berak

// images from
// https://drive.google.com/drive/folders/19tcwtgXRqyRxcNcfmyqUoBPM78mvfD9n?usp=sharing

int main(int argc, char **argv) {
    Ptr<MACE> mace = MACE::create();

    vector<string> fn;
    glob("face",fn);
    for (int i=0; i<fn.size(); i++) {
        vector<Mat> train_img;

berak

static PyObject* pyopencv_cv_solvePnPGeneric(PyObject* , PyObject* args, PyObject* kw)
{
    using namespace cv;

    {
    PyObject* pyobj_objectPoints = NULL;
    Mat objectPoints;
    PyObject* pyobj_imagePoints = NULL;
    Mat imagePoints;
    PyObject* pyobj_cameraMatrix = NULL;

berak

    Mat m1(3,3,CV_32FC3,Scalar(3,2,1));
    Mat m2;
    cout << m1 << endl << m2 << endl;

    Mat n = m1/m2;
    cout << n << endl;
    divide(1,m1,n);
    cout << n << endl;

berak

#include <fstream>
#include <iostream>
#include <sstream>

#include <opencv2/dnn.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>


using namespace cv;

berak

# colab - install latest
# !pip install opencv-python==4.1.0.25


import torch
import torch.nn as nn
import torchvision
import numpy as np
import cv2

berak

# this is a travis related oddity, ignore ...
import sys
sys.path.append('/home/travis/build/berak/tt/ocv/lib/python2.7/dist-packages/')

import cv2, numpy as np

img = cv2.imread("david2.jpg",0)
cas = cv2.CascadeClassifier("haarcascade_frontalface_alt2.xml")
faces = cas.detectMultiScale(img, 1.5, 5)
print("faces",faces)

berak

#include <opencv2/dnn.hpp>
#include <opencv2/opencv.hpp>
using namespace cv;
using namespace cv::dnn;
using namespace std;


int main(int argc, char **argv)
{
    String modelTxt = "c:/data/mdl/faceboxes/deploy.prototxt";

berak

node {
  name: "conv2d_input"
  op: "Placeholder"
  attr {
    key: "dtype"
    value {
      type: DT_FLOAT
    }
  }
  attr {

berak

#include <opencv2/opencv.hpp>
#include <opencv2/dnn.hpp>
#include <opencv2/dnn/layer.details.hpp>


class CropLayer : public cv::dnn::Layer
{
public:
    CropLayer(const cv::dnn::LayerParams &params) : Layer(params)
    {

berak

class CV_EXPORTS_W LDA
{
public:
    /** @brief constructor
    Initializes a LDA with num_components (default 0).
    */
    explicit LDA(int num_components = 0);

    /** Initializes and performs a Discriminant Analysis with Fisher's
     Optimization Criterion on given data in src and corresponding labels