I am a beginner in python & image processing.I have the following image.
I would like to detect the changes between this picture and another one where the other one may be
taken from a slightly different angle or the object is translated
have different light conditions
and the changes may be
a change in color in part of the object
an extra or missing part
After various searches I thought about using ORB to detect the matching parts and remove them from the picture then use contour to extract and compare the difference.
But I cannot seem to find a way to remove the matching parts from the two images.
I am open for all suggestions / better way to approach the problem.
edit:
Sorry , forgot to mention that the color change could either be white or pink
Looking at your image, it appears there are three dominant colors. If this is always the case the first thing that comes to mind is to apply a color K mean algorithm with three clusters, like explained here.
The center color of each cluster would then give you information on the color of the tubes, and the size of each cluster (# of pixels belonging to that cluster) would give you if there are extra or missing parts.
Related
Forgive me but I'm new in OpenCV.
I would like to delete the common background in 3 images, where there is a landscape and a man.
I tried some subtraction codes but I can't solve the problem.
I would like output each image only with the man and without landscape
Are there in OpenCV Algorithms what do this do? (then without any manual operation so no markers or other)
I tried this python code CV - Extract differences between two images
but not works because in my case i don't have an image with only background (without man).
I thinks that good solution should to Compare all the images and save those "points" that are the same at least in an image.
In this way I can extrapolate a background (which we call "Result.jpg") and finally analyze each image and cut those portions that are also present in "Result.jpg".
You say it's a good idea? Do you have other simplest ideas?
Without semantic segmentation, you can't do that.
Because all you can compute is where two images differ, and this does not give you the silhouette of the person, but an overlapping of two silhouettes. You'll never know the exact outline.
I am dealing with some images which contain tables and there are 1 or 2 stickers on them. What I am trying to do is getting rid of those stickers. Using color thresholding (in HSV) and contour detection I am able to create a mask for those stickers. Now I want those stickers to "dissolve" out from there (I don't know the correct term for this). While keeping those tables lines intact, so that my line detection works well (which I have to do after this cleaning).
I tried OpenCV's inpaint. But this doesn't work well here, because the sticker size is big enough.
See this example:
Part of the whole image where the sticker is sticking (inside contents are censored by me). It can be over horizontal lines, or vertical lines, or both. Basically, it's sticking somewhere on the table (maybe over some text too, but that can't be recovered anyway). The background won't be necessary whitish, it can be pink/orange/other colors.
This is the thresholded image, creating a mask of the sticker. We can also get the contour of this if required.
This is the result of cv.inpaint() with radius 3.
What I want is to reconstruct those lines.
My solution
Now my approach is to interpolate the colors in between the sticker contour, to fill it up. For each pixel inside the contour, I will do a vertical interpolation and a horizontal interpolation (interpolation of the boundary colors) and then fill that pixel with the average of both. I am hoping that this will preserve my vertical and horizontal lines at least. (Might fail if it's on a corner of the table). This will also keep the background smooth, my background can have some different colors.
Now my problem is how I can implement this. What I have are contours that I find using OpenCV's get_contours(). I don't know how to get the colors on its boundary and how to interpolate the in-between colors.
Any help is appreciated. Thanks in advance.
Due to confidentiality, I cannot share the whole image.
EDIT
I tried the seam-carving method (implementation). Here are the results:
Vertical seaming
Horizontal seaming
It works well once I know which one to use. And I am not sure how well it will do when we have both horizontal and vertical lines.
PS. Don't suggest a solution which needs to find lines and then work. Because there will be many lines in my whole image.
You can make synthetic example images. To better explain your issue.
As I got it you can use Poisson image editing. Just take a piece of clear paper image and paste it using poisson blending and the mask you extracted.
Check this github repo as instance for examples with code.
I am quite new to OpenCV and DIP in general so I need bit of help in stitching of two images. The problem background is, there are two pieces which have their adhesives/glue torn apart from two joined pieces of plastic. This is the image of "glue" on the base:
and this is the image of "glue" on the other attached face:
As the background of the the images is not the same, I read that it's not possible to do stitching (because different features). And these two pieces are like jigsaw pieces which needs to rotated, so the problem is not straightforward like panaroma stitching.
How do I join such images together?
I was thinking of finding the white color countours and then keeping one image fixed, rotating the other one and finding area of merged countours, also storing the angle of what I rotate. The area would become smallest when there would be perfect match.
This is not a complete answer, (no-one said answers have to be complete), but it may inspire you or someone else to work out a method.
I flipped vertically and flopped horizontally one of your images and then put them both into Photoshop on two separate layers. I then set the "Blending Mode" to Difference which is always a great way to align images - because they normally go black when images are aligned and there is no difference.
I then moved one layer around on the other. I guess you will need to do something similar to solve your problem - you just need to find something that your code can maximise or minimise.
In the image there are 2 insulators, the one on the left has a gap i.e a disk missing in between. I have to detect the missing disk with a rectangular box. I know alogorithms SIFT and SURF or by using absdiff() in opencv for calculating difference between two images.
How can i can detect the missing disk if I only have this image.
Image
You should find contours,bounding boxes and circles.After that you can find missing object or noise objectt. Other way to use AI to fit objects and search for that. But this one is very hard job
General algorithm (it's obviously):
find insulators
find gaps
find insulators with gaps.
I think, insulators are kinda standardized by size and look. So, probably, you can detect them by color/texture and/or some specific details. They can't be very "curve", so you can estimate them with lines and separate overlapped elements. If all insulators have same size, than you can normalize them, stretch by one axe, and then detect gaps.
Their is no way to do 100% correct recognizing in all cases, but you can use some knowledge about insulators and get good results.
how can I identify the presence or absence of regular stripes of different colors, but ranging from very very very very light pink to black inside of a scanned image (bitmap 200x200dpi 24-bit).
Carry a few examples.
Example 1
Example 2 (the lines are in all the columns except 7 in the second row of the last column)
For now try to identify (using python language) whether or not there is at least 5-10 pixels for the presence of different color from white to each strip, however, does not always work because the scanned image is not of high quality and the strip changes color very similar to color that surrounds it.
Thanks.
This looks to me a connected component labeling in an image to identify discrete regions of certain color range. You can have a look to cvBlobLib. Some pre-processing would be required to merge the pixels if there are holes or small variations between neighbors.
Not going to happen. The human visual system is far better than any image processing system, and I don't see anything in the 2nd row of #3. #1 and #5 are also debatable.
You need to find some way to increase the optical quality of your input.
Search for segmentation algorithm ,with a low threshold.
It should give you good results as the edges are sharp.
Sobel would be a good start ;)