B4A Library OpenCV 3.x

cesarcm

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Hi JordiCP,

How are you doing?

Please, one more question... yep! I am late


Private Sub List2Array3D(lst As List) As Object(,)
Dim rgb(3) As Double
Dim arr(lst.Size, 3) As Double

For i=0 To (lst.Size - 1)
rgb = lst.Get(i)
arr(i, 0) = rgb(0) : arr(i, 1) = rgb(1) : arr(i, 2) = rgb(2)
Next

return arr
End Sub


Dim mySignal As List
'PS: each list row contains an array regarding RGB colors

Dim outSignal As OCVMat
outSignal.Initialize2(mySignal.Size, 1, mType.CV_32FC3)

Dim arr(mySignal, 3) As Object = List2Array3D(mySignal)

outSignal.put(0, 0, arr) ???


'PS: how to put the array into OCVMat with PUT method???


Any idea?
Thanks.
Regards,
Cesar
 

cesarcm

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JCP and Erel,

Running your JordiCP's OpenCV code at "old" Activity model I was getting ~12fps (Motorola One Vision) ...

Now, using B4Xpages I am getting ~6-7fps

Any idea? Probably it is related to Erel's implementations of B4XPages ... or not?

Thanks.
Regards,
Cesar
 

cesarcm

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Erel,

Thanks but something is different ...

I have sent to JordiCP:


JCP,

I have tried -- using B4XPages -- both commands below:

mOpenCvCameraView.connectCamera2(pnlView, True) ' NEW

'mOpenCvCameraView.connectCamera(pnlView.Width,pnlView.Height)


No processing at all .. just to capture video frames => 9-10fps !!! (of course, after apply the algorithms it decreases to 5-6fps)

It means that using your original OpenCV library or the new one the FPS are the same ...

I will try using the same algorithm at the old Activuty project ...

Thanks.
Regards,
Cesar
 

cesarcm

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Erel,

I just have tested here again! Results below:

JCP,

Using your lib in "old standard" project: ACTIVITY

1) just grabbing video frame: 22-23fps

2) applying face detection: 10-11fps

3) full algorithm (from our project): 9-10fps

Weird ... it means that your LIB + our algorithms under B4XPages are 50% slower than the "old" Activity model ...

Thanks.
Regards,

Cesar
 

cesarcm

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Erel and JordiCP,

Running in RELEASE MODE we get 25fps !!!

Why so difference to DEBUG mode??? and why in debug mode there are almost 50% differences between Activity vs B4XPages projects ?

Thanks for all
Kind regards,
Cesar
 

JordiCP

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JavaCameraView uses a different thread in release mode, and the same UI thread in debug mode. I needed to do it this way to make it work in debug mode.

Besides, debug mode is optimized for a faster development/deployment cycle, where part of the code can be executed in the device and another part in the pc. That's why performance should ALWAYS be measured in release mode.

So, if everything is fast enough in release mode, you got it
 

Erel

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and why in debug mode there are almost 50% differences between Activity vs B4XPages projects ?
It has nothing to do with B4XPages.
As I've already wrote it two times, I will write again:
It has nothing to do with B4XPages.

Clean the project, remove breakpoints and it will run in the same speed. The debugger is very complex and has several execution pipelines that depend on all kinds of parameters.
Bottom line is to never test performance in debug mode.
 

cesarcm

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Erel,

Thanks a lot! YOU ROCK!

Please, accept my apologizes since you had to write 3x about B4XPages.

I just would like to understand what is the performance differences between Activity models under debug mode... It was making non sense when I was testing. Sorry about that.

Kind regards,
Cesar
 

PodSoft2016

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Hello, i'm sorry sir for my question. can you help me about detecting face parts such as mouth, nose or ear in this library?
Thanks alot .
 

Hypnos

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Hello JordiCP,

I found one of your video in youtube and seems that you can use OpenCV in B4I, may I know whether your have the library for B4i ?
I want to use OpenCV on my IOS project and it is great if you can make this library for B4I.

Thanks!

 

DonManfred

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may I know whether your have the library for B4i ?
as B4i is not using java (like B4A and B4J) it needs a wrapper in another language than java. Don´t know if JodiCP have written one.
I could not find a B4i Library with the forumsearch.
 

