@Manish, welcome to the forum. You’ll find this a very welcoming and supportive community. However, this question is far too broad encourage community engagement. You are encouraged to show what you have tried, what you are trying to achieve and to be clearer about what specifically you are asking for help with.

If you’ve not got any background in image processing then I suggest reading a few articles on “edge detection”. Then look at the OpenCV functions for edge detection which map to the eVision/OpenCV ones.

Some helpful resources for evision…

Also, please read the inline docs. Pulling up the inline docs and presenting them in a friendly way is a built-in feature of IEx. It only requires one to type h ModuleName.FunctionName. For example

iex> h Evision.findContours

                     def findContours(image, mode, method)

  @spec findContours(Evision.Mat.maybe_mat_in(), integer(), integer()) ::
          {[Evision.Mat.t()], Evision.Mat.t()} | {:error, String.t()}

Finds contours in a binary image.

##### Positional Arguments

  • **image**: `Evision.Mat`.
    Source, an 8-bit single-channel image. Non-zero pixels are treated as 1's.
    Zero pixels remain 0's, so the image is treated as binary . You can use
    #compare, #inRange, #threshold , #adaptiveThreshold, #Canny, and others to
    create a binary image out of a grayscale or color one. If mode equals to
    #RETR_CCOMP or #RETR_FLOODFILL, the input can also be a 32-bit integer
    image of labels (CV_32SC1).


  • **mode**: `int`.
    Contour retrieval mode, see #RetrievalModes


  • **method**: `int`.
    Contour approximation method, see #ContourApproximationModes


##### Keyword Arguments
...