I'm trying to make a simple 2D editor with the following capabilities:
Create/delete rectangles, polygons, circles, etc
Hierarchical grouping of these shapes
Move, rotate, scale these shapes
Apply a certain texture to them (with UV coordinates for each vertex)
I've had some success, but the code is messy. Are they any simple projects or articles that I can read to get some more info on the kinds of data structures used for such projects?
There is a lot of literature out there to get good ideas from. Some good ones:
IEEE Tutorial: Computer Graphics '79 has all the important graphics algorithms from the 60s and 70s, many are the original articles.
Graphics Gems (I) surveys important techniques from the 80s.
You might also want to look at PHIGS which focuses on Hierarchical Graphics.
Open Source 2D Game Engines can give you a good idea.
Not exactly "simple" in any sense, but Inkscape might be worth looking at.
Related
I am looking for a tool to start with simple 3d objects for Scene Kit. I know there are a lot professional tools out there, but just buying blind a program to try a bit how things work would be wasted money.
How do I create 3D content for a Scene Kit scene, beyond using the builtin geometry and geometry creation tools within Scene Kit?
Short answer: Blender.
Long answer: Blender, because it's free and as good as any other 3D app (they are unfortunately all quite bad) but take this to the Apple Developer forums if you'd like more info, as this question is not appropriate for Stack Overflow. The way you're thinking about this is also not appropriate; no app is a "Colada editor". That would be like saying Photoshop is a JPEG editor.
The Unity forum is also a great place to hear a bunch of opinions; unlike any of Apple's tools other than Logic or perhaps Final Cut, it has a wide-ranging user base of technically-minded artists from whom to gather opinions.
Use Polygon Modelling.
COLLADA content is created in 3D modelling programs. There are three main ways of creating 3D content: polygon modelling, clay/brush style push/pull modelling of highly complex meshes, and what's most commonly known as NURBS modelling.
Polygon modelling involves creating shapes by articulating and adding primitive pieces of geometry to primitive pieces of geometry and progressively building up complex shapes in this manner. It has several massive advantages for game engine content creation that have seen it become (far and away) the most popular manner of making content for game engines.
Namely:
Strict and absolute control of the amount and type of geometry (performance)
Strict and absolute seam and inter-object connectivity control (animation)
Absolute texture control and material/shader control via geometry and bitmaps with 1:1 UVW mapping (ideal for expressing textures onto geometry with great performance)
Well designed and commonly used smoothing and tessellation algorithms nondestructive to, and considerate of, the above points
Given these massive benefits for game content creation, it's imperative you learn Polygon modelling first, for any and all game content creation. Most nearly all polygon modellers output a format compatible with COLLADA.
Understanding Polygon modelling will also give you understanding of some of how Geometry Shaders work and what they act on. The addition of Geometry Shaders is a relatively new feature of Scene Kit that provides vastly more interesting ways to manipulate geometry than the basic geometry creation tools provided within Scene Kit.
I'm searching for algorithms/methods that are used to classify or differentiate between two outdoor environments. Given an image with vehicles, I need to be able to detect whether the vehicles are in a natural desert landscape, or whether they're in the city.
I've searched but can't seem to find relevant work on this. Perhaps because I'm new at computer vision, I'm using the wrong search terms.
Any ideas? Is there any work (or related) available in this direction?
I'd suggest reading Prince's Computer Vision: Models, Learning, and Inference (free PDF available). It covers image classification, as well as many other areas of CV. I was fortunate enough to take the Machine Vision course at UCL which the book was designed for and it's an excellent reference.
Addressing your problem specifically, a simple MAP or MLE model on pixel colours will probably provide a reasonable benchmark. From there you could look at more involved models and feature engineering.
Seemingly complex classifications similar to "civilization" vs "nature" might be able to be solved simply with the help of certain heuristics along with classification based on color. Like Gilevi said, city scenes are sure to contain many flat lines and right angles, while desert scenes are dominated by rolling dunes and so on.
To address this directly, you could use OpenCV's hough - lines algorithm on the images (tuned for this problem of course) and look at:
a) how many lines are fit to the image at a given threshold
b) of the lines that are fit what is the expected angle between two of them; if the angles are uniformly distributed then chances are its nature, but if the angles are clumped up around multiples of pi/2 (more right angles and straight lines) then it is more likely to be a cityscape.
