harsh2912
commented
3 years ago You need to look inside the detectron library and grab the input before it is applying the threshold. I did that a year back, so I don't exactly remember where you will find that. But there certainly is a way.
On Tue, 13 Jul, 2021, 7:33 am bokelai1989, @.***> wrote:
Hello Harsh,
First of all, I want to say thank you very much for sharing your work about this background removal. One of my current projects is quite related to it. I have read both your articles and your code repo and they are quite helpful to me! Thanks a lot!
I am using Detectron2 to do instance segmentation (human face + hair) on images and achieved 91% precision. My next step is to do background removal on those segmentations. I understand from your article https://heartbeat.fritz.ai/changing-backgrounds-with-image-segmentation-deep-learning-code-implementation-d848648ac477 that a trimap is needed, and you mentioned, " However, with a few tweaks, we can get the probability values from the Detectron model also.", I am wondering whether you can share those codes as well? I tried to track down the raw code of detectron2 and found it hard to get it out. Thanks a lot!!
I did look at your detectron_seg.py script, but I see the outputs (using out.pred_mask as below code shows) look like still just binary instead of pixel-level probability for the segmentation.
` def get_seg_output(self,image:np.array): out = self.model(image)['instances']
outputs = [(out.pred_masks[i],out.pred_classes[i]) for i in range(len(out.pred_classes)) if out.pred_classes[i]==0] return outputs`— You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub https://github.com/harsh2912/Background-removal/issues/3, or unsubscribe https://github.com/notifications/unsubscribe-auth/AENHXF2XQT7KQS4EABRYH7LTXONG7ANCNFSM5AH7NODQ .
bokelai1989
Hello Harsh,
First of all, I want to say thank you very much for sharing your work about this background removal! One of my current projects is quite related to it. I have read both your articles and your code repo and they are quite helpful to me! Thanks a lot! :)
I am using Detectron2 to do instance segmentation (human face + hair) on images and achieved 91% precision. My next step is to do background removal on those segmentations. I understand from your article that a trimap is needed, and you mentioned, " However, with a few tweaks, we can get the probability values from the Detectron model also.", I am wondering whether you can share those codes as well? I tried to track down the raw code of detectron2 and found it hard to get it out. Thanks a lot!!
I did look at your detectron_seg.py script, but I see the outputs (using out.pred_mask as below code shows) look like still just binary instead of pixel-level probability for the segmentation.
` def get_seg_output(self,image:np.array): out = self.model(image)['instances']