关于 skimage.transform.PolynomialTransform 的问题

Question

我有两幅图像（通道 1 和通道 2），我正在尝试计算将一幅图像变形为另一幅图像的多项式变换。首先，我创建了一个 ORB 对象并计算了两个图像之间的仿射变换 (post-仿射)。然后我决定尝试使用skimage.transform.PolynomialTransform。但是，当我尝试计算转换时，返回的 NumPy 数组具有 NaN 值或 0 值，即使原始图像在该位置具有非零浮点值（post-多项式）。我究竟做错了什么？代码包含在下面，图片在下面 link。 https://drive.google.com/drive/folders/1mWxUvLFLK5-rYCrxs3-uGKFxKq2wXDjS?usp=sharing 提前致谢！

注意：我知道 Image warping with scikit-image and transform.PolynomialTransform 这个问题很相似，但我认为这两个问题并不重复。虽然那个用户的问题是相同的功能，但他们转换后的图像中的像素有值，而我的大体上没有。

import cv2 
from ImageConversion import ImageConversion # self-written, irrelevant
import matplotlib
matplotlib.use('macosX')
import matplotlib.pyplot as plt
from scipy.ndimage import uniform_filter
from skimage.draw import circle_perimeter
from skimage.transform import PolynomialTransform, warp
    
    def affine_transform(self):

        channel1_u8 = self.channel1.astype('uint8') # necessary for detectAndCompute
        channel2_u8 = self.channel2.astype('uint8')
        orb = cv2.ORB_create(100)
        #kp1, des1 = orb.detectAndCompute(channel1_32, None)
        #kp2, des2 = orb.detectAndCompute(channel2_32, None)
        kp1, des1 = orb.detectAndCompute(channel1_u8, None)
        kp2, des2 = orb.detectAndCompute(channel2_u8, None)

        matcher = cv2.DescriptorMatcher_create(cv2.DESCRIPTOR_MATCHER_BRUTEFORCE_HAMMING)
        matches = matcher.match(des1, des2, None)
        matches = sorted(matches, key = lambda x:x.distance)

        points1 = np.zeros((len(matches), 2), dtype = np.float32)
        points2 = np.zeros((len(matches), 2), dtype = np.float32)

        for i, match in enumerate(matches):
            points1[i, :] = kp1[match.queryIdx].pt # index of descriptor in query descriptors, ie index of descriptor in channel 1 which is the image we wish to map to channel 2
            points2[i, :] = kp2[match.trainIdx].pt

        mat_coeff, inliers = cv2.estimateAffine2D(points1, points2) # inliers only here because estimateAffine2D returns both matrix coefficients and inliers
        print(mat_coeff)
        rows, cols = channel1_u8.shape
        #dst = cv2.warpAffine(channel1_u8, mat_coeff, (cols, rows))
        dst = cv2.warpAffine(self.channel1, mat_coeff, (cols, rows))
        return mat_coeff, dst


    tform = PolynomialTransform()
    tform.estimate(self.channel2, dst, order = 3)
    warped_1 = warp(dst, tform, mode = 'constant')

Answer 1

我发现了错误。我试图提供 PolynomialTransform.estimate 整个图像，而不是图像中识别的关键点。

关于 skimage.transform.PolynomialTransform 的问题

Question about skimage.transform.PolynomialTransform

image-processing

python-3.x

scikit-image