I am new to d3 geo. My task is to make a map of Boston and add some interactive features to it.
So far I've been able to get an outline of Boston. But the base map should be comparable to something you'd see in Google Maps - it should have buildings, roads, street names and city names, rivers, etc. A basic geography that makes the region more familiar.
For now, I don't need to pan, and may have just two or three zoom states.
All the visualizations I've seen that overlay interactive features onto maps like this seem to use images for the underlying maps: windhistory, polymaps, google maps and more. So I guess my questions are:
Why do some maps use images for the "backdrop"? Is it just the easiest way to build on top of existing maps? Is it more performant?
If I go with the images approach, are there any limitations to the features I can add? I'm hoping to do things like windmaps, animations, heatmaps, etc.
What are the copyright implications for using images? I imagine the answer to this is, "depends on which images I use," but are there some standard libraries that have no strings attached? For example I know if I use Google Maps, I have to display their logo, there's an API limit, etc. Are there any standard sources that are completely open?
Are there any examples where geography is added purely through TopoJSON?
Sorry if some of these seem obvious, but I am completely new to maps and just don't know the standard practices. Thanks for any help!
A quick take on your questions. Hopefully someone with more mapping experience can give you more detail:
Why do some maps use images for the "backdrop"?
File size and computation time, mostly. Drawing complete maps with buildings, roads, and topography requires a lot of data and a lot of time for the browser to render it. If your browser DOM gets too complicated, it can slow down all interactions even after the original drawing.
If I go with the images approach, are there any limitations to the features I can add?
There's a reason most interactive maps use multiple layers. The background images are best for the underlying "lay of the land" type imagery, anything you want to be interactive should be on top with SVG.
What are the copyright implications for using images?
If you're using someone's images, you have to follow their licence. You might want to look at the OpenStreetMap project.
Are there any examples where geography is added purely through TopoJSON?
I suppose that depends on what you mean by "geography"; Mike Bostock has generated topoJSON for a variety of features based on US Atlas data.
As for whether it makes sense: TopoJSON encodes paths/boundaries directly, and encodes regions as the area enclosed by a set of boundaries. You could use it to encode streets and rivers and even building outlines, but you're not saving any file size relative regular GeoJSON because those paths generally aren't duplicated the way that region boundaries are. Relative to using image tiles, any improvement in file size would be countered with increased processing time.
Related
there are same solutions for rendering vector tiles on client-side webbrowser. But i don't find one for my expectations.
I want to display a huge amount data (points, polygons) in a map viewer. I need vector data because of dynamic styling and interactions of the features. Its too much to load all in Google Maps and from my perspective its the right way to use vector tiles, because only nessesary and aggregated data for the viewpoint will be load.
So i dont need to style the basemap like i found thousands of examples. I only want to load my data as a vector tile layer on a raster (google satelite). But my features should by stylable, need to have normal events like clicking or mouseover and store properties. And last but not least it should be really fast ;-)
What viewer i need? And what is the workflow to create and serve the data as vector tiles?
I have been working on a similar problem, strech - technologies are evolving, but mapbox-gl.js is one viewer you can use. You might be able to use mapzen's system as well, but I haven't tried their system with large amounts of features, whereas I know mapbox does work better than leaflet and openlayers for your scenario.
I'm a structural engineering master student work on a seismic evaluation of a temple structure in Portugal. For the evaluation, I have created a 3D block model of the structure and will use a discrete element code to analyze the behaviour of the structure under a variety of seismic (earthquake) records. The software that I will use for the analysis has the ability to produce snapshots of the structure at regular intervals which can then be put together to make a movie of the response. However, producing the images slows down the analysis. Furthermore, since the pictures are 2D images from a specified angle, there is no possibility to rotate and view the response from other angles without re-running the model (a process that currently takes 3 days of computer time).
I am looking for an alternative method for creating a movie of the response of the structure. What I want is a very lightweight solution, where I can just bring in the block model which I have and then produce the animation by feeding in the location and the three principal axis of each block at regular intervals to produce the animation on the fly. The blocks are described as prisms with the top and bottom planes defining all of the vertices. Since the model is produced as text files, I can modify the output so that it can be read and understood by the animation code. The model is composed of about 180 blocks with 24 vertices per block (so 4320 vertices). The location and three unit vectors describing the block axis are produced by the program and I can write them out in a way that I want.
The main issue is that the quality of the animation should be decent. If the system is vector based and allows for scaling, that would be great. I would like to be able to rotate the model in real time with simple mouse dragging without too much lag or other issues.
I have very limited time (in fact I am already very behind). That is why I wanted to ask the experts here so that I don't waste my time on something that will not work in the end. I have been using Rhino and Grasshopper to generate my model but I don't think it is the right tool for this purpose. I was thinking that Processing might be able to handle this but I don't have any experience with it. Another thing that I would like to be able to do is to maybe have a 3D PDF file for distribution. But I'm not sure if this can be done with 3D PDF.
Any insight or guidance is greatly appreciated.
Don't let the name fool you, but BluffTitler DX9, a commercial software, may be what your looking for.
