I have a binary image and I want to perform closing on that image with the line as structuring element.
The openCv api has a function getStructuringElement that takes the following parameters
Shape
Size
Anchor Point
I can pass CV_SHAPE_CUSTOM in the first parameter to create a new shape but where do I
pass the size and the values of my structuring element.
My line will be 10 pixels wide and 1 pixels in length basically {1,1,1,1,1,1,1,1,1,1}.
There is an old function createStructringElementEx but I don't want to use that as it involves a lot of conversion of datatype.
Is this what you want?
Size = Size(10,1)
Anchor Point = Point(-1,-1)
Got it . Thanks to the comment from Niko.
Create a matrix as
Mat line = Mat::ones(1,10,CV_8UC1);
//now apply the morphology close operation
morphologyEx(img, img, MORPH_CLOSE, line,Point(-1,-1));
This solved my problem.
Related
I have a hard time solving the issue with mask creation.My image is large,
40959px X 24575px and im trying to create a mask for it.
I noticed that i dont have a problem for images up to certain size(I tested about 33000px X 22000px), but for dimensions larger than that i get an error inside my mask(Error is that it gets black in the middle of the polygon and white region extends itself to the left edge.Result should be without black area inside polygon and no white area extending to the left edge of image).
So my code looks like this:
pixel_points_list = latLonToPixel(dataSet, lat_lon_pairs)
print pixel_points_list
# This is the list im getting
#[[213, 6259], [22301, 23608], [25363, 22223], [27477, 23608], [35058, 18433], [12168, 282], [213, 6259]]
image = cv2.imread(in_tmpImgFilePath,-1)
print image.shape
#Value of image.shape: (24575, 40959, 4)
mask = np.zeros(image.shape, dtype=np.uint8)
roi_corners = np.array([pixel_points_list], dtype=np.int32)
print roi_corners
#contents of roi_corners_array:
"""
[[[ 213 6259]
[22301 23608]
[25363 22223]
[27477 23608]
[35058 18433]
[12168 282]
[ 213 6259]]]
"""
channel_count = image.shape[2]
ignore_mask_color = (255,)*channel_count
cv2.fillPoly(mask, roi_corners, ignore_mask_color)
cv2.imwrite("mask.tif",mask)
And this is the mask im getting with those coordinates(minified mask):
You see that in the middle of the mask the mask is mirrored.I took those points from pixel_points_list and drawn them on coordinate system and im getting valid polygon, but when using fillPoly im getting wrong results.
Here is even simpler example where i have only 4(5) points:
roi_corners = array([[ 213 6259]
[22301 23608]
[35058 18433]
[12168 282]
[ 213 6259]])
And i get
Does anyone have a clue why does this happen?
Thanks!
The issue is in the function CollectPolyEdges, called by fillPoly (and drawContours, fillConvexPoly, etc...).
Internally, it's assumed that the point coordinates (of integer type int32) have meaningful values only in the 16 lowest bits. In practice, you can draw correctly only if your points have coordinates up to 32768 (which is exactly the maximum x coordinate you can draw in your image.)
This can't be considered as a bug, since your images are extremely large.
As a workaround, you can try to scale your mask and your points by a given factor, fill the poly on the smaller mask, and then re-scale the mask back to original size
As #DanMašek pointed out in the comments, this is in fact a bug, not fixed, yet.
In the bug discussion, there is another workaround mentioned. It consists on drawing using multiple ROIs with size less than 32768, correcting coordinates for each ROI using the offset parameter in fillPoly.
I tryed to apply to the image the following code in octave:
sq = imread("Square BW.jpg");
figure(1), imshow(Square);
cont1 = edge(sq,"Sobel");
figure(2), imshow(cont1);
The image I get is:
And a similar image appears if I use the Prewitt function. Can anyone explain to me what is happening? The problem is that I can't visualize the process only the result, so I can't understand why the code isn't working.
The problem seems to be how threshold is computed in Octave. You can see how Octave does it by looking at its source by entering type edge at the Octave prompt, or online (I'm not copying the exact code since the code is GPL -- although quite simple)
To get the border, you will need to set the threshold yourself (hopefully, in future versions of Octave's image package this will be fixed but at the moment it's Matlab incompatible since Matlab documentation on their default is unclear).
