Thanks for the instructional video and all worked as explained. The image output is nice but now I would like to process the segments highlighted and do additional processing of these segments based on other details and positional relationships relative to other segments. def everything_prompt(self): if self.results == None: return [] return self.results[0].masks.data With ann is the everything_prompt and the below tensor array. Should be 4 but has 6 4 segments are highlighted in output but 6 tensors show in the array. The segment array data does not seem to correlate to the original image pixel coordinates from the data I see, but obviously the plot handled the conversion in the output image. tensor([[7.8642e+02, 7.7507e+01, 9.9292e+02, 4.7635e+02, 9.1396e-01, 0.0000e+00], [4.0099e+02, 7.8259e+01, 6.0955e+02, 4.7675e+02, 9.0963e-01, 0.0000e+00], [0.0000e+00, 7.8047e+01, 2.2261e+02, 4.7691e+02, 8.4697e-01, 0.0000e+00], [0.0000e+00, 0.0000e+00, 1.0800e+03, 5.4800e+02, 7.3205e-01, 0.0000e+00], [2.9916e+01, 8.8821e+01, 2.0837e+02, 4.6286e+02, 6.0771e-01, 0.0000e+00], [7.9572e+02, 8.4327e+01, 9.8209e+02, 4.7095e+02, 4.5648e-01, 0.0000e+00]], device='cuda:0')
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Thank you so much, I did It
Glad it helped
I have a question when I try this project it's work well thank you but I don't know how to get class name from annotations could you tell me this?
Thanks for the instructional video and all worked as explained. The image output is nice but now I would like to process the segments highlighted and do additional processing of these segments based on other details and positional relationships relative to other segments.
def everything_prompt(self):
if self.results == None:
return []
return self.results[0].masks.data
With ann is the everything_prompt and the below tensor array. Should be 4 but has 6
4 segments are highlighted in output but 6 tensors show in the array.
The segment array data does not seem to correlate to the original image pixel coordinates from the data I see, but obviously the plot handled the conversion in the output image.
tensor([[7.8642e+02, 7.7507e+01, 9.9292e+02, 4.7635e+02, 9.1396e-01, 0.0000e+00],
[4.0099e+02, 7.8259e+01, 6.0955e+02, 4.7675e+02, 9.0963e-01, 0.0000e+00],
[0.0000e+00, 7.8047e+01, 2.2261e+02, 4.7691e+02, 8.4697e-01, 0.0000e+00],
[0.0000e+00, 0.0000e+00, 1.0800e+03, 5.4800e+02, 7.3205e-01, 0.0000e+00],
[2.9916e+01, 8.8821e+01, 2.0837e+02, 4.6286e+02, 6.0771e-01, 0.0000e+00],
[7.9572e+02, 8.4327e+01, 9.8209e+02, 4.7095e+02, 4.5648e-01, 0.0000e+00]], device='cuda:0')
I wish SAM also classified detected masks (provided labels)
hi,I have a question, how can I obtain all the points that generate the segmented image
It will output the masks which is basically just every single pixel in that class. Or did I not understand your question correctly?
Is it not possible for us to get real time camera view instead of picture?
Yeah ill do that in the next video
@@NicolaiAI I am waiting...
@@NicolaiAI excited to see that!
Me too! Maybe both with points and with masks, please
Already up and running live! Will record a video over the weekend and upload start of next week
Hello, first of all, thank you for the video. I'm wondering about something, is it possible to train on our own dataset with fast-sam?
Hi @nicolaiAI love your videos, is there anyway you can share the py code for this video?
Awesome man, thanks! The code is on my GitHub under fastSam live
@@NicolaiAI fantastic, thank you!