I have a QImage of size 12x12 in GIF format. I want to rotate it on certain angle with very high frequency. My application involves a robot so when it changes its orientation(which it does very frequently) my QImage in simulation should also be rotated but it causes loss of information. I am doing it something like below.
robot_transform.rotate(angle);
*robot2 = robot->transformed(robot_transform,Qt::SmoothTransformation);
*robot2= robot2->scaled(12,12, Qt::KeepAspectRatio,Qt::SmoothTransformation);
I need suggestions that whats wrong in this approach and secondly is there any other optimal approach for the desired application?
Thanks
I would increase the resolution of the source image to at least double. Rotating an image to non-90-degree angles will cause loss of pixel information. An higher res source can compensate for that.
Most sprite based animations use pre-rendered images for each possible angle.
The problem is the scaling afterwards, you need to crop the center of the image. You can do this with QImage::copy.
Related
I have an problem: My 3d plot gets distorted, if I rotate or pan the plot. I know this behaviour is intended to show the user the most at all times, but it looks silly, so I want an orthogonal view or Axes which are equally long.
Earth distorted.
I have an WPF Application, where the ILN Form/Control is hosted via WindowsFormsHost.
I tried every possibility regarding Plotcube.Projection, have set Plotcube.Limits, changed ILN_Panel autosize etc. My next idea was that maybe I need to configure the WPF and/or Forms window itself.
Thanks!
I think that is not possible. At least not easy within PlotCube. If you do not need PlotCube just put the object into the Camera node and set Projection to Orthogonal. And you are done.
With PlotCube it is much more complicated. You must make sure that all containers have equal aspect ratio (width / height) WinformsHostControl, PlotCube, PlotCube.ScreenRect, PlotCube.DataScreenRect, PlotCube.Plots (data group), and its Limits. If you want to rotate everything free you must make sure to have equal aspect ration on all 3 dimensions wehere applicable.
Result of my code:
Basically, what the issue is, the transparent part of my image are not blending correctly with what is drawn before it. I know I can do a
if(alpha<=0){discard;}
in the fragment shader, the only issue is I plan on having a ton of fragments and don't want the if statement for each fragment on mobile devices.
Here is my code related to alpha, and depth testing:
var gl = canvas.getContext("webgl2",
{
antialias : false,
alpha : false,
premultipliedAlpha: false,
}
);
gl.enable(gl.BLEND);
gl.blendFunc(gl.SRC_ALPHA, gl.ONE_MINUS_SRC_ALPHA);
gl.enable(gl.DEPTH_TEST);
gl.depthFunc(gl.GREATER);
Also, these are textured gl.POINTS I am drawing. If I change the order the two images are drawn in the buffer, the problem doesn't exist. They will be dynamically rotating during the program's runtime so this is not an option.
It's not clear what your issue is without more code but it looks like a depth test issue.
Assuming I understand correctly you're drawing 2 rectangles? If you draw the red one before the blue one then depending on how you have the depth test setup the blue one will fail the depth test when the X area is drawn.
You generally solve this by sorting what you draw, making sure to draw things further away first.
For a grid of "tiles" you can generally sort by walking the grid itself in the correct direction instead of "sorting"
On the other hand, if all of your transparency is 100% draw or not draw then discard has its advantages and you can draw front to back. The reason is because in that case drawing front to back, the pixel drawn (not discarded) by the red quad will be rejected when drawing the blue quad by the depth test. The depth test is usually optimized to happen before running the fragment shader for a certain pixel. If the depth test says the pixel will not be drawn then no reason to even run the fragment shader for that pixel, time saved. Unfortunately as soon as you have any transparency that is not 100% opaque or 100% transparent then you need to sort and draw back to front. Some of these issues are covered in this article
A few notes:
you mentioned mobile devices and you mentioned WebGL2 in your code sample. There is no WebGL2 on iOS
you said you're drawing with POINTS. The spec says only POINTS of 1 pixel in size are required. It looks like you're safe up to points of size 60 but to be safe it's generally best to draw with triangles as there are other isses with points
you might also be interested in sprites with depth
I am new to the field of medical imaging - and trying to solve this (potentially basic problem). For a machine learning purpose, I am trying to standardize and normalize a library of DICOM images, to ensure that all images have the same rotation and are at the same scale (e.g. in mm). I have been playing around with the Mango viewer, and understand that one can create transformation matrices that might be helpful in this regard. I have however the following basic questions:
I would have thought that a scaling of the image would have changed the pixel spacing in the image header. Does this tag not provide the distance between pixels, and should this not change as a result of scaling?
