There is an image and a multiset of pixel coordinates. Each set corresponds to a polygon.
The problem at hand is to overlay all the polygons onto the image, adjust their shapes/sizes to cover specific areas in the image and then save the resulting view of the image.
Suggestions on how to get started on this would be highly appreciated!
I realize I was talking about image annotation. I found 'sloth'. I am giving it a try.
https://cvhci.anthropomatik.kit.edu/~baeuml/projects/a-universal-labeling-tool-for-computer-vision-sloth/
Related
I am working on a code in python and I came across a figure in a report that I would like to replicate.
Basically I would like to create a 'bounding' box onto the original image, and then subsequently crop and display the part of the image that has the bounding box on it. (basically to 'magnify' that section)
I've been googling but I can't seem to find the correct function to use so that I can achieve this. Currently, opencv is used to read my image, but if there is a function in matplotlib that does this, then you can suggest that too.
Thank you for your help!
I have an Image like the below:
and I want to make this image to heatmap by its contours.
like this image
Is there any solution in OpenCV or others for that?
(in fact, this Image is a slice of CT image that I extract contours of tumor and organ at risk from that and the raw image is like below:
raw image)
Thanks
based on solutions that others guided me and thanks a lot for their solutions, a sample for output now is something like that: heatmap based on distance but now another problem appears that sometimes it doesn't have segmentation for colorful contours accurately :(
One possible approach is to generate a distance field, where for each pixel within the closed contour, calculate its distance to the contour (several methods possible, please do your research), then color mapping the distance to a color. I guess this is what do you mean by a "heat map".
My goal is to transform an image captured by a camera and transform that image to orthographical image without effects of perspective.
I have a few objects of known size on a surface. I have a camera, placed above and directed to those objects, as exemplified in the scene. The camera is capturing images as in image captured by the camera. I want to get an orthographical image of the environment as in orthographical image I want to get.
I have read few posts, but did not really understand their relevance to my problem, as I am not expert on these transforms. The answer from this question made me think it is possible, although I did not get how.
I would appreciate a clear explanation or pointing a clear tutorial, using Python or Lua if possible.
Any help is appreciated.
This was not possible without distorting the image. A straightforward explanation is that the perspective causes some parts of the image to be not visible, for example the white line in the marked area is not visible, and there could be something small that we are not able to observe. For those parts, the algorithm is supposed to produce some kind of prediction based on heuristics.
I have a database of original images and for each original images there are various cropped versions.
This is an example of how the image look like:
Original
Horizontal Crop
Square Crop
This is a very simple example, but most images are like this, some might taken a smaller section of the original image than others.
I was looking at OpenCV in python but I'm very new to this kind of image processing.
The idea is to be able to save the cropping information separate from the image to save space and then generate all the cropping and different aspect ratio on the fly with a cache system instead.
The method you are looking for is called "template matching". You find examples here
https://docs.opencv.org/trunk/d4/dc6/tutorial_py_template_matching.html
For your problem, given the large images, it might be a good idea to constrain the search space by resizing both images by the same factor. So that searching a position that isn't as precise, but allows then to constrain the actual full pixel sized search to a smaller region around that point.
I have coordinate regions of an image. I am cropping out images from those coordinates. Now, I need complimentary regions of what that is cut out, with respect to the original image. How do I go about using Pillow?
If you crop a region you basically create a new, smaller image.
A complementary operation to that would be to fill the region with some value you consider invalid or zero as you will still have an image of the original size. Technically you cannot remove a region from an image. you can just change or ignore it.
PIL.ImageDraw.Draw.rectangle(xy, fill=None, outline=None)
is something I found quickly. Maybe there is something better. Just crawl through the reference.