Like the picture above, I want to take the image pattern of the part of the box or mask and paste it into another medical image, is there a way?
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I am working on a computer vision project that involves the segmentation of images. However, I do not want a 'normal' segmentation (like this), rather, I want a segmentation that only leaves the desired image itself, without even a blank background (similar to this).
Right now, I am thinking that the background needs to be set to the alpha channel after image segmentation, after which the image can be fed into a probabilistic program. Is that the proper way to approach this, or do I need to perform additional preprocessing to segment the desired image BTW, I am working with opencv-python.
The image you're providing is segmentation results for different approaches.
The image at first image is the result image of semantic segmentation. In this semantic segmentation task, you have do define which pixel are belonging to which class
The image at second image is the result image of hierarchical segmentation. In this hierachical segmentation task, you have to group pixel and find the relationship between them.
So from first image, you have to apply the "hierarchical analyzing", then you will have the result as second image. The easiest approach using opencv is :
considering each class as one contour
Find the contours hierarchy . Some tut here : contour hierachy
Hope this help.
I have a large set of "apple" images in various shapes, sizes, lighting, color, etc. These "apple" images were part of a larger image from different angles.
Now I want to train Darknet to detect "apple"s in images. I don't want to go through annotation process as I already have cropped out ready jpg images of apples.
Can I use these ready and cropped "apple" images to train Darknet or do I still have to go through annotation process?
In object detection models, you annotate the object in an image because it will understand where the object is in a particular image. If you have an entire dataset containing only apple images, the model will learn in a way such that every image you provide will contain the only apple. So even if you provide an "orange" as a test image, it might still give apple because it doesn't know another class except for apple.
So there are two important points to consider:
Have dataset in such a way that there are apples, apples with other fruits or other objects. This will help the model to understand clearly what apple is.
As the coordinates of the bounding box are inputs for the detection, although you can give the regular dimensions of the image as the bounding box, it won't learn effectively well as mentioned above. Therefore, have multiple objects in the image and then annotate well so that the model can learn well
Your answer relates to a process that we called "Data Augmentation". You can google it how others do.
Since your apple images are all cropped-ready, you can assume all apple images were already tagged by their full sizes. Then collect some background images of which the sizes are all bigger than any of your apple images. And now you can write a tool to randomly select an apple image and combine it to your randomly-selected background to generate 'new' apple images with backgrounds. Since you must know the size of each apple image, you can definitely calculate the size of the bounding box and its position and then generate its tag file.
I am working on a project, which will intake multiple images (lets say 2 for the moment) and combine them to generate a better image. The resultant image will be a combination of those input images. As a requirement I want to achieve this by using OpenCV. I read about Image Stitching and saw some example images in the process and now I am confused whether image overlapping is equal to image stitching, or can the Stitcher class in OpenCV do Image overlapping? A little clarity as to how can I achieve the above project problem thru OpenCV.
"Image overlapping" is not really a term used in the CV literature. The general concept of matching images via transformations is most often called image registration. Image registration is taking many images and inserting them all into one shared coordinate system. Image stitching relies on that same function, but additionally concerns itself with how to blend multiple images. Furthermore, image stitching tries to take into account multiple images at once and makes small adjustments to the paired image registrations.
But it seems you're interested in producing higher quality images from multiple images of the same space (or from video feed of the space for example). The term for that is not image overlapping but super-resolution; specifically, super-resolution from multiple images. You'll want to look into specialized filters (after warping to the same coordinates) to combine those multiple views into a high resolution image. There are many papers on this topic (e.g.). Even mean or median filters (that is, taking the mean or median at every pixel location across the images) can work well, assuming your transformations are very good.
Given an scanned image containing graphs and text , how can I extract only images from that picture . Can you mention any image processing algorithms .
You could do connected component analysis, filtering out everything that does not look like character bounding boxes. An example paper is Robust Text Detection from Binarized Document Images
(https://www.researchgate.net/publication/220929376_Robust_Text_Detection_from_Binarized_Document_Images), but there are a lot of approaches. It depends on your exact needs if you get away with something simple.
There is a lot more complex stuff available, too. One example: Fast and robust text detection in images and video frames (http://ucassdl.cn/publication/ivc.pdf).
I'm trying to separate some leaves(objects in image) from an image like below
Input Image
the output should look like this picture below
Output Image
I use OpenCvsharp in Visual studio 2010 (C# language)
I want to separate 4-5 leaves which are visible more than 80% of the complete leaf in the image, and get output image as the second image.
Please someone tell me how can achieve this?
I used watershed algorithm which is described here
but I couldn't get any segments rather than one segment covering the whole image. I want to know if there's any better approaches(algorithms) to do this?
I'm doing this kind of thing because after I separated that 4-5 leaves, that image is again processed for brown spots on those leaves and shape of the edge to identify some diseases again using image processing. Please someone who has knowledge in this area help me..