Color components, textures, and degree of smoothness(variation or gradient of image) may differentiate the desert and city background. You may also try Hough transform, which is used for line detection that can be viewed as city feature (building, road, bridge, cars,,,etc).
I would recommend you this research very similar with your project. This article presents a comparison of different classification techniques to obtain the scene classifier (urban, highway, and rural) based on images.
See my answer here: How to match texture similarity in images?
You can use the same method. I already solved in the past problems like the one you described with this method.
The problem you are describing is that of scene categorization. Search for works that use the SUN database.
However, you only working with two relatively different categories, so I don't think you need to kill yourself implementing state-of-the-art algorithms. I think taking GIST features + color features and training a non-linear SVM would do the trick.
Urban environments is usually characterized with a lot of horizontal and vertical lines, GIST captures that information.
I'm currently working on a vision system for a UAV I am building. The goal of the system is to find target objects, which are rather well defined (see below), in a video stream that will be a 2-D flyover view of the ground. So far I have tried training and using a Haar-like feature based cascade, a la Viola Jones, to do the detection. I am training it with 5000+ images of the targets at different angles (perspective shifts) and ranges (sizes in the frame), but only 1900 "background" images. This does not yield good results at all, as I cannot find a suitable number of stages to the cascade that balances few false positives with few false negatives.
I am looking for advice from anyone who has experience in this area, as to whether I should: 1) ditch the cascade, in favor of something more suitable to objects defined by their outline and color (which I've read the VJ cascade is not).
2) improve my training set for the cascade, either by adding positives, background frames, organizing/shooting them better, etc.
3) Some other approach I can't fathom currently.
A description of the targets:
Primary shapes: triangles, squares, circles, ellipses, etc.
Distinct, solid, primary (or close to) colors.
Smallest dimension between two and eight feet (big enough to be seen easily from a couple hundred feet AGL
Large, single alphanumeric in the center of the object, with its own distinct, solid, primary or almost primary color.
My goal is use something very fast, such as the VJ cascade, to find possible objects and their associated bounding box, and then pass these on to finer processing routines to determine the properties (color of the object and AN, value of the AN, actual shape, and GPS location). Any advice you can give me towards completing this goal would be much appreciated. The source code I currently have is a little lengthy for post here, but is freely available should you like to see it for reference. Thanks in advance!
-JB
I would recommend ditching Haar classification, since you know a lot about your objects. In these cases you should start by checking what features you can use:
1) overhead flight means, as you said, you can basically treat these as fixed shapes on a 2D plane. There will be scaling, rotations and some minor affine transformations, which depends a lot on how wide-angled your camera is. If it isn't particularly wide-angled, that part can probably be ignored. Also, you probably know your altitude, by which you can probably also make very good assumption on the target size (scaling).
2) You know the colors, which also makes it quite easy to find objects. If these are very defined as primary color, then you can just filter the image based on color and find those contours. If you want to do something a little more advanced (which to me doesn't seem necessary though...) you can do a backprojection, which in my experience is very effective and fast. Note, if you're creating the objects, it would be better to use Red Green and Blue instead of primary colors (red green and yellow). Then you can simply split the image into it's respective channels and use a very high threshold.
3) You know the geometric shapes. I've never done this myself, but as far as I know, the options are using moments or using Hough transforms (although openCV only has hough algorithms for lines and circles, so you'd have to write your own for other shapes...). You might already have sufficiently good results without this step though...
If you want more specific recommendations, it would be very useful to upload a couple sample images. :)
May be solved but I came across a paper recently with an open-source license for generic object detection using normalised gradient features : http://mmcheng.net/bing/comment-page-9/
The details of the algorithms performance against illumination, rotation and scale may require a little digging. I can't remember on the top of my head where the original paper is.
If I take a picture with a camera, so I know the distance from the camera to the object, such as a scale model of a house, I would like to turn this into a 3D model that I can maneuver around so I can comment on different parts of the house.
If I sit down and think about taking more than one picture, labeling direction, and distance, I should be able to figure out how to do this, but, I thought I would ask if someone has some paper that may help explain more.
What language you explain in doesn't matter, as I am looking for the best approach.
Right now I am considering showing the house, then the user can put in some assistance for height, such as distance from the camera to the top of that part of the model, and given enough of this it would be possible to start calculating heights for the rest, especially if there is a top-down image, then pictures from angles on the four sides, to calculate relative heights.
