I have images such as the one below from which I need to count the prominent white spots. Unfortunately my object counting algorithm is becoming confused due to those "fuzzy" white areas. It can sometimes see hundreds of objects there.
So what I'm wondering is whether there's some way to perhaps exaggerate the white spots and suppress the "fuzzy" areas either using filters in GIMP or Python libraries.
Thank you!
Increase the contrast in GIMP.
You probably want an adaptive threshold.
The modules that I know have this in Python are scikit-image and OpenCV.
I ended up using G'MIC's Bilateral Filtering, it was the perfect tool for the job.
Related
I have a set of images that look like this:
Using python need a way to find a contour around the yellow shape that ignores the isolated points and is not too complex. Something looking a bit like this :
I tried some methods such as the find_contours function from skimage,which gives this after keeping only the biggest contour:
which is not what I am looking for. A also tried active contour (snake) which had the problem of paying too much attention to isolated pixels. Is there a particular method that would help me in this situation ?
Thank you
Assuming the yellow blob is slightly different across your images, I recommend you look into either using Morphological Operations, or using Contour Approximation.
I've never used scikit-image, but it appears to have Morphological functionalities included.
You can take a look at this OpenCV tutorial for a quick guideline of the different operations.
But I think all you need is to use the "Opening" operation to preprocess your yellow shape; making it smoother and removing the random speckles.
Another approach is by approximating that contour you've extracted to make it smoother. For scikit-image, that is the measure.approximate_polygon function. Also another OpenCV tutorial for reference on how Contour Approximation works (the same algorithm as with scikit-image).
I am new to python and opencv. I am analysing images of clouds, and I need to remove the buildings, so that the subsequent analysis will have less noise. I tried using Canny edge detection and then fill in the contours, but did not get too far. I also tried thresholding by pixel colours, but cannot reliably exclude just the buildings and not other parts of the image containing the clouds.
Is there a way I can efficiently and accurately remove the buildings and keep all of the clouds/sky? Thanks for the tips in advance.
You could use a computer vision model that finds the buildings. There may be some open source ones out there. The only one I can think of at the moment is this semantic segmentation model. There should be details on how to implement it, but there could definitely be others out there.
https://github.com/CSAILVision/semantic-segmentation-pytorch
I think one of the classes is buildings and you could theoretically run the model and get the dimensions of the building and take it out.
I need your advice, guys! So I am trying to create a mask for a football (i.e. soccer) pitch, and if I only use filtering by the most common hue/saturation/value values, I get the following image
As you can see, the desired part is inside the grey boundary I drawn and there is a lot of noise here - trees, lines, external objects, and of course I would like to get rid of it. The desired outcome is something similar to this:
I thought about an algorithm that would transform the first image into another by analyzing each pixel's surrounding and color it white if more than threshold% of pixels into a (x, x) square is white, otherwise black.
Do you know if there is an implementation on openCV or similar libraries for this or I should build it from scratch?
Also, maybe you can propose other way to deal with the noise and external objects? I already tried the morphological transform and blurring techniques, but either I don't do it right or it doesn't work well for my problem.
Thank you in advance for your advice!
I actually found an easy implementation of the algo I proposed - I simply use cv2.blur on the image and then filter with cv2.inRange, so it does exactly what I wanted it to do.
fig:Shoe in the red circle is to be detected
I am trying to create a python script using cv2 that can recognize the shoe of the baller and determine whether the shoe is beyond, on or before the white line(refer to the image).
I have no idea about any kind of approach to use, what kind of algorithms might be helpful. Need some guideline, please help!
(Image is attached)
I realize this would work better as a comment because it isn't a full answer, but I don't have enough rep yet to leave comments, haha.
You may be interested in OpenCV's Canny Edge detection algorithm:
http://docs.opencv.org/trunk/da/d22/tutorial_py_canny.html
This will allow you to find shapes within your image.
Also, you can find similarly colored blobs using SimpleBlobDetector:
https://www.learnopencv.com/blob-detection-using-opencv-python-c/
This should make it fairly easy to detect the white line.
In order to detect a more complex object like the shoe, you'll probably have to make something like a object detection cascade file and use a CascadeClassifier to find it:
http://docs.opencv.org/2.4/doc/tutorials/objdetect/cascade_classifier/cascade_classifier.html#cascade-classifier
http://johnallen.github.io/opencv-object-detection-tutorial/
Basically, you take a bunch of pictures to "teach" what the object looks like, and output that info to a file that a CascadeClassifier can use to detect objects in input images. It may be hard to distinguish between different brands of shoe though, if you need it to be that specific. Also, you may need to adjust the input images (saturation, brightness, etc) before trying to detect objects in order to get good results.
How to remove the shadows of the seeds? Also I would like to know if there is a way to change the color of all the seeds to red colour?
It seems rather easy to detect the seeds since your background is homogeneous. You can start by some simple image processing (contrast enhancement, thresholding, contour detection) to detect the seeds and then you can plot red blobs (with the same area as the detected regions) on the original image. As for the shadows, you can check this question (How to remove the shadow in image by using openCV?).
I think you can solve with this paper and it will make you interesting.
The algorithm described there works quite well and this will be a good example for you in using opencv.
And you can find the source code here
Regards.