPoint提取到的特征点数量要少一些#xff0c;可以理解#xff0c;我想原因大…1. SIFTSuperPoint 都具有提取图片特征点并且输出特征描述子的特性本篇文章从特征点的提取数量特征点的正确匹配数量来探索一下二者的优劣。 SuperPoint提取到的特征点数量要少一些可以理解我想原因大概是SuperPoint训练使用的是合成数据集含有很多形状并且只标出了线段的一些拐点而sift对图像的像素值变化敏感。 在特征点匹配上感觉不出有什么明显的差异但是很明显SuperPoint的鲁棒性更高一些sift匹配有很多的错点比如SIFT第三幅图中的牛奶盒子由于物体没有上下的起伏可以认为连线中的斜线都是错匹配。 在形状较为复杂的情况下 正如上文所说SuperPoint对形状较多的图片敏感。 同样值得注意的是第一张图的窗子的点SuperPoint并没有检测出来。

1. 总结 在捕捉特征点的时候SuperPoint对形状的特征点敏感SIFT对像素的变化敏感 在进行特征点匹配的时候SuperPoint的特征描述子鲁棒性更好一些 视角变化较大的情况下二者的表现都差强人意 代码 SIFT.py from future import print_function import cv2 as cv import numpy as np import argparsepic1 ./1.ppm pic2 ./6.ppmparser argparse.ArgumentParser(descriptionCode for Feature Matching with FLANN tutorial.) parser.add_argument(–input1, helpPath to input image 1., defaultpic1) parser.add_argument(–input2, helpPath to input image 2., defaultpic2) args parser.parse_args() img_object cv.imread(pic1) img_scene cv.imread(pic2) if img_object is None or img_scene is None:print(Could not open or find the images!)exit(0)#– Step 1: Detect the keypoints using SURF Detector, compute the descriptors minHessian 600 detector cv.xfeatures2d_SURF.create(hessianThresholdminHessian) keypoints_obj, descriptors_obj detector.detectAndCompute(img_object, None) keypoints_scene, descriptors_scene detector.detectAndCompute(img_scene, None)#– Step 2: Matching descriptor vectors with a FLANN based matcher

   Since SURF is a floating-point descriptor NORM_L2 is used

   matcher cv.DescriptorMatcher_create(cv.DescriptorMatcher_FLANNBASED) knn_matches matcher.knnMatch(descriptors_obj, descriptors_scene, 2)#– Filter matches using the Lowes ratio test ratio_thresh 0.75 good_matches [] for m,n in knn_matches:if m.distance ratio_thresh * n.distance:good_matches.append(m)print(The number of keypoints in image1 is, len(keypoints_obj)) print(The number of keypoints in image2 is, len(keypoints_scene)) #– Draw matches img_matches np.empty((max(img_object.shape[0], img_scene.shape[0]), img_object.shape[1]img_scene.shape[1], 3), dtypenp.uint8) cv.drawMatches(img_object, keypoints_obj, img_scene, keypoints_scene, good_matches, img_matches, flagscv.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)cv.namedWindow(Good Matches of SIFT, 0) cv.resizeWindow(Good Matches of SIFT, 1024, 1024) cv.imshow(Good Matches of SIFT, img_matches) cv.waitKey() 使用sift.py时只需要修改第6,7行的图片路径即可。 SuperPoint import numpy as np import os import cv2 import torch# Jet colormap for visualization. myjet np.array([[0., 0., 0.5],[0., 0., 0.99910873],[0., 0.37843137, 1.],[0., 0.83333333, 1.],[0.30044276, 1., 0.66729918],[0.66729918, 1., 0.30044276],[1., 0.90123457, 0.],[1., 0.48002905, 0.],[0.99910873, 0.07334786, 0.],[0.5, 0., 0.]])class SuperPointNet(torch.nn.Module): Pytorch definition of SuperPoint Network. def init(self):super(SuperPointNet, self).init()self.relu torch.nn.ReLU(inplaceTrue)self.pool torch.nn.MaxPool2d(kernel_size2, stride2)c1, c2, c3, c4, c5, d1 64, 64, 128, 128, 256, 256# Shared Encoder.self.conv1a torch.nn.Conv2d(1, c1, kernel_size3, stride1, padding1)self.conv1b torch.nn.Conv2d(c1, c1, kernel_size3, stride1, padding1)self.conv2a