I am working on an image with a few objects, and it is crucial to know which measurement comes from which object. Is there any way to do it here with Python? You did not cover this in the video.
Thank you very much for the tutorials. I have used software previously to do analysis, but now trying to do it via Python. In my data, I have some surface-connected features that I do not want to remove. But, the issue is, that as these features are surface-connected, they are getting the same class as the background. I have tried closing operation, diameter closing operation but it is not working. What should I do to solve this?
Dr. Sreenivas, your video came just at the right time to my project progress. Thanks a lot for your contribution. I have one question: What if I want to take to objects that are close together as one singles object? The problem right now is that the output dictionary is giving me to many objects. I would like to consider as one the objects which are closed together.
i have an input image and i get this as a area Label: 1 Area: 8120 Label: 2 Area: 6614 Label: 3 Area: 366 Label: 4 Area: 3 Label: 5 Area: 21 Label: 6 Area: 1 Label: 7 Area: 5 Label: 8 Area: 5 I want know how can i label these area to an image to see which area is label 1, label 2 etc...
You can extract Bounding box dimensions along with many other measurements. The bounding boxes can be used to crop your original image and display the exact object of interest.
You need advanced segmentation techniques such as deep learning based instance segmentation to properly separate overlapped objects. Or you can simply use watershed to separate the overlapped objects but it literally draws a line between connected pixels.
I do not have experience with coordiantes, I believe it is depreciated in the latest version of scikit-image. In any case, the only explanation I can think of would be that you have an RGB image with 3 channels and it is reporting pairs of x, y coordinates corresponding to each channel, hence the reason why they may be exactly the same.
Great tutorial! Very clear and helpful ! Thanks for sharing and making it accessible !
where is this code
I am working on an image with a few objects, and it is crucial to know which measurement comes from which object. Is there any way to do it here with Python? You did not cover this in the video.
Thank you very much for the tutorials. I have used software previously to do analysis, but now trying to do it via Python. In my data, I have some surface-connected features that I do not want to remove. But, the issue is, that as these features are surface-connected, they are getting the same class as the background. I have tried closing operation, diameter closing operation but it is not working. What should I do to solve this?
hi sir, i have doubt, in csv file area column represents the area of real world image??
Dr. Sreenivas, your video came just at the right time to my project progress. Thanks a lot for your contribution. I have one question: What if I want to take to objects that are close together as one singles object? The problem right now is that the output dictionary is giving me to many objects. I would like to consider as one the objects which are closed together.
You can perform some image processing operations like dilate and erode to connect disconnected objects.
Your tutorials are amazing and very useful! Many thanks :))
Glad you think so!
great tutorial, thank you.
Is it possible to measure 3D objects?
Yes. Regionprops can handle 3D arrays and will give you volume, centroids, etc.
thank you. I am working on Multiplexed imaging data and this is very informative.
i have an input image and i get this as a area
Label: 1 Area: 8120
Label: 2 Area: 6614
Label: 3 Area: 366
Label: 4 Area: 3
Label: 5 Area: 21
Label: 6 Area: 1
Label: 7 Area: 5
Label: 8 Area: 5
I want know how can i label these area to an image to see which area is label 1, label 2 etc...
You can extract Bounding box dimensions along with many other measurements. The bounding boxes can be used to crop your original image and display the exact object of interest.
Thanks for this informative videos, but I want to know how can we measure the overlapped objects in an image ?
You need advanced segmentation techniques such as deep learning based instance segmentation to properly separate overlapped objects. Or you can simply use watershed to separate the overlapped objects but it literally draws a line between connected pixels.
One of my properties is 'coords'. I received ndarray as my result. May I ask why there are 6 coordinate values?
3 of the coords are almost similar and another 3 also almost similar to each other.
I do not have experience with coordiantes, I believe it is depreciated in the latest version of scikit-image. In any case, the only explanation I can think of would be that you have an RGB image with 3 channels and it is reporting pairs of x, y coordinates corresponding to each channel, hence the reason why they may be exactly the same.