如何在骨架的图像python openCV中找到插点或段？

Question

我试图将骨架化的结果转换为一组线段，其中顶点对应于骨架的接线点。形状不是封闭的多边形，它可能有些嘈杂（段不像应有的那样直接）。

这是一个示例输入图像：

，这是我要检索的点：

我尝试使用Harris Corner detter尝试调整算法的参数后（例如图像底部的角度部分）。以下是结果：

您知道有任何能够执行此操作的方法吗？我使用的是Python，主要是OpenCV和Numpy，但我没有绑定到任何库。提前致谢。

编辑：我对接线点有一些很好的回应，我真的很感激。我还要感谢有关从接线点提取线段的任何解决方案。我认为@nathancy 可以用来通过用交叉点面具减去口罩来提取线段，但是我是没有把握。

Answer 1

我的方法是基于我以前的答案在这里。它涉及卷动与特殊内核的图像。该卷积确定了线的终点以及交叉点。这将导致一个点掩模，其中包含与您要寻找的点相匹配的像素。之后，应用一点形态以加入可能的重复点。该方法对于骨骼产生的角度明智。

这是代码：

import cv2
import numpy as np

# image path
path = "D://opencvImages//"
fileName = "Repn3.png"

# Reading an image in default mode:
inputImage = cv2.imread(path + fileName)
inputImageCopy = inputImage.copy()

# Convert to grayscale:
grayscaleImage = cv2.cvtColor(inputImage, cv2.COLOR_BGR2GRAY)

# Compute the skeleton:
skeleton = cv2.ximgproc.thinning(grayscaleImage, None, 1)

# Threshold the image so that white pixels get a value of 10 and
# black pixels a value of 0:
_, binaryImage = cv2.threshold(skeleton, 128, 10, cv2.THRESH_BINARY)

# Set the convolution kernel:
h = np.array([[1, 1, 1],
              [1, 10, 1],
              [1, 1, 1]])

# Convolve the image with the kernel:
imgFiltered = cv2.filter2D(binaryImage, -1, h)

到目前为止，我将骨骼图像与我的特殊内核一起卷积。您可以检查产生的图像并搜索拐角和交叉点的数值。

这是到目前为止的输出：

接下来，确定角落或交集。这个位很棘手，因为阈值值直接取决于骨架图像，有时不会产生好（接近直的）角：

# Create list of thresholds:
thresh = [130, 110, 40]

# Prepare the final mask of points:
(height, width) = binaryImage.shape
pointsMask = np.zeros((height, width, 1), np.uint8)

# Perform convolution and create points mask:
for t in range(len(thresh)):
    # Get current threshold:
    currentThresh = thresh[t]
    # Locate the threshold in the filtered image:
    tempMat = np.where(imgFiltered == currentThresh, 255, 0)
    # Convert and shape the image to a uint8 height x width x channels
    # numpy array:
    tempMat = tempMat.astype(np.uint8)
    tempMat = tempMat.reshape(height,width,1)
    # Accumulate mask:
    pointsMask = cv2.bitwise_or(pointsMask, tempMat)

这是二进制掩码：

让我们扩张以加入近距离点：

# Set kernel (structuring element) size:
kernelSize = 3
# Set operation iterations:
opIterations = 4
# Get the structuring element:
morphKernel = cv2.getStructuringElement(cv2.MORPH_RECT, (kernelSize, kernelSize))
# Perform Dilate:
pointsMask = cv2.morphologyEx(pointsMask, cv2.MORPH_DILATE, morphKernel, None, None, opIterations, cv2.BORDER_REFLECT101)

这是输出：

现在简单提取外部轮廓。获取它们的边界框并计算其质心：

# Look for the outer contours (no children):
contours, _ = cv2.findContours(pointsMask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

# Store the points here:
pointsList = []

# Loop through the contours:
for i, c in enumerate(contours):

    # Get the contours bounding rectangle:
    boundRect = cv2.boundingRect(c)

    # Get the centroid of the rectangle:
    cx = int(boundRect[0] + 0.5 * boundRect[2])
    cy = int(boundRect[1] + 0.5 * boundRect[3])

    # Store centroid into list:
    pointsList.append( (cx,cy) )

    # Set centroid circle and text:
    color = (0, 0, 255)
    cv2.circle(inputImageCopy, (cx, cy), 3, color, -1)
    font = cv2.FONT_HERSHEY_COMPLEX
    cv2.putText(inputImageCopy, str(i), (cx, cy), font, 0.5, (0, 255, 0), 1)

    # Show image:
    cv2.imshow("Circles", inputImageCopy)
    cv2.waitKey(0)

这就是结果。错过了一些角落，您可能会在计算骨架之前改进解决方案。

Answer 2

这是一种简单的方法，这个想法是：

1. 获取二进制图像。加载图像，转换为灰度， gaussian blur”> gaussian blur ，然后 otsu的阈值。

2. 获得水平和垂直线蒙版。用 cv2.getStructuringElement 然后执行
   

3. 查找关节。我们 cv2.bitwise_and 两个蒙版一起获取关节。这个想法是两个口罩上的交点点是关节。

4. 在关节面膜上找到质心。我们查找轮廓然后计算
   

5. 查找剩余的端点。端点与十字路口无关，因此为了找到这些端点，我们可以使用 shi-tomasi corner corner detter

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和垂直线掩模的

结果

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”

import cv2
import numpy as np

# Load image, grayscale, Gaussian blur, Otsus threshold
image = cv2.imread('1.png')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (3,3), 0)
thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]

# Find horizonal lines
horizontal_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5,1))
horizontal = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, horizontal_kernel, iterations=1)

# Find vertical lines
vertical_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1,5))
vertical = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, vertical_kernel, iterations=1)

# Find joint intersections then the centroid of each joint
joints = cv2.bitwise_and(horizontal, vertical)
cnts = cv2.findContours(joints, 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
    x,y,w,h = cv2.boundingRect(c)
    centroid, coord, area = cv2.minAreaRect(c)
    cx, cy = int(centroid[0]), int(centroid[1])
    cv2.circle(image, (cx, cy), 5, (36,255,12), -1)

# Find endpoints
corners = cv2.goodFeaturesToTrack(thresh, 5, 0.5, 10)
corners = np.int0(corners)
for corner in corners:
    x, y = corner.ravel()
    cv2.circle(image, (x, y), 5, (255,100,0), -1)

cv2.imshow('thresh', thresh)
cv2.imshow('joints', joints)
cv2.imshow('horizontal', horizontal)
cv2.imshow('vertical', vertical)
cv2.imshow('image', image)
cv2.waitKey()