I just want to register UAV images side by side using pix4D software, but the problem is I don't have any GCPs. How do I get a mosaic where all the images are stitched properly?
Images with Pix4d are typically referenced with geo-information (data tagged from INS/GPS and IMU). If this is not the case, there is no way for Pix4d to know where your images are in space.
An alternative to geo-referenced images is to create Manual Tie Points. This is a tedious process and has an effect on accuracy + image distortion. Check out Pix4D tutorials on Youtube or Pix4d/FAQ's for how to tag your images with MTP's.
Hope that helps & good luck.
Related
When preforming image co-registration of multiple subjects, how should we select the reference image?
Can a randomly selected image form one dataset could be the reference image for an image from the other dataset?
If we do that, should all the images belonging to the reference image dataset be co-registered with the reference image as well?
I couldn't find any material in this area. Could someone please advice?
I'm not sure exactly what you mean by the term "dataset", but I will assume you are asking about co-registering multiple images from different patients (i.e. multiple 3D images per subject).
To answer your questions:
If there are no obvious choices about which image is best, then a random choice is fine. If you have e.g. a CT and an MRI for each subject, then co-registration using the CT images is likely going to give you better results because of intrinsic image characteristics (e.g. less distortion, image value linked to physical quantity).
I suppose that depends on what you want to do, but if it is important to have all imaging data in the same co-registered reference space then yes.
Another option is to try and generate an average image, and then use that as a reference to register other images to. Without more information about what you are trying to achieve it's hard to give any more specific advice.
What is suggested (optimal) image size to work with face API. Can't find anything about this.
Looks like images should not be to small but either too large. Probably any recommendation how to prepare them before train model?
Thanks.
This may help from the "Add Face" documentation:
JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed image file size is from 1KB to 4MB.
"targetFace" rectangle should contain one face. Zero or multiple faces will be regarded as an error. If the provided "targetFace" rectangle is not returned from Face - Detect, there’s no guarantee to detect and add the face successfully.
Out of detectable face size (36x36 - 4096x4096 pixels), large head-pose, or large occlusions will cause failures.
Adding/deleting faces to/from a same face list are processed sequentially and to/from different face lists are in parallel.
I did crawl the images in the Google Image Search window
but, the images are too small so I want to increased the size
I increased the size using PIL, but the picture is broken(Image quality is too low)
How can I increase the images size with good quality?
I used PIL this way
from PIL import Image
im = Image.open('filename')
im_new = im.resize((500, 500))
im_new.save('filename2')
No, I think you maybe get a wrong understanding of the real problem.
The images you got are just some thumbnails, so it contains little information. Your efforts to improve the image quality
by some algorithm may be very hard to make a difference. Probably only by using some machine learning tricks can you make the photos a little nicer.
In my opinion, what you need to do is to get original images you got with Google search rather than use thumbnails. You can do this by do a lot more analysis with image search results. Good luck :)
I am doing some studies on eye vascularization - my project contains a machine which can detect the different blood vessels in the retinal membrane at the back of the eye. What I am looking for is a possibility to segment the picture and analyze each segmentation on it`s own. The Segmentation consist of six squares wich I want to analyze separately on the density of white pixels.
I would be very thankful for every kind of input, I am pretty new in the programming world an I actually just have a bare concept on how it should work.
Thanks and Cheerio
Sam
Concept DrawOCTA PICTURE
You could probably accomplish this by using numpy to load the image and split it into sections. You could then analyze the sections using scikit-image or opencv (though this could be difficult to get working. To view the image, you can either save it to a file using numpy, or use matplotlib to open it in a new window.
First of all, please note that in image processing "segmentation" describes the process of grouping neighbouring pixels by context.
https://en.wikipedia.org/wiki/Image_segmentation
What you want to do can be done in various ways.
The most common way is by using ROIs or AOIs (region/area of interest). That's basically some geometric shape like a rectangle, circle, polygon or similar defined in image coordinates.
The image processing is then restricted to only process pixels within that region. So you don't slice your image into pieces but you restrict your evaluation to specific areas.
Another way, like you suggested is to cut the image into pieces and process them one by one. Those sub-images are usually created using ROIs.
A third option which is rather limited but sufficient for simple tasks like yours is accessing pixels directly using coordinate offsets and several nested loops.
Just google "python image processing" in combination with "library" "roi" "cropping" "sliding window" "subimage" "tiles" "slicing" and you'll get tons of information...
i am trying to read an image with ITK and display with VTK.
But there is a problem that has been haunting me for quite some time.
I read the images using the classes itkGDCMImageIO and itkImageSeriesReader.
After reading, i can do two different things:
1.
I can convert the ITK image to vtkImageData using itkImageToVTKImageFilter and the use vtkImageReslicer to get all three axes. Then, i use the classes vtkImageMapper, vtkActor2D, vtkRenderer and QVTKWidget to display the image.
In this case, when i display the images, there are several problems with colors. Some of them are shown very bright, others are so dark you can barely see them.
2.
The second scenario is the registration pipeline. Here, i read the image as before, then use the classes shown in the ITK Software Guide chapter about registration. Then i resample the image and use the itkImageSeriesWriter.
And that's when the problem appears. After writing the image to a file, i compare this new image with the image i used as input in the XMedcon software. If the image i wrote ahs been shown too bright in my software, there no changes when i compare both of them in XMedcon. Otherwise, if the image was too dark in my software, it appears all messed up in XMedcon.
I noticed, when comparing both images (the original and the new one) that, in both cases, there are changes in modality, pixel dimensions and glmax.
I suppose the problem is with the glmax, as the major changes occur with the darker images.
I really don't know what to do. Does this have something to do with color level/window? The most strange thing is that all the images are very similar, with identical tags and only some of them display errors when shown/written.
I'm not familiar with the particulars of VTK/ITK specifically, but it sounds to me like the problem is more general than that. Medical images have a high dynamic range and often the images will appear very dark or very bright if the window isn't set to some appropriate range. The DICOM tags Window Center (0028, 1050) and Window Width (0028, 1051) will include some default window settings that were selected by the modality. Usually these values are reasonable, but not always. See part 3 of the DICOM standard (11_03pu.pdf is the filename) section C.11.2.1.2 for details on how raw image pixels are scaled for display. The general idea is that you'll need to apply a linear scaling to the images to get appropriate pixel values for display.
What pixel types do you use? In most cases, it's simpler to use a floating point type while using ITK, but raw medical images are often in short, so that could be your problem.
You should also write the image to the disk after each step (in MHD format, for example), and inspect it with a viewer that's known to work properly, such as vv (http://www.creatis.insa-lyon.fr/rio/vv). You could also post them here as well as your code for further review.
Good luck!
For what you describe as your first issue:
I can convert the ITK image to vtkImageData using itkImageToVTKImageFilter and the use vtkImageReslicer to get all three axes. Then, i use the classes vtkImageMapper, vtkActor2D, vtkRenderer and QVTKWidget to display the image.
In this case, when i display the images, there are several problems with colors. Some of them are shown very bright, others are so dark you can barely see them.
I suggest the following: Check your window/level in VTK, they probably aren't adequate to your images. If they are abdominal tomographies window = 350 level 50 should be a nice color level.