add match_test to visual the matches with own image

README.md

@@ -1,3 +1,26 @@
+# LightGlue_CIUS
+Test using `meta_count` image pairs
+```sh
+conda activate superpoint # activate the conda env
+python match_test.py # to save the matches plot
+python match_test_loop.py # to make statistics of the matching time
+```
+- The image matched is shown below, seems good:
+  <p align="center">
+    <a><img src="assets/match_test.png" alt="match_show" width=100%></a>
+    <br>
+    <em>The match result in meta count. </em>
+  </p>
+
+- Average time cost of 640*480 image with 540 keypoints is 110ms using device:  RTX 2070 SUPER and  i7-9700K CPU @ 3.60GHz × 8
+
+## TODO:
+Since there is no C++ version of LightGlue, we will wait official update the C++ version with TensorRT
+
+---
+
+
+
 <p align="center">
   <h1 align="center"><ins>LightGlue ⚡️</ins><br>Local Feature Matching at Light Speed</h1>
   <p align="center">

match_test.py

@@ -0,0 +1,92 @@
+from operator import truediv
+from lightglue import LightGlue, SuperPoint, DISK
+from lightglue.utils import load_image, rbd
+
+# wzy add >>>>
+import cv2
+from pathlib import Path
+import argparse
+import random
+import numpy as np
+import matplotlib.cm as cm
+import torch
+
+from utils import (compute_pose_error, compute_epipolar_error,
+                            estimate_pose, make_matching_plot,
+                            error_colormap, AverageTimer, pose_auc, read_image,
+                            rotate_intrinsics, rotate_pose_inplane,
+                            scale_intrinsics)
+
+torch.set_grad_enabled(False)
+# wzy add <<<<
+
+
+# SuperPoint+LightGlue
+extractor = SuperPoint(max_num_keypoints=2048).eval().cuda()  # load the extractor
+matcher = LightGlue(features='superpoint').eval().cuda()  # load the matcher
+
+# or DISK+LightGlue
+# extractor = DISK(max_num_keypoints=2048).eval().cuda()  # load the extractor
+# matcher = LightGlue(features='disk').eval().cuda()  # load the matcher
+
+# load each image as a torch.Tensor on GPU with shape (3,H,W), normalized in [0,1]
+image0 = load_image('/home/zph/projects/LightGlue/assets/10_1683599558_173581.jpg').cuda()
+image1 = load_image('/home/zph/projects/LightGlue/assets/10_1683599558_070450.jpg').cuda()
+
+timer = AverageTimer(newline=True)
+# extract local features
+feats0 = extractor.extract(image0)  # auto-resize the image, disable with resize=None
+feats1 = extractor.extract(image1)
+
+# match the features
+matches01 = matcher({'image0': feats0, 'image1': feats1})
+feats0, feats1, matches01 = [rbd(x) for x in [feats0, feats1, matches01]]  # remove batch dimension
+matches = matches01['matches']  # indices with shape (K,2)
+points0 = feats0['keypoints'][matches[..., 0]]  # coordinates in image #0, shape (K,2)
+points1 = feats1['keypoints'][matches[..., 1]]  # coordinates in image #1, shape (K,2)
+print('number of points0: ', len(points0))
+# print(points0)
+
+# convert CUDA to CPU
+image0 = image0.cpu().numpy().transpose((1, 2, 0))  # CxHxW to HxWxC
+image1 = image1.cpu().numpy().transpose((1, 2, 0))  # CxHxW to HxWxC
+# print(image0)
+# print(image0.shape)
+
+# cv2.imshow("image0",image0)
+# cv2.waitKey(0)
+# image0 = cv2.cvtColor(image0, cv2.COLOR_BGR2GRAY)
+# image1 = cv2.cvtColor(image1, cv2.COLOR_BGR2GRAY)
+
+matches = matches.cpu().numpy()
+points0 = points0.cpu().numpy()
+points1 = points1.cpu().numpy()
+# print(points0)
+
+do_viz = True
+fast_viz = False
+opencv_display = True
+show_keypoints = True
+viz_path = '/home/zph/projects/LightGlue/assets/match_test.png'
+mconf = np.ones(len(points0))
+
+
+if do_viz:
+    # Visualize the matches.
+    color = cm.jet(mconf)
+    text = [
+        'LightGlue',
+        'Matches: {}'.format(len(points0)),
+    ]
+
+    # Display extra parameter info.
+    small_text = [
+        'Image Pair: {}:{}'.format('kun0', 'kun1'),
+    ]
+
+    make_matching_plot(
+        image0, image1, points0, points1, points0, points1, color,
+        text, viz_path, show_keypoints,
+        fast_viz, opencv_display, 'Matches', small_text)
+
+    timer.update('viz_match')

