The artifacts are common in computed topographies (CT) and can occur for various reasons. The ring and metal artifacts are two common types of them.
An example of the ring type
An example of the metal type
I would like to create these artifacts synthetically and randomly add them to my CT images. Is there any way to do this in python?
Related
I have two recordings from security cameras which are located on opposite corners. Each of the cameras has a dead spot (image attached).
I am looking for a technique in python that allows creating a map of the objects in the room space based on these two cameras' views.
Can you please suggest something?
Problem definition
Given two images from the same scene, stitch these images side-by-side by finding common parts (features) that overlap with each other.
Approach
In theory, the steps followed to solve the problem are as follows:
Detect feature points using Harris corner detection.
Create key-point descriptors by creating patches around them
Estimate key-point matches based on the Euclidean distance
Perform RANSAC to find the affine transformation matrix
Warp and apply geometric transformation
For the abovementioned steps you can refer to the OpenCV tutorial and other libraries that might come in use for some specific step sklearn.feature module used on the feature extraction step.
Also, you can find many image stitching projects on github in case you don't want to reinvent the wheel.
I am trying to perform image registration on potentially hundreds of aerial images taken from a camera mounted on a UAV. I think it is safe to assume that I know the ordering of the images, and hopefully, sequential images will overlap.
I have read some papers that suggest using a CNN to find the homography matrix can vastly outperform the old school feature descriptor matching with RANSAC song and dance. My issue is that I don't quite understand how to stitch more than 2 images together. It seems to me that to register image 100 in the same coordinate frame as image 1 using the cv2.warpPerspective function, I would do I100H1H2*H3...H99. Even if the error in each transform is small after 100 applications it seems like it would be huge. My understanding is that the solution to this problem is bundle adjustment.
I have looked into bundle adjustment a little bit but Im struggling to see how exactly I can use it. I have read the paper that many related stack overflow posts suggest "Automatic Panoramic Image Stitching using Invariant Features". In the section on bundle adjustment IF I understand the authors suggest that after building the initial panorama it is likely that image A will eventually overlap with multiple other images. Using the matched feature points in any images that overlap with A they basically calculate some adjustment...? I think to image A?
My question is using openCV how do I apply this adjustment? Let's say I have 3 images I1, I2, I3 all overlapping for a minimal example.
#assuimg CNN model predicts transform
#I think the first step is find the homography between all images
H12 = cnnMod.predict(I1,I2)
H13 = cnnMod.predict(I1,I3)
H23 = cnnMod.predict(I2,I3)
outI2 = cv2.warpPerspective(I2,H12,(maxWidth, maxHeight),flags=cv2.INTER_LINEAR)
outI3 = cv2.warpPerspective(I2,H23,(maxWidth, maxHeight),flags=cv2.INTER_LINEAR)
#now would I do some bundle voodoo?
#what would it look like?
#which of the bundler classes should I use?
#would it look like this?
#or maybe the input is features?
voodoo = cv2.bundleVoodoo([H12,H13,H23])
golaballyRectifiedI2 = cv2.warpPerspective(outI2,voodoo[2],(maxWidth, maxHeight),flags=cv2.INTER_LINEAR)
The code is my best guess at what a solution might look like but clearly I have no idea what I am doing. I've not been able to find anything that actually shows how the bundle adjustment is done.
The basic idea underlying image alignment through bundle adjustment is that, rather than matching pairs of 2D points (x, x') across pairs of images, you posit the existence of 3d points X that, ideally, project onto matched tuples of 2D points (x, x', x'', ...) matched among corresponding tuples of images. You then solve for the location of the X's and the camera parameters (extrinsics, and intrinsics if the camera is uncalibrated) that minimize the (robustified, usually) RMS reprojection error over all 2d points and images.
Depending on your particular setup and scene, you may make some simplifying assumptions, e.g.:
That the X's all belong to the same plane (which you can arbitrarily choose as the world's Z=0 plane). This is useful, for example, when stitching images of a painting, or aerial images on relatively flat ground with relatively small extent so one can ignore the earth's curvature.
Or that the X's are all on the WGS84 ellipsoid.
Both the above assumptions remove one free coordinate from X, effectively reducing the problem's dimensionality.
I intend to make a 3D model based on multi view stereo images ( basically 2D plane images of the same object from different angles and orientation) inside Blender from scratch.However, I am new to Blender.
