I'm looking into Google's ML Kit for barcode scanning. One of the issues I'm having is being able to scan inverse barcodes, which are dark/black barcodes on a light/white background. I was wondering if anyone knows if this is possible to achieve with ML Kit?
There is no support in the ML KIT for inverse barcodes. However your program decides whether it accesses the kit with a file or a snapshot, so you could make a function that simply flips every byte of the picture before sending it.
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Being a novice I need an advice how to solve the following problem.
Say, with photogrammetry I have obtained a point cloud of the part of my room. Then I upload this point cloud to an android phone and I want it to track its camera pose relatively to this point cloud in real time.
As far as I know there can be problems with different cameras' (simple camera or another phone camera VS my phone camera) intrinsics that can affect the presision of localisation, right?
Actually, it's supposed to be an AR-app, so I've tried existing SDKs - vuforia, wikitude, placenote (haven't tried arcore yet cause my device highly likely won't support it). The problem is they all use their own clouds for their services and I don't want to depend on them. Ideally, it's my own PC where I perform 3d reconstruction and from where my phone downloads a point cloud.
Do I need a SLAM (with IMU fusion) or VIO on my phone, don't I? Are there any ready-to-go implementations within libs like ARtoolKit or, maybe, PCL? Will any existing SLAM catch up a map, reconstructed with other algorithms or should I use one and only SLAM for both mapping and localization?
So, the main question is how to do everything arcore and vuforia does without using third party servers. (I suspect the answer is to device the same underlay which vuforia and other SDKs use to employ all available hardware..)
Earlier this week I received the Intel RealSense D435 camera and now I am discovering its capabilities. After doing a few hours of research, I discovered the previous version of the SDK had a 3D model scan example application. Since SDK 2.0, this example application is no longer present making it harder to create 3D models with the camera.
I have managed to create various Point cloud (.ply) files with the camera, and now I try to use CloudCompare to generate a 3D model of it. However, without any success. Since my knowledge about computer vision is rather basic, I reach out to the community how it's possible to accomplish a 3D model scan using only PointClouds. The model can be rough, but preferable most noisy data needs to be removed.
Try recfusion 1.7.3 for scanning. 99 euro
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 am right now working on one application where I need to find out user's heartbeat rate. I found plenty of applications working on the same. But not able to find a single private or public API supporting the same.
Is there any framework available, that can be helpful for the same? Also I was wondering whether UIAccelerometer class can be helpful for the same and what can be the level of accuracy with the same?
How to implement the same feature using : putting the finger on iPhone camera or by putting the microphones on jaw or wrist or some other way?
Is there any way to check the blood circulation changes ad find the heart beat using the same or UIAccelerometer? Any API or some code?? Thank you.
There is no API used to detect heart rates, these apps do so in a variety of ways.
Some will use the accelerometer to measure when the device shakes with each pulse. Other use the camera lens, with the flash on, then detect when blood moves through the finger by detecting the light levels that can be seen.
Various DSP signal processing techniques can be used to possibly discern very low level periodic signals out of a long enough set of samples taken at an appropriate sample rate (accelerometer or reflected light color).
Some of the advanced math functions in the Accelerate framework API can be used as building blocks for these various DSP techniques. An explanation would require several chapters of a Digital Signal Processing textbook, so that might be a good place to start.
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.