2019-10-29 04:32:02 -0500 received badge ● Notable Question (source) 2019-02-01 04:12:52 -0500 received badge ● Popular Question (source) 2017-02-20 02:16:01 -0500 commented answer remove small object faster great to see that ! thank you for pointing this to me ! I finished my project but I will test it again 3.2 and see how it goes !! I hope it's not going to be painful !!! thanks again :) 2017-02-13 09:40:47 -0500 received badge ● Self-Learner (source) 2017-02-13 08:44:37 -0500 answered a question build OpenCV 3.1 for android with openCL support Turn out that there is a python script call build_sdk.py located at: /opencv-3.1.0/platforms/android. With this script you will be able to created your own android sdk. Before started I had to install ant (a build tool in the java ecosystem). For the NDK I used the version r10e. I wanted to turn ON OpenCL to have some performance improvement. So inside the script look for DWITH_OPENCL and change the flag from OFF to ON. I also had to turn off DBUILD_ANDROID_EXAMPLES and DINSTALL_ANDROID_EXAMPLES because I had some issue with them. I created a folder opencv-build-android and inside this folder I invoke the script like this: ../opencv-3.1.0/platforms/android/build_sdk.py --ndk_path $ANDROID_NDK --sdk_path$ANDROID_SDK . ../opencv-3.1.0  be careful to the . before ../opencv-3.1.0 I hope It will help you ! 2017-02-10 09:14:05 -0500 asked a question build OpenCV 3.1 for android with openCL support Hi All! I tried OpenCL on my OpenCV project and the result is a huge improvement in terms of performance so I would like to try OpenCL on android. I use this article to guide me. Here is my cmake command: cmake -GNinja -DCMAKE_MAKE_PROGRAM="/usr/bin/ninja" -DCMAKE_TOOLCHAIN_FILE="../opencv-3.1.0/platforms/android/android.toolchain.cmake" -DCMAKE_BUILD_WITH_INSTALL_RPATH=ON -DWITH_OPENCL=YES -DANDROID_ABI="armeabi-v7a" ../opencv-3.1.0  and I use this command ninja install/strip  I have no errors neither on compilation nor installation. And an install folder has been created. I think all is perfectly fine except that I can't find the libopencv_java3.so file !!!!! so: Do you know how I can generate libopencv_java3.so ? Do you know how I can target several abis ? I tried -DANDROID_ABI="armeabi-v7a" -DANDROID_ABI="x86" and -DANDROID_ABI="armeabi-v7a x86" but it is not correct. Thank you ! 2017-02-09 09:43:59 -0500 commented answer remove small object faster Yes I tried this one but it breaks my behavior. I'm stuck with contourArea(contours[i]) unfortunately ... 2017-02-08 04:33:52 -0500 commented answer remove small object faster Hi. I stuck with contourArea(contours[i]) unfortunately ... 2017-02-03 08:54:41 -0500 commented question Which resolution will you choose ? Thank you for your answer! What resolution will you choose for image processing ? 2017-02-03 06:52:31 -0500 asked a question Which resolution will you choose ? Hi all ! I have some general question about image processing. I have a project that process an image in real time. It extract a puzzle, write on it and merge the puzzle in the original image. If you start a project right now with real time image processing which resolution will you choose for: The camera (in my case android) ? The image that is going to be modified (puzzle in my case) ? At the beginning of the project I chose 640x360 (don't ask me why ...) for the image that being processed The camera is 1920*1080 so I must resize the image in order to process it and now my Algo is not accurate anymore. well I think my question is do you have some advice ? Thank you ! 2017-02-01 07:47:48 -0500 received badge ● Student (source) 2017-02-01 07:42:03 -0500 commented answer remove small object faster Thank you a lot for your answer ! I had to think a lot on how I preprocess my picture. Unfortunatly I think it is not possible to "reuse" the result of findContour because. - I preprocess the original picture to extract the puzzle - I extract the cell on the puzzle - I preprocess the cell to extract the number I realize that I never apply findContour on the same image or part of image so unfortunately for me I can't apply what you advise me :( and unfortunately for me again rectangle(output, cv::boundingRect(contours[i]), color, -1, 8);  breaks the behavior of my app so zero optimization for me today !! But thanks because it makes me think about my process again. 