After some color detection, binary thresholding, and using cvFindContours() and drawing the contours and detected blue rectangle on the image I have:
My problem is to some simple collision avoidance (the blue rectangle in the center cannot hit the red "walls"). It would be helpful for my purposes to have the red wall contours be approximated as with rectangles. However, using a simple cvBoundingRect and drawing red rectangles around the white contours I get:
The edges are a little cropped off, but you may get the idea of what we would expect using a bounding rectangle for the contours, as the entire contour is used for the approximation of the bounding rectangle and hence the large overlapping rectangles. What I would like to have is the wall contours be divided into multiple bounding rectangles, such as the the left wall be approximated as one rectangle, the right wall, the forward wall, etc...as in my illustrative rendition below:
Any help in doing so would be greatly appreciated.
Detecting lines (typically Hough, RANSAC) together with some other information you have about the problem should be enough, maybe even overkill. For instance, starting with the below image at left, we get the below image at right.
But if you have the above image at left (which you should have already), the problem is already solved. Just draw both internal and external contours of the walls and you are set.
Related
Let's say I have a 16x16 black & white bitmap image
Here white pixels indicate empty space and black pixels indicate filled space.
I want to extract all of it's contour lines that surround black pixels, including holes and nested contour lines. (see the second image)
Let's define a coordinate space for pixels
top-left pixel -> index (0,0)
top-right pixel -> index (15,0)
bottom-left pixel -> index (0,15)
bottom-right pixel -> index (15,15)
Contour lines also have their coordinate space
top-left corner of top-left pixel -> index (0,0)
top-right corner of top-right pixel -> index (16,0)
bottom-left corner of bottom-left pixel -> index (0,16)
bottom-right corner of bottom-right pixel -> index (16,16)
Finally, contour lines are defined as a sequence of points in that coordinate space.
On the second image I marked 3 contours to demonstrate what the desired output should look like.
Path1 (RED): 1(1,0) 2(2,0) 3(2, 3) 4(3,3) 5(0,3) ... 23(4,4) 24(1, 4)
Hole1 of Path1 (BLUE): 1(7,5) 2(7,6) 3(6,6) ... 13(11,6) 14(11,5)
Path2 (RED again): 1(8,6) 2(10,6) 3(10,8) 4(8,8)
...
Note that the order of points in contour is important. Winding difference for holes is not that important, but we should somehow indicate "hole" property of that contour.
I solved this problem using ClipperLib, but it is more like a brute-force approach in my opinion, if we ignore what happens inside the ClipperLib.
Here's a brief description of the algorithm.
First, define a 16x16 subject polygon from which we will be subtracting all white pixels
Scan the image matrix row by row
On each row extract all contiguous white rectangle shapes as a clipping polygon
Do the polygon clipping by subtracting all collected white rectangular polygons from initial 16x16 subject polygon
Extract path data (including holes) from ClipperLib's PolyTree solution
I'm wondering if there is a better way to solve this problem?
Using ClipperLib seems overkill here, as it addresses general polygons by means of complex intersection detection and topological reconstruction algorithms, whereas your problem is more "predictable".
You can proceed in two steps:
use a standard contouring algorithm, such as used by cv.findContours. (It is an implementation of "Satoshi Suzuki and others. Topological structural analysis of digitized binary images by border following. Computer Vision, Graphics, and Image Processing, 30(1):32–46, 1985.")
from the contours, which link pixel centers to pixel centers, derive the contours that follow the pixel edges. This can probably be achieved by studying the different configurations of sequences of three pixels along the outline.
You can use boundary tracing algorithms for this. I personally use Moore-Neighbor tracing, because it's intuitive and straightforward to implement. You first find the boundary contours, and then come up with a hole searching algorithm (you may need to combine parts of scanline fill algorithm). Once you find a hole, you can apply the same boundary tracing algorithm, but in opposite direction.
You can definitely use libraries like OpenCV to find contours, but it my experience, it may produce degenerate output incompatible with other libraries, such as poly2tri used to decompose polygons into triangles.
If we take your input sample image, then the red path could be considered self-intersecting (vertices 7 and 23 are touching), which may lead to failed polygon decomposition. You may need to figure out a way to find and treat those objects as separate, if that's a problem. However, the newest Clipper2 is going to have triangulation unit that could handle such degenerate input, if you ever need to solve this problem down the road.
I have an image with only black and white pixels. The image contains edges (the black pixels) with the width of one pixel (each black pixel has exactly one or two black neighbourpixels). Now i want to group the edges into different shape classes (e.g. line, triangle, ellipse). Problem: the edges are not perfect lines, triangles or ellipses.
I think i can partially solve the problem by logical thinking. But i also have more complex geometries where this will be more difficult.
Does anyone know how to solve this kind of problem? Or can anyone give me some ideas?
A general way to find the shape of the edges will be to find the convex hull of the points. After that you can try to discard sides in the convex hull which are small than a certain threshold.
I want to fit an image of a clown like face into a contour of another face (a person).
I am detecting the persons face and getting a elliptical-like contour.
I can figure out the center, radius, highest, lowest, left-most and right-most points.
How do I fit the clown face (a square image which I can make elliptical by cutting the face out of the empty background of a png and then detecting the contour) into the persons face?
Or at the least, how do I fit a polygon into another polygon.
I can fit a rectangular image into a rectangular contour with ease, but faces aren't that shape.
Python preferable, but C++ is also manageable, thank you.
Edit: Visual representation as requested:
I have
and I want to make it like this:
but I want the clown face to stretch over the guys face and fit within the blue contour.
I think the keyword you are looking for is Active Appearance Models. First, you need to fit a model to first face (such as this one), which lays inside the contour. Then, you should fit the same model to the clown face. After that, since you have fitted same model to both faces, you can stretch it as you need.
I haven't use AAM myself and I'm not an expert about it, so my explanation might not be enough or might not be exactly correct, but I'm sure it will give you some insight.
A simple and good answer to this question is to find the extreme top, bottom, left, and right points on your contour (head) and then resize your mask to match the aspect ration and place it to cover the 4 points.
Because human heads are elliptical you can use fitEllipse() to give you those 4 points. This will automagically fix any problems with the person tilting their head because regardless of the angle you will know which point is top, bottom, left, and right.
The relevant code for finding the ellipse is:
vector<Point> contour;
// Do whatever you are doing to populate this vector
RotatedRect ellipse = fitEllipse(Mat(contour));
There is also an example as well as documentation for RotatedRect.
// Resize your mask with these sizes for optimum fit
ellipse.size.width
ellipse.size.height
You can rotate your image like this.
UPDATE:
You may also want to find the contour's extreme points to know how much you need to scale your image to ensure that all of the face is covered.
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.