I'm trying to use offline open street map in a react native application, for that reason, and according to react native maps I need to store the tiles in a specific format :
The path template of the locally stored tiles. The patterns {x} {y} {z} will be replaced at runtime
For example, /storage/emulated/0/mytiles/{z}/{x}/{y}.png
I tried to download the tiles using tiles servers, however, I find out that It will take a lot of time (it is almost impossible) I also looked at the proposed ways to download tiles, however, I don't know the files extension and I don't know if I could convert one of them to png, therefore, I wonder I there is an opensource/free way to do that
I find also, this software but I can only use it up to zoom=13, otherwise its not for free.
Bulk downloads are usually forbidden. See the tile usage policy. Quoting the important parts:
OpenStreetMap’s own servers are run entirely on donated resources.
OpenStreetMap data is free for everyone to use. Our tile servers are not.
Bulk downloading is strongly discouraged. Do not download tiles unnecessarily.
In particular, downloading significant areas of tiles at zoom levels 17 and higher for offline or later usage is forbidden [...]
You can render your own raster tiles by installing a rendering software such as TileMill or by installing your own tile-server. Alternatively take a look at Commercial OSM software and services.
Alternatively switch to vector tiles. Obtaining raw OSM data is rather easy. Vector tiles allow you to render tiles on your device on the fly.
Related
Does soneone of you have a good solution for external rendering for Microsoft HoloLens Apps? Specified: Is it possible to let my laptop render an amount of 3D objects that is too much for the HoloLens GPU and then display them with the HoloLens by wifi including the spatial mapping and interaction?
It's possible to render remotely both directly from the unity editor and from a built application.
While neither achieves your goal of a "good solution" they both allow very intensive applications to at least run at all.
This walks you through how to add it to an app you're building.
https://learn.microsoft.com/en-us/windows/mixed-reality/add-holographic-remoting
This is for running directly from the editor:
https://blogs.unity3d.com/2018/05/30/create-enhanced-3d-visuals-with-holographic-emulation-in-uwp/
I don't think this is possible since, you can't really access the OS or the processor at all on the HoloLens. Even if you do manage to send the data to a 3rd party to process, the data will still need to be run back through the HoloLens which is really just the same as before.
You may find a way to perhaps hook up a VR backpack to it but even then, I highly doubt it would be possible.
If you are having trouble rendering 3D objects, then you should reduce the number of triangles, get a lower resolution shader on it, or reduce the size of the object. The biggest factor in processing 3D objects on the HoloLens is how much space is being drawn on the lens. If your object takes up 25% of the view instead of 100% it will be easier to process on the HoloLens.
Also if you can't avoid a lot of objects in the scene maybe check out LOD, which reduces the resolution of objects based off of distance to it and vice versa.
I want to Detect 360 degree video
I want to find if the video format is 360 degree and based on that manage the player supported the 360 degree
So anybody can help ?
It seems that the 360video/VR standards are still in flux. This article, http://labs.dash.umn.edu/etc-lab/detecting-spherical-media-files/, (dated April 2016) suggests that inspecting the EXIF properties of the photo/video might reveal a pattern in the metadata. There are libraries available to access EXIF data from files, such as this: https://github.com/exif-js/exif-js. PHP libraries exist as well. Phil Harvey's website ( http://www.sno.phy.queensu.ca/~phil/exiftool/) has a wealth of information and coding examples. There are compiled tools for WIN, MAC and NIX and a C++ library as well. From personal experience, EXIF values can be read from JPEG files. I have never tried accessing 360 JPEGs, but it is my understanding that the photo files produced from spherical cameras are nothing but regular JPEGs (although the images appear distorted when viewed without a 360 viewer). The EXIF tool that Phil Harvey has written shows support for mp4. You may be able to use the tool to analyze 360 videos to look for discernible patterns in the metadata.
The short question: I am wondering if the kinect SDK / Nite can be exploited to get a depth image IN, skeleton OUT software.
The long question: I am trying to dump depth,rgb,skeleton data streams captured from a v2 Kinect into rosbags. However, to the best of my knowledge, capturing the skeleton stream on Linux with ros, kinect v2 isn't possible yet. Therefore, I was wondering if I could dump rosbags containing rgb,depth streams, and then post-process these to get the skeleton stream.
I can capture all three streams on windows using the Microsoft kinect v2 SDK, but then dumping them to rosbags, with all the metadata (camera_info, sync info etc) would be painful (correct me if I am wrong).
It's quite some time ago that I worked with NITE (and I only used Kinect v1) so maybe someone else can give a more up-to-date answer, but from what I remember, this should easily be possible.
