Use pre-trained cnn to classify 6-channel-mats

asked 2019-05-24 02:48:02 -0600

LuisK
21 ●1 ●4

Hey there,

I have a pre trained cnn, which was trained on 6 channel inputs (created out of two bgr images of same size).

vector<Mat> inputs;
vector<Mat> temp;
Mat blob;
cv::split(image_1, inputs);
cv::split(image_2, temp);
inputs.insert(inputs.end(), temp.begin(), temp.end());
dnn::blobFromImages(inputs, blob, 1.0/255.0, m_patchSize, 0, true, false);
net.setInput(blob);
Mat out = net.forward();

The program crashes in line blobFromImages. What am I doing wrong? Is it possible to classify Mats with more than 3 channels?

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what does "6-channel Mat's" really mean here ? do you have a link to a repo / paper ? a prototxt ?

blobFromImages is meant to produce a "batch" of several images, not "channels" ( is that what you wanted ???)

berak ( 2019-05-24 02:52:24 -0600 )edit

I don't have a link or something, because I trained the model myself with keras/tensorflow for python. What I want to do: classify two images / consecutive frames (Don't ask me why). I trained the model by mixing these two images into one 6 channel matrix.

LuisK ( 2019-05-24 03:35:04 -0600 )edit

I don't have a link or something

but you'll have code to train it, right ? please also have a look here , you can't use the keras output directly, but you'll have to process it in a similar way (freezing the graph, applying transforms, weeding out unneeded consts, etc.

here's unfortunately, where the real work starts.

berak ( 2019-05-24 03:39:57 -0600 )edit

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Mat img1; // bgr Mat img2; // also, same size. int W = img1.cols; int H = img1.rows; int sz[] = {1, 6, H, W}; // 1 (batch)img, 6 channels Mat blob(4, sz, CV_32F); // allocate data vector<Mat> ch1, ch2; // original image channels // setup channel pointers: // 1st image goes into planes [0,1,2] ch1.push_back(Mat(H, W, CV_32F, blop.ptr<float>(0,0)); ch1.push_back(Mat(H, W, CV_32F, blop.ptr<float>(0,1)); ch1.push_back(Mat(H, W, CV_32F, blop.ptr<float>(0,2)); cv::split(img1, ch1); // this will handle the actual copy into our blob !) // 2nd image goes into planes [3,4,5] ch2.push_back(Mat(H, W, CV_32F, blop.ptr<float>(0,3)); ch2.push_back(Mat(H, W, CV_32F, blop.ptr<float>(0,4)); ch2.push_back(Mat(H, W, CV_32F, blop.ptr<float>(0,5)); cv::split(img2, ch2);

Use pre-trained cnn to classify 6-channel-mats

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Use pre-trained cnn to classify 6-channel-mats