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stpete2
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memo

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Args:
image_dir (str): Path to the directory containing images.
weights_path (str): Path to the AsymmetricMASt3R model weights.
retrieval_model_path (str): Path to the retrieval model (e.g., "trainingfree.pth").
scene_graph (str, optional): String defining the scene graph construction strategy.
Defaults to 'retrieval-20-1-10-1'.
device (str, optional): PyTorch device to use ('cuda' or 'cpu'). Defaults to 'cuda'.
Returns:
sorted_pairs: List[Tuple[str, str]], where each tuple contains
the relative paths of the paired images (img1, img2).
"""
print("🖼️ Scanning images...")
imgs = get_image_list(image_dir)
imgs_fp = [os.path.join(image_dir, f) for f in imgs]
if not imgs:
print("⚠️ No valid images found in the directory. Returning empty pairs.")
return []
print(f"⚙️ Loading backbone model from {weights_path}...")
backbone = AsymmetricMASt3R.from_pretrained(weights_path).to(device).eval()
# print("🔍 Running ASMK retrieval...")
retriever = Retriever(retrieval_model_path, backbone=backbone)
with torch.no_grad():
sim_matrix_np = retriever(imgs_fp)
# Cleanup GPU cache
del retriever
del backbone
torch.cuda.empty_cache()
# print("🧮 Generating image pairs...")
# make_pairs 应该期望一个 PyTorch 张量作为 sim_mat
raw_pairs = make_pairs(imgs, scene_graph, prefilter=None, symmetrize=True, sim_mat=sim_matrix_np)
# print(raw_pairs)
sorted_pairs = sorted(set(tuple(sorted([a, b])) for a, b in raw_pairs))
print(f"✅ Generated {len(sorted_pairs)} unique image pairs.")
return sorted_pairs
import kornia as K
import kornia.feature as KF
# --- Helper function for image loading (if not already defined) ---
def load_torch_image(fname, device=torch.device('cpu')):
img = K.io.load_image(fname, K.io.ImageLoadType.RGB32, device=device)[None, ...]
return img
# Must Use efficientnet global descriptor to get matching shortlists.
def get_global_desc(fnames, device = torch.device('cpu')):
processor = AutoImageProcessor.from_pretrained('/kaggle/input/dinov2/pytorch/base/1')
model = AutoModel.from_pretrained('/kaggle/input/dinov2/pytorch/base/1')
model = model.eval()
model = model.to(device)
global_descs_dinov2 = []
for i, img_fname_full in tqdm(enumerate(fnames),total= len(fnames)):
key = os.path.splitext(os.path.basename(img_fname_full))[0]
timg = load_torch_image(img_fname_full)
with torch.inference_mode():
inputs = processor(images=timg, return_tensors="pt", do_rescale=False).to(device)
outputs = model(**inputs)
dino_mac = F.normalize(outputs.last_hidden_state[:,1:].max(dim=1)[0], dim=1, p=2)
global_descs_dinov2.append(dino_mac.detach().cpu())
global_descs_dinov2 = torch.cat(global_descs_dinov2, dim=0)
return global_descs_dinov2
def get_img_pairs_exhaustive(img_fnames):
index_pairs = []
for i in range(len(img_fnames)):
for j in range(i+1, len(img_fnames)):
index_pairs.append((i,j))
return index_pairs
def get_image_pairs_shortlist_org(fnames,
sim_th = 0.6, # should be strict
min_pairs = 60,
exhaustive_if_less = 20,
device=torch.device('cpu')):
num_imgs = len(fnames)
if num_imgs <= exhaustive_if_less:
return get_img_pairs_exhaustive(fnames)
descs = get_global_desc(fnames, device=device)
dm = torch.cdist(descs, descs, p=2).detach().cpu().numpy()
mask = dm <= sim_th
total = 0
matching_list = []
ar = np.arange(num_imgs)
already_there_set = []
for st_idx in range(num_imgs-1):
mask_idx = mask[st_idx]
to_match = ar[mask_idx]
if len(to_match) < min_pairs:
to_match = np.argsort(dm[st_idx])[:min_pairs]
for idx in to_match:
if st_idx == idx:
continue