OpenCV center of contour


Today, we are going to start a new 3-part series of tutorials on shape detection and analysis.

Throughout this series, we’ll learn how to:

  1. Compute the center of a contour/shape region.
  2. Recognize various shapes, such as circles, squares, rectangles, triangles, and pentagons using only contour properties.
  3. Label the color of a shape.

While today’s post is a bit basic (at least in context of some of the more advanced concepts on the PyImageSearch blog recently), it still addresses a question that I get asked a lot:

“How do I compute the center of a contour using Python and OpenCV?

In today’s post, I’ll answer that question.

And in later posts in this series, we’ll build upon our knowledge of contours to recognize shapes in images.

Looking for the source code to this post?
Jump right to the downloads section.

OpenCV center of contour

Figure 1: An example image containing a set of shapes that we are going to compute the center of the contour for.

Figure 1: An example image containing a set of shapes that we are going to compute the center of the contour for.

In above image, you can see a variety of shapes cut out from pieces of construction paper. Notice how these shapes are not perfect. The rectangles aren’t quite rectangular — and the circles are not entirely circular either. These are human drawn and human cut out shapes, implying there is variation in each shape type.

With this in mind, the goal of today’s tutorial is to (1) detect the outline of each shape in the image, followed by (2) computing the center of the contour — also called the centroid of the region.

In order to accomplish these goals, we’ll need to perform a bit of image pre-processing, including:

  • Conversion to grayscale.
  • Blurring to reduce high frequency noise to make our contour detection process more accurate.
  • Binarization of the image. Typically edge detection and thresholding are used for this process. In this post, we’ll be applying thresholding.

Before we start coding, make sure you have the imutils Python package installed on your system:

From there, we can go ahead and get started.

Open up a new file, name it , and we’ll get coding:

We start off on Lines 2-4 by importing our necessary packages, followed by parsing our command line arguments. We only need a single switch here, --image , which is the path to where the image we want to process resides on disk.

We then take this image, load it from disk, and pre-process it by applying grayscale conversion, Gaussian smoothing using a 5 x 5 kernel, and finally thresholding (Lines 14-17).

The output of the thresholding operation can be seen below:

Figure 2: Thresholding our image returns a binary image, where the shapes appear as white on a black foreground.

Figure 2: Thresholding our image returns a binary image, where the shapes appear as white on a black foreground.

Notice how after applying thresholding the shapes are represented as a white foreground on a black background.

The next step is to find the location of these white regions using contour detection:

A call to cv2.findContours  on Lines 20 and 21 returns the set of outlines (i.e., contours) that correspond to each of the white blobs on the image. Line 22 then grabs the appropriate tuple value based on whether we are using OpenCV 2.4, 3, or 4. You can read more about how the return signature of cv2.findContours  changed between OpenCV versions in this post.

We are now ready to process each of the contours:

On Line 25 we start looping over each of the individual contours, followed by computing image moments for the contour region on Line 27.

In computer vision and image processing, image moments are often used to characterize the shape of an object in an image. These moments capture basic statistical properties of the shape, including the area of the object, the centroid (i.e., the center (x, y)-coordinates of the object), orientation, along with other desirable properties.

Here we are only interested in the center of the contour, which we compute on Lines 28 and 29.

From there, Lines 32-34 handle:

  • Drawing the outline of the contour surrounding the current shape by making a call to cv2.drawContours .
  • Placing a white circle at the center (cX, cY) -coordinates of the shape.
  • Writing the text center  near the white circle.

To execute our script, just open up a terminal and execute the following command:

Your results should look something like this:

Figure 3: Looping over each of the shapes individually and then computing the center (x, y)-coordinates for each shape.

Figure 3: Looping over each of the shapes individually and then computing the center (x, y)-coordinates for each shape.

Notice how each of the shapes are successfully detected, followed by the center of the contour being computed and drawn on the image.


In this lesson, we learned how to compute the center of a contour using OpenCV and Python.

This post is the first in a three part series on shape analysis.

In next week’s post, we’ll learn how to identify shapes in an image.

Then, two weeks from now, we’ll learn how to analyze the color of each shape and label the shape with a specific color (i.e., “red”, “green”, “blue”, etc.).

