寻找矩形的角

Question

我试图得到这个矩形的角：

.

我尝试使用cv2.cornerHarris(rectangle, 2, 3, 0.04)，但由于图像亮度，左边缘未显示，我猜。因此，我在使用cornerHarris 之前尝试应用阈值，但生成的图像显示边缘有很多顶点，无法过滤角点。

我知道我需要在使用cornerHarris 之前对其进行过滤，但我不知道如何进行。有人可以帮我解决这个问题吗？

附言。我已经尝试使用模糊，但它也不起作用。

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

rectangle = cv2.imread('rectangle.png', cv2.IMREAD_GRAYSCALE)
rectangle = np.where(rectangle > np.mean(rectangle), 255, 0).astype(np.uint8)

dst_rectangle = cv2.cornerHarris(rectangle, 2, 3, 0.04)
dst_rectangle = cv2.dilate(dst_rectangle, None)

mask = np.where(dst_rectangle > 0.01*np.max(dst_rectangle), 255, 0).astype(np.uint8)
points = np.nonzero(mask)

plt.imshow(dst_rectangle, cmap='gray')
plt.plot(points[1], points[0], 'or')
plt.show()

Answer 1

我会通过在自适应阈值化后获取轮廓的旋转边界框的角来以不同的方式处理它。这是我在 Python/OpenCV 中的代码。

输入：

import cv2
import numpy as np

# read image
img = cv2.imread("rectangle.png")

# convert img to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
gray = 255-gray

# do adaptive threshold on gray image
thresh = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 17, 1)
thresh = 255-thresh

# apply morphology
kernel = np.ones((3,3), np.uint8)
morph = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)
morph = cv2.morphologyEx(morph, cv2.MORPH_CLOSE, kernel)

# separate horizontal and vertical lines to filter out spots outside the rectangle
kernel = np.ones((7,3), np.uint8)
vert = cv2.morphologyEx(morph, cv2.MORPH_OPEN, kernel)
kernel = np.ones((3,7), np.uint8)
horiz = cv2.morphologyEx(morph, cv2.MORPH_OPEN, kernel)

# combine
rect = cv2.add(horiz,vert)

# thin
kernel = np.ones((3,3), np.uint8)
rect = cv2.morphologyEx(rect, cv2.MORPH_ERODE, kernel)

# get largest contour
contours = cv2.findContours(rect, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours = contours[0] if len(contours) == 2 else contours[1]
for c in contours:
    area_thresh = 0
    area = cv2.contourArea(c)
    if area > area_thresh:
        area = area_thresh
        big_contour = c

# get rotated rectangle from contour
rot_rect = cv2.minAreaRect(big_contour)
box = cv2.boxPoints(rot_rect)
box = np.int0(box)
print(box)

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

# write img with red rotated bounding box to disk
cv2.imwrite("rectangle_thresh.png", thresh)
cv2.imwrite("rectangle_outline.png", rect)
cv2.imwrite("rectangle_bounds.png", rot_bbox)

# display it
cv2.imshow("IMAGE", img)
cv2.imshow("THRESHOLD", thresh)
cv2.imshow("MORPH", morph)
cv2.imshow("VERT", vert)
cv2.imshow("HORIZ", horiz)
cv2.imshow("RECT", rect)
cv2.imshow("BBOX", rot_bbox)
cv2.waitKey(0)

阈值图像：

矩形区域提取：

图像上的旋转边界框：

旋转的边界框角：

[[446 335]
 [163 328]
 [168 117]
 [451 124]]

添加：

这是代码的略短版本，可以通过在阈值化之前添加一些高斯模糊来实现。

import cv2
import numpy as np

# read image
img = cv2.imread("rectangle.png")

# convert img to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
gray = 255-gray

# blur image
blur = cv2.GaussianBlur(gray, (3,3), 0)

# do adaptive threshold on gray image
thresh = cv2.adaptiveThreshold(blur, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 75, 2)
thresh = 255-thresh

# apply morphology
kernel = np.ones((5,5), np.uint8)
rect = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)
rect = cv2.morphologyEx(rect, cv2.MORPH_CLOSE, kernel)

# thin
kernel = np.ones((5,5), np.uint8)
rect = cv2.morphologyEx(rect, cv2.MORPH_ERODE, kernel)

# get largest contour
contours = cv2.findContours(rect, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours = contours[0] if len(contours) == 2 else contours[1]
for c in contours:
    area_thresh = 0
    area = cv2.contourArea(c)
    if area > area_thresh:
        area = area_thresh
        big_contour = c

# get rotated rectangle from contour
rot_rect = cv2.minAreaRect(big_contour)
box = cv2.boxPoints(rot_rect)
box = np.int0(box)
for p in box:
    pt = (p[0],p[1])
    print(pt)

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

# write img with red rotated bounding box to disk
cv2.imwrite("rectangle_thresh.png", thresh)
cv2.imwrite("rectangle_outline.png", rect)
cv2.imwrite("rectangle_bounds.png", rot_bbox)

# display it
cv2.imshow("IMAGE", img)
cv2.imshow("THRESHOLD", thresh)
cv2.imshow("RECT", rect)
cv2.imshow("BBOX", rot_bbox)
cv2.waitKey(0)

阈值图像：

矩形区域提取：

图像上的旋转边界框：

旋转的边界框角：

(444, 335)
(167, 330)
(170, 120)
(448, 125)