在下图中，我使用opencv harris角点检测器仅检测正方形的角点(以及外部正方形中较小的正方形).然而，我也会检测到图像侧面数字的角点.我怎样才能让它只关注正方形而不是数字呢？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)
Input image
Output from Harris corner detector
下面是一种使用传统图像处理的潜在方法:
Obtain binary image.我们load the image，转换到grayscale，Gaussian blur，然后adaptive threshold，以获得一个黑白二值图像.然后我们使用contour area filtering消除小噪音.在此阶段，我们还创建了两个空白遮罩.
Detect horizontal and vertical lines.现在我们通过创建一个水平形状的kernel来隔离水平线，并执行morphological operations.为了检测垂直线，我们也会做同样的事情，但要使用垂直形状的内核.我们把探测到的线画在不同的遮罩上.
Find intersection points.这个 idea 是，如果我们结合水平和垂直遮罩，交点将是角.我们可以在两个口罩上进行bitwise-and operation次测试.最后，我们找到每个交点的centroid，并通过画一个圆来突出显示角点.
这是管道的可视化
输入图像->二进制图像
检测到水平线->水平遮罩
检测到垂直线->垂直遮罩
按位和两个遮罩->个检测到的交点->个角->个清理过的角
结果并不完美，但非常接近.问题源于倾斜图像导致垂直遮罩上的噪声.如果图像在没有Angular 的情况下居中，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.
密码
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()