计算图形的方向以将其拉直（在python中）

Question

我有一个计算图形方向的代码和一个根据计算的方向拉直图形的函数。当我运行代码时，方向似乎很好，但是当函数试图拉直图形时，看起来图形已经变成了另一个形状。会不会是代码有问题？

代码：

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

img = cv2.imread('path_to_input_image',0) 
edges = cv2.Canny(img,1,2,70,3)

img = edges 
y, x = np.nonzero(img)

x = x - np.mean(x) 
y = y - np.mean(y)
coords = np.vstack([x, y])

cov = np.cov(coords) 
evals, evecs = np.linalg.eig(cov) 

sort_indices = np.argsort(evals)[::-1] 
x_v1, y_v1 = evecs[:, sort_indices[0]]  
x_v2, y_v2 = evecs[:, sort_indices[1]]

scale = 30
plt.plot([x_v1*-scale*2, x_v1*scale*2], 
         [y_v1*-scale*2, y_v1*scale*2], color='red')
plt.plot([x_v2*-scale, x_v2*scale],
         [y_v2*-scale, y_v2*scale], color='blue')
plt.plot(x, y, 'k.')
plt.axis('equal')
plt.gca().invert_yaxis()  
plt.show()

def rechtzetten(x_v1,y_v1,coords):
    theta = np.arctan((x_v1)/(y_v1))
    rotation_mat =np.matrix([[np.cos(theta), -np.sin(theta)],[np.sin(theta),np.cos(theta)]])
    transformed_mat = rotation_mat*coords


    x_transformed, y_transformed = transformed_mat.A

    fig = plt.figure()

    ax = plt.Axes(fig, [0.,0.,1.,1.])
    ax.set_axis_off()
    fig.add_axes(ax)
    ax = plt.plot(x_transformed,y_transformed)

    plt.savefig("ja.png",pdi=300)
    plt.show(ax)
    #plt.savefig("rotation.png") 
    img3 = cv2.imread('ja.png',100)
    edges2 = cv2.Canny(img3,1,4)

    cv2.imwrite('rotated_with_border.png', edges2)
    return transformed_mat, edges2

transformed_mat, edges = rechtzetten(x_v1,y_v1,coords)

我使用的输入图片：

我得到的输出：

输出的第一个图使用蓝色和红色轴显示方向。 输出的第二个数字应该是第一个数字的拉直版本。

*我的意思是把第一个图上的蓝色和红色轴与基本坐标系上的 x 和 y 轴匹配。

Answer 1

这是一种在 Python/OpenCV 中使用旋转的边界矩形进行纠偏的方法

输入：

import cv2
import numpy as np

# load image
img = cv2.imread("object.png")

# convert to gray
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# threshold the grayscale image
ret, thresh = cv2.threshold(gray,0,255,0)

# find outer contour
cntrs = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cntrs = cntrs[0] if len(cntrs) == 2 else cntrs[1]

# get rotated rectangle from contour
rotrect = cv2.minAreaRect(cntrs[0])
box = cv2.boxPoints(rotrect)
box = np.int0(box)

# draw rotated rectangle on copy of img
rot_bbox = img.copy()
cv2.drawContours(rot_bbox,[box],0,(0,0,255),2)

# get orientation angle relative to horizontal of the rotated rectangle
angle = rotrect[-1]

# from https://www.pyimagesearch.com/2017/02/20/text-skew-correction-opencv-python/
# the `cv2.minAreaRect` function returns values in the
# range [-90, 0); as the rectangle rotates clockwise the
# returned angle tends to 0 -- in this special case we
# need to add 90 degrees to the angle
if angle < -45:
    angle = -(90 + angle)

# otherwise, just take the negative of the angle to make
# it positive
else:
    angle = -angle

print(angle,"deg")

# negate the angle for deskewing
neg_angle = -angle

# Get rotation matrix
(h, w) = img.shape[:2]
center = (w // 2, h // 2)
M = cv2.getRotationMatrix2D(center, neg_angle, scale=1.0)

# rotate the image to deskew it
deskewed = cv2.warpAffine(img, M, (w, h), flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE)


cv2.imshow("THRESH", thresh)
cv2.imshow("ROTATED BBOX", rot_bbox)
cv2.imshow("DESKEWED", deskewed)
cv2.waitKey(0)
cv2.destroyAllWindows()

# write result to disk
cv2.imwrite("object_deskewed.png", deskewed)