torch.nn.Conv2d(c1, c2, kernel_size3, stride1, padding1)self.conv2b torch.nn.Conv2d(c2, c2, kernel_size3, stride1, padding1)self.conv3a torch.nn.Conv2d(c2, c3, kernel_size3, stride1, padding1)self.conv3b torch.nn.Conv2d(c3, c3, kernel_size3, stride1, padding1)self.conv4a torch.nn.Conv2d(c3, c4, kernel_size3, stride1, padding1)self.conv4b torch.nn.Conv2d(c4, c4, kernel_size3, stride1, padding1)# Detector Head.self.convPa torch.nn.Conv2d(c4, c5, kernel_size3, stride1, padding1)self.convPb torch.nn.Conv2d(c5, 65, kernel_size1, stride1, padding0)# Descriptor Head.self.convDa torch.nn.Conv2d(c4, c5, kernel_size3, stride1, padding1)self.convDb torch.nn.Conv2d(c5, d1, kernel_size1, stride1, padding0)def forward(self, x): Forward pass that jointly computes unprocessed point and descriptortensors.Inputx: Image pytorch tensor shaped N x 1 x H x W.Outputsemi: Output point pytorch tensor shaped N x 65 x H/8 x W/8.desc: Output descriptor pytorch tensor shaped N x 256 x H/8 x W/8.# Shared Encoder.x self.relu(self.conv1a(x))x self.relu(self.conv1b(x))x self.pool(x)x self.relu(self.conv2a(x))x self.relu(self.conv2b(x))x self.pool(x)x self.relu(self.conv3a(x))x self.relu(self.conv3b(x))x self.pool(x)x self.relu(self.conv4a(x))x self.relu(self.conv4b(x))# Detector Head.cPa self.relu(self.convPa(x))semi self.convPb(cPa)# Descriptor Head.cDa self.relu(self.convDa(x))desc self.convDb(cDa)dn torch.norm(desc, p2, dim1) # Compute the norm.desc desc.div(torch.unsqueeze(dn, 1)) # Divide by norm to normalize.return semi, descclass SuperPointFrontend(object): Wrapper around pytorch net to help with pre and post image processing. def init(self, weights_path, nms_dist, conf_thresh, nn_thresh,cudaFalse):self.name SuperPointself.cuda cudaself.nms_dist nms_distself.conf_thresh conf_threshself.nn_thresh nn_thresh # L2 descriptor distance for good match.self.cell 8 # Size of each output cell. Keep this fixed.self.border_remove 4 # Remove points this close to the border.# Load the network in inference mode.self.net SuperPointNet()if cuda:# Train on GPU, deploy on GPU.self.net.load_state_dict(torch.load(weights_path))self.net self.net.cuda()else:# Train on GPU, deploy on CPU.self.net.load_state_dict(torch.load(weights_path,map_locationlambda storage, loc: storage))self.net.eval()def nms_fast(self, in_corners, H, W, dist_thresh):Run a faster approximate Non-Max-Suppression on numpy corners shaped:3xN [x_i,y_i,conf_i]^TAlgo summary: Create a grid sized HxW. Assign each corner location a 1, restare zeros. Iterate through all the 1s and convert them either to -1 or 0.Suppress points by setting nearby values to 0.Grid Value Legend:-1 : Kept.0 : Empty or suppressed.1 : To be processed (converted to either kept or supressed).NOTE: The NMS first rounds points to integers, so NMS distance might notbe exactly dist_thresh. It also assumes points are within image boundaries.Inputsin_corners - 3xN numpy array with corners [x_i, y_i, confidence_i]^T.H - Image height.W - Image width.dist_thresh - Distance to suppress, measured as an infinty norm distance.Returnsnmsed_corners - 3xN numpy matrix with surviving corners.nmsed_inds - N length numpy vector with surviving corner indices.grid np.zeros((H, W)).astype(int) # Track NMS data.inds np.zeros((H, W)).astype(int) # Store indices of points.