二维视角下的模板匹配

目录

• 相关算法
  • 基于灰度值的模板匹配
  • 基于描述符的模板匹配
  • 基于学习的特征提取算法与SIFT进行对比
• 代码
  • SIFT.py
  • SuperPoint.py

涉猎了二维视角下的模板匹配，用这篇博客记录一下，顺便再理一理思路，方便向三维的模板匹配扩展。

模板匹配定义：

模板匹配是一种最原始、最基本的模式识别方法，研究某一特定对象物的图案位于图像的什么地方，进而识别对象物，这就是一个匹配问题。

可以这样理解，手中有两张图片，一张是模板图片，另一张是场景图片，要找到模板在场景中的位置，同时确定模板图片到场景图片的变换矩阵\(H\)，这样的一个任务。

相关算法

基于灰度值的模板匹配

解释一下相关的原理：

在OpenCV中支持以下6种对比方式：

• CV_TM_SQDIFF 平方差匹配法：该方法采用平方差来进行匹配；最好的匹配值为0；匹配越差，匹配值越大。
• CV_TM_CCORR 相关匹配法：该方法采用乘法操作；数值越大表明匹配程度越好。
• CV_TM_CCOEFF 相关系数匹配法：1表示完美的匹配；-1表示最差的匹配。
• CV_TM_SQDIFF_NORMED 归一化平方差匹配法
• CV_TM_CCORR_NORMED 归一化相关匹配法
• CV_TM_CCOEFF_NORMED 归一化相关系数匹配法

我们看一下匹配的实际情况：

这个方法其实是很不"智能"，结合原理可知。一旦模板发生大幅度的旋转，放缩，错切，则匹配就会很难成功。而且也很难应对相机拍摄角度发生变化的情况。如果有小伙伴有针对这个的优化想法，欢迎留言讨论。

基于描述符的模板匹配

这个是很值得探讨的一种模板匹配的方法，我们可以先看一下流程图：

模板图和场景图分别通过特征提取算法得到它们各自的特征点， 描述子，然后将描述子送入KNN进行匹配，建立起点到点的对应关系。之后再用OpenCV里的findHomography()函数求出点到点的映射矩阵。之后再用模板图片边框四个点的坐标乘以这个映射矩阵，就得到了模板图片在场景图片中的位置。

这个算法最重要的地方是特征提取算法。算法提取到的特征点，描述子是否能够对图片的旋转，放缩，错切等变换具有鲁棒性，将直接决定KNN中能有多少点匹配上，进而影响后面的操作。举个例子，一个模板图片，相对于场景图片发生了一些透视变换，如果导致提取到的描述子的“距离变远了”，那这个算法就不能很好的应对这些变换。

我们以sift举例，表现还不错：

基于学习的特征提取算法与SIFT进行对比

• SuperPoint结构
  
  

基于学习的特征提取算法，以SuperPoint为例。

总结如下：

SuperPoint无法匹配较大透视变换的原因推测：

原文：

代码

SIFT.py

import numpy as np
import cv2 as cv
from matplotlib import pyplot as plt

MIN_MATCH_COUNT = 10

#
img1 = cv.imread('data/box.png',0)          # queryImage
img2 = cv.imread('data/box_in_scene.png',0) # trainImage

# Initiate SIFT detector
sift = cv.SIFT_create()
# find the keypoints and descriptors with SIFT
kp1, des1 = sift.detectAndCompute(img1,None)
kp2, des2 = sift.detectAndCompute(img2,None)

FLANN_INDEX_KDTREE = 1
index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
search_params = dict(checks = 50)
flann = cv.FlannBasedMatcher(index_params, search_params)
matches = flann.knnMatch(des1,des2,k=2)
# store all the good matches as per Lowe's ratio test.
good = []
for m,n in matches:
    if m.distance < 0.7*n.distance:
        good.append(m)

print("The number of keypoints in image1 is", len(kp1))
print("The number of keypoints in image2 is", len(kp2))

