图像处理中的角点检测Opencv Python

我有一个盒子的图像.我正在尝试检测角落并从圆圈标记这些角落.我使用以下代码:

import cv2
import numpy as np

img_file = 'Image.jpg'
img = cv2.imread(img_file, cv2.IMREAD_COLOR)

imgDim = img.shape
dimA = imgDim[0]
dimB = imgDim[1]

# RGB to Gray scale conversion
img_gray = cv2.cvtColor(img,cv2.COLOR_RGB2GRAY)
# Noise removal with iterative bilateral filter(removes noise while preserving edges)
noise_removal = cv2.bilateralFilter(img_gray,9,75,75)
# Thresholding the image
ret,thresh_image = cv2.threshold(noise_removal,220,255,cv2.THRESH_OTSU)
th = cv2.adaptiveThreshold(noise_removal, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 2)

# Applying Canny Edge detection
canny_image = cv2.Canny(th,250,255)
canny_image = cv2.convertScaleAbs(canny_image)

# dilation to strengthen the edges
kernel = np.ones((3,3), np.uint8)
# Creating the kernel for dilation
dilated_image = cv2.dilate(canny_image,kernel,iterations=1)
np.set_printoptions(threshold=np.nan)

_, contours, h = cv2.findContours(dilated_image, 1, 2)
contours= sorted(contours, key = cv2.contourArea, reverse = True)[:1]


corners    = cv2.goodFeaturesToTrack(thresh_image,6,0.06,25)
corners    = np.float32(corners)

for item in corners:
    x,y    = item[0]
    cv2.circle(img,(x,y),10,255,-1)
cv2.namedWindow("Corners", cv2.WINDOW_NORMAL)
cv2.imshow("Corners",img)
cv2.waitKey()

此代码将返回带有圆圈的指向边缘的图像,但您可以看到两个边缘(框背面的边缘)检测不正确.我知道在确定角落时存在一些问题,因为我们只是在绘制角落.任何人都可以指导我在哪里做错了吗？谢谢

我不会说我已经达到了最佳解决方案,但经过相当多的编码后,我能够获得以下内容:

为此,我按照以下步骤操作:

1.首先:获取盒子的边缘

我对灰度图像进行了双边滤波.

使用Canny边缘检测找到边缘.

使用形态膨胀增强边缘.

这是上述结果:

现在,当我进行角落检测时,我一点都不满意:

那我该怎么办？

2.寻找理想的角落

我使用尺寸为9x9的窗口模糊了扩张的图像.

然后将Harris角点检测应用于此模糊图像.

结果我得到了这个:

我知道它并不完美,但它总是可以很好地调整.

以下是角点检测的代码:

dst = cv2.cornerHarris(dilate,2,3,0.04)
#----result is dilated for marking the corners, not important-------------
dst = cv2.dilate(dst,None) 
#----Threshold for an optimal value, it may vary depending on the image---
img[dst>0.01*dst.max()]=[0,0,255]