What is the easiest way to standardize a library of images (ideally in python)? Is it possible and should one extract a mean pixel spacing across all images, and then scaling all images to match that mean? or is there a smarter way to ensure consistency in scaling and rotation?
Many thanks in advance, W
Does this tag not provide the distance between pixels, and should this
not change as a result of scaling?
Think of the image voxels as fixed units of space, which are sampling your image. When you apply your transform, you are translating/rotating/scaling your image around within these fixed units of space. That is, the size and shape of the voxels doesn't change. They just sample different parts of your image.
You can resample your image by making your voxels bigger or smaller or changing their shape (pixel spacing), but this can be independent of the transform you are applying to the image.
What is the easiest way to standardize a library of images (ideally in
python)?
One option is FSL-FLIRT, although it only accepts data in NIFTI format, so you'd have to convert your DICOMs to NIFTI. There is also this Python interface to FSL.
Is it possible and should one extract a mean pixel spacing across all
images, and then scaling all images to match that mean? or is there a
smarter way to ensure consistency in scaling and rotation?
I think you'd just to have pick a reference image to register all your other images too. There's no right answer: picking the highest resolution image/voxel dimensions or an average or some resampling into some other set of dimensions all sound reasonable.
I create a big image stitched out of many single microscope images.
Suddenly, (after several month of working properly) the stitched overview images became blurry and they are containing strange structural artefacts like askew lines (not the rectangulars, they are because of not perfect stitching)
If I open any particular tile in full size, they are not blurry and the artefacts are hardly observable. (Consider, the image below is already 4x scaled)
The overview image is created manually by scaling each tile using QImage::scaled and copying all of them to the corresponding region in the big image. I'm not using opencv's stitching.
I assume, this happens because of image contents, because most if the overview images are ok.
The question is, how can I avoid such hardly observable artefacts to become very clearly visible after scaling? Is there some means in OpenCV or QImage?
Is there any algorithms to find out, if image content could lead to such effect for defined scale-factor?
Many thanks in advance!
Are you sure the camera is calibrated properly? That the lightning is uniform? Is the lens clear? Do you have electrical components that interfere with the camera connection?
If you add image frames of photos on a uniform material (or non-uniform material, moved randomly for significant time), the resultant integrated image should be completely uniform.
If your produced image is not uniform, especially if you get systematic noise (like the apparent sinusoidal noise in the provided pictures), write a calibration function that transforms image -> calibrated image.
Filtering in Fourier space is another way to filter out the noise but considering that the image is rotated you will lose precision, and you'll be cutting off components of the real signal, too. The following empiric method will reduce the noise in your particular case significantly:
ground_output: composite image with per-pixel sum of >10 frames (more is better) over uniform material (e.g. excited slab of phosphorus)
ground_input: the average(or sqrt(sum of px^2)) in ground_output
calib_image: ground_input /(per px) ground_output. Saved for the session, or persistent in a file (important: ensure no lossy compression! (jpeg)).
work_input: the images to work on
work_output = work_input *(per px) calib_image: images calibrated for systematic noise.
If you can't create a perfect ground_input target such as having a uniform material on hand, do not worry too much. If you move any material uniformly (or randomly) for enough time, it will act as a uniform material in this case (think of a blurred photo).
