berak

> make install
...
-- Up-to-date: C:/prog/Python37/Lib/site-packages/cv2/__init__.py
-- Up-to-date: C:/prog/Python37/Lib/site-packages/cv2/load_config_py2.py
-- Up-to-date: C:/prog/Python37/Lib/site-packages/cv2/load_config_py3.py
-- Up-to-date: C:/prog/Python37/Lib/site-packages/cv2/config.py
-- Up-to-date: C:/prog/Python37/Lib/site-packages/cv2/misc/__init__.py
-- Up-to-date: C:/prog/Python37/Lib/site-packages/cv2/misc/version.py
-- Up-to-date: C:/prog/Python37/Lib/site-packages/cv2/mat_wrapper/__init__.py
-- Up-to-date: C:/prog/Python37/Lib/site-packages/cv2/utils/__init__.py

berak

static PyObject* pyopencv_cv_calcHist(PyObject* , PyObject* py_args, PyObject* kw)
{
    using namespace cv;

    pyPrepareArgumentConversionErrorsStorage(2);

    {
    PyObject* pyobj_images = NULL;
    vector_Mat images;
    PyObject* pyobj_channels = NULL;

berak

!git clone https://github.com/opencv/opencv
!git clone https://github.com/opencv/opencv_contrib
!mkdir /content/build
%cd /content/build

!cmake -DOPENCV_EXTRA_MODULES_PATH=/content/opencv_contrib/modules  \
       -DBUILD_SHARED_LIBS=OFF \
       -DBUILD_TESTS=OFF \
       -DBUILD_PERF_TESTS=OFF \
       -DBUILD_EXAMPLES=OFF \

berak

template<class _Tp>
struct WeightedBoxesFusion {
	typedef Rect_<_Tp> RECT;

	struct Box {
		int label;
		float x1,y1,x2,y2, score, weight;

		Box() {}
		Box(int label, float x1,float y1,float x2,float y2, float score, float weight)

berak

"""
using cv2, open a webcam and record a video file
"""
def rec(cap_id, vid_filename):
    cap = cv2.VideoCapture(cap_id)
    fourcc = cv2.VideoWriter_fourcc(*'XVID')
    out = cv2.VideoWriter(vid_filename, fourcc, 20.0, (640,480))
    while(cap.isOpened()):
        ret, frame = cap.read()
        if ret==True:

berak

#include "opencv2/opencv.hpp"
using namespace cv;

#include <iostream>
using namespace std;

int main()
{
	int IMAGE_SIZE = 513;
	cv::Mat faceImg= cv::imread("C:/data/persons/unsorted/139539.png");

berak

# i ran this on colab to convert the pth to onnx, so opencv can read it !

import torch;
import torch.nn as nn
import torch.nn.functional as F

class SPCH2FLM(nn.Module):
    def __init__(self, numFilters=64, filterWidth=21):
        super(SPCH2FLM, self).__init__()
        self.numFilters = numFilters

berak

code running https://berak.pythonanywhere.com/

run get_model.sh to download the pretrained 'unet.onnx'
wsgi.py is a webserver, receiving images of indoor scenes and sending back depth images.
up.html is the main webpage

berak

input: "img0"
input: "img1"
input_shape {
  dim: 1
  dim: 3
  dim: 720
  dim: 1280
}
input_shape {
  dim: 1

berak

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


using namespace cv;
using namespace std;
int main(int argc, char** argv) {
    string folder = "c:/data/dnn/tcnn/";