To be notified when these posts go live, be sure to enter your email address using the form below!


If you would like to download the code and images used in this post, please enter your email address in the form below. Not only will you get a .zip of the code, I’ll also send you a FREE 17-page Resource Guide on Computer Vision, OpenCV, and Deep Learning. Inside you'll find my hand-picked tutorials, books, courses, and libraries to help you master CV and DL! Sound good? If so, enter your email address and I’ll send you the code immediately!

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100 Responses to OpenCV center of contour

  1. Luis Jose February 1, 2016 at 1:41 pm #

    Hi Adrian!

    Great job. I’m looking forward to completing this series of posts.

    Thanks for sharing your knowledge with the world!!!

    • Adrian Rosebrock February 2, 2016 at 10:32 am #

      Thanks Luis!

  2. Marco February 1, 2016 at 2:28 pm #

    Hi Adrian, another great Tutorial, but how to run the script into the Python Idle directly?

    • Adrian Rosebrock February 2, 2016 at 10:31 am #

      You would have to copy and paste each command into IDLE one-by-one. If you like using IDLE, you should also look into using IPython Notebooks as they are a bit more user friendly.

  3. Harley Mackenzie February 1, 2016 at 6:59 pm #

    There seems to be good support for opencv for shapes and finding centroids but are there equivalent routines for line detection. I have found this to be quite challenging especially discriminating between lots of small noise lines and what I think should be dominant significant lines.

    • Adrian Rosebrock February 2, 2016 at 10:29 am #

      Line detection is much, much more challenging for a variety of reasons. The “standard” method to perform line detection is to use the Hough Lines transform. But for noisy images, you’ll often get mixed results.

  4. Boško February 2, 2016 at 8:06 am #

    Hello Andrian,

    I got “ZeroDivisionError: float division by zero”, because all “m” values are 0. Why? Where I wrong? I trying solve it but do not have luck. I use Python 2.7 and openCV 3.1.

    Best Regards,

    • Adrian Rosebrock February 2, 2016 at 10:24 am #

      It seems like both you and Ruttunenn are getting the same error message. It seems like the segmentation may not be perfect and there is some noise left over in the thresholding. A simple check would be to use:

      Where you can set MIN_THRESH to be a suitable value to filter out small contour regions.

      • Boško February 2, 2016 at 3:17 pm #

        Thanks! It’s work now

        • Adrian Rosebrock February 4, 2016 at 9:23 am #

          Awesome, I’m happy it worked for you! 🙂

      • Amir April 19, 2017 at 9:50 am #

        Hi Adrian, where should i put this command? And what is the range of the MIN_THRESH?


        • Adrian Rosebrock April 19, 2017 at 12:43 pm #

          You would typically define MIN_THRESH at the top of your file, but you can place it anywhere that you think is good from a code organization perspective. The actual range of MIN_THRESH will vary on your application and will have to be experimentally determined.

    • leena February 9, 2016 at 3:49 am #

      I also got the same and resolved with following:

      if (M[“m00”] == 0):


      • ESPLondon February 26, 2016 at 9:33 am #

        Thanks leena, that worked

      • Ashay December 16, 2016 at 11:33 am #

        Can also resolve as following:

        #find centroid

  5. Ruttunenn February 2, 2016 at 10:21 am #


    Just run to a minor glitch in the example as I was getting zeros on the M = cv2.moments(c) on the first iteration, leading to float division by zero. A simple work around was to implement a check for 0.0 results.

    Cheers for awesome tutorials anyway.

  6. David February 3, 2016 at 10:20 pm #

    Hi, excellent post Adrian!!!

    Could you please explain a bit more why on the pre-processing stage you slightly blur the image???

    David Darias

    • Adrian Rosebrock February 4, 2016 at 9:13 am #

      Blurring (also called “smoothing”) is used to smooth high frequency noise in the image. Simply put, this allows us to ignore the details in the image and focus on what matters — the shapes. So by blurring, we smooth over less interesting regions of the image, allowing the thresholding and contour extraction phase to be more accurate.

  7. Ken Doman February 9, 2016 at 6:42 pm #

    Great post, Adrian!

    It may be a little off topic, but I’m curious how the tool to find the center would fair against crescent-shaped features. Would the centroid be inside the shape, or in the middle possibly blank area?

