# Opencv moments

I used opencv 2. My problems are. For Hu-Moments you need to compute the moments first.

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Moments you can compute of contours as in your example i. For your second question: For what do you want to use the moments? Moments give you certain statistics about your image, like the sum of gray-values or center of gravity. Hu-Moments are also rotational invariant.

So, they may be nice features depending on your application. Asked: What kind of features that can be extracted from binary image. Do all opencv functions support in-place mode for their arguments? What is the most effective way to access cv::Mat elements in a loop? Sobel derivatives in the 45 and degree direction. Saving an image with unset pixels. How to enable vectorization in OpenCV? First time here?

Check out the FAQ! Hi there! Please sign in help. My problems are, is it possible to calculate Hu-Moments only for a one contour?

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Question Tools Follow. Related questions What kind of features that can be extracted from binary image Do all opencv functions support in-place mode for their arguments? Sobel derivatives in the 45 and degree direction finding centroid of a mask Extract a RotatedRect area Saving an image with unset pixels How to enable vectorization in OpenCV? Object detection slow Difference of Gaussian Filtering. Copyright OpenCV foundation Powered by Askbot version 0. Please note: OpenCV answers requires javascript to work properly, please enable javascript in your browser, here is how.

What are moments of a contour? Could someone explain this in simplistic, non-mathematical terms? Possibly with an example? The official explanation is "integration over all the pixels in a contour". I have no idea what integration. And also what can contour moments be used for? Moment of 1st degree for x-axis and some particular point X on the x-axis: this is the sum of the white pixel distances from X. If you divide this by the number of white pixels 0th moment you get the average white pixel position wrt.

For degree 0 the last part is just 1 so that you simply sum the pixel values. For degree 1 it becomes a sum of positions, which can give you an average position, and for degree 2 it can reportedly give you a kind of direction. Learn more. OpenCV Contours Moments? Ask Question. Asked 8 years, 6 months ago.

Post as a guest Name. Email Required, but never shown. The Overflow Blog.In middle school, we learned about various shapes in geometry. It was relatively easy to find the centers of standard shapes like the circle, square, triangle, ellipse, etc. But when it came to finding the centroid of an arbitrary shape, the methods were not straightforward.

Some nerdy friends said it would require calculus. Other practical friends suggested intersecting plumblines. The same problem of finding centroid is relevant when you work in Computer Vision — except, you are dealing with pixels instead of atoms!

In this post, we will first discuss how to find the center of an arbitrarily shaped blob and then we will move to the case of multiple blobs. A blob is a group of connected pixels in an image that shares some common property e.

If the shape we are interested in is not binary, we have to binarize it first. The centroid of a shape is the arithmetic mean i. Suppose a shape consists of distinct pointsthen the centroid is given by. In the context of image processing and computer vision, each shape is made of pixels, and the centroid is simply the weighted average of all the pixels constituting the shape. We can find the center of the blob using moments in OpenCV. Image Moment is a particular weighted average of image pixel intensities, with the help of which we can find some specific properties of an image, like radius, area, centroid etc.

To find the centroid of the image, we generally convert it to binary format and then find its center. Finding the center of only one blob is quite easy, but what if there are multiple blobs in the Image? Well then, we will have to use findContours to find the number of contours in the Image and find the center of each of them. Let us see how it works! You can include, the below code snippet to prevent getting errors, this simply neglects the contours which are not segmented properly.

You will also receive a free Computer Vision Resource Guide. Subscribe Now. Skip to primary navigation Skip to main content Skip to primary sidebar In middle school, we learned about various shapes in geometry. What is a blob? What is the centroid of a shape? Steps for finding Centroid of a Blob in OpenCV To find the center of the blob, we will perform the following steps:- 1. Convert the Image to grayscale. Perform Binarization on the Image.

Find the center of the image after calculating the moments. Some of the functions may change according to your version. Download Code To easily follow along with this tutorial, please download code by clicking on the button below. Download Code.The function computes moments, up to the 3rd order, of a vector shape or a rasterized shape. The results are returned in the structure Moments defined as:. In case of a raster image, the spatial moments are computed as:.

The central moments are computed as:. The normalized central moments are computed as:.

Image Moments

So, due to a limited raster resolution, the moments computed for a contour are slightly different from the moments computed for the same rasterized contour. Since the contour moments are computed using Green formula, you may get seemingly odd results for contours with self-intersections, e. These values are proved to be invariants to the image scale, rotation, and reflection except the seventh one, whose sign is changed by reflection.

This invariance is proved with the assumption of infinite image resolution. In case of raster images, the computed Hu invariants for the original and transformed images are a bit different. The function retrieves contours from the binary image using the algorithm [Suzuki85].

The contours are a useful tool for shape analysis and object detection and recognition. See squares. Source image is modified by this function. The function draws contour outlines in the image if or fills the area bounded by the contours if.

The example below shows how to retrieve connected components from the binary image and label them:.

This is a standalone contour approximation routine, not represented in the new interface. When FindContours retrieves contours as Freeman chains, it calls the function to get approximated contours, represented as polygons. The function calculates and returns the minimal up-right bounding rectangle for the specified point set. The function computes a contour area.

Similarly to momentsthe area is computed using the Green formula. Thus, the returned area and the number of non-zero pixels, if you draw the contour using drawContours or fillPolycan be different. Also, the function will most certainly give a wrong results for contours with self-intersections.In this post, we will show how to use Hu Moments for shape matching. You will learn the following. Image moments are a weighted average of image pixel intensities. For simplicity, let us consider a single channel binary image. The pixel intensity at location is given by. Note for a binary image can take a value of 0 or 1. All we are doing in the above equation is calculating the sum of all pixel intensities. In other words, all pixel intensities are weighted only based on their intensity, but not based on their location in the image. So far you may not be impressed with image moments, but here is something interesting. Figure 1 contains three binary images — S S0. This image moment for S and rotated S will be very close, and the moment for K will be different.

For two shapes to be the same, the above image moment will necessarily be the same, but it is not a sufficient condition.

We can easily construct two images where the above moment is the same, but they look very different. These moments are often referred to as raw moments to distinguish them from central moments mentioned later in this article. Note the above moments depend on the intensity of pixels and their location in the image.

So intuitively these moments are capturing some notion of shape. The centroid of a binary blob is simply its center of mass. The centroid is calculated using the following formula. We have explained this in a greater detail in our previous post. Central moments are very similar to the raw image moments we saw earlier, except that we subtract off the centroid from the and in the moment formula.

Notice that the above central moments are translation invariant. In other words, no matter where the blob is in the image, if the shape is the same, the moments will be the same.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.

The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I am trying to use the Python opencv function Moments on a grayscale image, but I receive the following TypeError :. I am confident this usage is correct as it is demonstrated in the opencv docs herewhere GetHuMoments uses the results from Moments. I believe I have opencv and numpy installed correctly, as I have been successfully using them for many other things, and I encounter this on both OS X The same question is posed in the opencv user groupbut I don't want to convert the image to a contour first as the reply instructs.

Which python and opencv version are you using? I am getting working results on python 2. Learn more. Asked 8 years, 7 months ago.

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