JordiCP

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There's not Opencv for B4i yet.
The video was using B4i plus a small library made for that specific purpose, and all the Opencv processing work was made inside that lib.
 

biometrics

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Hi @JordiCP,

readNetFromCaffe is working fine for me with Caffe models but readNetFromTensorflow is failing to load Tensorflow models. I've read many Pyhton and C++ examples/tutorials online that use them and there are no mention of issues with it. Would you mind loading the attached models and see if there is an obvious error that can be fixed. Else I'll have to live with the Caffe models. Was hoping to use the Tensorflow models to speed up processing.

This works fine (Caffe model):
B4X:
libOpenCvNet = libOpenCvDnn.readNetFromCaffe(File.Combine(File.DirInternal, "deploy.prototxt"), File.Combine(File.DirInternal, "res10_300x300_ssd_iter_140000_fp16.caffemodel"))

But this fails (Tensorflow model):
B4X:
libOpenCvNet = libOpenCvDnn.readNetFromTensorflow(File.Combine(File.DirInternal, "opencv_face_detector.pbtxt"), File.Combine(File.DirInternal, "opencv_face_detector_uint8.pb"))

with this error:
B4X:
CvException [org.opencv.core.CvException: cv::Exception: OpenCV(3.4.1) /build/master_pack-android/opencv/modules/dnn/src/tensorflow/tf_io.cpp:42: error: (-2) FAILED: ReadProtoFromBinaryFile(param_file, param). Failed to parse GraphDef file: /data/user/0/isenzo.audiencemeasurementservice/files/opencv_face_detector.pbtxt in function void cv::dnn::ReadTFNetParamsFromBinaryFileOrDie(const char*, tensorflow::GraphDef*)

Edit: seems a 1.5MB attachment is too large. You can get those two files from here:

The article: https://learnopencv.com/face-detection-opencv-dlib-and-deep-learning-c-python/
The model files (note to view in Raw or else you get HTML when downloading the small file): https://github.com/spmallick/learnopencv/tree/master/FaceDetectionComparison/models

I have downloaded the Tensorflow models from several other tutorials thinking that perhaps this set is corrupt but they all do this.

Happy to send you a donation for your effort.
 

JordiCP

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Can't help you much (now I don't have time to test it).

DNN adoption by OpenCV was 'quite recent' in version 3.4.1, and it is possible that some things didn't work properly. In fact not all combinations did work for me when I tested it: sometimes a module only worked when initted with the description file and others without it (if I remember correctly). If you read the Release Notes, starting with version 3.4.1 and then scroll upwards to see what was changed in the next versions, you'll see that there are lots of references to DNN improvements and bug fixes.

Anyhow, you could try:
  • Just in case there is a difference somewhere, test with these files (they are the exact ones they mention in the release notes): network and description.
  • Also, try to load the network using the 2nd syntax (i.e., without the description file): mDnn.readNetFromTensorflow1(model As String) As OCVNet

If none of them work for you I'll try to check it during the weekend. But if it does not work, I fear that there will not be much to do, and a higher OpenCV version will be needed.
 
Last edited:

biometrics

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No luck, same problem. Thanks for explaining though, didn't realise this is still buggy stuff (tensorflow).

I did some benchmarks on only the face extraction for Caffe vs Haar Cascade on a 640x388 image with 15 faces and Haar Cascade was about 150ms and Caffe about 1000ms. This is on a Rockchip RK3328 with 2 GB RAM and Android 7.1 With Caffe I still have to loop through the array of detections and compare the confidence level which also takes time, I don't see a way to pass in the confidence level to reduce the array size (which is typical from 500-1,000).

So though Caffe is better at face detection for faces that are sideways, up/down and occluded, Haar Cascade still works well for frontal faces and is faster.

So I think I'll combine Haar Cascade for face detection and use Caffe for gender and age detection. I still have to match and track faces as they move too. So I need as much speed as I can get.

Would still like to benchmark the tensorflow model though. The two models are described as follows and I'm thinking that floating point would be slower...
  1. Floating point 16 version of the original caffe implementation ( 5.4 MB )
  2. 8 bit quantized version using Tensorflow ( 2.7 MB )
Another thing I noticed is that Caffe requires RGB (3 channel) images and Haar Cascade can work on B&W (1 channel). I guess that will also influence the speed. Don't know what tensorflows requirement is but the example was RGB.
 

roberto64

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Hi, aito, I'm using CamEx2 with opencv3 I don't understand why I can't see the rectangle in the camera,
Greetings
 
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