OpenCV中的级联Hough变换

Question

Is it possible to perform a Cascaded Hough Transform in OpenCV? I understand its just a HT followed by another one. The problem I'm facing is that the values returned are always rho and theta and never in y-intercept form.

Is it possible to convert these values back to y-intercept and split them into sub-spaces so I can detect vanishing points?

Or is it just better to program an implementation of HT myself in, say, Python?

Answer 1

I don't think OpenCV can perform cascaded hough transforms. You should convert them to xy space yourself. This article might help you:

http://aishack.in/tutorials/converting-lines-from-normal-to-slopeintercept-form/

Answer 2

you could try to populate the Hough domain with m and c parameters instead, so that y = mx + c can be re-written as c = y - mx so instead of the usual rho = x cos(theta) + y sin(theta), you have c = y - mx

normally, you'd go through the thetas and calculate the rho, then you increment the accumulator value for that pair of rho and theta. Here, you'd go through the value of m and calculate the values of c, then accumulate that m,c element in the accumulator. The bin with the most votes would be the right m,c

// going through the image looking for edge pixels
for (i = 0;i<numrows;i++)
    {
        for (j = 0;j<numcols;j++)
        {
            if (img[i*numcols + j] > 1)
            {
                for (n = first_m;n<last_m;n++)
                {   
                    index = i - n * j;
                    accum[n][index]++;
                }
            }
        }
    }

I guess where this becomes ineffective is that its hard to define the step size for going through m as they should technically go from -infinity to infinity so you'd kind of have trouble. yeah, so much for Hough transform in terms of m,c. Lol

I guess you could go the other way and isolate m so it would be m = (yc)/x so that now, you cycle through a bunch of y values that make sense and its much more manageable though it's still hard to define your accumulator matrix because m still has no limit. I guess you could limit the values of m that you would be interested in looking for.

Yeah, much more sense to go with rho and theta and convert them into y = mx + c and then even making a brand new image and re-running the hough transform on it.