    • Adrian Rosebrock February 10, 2016 at 4:41 pm #

      Great question. It would still be inside the shape, in the center, but towards the rim. An example can be found here. Keep in mind that only non-zero pixels are included in the calculation of the centroid.

  8. gary February 11, 2016 at 4:43 pm #

    Hi Adrian,
    I have a question about the value of cX and cY. As i want to know what is the pixel value at the point (cX, cY), i tried to print it by image[cX,cY]. However, I got error like:
    IndexError: index 1040 is out of bounds for axis 0 with size 1024
    which means that cX and cY is outside of range of the image size. Therefore, I want to ask how can i find out the pixel coordinate at point (cX, cY)?

    • Adrian Rosebrock February 12, 2016 at 3:19 pm #

      When accessing pixel values in OpenCV + NumPy, you actually specify them in (y, x) order rather than (x, y) order. Thus, you need to use: image[cY, cX]

  9. thecanadiran October 12, 2016 at 4:15 pm #

    Thanks for a great tutorial Adrian.
    Could you please explain here or in another tutorial how to use image moments to characterize the other shape and statistical properties of an object?

  10. Rock January 30, 2017 at 1:52 pm #

    After running the code it gives me the following error: –

    usage: [-h] -i IMAGE error: argument -i/–image is required

    What i simply did was that i just downloaded the code and ran it and the above error occured.

    Also could you suggest me a book which i can use to learn open CV with python from scratch. I know python but i don’t have any clue about open CV.

    • Adrian Rosebrock January 30, 2017 at 4:09 pm #

      Hey Rock — you error is coming from not supplying the image path via command line argument. You should be executing the command line this:

      $ python --image shapes_and_colors.png

      If you want to learn OpenCV + Python from scratch, I would highly recommend that you take a look at my book, Practical Python and OpenCV.

      • Rock January 31, 2017 at 12:29 pm #

        Thank you for your reply. i shall definitely refer that book.

        Could you please elaborate i still am not able to figure out what i have to do. Where do i have to make changes in the code.

        • Rock January 31, 2017 at 12:51 pm #

          Just wanted to add that i’m running the exact same code on IDLE and tis is what it shows as error. Do i have to run it on CMD as admin?

          • Rock January 31, 2017 at 1:02 pm #

            Thank you so much for your help. I finally figured it out! :p

      • Mustafa October 25, 2019 at 1:51 am #

        Kindly elaborate me, i didn’t understand what to do?

        • Jaan January 6, 2020 at 8:12 pm #

          Just in case you, or anyone else is running this in a windows environment using Visual Studio, and not running directly from the command prompt, you’ll may need to add the arguments to your project.
          Right click your project -> Properties -> Debug -> Script Arguments -> [add your arguments here, like –image images\shapes.bmp]

  11. alcreek February 21, 2017 at 8:56 am #

    thanks Adrian! I do not use to leave comments. But you saved me! Pleasure to read you again!

    • Adrian Rosebrock February 22, 2017 at 1:38 pm #

      I’m happy to hear it! 🙂

  12. Jonathan April 1, 2017 at 10:38 am #

    If I am doing this in a Jupiter notebook, and what to display the results using matplotlib, how would I do so for the very last step as you do with:

    # show the image
    cv2.imshow(“Image”, image)
    I’ve tried placing: plt.imshow(image) inside of the for loop as I thought this would work. It will run the cell with no error but not display any image.

    • Adrian Rosebrock April 3, 2017 at 2:09 pm #

      If you’re using Jupyter notebooks make sure you declare matplotlib to be inline:

      %matplotlib inline

      From there, follow this tutorial on displaying images with matplotlib.

  13. Amit April 13, 2017 at 7:43 am #

    Adrian will these help me in detecting shapes in real time images also ,or will it throw errors in them

    • Adrian Rosebrock April 16, 2017 at 9:04 am #

      You can use this code to detect shapes in real-time as well.

  14. Abhinav April 18, 2017 at 12:04 pm #

    Sir can you provide me with what changes to make in shape detector program so that i can take object from webcam feed and classify it ,it will be very helpful if you can provide with the code modification

    • Adrian Rosebrock April 19, 2017 at 12:49 pm #

      You should use this post as a starting point to access your webcam.

