如何在OpenCV中将像素投影到特征向量上？

如何解决如何在OpenCV中将像素投影到特征向量上？

给出轮廓，我可以通过执行PCA来提取均值和特征向量。然后我想将所有像素投影到特征向量上的轮廓内。下面是我的代码和图片

我的输入图片

读取图像，提取轮廓并绘制第一个组件

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

src = cv.imread(cv.samples.findFile('/Users/bryan/Desktop/lung.png'))
gray = cv.cvtColor(src,cv.COLOR_BGR2GRAY)

def drawAxis(img,p_,q_,colour,scale):
    p = list(p_)
    q = list(q_)
    ## [visualization1]
    angle = atan2(p[1] - q[1],p[0] - q[0])  # angle in radians
    hypotenuse = sqrt((p[1] - q[1]) * (p[1] - q[1]) + (p[0] - q[0]) * (p[0] - q[0]))

    # Here we lengthen the arrow by a factor of scale
    q[0] = p[0] - scale * hypotenuse * cos(angle)
    q[1] = p[1] - scale * hypotenuse * sin(angle)
    cv.line(img,(int(p[0]),int(p[1])),(int(q[0]),int(q[1])),1,cv.LINE_AA)

    # create the arrow hooks
    p[0] = q[0] + 9 * cos(angle + pi / 4)
    p[1] = q[1] + 9 * sin(angle + pi / 4)
    cv.line(img,cv.LINE_AA)

    p[0] = q[0] + 9 * cos(angle - pi / 4)
    p[1] = q[1] + 9 * sin(angle - pi / 4)
    cv.line(img,cv.LINE_AA)
    
def getOrientation(pts,img,scale_factor=25):
    ## [pca]
    # Construct a buffer used by the pca analysis
    sz = len(pts)
    data_pts = np.empty((sz,2),dtype=np.float64)
    for i in range(data_pts.shape[0]):
        data_pts[i,0] = pts[i,0]
        data_pts[i,1] = pts[i,1]

    # Perform PCA analysis
    mean = np.empty(0)
    mean,eigenvectors,eigenvalues = cv.PCACompute2(data_pts,mean)

    # Store the center of the object
    cntr = (int(mean[0,0]),int(mean[0,1]))
    ## [pca]

    ## [visualization]
    # Draw the principal components
    cv.circle(img,cntr,3,(255,255),2)

    p1 = (
        cntr[0] + scale_factor * eigenvectors[0,0],cntr[1] + scale_factor * eigenvectors[0,1])
    p2 = (
        cntr[0] - scale_factor * eigenvectors[1,cntr[1] - scale_factor * eigenvectors[1,1])
    drawAxis(img,p1,(0,255,0),4)
    
    ## [visualization]

    # doing projections along eigenvectors
    dim1_ = []
    for _ in data_pts:
        p = make_vector_projection(_,np.array(p1))
        dim1_.append(p.astype(int))
    dim1 = np.array(dim1_)

    dim2_ = []
    for _ in data_pts:
        p = make_vector_projection(_,np.array(p2))
        dim2_.append(p.astype(int))
    dim2 = np.array(dim2_)

    return mean,eigenvalues,p2,dim1,dim2

for i,c in enumerate(contours):
    mean,evecs,evalues,dim2 = getOrientation(c,src)

    
plt.figure(figsize=(10,10))
plt.axis('equal')
plt.gca().invert_yaxis()
plt.imshow(src)

如我所料，但我想仔细检查一下，我计算了第一维随机矢量和第一个特征矢量之间的角度。我定义了

def unit_vector(vector):
    """ Returns the unit vector of the vector.  """
    return vector / np.linalg.norm(vector)


def angle_between(v1,v2):
    """ Returns the angle in radians between vectors 'v1' and 'v2'::

            >>> angle_between((1,0))
            1.5707963267948966
            >>> angle_between((1,(1,0))
            0.0
            >>> angle_between((1,(-1,0))
            3.141592653589793
    """
    v1_u = unit_vector(v1)
    v2_u = unit_vector(v2)
    return np.arccos(np.clip(np.dot(v1_u,v2_u),-1.0,1.0))



for i,src)
    draw_point = dim1[45].astype(int)  # extract the first dimension
    print(draw_point,evecs[0],angle_between(draw_point,p1))
    print('#'* 10)

我得到如下输出，angle_between是弧度的，接近零意味着它们是平行的

[ 97 148] [ 0.14189901 -0.98988114] 0.002321780300502494
##########
[332 163] [-0.22199134 -0.97504864] 0.0006249775357550807
##########

我想在特征向量上绘制投影点时出现问题。我绘制的点不在绿线（我的特征向量）上。我的代码是

point_colors = [
    (0,# blue
    (0,0) # green
]

for i,src)
    draw_point = dim1[45].astype(int)
    print(draw_point,p1))
    cv.circle(src,(draw_point[1],draw_point[0]),7,point_colors[i],2) # plot point
    print('#'* 10)

并输出

我的问题是，我想在本征向量线上绘制所有投影像素，但是由于点不在本征向量线上，所以我的计算似乎不正确。你能帮忙吗？