I haven't found any method to train new latent svm detector models using openCV. I'm currently using the existing models given in the xml files, but I would like to train my own.
Is there any method for doing so?
Thank you,
Gil.
As of now only DPM-detection is implemented in OpenCV, not training.
If you want to train your own models, the most reliable approach is to use Felzenszwalb's and Girshick's matlab code (most of the heavy stuff is implemented in C) (http://www.cs.berkeley.edu/~rbg/latent/)(http://www.rossgirshick.info/latent/) It is reliable and works reasonably fast
If you want to do it in C-only, there is an implementation here (http://libccv.org/doc/doc-dpm/) that I haven't tried myself.
I think there is a function in the octave version of the author's code here
(Octave Version of DPM). It is in step #5,
mat2opencvxml('./INRIA/inriaperson_final.mat', 'inriaperson_cascade_cv.xml');
I will try it and let you know about the result.
EDIT
I tried to convert the .mat file from the octave version i mentioned before to .xml file, and compared the result with the built in opencv .xml model and the construction of the 2 xmls was different (tags, #components,..), it seems that this version of octave dpm generates xml files for later opencv version (i am using 2.4).
VOC-release3.1 is the one matches opencv2.4.14. I tried to convert the already trained model from this version using mat2xml function available in opencv and the result xml file is successfully loaded and working with opencv. Here are some helpful links:
mat2xml code
VOC-release-3.1
How To Train DPM on a New Object
Related
My task is to perform inference for face detection using Intel Movidius and Raspberry Pi. The error is that the model only returns "Scores" -> (1, 3000, 2) and not "Boxes".
Steps:
On my local machine, I trained several models(mb1-ssd, mb1-ssd-lite, vgg16-ssd) from the repository https://github.com/qfgaohao/pytorch-ssd and converted them to onnx. Then, using open vino model optimizer from openvinotoolkit = 2020.1, I obtained the '.bin', '.xml' files for each model.
Then, using the obtained files, I performed the infference on the Rasberry Pi and hit the mentioned error.
Note: The inference works using pretrained face detection models from model zoo, the only difference I found looking at the .xml files and my .xml files is that the last layer, "Detection output" is missing. However, when I visualize the .xml file using netron, the conversion seems to be correct.
Link to repo: https://github.com/cocacola0/bsc_thesis
OpenVINO™ 2020.3 release is the last OpenVINO™ version that supports Intel® Movidius™ Neural Compute Stick powered by the Intel® Movidius™ Myriad™ 2.
Use ssd_mobilenet_v2_coco and ssdlite_mobilenet_v2, alternative models that are available in Open Model Zoo. Both models are working well with your code.
I try to model a CNN with deeplearing4j using SVHN dataset (http://ufldl.stanford.edu/housenumbers/), in particular I'm using
Format 2: Cropped Digits
This is matlab's files and each one contains a struct with a tensor (4-D) and an array with label. I would open this one into my deeplearing4j code, so I wondered and I find this class MatlabRecordReader.java into deeplearning4j/DataVec (https://github.com/deeplearning4j/DataVec/blob/master/datavec-api/src/main/java/org/datavec/api/records/reader/impl/misc/MatlabRecordReader.java) but I can't understand how use it. Anybody has experience whit this?
Thanks in advance
Here is a reference for "datavec":
http://deeplearning4j.org/DataVec
So if you look at:
http://nd4j.org/tensor
All of deeplearning4j's neural nets are written using nd4j (matlab for java) so this should be pretty easy to map.
You'll see it more or less maps to matlab.
What might be easier is if you could just write out the values as a csv
and reshape them to be the proper value instead. If you use c ordering it should work fine.
If you do that you can just use the csvrecord reader.
That matlab record reader hasn't been used by a lot of people and I think may only work with matrices (it's been a while)
I would try the csv one first.
everyone, I am new to caffe. Currently, I try to use the trained GoogleNet which was downloaded from model zoo to classify some images. However, the network's output seem to be a vector rather than real label(like dog, cat).
Where can I find the label-map between trained model like googleNet's output to their real class label?
Thanks.
If you got caffe from git you should find in data/ilsvrc12 folder a shell script get_ilsvrc_aux.sh.
This script should download several files used for ilsvrc (sub set of imagenet used for the large scale image recognition challenge) training.
The most interesting file (for you) that will be downloaded is synset_words.txt, this file has 1000 lines, one line per class identified by the net.
The format of the line is
nXXXXXXXX description of class
Since there is randomness involved in the computation of a random forest classifier, it is necessary to define a random seed to get reproducible results. How does one do this for OpenCV CvRTrees? I do not see such a parameter in CvRTParams.
Update: The API change of OpenCV 3 removed CvRTParams. However, the title question remains.
it depends on the opencv version you are using.
While 2.4.9 seems to use the global cv::theRNG() , where you can just set theRNG().state = something,
This no longer seems to be possible in opencv3.0
I Have decided to use OpenCV to build a 3d scene by using a series of 2D Images. I found the example code that came with OpenCV [ build3dmodel.cpp Here ].
I just want to run this once and see what kind of outcome this gives. My knowledge with OpenCV is low, I don't want to understand the whole code, I just want to know how to give inputs to this program (the image set) to see the output.
The line command of this code example requires the following parameters:
build3dmodel -i intrinsics_filename.yml [-d detector] [-de
descriptor_extractor] -m model_name.yml
The first file is the camera matrix which you obtain after the calibration process (there is an especific example with it). Detector and descriptor detector must match with valid FeatureDetector and DescriptorExtractor names. Model name is a bit confusing, it looks like part of the yml file name where data will be saved.
First see some tutorial like introduction to OpenCv or OpenCV tutorial. Also, see input and output with OpenCv.