if len(good)>MIN_MATCH_COUNT:
    src_pts = np.float32([ kp1[m.queryIdx].pt for m in good ]).reshape(-1,1,2)
    dst_pts = np.float32([ kp2[m.trainIdx].pt for m in good ]).reshape(-1,1,2)
    M, mask = cv.findHomography(src_pts, dst_pts, cv.RANSAC,5.0)
    matchesMask = mask.ravel().tolist()
    h,w = img1.shape
    pts = np.float32([ [0,0],[0,h-1],[w-1,h-1],[w-1,0] ]).reshape(-1,1,2)
    dst = cv.perspectiveTransform(pts,M)
    img2 = cv.polylines(img2,[np.int32(dst)],True,255,3, cv.LINE_AA)
else:
    print( "Not enough matches are found - {}/{}".format(len(good), MIN_MATCH_COUNT) )
    matchesMask = None


print("the number of matches is ", len(matchesMask))

draw_params = dict(matchColor = (0,255,0), # draw matches in green color
                   singlePointColor = None,
                   matchesMask = matchesMask, # draw only inliers
                   flags = 2)
img3 = cv.drawMatches(img1,kp1,img2,kp2,good,None,**draw_params)
plt.imshow(img3),plt.show()

SuperPoint.py

import numpy as np
import os
import cv2
import torch
from matplotlib import pyplot as plt

MIN_MATCH_COUNT = 10

# 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(inplace=True)
        self.pool = torch.nn.MaxPool2d(kernel_size=2, stride=2)
        c1, c2, c3, c4, c5, d1 = 64, 64, 128, 128, 256, 256
        # Shared Encoder.
        self.conv1a = torch.nn.Conv2d(1, c1, kernel_size=3, stride=1, padding=1)
        self.conv1b = torch.nn.Conv2d(c1, c1, kernel_size=3, stride=1, padding=1)
        self.conv2a = torch.nn.Conv2d(c1, c2, kernel_size=3, stride=1, padding=1)
        self.conv2b = torch.nn.Conv2d(c2, c2, kernel_size=3, stride=1, padding=1)
        self.conv3a = torch.nn.Conv2d(c2, c3, kernel_size=3, stride=1, padding=1)
        self.conv3b = torch.nn.Conv2d(c3, c3, kernel_size=3, stride=1, padding=1)
        self.conv4a = torch.nn.Conv2d(c3, c4, kernel_size=3, stride=1, padding=1)
        self.conv4b = torch.nn.Conv2d(c4, c4, kernel_size=3, stride=1, padding=1)
        # Detector Head.
        self.convPa = torch.nn.Conv2d(c4, c5, kernel_size=3, stride=1, padding=1)
        self.convPb = torch.nn.Conv2d(c5, 65, kernel_size=1, stride=1, padding=0)
        # Descriptor Head.
        self.convDa = torch.nn.Conv2d(c4, c5, kernel_size=3, stride=1, padding=1)
        self.convDb = torch.nn.Conv2d(c5, d1, kernel_size=1, stride=1, padding=0)

    def forward(self, x):
        """ Forward pass that jointly computes unprocessed point and descriptor
        tensors.
        Input
          x: Image pytorch tensor shaped N x 1 x H x W.
        Output
          semi: 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, p=2, dim=1)  # Compute the norm.
        desc = desc.div(torch.unsqueeze(dn, 1))  # Divide by norm to normalize.
        return semi, desc


class 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,
                 cuda=False):
        self.name = 'SuperPoint'
        self.cuda = cuda
        self.nms_dist = nms_dist
        self.conf_thresh = conf_thresh
        self.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_location=lambda 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]^T

        Algo summary: Create a grid sized HxW. Assign each corner location a 1, rest
        are zeros. Iterate through all the 1's 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 not
        be exactly dist_thresh. It also assumes points are within image boundaries.

        Inputs
          in_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.
        Returns
          nmsed_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]] = 1
            inds[rcorners[1, i], rcorners[0, i]] = i
        # Pad the border of the grid, so that we can NMS points near the border.
        pad = dist_thresh
        grid = np.pad(grid, ((pad, pad), (pad, pad)), mode='constant')
        # Iterate through points, highest to lowest conf, suppress neighborhood.
        count = 0
        for 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] = 0
                grid[pt[1], pt[0]] = -1
                count += 1
        # Get all surviving -1's and return sorted array of remaining corners.
        keepy, keepx = np.where(grid == -1)
        keepy, keepx = keepy - pad, keepx - pad
        inds_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_inds

