Hello, i am working on a final year project on instance segmentation for plant disease and i found your video "Fine tuning Detectron2 for instance segmentation" very useful. I however need to calculate the area of an instance object or objects out of the entire image. Using your earlier video as an example, i wish to calculate the percentage of area occupied by the Alpha Granule on a single Cell image so that i can display that Alpha Granule occupy 5% of the Cell image. I will be glade if you can assist me on this or point me to further references that can help.
Hi @Cammpopp , if you were able to segment the region of interest and you got the segmentation map, you can calculate the area using regionprops from the sci-kit image library or calculate it by counting the pixels that belongs to that object. Once you now that, you can calculate the ratio by dividing the ROI area through the total area.
I would like to ask you for an advice, if I can. I have a couple of decade-old videos (talk shows), which are rather small and blurry. I learned about this thing called "upscaling" and decided to give it a try. Essentially, I took a 10 sec video fragment from video, extracted frames (i.e. images) and passed them through the upscaler (if memory serves, ESRGAN). The end result was not particularly satisfying, but for some reason I got interested. I understood that there is no "magic button" and I need to learn what I am doing, if I want better results. Then I found your channel, which probably has the best explanation on image analysis in Python. The difficulty is that I do not know where to start and what I need to learn. As I said, I am interested in image upscaling. I would like to understand this technology just enough, so I can start experimenting on my own. I desperately need a "roadmap" for my learning. What would be the best learning map for this? Your advice will be appreciated. Thank you for your tutorials, Sreeni!
Not sure how I can help here. I do not have access to any WSI or any experience with the cancer genome atlas. Hopefully someone else with the right experience can help.
Sir nenu mee videos regular ga follow avuthanu I am a professor actually but a student in deep learning and python chala proud ga vundi sir I shared your videos to all my students colleagues and relatives Ayurarogya prapithirastu sir meeku God bless you. Mee nunchi direct ga training thisuko vacha sir plz inform if you can
I wish I had time for direct training. నా ఆఫీస్ పని కొంచం ఎక్కువగానే ఉంటుంది. నా ఆఫీస్ పని కొంచం ఎక్కువగానే ఉంటుంది. Plus, on the weekends, ఫామిలీ టైం అండ్ ఫ్యూచర్ వీడియోస్ కి రీసెర్చ్ తో టైం అయిపోతుంది.
First, i would say that ome.tiff is not really a format that aims to be the standard. It's rather the ome.xml metadata that is important. Ome.tiff is just tiff files with ome.xml metadata. And that metadata model is a standard. Then I would have expected that you talk about biofirmats, which does a good job not only at reading many image formats, but also extracts the metadata and convert it to ome.xml
Hello, i am working on a final year project on instance segmentation for plant disease and i found your video "Fine tuning Detectron2 for instance segmentation" very useful.
I however need to calculate the area of an instance object or objects out of the entire image. Using your earlier video as an example, i wish to calculate the percentage of area occupied by the Alpha Granule on a single Cell image so that i can display that Alpha Granule occupy 5% of the Cell image. I will be glade if you can assist me on this or point me to further references that can help.
Hi @Cammpopp , if you were able to segment the region of interest and you got the segmentation map, you can calculate the area using regionprops from the sci-kit image library or calculate it by counting the pixels that belongs to that object. Once you now that, you can calculate the ratio by dividing the ROI area through the total area.
Thanks for everything you keep sharing so far.
I joined your channel while I had a project for Honey-pollen analysis and classifications.
I would like to ask you for an advice, if I can.
I have a couple of decade-old videos (talk shows), which are rather small and blurry. I learned about this thing called "upscaling" and decided to give it a try. Essentially, I took a 10 sec video fragment from video, extracted frames (i.e. images) and passed them through the upscaler (if memory serves, ESRGAN). The end result was not particularly satisfying, but for some reason I got interested. I understood that there is no "magic button" and I need to learn what I am doing, if I want better results.
Then I found your channel, which probably has the best explanation on image analysis in Python. The difficulty is that I do not know where to start and what I need to learn. As I said, I am interested in image upscaling. I would like to understand this technology just enough, so I can start experimenting on my own. I desperately need a "roadmap" for my learning.
What would be the best learning map for this? Your advice will be appreciated. Thank you for your tutorials, Sreeni!
Hello Sreeni, very much informative your videos are. I am struggling to find HER2 positive WSIs fro TCGA. Can you just give some info
Not sure how I can help here. I do not have access to any WSI or any experience with the cancer genome atlas. Hopefully someone else with the right experience can help.
@@DigitalSreeni Thanks
Sir nenu mee videos regular ga follow avuthanu I am a professor actually but a student in deep learning and python chala proud ga vundi sir I shared your videos to all my students colleagues and relatives Ayurarogya prapithirastu sir meeku God bless you. Mee nunchi direct ga training thisuko vacha sir plz inform if you can
I wish I had time for direct training. నా ఆఫీస్ పని కొంచం ఎక్కువగానే ఉంటుంది. నా ఆఫీస్ పని కొంచం ఎక్కువగానే ఉంటుంది. Plus, on the weekends, ఫామిలీ టైం అండ్ ఫ్యూచర్ వీడియోస్ కి రీసెర్చ్ తో టైం అయిపోతుంది.
@@DigitalSreeni Sir please provide all the datasets which are use in this python Tutorial
@@DigitalSreeni please sir 🥲🥲
First, i would say that ome.tiff is not really a format that aims to be the standard. It's rather the ome.xml metadata that is important. Ome.tiff is just tiff files with ome.xml metadata. And that metadata model is a standard.
Then I would have expected that you talk about biofirmats, which does a good job not only at reading many image formats, but also extracts the metadata and convert it to ome.xml