Then I expect that parts will also need to differ in color to help separate out the various parts of the model.
As mentioned, the problem is very hard and is often also referred to as multi-view object reconstruction. It is usually approached by solving the stereo-view reconstruction problem for each pair of consecutive images.
Performing stereo reconstruction requires that pairs of images are taken that have a good amount of visible overlap of physical points. You need to find corresponding points such that you can then use triangulation to find the 3D co-ordinates of the points.
Epipolar geometry
Stereo reconstruction is usually done by first calibrating your camera setup so you can rectify your images using the theory of epipolar geometry. This simplifies finding corresponding points as well as the final triangulation calculations.
If you have:
the intrinsic camera parameters (requiring camera calibration),
the camera's position and rotation (it's extrinsic parameters), and
8 or more physical points with matching known positions in two photos (when using the eight-point algorithm)
you can calculate the fundamental and essential matrices using only matrix theory and use these to rectify your images. This requires some theory about co-ordinate projections with homogeneous co-ordinates and also knowledge of the pinhole camera model and camera matrix.
If you want a method that doesn't need the camera parameters and works for unknown camera set-ups you should probably look into methods for uncalibrated stereo reconstruction.
Correspondence problem
Finding corresponding points is the tricky part that requires you to look for points of the same brightness or colour, or to use texture patterns or some other features to identify the same points in pairs of images. Techniques for this either work locally by looking for a best match in a small region around each point, or globally by considering the image as a whole.
If you already have the fundamental matrix, it will allow you to rectify the images such that corresponding points in two images will be constrained to a line (in theory). This helps you to use faster local techniques.
There is currently still no ideal technique to solve the correspondence problem, but possible approaches could fall in these categories:
Manual selection: have a person hand-select matching points.
Custom markers: place markers or use specific patterns/colours that you can easily identify.
Sum of squared differences: take a region around a point and find the closest whole matching region in the other image.
Graph cuts: a global optimisation technique based on optimisation using graph theory.
For specific implementations you can use Google Scholar to search through the current literature. Here is one highly cited paper comparing various techniques:
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms.
Multi-view reconstruction
Once you have the corresponding points, you can then use epipolar geometry theory for the triangulation calculations to find the 3D co-ordinates of the points.
This whole stereo reconstruction would then be repeated for each pair of consecutive images (implying that you need an order to the images or at least knowledge of which images have many overlapping points). For each pair you would calculate a different fundamental matrix.
Of course, due to noise or inaccuracies at each of these steps you might want to consider how to solve the problem in a more global manner. For instance, if you have a series of images that are taken around an object and form a loop, this provides extra constraints that can be used to improve the accuracy of earlier steps using something like bundle adjustment.
As you can see, both stereo and multi-view reconstruction are far from solved problems and are still actively researched. The less you want to do in an automated manner the more well-defined the problem becomes, but even in these cases quite a bit of theory is required to get started.
Alternatives
If it's within the constraints of what you want to do, I would recommend considering dedicated hardware sensors (such as the XBox's Kinect) instead of only using normal cameras. These sensors use structured light, time-of-flight or some other range imaging technique to generate a depth image which they can also combine with colour data from their own cameras. They practically solve the single-view reconstruction problem for you and often include libraries and tools for stitching/combining multiple views.
Epipolar geometry references
My knowledge is actually quite thin on most of the theory, so the best I can do is to further provide you with some references that are hopefully useful (in order of relevance):
I found a PDF chapter on Multiple View Geometry that contains most of the critical theory. In fact the textbook Multiple View Geometry in Computer Vision should also be quite useful (sample chapters available here).
Here's a page describing a project on uncalibrated stereo reconstruction that seems to include some source code that could be useful. They find matching points in an automated manner using one of many feature detection techniques. If you want this part of the process to be automated as well, then SIFT feature detection is commonly considered to be an excellent non-real-time technique (since it's quite slow).
A paper about Scene Reconstruction from Multiple Uncalibrated Views.
A slideshow on Methods for 3D Reconstruction from Multiple Images (it has some more references below it's slides towards the end).
A paper comparing different multi-view stereo reconstruction algorithms can be found here. It limits itself to algorithms that "reconstruct dense object models from calibrated views".