2017-01-31 11:39:51 -0500 commented question remove small object faster 2017-01-31 11:38:58 -0500 asked a question remove small object faster Hello all ! In my project I have a function that find contour within an image and if this contour is too small then the contour is set to the color of the background in other words I remove it. here is the function Mat removeTinyVolume(Mat input, int area, Scalar color) { // we draw to the color of the background Mat output = input.clone(); vector> contours; findContours(input, contours, RETR_LIST, CV_CHAIN_APPROX_SIMPLE); // cout << "contours : " << contours.size() << endl; for (int i = 0; i < contours.size(); i++) { if (contourArea(contours[i]) < area) { drawContours(output, contours, i, color, -1, 8); } } return output; }  It accurate I'm happy but it is really slow so I was wondering if you guys have some tips&ticks for that ? Thank you a lot ;) By the way this function is part of an open source project that you can find in comments. 2017-01-31 09:16:40 -0500 commented question switching from knn to svm (opencv 3.1) My grabNumbers function use to take ~110ms now it's ~40ms this is huge !!! 2017-01-31 09:12:29 -0500 commented question switching from knn to svm (opencv 3.1) I had edited the code and now it works ! Thank you Berak ! 2017-01-31 08:24:03 -0500 commented question switching from knn to svm (opencv 3.1) I made some modification I hope it is better now ! by the wat this is an open source project, you can check it out https://github.com/BenNG/sudoku-recognizer (here) 2017-01-31 02:57:50 -0500 commented question switching from knn to svm (opencv 3.1) The error is weird because I think that my Mat labels has integer inside ! 2017-01-31 02:55:59 -0500 commented question switching from knn to svm (opencv 3.1) Hi! I simplify a bit my code and change the Mat labels to be "svm compliant" it still work for my knn algo. It still not work but I have a new error : OpenCV Error: Bad argument (in the case of classification problem the responses must be categorical; either specify varType when creating TrainData, or pass integer responses) in train, file /home/benng/bin/opencv_working_dir/opencv-3.1.0/modules/ml/src/svm.cpp, line 1618 terminate called after throwing an instance of 'cv::Exception' what(): /home/benng/bin/opencv_working_dir/opencv-3.1.0/modules/ml/src/svm.cpp:1618: error: (-5) in the case of classification problem the responses must be categorical; either specify varType when creating TrainData, or pass integer responses in function train  2017-01-30 11:36:29 -0500 asked a question switching from knn to svm (opencv 3.1) Hello ! I would like to switch from knn to svm in order to see how performance goes: This is the "script" I use to generate a file call "raw-features.yml". int main(int argc, char **argv) { // data to return Mat features(nbrOfCells, normalizedSizeForCell * normalizedSizeForCell, CV_8UC1); Mat labels(1, nbrOfCells, CV_8UC1); Mat svm_labels(nbrOfCells, 1, CV_32S); // Ptr knn(ml::KNearest::create()); std::map> knownCellValues(cellValues()); int value; string fileName; Mat raw, sudoku; ExtractionInformation extractInfo; string raw_features_path("./../assets/raw-features.yml"); cv::FileStorage raw_features(raw_features_path, cv::FileStorage::WRITE); // open the classifications file int incrCell = 0; // --> 1184 Mat roi, normalized; for (int i = 0; i <= lastTrainingPuzzle; i++) { // cout << i << endl; stringstream ss; ss << "./../assets/puzzles/s"; ss << i; ss << ".jpg"; string fileName(ss.str()); raw = imread(fileName, CV_LOAD_IMAGE_GRAYSCALE); vector biggestApprox = findBiggestBlob(raw); extractInfo = extractPuzzle(raw, biggestApprox); Mat sudoku = recursiveExtraction(extractInfo.image); for (int k = 0; k < 81; k++) { roi = extractRoiFromCell(sudoku, k); if (!roi.empty()) { value = knownCellValues[i][k]; Mat feat = roi.reshape(1, 1); feat.copyTo(features.row(incrCell)); labels.at(0, incrCell) = value; svm_labels.at(incrCell, 0) = value; incrCell++; } } } features.convertTo(features, CV_32F); labels.convertTo(labels, CV_32F); raw_features << "features" << features; raw_features << "labels" << labels; raw_features << "svm_labels" << svm_labels; raw_features.release(); return 0; }  Once "raw-features.yml" is ready, I can use it like that ! I use this the "getKnn" function from my project to train the knn