The other answers did not work for me. Back then, I spent so much time to find a good masking function. Here are two simple answers with numpy only. Show
Hello, I'm try to create binary mask for medical images, im new to OpenCV and any advice will help on how, or if it possible, to do so with OpenCV. Below is the type of pictures I try to create mask for: I need to color the vocal cords area in white(Marked with triangle) and the rest in black, so it will look something like that: i tried to find the contours using that code:
this is the result: i tried to clean the rest of the image using some adaptiveThreshold but the result was worse. i also try to detect the triangle shape that created by the Vocal cords, also without success. Does anyone have any suggestions on how I can get the desired result or what method I can use? Any advice will help, Thanks. The original image for example: The problem had been solved. That is all I can't go any furthers. For opencv 3.x add this `_, contours, _ = . New python code has been changed. For opencv 4.x, see below:
Output: In this 5th part of the image processing series, we discuss more on the Arithmetic and bitwise operations, and masking of images in Python. It is recommended that the previous articles be run through, before starting off on your masked learning adventure here. Setting up the environmentThe following lines of code are used in all of the applications given below. We’ll include those here instead so you don’t have to read through a huge block of code. Helps reduce clutter :)
Arithmetic Operations on Images using PythonArithmetic Operations allow us to enhance a lot of aspects of an image. We can work with lighting, shadows, the red, blue, and green color enhancement. A lot of image filters on applications use the same method to alter and beautify photographs as well. So, let’s get started with all of the code! First, in order to understand whether the limit can go over 255 or 0, we can conduct a simple test, which provides us with
In this example, we are increasing the intensity of all the pixels in the image by 100.
This is done by constructing a matrix with the same size as our images using the
In case we wish to darken an image, we subtract from the pixel values of the image, as shown below,
This should provide you with two different variations of the original image, one lighter, and the other darker. Bitwise OperationsWe use Bitwise operations a lot of the times while attempting to mask images. This feature of OpenCV allows us to filter out the part of the image that is relevant to us. Setting upTo work on Bitwise operations, we’ll first need two variables or images that we can conduct the operations on. So, let’s create a bitwise square and a bitwise circle through which we can use the bitwise operations. Note that bitwise operations require the images to be black and white.
The output images that you receive should look like this, Bit SquareCombine with the AND operationBitwise addition refers to the addition of two different images, and decide which is to be displayed using an
Bitwise addition of both the circle and the square gives us an output which should look like this, AND Bit SquareGiven a choice with the OR operationBitwise OR provides us with a product of the two images with an
Upon performing the operation Bitwise OR, you should receive something like this, OR Bit SquareExclusivity with the XOR operationAnother operation that is provided by the XOR Bit Square
Negation using the NOT operationLastly, we have the negation operation, which is performed using the The NOT operation only requires a single image as we’re not adding or subtracting anything here. We still use it on both here however, that’s also an option.
The circle is inside the square in this case, and as such is not visible, Not Bit SquareMasking of images using Python OpenCVMasking is used in Image Processing to output the Region of Interest, or simply the part of the image that we are interested in. We tend to use bitwise operations for masking as it allows us to discard the parts of the image that we do not need. So, let’s get started with masking! The process of masking images We have three steps in masking.
Following the same process, let’s create a few masks and use them on our image. First, let’s work with a rectangle mask.
Now, let’s try it out with a circle mask.
If everything works out just fine, we should receive outputs which look something like this, Rectangular MaskConclusionWe’re finally getting started with the core of Image Processing, and understanding bitwise operations and masking in it is important. It helps us to block out parts or only take in parts of the image that we are interested in, so, quite a useful concept. We’re proceeding at a decent pace, but, in case you wish to time skip and get to the end, be my guest! Here’s articles which let you look into OpenCV and Facial Recognition, and a Java implementation of Android and CameraX OpenCV. References
How do you make a binary mask in Python?import numpy as np arr = np. arange(27). reshape(3,3,3) #3 channel image mask = np. zeros(shape=(3,3)) mask[1,1] = 1 # binary mask mask_3d = np.
How do you make an image mask in Python?Masking of images using Python OpenCV. Creating a black canvas with the same dimensions as the image, and naming it as mask .. Changing the values of the mask by drawing any figure in the image and providing it with a white color.. Performing the bitwise ADD operation on the image with the mask.. How do you make a binary mask?You can create this binary mask by specifying the vertices of the polygon using the using the roipoly function, or by specifying the vertices and the target size of the mask using the poly2mask function. poly2mask does not require an input image.
How do I crop an image using binary mask?use findContours or extract all mask points (manually) and use the minBoundingRect function. Afterwards use subimage to get the cropped image.
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