Okay, so I'd like to implement something similar to this image using NSMatrix.
My "table" will be large (scrollable in both directions).
I realize the prerequisite that NSMatrix cells are all the same size.
Here's a proposed solution:
The white cells comprise the main NSMatrix
The top row of dark gray header cells (except the leftmost one), are another NSMatrix
The leftmost column of cells (including the top one), are another NSMatrix.
I would embed the white cells in a NSClipView/NSScrollView, with visible scrollers. I would also embed the top and left matrices similarly but with separate NSClipView/NSScrollView pairs, without visible scrollers.
Then, the main visible NSScrollView would notify the other NSScrollView's of movement as appropriate.
Is this a typical solution to this issue? Or is there some other custom-ish view I'm unaware of?
One other thing: if the content of the main NSMatrix is bound to an NSArrayController, how is the 2D-ness of the matrix fleshed out with a single-dimension NSArray?
There are some custom-ish views indeed: LIGridControl, MBTableGrid
Related
I have an array of text items with varying amounts of text in them.
I want to display them as rows of line number and text with boxes around number and txt separately. The catch is I want the box around the number to be the same height as the box around the text.
I found this question and answer which almost makes it, except every line is the same height, ie sized to the largest visible row. I want each row to be sized according to its own text size, ie each row might be a different height.
I tried changing the lazy grid to a VStack (there's unlikely to be more than forty rows) so that the sizing wasn't limited to visible rows, but then I couldn't get height or width to cooperate.
Is there a way?
As #Yrb suggests a solution (which seems to me the simplest) is to modify the answer I linked by creating a struct for the rows and then moving the height calculating extension to extend that struct rather than the entire view.
This beautifully calculates the height of each row independently of the others.
I've a sprite sheet containing a set of icons as shown here:
I'd like to get the bounding box (at pixel precision) of all icons inside it, some cases like list, grid have to be considered as only one icons. Any ideas are more than welcome.
I think the main issue in your problem is that some icons contain disjoint parts.
If all the icons were in only one part, you could just find the "connected components" (groups of white pixels) in your image and isolate them.
I don't know your level in image processing but to connect the parts of one icons, I would probably use dilation, which is a morphological method to expand (under constraints) the areas of maximum intensity in an image.
If you need any clarification, please let me know !
In general, it is not possible: only the humans have enough context to determine which of the disjoint parts belong together. You can approximate it using various ways, but it's a lost cause - and IMHO completely unnecessary. Imagine writing a test for this functionality - it's impossible, it requires a human in the loop, since the results for any particular icon sheet don't generalize. Knowing that the algorithm works for some sheet tells you nothing about whether it will work for some other sheet that you know nothing about a-priori.
It'd be simpler to manually colorize each sprite to have a color different than that of its neighbors. Then a greedy algorithm could find the bounding boxes easily without having to approximate anything.
(a) what I have, (b) what I get, (c) what I want
I have a simple vector graphic in Inkscape, which consists of a rectangle, filled points and stars. Since the axis ranges are not really nice (the height equals approximatly 3 times the width of the picture) for a publication, I want to rescale the picture. However, I do not have the raw data, such that I can plot it again. How can I rescale my graphic (see figure (a)), such that the x-range is more wide (see figure (c)) without getting distortions (see figure (b))? In the end I want to create a PDF file out of it.
Any ideas on that?
Thanks for your help.
You can try to do it in 2 steps, using the Object -> Transform tool (Shift-Ctrl-M).
First, select everything, and with the transform tool select the Scale tab, and scale horizontally by, say, 300%. All figures will be distorted.
Now, unselect the rectangle, and scale horizontally again by 33.3%, but first click on Apply to each object separately. This will undo the distortion (but not the translation) of each object.
Note that 300% followed by 33.3% should leave the individual objects with the same size.
Documentation here.
I have a list of bounding boxes, I was wondering how I could calculate which ones were redundant / duplicates.
The reason being is I have 2 million of these I send to a API and I want to know which are overlapping others so I can reduce them down so each box only covers a unique area of land, so no two bounding boxes cover the same piece of geo space.
How would I calculate it so that these bounding boxes were each covering their own unique space of geo land ?
I am writing this program in C++ btw.
I think that this task is more complex then you think.
You would have to split existing boxes, untill no overlapping exists, and then remove the boxes totally contained in another.
Instead giving you a solution to that, I recomend to check if you can live with:
1) remove the boxes that are totally contained in another box.
2) leave (partly-)overlapping boxes as they are.
For 2 millions you need a spatial index (QuadTree), to get a list of all boxes nearby one box.
If you have to avoid any overlappings, then you must continue to think what should be the result?
A) A union of overlapping rectangles that therfore is not an rectangle anymore, but a polygon.
or B) The result should be rectangles.
You could check if X% of a box's vertices are inside another box to find if it's overlapped but I suppose this isn't the optimal solution.
I'm relatively new to OpenCV, and I'm working on a project where I need to count the number of objects on a grid. the grid is the background of the image, and there's either an object in each space or there isn't; I need to count the number present, and I don't really know where to start. I've searched here and other places, but can't seem to find what I'm looking for. I will need to be tracking the space numbers of the grid in the future, so I will also eventually need to know whether each grid space is occupied or empty. I'm not going so far as to ask for a coded example, but does anybody know of any source or tutorials to accomplish this task or one similar to it? Thanks for your help!
Further Details: images will come from a stable-mounted camera, objects are of relatively uniform shape, but varying size and color.
I would first answer a few questions:
Will an object be completely enclosed in a grid cell? Or can it be placed on top of a grid line? (In other words, will the object hide a line from the camera?)
Will more than one object be in one cell?
Can an object occupy more than one cell? (closely related to question 1)
Given reasonable answers to those questions, I believe the problem can be broken into two parts: first, identify the centers of each grid space. To count objects, you can then sample that region to see if anything "not background" is there.
You can then assume that a grid space is defined by four strong, regularly-placed, corner features. (For the sake of discussion, I'll assume you've performed the initial image preparation as needed: histogram equalization, gaussian blur for noise reduction, etc.) From there, you might try some of OpenCV's methods for finding corners (Harris corner detector, cvGoodFeaturesToTrack, etc). It's likely that you can borrow some of the techniques found in OpenCV's square finding example (samples/c/square.c). For this task, it's probably sufficient to assume that the grid center is just the centroid of each set of "adjacent" (or sufficiently near) corners.
Alternatively, you might use the Hough transform to identify the principal horizontal and vertical lines in the image. You can then determine the intersection points to identify the extents of each grid cell. This implementation might be more challenging since inferring structure (or adjacency) from "nearby" vertices in order to find a grid center seems more difficult.