I have an image with a list of segmentation polygons. Getting the bounding box with the polygons is easy enough. I used the bounding box to crop out the objects of interest.
Now, I want to scale the polygons so that they wrap around the cropped object. I scaled the polygons using the ratio of the original image size to the crop size. However, I do not know how to add the correct offsets to the bounding boxes. As a result, the polygon points incorrectly wrap around the object.
For object crops, what is the correct transformation for scaling and translating arbitrary points within the crops?
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I have a dataset of contour images.
In my dataset, each image contain SINGLE object (on black background) which corresponds to a contour-image (i.e. image corresponding to a particular detected contour earlier), retrieved earlier.
I just have these images, and no other contour information.
I need to get contour polygon (height, width, polygon coordinates) for each image so that I can use this dataset for training in Tensorflow models.
Will running cv2.findContours() make sense (as each image is already a single contour) or is there another faster way to extract the bounding polygon from the contour images ?
Thank you so much in advance.
I need to find the size or coordinates of a rectangle that is displayed as a quadrilateral in a 3D image. The quadrilateral is on a plane that lines up with 3d world vanishing points. To clarify, the quadrilateral IS a rectangle in the 3D world, and that's the rectangle I want the size of.
I do not need to get all the textures and make a new image. I also do not know the coordinates of the target rectangle as required by the homography (perspective transformation) solutions I've seen, because I don't know the aspect ratio it's supposed to have.
I've read through this thread: proportions of a perspective-deformed rectangle and the guy seemed to find an algorithm that works. However I've read other research papers that claim to calculate a homography yet they don't say how they did it. Also it seems such a basic function there would be something in the existing openCV library.
Thanks.
I'm going to find the most look-like rectangles among shapes. The first image is the original image with shapes which possibly be rectangles but they are not. The green rectangles in the second image is what I want. So is there a way to do this with opencv? I've tried hough lines but the result's not good
The source image:
And what I want is to find out the most look-like rectangle among these shapes, like the rectangles in green.
What I want:
A very simple approach is, after you have a rectangle bounding box around your shape, count the percentage of pixels inside the box which are white.
The higher the percentage of white pixels, the closest to a rectangle it is.
To get the bounding boxes you should take a look at either findContours from opencv, or some Blob extracting algorithm, you will find plenty of questions regarding those.
Edit:
Maybe you should first get the Minimum bounding rectangles of the shapes and then do this kind of heuristic:
Shrink the rectangle dimensions until the white-pixel percentage inside the rectangle reaches some threshold defined by you (like 90% of white pixels inside the rectangle).
To get the Minimum bounding rectangle (the smallest rectangle which contains the whole shape), you might check this tutorial:
http://docs.opencv.org/doc/tutorials/imgproc/shapedescriptors/bounding_rects_circles/bounding_rects_circles.html
One thing that might also help is doing the difference of sizes from the minimum bounding rectangle and the maximum inner rectangle (the biggest rectangle you can fit inside the white shape). The less difference there is between those rectangle's properties (width, height, area, center coordinates) the closest is the shape to a rectangle.
I am trying to crop a picture on right on along the contour. The object is detected using surf features and than i want to crop the image of extactly as detected.
When using crop some outside boundaries of other object is includes. I want to crop along the green line below. OpenCV has RotatedRect but i am unsure if its good for cropping.
Is there way to perfectly crop along the green line
I assume you get you get your example from http://docs.opencv.org/doc/tutorials/features2d/feature_homography/feature_homography.html, so what you can do is to find the minimum axis aligned bounding box around the green bounding box, crop it from the image, use the inverted homography (H.inv()) matrix to transform that sub image into a new image (call cv::warpPerspective), and then crop your green bounding box (it should be axis aligned in your new image).
You can get the equations of the lines from the end points for each. Use these equations to check whether any given pixel lies within the green box or not i.e. does it lie between the left and right lines and between the top and bottom lines. Run this over the entire image and reset anything that doesn't lie within the box to black.
Not sure about in-built functionality to do this, but this simple methodology is guaranteed to work. For higher accuracy, you may want to consider sub-pixel checks.
I have a picture of a checkerboard taken from an arbitrary camera angle. I find the two vanishing points corresponding to the two sets of lines that form the checkerboard grid. From these two vanishing points, I compute a homography from the checkerboard plane to the image plane.
I then apply the inverse homography to re-render the checkerboard from a top view. However, for certain images, the re-rendered top view is very large. That is, due to the camera angle, the inverse homography stretches certain parts of the image (i.e. the regions of the image that are very close to one of the vanishing points) to be very large.
This takes up an unnecessarily large amount of memory, and most of the region that becomes highly stretched is stuff I do not need. So, when applying the inverse homography, I would like to avoid rendering regions of the image that will be highly stretched. What is a good way to do this?
(I am coding in MATLAB)
If you just need to render the checkerboard, without the background, you could just extract the four corners of the checkerboard and compute the homography that maps them to the four corners of a square.
Then you can obtain a rectified image of the checkerboard by warping your input image with this homography, paying attention to render only the needed region (ie the square on which you map the checkerboard).