I cannot find any c++ examples of ML alghoritms like svm, k-nn recognizing patterns, images. I want to train my classifier to recognize cat or dog and test it but I have no idea how to do this. I've read tutorials about svm, k-nn training but they are about digit or text recognition.
You can find some examples : https://github.com/opencv/opencv/blob...
imho, dnn's rule for this kind of task, nowadays.
we could try to use transfer learning ,
that is: use an existing, pretrained model, and try to teach it some new tricks !
we can just "pipe" our images through the network, stop it at some layer (before it would do the final classification), grab the output neurons from there, and feed our own ml classifier with this data (instead of using the "raw" images) , like this:
(colour) image --> DNN --> 1000 numbers --> our own classifier (ANN_MLP for today)
since opencv's dnn module already supports various classification models, let's try with squeezenet (which is also small, and quite fast !)
it was trained on millions of images (imagenet), among them cats & dogs. so, it has "seen the world", already. ;)
there are 67 layers (!), here's how the last 10 look like: (i=input,o=output)
fire9/squeeze1x1 Convolution i[1, 512, 14, 14] o[1, 64, 14, 14]
fire9/relu_squeeze1x1 ReLU i[1, 64, 14, 14] o[1, 64, 14, 14]
fire9/expand1x1 Convolution i[1, 64, 14, 14] o[1, 256, 14, 14]
fire9/relu_expand1x1 ReLU i[1, 256, 14, 14] o[1, 256, 14, 14]
fire9/expand3x3 Convolution i[1, 64, 14, 14] o[1, 256, 14, 14]
fire9/relu_expand3x3 ReLU i[1, 256, 14, 14] o[1, 256, 14, 14]
fire9/concat Concat i[1, 256, 14, 14] i[1, 256, 14, 14] o[1, 512, 14, 14]
drop9 Dropout i[1, 512, 14, 14] o[1, 512, 14, 14]
conv10 Convolution i[1, 512, 14, 14] o[1, 1000, 14, 14]
relu_conv10 ReLU i[1, 1000, 14, 14] o[1, 1000, 14, 14]
pool10 Pooling i[1, 1000, 14, 14] o[1, 1000, 1, 1]
prob Softmax i[1, 1000, 1, 1] o[1, 1000, 1, 1]
so, pool10 looks like a good place to tap it !
(1000 features are a good number, if we have ~1000 images in our dataset)
you'll need to download the caffemodel and the prototxt , then we can start playing with your cats vs dogs dataset
#include "opencv2/opencv.hpp"
#include "opencv2/dnn.hpp"
using namespace cv;
using namespace std;
int main(int argc, char** argv)
{
vector<String> fn;
glob("c:/data/cat-dog/*.jpg", fn, true);
// glob() will conveniently sort names lexically, so the cats come first!
// so we have 700 cats, 699 dogs, and split it into:
// 100 test cats
// 600 train cats
// 100 test dogs
// 599 train dogs
std::string modelTxt = "c:/data/mdl/squeezenet/deploy.prototxt";
std::string modelBin = "c:/data/mdl/squeezenet/squeezenet_v1.1.caffemodel";
dnn::Net net = dnn::readNetFromCaffe(modelTxt, modelBin);
cv::Size inputImgSize = cv::Size(227, 227); // model was trained with this size
Mat_<int> layers(4, 1);
layers << 1000, 400, 100, 2; // the sqeezenet pool10 layer has 1000 neurons
Ptr<ml::ANN_MLP> nn = ml::ANN_MLP::create();
nn->setLayerSizes(layers);
nn->setTrainMethod(ml::ANN_MLP::BACKPROP, 0.0001);
nn->setActivationFunction(ml::ANN_MLP::SIGMOID_SYM);
nn->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 300, 0.0001));
Mat train, test;
Mat labels ...I want to train my classifier to recognize cat or dog and test it but I have no idea how to do this.
(from the question above)
((it also turned out, that simply throwing images at knn or such, does not work here, and that it needs "a larger cannon"))
(((and maybe, we also need to mention more modern parts of machine-learning here)))
Yes you are right. "it also turned out, that simply throwing images at knn or such, does not work here, and that it needs "a larger cannon" : may be a test is necessary to check this if knn input is pool10 output
Finally answer to first question is You cannot train deep learning network using opencv
@sjhalayka , as long as you're trying with python, i'd recommend using google's colab utility
(you can burn their gpu's all day long, also, downloading data is for free ! also, cv2 preinstalled !)
@sjhalayka, ask a new question, and provide details, please. (it's getting somewhat crowded here..)
There is the code for the OpenCV Multi Layer Perceptron Artificial Neural Network, to do image classification:
https://github.com/sjhalayka/opencv_a...
The code uses your cat and dog image database. The code is written in Python and C++. The problem -- and it affects both the Python and C++ versions -- is that the predictor acts like a pseudorandom coin toss. Try getting mugshots of cats and dogs instead.
If you learn SVM and k-NN, you are encouraged to please post your code as an answer to your own question. Thank you.
I found this re: SVM https://docs.opencv.org/3.4.1/d1/d73/...
I've done .csv file containing cat, dog images with labels cat=0, dog=1 https://github.com/yoggasek/Train_Data what to do next??
Right, you're using C++.
There's lots to do. Are you interested in the OpenCV MLP ANN ('the neural network')?
I noticed that you have quite a lot of dog and cat pictures. Very nice to have when it comes to training and testing the network.
Do you know the difference between binary encoding and one-hot encoding? Have you read any books or tutorials on neural networks?
Did you look at my neural network code that does image classification?
I can try mlp ann, but I'm looking for alghorithm that is the simpliest and does not take too much memory cause I want to launch my recognizing software on raspberry pi3. I did read some tutorials and your code but I don't know how to put my training data as csv file and train it.
That's OK, we will read in the images one at a time, which uses the least amount of memory. I don't think you'll need the CSV file at all, since I have code to enumerate a directory for all of the images. I will work on the base code, before I add in the neural network code. I will post it when it's ready.
I put the Python code up at https://github.com/sjhalayka/opencv_a...
The steps you need to follow are in the repository readme:
Step 1) Copy your cat and dog images into the Images directory, then run get_files.py
Step 2) Run ann_image.py to train and test the network
Now to do the C++ version. I'll do that after I train the network 10000 times. If the network doesn't do much better than a random coin toss at 10000 iterations, it's not worth porting the code to C++.
@berak -- I'm wondering: Should the neural network do much better than predicting just a little above 50% correct? That's what I'm seeing with this network -- it classifies correctly only a little more than half the time. Do you have any feedback to make the network operate better?
Asked: 2018-05-11 12:34:38 -0600
Seen: 2,813 times
Last updated: May 18 '18
we cannot mend your lack of research skill, but at least have a read here