Opencv Detect boundary and ROI mask - opencv

Hi , I have attached the image below with an yellow bounding box. Is there any algorithm or (sequence of algorithms) in Opencv by which I can detect the yellow pixels and create a ROI mask (which will block out all the pixels outside of it).

You can do:
Find the yellow polygon
Fill the inside of the polygon
Copy only the inside of the polygon to a black-initialized image
Find the yellow polygon
Unfortunately, you used anti-aliasing to draw the yellow line, so the yellow color is not pure yellow, but has a wider range due to interpolation. This affects also the final results, since some not yellow pixels will be included in the result image. You can easily correct this by not using anti-aliasing.
So the best option is to convert the image in the HSV space (where it's easier to segment a single color) and keep only values in a range around the pure yellow.
If you don't use anti-aliasing, you don't even need to convert to HSV and simply keep points whose value is pure yellow.
Fill the inside of the polygon
You can use floodFill to fill the polygon. You need a starting point for that. Since we don't know if a point is inside the polygon (and taking the baricenter may not be safe since the polygon is not convex), we can safely assume that the point (0,0), i.e. the top-left corner of the image is outside the polygon. We can then fill the outside of the polygon, and then invert the result.
Copy only the inside of the polygon to a black-initialized image
Once you have the mask, simply use copyTo with that mask to copy on a black image the content under non-zero pixels in the mask.
Here the full code:
#include <opencv2\opencv.hpp>
using namespace cv;
int main()
{
Mat3b img = imread("path_to_image");
// Convert to HSV color space
Mat3b hsv;
cvtColor(img, hsv, COLOR_BGR2HSV);
// Get yellow pixels
Mat1b polyMask;
inRange(hsv, Scalar(29, 220, 220), Scalar(31, 255, 255), polyMask);
// Fill outside of polygon
floodFill(polyMask, Point(0, 0), Scalar(255));
// Invert (inside of polygon filled)
polyMask = ~polyMask;
// Create a black image
Mat3b res(img.size(), Vec3b(0,0,0));
// Copy only masked part
img.copyTo(res, polyMask);
imshow("Result", res);
waitKey();
return 0;
}
Result:
NOTES
Please note that there are almost yellow pixels in the result image. This is due to anti-aliasing, as explained above.

Related

How to make a mask of everything outside the result of FindContours()

I've detected the triangular shape of the traffic sign I want to detect. To use this image for further processing, I'd like to create a mask to make the background white (or black, doesn't really matter).
To detect this shape I used:
VectorOfVectorOfPoint contours = new VectorOfVectorOfPoint();
Mat hierarchy = new Mat();
CvInvoke.FindContours(input.GetImage(), contours, hierarchy,Emgu.CV.CvEnum.RetrType.External,Emgu.CV.CvEnum.ChainApproxMethod.ChainApproxSimple);
With the DrawContours method, I can easily draw them, with the correct result: image of the contours.
How do I make a mask so that I can clear everything outside the contours?
You can use the contours you have and draw to another similar empty image if all what you want is the outline.
If you want to fill, you can use the fillPoly function to fill the contours directly with specified color, and then use the binary filter to make all pixels with that color while and others black.

Region of interest in fingerprint image

I am working on a fingerprint recognition project with OpenCV. Currently I need to extract the inner region in fingerprint (ellipse in image), but I am not sure how to do it.
Any suggestion is appreciated.
EDIT:
I need to check if a fingerprint from sensor device and another from identification card match or not. The fingerprint in sensor is as follow (left) meanwhile in identification card is as right fingerprint. In order to validate them, it is required to segment this fingerprint (outside the ellipse doesn't provide useful information but indeed adds "noise" for this purpose).
Thank you.
#API55's comment is correct, for clarity:
create a mask (white inside the ellipse and black outside) you can do this with ellipse function and -1 in the thickness. Then copy the image using the mask (bitwise_and for python or copyTo for c++ should do it)... you will always have a squared image, but you will have black (or the color you want) outside the ellipse
These steps are pretty much spot on,
Create your circular mask in the correct place in the image
Copy the image using that mask
Your new image contains the mask data, and black data everywhere else.
below is an example of how to implement this in code:
( I lovingly borrowed from here)
Mat img = imread("small1.png", 0); // load gray
Rect region(10,10,40,40); // example roi
Mat roi(img, region); // part of img
Mat mask(Size(40,40), CV_8U, Scalar(0)); // all black
circle(mask, Point(20,20), 20, Scalar(255), -1, LINE_AA); // filled circle
Mat circRoi;
bitwise_and(roi, roi, circRoi, mask); // retain only pixels inside the circle
//
// now you can do your intensity calculation on circRoi
//
imshow("circle masked", mask);
imshow("masked roi", circRoi);
waitKey();
Useful Links
Why ROIs don't have to be circular but Mats do
Older code example, useful for learning the theory but I wouldnt recommend implementing using IPLimage
Creating a custom ROI of any shape or size

