import torch import cv2 import numpy as np from copy import deepcopy from src.loftr import LoFTR, full_default_cfg, reparameter # Initialize the matcher with default settings _default_cfg = deepcopy(full_default_cfg) matcher = LoFTR(config=_default_cfg) # Load pretrained weights matcher.load_state_dict(torch.load("weights/eloftr_outdoor.ckpt")['state_dict']) matcher = reparameter(matcher) # Essential for good performance matcher = matcher.eval().cuda() # Load and preprocess images img0_raw = cv2.imread("path/to/image0.jpg", cv2.IMREAD_GRAYSCALE) img1_raw = cv2.imread("path/to/image1.jpg", cv2.IMREAD_GRAYSCALE) # Resize images to be divisible by 32 img0_raw = cv2.resize(img0_raw, (img0_raw.shape[1]//32*32, img0_raw.shape[0]//32*32)) img1_raw = cv2.resize(img1_raw, (img1_raw.shape[1]//32*32, img1_raw.shape[0]//32*32)) # Convert to tensors img0 = torch.from_numpy(img0_raw)[None][None].cuda() / 255. img1 = torch.from_numpy(img1_raw)[None][None].cuda() / 255. batch = {'image0': img0, 'image1': img1} # Inference with torch.no_grad(): matcher(batch) mkpts0 = batch['mkpts0_f'].cpu().numpy() # Matched keypoints in image0 mkpts1 = batch['mkpts1_f'].cpu().numpy() # Matched keypoints in image1 mconf = batch['mconf'].cpu().numpy() # Matching confidence scores

import torch import cv2 import numpy as np import matplotlib.cm as cm from copy import deepcopy from src.loftr import LoFTR, full_default_cfg, opt_default_cfg, reparameter from src.utils.plotting import make_matching_figure # Model configuration options model_type = 'full' # Choose: 'full' for best quality, 'opt' for best efficiency precision = 'fp32' # Choose: 'fp32', 'mp' (mixed precision), 'fp16' for best efficiency # Load appropriate config if model_type == 'full': _default_cfg = deepcopy(full_default_cfg) elif model_type == 'opt': _default_cfg = deepcopy(opt_default_cfg) # Set precision options if precision == 'mp': _default_cfg['mp'] = True elif precision == 'fp16': _default_cfg['half'] = True # Initialize matcher matcher = LoFTR(config=_default_cfg) matcher.load_state_dict(torch.load("weights/eloftr_outdoor.ckpt")['state_dict']) matcher = reparameter(matcher) # Apply precision settings if precision == 'fp16': matcher = matcher.half() matcher = matcher.eval().cuda() # Load and preprocess images img0_raw = cv2.imread("path/to/image0.jpg", cv2.IMREAD_GRAYSCALE) img1_raw = cv2.imread("path/to/image1.jpg", cv2.IMREAD_GRAYSCALE) img0_raw = cv2.resize(img0_raw, (img0_raw.shape[1]//32*32, img0_raw.shape[0]//32*32)) img1_raw = cv2.resize(img1_raw, (img1_raw.shape[1]//32*32, img1_raw.shape[0]//32*32)) # Convert to tensors with appropriate precision if precision == 'fp16': img0 = torch.from_numpy(img0_raw)[None][None].half().cuda() / 255. img1 = torch.from_numpy(img1_raw)[None][None].half().cuda() / 255. else: img0 = torch.from_numpy(img0_raw)[None][None].cuda() / 255. img1 = torch.from_numpy(img1_raw)[None][None].cuda() / 255. batch = {'image0': img0, 'image1': img1} # Inference with different precision modes with torch.no_grad(): if precision == 'mp': with torch.autocast(enabled=True, device_type='cuda'): matcher(batch) else: matcher(batch) mkpts0 = batch['mkpts0_f'].cpu().numpy() mkpts1 = batch['mkpts1_f'].cpu().numpy() mconf = batch['mconf'].cpu().numpy() # Post-process confidence scores for 'opt' model if model_type == 'opt': mconf = (mconf - min(20.0, mconf.min())) / (max(30.0, mconf.max()) - min(20.0, mconf.min())) # Visualize matches color = cm.jet(mconf) text = ['EfficientLoFTR', 'Matches: {}'.format(len(mkpts0))] fig = make_matching_figure(img0_raw, img1_raw, mkpts0, mkpts1, color, text=text)

from transformers import AutoImageProcessor, AutoModel import torch from PIL import Image import requests # Load example images (same as in the original paper) url_image1 = "https://raw.githubusercontent.com/magicleap/SuperGluePretrainedNetwork/refs/heads/master/assets/phototourism_sample_images/united_states_capitol_98169888_3347710852.jpg" image1 = Image.open(requests.get(url_image1, stream=True).raw) url_image2 = "https://raw.githubusercontent.com/magicleap/SuperGluePretrainedNetwork/refs/heads/master/assets/phototourism_sample_images/united_states_capitol_26757027_6717084061.jpg" image2 = Image.open(requests.get(url_image2, stream=True).raw) images = [image1, image2] # Load processor and model processor = AutoImageProcessor.from_pretrained("zju-community/efficientloftr") model = AutoModel.from_pretrained("zju-community/efficientloftr") # Process images and run inference inputs = processor(images, return_tensors="pt") with torch.no_grad(): outputs = model(**inputs) # keypoints = outputs.keypoints # Keypoints in both images # matches = outputs.matches # Matching indices  # matching_scores = outputs.matching_scores # Confidence scores

# Post-process to get keypoints and matches in a readable format image_sizes = [[(image.height, image.width) for image in images]] outputs = processor.post_process_keypoint_matching(outputs, image_sizes, threshold=0.2) # Print matching results for i, output in enumerate(outputs): print(f"Image pair {i}:") print(f"Found {len(output['keypoints0'])} matches") for keypoint0, keypoint1, matching_score in zip( output["keypoints0"], output["keypoints1"], output["matching_scores"] ): print( f"Keypoint {keypoint0.numpy()} ↔ {keypoint1.numpy()} (score: {matching_score:.3f})" ) # Visualize matches processor.visualize_keypoint_matching(images, outputs)