There's definitely a problem with the way the threshold is computed, however I wasn't able to find the correct value to use in this picture. After many attempts I found this code that seems to work perfectly:
sq = imread("Square BW.jpg");
maskSobel = fspecial("sobel");
mSobel = uint8(zeros(size(BW)));
for i = 0:3
mSobel += imfilter(sq, rot90(maskSobel, i));
end
figure(1), imshow(mSobel);
First we create the Sobel matrix/operator and a zero matrix the same size of the image Square BW. Then we rotate the Sobel matrix four times (by 90 degrees), in order filter the image in all directions (left-right, up-down, right-left and down-up), always adding the result to the mSobel matrix that was created.
Here's the final result:
I'm trying to fill the contourn with cvDrawContourns after cvFindContourns in cvCanny detection, but i'm not success in this
How can I fill the whole contourn?
Thanks
In C++, you request the function to fill the contours by passing the value CV_FILLED (which is equal to -1 if I remember correctly) to the thickness argument. This is probably the same for the Java API.
I've ran in to an issue concerning generating floating point coordinates from an image.
The original problem is as follows:
the input image is handwritten text. From this I want to generate a set of points (just x,y coordinates) that make up the individual characters.
At first I used findContours in order to generate the points. Since this finds the edges of the characters it first needs to be ran through a thinning algorithm, since I'm not interested in the shape of the characters, only the lines or as in this case, points.
Input:
thinning:
So, I run my input through the thinning algorithm and all is fine, output looks good. Running findContours on this however does not work out so good, it skips a lot of stuff and I end up with something unusable.
The second idea was to generate bounding boxes (with findContours), use these bounding boxes to grab the characters from the thinning process and grab all none-white pixel indices as "points" and offset them by the bounding box position. This generates even worse output, and seems like a bad method.
Horrible code for this:
Mat temp = new Mat(edges, bb);
byte roi_buff[] = new byte[(int) (temp.total() * temp.channels())];
temp.get(0, 0, roi_buff);
int COLS = temp.cols();
List<Point> preArrayList = new ArrayList<Point>();
for(int i = 0; i < roi_buff.length; i++)
{
if(roi_buff[i] != 0)
{
Point tempP = bb.tl();
tempP.x += i%COLS;
tempP.y += i/COLS;
preArrayList.add(tempP);
}
}
Is there any alternatives or am I overlooking something?
UPDATE:
I overlooked the fact that I need the points (pixels) to be ordered. In the method above I simply do scanline approach to grabbing all the pixels. If you look at the 'o' for example, it would grab first the point on the left hand side, then the one on the right hand side. I would need them to be ordered by their neighbouring pixels since I want to draw paths with the points later on (outside of opencv).
Is this possible?
You should look into implementing your own connected components labelling. The concept is very simple: you scan the first line and assign unique labels to each horizontally connected strip of pixels. You basically check for every pixel if it is connected to its left neighbour and assign it either that neighbour's label or a new label. In the second row you do the same, but you also check against the pixels above it. Sometimes you need a label merge: two strips that were not connected in the previous row are joined in the current row. The way to deal with this is either to keep a list of label equivalences or use pointers to labels (so you can easily do a complete label change for an object).
This is basically what findContours does, but if you implement it yourself you have the freedom to go for 8-connectedness and even bridge a single-pixel or two-pixel gap. That way you get "almost-connected components labelling". It looks like you need this for the "w" in your example picture.
Once you have the image labelled this way, you can push all the pixels of a single label to a vector, and order them something like this. Find the top left pixel, push it to a new vector and erase it from the original vector. Now find the pixel in the original vector closest to it, push it to the new vector and erase from the original. Continue until all pixels have been transferred.
It will not be very fast this way, but it should be a start.
I am new to Open Cv, I want to transform the two images,
here are my images,Left image and right image.
here is my Code
cv::Mat transformMat = cv::estimateRigidTransform(leftImageMat, rightImageMat, true);
transform(leftImageMat, reconMat, transformMat);
but the problem is that reconMat is of 2 channel. so how can I show it in openCv or convert to 1 channel image as shown above right and left images.
You have a fundamental misunderstanding of what cv::transform() does. The documentation
states:
Performs the matrix transformation of every array element.
This means that the numerical value of each element is transformed by the specified matrix.
It looks like you want a geometric transformation. This can be achieved using cv::warpAffine():
cv::Mat transformMat = cv::estimateRigidTransform(leftImageMat, rightImageMat, true);
cv::Mat output;
cv::Size dsize = leftImageMat.size(); //This specifies the output image size--change as needed
cv::warpAffine(leftImageMat, output, transformMat, dsize);