match_test_loop.py

@@ -0,0 +1,87 @@
+from operator import truediv
+from time import time
+from lightglue import LightGlue, SuperPoint, DISK
+from lightglue.utils import load_image, rbd
+
+# wzy add >>>>
+import cv2
+from pathlib import Path
+import argparse
+import random
+import numpy as np
+import matplotlib.cm as cm
+import torch
+
+from utils import (compute_pose_error, compute_epipolar_error,
+                            estimate_pose, make_matching_plot,
+                            error_colormap, AverageTimer, pose_auc, read_image,
+                            rotate_intrinsics, rotate_pose_inplane,
+                            scale_intrinsics)
+
+torch.set_grad_enabled(False)
+# wzy add <<<<
+
+
+# SuperPoint+LightGlue
+extractor = SuperPoint(max_num_keypoints=2048).eval().cuda()  # load the extractor
+matcher = LightGlue(features='superpoint').eval().cuda()  # load the matcher
+
+# or DISK+LightGlue
+# extractor = DISK(max_num_keypoints=2048).eval().cuda()  # load the extractor
+# matcher = LightGlue(features='disk').eval().cuda()  # load the matcher
+
+# load each image as a torch.Tensor on GPU with shape (3,H,W), normalized in [0,1]
+image0 = load_image('/home/zph/projects/LightGlue/assets/10_1683599558_173581.jpg').cuda()
+image1 = load_image('/home/zph/projects/LightGlue/assets/10_1683599558_070450.jpg').cuda()
+
+timer = AverageTimer(newline=True)
+for i in range(1,20):
+    # extract local features
+    feats0 = extractor.extract(image0)  # auto-resize the image, disable with resize=None
+    feats1 = extractor.extract(image1)
+
+    # match the features
+    matches01 = matcher({'image0': feats0, 'image1': feats1})
+    feats0, feats1, matches01 = [rbd(x) for x in [feats0, feats1, matches01]]  # remove batch dimension
+    matches = matches01['matches']  # indices with shape (K,2)
+    points0 = feats0['keypoints'][matches[..., 0]]  # coordinates in image #0, shape (K,2)
+    points1 = feats1['keypoints'][matches[..., 1]]  # coordinates in image #1, shape (K,2)
+    print('number of points0: ', len(points0))
+
+    # convert CUDA to CPU
+    image0_cpu = image0.cpu().numpy().transpose((1, 2, 0))  # CxHxW to HxWxC
+    image1_cpu = image1.cpu().numpy().transpose((1, 2, 0))  # CxHxW to HxWxC
+    matches = matches.cpu().numpy()
+    points0 = points0.cpu().numpy()
+    points1 = points1.cpu().numpy()
+timer.update('match loop sum')
+timer.print()
+
+do_viz = False
+fast_viz = False
+opencv_display = True
+show_keypoints = True
+viz_path = '/home/zph/projects/LightGlue/assets/match_test.png'
+mconf = np.ones(len(points0))
+
+
+if do_viz:
+    # Visualize the matches.
+    color = cm.jet(mconf)
+    text = [
+        'LightGlue',
+        'Keypoints: {}:{}'.format(len(points0), len(points1)),
+        'Matches: {}'.format(len(points0)),
+    ]
+
+    # Display extra parameter info.
+    small_text = [
+        'Image Pair: {}:{}'.format('kun0', 'kun1'),
+    ]
+
+    make_matching_plot(
+        image0, image1, points0, points1, points0, points1, color,
+        text, viz_path, show_keypoints,
+        fast_viz, opencv_display, 'Matches', small_text)
+
+    timer.update('viz_match')