algo. Ptr getKnn(cv::FileStorage raw_features) { int trainingNbr = nbrOfCells * 0.9; int testingNbr = nbrOfCells - trainingNbr; Mat features(nbrOfCells, normalizedSizeForCell * normalizedSizeForCell, CV_8UC1); Mat labels(1, nbrOfCells, CV_8UC1); Ptr knn(ml::KNearest::create()); if (raw_features.isOpened() == false) { throw std::logic_error("error, unable to open training classifications file, exiting program\n\n"); } raw_features["features"] >> features; raw_features["labels"] >> labels; raw_features.release(); Mat sub_features = features(cv::Range(0, trainingNbr), cv::Range::all()); Mat sub_labels = labels(cv::Range::all(), cv::Range(0, trainingNbr)); knn->train(sub_features, ml::ROW_SAMPLE, sub_labels); return knn; }  I'm really happy of it, it works perfectly but I have a problem of performance so I would like to test other algo. I created the same function for SVM: Ptr getSvm(FileStorage raw_features) { Mat features(nbrOfCells, normalizedSizeForCell * normalizedSizeForCell, CV_32F); Mat svm_labels(nbrOfCells, 1, CV_32S); raw_features["features"] >> features; raw_features["svm_labels"] >> svm_labels; raw_features.release(); Ptr svm = ml::SVM::create(); svm->setType(ml::SVM::C_SVC); svm->setKernel(ml::SVM::POLY); svm->setDegree(3); // I had to put it if not it fails svm->setGamma(3); Ptr tData = ml::TrainData::create(features, ml::SampleTypes::ROW_SAMPLE, svm_labels); svm->train(tData); return svm; }  I use it like that: string grabNumbers(Mat extractedPuzzle, Ptr svm) { Mat roi, response, dist; stringstream ss; int K = 1; for (int k = 0; k < 81; k++) { roi = extractRoiFromCell(extractedPuzzle, k); if (!roi.empty()) { roi.convertTo(roi, CV_32F); float res = svm->predict(roi.reshape(1, 1)); ss << res; } else { ss << "0"; } } return ss.str(); }  But I have this weird error I say weird because I already done the conversion !  Gtk-Message: Failed ... 2017-01-05 04:19:16 -0500 commented question What is the reverse of warpPerspective ? By using this:  warpPerspective(sudoku, original, trans_mat33, input.size(), WARP_INVERSE_MAP, BORDER_TRANSPARENT);  I managed to re introduce the sudoku I had modified Thank you ! 2017-01-05 03:35:11 -0500 commented question What is the reverse of warpPerspective ? It does the job yes but the background is black :( 2017-01-05 03:05:48 -0500 commented question What is the reverse of warpPerspective ? Somebody ask the same question here without answer: 2017-01-05 03:03:11 -0500 asked a question What is the reverse of warpPerspective ? Hi all ! At the bottom of this link, a transformation extract the sudoku in "full screen". I would like to draw some text on the extracted sudoku and re introduce it in the big picture. Do you know how can I re introduce it ? Here is the code I use: /** coordinates is a std::vector with inside: [topLeft, topRight, bottomLeft, bottomRight] original is the original picture (the left one in the link) and sudoku is the right one */ float w = (float)input.cols; float h = (float)input.rows; float hw = w / 2.0f; float hh = h / 2.0f; // to points dst_p[0] = Point2f(0.0f, 0.0f); dst_p[1] = Point2f(w, 0.0f); dst_p[2] = Point2f(w, h); dst_p[3] = Point2f(0.0f, h); // create perspective transform matrix Mat trans_mat33 = getPerspectiveTransform(coordinates, dst_p); //CV_64F->double Mat inv_trans_mat33 = getPerspectiveTransform(dst_p, coordinates); // perspective transform operation using transform matrix warpPerspective(input, sudoku, trans_mat33, input.size(), INTER_LINEAR); sudoku = writeOnPuzzle(sudoku, "012345000000000000000000000000000000000000000000000000000000000000000000000000000"); invert(trans_mat33, inv_trans_mat33); warpPerspective(sudoku, original, trans_mat33, input.size(), WARP_INVERSE_MAP);  Thank you ! 2016-12-14 03:36:26 -0500 asked a question How to use my cpp project on Android ? Hi guys ! I finally finished a big millestone on my project here but it's not 100% finished. I would like to port my project on android (ios ?) and I need your experience to win a bit of time because I start to be a little bit out of it... Could you tell me: How do I need to package my lib to be eatable by android studio ? ( my build here ) how to use a native lib which depends on OpenCV ! as usual Thank you for helping ! 2016-10-26 13:19:40 -0500 commented question Why is my knn network is so bad ? That's anoying I don't know what to do now ... It works but it sucks ... Is the knn efficient for that use case ? 