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if dm[st_idx, idx] < 10000:
matching_list.append(tuple(sorted((st_idx, idx.item()))))
total+=1
matching_list = sorted(list(set(matching_list)))
return matching_list
import pycolmap
print(f"pycolmap version: {pycolmap.__version__}")
# Collect vital info from the dataset
@dataclasses.dataclass
class Prediction:
image_id: str | None # A unique identifier for the row -- unused otherwise. Used only on the hidden test set.
dataset: str
filename: str
cluster_index: int | None = None
rotation: np.ndarray | None = None
translation: np.ndarray | None = None
# Set is_train=True to run the notebook on the training data.
# Set is_train=False if submitting an entry to the competition (test data is hidden, and different from what you see on the "test" folder).
is_train = False
data_dir = '/kaggle/input/image-matching-challenge-2025'
workdir = '/kaggle/working/result/'
os.makedirs(workdir, exist_ok=True)
if is_train:
sample_submission_csv = os.path.join(data_dir, 'train_labels.csv')
else:
sample_submission_csv = os.path.join(data_dir, 'sample_submission.csv')
samples = {}
competition_data = pd.read_csv(sample_submission_csv)
for _, row in competition_data.iterrows():
# Note: For the test data, the "scene" column has no meaning, and the rotation_matrix and translation_vector columns are random.
if row.dataset not in samples:
samples[row.dataset] = []
samples[row.dataset].append(
Prediction(
image_id=None if is_train else row.image_id,
dataset=row.dataset,
filename=row.image
)
)
for dataset in samples:
print(f'Dataset "{dataset}" -> num_images={len(samples[dataset])}')
import multiprocessing
import os
import numpy as np
import torch
import matplotlib.pyplot as plt
import cv2
def save_match_viz_image(
key1,
key2,
view1,
view2,
matches_im0,
matches_im1,
feature_dir,
n_viz: int = 100
):
"""
Save a visual match image for a pair of images using descriptor matches.
Parameters:
key1, key2: str
Base filenames of the matched image pair (used for naming the output).
view1, view2: dict
MASt3R inference outputs containing 'img' and 'true_shape'.
matches_im0, matches_im1: np.ndarray of shape (N, 2)
Coordinates of matched keypoints in image 0 and image 1.
feature_dir: str
Path to save the visualized match image.
n_viz: int
Number of matches to visualize (default: 100).
"""
if matches_im0.shape[0] == 0:
return # nothing to draw
n_viz = min(n_viz, matches_im0.shape[0])
idx = np.round(np.linspace(0, matches_im0.shape[0] - 1, n_viz)).astype(int)
viz_matches_im0 = matches_im0[idx]
viz_matches_im1 = matches_im1[idx]
image_mean = torch.tensor([0.5, 0.5, 0.5]).reshape(1, 3, 1, 1)
image_std = torch.tensor([0.5, 0.5, 0.5]).reshape(1, 3, 1, 1)
viz_imgs = []
for view in [view1, view2]:
rgb_tensor = view['img'].cpu() * image_std + image_mean
rgb_np = rgb_tensor.squeeze(0).permute(1, 2, 0).clamp(0, 1).numpy()
viz_imgs.append((rgb_np * 255).astype(np.uint8))
H0, W0 = viz_imgs[0].shape[:2]
H1, W1 = viz_imgs[1].shape[:2]
img0 = np.pad(viz_imgs[0], ((0, max(H1 - H0, 0)), (0, 0), (0, 0)), 'constant')
img1 = np.pad(viz_imgs[1], ((0, max(H0 - H1, 0)), (0, 0), (0, 0)), 'constant')