Do you have any links/books with information about digital image processing (filters/effects)? I'd like a large list of filters and information on how they work and how to implement them.
Here is a large list
Among them my favorites are Invert, GaussianBlur, Canny, HoughLinesP, Lanczos, cvBlobsLib
Related
I am starting the development of a software in which through an image of a touristic spot (for example: San Peter Basilica, the Colosseum, etc.) I should retrieve which is the name of the spot (plus its related information). In addition to the image I will have with me the picture coordinates (embedded as metadata). I know I can support me with Google Images API using reverse search in which I give my image as an input, and I will have as a response a big set of images.
However, my advice request for you, is that now having all the similar images, which approach can I make in order to retrieve the correct place name which is in the photo.
A second approach that I am managing is to construct my own dataset in my database, and do my own heuristic (filtering images by their location and then to make the comparation over the resulting subset after having done that filtering). Suggestions and advices are heard, and thanks in advance.
An idea is to use the captions of the images (if available) as a query, retrieve a list of candidates and make use of a structured knowledge base to deduce the location name.
The situation is lot trickier if there're no captions associated with the images, in which case, you may use the fc7 layer output of a pre-trained convolutional net and query into the ImageNet to retrieve a ranked list of related images. Since those images have captions, you could again use them to get the location name.
I'm new to OSM querying, but would like to query vector data for a large area. Thus I need to limit the results I would like to get by tagging the request.
http://www.informationfreeway.org/api/0.6/way[tag=value][bbox=x,y,z,j]
I'd like to filter for specific tag/values when querying for a way. Though I don't know which tags/values exist. Is there a list listing the most common of them?
You are approaching your problem from the wrong direction. The number of different tags is almost unlimited. According to taginfo there are currently 75 380 856 different tags. I'm pretty sure you are not interested in most of them. Likewise you are probably not even interested in many of the most common tags.
What data do you want to query?
The OSM wiki should be your starting point for generating a list of tags you are interested in. For a generic overview take a look at the map features. Are you interested in streets? Then visit at the highway key. Routing? Then take a look at the routing wiki page.
Always remember that these lists aren't complete. People can use any tag they like (but should use well-established tags whenever possible of course).
Also consider using Overpass API instead of XAPI. Overpass API is much more powerful.
I'm looking for a way to specify that the images returned by the Google Custom Search API have a square format.
I've tried tbs=iar:s (because I've read Using the Custom Search API (REST JSON) to search for square images), but it doesn't work.
Have you an idea please ?
The problem is that tbs query parameter only applies to a regular image search on Google. For example, if you wanted to search for cat pictures with a square aspect ratio, you could do a search like this:
http://images.google.com/?q=cat&tbs=iar:s
But the Custom Search API uses a completely different set of parameters. The full list of supported parameters is shown in the REST documentation.
Some of the tbs queries do have equivalents. For example:
tbs=ic:gray translates to imgColorType=gray
tbs=isz:m translates to imgSize=medium
tbs=itp:clipart translates to imgType=clipart
But sadly there appears to be no equivalent for the iar aspect ratio filter. I tried guessing a few queries (things like imgAspectRatio=square) in case there was an undocumented parameter, but didn't have any luck with that.
The best alternative I could suggest is using imgSize=icon. This tends to return images that have a square aspect ratio, but with the unfortunate side effect that the images also tend to be rather small (the largest size I've seen returned is 256x256). Depending on your needs though, this may be good enough.
I apologise if this isn't particularly useful to you. I'm not just trying to grab the bounty on this question, so feel free not to vote this answer up. I just wanted to let you know what I had found in case it was of some help.
You can simply use both tbs=isz:l,iar:s that way it will return only large images with same aspect ration.
In my web application I am going to use OpenLayers.Strategy.AnimatedCluster strategy due to the fact that I need to visualize a great amount of point features. Here is a very good example of what it looks like. In both examples in above mentioned example the data (point features) are generated of taken from the GeoJSON file.
So, can anybody provide me with a file containing 100 000+ (better is even 500 000+) features (world cities, for instance), or explain how I can generate them so that they will be located all over the world (not like in Spain in the first example in above mentioned link).
use a geolocation database to supply you the data you need. GeoLite, for example
If 400K+ locations is ok, use download their CSV CITY LIST
If you want more, then you might want to give the Nominatim downloads, but they are quite bulky (more than 25GB) and parsing data is not as simple as a csv file.
I am trying to get user input from matplotlib XY plot. The plot contains multiple datasets and I need get from user selection of which dataset to use and the range. I need this to fit model to right dataset and range.
Therefore I need two indicators, which would be "attached" to specific dataset, per user choosing. I need to get from them both the dataset info and the range info.
Somehow in line with what commercial plotting packages (Igor Pro, Kaleidagraph, Sigmaplot...) provide as "cursors" and similarly named widgets for control of their fitting interface, which is what I am trying to reproduce.
I have checked various examples with rangeselector and other methods I was able to Google on the web, but none I was able to find seems to be able to provide what I need.
Would anyone have any pointers to where to look or what to start with, please?
You might want to look at this example: http://matplotlib.sourceforge.net/examples/pylab_examples/ginput_manual_clabel.html
The interesting functions are ginput, waitforbuttonpress.