Here's a paper that goes into lots of detail for the case that you have stereo cameras that take multiple images: Towards robust metric reconstruction
via a dynamic uncalibrated stereo head. They then find methods to self-calibrate the cameras.
I'm not sure how helpful all of this is, but hopefully it includes enough useful terminology and references to find further resources.
Research has made significant progress and these days it is possible to obtain pretty good-looking 3D shapes from 2D images. For instance, in our recent research work titled "Synthesizing 3D Shapes via Modeling Multi-View Depth Maps and Silhouettes With Deep Generative Networks" took a big step in solving the problem of obtaining 3D shapes from 2D images. In our work, we show that you can not only go from 2D to 3D directly and get a good, approximate 3D reconstruction but you can also learn a distribution of 3D shapes in an efficient manner and generate/synthesize 3D shapes. Below is an image of our work showing that we are able to do 3D reconstruction even from a single silhouette or depth map (on the left). The ground-truth 3D shapes are shown on the right.
The approach we took has some contributions related to cognitive science or the way the brain works: the model we built shares parameters for all shape categories instead of being specific to only one category. Also, it obtains consistent representations and takes the uncertainty of the input view into account when producing a 3D shape as output. Therefore, it is able to naturally give meaningful results even for very ambiguous inputs. If you look at the citation to our paper you can see even more progress just in terms of going from 2D images to 3D shapes.
This problem is known as Photogrammetry.
Google will supply you with endless references, just be aware that if you want to roll your own, it's a very hard problem.
Check out The Deadalus Project, althought that website does not contain a gallery with illustrative information about the solution, it post several papers and info about the working method.
I watched a lecture from one of the main researchers of the project (Roger Hubbold), and the image results are quite amazing! Althought is a complex and long problem. It has a lot of tricky details to take into account to get an approximation of the 3d data, take for example the 3d information from wall surfaces, for which the heuristic to work is as follows: Take a photo with normal illumination of the scene, and then retake the picture in same position with full flash active, then substract both images and divide the result by a pre-taken flash calibration image, apply a box filter to this new result and then post-process to estimate depth values, the whole process is explained in detail in this paper (which is also posted/referenced in the project website)
Google Sketchup (free) has a photo matching tool that allows you to take a photograph and match its perspective for easy modeling.
EDIT: It appears that you're interested in developing your own solution. I thought you were trying to obtain a 3D model of an image in a single instance. If this answer isn't helpful, I apologize.
Hope this helps if you are trying to construct 3d volume from 2d stack of images !! You can use open source tool such as ImageJ Fiji which comes with 3d viewer plugin..
https://quppler.com/creating-a-classifier-using-image-j-fiji-for-3d-volume-data-preparation-from-stack-of-images/
I have a number of images where I know the focal length, pixel count, dimensions and position (from GPS). They are all in a high oblique manner, taken on the ground with commercially available cameras.
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What would be the best method for calculating the euclidean distances between certain pixels within an image? If it is indeed possible.
Assuming you're not looking for full landscape modelling but a simple approximation then this shouldn't be too hard. Basically a first approximation of your image reduces to a camera with know focal length looking along a plane. So we can create a model of the system in 3D very easily - it's not too far from the classic observer looking over a checkerboard demo.
Normally our graphics problem would be to project the 3D model into 2D so we could render the image. Although most programs nowadays use an API (such as OpenGL) to do this the equations are not particularly complex or difficult to understand. I wrote my first code using the examples from 3D Graphics In Pascal which is a nice clear treatise, but there will be lots of other similar source (although probably less nowadays as a hardware API is invariably used).
What's useful about this is that the projection equations are commutative, in that if you have a point on the image and the model you can run the data back though the projection to retrieve the original 3D coordinates - which is what you wish to do.
So a couple of approaches suggest: either write the code to do the above yourself directly, or probably more simply use OpenGL (I'd recommend the GLUT toolkit for this). If your math is good and manipulating matrices causes you no issue then I'd recommend the former as the solution will be tighter and it's interesting stuff - otherwise take the OpenGL approach. You'd probably want to turn the camera/plane approximation into camera/sphere fairly early too.
If this isn't sufficient for your needs then in theory going to actual landscape modelling would be feasible. The SRTM data is freely available (albeit not in the friendliest of forms) so combined with your GPS position it should be possible to create a mesh model in with which you apply the same algorithms as above.