Python OpenCV - 在二进制图像中查找黑色区域 [英] Python OpenCV - Find black areas in a binary image

问题描述

Opencv的python包装器中有任何方法/函数在二进制映像中找到黑色区域？ （像Matlab中的 regionprops ）
到目前为止，我加载了源图像，通过阈值将其转换为二进制图像，然后反转它以突出显示黑色区域（现在是白色）。

There is any method/function in the python wrapper of Opencv that finds black areas in a binary image? (like regionprops in Matlab) Up to now I load my source image, transform it into a binary image via threshold and then invert it to highlight the black areas (that now are white).

我无法使用第三方库，例如cvblobslob或cvblob

I can't use third party libraries such as cvblobslob or cvblob

推荐答案

基本上，您可以使用 findContours 函数，结合OpenCV特别为此目的提供的许多其他函数。

Basically, you use the findContours function, in combination with many other functions OpenCV provides for especially this purpose.

有用的函数em>全部出现在OpenCV文档中的结构分析和形状描述符页面上）：

Useful functions used (surprise, surprise, they all appear on the Structural Analysis and Shape Descriptors page in the OpenCV Docs):

• findContours


• drawContours


• moments


• contourArea


• arcLength li>
  
• boundingRect


• convexhull


• fitEllipse

• findContours
• drawContours
• moments
• contourArea
• arcLength
• boundingRect
• convexHull
• fitEllipse

示例代码（我拥有所有属性来自Matlab的 regionprops 除了 WeightedCentroid 和 EulerNumber - 您可以通过 EulerNumber 在 findContours 中使用 cv2.RETR_TREE 并查看结果层次结构，我确定 WeightedCentroid 也不会那么难。

example code (I have all the properties from Matlab's regionprops except WeightedCentroid and EulerNumber - you could work out EulerNumber by using cv2.RETR_TREE in findContours and looking at the resulting hierarchy, and I'm sure WeightedCentroid wouldn't be that hard either.

# grab contours
cs,_ = cv2.findContours( BW.astype('uint8'), mode=cv2.RETR_LIST,
                             method=cv2.CHAIN_APPROX_SIMPLE )
# set up the 'FilledImage' bit of regionprops.
filledI = np.zeros(BW.shape[0:2]).astype('uint8')
# set up the 'ConvexImage' bit of regionprops.
convexI = np.zeros(BW.shape[0:2]).astype('uint8')

# for each contour c in cs:
# will demonstrate with cs[0] but you could use a loop.
i=0
c = cs[i]

# calculate some things useful later:
m = cv2.moments(c)

# ** regionprops ** 
Area          = m['m00']
Perimeter     = cv2.arcLength(c,True)
# bounding box: x,y,width,height
BoundingBox   = cv2.boundingRect(c)
# centroid    = m10/m00, m01/m00 (x,y)
Centroid      = ( m['m10']/m['m00'],m['m01']/m['m00'] )

# EquivDiameter: diameter of circle with same area as region
EquivDiameter = np.sqrt(4*Area/np.pi)
# Extent: ratio of area of region to area of bounding box
Extent        = Area/(BoundingBox[2]*BoundingBox[3])

# FilledImage: draw the region on in white
cv2.drawContours( filledI, cs, i, color=255, thickness=-1 )
# calculate indices of that region..
regionMask    = (filledI==255)
# FilledArea: number of pixels filled in FilledImage
FilledArea    = np.sum(regionMask)
# PixelIdxList : indices of region. 
# (np.array of xvals, np.array of yvals)
PixelIdxList  = regionMask.nonzero()

# CONVEX HULL stuff
# convex hull vertices
ConvexHull    = cv2.convexHull(c)
ConvexArea    = cv2.contourArea(ConvexHull)
# Solidity := Area/ConvexArea
Solidity      = Area/ConvexArea
# convexImage -- draw on convexI
cv2.drawContours( convexI, [ConvexHull], -1,
                  color=255, thickness=-1 )

# ELLIPSE - determine best-fitting ellipse.
centre,axes,angle = cv2.fitEllipse(c)
MAJ = np.argmax(axes) # this is MAJor axis, 1 or 0
MIN = 1-MAJ # 0 or 1, minor axis
# Note: axes length is 2*radius in that dimension
MajorAxisLength = axes[MAJ]
MinorAxisLength = axes[MIN]
Eccentricity    = np.sqrt(1-(axes[MIN]/axes[MAJ])**2)
Orientation     = angle
EllipseCentre   = centre # x,y

# ** if an image is supplied with the BW:
# Max/Min Intensity (only meaningful for a one-channel img..)
MaxIntensity  = np.max(img[regionMask])
MinIntensity  = np.min(img[regionMask])
# Mean Intensity
MeanIntensity = np.mean(img[regionMask],axis=0)
# pixel values
PixelValues   = img[regionMask]        

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