berak

## landmarks.py
# 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)

## fbox.cpp
#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";

## lenet2.pbtxt
node {
  name: "conv2d_input"
  op: "Placeholder"
  attr {
    key: "dtype"
    value {
      type: DT_FLOAT
    }
  }
  attr {

## dnn_edges.cpp
#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)
    {

## core.hpp
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

## exudates.cpp
#include <iostream>
#include <fstream>
#include "opencv2/opencv.hpp"
#include "opencv2/ximgproc.hpp"
using namespace cv;
using namespace cv::ximgproc;
using std::cout;
using std::endl;

// https://ieeexplore.ieee.org/document/8015123

## test10.cpp
// Sample of using Halide backend in OpenCV deep learning module.
// Based on caffe_googlenet.cpp.

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

## Tracker.cpp
/*******************************************************************************
* MKCFup										Version 1.0
* Copyright 2018 Bin Yu, UCAS&NLPR, Beijing.	[yubin2017@ia.ac.cn]
* Paper [High-speed Tracking with Multi-kernel Correlation Filters]
*******************************************************************************/
#include <fftw3.h>
#include <opencv2\opencv.hpp>
#include <opencv2\tracking.hpp>
#include <stdio.h>
#include <stdlib.h>

## lbdmodel.yaml
C:\data\mdl>ls -l lbfmodel.yaml
-rw-r--r-- 1 ppp 197121 56375857 Jun 14 12:27 lbfmodel.yaml

C:\data\mdl>head -c 1000 lbfmodel.yaml
%YAML:1.0
---
stages_n: 5
tree_n: 6
tree_depth: 5
n_landmarks: 68

## output.txt
benchmark --gt=david.gt.txt --video=david.webm --start=300 --plot CSRT,MOSSE,MEDIAN_FLOW,BACF,CMT,MIL,BOOSTING,TLD
==========
CSRT
       Overlap > 0                   100%                 469
       Overlap > 0.5              80.597%                 378
           Precision              80.597%
           Recall                 80.597%
           f-measure              80.597%
                 AUC            0.438657
         Performance             103.987 ms/frame             9.61663 fps