How can I spatially average the colors of every single pixel in a photo? Im trying to spatially average the pixels themselves. By the way, I am working in python.
I haven't really implemented code for this issue, as I am still researching the process for coding this. I was originally planning on spatially averaging the photo as a whole.
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I´m currently working on a project to measure the surface of plant leaves. Until now I´ve successfully implemented an RCNN model to segment individual leaves and also generated a depth map using stereo computer vision which allows me to calculate distances between any two points.
Now I´m stuck trying to connect everything together in order to calculate the area of a leaf/polygon.
**I got original RGB images, Binary masks containing leaves, and also the depth information of every pixel.
Can someone please point me in the right direction?**
I reckon the right way would be to use Delauney triangulation on the polygons in the binary masks and then calculate the surface using the distance between the 3 points of each triangle. I haven't been able to find something quite similar to my problem which is implemented in python.
Thanks so much for your help in advance. I´ll upload a picture of an RGB image with the masks plotted.
leaf instance segmentation
Count the pixels inside the outlines (by polygon filling) or use the shoelace formula.
I'm trying to work out how to measure an object in a photograph. I want to measure the actual, real-world size of it. Luckily, this object has a scale in cm. My thinking is that I measure the pixels in the scale, and use that to then determine the size of the other object/s in the photo. I've been working on this in scikit images with mixed results. One of the issues I get is that the resolution of the image changes the measurement. So, it seems that thresholding for the scale and extracting pixel counts does not work.
I know that OpenCV has the ability to measure objects with a bounding box, however the objects I'm trying to measure have uneven sides/edges, and this needs to be accounted for (the shape of the object is important too, and I can capture that using thresholding and contours).
I'm hoping that people on this board can point me in alternate/better directions for trying to solve this issue. Perhaps my approach is all wrong. Thank you.
Example photo of vase with 10 cm scale. (https://i.stack.imgur.com/dK2BZ.jpg)
I have a camera in a fixed position looking at a target and I want to detect whether someone walks in front of the target. The lighting in the scene can change so subtracting the new changed frame from the previous frame would therefore detect motion even though none has actually occurred. I have thought to compare the number of contours (obtained by using findContours() on a binary edge image obtained with canny and then getting size() of this) between the two frames as a big change here could denote movement while also being less sensitive to lighting changes, I am quite new to OpenCV and my implementations have not been successful so far. Is there a way I could make this work or will I have to just subtract the frames. I don't need to track the person, just detect whether they are in the scene.
I am a bit rusty but there are various ways to do this.
SIFT and SURF are very expensive operations, so I don't think you would want to use them.
There are a couple of 'background removal' methods.
Average removal: in this one you get the average of N frames, and consider it as BG. This is vulnerable to many things, light changes, shadow, moving object staying at a location for long time etc.
Gaussian Mixture Model: a bit more advanced than 1. Still vulnerable to a lot of things.
IncPCP (incremental principal component pursuit): I can't remember the algorithm totally but basic idea was they convert each frame to a sparse form, then extract the moving objects from sparse matrix.
Optical flow: you find the change across the temporal domain of a video. For example, you compare frame2 with frame1 block by block and tell the direction of change.
CNN based methods: I know there are a bunch of them, but I didn't really follow them. You might have to do some research. As far as I know, they often are better than the methods above.
Notice that, for a #30Fps, your code should complete in 33ms per frame, so it could be real time. You can find a lot of code available for this task.
There are a handful of ways you could do this.
The first that comes to mind is doing a 2D FFT on the incoming images. Color shouldn't affect the FFT too much, but an object moving, entering/exiting a frame will.
The second is to use SIFT or SURF to generate a list of features in an image, you can insert these points into a map, sorted however you like, then do a set_difference between the last image you took, and the current image that you have. You could also use the FLANN functionality to compare the generated features.
I was looking for ways to classify the different colours present in the bands of the resistor using openCV and python.
What algorithm can be used to segment the image into different bands etc. I tried using the watershed algorithm but i couldnt get the markers right and didnt get the desired results.
Sample Image I used:
The possible colors of resistor codes are known apriori.
You could simply scan across the resistor and check which of those colors are present in which order.
The final implementation would of course depend on many things.
If you just have a random snapshot of a resistor it will be more difficult than having the same orientation, position, perspective and scale every time.
Finding the resistor and its main axis should be rather simple, then all you need is a scan line.
Another option: Transform the image to HUE, then use the resistors body colour and the background colour for two threshold operations, which should leave you with the colour bands.
I'm newbie in computer vision. My goal is to distinguish individual cells on a set of pictures like this: Example
Basically, I blur whole image, find region maximum on it and use it like seed in watershed algorithm on distance tranfsform of threesholded blurred image. In fact I'm following tutorial which you can find here:
github/luispedro/python-image-tutorial
(sorry, can't post more than 2 links).
My problem is that some cells in my set have very distinguishable dark nucleus (which you can see on the example) and my algorithm produce results like this which are cleary wrong.
Of course it's possible to fix it by increasing strength of gaussian blur but it will merge some other cells toghether which is even worse.
What can be done to solve this problem? What are other possibilites if watershed just isn't situable for this case (keeping in mind that my set is pretty small and learning seems impossible)?
The watershed tends to over-segment if you don't use a watershed with markers.
Usually, we start with DNA/DAPI segmentation that is easy, and it provides the number of cells and the inner markers for the watershed.
If you blur the images, you smooth all the patterns. You should use an alternate sequential filter (opening / closing) in order to simplify each zone, and then try an ultimate eroded in order to find the number of inner seed for your watershed.