# Sort by confidence and round to nearest int.inds1 np.argsort(-in_corners[2, :])corners in_corners[:, inds1]rcorners corners[:2, :].round().astype(int) # Rounded corners.# Check for edge case of 0 or 1 corners.if rcorners.shape[1] 0:return np.zeros((3, 0)).astype(int), np.zeros(0).astype(int)if rcorners.shape[1] 1:out np.vstack((rcorners, in_corners[2])).reshape(3, 1)return out, np.zeros((1)).astype(int)# Initialize the grid.for i, rc in enumerate(rcorners.T):grid[rcorners[1, i], rcorners[0, i]] 1inds[rcorners[1, i], rcorners[0, i]] i# Pad the border of the grid, so that we can NMS points near the border.pad dist_threshgrid np.pad(grid, ((pad, pad), (pad, pad)), modeconstant)# Iterate through points, highest to lowest conf, suppress neighborhood.count 0for i, rc in enumerate(rcorners.T):# Account for top and left padding.pt (rc[0] pad, rc[1] pad)if grid[pt[1], pt[0]] 1: # If not yet suppressed.grid[pt[1] - pad:pt[1] pad 1, pt[0] - pad:pt[0] pad 1] 0grid[pt[1], pt[0]] -1count 1# Get all surviving -1s and return sorted array of remaining corners.keepy, keepx np.where(grid -1)keepy, keepx keepy - pad, keepx - padinds_keep inds[keepy, keepx]out corners[:, inds_keep]values out[-1, :]inds2 np.argsort(-values)out out[:, inds2]out_inds inds1[inds_keep[inds2]]return out, out_indsdef run(self, img): Process a numpy image to extract points and descriptors.Inputimg - HxW numpy float32 input image in range [0,1].Outputcorners - 3xN numpy array with corners [x_i, y_i, confidence_i]^T.desc - 256xN numpy array of corresponding unit normalized descriptors.heatmap - HxW numpy heatmap in range [0,1] of point confidences.assert img.ndim 2, Image must be grayscale.assert img.dtype np.float32, Image must be float32.H, W img.shape[0], img.shape[1]inp img.copy()inp (inp.reshape(1, H, W))inp torch.from_numpy(inp)inp torch.autograd.Variable(inp).view(1, 1, H, W)if self.cuda:inp inp.cuda()# Forward pass of network.outs self.net.forward(inp)semi, coarse_desc outs[0], outs[1]# Convert pytorch - numpy.semi semi.data.cpu().numpy().squeeze()# — Process points.# C np.max(semi)# dense np.exp(semi - C) # Softmax.# dense dense / (np.sum(dense)) # Should sum to 1.dense np.exp(semi) # Softmax.dense dense / (np.sum(dense, axis0) .00001) # Should sum to 1.# Remove dustbin.nodust dense[:-1, :, :]# Reshape to get full resolution heatmap.Hc int(H / self.cell)Wc int(W / self.cell)nodust nodust.transpose(1, 2, 0)heatmap np.reshape(nodust, [Hc, Wc, self.cell, self.cell])heatmap np.transpose(heatmap, [0, 2, 1, 3])heatmap np.reshape(heatmap, [Hc * self.cell, Wc * self.cell])xs, ys np.where(heatmap self.conf_thresh) # Confidence threshold.if len(xs) 0:return np.zeros((3, 0)), None, Nonepts np.zeros((3, len(xs))) # Populate point data sized 3xN.pts[0, :] yspts[1, :] xspts[2, :] heatmap[xs, ys]pts, _ self.nms_fast(pts, H, W, dist_threshself.nms_dist) # Apply NMS.inds np.argsort(pts[2, :])pts pts[:, inds[::-1]] # Sort by confidence.