As long as all relevant data is published via ROS topics, it is quite easy to record them with rosbag and play them back afterwards. Every node that can handle live data from the sensor will also be able to do the same work on recorded data coming from a bag file.
One issue you may encounter is that if you record kinect-data, the bag files are quickly becoming very large (several gigabytes). This can be problematic if you want to edit the file afterwards on a machine with very little RAM. If you only want to play the file or if you have enough RAM, this should not really be a problem, though.
Indeed it is possible to perform a NiTE2 skeleton tracking on any depth-image-stream.
Refer to:
https://github.com/VIML/VirtualDeviceForOpenNI2/wiki/How-to-use
and
https://github.com/VIML/VirtualDeviceForOpenNI2/wiki/About-PrimeSense-NiTE
With this extension one can add a virtual device which allows to manipulate each pixel of the depth stream. This device can then be used for creation of a userTracker object. As long as the right device name is provided skeleton tracking can be done
\OpenNI2\VirtualDevice\Kinect
but consider usage limits:
NiTE only allow to been used with "Authorized Hardware"
I want to embed a video stream into my web page, which is part of our own cloud based software. The video should be low-latency (like video conferencing), and it would be preferable, but not required, for it to include audio. I am comfortable serving streaming binary data from the server-side, and embedding it into the page using HTML5 video.
What I am not comfortable with is the ability to capture the video data to begin with. The client does not already have a solution in place, and is looking to us for assistance. The video would be routed through our server equipment, and not be an embedded peice that connects directly to the video source.
It is a known quantity for us to use a USB or built-in camera from the computer. What I would like more information is about stand-alone cameras.
Some models of cameras have their own API documentation (example). It would seem from what I am reading that a manufacturer would typically have their own API which they repeat on many or all of their models, and that each manufacturer would be different in their API. However, I have only done surface reading and hope to gain more knowledge from someone who has already researched this, or perhaps even had first hand experience.
Do stand-alone cameras generally include an API? (Wouldn't this is a common requirement, so that security software can use multiple lines of cameras?) Or if not an API, how is the data retrieved from the on-board webserver? Is it usually flash based? Perhaps there is a re-useable video stream I could capture from there? Or is the stream formatting usually diverse?
What would I run into when trying to get the server-side to capture that data?
How does latency on a stand-alone device compare with a USB camera solution?
Do you have tips on picking out a stand-alone camera that would be a good fit for streaming through a server?
I am experienced at using JavaScript (both HTML5 and Node.JS), Perl and Java.
Each camera manufacturer has their own take on this from the point of access points; generally you should be able to ask for a snapshot or a MJPEG stream, but it can vary. Take a look at this entry on CodeProject; it tackles two common methodologies. Here's another one targeted at Foscam specifically.
Get a good NAS, I suggest Synology, check out their long list of supported IP Web Cams. You can connect them with a hub or with a router or whatever you wish. It's not a "computer" as-in "tower", but it does many computer jobs, and it can stay on while your computer is off or away, and do thing like like video feeds, torrents, backups, etc.
I'm not an expert on all the features, so I don't know how to get it to broadcast without recording, but even if it does then at least it's separate. Synology is a popular brand and there are lot of authorized and un-authorized plugins for it. Check them out and see if one suits you.
Is there any code out there, that I can use in Cocoa, to recognize text from photos? Let's say I snap a photo with my iPhone of a page of a book. I'd like to capture the text in it.
There is the Tesseract OCR toolkit that is an open source OCR engine, currently maintained by Google. "Olipion" created a cross compilation tutorial to get in on the iPhone. I would say that this is a good place to start.
However, there are reasons why you might not want to to OCR on the Phone even if you could. Some of these include:
Even the new iPhone 4's processor is not that fast and since you app can't really run in the background doing the processing, the user experience might not be optimal.
Running OCR on a mobile device would probably be a killer for battery life.
Every time you would want to update the OCR engine everybody who installed your app would have to upgrade.
For an always connected mobile device running the OCR on a server somewhere would be probably better. You could upgrade your OCR software easily, you could run much more powerful algorithms then a mobile device could handle and so on.
I am not so sure that you would be able to get good results from photos taken using a mobile camera -- accuracy of OCR systems goes way down with the kind of poorly lit, noisy, distorted images likely to be captured using a phone camera.
As far as commercial products out there, there is Evernote that gives you a OCR capability if you buy their premium service.
As an alternative to machine OCR, there is always Mechanical Turk, where you could pay people small amount to do the OCR for you. Would probably do better at transcription given the image source.