如何使用 Python OpenCV 检测正方形的角点？

Question

在下图中，我使用 OpenCV 哈里斯角检测器来检测正方形的角（以及外部正方形内的较小正方形）。但是，我也检测到图像侧面数字的角点。我如何让它只关注方块而不是数字？在执行 OpenCV 角点检测时，我需要一种忽略数字的方法。代码、输入图像和输出图像如下：

import cv2 as cv
img = cv.imread(filename)
gray = cv.cvtColor(img,cv.COLOR_BGR2GRAY)
gray = np.float32(gray)
dst = cv.cornerHarris(gray, 2, 3, 0.04)
dst = cv.dilate(dst,None)
# Threshold for an optimal value, it may vary depending on the image.
img[dst>0.01*dst.max()]=[0,0,255]
cv.imshow('dst', img)

输入图片

哈里斯角点检测器的输出

Answer 1

这是一种使用传统图像处理的潜在方法：

获取二值图像。我们load the image, convert to grayscale, Gaussian blur, then adaptive threshold to obtain a black/white binary image. We then remove small noise using contour area filtering。在此阶段我们还创建了两个空白蒙版。
检测水平线和垂直线。 现在我们通过创建水平形状 kernel and perform morphological operations 来隔离水平线。为了检测垂直线，我们做同样的事情，但使用垂直形状的内核。我们将检测到的线条绘制到单独的蒙版上。
找到交点。 想法是，如果我们将水平和垂直蒙版组合起来，交点就是角。我们可以对每个交点执行 bitwise-and operation on the two masks. Finally we find the centroid 并通过画圆来突出角。

这是管道的可视化图

输入图像->二值图像

检测到水平线->水平蒙版

检测到垂直线->垂直蒙版

Bitwise-and 两个蒙版 -> 检测到交叉点 -> 角 -> 清理角

结果并不完美，但已经很接近了。问题来自于倾斜图像导致的垂直蒙版上的噪声。如果图像没有角度地居中，结果将是理想的。您可能可以微调内核大小或迭代以获得更好的结果。

代码

import cv2
import numpy as np

# Load image, create horizontal/vertical masks, Gaussian blur, Adaptive threshold
image = cv2.imread('1.png')
original = image.copy()
horizontal_mask = np.zeros(image.shape, dtype=np.uint8)
vertical_mask = np.zeros(image.shape, dtype=np.uint8)
gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (3,3), 0)
thresh = cv2.adaptiveThreshold(blur, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 23, 7)

# Remove small noise on thresholded image
cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
    area = cv2.contourArea(c)
    if area < 150:
        cv2.drawContours(thresh, [c], -1, 0, -1)

# Detect horizontal lines
dilate_horizontal_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (10,1))
dilate_horizontal = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, dilate_horizontal_kernel, iterations=1)
horizontal_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (40,1))
detected_lines = cv2.morphologyEx(dilate_horizontal, cv2.MORPH_OPEN, horizontal_kernel, iterations=1)
cnts = cv2.findContours(detected_lines, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
    cv2.drawContours(image, [c], -1, (36,255,12), 2)
    cv2.drawContours(horizontal_mask, [c], -1, (255,255,255), 2)

# Remove extra horizontal lines using contour area filtering
horizontal_mask = cv2.cvtColor(horizontal_mask,cv2.COLOR_BGR2GRAY)
cnts = cv2.findContours(horizontal_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
    area = cv2.contourArea(c)
    if area > 1000 or area < 100:
        cv2.drawContours(horizontal_mask, [c], -1, 0, -1)

# Detect vertical 
dilate_vertical_kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (1,7))
dilate_vertical = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, dilate_vertical_kernel, iterations=1)
vertical_kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (1,2))
detected_lines = cv2.morphologyEx(dilate_vertical, cv2.MORPH_OPEN, vertical_kernel, iterations=4)
cnts = cv2.findContours(detected_lines, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
    cv2.drawContours(image, [c], -1, (36,255,12), 2)
    cv2.drawContours(vertical_mask, [c], -1, (255,255,255), 2)

# Find intersection points
vertical_mask = cv2.cvtColor(vertical_mask,cv2.COLOR_BGR2GRAY)
combined = cv2.bitwise_and(horizontal_mask, vertical_mask)
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (2,2))
combined = cv2.morphologyEx(combined, cv2.MORPH_OPEN, kernel, iterations=1)

# Highlight corners
cnts = cv2.findContours(combined, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
    # Find centroid and draw center point
    try:
        M = cv2.moments(c)
        cx = int(M['m10']/M['m00'])
        cy = int(M['m01']/M['m00'])
        cv2.circle(original, (cx, cy), 3, (36,255,12), -1)
    except ZeroDivisionError:
        pass

cv2.imshow('thresh', thresh)
cv2.imshow('horizontal_mask', horizontal_mask)
cv2.imshow('vertical_mask', vertical_mask)
cv2.imshow('combined', combined)
cv2.imshow('original', original)
cv2.imshow('image', image)
cv2.waitKey()

如何使用 Python OpenCV 检测正方形的角点？

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python

opencv

image

image-processing

computer-vision