As I know HoughCircles() function,
param1 is used upper limit of canny edge.
I tried to detect circle in my image which circle border pixel values are 190~210.
(background is 255)
When I try HoughCircles() param1=40~80,
detected circle.
But param1=220 failed to detect circle.
I confirmed HoughCircle() param1 is canny edge upper limit so I thought
from pixel value half of param1(110) to param1(220) will detect circle.
How does the HoughCircle() works with parameter?
Related
I'm trying to use Hough circle detection method to find all the circle as shown in the image(shown in image 1 and 2). Initially, I used canny and findContour method (shown in image 3). I'm still unsure which method will be more suitable.The problem with the canny and findContour method was that it didn't find all the circles as well as getting lot of noise. But when using the Hough circle detection, the circle is sometimes catching the outer perimeter instead of the inner as shown in image 2.
The canny+findcontour methods finds the circle contour well but has a lot of noise whereas Hough circle works well but the circles sometimes blends with the outer circle.
Hough circle
Hough circle-zoomed in
canny + findcontour method
I think you can get better results if you pre-process the image. First apply Otsu thresholding, if that doesn't work well use a manual value for threshold. After that use the cv.erode() function to get a crisp boundary. Then try to apply the Hough circle transform.
If performance is not an issue, another interesting thing would be to to look at Holistically Nested Edge Detection and then apply Hough circle transform.
Also have a look at the following:
https://www.learnopencv.com/filling-holes-in-an-image-using-opencv-python-c/
https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_imgproc/py_watershed/py_watershed.html
I am trying to detect two concentric circles using opencv in Android. Big outer circle is red, inner smaller circle is blue. The idea is to detect big circle while distance is long and detect inner circle as the distance becomes short.
Sample picture
I am using simple code:
Mat matRed = new Mat();
Core.inRange(matHsv, getScalar(hue - HUE_D, saturation - SAT_D, brightness - BRIGHT_D), getScalar(hue + HUE_D, saturation + SAT_D, brightness + BRIGHT_D), matRed);
//here we have black-white image
Imgproc.GaussianBlur(matRed, matRed, new Size(0, 0), 6, 6);
Mat matCircles = new Mat();
Imgproc.HoughCircles(matRed, matCircles, CV_HOUGH_GRADIENT, 1, matRed.rows()/8, 100, param2, 0, 0);
After calling inRange we have white ring on black background. HoughCircles function detects only inner black circle.
How can I make it to detect outer white circle instead?
Without seeing a sample image (or being quite sure what you mean by 'detect big circle while distance is long and detect inner circle as the distance becomes short'), this is somewhat of a guess, but I'd suggest using Canny edge detect to get the boundaries of your circles and then using contours to extract the edges. You can use the contour hierarchy to determine which is inside which if you need to extract one or the other.
Additionally, given the circles are different colours, you might want to look at using inRange to segment based on colour; for example, this post from PyImageSearch contains a Python application which does colour-based tracking.
Is there a way to fill the results of a Canny edge detector with white?
An alternative could be to detect whether a pixel or object is on the concave size of a curved edge.
Canny detects edges, it does not make any guarantees about generating closed contours. Thus, "filling" is not always defined for the result.
If by some other means you know that some area is enclosed in a continuous closed contour, you could use e.g. flood-fill to fill it.
I have lots of curve But curves should completed to circle How can I do this process with hough circle I tried this method but I cant get a result
Detect semi-circle in opencv
My image is here:
Hough circles is the only way to do this.
The image is noisy but I suspect the problem is that there are very few points and you are trying a wide range of radii. This means that the Hough probability for each circle is very low.
Do you know what the radii should be? If you do try a reduced radius range.
If not I would run the image with a set of small radii ranges and see which produce any result
I have been trying to detect and track vehicle in a video stream. Recently I decided to implement a hard-coded method which find out the shadow of a vehicle and detect entire vehicle with respect to tire position. At the end, I partially done with my implementation. Here is the video link of demonstration.
At the first step I used canny edge detector to subtract edge of the video frames.
Then I used hough transform funciton in opencv.
However this functions finds all the horizontal and vertical lines while I only interested in horizontal lines which are possibly shadow of the vehicle.
My question is how I can use hough line transform function where it only checks the lines which are in a spesific range of angle and within a spesific area. Is there any parameter that tresholds the angle ? Or should I implement the function by myself ?
Since you end up with a binary image after the Canny operation, it may be easiest to convolve the image with a simple horizontal Prewitt operator before applying the Hough transform:
1 1 1
0 0 0
-1 -1 -1
which will give you a map of the grayscale intensities of each pixel, with pixels along horizontal edge giving the strongest signal. The advantage of using only the horizontal operator is that vertical edges receive no amplification, horizontal edges receive maximum amplification, and any edge within 45° of horizontal should have a signal somewhere between the two. You can use the resulting image to decide which pixels from the Canny mask to keep when you apply the detect edges to the original image: If the Prewitt signal is above a certain threshold for a pixel, that pixel is assumed to be along a 'horizontal-enough' edge that gets kept, discard otherwise. I believe opencv has this feature, but it's trivial to implement if not.