This method has the added advantage of calibrating solitary faulty pixels that ccd cameras have (eg NormalPixel.value(signal)).
If you want to have more fun you can always fit the calibration function to something more complex than a zero-intercept line (steps 3. and 5.).
I suggest scaling the image with some other software to verify if the artifacts are in fact caused by Qt or are inherent in the image you've captured.
The askew lines look a lot like analog tv interference, or CCTV noise induced by 50 or 60 Hz power lines running alongside the signal cable or some other electrical interference on the signal.
If the image distortion is caused by signal interference then you can try to mitigate it by moving the signal lines away from whatever could be the source of the problem, or fit something to try to filter the noise (baluns for example).
I'm doing some image processing, and I need to find some information on line growing algorithms - not sure if I'm using the right terminology here, so please call me out on this is needs be.
Imagine my input image is simply a circle on a black background. I'd basically like extract the coordinates, so that I may draw this circle elsewhere based on the coordinates.
Note: I am already using edge detection image filters, but I thought it best to explain with a simple example.
Basically what I'm looking to do is detect lines in an image, and store the result in a data type where by I have say a class called Line, and various different Point objects (containing X/Y coordinates).
class Line
{
Point points[];
}
class Point
{
int X, Y;
}
And this is how I'd like to use it...
Line line;
for each pixel in image
{
if pixel should be added to line
{
add pixel coordinates to line;
}
}
I have no idea how to approach this as you can probably establish, so pointers to any subject matter would be greatly appreciated.
I'm not sure if I'm interpreting you right, but the standard way is to use a Hough transform. It's a two step process:
From the given image, determine whether each pixel is an edge pixel (this process creates a new "binary" image). A standard way to do this is Canny edge-detection.
Using the binary image of edge pixels, apply the Hough transform. The basic idea is: for each edge pixel, compute all lines through it, and then take the lines that went through the most edge pixels.
Edit: apparently you're looking for the boundary. Here's how you do that.
Recall that the Canny edge detector actually gives you a gradient also (not just the magnitude). So if you pick an edge pixel and follow along (or against) that vector, you'll find the next edge pixel. Keep going until you don't hit an edge pixel anymore, and there's your boundary.
What you are talking about is not an easy problem! I have found that this website is very helpful in image processing: http://homepages.inf.ed.ac.uk/rbf/HIPR2/wksheets.htm
One thing to try is the Hough Transform, which detects shapes in an image. Mind you, it's not easy to figure out.
For edge detection, the best is Canny edge detection, also a non-trivial task to implement.
Assuming the following is true:
Your image contains a single shape on a background
You can determine which pixels are background and which pixels are the shape
You only want to grab the boundary of the outside of the shape (this excludes donut-like shapes where you want to trace the inside circle)
You can use a contour tracing algorithm such as the Moore-neighbour algorithm.
Steps:
Find an initial boundary pixel. To do this, start from the bottom-left corner of the image, travel all the way up and if you reach the top, start over at the bottom moving right one pixel and repeat, until you find a shape pixel. Make sure you keep track of the location of the pixel that you were at before you found the shape pixel.
Find the next boundary pixel. Travel clockwise around the last visited boundary pixel, starting from the background pixel you last visited before finding the current boundary pixel.
Repeat step 2 until you revisit first boundary pixel. Once you visit the first boundary pixel a second time, you've traced the entire boundary of the shape and can stop.
You could take a look at http://processing.org/ the project was created to teach the fundamentals of computer programming within a visual context. There is the language, based on java, and an IDE to make 'sketches' in. It is a very good package to quickly work with visual objects and has good examples of things like edge detection that would be useful to you.
Just to echo the answers above you want to do edge detection and Hough transform.
Note that a Hough transform for a circle is slightly tricky (you are solving for 3 parameters, x,y,radius) you might want to just use a library like openCV