使用 Canny 在倾斜图像中寻找边缘 [英] finding edge in tilted image with Canny

问题描述

我试图在一系列类似于下面创建的示例数据的图像中找到倾斜角度.应该有一个肉眼可见的清晰边缘.但是，到目前为止，我正在努力提取边缘.Canny 是在这里找到边缘的正确方法还是有更好的方法来找到边缘?

I'm trying to find the tilt angle in a series of images which look like the created example data below. There should be a clear edge which is visible by eye. However I'm struggling in extracting the edges so far. Is Canny the right way of finding the edge here or is there a better way of finding the edge?

import cv2 as cv
import numpy as np
import matplotlib.pyplot as plt
from scipy.ndimage.filters import gaussian_filter

# create data
xvals = np.arange(0,2000)
yvals = 10000 * np.exp((xvals - 1600)/200) + 100
yvals[1600:] = 100
blurred = gaussian_filter(yvals, sigma=20)

# create image
img = np.tile(blurred,(2000,1))
img = np.swapaxes(img,0,1)

# rotate image
rows,cols = img.shape
M = cv.getRotationMatrix2D((cols/2,rows/2),3.7,1)
img = cv.warpAffine(img,M,(cols,rows))

# convert to uint8 for Canny
img_8 = cv.convertScaleAbs(img,alpha=(255.0/65535.0))
fig,ax = plt.subplots(3)
ax[0].plot(xvals,blurred)
ax[1].imshow(img)

# find edge
ax[2].imshow(cv.Canny(img_8, 20, 100, apertureSize=5))

推荐答案

kavko 建议的阈值处理效果不佳，因为强度因图像而异(我当然可以考虑每个图像的直方图以改进这种方法).我最终在 y 方向上取了梯度的最大值:

Thresholding as suggested by kavko didn't work that well, as the intensity varied from image to image (I could of course consider the histogram for each image to imrove this approach). I ended up with taking the maximum of the gradient in the y-direction:

def rotate_image(image):
    blur = ndimage.gaussian_filter(image, sigma=10)    # blur image first
    grad = np.gradient(blur, axis= 0)    # take gradient along y-axis
    grad[grad>10000]=0    # filter unreasonable high values
    idx_maxline = np.argmax(grad, axis=0)    # get y-indices of max slope = indices of edge

    mean = np.mean(idx_maxline)
    std = np.std(idx_maxline)
    idx = np.arange(idx_maxline.shape[0])
    idx_filtered = idx[(idx_maxline < mean+std) & (idx_maxline > mean - std)]    # filter positions where highest slope is at different position(blobs)
    slope, intercept, r_value, p_value, std_err = stats.linregress(idx_filtered, idx_maxline[idx_filtered])
    out = ndimage.rotate(image,slope*180/np.pi, reshape = False)
    return out
out = rotate_image(img)
plt.imshow(out)

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