I wanted to know if there are any tutorials of how to project a single pixel or point in the space of Blender's 3D environment using python. If not tutorial, any documentation. I am still learning about this whole 3D construction thing and pretty new to this, so I am not sure maybe these points are displayed using a 3 dimensional matrix/array ?
Basically I want to implement 3D construction based on a paper written by some researchers. Mostly every such project is in C++. I want to do it in Python in Blender, and if I am capable enough, make these libraries open source.
Suggest me any pre-requisite if you think that shall help me. I have just started my 3rd year of BSc Computer Science course, and very new to the world of Computer Graphics.
(My skillset is C, Java and Python.)
I would be very glad and appreciate any help.
Thank You
[Link to websitehttps://vision.in.tum.de/research/image-based_3d_reconstruction/multiviewreconstruction[][1]]
image2
Yes, it can very likely be done in Blender, and in Python at least for small geometries / low resolution.
A valid approach for the kind of scenarios you seem to want to play with is based on the idea of "space carving" or "silhouette projection". A good description in is an old paper by Kutulakos and Seitz, which was based in part on earlier work by Szelisky.
Given a good estimation of the silhouettes, these methods can correctly reconstruct all convex portions of the object's surface, and the subset of concavities that are resolved in the photo hull. The remaining concavities are "patched" over and need to be reconstructed using a different method (e.g. stereo, or structured light). For the surfaces that can be reconstructed, space carving is generally more robust than stereo (since it is insensitive to the color and surface texture of the object), and can work on surfaces where structured light struggles (e.g. surfaces with specularities, or very dark objects with low reflectance for a laser stripe)
The basic idea is to use the silhouettes of the projection of the object in cameras around it to "remove" mass from an initial volume (e.g. a box) encompassing the object, a bit like a sculptor carving a statue by removing material from a block of marble.
Computationally, you can do it representing the volume of space of interest using an octree, initialized with a minimal level of subdivision, and then progressively refined. The refinement consists of projecting the vertices of the octree leaves in the cameras, and identifying which leaves are completely outside or partially inside the silhouettes. The former are pruned, while the latter are split, and the process continues until no more leaves can be split or a maximul level of subdivision is reached. The hull of the octree is then extracted as a "watertight" mesh using standard methods.
Apart from the above paper, a way more detailed description can be found on an old patent by Geometrix - it sold a scanner based on the above ideas around year 2000. Here is what it looked like:
I am currently working on the problem of generating roads from Openstreetmap files. I use these Osm files to create a shapefile from all different road types with different roadway numbers, widths and so on.
Later on, I would also like to use digital orthophotos (DOP) to eliminate inconsistencies and errors in the Osm files.
In the appendix you can see an example image of a DOP (with corrected shadows) and a generated mask, in addition to the mask there is the generated shapefile (with the different information contained).
So much for the introduction, now for the main question:
In order to generate road markings on the generated roads it is first necessary to split shapefiles according to individual lanes. How is that possible in Python?
Many thanks and greetings
To detect a car trailer its very hard. There're a lot of car trailers that has the same license plate as the car itself. To detect if its a trailer I need to search in a area around the license plate. I already making a detector for the license plate with Viola and Jones. Only to detect where the triangle is you don't see it very clearly on the image. The images coming from section control so a lot of different thresholds in the day time.
For the privacy I have to delete the license plate details
So my question are there special ways of image processing that would be help full. I thought about:
- canny
- adapting threshold
- image Gradients
But big difficulty is that the application needs to be real-time.
The way I see it, you will need edge-detection (canny) and image correlation (finding similar shapes; recognition of object in the image).
Your two basic shapes (patterns to seek) would consist of the line-image of license plate, the base line of the bumper, edges of the lights, and respectively the triangles for one and no triangles in the other. The presence and absence of the triangles should be the only difference between the two images to be sought.
First, you process the image through Canny or Sobel or some other edge-detect to get the edges. Then you correlate it with the two patterns. The correlation function should produce "quality of match" value - how well the shape found in the image matches the pattern. If the one with triangles matches better, it's the trailer.
Don't try to detect just the triangles. They are too dim to produce a decent match, and too common a shape, possibly producing countless false positives. Seek bumper with lights and license plate, and then once that is found, compare it to example bumper with triangles, and example bumper without triangles. That way no matter how poor the triangle detection, the match against the image with triangles will always be better if they are there.