I have the following image as a test image:
I attempt to find the shapes on the image (and other images). My approch right now is the following:
Gaussian blur with a 3x3 kernel
Canny edge detection using
list (to get all shapes)
Morphology with MorphOp.Close to close
the edges
FindContours to find contours
Iteration of each contour:
Find ApproxPolyDP
Find ConvexHull
Discard if
hull size < 2, approx area < 200 or hull size > 50000, or arclength
of the approx < 100
Draw convexhull
This method yields the following images where the convex hulls are drawn:
This is almost perfect, but notice that the lines are seen as a contours events->suppliers and events->documents). When looking at the edge information, it becomes apparent why this is so:
The lines are detected as a contour. How could I prepare/find the shapes so the lines are not detected? I though of some thinning algorithm, but since I also work on real life images it is difficult to find a threshold that works. Here is an example of a real life image where thinning is difficult to do because thinning typically requires the images to be monochrome in black and white.
How would you do it? Is there some method to determine if the contour/convex hull is a line, rectangle or something like this?
I ended up using a mix of overlapping test and convexity scan. The convexity scans for the error between the convex hull and the actual contour. If this error exceeds a certain amount, the hull is ignored. The overlapping simply use bitwise and to detech if two convex hull's overlap. If they overlap more than 95% percent, one of them is ignored.
Related
I'm trying to extract the geometries of the papers in the image below, but I'm having some trouble with grabbing the contours. I don't know which threshold algorithm to use (here I used static threshold = 10, which is probably not ideal.
And as you can see, I can get the correct number of images, but I can't get the proper bounds using this method.
Simply applying Otsu just doesn't work, it doesn't capture the geometries.
I assume I need to apply some edge detection, but I'm not sure what to do once I apply Canny or some other.
I also tried sobel in both directions (+ve and -ve in x and y), but unsure how to extract these contours from there.
How do I grab these contours?
Below is some previews of the images in the process of the final convex hull results.
**Original Image** **Sharpened**
**Dilate,Sharpen,Erode,Sharpen** **Convex Of Approximated Polygons Hulls (which doesn't fully capture desired regions)**
Sorry in advance about the horrible formatting, I have no idea how to make images smaller or title them nicely in SOF
As a follow-up question to finding contiguous black pixels in image, I use OpenCV's findContours() to detect shapes of black on white (I invert the colors for the function to work better). In the images below, OpenCV detects the outer shape of the "g" and the inner bowl as different shapes:
I could use the hierarchy to discard shapes inside other shapes, but I would rather avoid it in case OpenCV detects an overarching contour around the whole image. Does findContours have some tuning that cause it to find only contiguous pixels and not the inside negative shape?
I am trying to detect ROI for a fixed repetitive pattern in an image using opencv C++.
The ROI which I am trying to find - is shown with red boundary as shown in the pic:
I tried canny edge detection after blurring but it detects edge of the vertical/horizontal black and white lines. This is not something I am trying to detect.
What is the best approach to my problem?
Since you're starting with a binary image you could use
findContours()
to get the contours for the individual strips. Since there are a couple of solitary pixels from noise you should then filter for size using
contourArea(contour)
and merge the points of all contours meeting your size criteria into a combined contour. Then get the bounding box for the combined contour:
boundingRect(combinedContour)
I've been working off a variant of the opencv squares sample to detect rectangles. It's working fine for closed rectangles, but I was wondering what approaches I could take to detect rectangles that have openings ie missing corners, lines that are too short.
I perform some dilation, which closes small gaps but not these larger ones.
I considered using a convex hull or bounding rect to generate a contour for comparison but since the edges of the rectangle are disconnected, each would read as a separate contour.
I think the first step is to detect which lines are candidates for forming a complete rectangle, and then perform some sort of line extrapolation. This seems promising, but my rectangle edges won't lie perfectly horizontally or vertically.
I'm trying to detect the three leftmost rectangles in this image:
Perhaps this paper is of interest? Rectangle Detection based on a Windowed Hough Transform
Basically, take the hough line transform of the image. You will get maximums at the locations in (theta, rho) space which relate to the places where there are lines. The larger the value, the longer/straighter the line. Maybe do a threshold to only get the best lines. Then, we are trying to look for pairs of lines which are
1) parallel: the maximums occur at similar theta values
2) similar length: the values of the maximums are similar
3) orthogonal to another pair of lines: theta values are 90 degrees away from other pairs' theta values
There are some more details in the paper, such as doing the transform in a sliding window, and then using an error metric to consolidate multiple matches.
I want to find the corners of a object & detect if there is a cut in the corner. The Real Image is so Big & it consist of lot of noise inside the Contour Area. So far I've tried...
1)find the contours
2)approximate the contour to find the approximate corners points
3)crop each corner image & compare it with cvMatchShapes() Rotated # Corners.
But the results was not accurate & i need some guidance.Here is the sample canny output image for which i wanna detect the Cut which is CIRCLED. Also in real Image I'm getting lot of noise in the canny output so Pls suggest me how to detect this shape defect at Corners.
![enter image description here][1]Regards, Balaji.R
http://answers.opencv.org/question/25730/finding-a-defective-cornercircled-from-contours/
after you do the step 3, you may want to do hausdorff distance. OpenCV function can be found here. I think hausdoff distance best suites your requirement. Find the distance between the corners and if the distance is more than a certain value, then it is defective. It can also take care of noise upto certain extent.