  15. MOHAMED AWNI HAMED April 21, 2017 at 7:49 am #

    I can’t understand why you made this line
    cnts = cnts[0] if imutils.is_cv2() else cnts[1]

    • Adrian Rosebrock April 21, 2017 at 10:41 am #

      The cv2.findContours function in OpenCV 2.4 returns a 2-tuple while in OpenCV 3 it returns a 3-tuple. You can read more about the change in this blog post.

  16. pınar May 25, 2017 at 6:04 pm #

    I need to find radius of the given x,y coordinates of the center of a detected region? How can find the radius of each detected image?

    • Adrian Rosebrock May 28, 2017 at 1:16 am #

      I would suggest computing the cv2.minEnclosingCircle of the contour region to obtain the radius.

  17. Debajyoti Dutta June 1, 2017 at 3:01 am #

    Suppose three boxes are kept on top of one another. Does this method apply to finding the individual centres of each box, or it will find the single centre for the entire image?

    • Adrian Rosebrock June 4, 2017 at 6:26 am #

      You would want to change the cv2.findContours function call to either return a list of contours or a hierarchy, otherwise the script would find the center of the largest outer rectangle. In particular, take a look at the cv2.RETR_LIST flag.

  18. AMBIKA June 20, 2017 at 2:19 am #

    Hello Adrian!

    I tried to run this code on my system. But if I keep the code just the way it is, it shows me lots of centres (probably because it is in the loop). But when I keep that lines 33-39 outside the loop, there comes only one circle named centre but that is not on the center point but is somewhere at the bottom left corner of the contour. Can you please help me out with this?

    • Adrian Rosebrock June 20, 2017 at 10:48 am #

      Hi Ambika — this code will draw the center of each shape on Line 33. It does this by looping over all contours that have been detected. If you move Line 33 outside of the for loop then the coordinates will be incorrect. What exactly are you trying to accomplish?

  19. AMBIKA June 20, 2017 at 2:22 am #

    For your kind information, I am using OpenCV 2.4.9 and Python 2.7.

  20. Yassine RHAZ July 18, 2017 at 5:54 am #

    Thank you very much sir 🙂

  21. Dani August 3, 2017 at 8:21 pm #

    Hi Adrian! Great tutorial. Any idea why when I execute the files in terminal it is only producing a still image showing the centre of only one object? Similarly with the next tutorial on shape detection, only displaying the name of one shape. How do I get it to show them all? Thanks

    • Adrian Rosebrock August 4, 2017 at 6:48 am #

      You need to click on the active window and press any key on your keyboard to advance the execution of the script. That is what the cv2.waitKey call does.

  22. Dani August 3, 2017 at 8:52 pm #

    Also, if I wanted to add the additional functionality of counting the number of shapes/objects in the image, what would be the best way to go about this?


    • Adrian Rosebrock August 4, 2017 at 6:50 am #

      You need to be more specific regarding counting the number of shapes/objects. What is your end goal? Are there multiple objects of the same shape? Or are you looking just to get the total number of objects in an image?

      • Dani August 4, 2017 at 9:35 am #

        Yes, I would like to get the total number of objects in an image, basically counting the number of “centers” that it is detecting. Thanks for your help

        • Adrian Rosebrock August 10, 2017 at 9:13 am #

          In that case, simply apply cv2.findContours. The number of contours returned will be the total number of objects in the image. You might have to filter the contours based on width/height/aspect ratio to help protect against false-positive detections, but that’s basically the gist.

  23. Eloque September 11, 2017 at 6:00 pm #

    Very nice,good to read and made easy to understand. But am I correct in assuming that if my basic image has a white background instead of your example black background, the contouring doesn’t work?

    Image included for reference:

    • Adrian Rosebrock September 12, 2017 at 8:21 pm #

      Why not just flip the image via cv2.bitwise_not that way your white background becomes black?