Create a mask from a boundingRect in openCV

Lets say I was given a boundingRect based on some points and stored it into a Rect object.
How can I use those points and create a mask in openCV? that is, everything outside the bounding rectangle is masked (or set white)
I've tried several different methods and was able to get it to work using a convexHull and fillign with a polygon but can't seem to get it to work with the boundingRect
You can call fillConvexPoly() by passing the four end points of the bounding Rect.
// assume all four end points are stored in "vector<Point> roi_vertices" already
// the order of the vertices don't matter
Mat mask = Mat(height, width, CV_8UC1, Scalar(0));
// Create Polygon from vertices
vector<Point> roi_poly;
approxPolyDP(roi_vertices, roi_poly, 1.0, true);
// Fill polygon white
fillConvexPoly(mask, &roi_poly[0], (int)roi_poly.size(), 255, 8, 0);
P.S.: the above method will also work for generating masks for any (convex) polygons.
Draw your rectangle with CV_FILLED option and invert it, like
Rect boundRect(x,y,W,H);
Mat mask(rows,cols,CV_8UC1,Scalar(0));
rectangle(mask,boundRect,Scalar(255),CV_FILLED,8,0);
bitwise_not(mask,mask);
or in another way without using invert, just create a white image and then draw rectangle using CV_FILLED option but with black color(Scalar(0)).
That is
Rect boundRect(x,y,W,H);
Mat mask(rows,cols,CV_8UC1,Scalar(255));
rectangle(mask,boundRect,Scalar(255),CV_FILLED,8,0);

Cropping circular image in IOS using OpenCv

I am developing face features detection in my project.
Heretofore i have developed detecting the face, then finding the eyes within the face.
I want to crop the eyes which are in circular .
circle( mask, center, radius, cv::Scalar(255,255,255), -1, 8, 0 );
image.copyTo( dst, mask );
Here in the above code , I am able to Mask image with black color leaving eye region. now I am want to crop only the Eye region.
Can anybody help me out on this issue.Please check below image
Cropping, by definition, means cutting an axis aligned rectangle from a larger image, leaving a smaller image.
If you want to "crop" a non-axis-aligned rectangle, you will have to use a mask. The mask can be the size of the full image (this is sometimes convenient), or as small and the smallest bounding (axis-aligned) rectangle containing all the pixels you want to leave visible.
This mask can be binary, meaning that it indicates whether or not a pixel is visible, or it can be an alpha-mask which indicated the degree of transparency of any pixel within it, with 0 indicating a non-visible pixel and (for 8-bit mask image) 255 indicating full opacity.
In your example above you can get the sub-image ROI (Region-Of-Interest) like this:
cv::Mat eyeImg = image(cv::Rect(center.x - radius, // ROI x-offset, left coordinate
center.y - radius, // ROI y-offset, top coordinate
2*radius, // ROI width
2*radius)); // ROI height
Note that eyeImg is not a copy, but refers to the same pixels within image. If you want a copy, add a .clone() at the end.

Outlying pixels in OpenCV warpPerspective()

My application requires mapping one quadrilateral to another quadrilateral. Neither of these are rectangles.
However, the result I get from warpPerspective() is always a rectangle. I have tried setting the "outlier" flag to different values to prevent pixels from outside the warped quad from appearing in the destination image but nothing seems to work. What I want is a warped quad with the pixels outside the warped quad set to transparency.
How do I achieve this?
Alternatively, is there a straightforward way to mask the region outside a quadrilateral in OpenCV so that I can copy just the quad to another image?
In case it is relevant, I am using the Python binding to OpenCV.
Here is my current code:
def warpImage(image, corners, target, width, height):
mat = cv2.getPerspectiveTransform(corners, target)
out = numpy.zeros(shape=(width, height), dtype="uint8")
out = cv2.warpPerspective(image, mat, (width,height), out, cv2.INTER_CUBIC)
return out
corners and target are both non-rectangular quads. The output is a full widthxheight rectangle, however. None of the pixels are black or transparent. Instead they are pixels from the image both inside and outside the corners quad. I only want the ones inside.
The best option I have found is to cycle through the pixels and copy the ones in the warped quad to a remap array using the matplotlib pnpoly() function, as so:
import matplotlib.nxutils as nx
def warpImage(image, corners, target, width, height, x0, y0, remap):
mat = cv2.getPerspectiveTransform(corners, target)
out = cv2.warpPerspective(image, mat, (width,height), flags=cv2.INTER_CUBIC)
for x in range(0,width):
for y in range(0,height):
if nx.pnpoly(x,y,target) == 1:
for i in range(0,3):
remap[y+y0,x+x0,i] = out[y,x,i]
return remap
I loop through all the quads in image and accumulate transformed versions in remap.
Having to access each pixel is not very efficient but fortunately this is a one time transformation.

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