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kpts_original = kpts_resized/ scale_factor
return kpts_original
import os
import numpy as np
import cv2
import matplotlib.pyplot as plt
def draw_matches_on_original_images(img_path1, img_path2, matches_im0, matches_im1, save_path, n_viz=100):
"""
画出两张原图上的匹配点连线，并保存图像
Args:
img_path1: str, 原图1路径
img_path2: str, 原图2路径
matches_im0: (N, 2) numpy array，图1上的匹配点坐标（在原图上）
matches_im1: (N, 2) numpy array，图2上的匹配点坐标（在原图上）
save_path: str, 输出图像路径
n_viz: int, 可视化前n个匹配（默认100）
"""
os.makedirs(os.path.dirname(save_path), exist_ok=True)
# 读取图像
img0 = cv2.imread(img_path1)
img1 = cv2.imread(img_path2)
key1 = os.path.basename(img_path1)
key2 = os.path.basename(img_path2)
if img0 is None or img1 is None:
print(f"Error: Cannot load {img_path1} or {img_path2}")
return
img0 = cv2.cvtColor(img0, cv2.COLOR_BGR2RGB)
img1 = cv2.cvtColor(img1, cv2.COLOR_BGR2RGB)
H0, W0 = img0.shape[:2]
H1, W1 = img1.shape[:2]
canvas_h = max(H0, H1)
canvas = np.zeros((canvas_h, W0 + W1, 3), dtype=np.uint8)
canvas[:H0, :W0] = img0
canvas[:H1, W0:] = img1
# 可视化子集
num_matches = min(len(matches_im0), n_viz)
idxs = np.round(np.linspace(0, len(matches_im0) - 1, num_matches)).astype(int)
cmap = plt.get_cmap('rainbow')
for i, idx in enumerate(idxs):
(x0, y0) = matches_im0[idx]
(x1, y1) = matches_im1[idx]
color = tuple((np.array(cmap(i / n_viz))[:3] * 255).astype(int).tolist())
pt1 = (int(round(x0)), int(round(y0)))
pt2 = (int(round(x1 + W0)), int(round(y1)))
cv2.line(canvas, pt1, pt2, color, thickness=1, lineType=cv2.LINE_AA)
cv2.circle(canvas, pt1, 2, color, -1, lineType=cv2.LINE_AA)
cv2.circle(canvas, pt2, 2, color, -1, lineType=cv2.LINE_AA)
# 保存
cv2.imwrite(f'{save_path}/{key1}_{key2}.jpg', cv2.cvtColor(canvas, cv2.COLOR_RGB2BGR))
# print(f"Saved match debug image to {save_path}")
img_path1 = '/kaggle/input/image-matching-challenge-2025/train/ETs/et_et003.png'
img_path2 = '/kaggle/input/image-matching-challenge-2025/train/ETs/et_et006.png'
images = load_images([img_path1, img_path2], size=512)
output = inference([tuple(images)], mast3r_model, device, batch_size=1, verbose=True)
# at this stage, you have the raw dust3r predictions
view1, pred1 = output['view1'], output['pred1']
view2, pred2 = output['view2'], output['pred2']
desc1, desc2 = pred1['desc'].squeeze(0).detach(), pred2['desc'].squeeze(0).detach()
# find 2D-2D matches between the two images
# matches_im0, matches_im1 = fast_reciprocal_NNs(desc1, desc2, subsample_or_initxy1=8,
# device=device, dist='dot', block_size=2**13)
conf1, conf2 = pred1['desc_conf'].squeeze(0).detach(), pred2['desc_conf'].squeeze(0).detach()
print(f"desc1 shape: {desc1.shape}")
print(f"conf1 shape: {conf1.shape}")
corres = extract_correspondences_nonsym(desc1, desc2, conf1, conf2,
device=device, subsample=8, pixel_tol=5)
print(f"corres[0].shape: {corres[0].shape}")
print(f"corres[2].shape: {corres[2].shape}")
score = corres[2]
print("conf min:",score.min().item())
print("conf max:", score.max().item())
print("conf mean:", score.float().mean().item())
print("conf std:", score.float().std().item())
print("conf median:", score.float().median().item())
mask = score >= CONFIG.MATCH_CONF_TH
matches_im0 = corres[0][mask].cpu().numpy()
matches_im1 = corres[1][mask].cpu().numpy()
print(f"matches_im0.shape: {matches_im0.shape}")
# matches_im0 = corres[0].cpu().numpy()
# matches_im1 = corres[1].cpu().numpy()