SuperGlue

<img src="assets/magicleap.png" width="240"> ### Research @ Magic Leap (CVPR 2020, Oral) # SuperGlue Inference and Evaluation Demo Script ## Introduction SuperGlue is a CVPR 2020 research project done at Magic Leap. The SuperGlue network is a Graph Neural Network combined with an Optimal Matching layer that is trained to perform matching on two sets of sparse image features. This repo includes PyTorch code and pretrained weights for running the SuperGlue matching network on top of [SuperPoint](https://arxiv.org/abs/1712.07629) keypoints and descriptors. Given a pair of images, you can use this repo to extract matching features across the image pair. <p align="center"> <img src="assets/teaser.png" width="500"> </p> SuperGlue operates as a "middle-end," performing context aggregation, matching, and filtering in a single end-to-end architecture. For more details, please see: * Full paper PDF: [SuperGlue: Learning Feature Matching with Graph Neural Networks](https://arxiv.org/abs/1911.11763). * Authors: *Paul-Edouard Sarlin, Daniel DeTone, Tomasz Malisiewicz, Andrew Rabinovich* * Website: [psarlin.com/superglue](https://psarlin.com/superglue) for videos, slides, recent updates, and more visualizations. * `hloc`: a new toolbox for visual localization and SfM with SuperGlue, available at [cvg/Hierarchical-Localization](https://github.com/cvg/Hierarchical-Localization/). Winner of 3 CVPR 2020 competitions on localization and image matching! We provide two pre-trained weights files: an indoor model trained on ScanNet data, and an outdoor model trained on MegaDepth data. Both models are inside the [weights directory](./models/weights). By default, the demo will run the **indoor** model. ## Dependencies * Python 3 >= 3.5 * PyTorch >= 1.1 * OpenCV >= 3.4 (4.1.2.30 recommended for best GUI keyboard interaction, see this [note](#additional-notes)) * Matplotlib >= 3.1 * NumPy >= 1.18 Simply run the following command: `pip3 install numpy opencv-python torch matplotlib` ## Contents There are two main top-level scripts in this repo: 1. `demo_superglue.py` : runs a live demo on a webcam, IP camera, image directory or movie file 2. `match_pairs.py`: reads image pairs from files and dumps matches to disk (also runs evaluation if ground truth relative poses are provided) ## Live Matching Demo Script (`demo_superglue.py`) This demo runs SuperPoint + SuperGlue feature matching on an anchor image and live image. You can update the anchor image by pressing the `n` key. The demo can read image streams from a USB or IP camera, a directory containing images, or a video file. You can pass all of these inputs using the `--input` flag. ### Run the demo on a live webcam Run the demo on the default USB webcam (ID #0), running on a CUDA GPU if one is found: ```sh ./demo_superglue.py ``` Keyboard control: * `n`: select the current frame as the anchor * `e`/`r`: increase/decrease the keypoint confidence threshold * `d`/`f`: increase/decrease the match filtering threshold * `k`: toggle the visualization of keypoints * `q`: quit Run the demo on 320x240 images running on the CPU: ```sh ./demo_superglue.py --resize 320 240 --force_cpu ``` The `--resize` flag can be used to resize the input image in three ways: 1. `--resize` `width` `height` : will resize to exact `width` x `height` dimensions 2. `--resize` `max_dimension` : will resize largest input image dimension to `max_dimension` 3. `--resize` `-1` : will not resize (i.e. use original image dimensions) The default will resize images to `640x480`. ### Run the demo on a directory of images The `--input` flag also accepts a path to a directory. We provide a directory of sample images from a sequence. To run the demo on the directory of images in `freiburg_sequence/` on a headless server (will not display to the screen) and write the output visualization images to `dump_demo_sequence/`: ```sh ./demo_superglue.py --input assets/freiburg_sequence/ --output_dir dump_demo_sequence --resize 320 240 --no_display ``` You should see this output on the sample Freiburg-TUM RGBD sequence: <img src="assets/freiburg_matches.gif" width="560"> The matches are colored by their predicted confidence in a jet colormap (Red: more confident, Blue: less confident). ### Additional useful command line parameters * Use `--image_glob` to change the image file extension (default: `*.png`, `*.jpg`, `*.jpeg`). * Use `--skip` to skip intermediate frames (default: `1`). * Use `--max_length` to cap the total number of frames processed (default: `1000000`). * Use `--show_keypoints` to visualize the detected keypoints (default: `False`). ## Run Matching+Evaluation (`match_pairs.py`) This repo also contains a script `match_pairs.py` that runs the matching from a list of image pairs. With this script, you can: * Run the matcher on a set of image pairs (no ground truth needed) * Visualize the keypoints and matches, based on their confidence * Evaluate and visualize the match correctness, if the ground truth relative poses and intrinsics are provided * Save the keypoints, matches, and evaluation results for further processing * Collate evaluation results over many pairs and generate result tables ### Matches only mode The simplest usage of this script will process the image pairs listed in a given text file and dump the keypoints and matches to compressed numpy `npz` files. We provide the challenging ScanNet pairs from the main paper in `assets/example_indoor_pairs/`. Running the following will run SuperPoint + SuperGlue on each image pair, and dump the results to `dump_match_pairs/`: ```sh ./match_pairs.py ``` The resulting `.npz` files can be read from Python as follows: ```python >>> import numpy as np >>> path = 'dump_match_pairs/scene0711_00_frame-001680_scene0711_00_frame-001995_matches.npz' >>> npz = np.load(path) >>> npz.files ['keypoints0', 'keypoints1', 'matches', 'match_confidence'] >>> npz['keypoints0'].shape (382, 2) >>> npz['keypoints1'].shape (391, 2) >>> npz['matches'].shape (382,) >>> np.sum(npz['matches']>-1) 115 >>> npz['match_confidence'].shape (382,) ``` For each keypoint in `keypoints0`, the `matches` array indicates the index of the matching keypoint in `keypoints1`, or `-1` if the keypoint is unmatched. ### Visualization mode You can add the flag `--viz` to dump image outputs which visualize the matches: ```sh ./match_pairs.py --viz ``` You should see images like this inside of `dump_match_pairs/` (or something very close to it, see this [note](#a-note-on-reproducibility)): <img src="assets/indoor_matches.png" width="560"> The matches are colored by their predicted confidence in a jet colormap (Red: more confident, Blue: less confident). ### Evaluation mode You can also estimate the pose using RANSAC + Essential Matrix decomposition and evaluate it if the ground truth relative poses and intrinsics are provided in the input `.txt` files. Each `.txt` file contains three key ground truth matrices: a 3x3 intrinsics matrix of image0: `K0`, a 3x3 intrinsics matrix of image1: `K1` , and a 4x4 matrix of the relative pose extrinsics `T_0to1`. To run the evaluation on the sample set of images (by default reading `assets/scannet_sample_pairs_with_gt.txt`), you can run: ```sh ./match_pairs.py --eval ``` Since you enabled `--eval`, you should see collated results printed to the terminal. For the example images provided, you should get the following numbers (or something very close to it, see this [note](#a-note-on-reproducibility)): ```txt Evaluation Results (mean over 15 pairs): AUC@5 AUC@10 AUC@20 Prec MScore 26.99 48.40 64.47 73.52 19.60 ``` The resulting `.npz` files in `dump_match_pairs/` will now contain scalar values related to the evaluation, computed on the sample images provided. Here is what you should find in one of the generated evaluation files: ```python >>> import numpy as np >>> path = 'dump_match_pairs/scene0711_00_frame-001680_scene0711_00_frame-001995_evaluation.npz' >>>