There are multiple rectangular areas in the 2d-numpy array. All the rectangular areas have value 1, other areas are zero. I want to extract a minimum number of rectangular connected components from the numpy array. These connected components can touch each other in any direction.
I tried extracting connected components using label function from scipy.ndimage.measurements but it assigns the same label to rectangles which touch each other.
I also tried, morphological opening but I do not want to lose the original shape of the rectangle.
The image shows the expected output for a better understanding of the problem.
Is there a better way to extract a minimum number of perfectly rectangular regions?
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I have curved rectangular object based images. There is a reference object on which the images have to be compared and the differences need to be identified.
Reference Image:
New Images:
I want to identify the difference between these images and highlight the difference.
Key Pointers:
I cannot do pixel by pixel comparison as the objects are not exactly in the same pixel
Approximate shape as to that of reference image also is acceptable
I have tried identifying the contours but as the lines are continuous it is difficult identify the defective part only
I have the following original image:
and the cropped image:
I will like to obtain the left (a), right (c), upper (b) and lower shifts (d) from the original image to obtain the crop:
As of now, I can only think of matching the pixel array values (row and column-wise) and then subtracting the the overlapping pixel arrays coordinates from the original image's coordinates to get the shifts. However this approach seems computationally expensive and a search on 4 sides will have to be undertaken. Also if it helps, I do not have the transformations that led to the cropped image, and I'm assuming that there are no pixel value changes between the original and cropped image on regions of overlap.
Is there a more efficient approach for this? I'm not sure if there are existing built-in functions in OpenCV or other imaging libraries that can do this, so some insights on this will be deeply appreciated.
I have a 2d grid where pixel centers are at the intersection of two half-grid lines, as shown below.
I also have a shape that is drawn on this grid. In my case the shape is a glyph, and is described by segments. Each segment has a start point, end point and a number of off-curve points. These segments can be quadratic curves or lines. What's important is that I can know the points and functions that make up the outline of the shape.
The rule for deciding which pixels should be turned on is simple: if the center of the pixel falls within the shape outline, turn that pixel on. The following image shows an example of applying this rule.
Now the problem I'm facing has to do with anti aliasing. What I'd like to do is to calculate what percentage of the area of a given pixel falls within the outline. As an example, in the image above, I've drawn a red square around a pixel that would be about 15% inside the shape.
The purpose of this would be so that I can then turn that pixel on only by 15% and thus get some cleaner edges for the final raster image.
While I was able to find algorithms for determining if a given point falls within a polygon (ray casting), I wasn't able to find anything about this type of problem.
Can someone can point me toward some algorithms to achieve this? Also let me know if I'm going about this problem in the wrong way!
This sounds like an X, Y problem.
You are asking for a way to calculate the perecentage of pixel coverage, but based on your question, it sounds that what you want to do is anti alias a polygon.
If you are working only with single color 2D shapes (i.e red, blue, magenta... squares, lines, curves...) A very simple solution is to create your image and blur the result afterwards.
This will automatically give you a smooth outline and is simple to implement in many languages.
I have brown filled svg paths and i want to detect and alert my user if there is any shape behind or above another shape. I know intersection list gets if they intersect at the edges but what happens if i want to detect a shape that is behind another shape but doesnt intersect at the edges?
The encoluseList method seems to be dealing with bounding boxes and not this.
Any ideas?
To detect if a path/shape overlaps another
1. Calculating the area covered by the final shape achieved
2. Calculating the sum of areas of all the shapes independently(since this is SVG and the details of each path element is known, this can be done)
3. Comparing the 2 areas.If the 2 areas are the same, then there is no overlapping otherwise at least 2 shapes overlap.
The tricky step is step 1 which can be approximately calculated using pixel painting algorithm(my preference). For other methods, you can go through the following stackoverflow question concerning area of overlapping circles
Given this grid ( http://i.stack.imgur.com/Nz39I.jpg is a trapezium/trapezoid, not a square), how do you find the point clicked by the user? I.e. When the user clicks a point in the grid, it should return the coordinates like A1 or D5.
I am trying to write pseudo code for this and I am stuck. Can anyone help me? Thanks!
EDIT: I am still stuck... Does anyone know of any way to find the height of the grid?
If it is a true perspective projection, you can run the click-point through the inverse projection to find it's X,Z coordinates in the 3D world. That grid has regular spacing and you can use simple math to get the A1,D5,etc.
If it's just something you drew, then you'll have to compare the Y coordinates to the positions of the horizontal lines to figure out which row. Then you'll need to check its position (left/right) relative to the angled lines to get the column - for that, you'll need either coordinates of the end-points, or equations for the lines.
Yet another option is to store an identical image where each "square" is flood-filled with a different color. You then check the color of the pixel where the user clicked but in this alternate image. This method assumes that it's a fixed image and is the least flexible.
If you have the coordinates of end points of the grid lines then
Try using the inside-outside test for each grid line and find the position
Since this grid is just a 3D view of a 2D grid plane, there is a projective transform that transforms the coordinates on the grid into coordinates on the 2D plane. To find this transform, it is sufficient to mark 4 different points on the plane (say, the edges), assign them coordinates on the 2D plane and solve the resulting linear equation system.