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Here we will learn to extract some frequently used properties of objects like Solidity, Equivalent Diameter, Mask image, Mean Intensity etc. More features can be found at Matlab regionprops documentation.
(NB : Centroid, Area, Perimeter etc also belong to this category, but we have seen it in last chapter)
1. Aspect Ratio
It is the ratio of width to height of bounding rect of the object.
aspect_ratio = float(w)/h
Rect boundingRect(InputArray array)
Calculates the up-right bounding rectangle of a point set or non-zero pixels of gray-scale image.
2. Extent
Extent is the ratio of contour area to bounding rectangle area.
rect_area = w*h
extent = float(area)/rect_area
double contourArea(InputArray contour, bool oriented=false)
Calculates a contour area.
3. Solidity
Solidity is the ratio of contour area to its convex hull area.
solidity = float(area)/hull_area
void convexHull(InputArray points, OutputArray hull, bool clockwise=false, bool returnPoints=true)
Finds the convex hull of a point set.
4. Equivalent Diameter
Equivalent Diameter is the diameter of the circle whose area is same as the contour area.
equi_diameter = np.sqrt(4*area/np.pi)
5. Orientation
Orientation is the angle at which object is directed. Following method also gives the Major Axis and Minor Axis lengths.
RotatedRect fitEllipse(InputArray points)
Fits an ellipse around a set of 2D points.
6. Mask and Pixel Points
In some cases, we may need all the points which comprises that object. It can be done as follows:
mask = np.zeros(imgray.shape,np.uint8)
pixelpoints = np.transpose(np.nonzero(mask))
void drawContours(InputOutputArray image, InputArrayOfArrays contours, int contourIdx, const Scalar &color, int thickness=1, int lineType=LINE_8, InputArray hierarchy=noArray(), int maxLevel=INT_MAX, Point offset=Point())
Draws contours outlines or filled contours.
Here, two methods, one using Numpy functions, next one using OpenCV function (last commented line) are given to do the same. Results are also same, but with a slight difference. Numpy gives coordinates in (row, column) format, while OpenCV gives coordinates in (x,y) format. So basically the answers will be interchanged. Note that, row = y and column = x.
7. Maximum Value, Minimum Value and their locations
We can find these parameters using a mask image.
min_val, max_val, min_loc, max_loc =
cv.minMaxLoc(imgray,mask = mask)
void minMaxLoc(InputArray src, double *minVal, double *maxVal=0, Point *minLoc=0, Point *maxLoc=0, InputArray mask=noArray())
Finds the global minimum and maximum in an array.
8. Mean Color or Mean Intensity
Here, we can find the average color of an object. Or it can be average intensity of the object in grayscale mode. We again use the same mask to do it.
Scalar mean(InputArray src, InputArray mask=noArray())
Calculates an average (mean) of array elements.
9. Extreme Points
Extreme Points means topmost, bottommost, rightmost and leftmost points of the object.
leftmost = tuple(cnt[cnt[:,:,0].argmin()][0])
rightmost = tuple(cnt[cnt[:,:,0].argmax()][0])
topmost = tuple(cnt[cnt[:,:,1].argmin()][0])
bottommost = tuple(cnt[cnt[:,:,1].argmax()][0])
For eg, if I apply it to an Indian map, I get the following result :
image
Exercises
- There are still some features left in matlab regionprops doc. Try to implement them.