It's simple interface provides a fast learning curve, may quick tutorials to either watch or dissect. Depending on how fast your GPU is, real-time previews are scalable.
Reference:
Model Layer Page
User Submitted Gallery (3D models)
Jim Merry from tetra4D here. We make the 3D CAD conversion tools for Acrobat X to generate 3D PDFs. Acrobat has a 3D javascript API that enables you to manipulate objects, i.e, you could drive translations, rotations, etc of objects from your animation information after translating your model to 3D PDF. Not sure I would recommend this approach if you are in a hurry however. Also - I don't think there are any commercial 3D PDF generation tools for the formats you are using (Rhino, Grasshopper, Processing).
If you are trying to animate geometric deformations, 3D PDF won't really help you at all. You could capture the animation and encode it as flash video and embed in a PDF, but this a function of the multimedia tool in Acrobat Pro, i.e, is not specific to 3D.
I 'm trying to find an efficient way of acceptable complexity to
detect an object in an image so I can isolate it from its surroundings
segment that object to its sub-parts and label them so I can then fetch them at will
It's been 3 weeks since I entered the image processing world and I've read about so many algorithms (sift, snakes, more snakes, fourier-related, etc.), and heuristics that I don't know where to start and which one is "best" for what I'm trying to achieve. Having in mind that the image dataset in interest is a pretty large one, I don't even know if I should use some algorithm implemented in OpenCV or if I should implement one my own.
Summarize:
Which methodology should I focus on? Why?
Should I use OpenCV for that kind of stuff or is there some other 'better' alternative?
Thank you in advance.
EDIT -- More info regarding the datasets
Each dataset consists of 80K images of products sharing the same
concept e.g. t-shirts, watches, shoes
size
orientation (90% of them)
background (95% of them)
All pictures in each datasets look almost identical apart from the product itself, apparently. To make things a little more clear, let's consider only the 'watch dataset':
All the pictures in the set look almost exactly like this:
(again, apart form the watch itself). I want to extract the strap and the dial. The thing is that there are lots of different watch styles and therefore shapes. From what I've read so far, I think I need a template algorithm that allows bending and stretching so as to be able to match straps and dials of different styles.
Instead of creating three distinct templates (upper part of strap, lower part of strap, dial), it would be reasonable to create only one and segment it into 3 parts. That way, I would be confident enough that each part was detected with respect to each other as intended to e.g. the dial would not be detected below the lower part of the strap.
From all the algorithms/methodologies I've encountered, active shape|appearance model seem to be the most promising ones. Unfortunately, I haven't managed to find a descent implementation and I'm not confident enough that that's the best approach so as to go ahead and write one myself.
If anyone could point out what I should be really looking for (algorithm/heuristic/library/etc.), I would be more than grateful. If again you think my description was a bit vague, feel free to ask for a more detailed one.
From what you've said, here are a few things that pop up at first glance:
Simplest thing to do it binarize the image and do Connected Components using OpenCV or CvBlob library. For simple images with non-complex background this usually yeilds objects
HOwever, looking at your sample image, texture-based segmentation techniques may work better - the watch dial, the straps and the background are wisely variant in texture/roughness, and this could be an ideal way to separate them.
The roughness of a portion can be easily found by the Eigen transform (explained a bit on SO, check the link to the research paper provided there), then the Mean Shift filter can be applied on the output of the Eigen transform. This will give regions clearly separated according to texture. Both the pyramidal Mean Shift and finding eigenvalues by SVD are implemented in OpenCV, so unless you can optimize your own code its better (and easier) to use inbuilt functions (if present) as far as speed and efficiency is concerned.
I think I would turn the problem around. Instead of hunting for the dial, I would use a set of robust features from the watch to 'stitch' the target image onto a template. The first watch has a set of squares in the dial that are white, the second watch has a number of white circles. I would per type of watch:
Segment out the squares or circles in the dial. Segmentation steps can be tricky as they are usually both scale and light dependent
Estimate the centers or corners of the above found feature areas. These are the new feature points.
Use the Hungarian algorithm to match features between the template watch and the target watch. Alternatively, one can take the surroundings of each feature point in the original image and match these using cross correlation
Use matching features between the template and the target to estimate scaling, rotation and translation
Stitch the image
As the image is now in a known form, one can extract the regions simply via pre set coordinates
I am looking for a solution to do the following:
( the focus of my question is step 2. )
a picture of a house including the front yard
extract information from the picture like the dimensions and location of the house, trees, sidewalk, and car. Also, the textures and colors of the house, cars, trees, and sidewalk.
use extracted information to generate a model
How can I extract that information?
You could also consult Tatiana Jaworska research on this. As I understood, this details at least 1 new algorithm to feature extraction (targeted at roofs, doors, ...) by colour (RGB). More intriguing, the last publication also uses parameterized objects to be identified in the house images... that must might be a really good starting point for what you're trying to do.
link to her publications:
http://www.springerlink.com/content/w518j70542780r34/
http://portal.acm.org/citation.cfm?id=1578785
http://www.ibspan.waw.pl/~jaworska/TJ_BOS2010.pdf
Yes. You can extract these information from a picture.