    def run(self, img):
        """ Process a numpy image to extract points and descriptors.
        Input
          img - HxW numpy float32 input image in range [0,1].
        Output
          corners - 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, axis=0) + .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, None
        pts = np.zeros((3, len(xs)))  # Populate point data sized 3xN.
        pts[0, :] = ys
        pts[1, :] = xs
        pts[2, :] = heatmap[xs, ys]
        pts, _ = self.nms_fast(pts, H, W, dist_thresh=self.nms_dist)  # Apply NMS.
        inds = np.argsort(pts[2, :])
        pts = pts[:, inds[::-1]]  # Sort by confidence.
        # Remove points along border.
        bord = self.border_remove
        toremoveW = np.logical_or(pts[0, :] < bord, pts[0, :] >= (W - bord))
        toremoveH = np.logical_or(pts[1, :] < bord, pts[1, :] >= (H - bord))
        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_pts[0, :] = (samp_pts[0, :] / (float(W) / 2.)) - 1.
            samp_pts[1, :] = (samp_pts[1, :] / (float(H) / 2.)) - 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, axis=0)[np.newaxis, :]
        return pts, desc, heatmap



if __name__ == '__main__':


    print('==> Loading pre-trained network.')
    # This class runs the SuperPoint network and processes its outputs.
    fe = SuperPointFrontend(weights_path="./model/superpoint_v1.pth",
                            nms_dist=4,
                            conf_thresh=0.015,
                            nn_thresh=0.7,
                            cuda=True)
    print('==> Successfully loaded pre-trained network.')

    pic1 = "data/wulala.png"
    pic2 = "data/viewPoint2.png"

    image1_origin = cv2.imread(pic1)
    image2_origin = cv2.imread(pic2)

    img1 = cv2.imread(pic1, cv2.IMREAD_GRAYSCALE).astype(np.float32)
    img2 = cv2.imread(pic2, cv2.IMREAD_GRAYSCALE).astype(np.float32)
    img1 = img1 / 255.
    img2 = img2 / 255.

    if img1 is None or img2 is None:
        print('Could not open or find the images!')
        exit(0)

    # -- Step 1: Detect the keypoints using SURF Detector, compute the descriptors

    kp1, des1, h1 = fe.run(img1)
    kp2, des2, h2 = fe.run(img2)

    ## to transfer array ==> KeyPoints
    kp1 = [cv2.KeyPoint(kp1[0][i], kp1[1][i], 1)
           for i in range(kp1.shape[1])]
    kp2 = [cv2.KeyPoint(kp2[0][i], kp2[1][i], 1)
           for i in range(kp2.shape[1])]

    print("The number of keypoints in image1 is", len(kp1))
    print("The number of keypoints in image2 is", len(kp2))

  # -- Step 2: Matching descriptor vectors with a FLANN based matcher
    # Since SURF is a floating-point descriptor NORM_L2 is used
    matcher = cv2.DescriptorMatcher_create(cv2.DescriptorMatcher_FLANNBASED)
    knn_matches = matcher.knnMatch(des1.T, des2.T, 2)

    # -- Filter matches using the Lowe's ratio test
    ratio_thresh = 0.7
    good = []
    for m, n in knn_matches:
        if m.distance < ratio_thresh * n.distance:
            good.append(m)

if len(good)>MIN_MATCH_COUNT:
    src_pts = np.float32([ kp1[m.queryIdx].pt for m in good ]).reshape(-1,1,2)
    dst_pts = np.float32([ kp2[m.trainIdx].pt for m in good ]).reshape(-1,1,2)
    M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC,5.0)
    matchesMask = mask.ravel().tolist()
    h,w = img1.shape
    pts = np.float32([ [0,0],[0,h-1],[w-1,h-1],[w-1,0] ]).reshape(-1,1,2)
    dst = cv2.perspectiveTransform(pts,M)
    img2 = cv2.polylines(image2_origin,[np.int32(dst)],True,255,3, cv2.LINE_AA)
else:
    print( "Not enough matches are found - {}/{}".format(len(good), MIN_MATCH_COUNT) )
    matchesMask = None

print("the number of matches is ", len(matchesMask))

draw_params = dict(matchColor = (0,255,0), # draw matches in green color
                   singlePointColor = None,
                   matchesMask = matchesMask, # draw only inliers
                   flags = 2)
img3 = cv2.drawMatches(image1_origin,kp1,image2_origin,kp2,good,None,**draw_params)
plt.imshow(img3),plt.show()