Python OpenCV - 在二进制图像中查找黑色区域

Question

在Opencv的python包装器中有任何方法/函数可以在二进制图像中找到黑色区域吗？(就像Matlab中的regionprops)到目前为止,我加载了我的源图像,通过阈值将其转换为二进制图像,然后将其反转以突出显示黑色区域(现在是白色).

我不能使用第三方库,如cvblobslob或cvblob

Answer 1

基本上,您使用该findContours功能,结合OpenCV为此特别提供的许多其他功能.

使用的有用函数(惊喜,惊喜,它们都出现在OpenCV Docs 的Structural Analysis和Shape Descriptors页面中):

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

示例代码(我都从MATLAB的性质regionprops,除了WeightedCentroid和EulerNumber-你可以计算出EulerNumber使用cv2.RETR_TREE中findContours来看,在造成层次,我敢肯定WeightedCentroid不会是很难无论是.

# 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]        

Answer 2

反转二值图像以将黑色区域变为白色区域后，应用 cv.FindContours 函数。它将为您提供所需区域的边界。

稍后您可以使用 cv.BoundingRect 来获取区域周围的最小边界矩形。一旦你得到了矩形的顶点，你就可以找到它的中心等。

或者要找到区域的质心，请在找到轮廓后使用 cv.Moment 函数。然后在 x 和 y 方向上使用 cv.GetSpatialMoments。opencv手册中有解释。

要查找面积，请使用 cv.ContourArea 函数。