1. You just identify these objects in a picture using some detection algorithms.
2. Measure these objects dimensions and generate a model using extracted information.
well actually your desired goal is not so easy to achieve. First of all you'll need a good way to figure what what is what and what is where on your image. And there simply is no easy "algorithm" for detecting houses/cars/whatsoever on an image. There are ways to segment different objects (like cars) from an image, but those don't work generally. Especially on houses this would be hard since each house looks different and it's hard to find one solid measurement for "this is house and this is not"...
Am I assuming it right that you are trying to simply photograph a house (with front yard) and build a texturized 3D-model out of it? This is not going to work since you need several photos of the house to get positions of walls/corners and everything in 3D space (There are approaches that try a mesh reconstruction with one image only but they lack of depth information and results are fairly poor). So if you would like to create 3D-mdoels you will need several photos of different angles of the house.
There are several different approaches that use this kind of technique to reconstruct real world objects to triangle-meshes.
Basically they work after the principle:
Try to find points in images of different viewpoint which are the same on an object. Considering you are photographing a house this could be salient structures likes corners of windows/doors or corners or edges on the walls/roof/...
Knowing where one and the same point of your house is in several different photos and knowing the position of the camera of both photos you can reconstruct this point in 3D-space.
Doing this for a lot of equal points will "empower" you to reconstruct the shape of your house as a 3D-model by triangulating the points.
Taking parts of the image as textures and mapping them on the generated model would work as well since you know where what is.
You should have a look at these papers:
http://www.graphicon.ru/1999/3D%20Reconstruction/Valiev.pdf
http://people.csail.mit.edu/wojciech/pubs/LabeledRec.pdf
http://people.csail.mit.edu/sparis/publi/2006/oceans/Paris_06_3D_Reconstruction.ppt
The second paper even has an example of doing exactly what you try to achieve, namely reconstruct a textured 3D-model of a house photographed from different angles.
The third link is a powerpoint presentation that shows how the reconstruction works and shows the drawbacks there are.
So you should get familiar with these papers to see what problems you are up to... If you then want to try this on your own have a look at OpenCV. This library provides some methods for feature extraction in images. You then can try to find salient points in each image and try to match them.
Good luck on your project... If you have problems, please keep asking!
I suggest to look at this blog
https://jwork.org/main/node/35
that shows how to identify certain features on images using a convolutional neural network. This particular blog discusses how to identify human faces on images from a large set of random images. You can adjust this example to train neural network using some other images. Note that even in the case of human faces, the identification rate is about 85%, therefore, more complex objects can be even harder to identify
I am amazed at how well (and fast) this software works. I hovered my phone's camera over a small area of a book cover in dim light and it only took a couple of seconds for Google Shopper to identify it. It's almost magical. Does anyone know how it works?
I have no idea how Google Shopper actually works. But it could work like this:
Take your image and convert to edges (using an edge filter, preserving color information).
Find points where edges intersect and make a list of them (including colors and perhaps angles of intersecting edges).
Convert to a rotation-independent metric by selecting pairs of high-contrast points and measuring distance between them. Now the book cover is represented as a bunch of numbers: (edgecolor1a,edgecolor1b,edgecolor2a,edgecolor2b,distance).
Pick pairs of the most notable distance values and ratio the distances.
Send this data as a query string to Google, where it finds the most similar vector (possibly with direct nearest-neighbor computation, or perhaps with an appropriately trained classifier--probably a support vector machine).
Google Shopper could also send the entire picture, at which point Google could use considerably more powerful processors to crunch on the image processing data, which means it could use more sophisticated preprocessing (I've chosen the steps above to be so easy as to be doable on smartphones).
Anyway, the general steps are very likely to be (1) extract scale and rotation-invariant features, (2) match that feature vector to a library of pre-computed features.
In any case, the Pattern Recognition/Machine Learning methods often are based on:
Extract features from the image that can be described as numbers. For instance, using edges (as Rex Kerr explained before), color, texture, etc. A set of numbers that describes or represents an image is called "feature vector" or sometimes "descriptor". After extracting the "feature vector" of an image it is possible to compare images using a distance or (dis)similarity function.
Extract text from the image. There are several method to do it, often based on OCR (optical character recognition)
Perform a search on a database using the features and the text in order to find the closest related product.
It is also likely that the image is also cuted into subimages, since the algorithm often finds a specific logo on the image.
In my opinion, the image features are send to different pattern classifiers (algorithms that are able to predict a "class" using as input a feature vector), in order to recognize logos and, afterwards, the product itself.
Using this approach, it can be: local, remote or mixed. If local, all processing is carried out on the device, and just the "feature vector" and "text" are sent to a server where the database is. If remote, the whole image goes to the server. If mixed (I think this is the most probable one), partially executed locally and partially at the server.
Another interesting software is the Google Googles, that uses CBIR (content-based image retrieval) in order to search for other images that are related to the picture taken by the smartphone. It is related to the problem that is addressed by Shopper.
Pattern Recognition.