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WTH OpenGL 4? Rendering elements arrays with VAOs and VBOs in a QGLWidget

I spent an entire day getting OpenGL 4 to display data from a VAO with VBOs so I thought I'd share the results with you guys, save you some pain.

I'm using the excellent GL wrappers from Qt, and in particular QGLShaderProgram.
This is pretty straightforward, but the thing to remember is that OpenGL is looking for the vertices/other elements (color? tex coords?) to come from some bound GL buffer or from the host. So if your app is not working and nothing appears on screen, just make sure GL has a bound buffer and the shader locations match up and consistent (see the const int I have on the class here).

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[Python] OpenCV capturing from a v4l2 device

I tried to set the capture format on a webcam from OpenCV's cv2.VideoCapture and ran into a problem: it's using the wrong IOCTL command.
So I used python-v4l2capture to get images from the device, which allows more control.
Here is the gist:

Enjoy!
Roy

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OpenCV Python YAML persistance

I wasn't able to find online a complete example on how to persist OpenCV matrices in Python (so really NumPy arrays) to YAML like what cv::FileStorage will give you.

So here's a short snippet:

import numpy as np
import yaml

# A yaml constructor is for loading from a yaml node.
# This is taken from: http://stackoverflow.com/a/15942429
def opencv_matrix_constructor(loader, node):
    mapping = loader.construct_mapping(node, deep=True)
    mat = np.array(mapping["data"])
    mat.resize(mapping["rows"], mapping["cols"])
    return mat
yaml.add_constructor(u"tag:yaml.org,2002:opencv-matrix", opencv_matrix_constructor)

# A yaml representer is for dumping structs into a yaml node.
# So for an opencv_matrix type (to be compatible with c++'s FileStorage) we save the rows, cols, type and flattened-data
def opencv_matrix_representer(dumper, mat):
    mapping = {'rows': mat.shape[0], 'cols': mat.shape[1], 'dt': 'd', 'data': mat.reshape(-1).tolist()}
    return dumper.represent_mapping(u"tag:yaml.org,2002:opencv-matrix", mapping)
yaml.add_representer(np.ndarray, opencv_matrix_representer)


#example
with open('output.yaml', 'w') as f:
    f.write("%YAML:1.0")
    yaml.dump({"a matrix": np.zeros((10,10)), "another_one": np.zeros((2,4))}, f)

#   a matrix: !!opencv-matrix
#     cols: 10
#     data: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
#       0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
#       0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
#       0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
#       0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
#       0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
#       0.0, 0.0, 0.0, 0.0, 0.0]
#     dt: d
#     rows: 10
#   another_one: !!opencv-matrix
#     cols: 4
#     data: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
#     dt: d
#     rows: 2

with open('output.yaml', 'r') as f:
    print yaml.load(f)
  
#  {'a matrix': array([[ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
#         [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
#         [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
#         [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
#         [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
#         [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
#         [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
#         [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
#         [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
#         [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.]]), 'another_one': array([[ 0.,  0.,  0.,  0.],
#         [ 0.,  0.,  0.,  0.]])}

There you go

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Quickly: How to render a PDF to an image in C++?

Using Poppler, of course!
Poppler is a very useful tool for handling PDF, so I've discovered lately. Having tried both muPDF and ImageMagick's Magick++ and failed, Poppler stepped up to the challenge and paid off.

So here's a small example of how work the API (with OpenCV, naturally):

#include <iostream>
#include <fstream>
#include <sstream>
#include <opencv2/opencv.hpp>
#include <poppler-document.h>
#include <poppler-page.h>
#include <poppler-page-renderer.h>
#include <poppler-image.h>

using namespace cv;
using namespace std;
using namespace poppler;

Mat readPDFtoCV(const string& filename,int DPI) {
    document* mypdf = document::load_from_file(filename);
    if(mypdf == NULL) {
        cerr << "couldn't read pdf\n";
        return Mat();
    }
    cout << "pdf has " << mypdf->pages() << " pages\n";
    page* mypage = mypdf->create_page(0);

    page_renderer renderer;
    renderer.set_render_hint(page_renderer::text_antialiasing);
    image myimage = renderer.render_page(mypage,DPI,DPI);
    cout << "created image of  " << myimage.width() << "x"<< myimage.height() << "\n";

    Mat cvimg;
    if(myimage.format() == image::format_rgb24) {
        Mat(myimage.height(),myimage.width(),CV_8UC3,myimage.data()).copyTo(cvimg);
    } else if(myimage.format() == image::format_argb32) {
        Mat(myimage.height(),myimage.width(),CV_8UC4,myimage.data()).copyTo(cvimg);
    } else {
        cerr << "PDF format no good\n";
        return Mat();
    }
    return cvimg;
}

All you have to do is give it the DPI (say you want to render in 100 DPI) and a filename.
Keep in mind it only renders the first page, but getting the other pages is just as easy.

That's it, enjoy!
Roy.

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