pctroll - 5 months ago 32

Python Question

I've been reading about the subject but cannot get the idea in "plain English" about the usage and parameters for

`HoughCircles`

`CV_HOUGH_GRADIENT`

What's an accumulator threshold? Are 100 "votes" a right value?

I could find and "mask" the pupil, and worked my way through the

`Canny`

`HoughCircles`

And this is the function I'm working on:

`def getRadius(area):`

r = 1.0

r = math.sqrt(area/3.14)

return (r)

def getIris(frame):

grayImg = cv.CreateImage(cv.GetSize(frame), 8, 1)

cv.CvtColor(frame,grayImg,cv.CV_BGR2GRAY)

cv.Smooth(grayImg,grayImg,cv.CV_GAUSSIAN,9,9)

cv.Canny(grayImg, grayImg, 32, 2)

storage = cv.CreateMat(grayImg.width, 1, cv.CV_32FC3)

minRad = int(getRadius(pupilArea))

circles = cv.HoughCircles(grayImg, storage, cv.CV_HOUGH_GRADIENT, 2, 10,32,200,minRad, minRad*2)

cv.ShowImage("output", grayImg)

while circles:

cv.DrawContours(frame, circles, (0,0,0), (0,0,0), 2)

# this message is never shown, therefore I'm not detecting circles

print "circle!"

circles = circles.h_next()

return (frame)

Answer

`HoughCircles`

can be kind of tricky, I suggest looking through this thread. Where a bunch of people, including me ;), discuss how to use it. The key parameter is `param2`

, the so-called `accumulator threshold`

. Basically, the higher it is the less circles you get. And these circles have a higher probability of being correct. The best value is different for every image. I think the best approach is to use a parameter search on `param2`

. Ie. keep on trying values until your criteria is met (such as: there are 2 circles, or max. number of circles that are non-overlapping, etc.). I have some code that does a binary search on 'param2', so it meet the criteria quickly.

The other crucial factor is pre-processing, try to reduce noise, and simplify the image. Some combination of blurring/thresholding/canny is good for this.

Anyhow, I get this:

From your uploded image, using this code:

```
import cv
import numpy as np
def draw_circles(storage, output):
circles = np.asarray(storage)
for circle in circles:
Radius, x, y = int(circle[0][3]), int(circle[0][0]), int(circle[0][4])
cv.Circle(output, (x, y), 1, cv.CV_RGB(0, 255, 0), -1, 8, 0)
cv.Circle(output, (x, y), Radius, cv.CV_RGB(255, 0, 0), 3, 8, 0)
orig = cv.LoadImage('eyez.png')
processed = cv.LoadImage('eyez.png',cv.CV_LOAD_IMAGE_GRAYSCALE)
storage = cv.CreateMat(orig.width, 1, cv.CV_32FC3)
#use canny, as HoughCircles seems to prefer ring like circles to filled ones.
cv.Canny(processed, processed, 5, 70, 3)
#smooth to reduce noise a bit more
cv.Smooth(processed, processed, cv.CV_GAUSSIAN, 7, 7)
cv.HoughCircles(processed, storage, cv.CV_HOUGH_GRADIENT, 2, 32.0, 30, 550)
draw_circles(storage, orig)
cv.ShowImage("original with circles", orig)
cv.WaitKey(0)
```

**Update**

I realise I somewhat miss-read your question! You actually want to find the **iris** edges. They are not so clearly defined, as the pupils. So we need to help `HoughCircles`

as much as possible. We can do this, by:

- Specifying a size range for the iris (we can work out a plausible range from the pupil size).
- Increasing the minimum distance between circle centres (we know two irises can never overlap, so we can safely set this to our minimum iris size)

And then we need to do a param search on `param2`

again. Replacing the 'HoughCircles' line in the above code with this:

```
cv.HoughCircles(processed, storage, cv.CV_HOUGH_GRADIENT, 2, 100.0, 30, 150,100,140)
```

Gets us this:

Which isn't too bad.