  24. Sanup s babu October 20, 2017 at 1:16 pm #

    Hi Adrian,
    Can i find the largest contour area from many contours in a frame from a video.
    If there are 3 circles: left, middle and right, if left is having more area than other 2.i want to tell its left circle,is it possible?
    Iam using Rpi3 python and opencv

    • Adrian Rosebrock October 22, 2017 at 8:41 am #

      I would suggest sorting your contours and maintaining a list of (x, y)-coordinates for each ball. A dequeue data structure like in this post would be really helpful. From there, monitor the (x, y)-coordinates in the dequeue. If there is a lot of variation, you know the ball is moving.

  25. Markus February 1, 2018 at 1:51 am #

    Hi! We are developing a mobile application that aims to measure an object’s area, to identify the real object’s (poultry eggs) size. We are about to use the formula for ellipse to get the area of the object, and the centroid to identify the axes needed. However, we are not sure if there is an available code in java for this. Thank you!

    • Adrian Rosebrock February 3, 2018 at 11:14 am #

      Hey Markus — I only provide Python + OpenCV code here. You would need to port the code to Java yourself.

  26. Aadil Popat February 7, 2018 at 12:03 pm #

    Hi Adrian!
    i’m running the code and i get this error, could you please help?! i don’t know where i’m going wrong.

    File “”, line 14, in
    image = cv2.imread(args[“shapes_and_colors.jpg”])
    KeyError: ‘shapes_and_colors.jpg’

    • Adrian Rosebrock February 8, 2018 at 7:56 am #

      Hey Aadil — you do not need to modify the code at all if you are using command line arguments. If you want to skip command line arguments you should hardcode the path to cv2.imread, like this:


  27. Jacob March 7, 2018 at 4:44 pm #

    Hi — how would you deal with having a light gray background with other colored objects (red, orange) ? currently it returns one green rectangle around the perimeter of the entire image

    • Adrian Rosebrock March 9, 2018 at 9:25 am #

      It sounds like the color threshold parameters need to be tuned a bit. Perhaps your environment has a bit of a “green tint” to it. You would need to define a color threshold range for each color you wished to detect.

  28. Jason March 23, 2018 at 4:43 am #

    Hi, Adrian! Your post is great! But when I run this in my computer, it didn’t work well. I don’t how to do with it. The tip is: usage: [-h] –path IMAGE PATH error: argument –path is required. And the code is :
    ap.add_argument(“–path”, dest=’image path’, required=True,
    help=”path to the input image”,default=’/home/jason/桌面/Shape Segmentation/dddd’, type=str)

    • Adrian Rosebrock March 27, 2018 at 6:37 am #

      Hey Jason — the issue is that you are not correctly providing the command line arguments to the script. Please take a look at this post for more details.

  29. Nimya June 14, 2018 at 7:31 am #

    Hi, I am trying to find centroid for moving object in that I am facing difficulty. Can any one please help me in that ??

    • Adrian Rosebrock June 15, 2018 at 12:06 pm #

      What object are you trying to compute the centroid of? Without seeing it’s example it’s hard to point you in the right direction.

  30. Ritu July 6, 2018 at 7:51 am #

    How can i get centroid(rectangle)-centroid(circle) value in this code. Your help may be appreciated.

  31. Secret July 16, 2018 at 4:28 am #

    Hi..I want to ask why I use a picture whose background is white and this code didn’t work. Dose it relate to the color of background?

    • Adrian Rosebrock July 17, 2018 at 7:21 am #

      The code assumes that white is foreground and black is background. You may want to invert your image with a “cv2.bitwise_not”.

  32. Andrea September 12, 2018 at 12:19 pm #

    Hi Adrian, first of all let me say the way you explain things is amazing. Congratulations for the fantastic job. I have one question for you. I took a screenshot of this website: it’s background is white, hence I added cv2.bitwise_not to your code. The problem I still face though is that, even if the code captures most of the objects (shapes) it does also capture shapes that are purely words next to each other. For example the radio button labeled “BMW” is considered as a rectangle.
    Is there anything I could do to avoid this behaviour? Thanks

    • Adrian Rosebrock September 12, 2018 at 1:52 pm #

      There are a few ways to approach the problem but I would suggest inspecting the gradient magnitude representation of the image like I do in this tutorial. You can use that representation to filter out text vs. other elements.

  33. ReginaLe September 26, 2018 at 10:06 pm #

    for the autonomous quadcopter project, I need this in realtime video. but I found an error in cX = int (M [“m10”] / M [“m00”]) and the terminal written zeroDivisionError: float division by zero
    how do I change it so that it doesn’t error?

