The code performs poorly

[lyfadvance]

I found that without lightglue, the effect is very poor.

Besides, when I printed the head map, the effect was also very poor.

imshow code is

@torch.inference_mode()
	def detectAndCompute(self, x, top_k = None, detection_threshold = None):
		"""
			Compute sparse keypoints & descriptors. Supports batched mode.

			input:
				x -> torch.Tensor(B, C, H, W): grayscale or rgb image
				top_k -> int: keep best k features
			return:
				List[Dict]: 
					'keypoints'    ->   torch.Tensor(N, 2): keypoints (x,y)
					'scores'       ->   torch.Tensor(N,): keypoint scores
					'descriptors'  ->   torch.Tensor(N, 64): local features
		"""
		if top_k is None: top_k = self.top_k
		if detection_threshold is None: detection_threshold = self.detection_threshold
		x, rh1, rw1 = self.preprocess_tensor(x)

		B, _, _H1, _W1 = x.shape
        
		M1, K1, H1 = self.net(x)
		mod = torch.jit.trace(self.net, x)
		torch.jit.save(mod,"feature.pt")
		M1 = F.normalize(M1, dim=1)

		#Convert logits to heatmap and extract kpts
		K1h = self.get_kpts_heatmap(K1)

		array1=K1h.numpy()#将tensor数据转为numpy数据
		maxValue=array1.max()
		array1=array1*255#normalize，将图像数据扩展到[0,255]
		mat=np.uint8(array1)#float32-->uint8
		print('mat_shape:',mat.shape)#mat_shape: (3, 982, 814)
		mat=mat[0, : , :, :].transpose(1,2,0)#mat_shape: (982, 814，3)
		cv2.imshow("img",mat)
		
		array2=H1.numpy()#将tensor数据转为numpy数据
		maxValue=array2.max()
		print(maxValue)
		#array2=array2*255/maxValue#normalize，将图像数据扩展到[0,255]
		array2=array2 * 255#normalize，将图像数据扩展到[0,255]
		mat=np.uint8(array2)#float32-->uint8
		print('mat_shape:',mat.shape)#mat_shape: (3, 982, 814)
		mat=mat[0, : , :, :].transpose(1,2,0)#mat_shape: (982, 814，3)
		cv2.imshow("img2",mat)
		cv2.waitKey()

		mkpts = self.NMS(K1h, threshold=detection_threshold, kernel_size=5)

		#Compute reliability scores
		_nearest = InterpolateSparse2d('nearest')
		_bilinear = InterpolateSparse2d('bilinear')
		scores = (_nearest(K1h, mkpts, _H1, _W1) * _bilinear(H1, mkpts, _H1, _W1)).squeeze(-1)
		scores[torch.all(mkpts == 0, dim=-1)] = -1

		#Select top-k features
		idxs = torch.argsort(-scores)
		mkpts_x  = torch.gather(mkpts[...,0], -1, idxs)[:, :top_k]
		mkpts_y  = torch.gather(mkpts[...,1], -1, idxs)[:, :top_k]
		mkpts = torch.cat([mkpts_x[...,None], mkpts_y[...,None]], dim=-1)
		scores = torch.gather(scores, -1, idxs)[:, :top_k]

		#Interpolate descriptors at kpts positions
		feats = self.interpolator(M1, mkpts, H = _H1, W = _W1)

		#L2-Normalize
		feats = F.normalize(feats, dim=-1)

		#Correct kpt scale
		mkpts = mkpts * torch.tensor([rw1,rh1], device=mkpts.device).view(1, 1, -1)

		valid = scores > 0
		return [  
				   {'keypoints': mkpts[b][valid[b]],
					'scores': scores[b][valid[b]],
					'descriptors': feats[b][valid[b]]} for b in range(B) 
			   `]`

Can you take a look at what's going on? thank you

[guipotje]

hi @lyfadvance, this result is weird, since we have an example without LightGlue that is working fine. Please check this notebook.