2016-10-26 12:27:58 -0500 commented question Why is my knn network is so bad ? Do you think I can train the cnn with a CV_8UC1 Mat ? I think there is a problem with the type of the trained data and the data I extract from a puzzle 2016-10-26 08:56:42 -0500 commented question Why is my knn network is so bad ? thx a lot :) 2016-10-26 08:51:28 -0500 commented question Why is my knn network is so bad ? I added some code ! 2016-10-26 08:47:43 -0500 edited question Why is my knn network is so bad ? Hello everyone ! I used a k-Nearest Neighbors algorithm (knn) and I trained it with the MNIST database Here is the code for the training: Ptr getKnn() { Ptr knn(ml::KNearest::create()); FILE *fp = fopen("/keep/Repo/USELESS/_sandbox/cpp/learning-cpp/sudoku/assets/train-images-idx3-ubyte", "rb"); FILE *fp2 = fopen("/keep/Repo/USELESS/_sandbox/cpp/learning-cpp/sudoku/assets/train-labels-idx1-ubyte", "rb"); if (!fp || !fp2) { cout << "can't open file" << endl; } int magicNumber = readFlippedInteger(fp); int numImages = readFlippedInteger(fp); int numRows = readFlippedInteger(fp); int numCols = readFlippedInteger(fp); fseek(fp2, 0x08, SEEK_SET); int size = numRows * numCols; cout << "size: " << size << endl; cout << "rows: " << numRows << endl; cout << "cols: " << numCols << endl; Mat_ trainFeatures(numImages, size); Mat_ trainLabels(1, numImages); BYTE *temp = new BYTE[size]; BYTE tempClass = 0; for (int i = 0; i < numImages; i++) { fread((void *)temp, size, 1, fp); fread((void *)(&tempClass), sizeof(BYTE), 1, fp2); trainLabels[0][i] = (int)tempClass; for (int k = 0; k < size; k++) { trainFeatures[i][k] = (float)temp[k]; } } knn->train(trainFeatures, ml::ROW_SAMPLE, trainLabels); return knn;  } When I test the algorithm with the 10k images file MNIST provide I have: Accuracy: 96.910000 which is a good news :) The code to test the knn trained is here: void testKnn(Ptr knn, bool debug) { int totalCorrect = 0; FILE *fp = fopen("/keep/Repo/USELESS/_sandbox/cpp/learning-cpp/sudoku/assets/t10k-images-idx3-ubyte", "rb"); FILE *fp2 = fopen("/keep/Repo/USELESS/_sandbox/cpp/learning-cpp/sudoku/assets/t10k-labels-idx1-ubyte", "rb"); int magicNumber = readFlippedInteger(fp); int numImages = readFlippedInteger(fp); int numRows = readFlippedInteger(fp); int numCols = readFlippedInteger(fp); fseek(fp2, 0x08, SEEK_SET); int size = numRows * numCols; Mat_ testFeatures(numImages, size); Mat_ expectedLabels(1, numImages); BYTE *temp = new BYTE[size]; BYTE tempClass = 0; int K = 1; Mat response, dist, m; for (int i = 0; i < numImages; i++) { if (i % 1000 == 0 && i != 0) { cout << i << endl; } fread((void *)temp, size, 1, fp); fread((void *)(&tempClass), sizeof(BYTE), 1, fp2); expectedLabels[0][i] = (int)tempClass; for (int k = 0; k < size; k++) { testFeatures[i][k] = (float)temp[k]; } // test to verify if createMatFromMNIST and createMatToMNIST are well. m = testFeatures.row(i); knn->findNearest(m, K, noArray(), response, dist); if (debug) { cout << "response: " << response << endl; cout << "dist: " << dist << endl; Mat m2 = createMatFromMNIST(m); showImage(m2); // Mat m3 = createMatToMNIST(m2); // showImage(m3); } if (expectedLabels[0][i] == response.at(0)) { totalCorrect++; } } printf("Accuracy: %f ", (double)totalCorrect * 100 / (double)numImages);  } By the way, you can test the knn I have implemented in my project here: (see the actions part) https://bitbucket.org/BenNG/sudoku-recognizer But when it comes to use my own data against the algo, it has a bad behavior. What is the data I give to the algo ? To answer that I will present a bit my project. My project is a sudoku grabber. So on a picture that holds a sudoku, I'm able to find the sudoku and extract it. Then I'm able to extract every cell in the puzzle. Each cell is preprocessed before I send it to the knn. By ... 2016-10-26 08:26:07 -0500 commented question Why is my knn network is so bad ? Well you are right for the code it's better to have it on the website but It's started to be big and I thought It was better for the reader to have all the code. For your answer I don't understand what it means 2016-10-26 08:09:53 -0500 commented question Why is my knn network is so bad ? I double check and it is accessible here ! https://bitbucket.org/BenNG/sudoku-re... even if you dont have an account !