    • Adrian Rosebrock October 8, 2018 at 12:36 pm #

      Add a small epsilon value to the division:

      cX = int(M["m10"] / (M["m00"] + 1e-7))

  34. Trent September 29, 2018 at 9:18 pm #

    If anyone is seeing only 1 shape outline being drawn, the solution is that the following code should be OUTSIDE (not indented) of the “for” loop:

    # show the image
    cv2.imshow(“Image”, image)

    • Adrian Rosebrock October 8, 2018 at 12:10 pm #

      Or you can click the active window opened by OpenCV and then hit any key on your keyboard to advance the execution of the script.

  35. Perizat November 15, 2018 at 3:28 pm #

    Hey, Adrian!
    How can I find width and height of specific shape?

  36. Perizat November 18, 2018 at 11:38 am #

    Hey, Adrian!
    Great job! How can I find height and width of each elements or for one of them?

  37. Sebastian December 19, 2018 at 10:54 am #

    Hi adrian, thanks a million for making this post. saved me a ton of work!!!

    • Adrian Rosebrock December 19, 2018 at 1:45 pm #

      Thanks Sebastian, you are more than welcome.

  38. Dan March 12, 2019 at 9:12 pm #

    Thank you very much, it was really useful!!!

    • Adrian Rosebrock March 13, 2019 at 3:14 pm #

      Thanks Dan, I’m glad you enjoyed it!

  39. reuben March 18, 2019 at 5:30 am #

    Sorry! i got it. but i get this error! error: the following arguments are required: -i/–image
    An exception has occurred, use %tb to see the full traceback.”

    what does this mean ?

  40. Martha March 21, 2019 at 8:38 am #

    We tried running dis script but got this error:
    python: can’t open file ‘’: [Errno 2] No such file or directory.

    We are using Spyder, launched through anaconda navigator.

    Do you have a solution?

    • Adrian Rosebrock March 22, 2019 at 8:38 am #

      I would recommend executing the script via the command line instead.

  41. Kevin June 10, 2019 at 7:36 am #

    Thanks you for sharing well documented post with us. I like the way you explain code line by line. I am following you since my college time 2013 and even today your posts are helpful to me in my company.. Keep it up

  42. Alyssa September 23, 2019 at 4:01 pm #

    Hey Adrian,

    Thank you for this code! I am super new to programming but am learning image processing for school. I am having a lot of trouble getting the ‘args’ code to work – keep getting an ipykernel_launcher error with Exit Code 2. Any advice on alternate code to use?

    • Adrian Rosebrock September 25, 2019 at 10:35 am #

      It’s okay if you are new to programming but you need to learn command line arguments if you want to study advanced Computer Science topics like Computer Vision. Start by reading this tutorial on command line arguments.

  43. Alex November 1, 2019 at 6:34 pm #

    Hi Adrian and and the community !
    Thank you for providing such great contents andso much tutorial !!
    I installed pyton 3 and opencv4 following your tutorial and then I started this tutorial. I did everything you wrote in this tutorial but I have an error concerning the “tresh.copy()” which says “AttributeError: ‘tuple’ object has no attribute ‘copy'”
    I don’t understand why, I had a look to your findContours memo and tried with the specific code line for OpenCv4 but still get an error. Any explanations about this ?


    Thanks !!

    • Adrian Rosebrock November 7, 2019 at 10:27 am #

      Make sure you’re downloading the source code to this post rather than copying and pasting it. You likely forgot the “[1]” after this line:

      thresh = cv2.threshold(blurred, 60, 255, cv2.THRESH_BINARY)[1]

  44. Dror December 3, 2019 at 9:08 am #

    Great post, thanks.
    Any ideas on how to compute the CoG for a non-integer contour? (meaning that the contour is not a result of thresholding over some mask, but rather a result of a different algorithm). It seems that cv2.moments requires integer inputs.


  1. OpenCV shape detection - PyImageSearch - February 8, 2016

    […] Last week we learned how to compute the center of a contour using OpenCV. […]

  2. Determining object color with OpenCV - PyImageSearch - February 15, 2016

    […] Compute the center of a contour […]

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