# Remove points along border.bord self.border_removetoremoveW np.logical_or(pts[0, :] bord, pts0, :)toremoveH np.logical_or(pts[1, :] bord, pts1, :)toremove np.logical_or(toremoveW, toremoveH)pts pts[:, ~toremove]# — Process descriptor.D coarse_desc.shape[1]if pts.shape[1] 0:desc np.zeros((D, 0))else:# Interpolate into descriptor map using 2D point locations.samp_pts torch.from_numpy(pts[:2, :].copy())samp_pts0, : - 1.samp_pts1, : - 1.samp_pts samp_pts.transpose(0, 1).contiguous()samp_pts samp_pts.view(1, 1, -1, 2)samp_pts samp_pts.float()if self.cuda:samp_pts samp_pts.cuda()desc torch.nn.functional.grid_sample(coarse_desc, samp_pts)desc desc.data.cpu().numpy().reshape(D, -1)desc / np.linalg.norm(desc, axis0)[np.newaxis, :]return pts, desc, heatmapif name main:print( Loading pre-trained network.)# This class runs the SuperPoint network and processes its outputs.fe SuperPointFrontend(weights_pathsuperpoint_v1.pth,nms_dist4,conf_thresh0.015,nn_thresh0.7,cudaTrue)print( Successfully loaded pre-trained network.)pic1 ./1.ppmpic2 ./6.ppmimage1_origin cv2.imread(pic1)image2_origin cv2.imread(pic2)image1 cv2.imread(pic1, cv2.IMREAD_GRAYSCALE).astype(np.float32)image2 cv2.imread(pic2, cv2.IMREAD_GRAYSCALE).astype(np.float32)image1 image1 / 255.image2 image2 / 255.if image1 is None or image2 is None:print(Could not open or find the images!)exit(0)# – Step 1: Detect the keypoints using SURF Detector, compute the descriptorskeypoints_obj, descriptors_obj, h1 fe.run(image1)keypoints_scene, descriptors_scene, h2 fe.run(image2)## to transfer array KeyPointskeypoints_obj [cv2.KeyPoint(keypoints_obj[0][i], keypoints_obj[1][i], 1)for i in range(keypoints_obj.shape[1])]keypoints_scene [cv2.KeyPoint(keypoints_scene[0][i], keypoints_scene[1][i], 1)for i in range(keypoints_scene.shape[1])]print(The number of keypoints in image1 is, len(keypoints_obj))print(The number of keypoints in image2 is, len(keypoints_scene))# – Step 2: Matching descriptor vectors with a FLANN based matcher# Since SURF is a floating-point descriptor NORM_L2 is usedmatcher cv2.DescriptorMatcher_create(cv2.DescriptorMatcher_FLANNBASED)knn_matches matcher.knnMatch(descriptors_obj.T, descriptors_scene.T, 2)# – Filter matches using the Lowes ratio testratio_thresh 0.75good_matches []for m, n in knn_matches:if m.distance ratio_thresh * n.distance:good_matches.append(m)# – Draw matchesimg_matches np.empty((max(image1_origin.shape[0], image2_origin.shape[0]), image1_origin.shape[1] image2_origin.shape[1], 3),dtypenp.uint8)cv2.drawMatches(image1_origin, keypoints_obj, image2_origin, keypoints_scene, good_matches, img_matches,flagscv2.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)cv2.namedWindow(Good Matches of SuperPoint, 0)cv2.resizeWindow(Good Matches of SuperPoint, 1024, 1024)cv2.imshow(Good Matches of SuperPoint, img_matches)cv2.waitKey() superpoint.py是基于官方给出的代码修改得到使用步骤如下 去官网下载模型的预训练文件https://github.com/magicleap/SuperPointPretrainedNetwork 3. 笔者自己也操作跑了一个小视频 4. https://download.csdn.net/download/Darlingqiang/88387732 参考SIFTSuperPoint在图像特征提取上的对比实验