I'm writing a connected component system and one of the descriptors I can easily compute is the surface area along with the component's rectangular bounding box.
What is surface area divided by bounding area called? (or any mixture of these two parameters).
For example, if my object were a rectangle, this parameter would be 1.0.
Extent or rectangularity, apparently:
Extent of an image object is defined as area of the image object
divided by the area of its bounding rectangle.
Source: Question text in https://dsp.stackexchange.com/questions/49026/what-is-the-application-difference-between-extent-and-solidity-in-image-processi
Rectangularity is the ratio of the object to the area of the minimum
bounding rectangle.
Source: Page 45 in http://www.cyto.purdue.edu/cdroms/micro2/content/education/wirth10.pdf
But the definitions I've run across do not always fully specify the rectangle. The ambiguity is related to the concept of "ferret box". Ferret boxes' edges do not have to be parallel to the image axes like good old bounding boxes. So depending on which you choose, your "extent" value might change.
Related
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?
I am trying to find a reliable method to calculate the corner points of a container. From these corner point’s idea is to calculate the center point of the container for the localization of robot, it means that the calculated center point will be the destination of robot in order to pick the container. For this I am looking for any suggestions to calculate the corner points or may be if any possibility to calculate the center point directly. Up to this point PCL library C/C++ is used for the processing of the 3D data.
The image below is the screenshot of the container.
thanks in advance.
afterApplyingPassthrough
I did the following things:
I binarized the image (black pixels = 0, green pixels = 1),
inverted the image (black pixels = 1, green pixels = 0),
eroded the image with 3x3 kernel N-times and dilated it with same kernel M-times.
Left: N=2, M=1;Right: N=6, M=6
After that:
I computed contours of all non-zero areas and
removed the contour that surrounded entire image.
This are the contours that remained:
I do not know how "typical" input image looks like in your case. Since I only have access to one sample image, I would rather not speculate about "general solution" that will be suitable for you. But to solve this particular case, you could analyze every contour in the following way:
compute rotatated rectangle that fits best around your contour (you need something similar to minAreaRect from OpenCV)
compute areas of rectangle and contour interior
if the difference between contour area and the area of the rotated bounding rectangle is small, the contour has approximately rectangular shape
find the contour that is both rectangular and satisfies some other condition (for example: typical area of the container). Assume that this belongs to container and compute its center.
I am not claiming that this is a solution that will work well in real world scenarios. It is also not fast. You should view it as a "sketch" that shows how to extract some useful information.
I assume the wheels maintain the cart a known offset from the floor and you can identify the floor. Filter out all points which are too close to the floor (this will remove wheels and everything but cart which will help limit data and simplify later steps.
If you isolate the cart, you could apply a simple average point (centroid), alternately, if that is not precise, you could try finding the bounding box of the isolated cart (min max in primary directions) and then take the centroid of that bounding box (this should be more accurate, but will still need a slight vertical offset due to the top handles).
If you can not isolate the cart or the other methods are not working well, you could try using PCL sample consensus specifically SACMODEL_LINE. This will be an involved strategy, but will give very solid results, basically run through and find each line and subtract its members from the cloud so as to find the next best line. After you have your 4 primary cart lines, use their parameters to find your centroid. *this would also be robust against random items being in or on the cart as well as carts of various sizes (assuming they always had linear perpendicular walls)
What is Distance Transform?What is the theory behind it?if I have 2 similar images but in different positions, how does distance transform help in overlapping them?The results that distance transform function produce are like divided in the middle-is it to find the center of one image so that the other is overlapped just half way?I have looked into the documentation of opencv but it's still not clear.
Look at the picture below (you may want to increase you monitor brightness to see it better). The pictures shows the distance from the red contour depicted with pixel intensities, so in the middle of the image where the distance is maximum the intensities are highest. This is a manifestation of the distance transform. Here is an immediate application - a green shape is a so-called active contour or snake that moves according to the gradient of distances from the contour (and also follows some other constraints) curls around the red outline. Thus one application of distance transform is shape processing.
Another application is text recognition - one of the powerful cues for text is a stable width of a stroke. The distance transform run on segmented text can confirm this. A corresponding method is called stroke width transform (SWT)
As for aligning two rotated shapes, I am not sure how you can use DT. You can find a center of a shape to rotate the shape but you can also rotate it about any point as well. The difference will be just in translation which is irrelevant if you run matchTemplate to match them in correct orientation.
Perhaps if you upload your images it will be more clear what to do. In general you can match them as a whole or by features (which is more robust to various deformations or perspective distortions) or even using outlines/silhouettes if they there are only a few features. Finally you can figure out the orientation of your object (if it has a dominant orientation) by running PCA or fitting an ellipse (as rotated rectangle).
cv::RotatedRect rect = cv::fitEllipse(points2D);
float angle_to_rotate = rect.angle;
The distance transform is an operation that works on a single binary image that fundamentally seeks to measure a value from every empty point (zero pixel) to the nearest boundary point (non-zero pixel).
An example is provided here and here.
The measurement can be based on various definitions, calculated discretely or precisely: e.g. Euclidean, Manhattan, or Chessboard. Indeed, the parameters in the OpenCV implementation allow some of these, and control their accuracy via the mask size.
The function can return the output measurement image (floating point) - as well as a labelled connected components image (a Voronoi diagram). There is an example of it in operation here.
I see from another question you have asked recently you are looking to register two images together. I don't think the distance transform is really what you are looking for here. If you are looking to align a set of points I would instead suggest you look at techniques like Procrustes, Iterative Closest Point, or Ransac.
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.
The application PhotoFiltre has an option to stretch part of an image. You select a rectangular shape and you can then grab and move the vertexes somewhere else to make any quadrangle. The image part which you selected will stretch along. Hopefully these images make my point a little clearer:
Is there a general algorithm which can handle this? I would like to obtain the same effect on HTML5 canvas - given an image and the resulting corner points, I would like to be able to draw the stretched image in such a way that it fills the new quadrangle neatly.
A while ago I asked something similar, where the solution was to divide the image up in triangles and stretch each triangle so that each three points correspond to the three points on the original image. This technique turned out to be rather exprensive and I would like if there is a more general method of accomplishing this.
I would like to use this in a 3D renderer, but I would like to work with a (2D) quadrangle.
I don't know whether PhotoFiltre internally also uses triangles, or whether it uses another (cheaper) algorithm to stretch an image like this.
Does someone perhaps know if there is a cheaper or more general method/algorithm to stretch a rectangular image, so that it fills a quadrangle given four points?
The normal method is to start with the destination, pick an appropriate grid size and then for each point in the new shape calculate the corresponding point in the source image (possibly with interpolation depending on the quality you need)
Affine transform.
Given four points for the "stretched" figure and four points for the figure it should match (e.g. a rectangle), an affine transform provides the spatial mapping you need. For each point (x1,y1) in the original image there is a corresponding point (x2,y2) in the second, "stretched" image.
For each integer-valued pixel (x2, y2) in the stretched image, use the affine transform to find the corresponding real-valued point (x1, y1) in the original image and apply its color to (x2,y2).
http://demonstrations.wolfram.com/AffineTransform/
You'll find sample code for Java and other languages online. .NET has the Matrix class.