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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# Adapted from [VGGT-Long](https://github.com/DengKaiCQ/VGGT-Long)
import argparse
import os
import sys
from pathlib import Path
import faiss
import torch
import torchvision.transforms as T
from PIL import Image
from torch import nn
from tqdm import tqdm
CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
SALAD_ROOT = os.path.join(CURRENT_DIR, "salad")
if SALAD_ROOT not in sys.path:
sys.path.insert(0, SALAD_ROOT)
from loop_utils.salad.models import helper
class VPRModel(nn.Module):
"""This is the main model for Visual Place Recognition
we use Pytorch Lightning for modularity purposes.
Args:
pl (_type_): _description_
"""
def __init__(
self,
# ---- Backbone
backbone_arch="resnet50",
backbone_config={},
# ---- Aggregator
agg_arch="ConvAP",
agg_config={},
):
super().__init__()
# Backbone
self.encoder_arch = backbone_arch
self.backbone_config = backbone_config
# Aggregator
self.agg_arch = agg_arch
self.agg_config = agg_config
# ----------------------------------
# get the backbone and the aggregator
self.backbone = helper.get_backbone(backbone_arch, backbone_config)
self.aggregator = helper.get_aggregator(agg_arch, agg_config)
# the forward pass of the lightning model
def forward(self, x):
x = self.backbone(x)
x = self.aggregator(x)
return x
class LoopDetector:
"""Loop detector class for detecting loop closures in image sequences"""
def __init__(self, image_dir, output="loop_closures.txt", config=None):
"""Initialize the loop detector
Args:
image_dir: Directory path containing images
ckpt_path: Model checkpoint path
image_size: Image resize dimensions [height width]
batch_size: Batch size for processing
similarity_threshold: Similarity threshold for loop closure
top_k: Number of nearest neighbors to check for each image
use_nms: Whether to use Non-Maximum Suppression (NMS) filtering
nms_threshold: NMS threshold for minimum frame difference between loop pairs
output: Output file path
"""
self.config = config
self.image_dir = image_dir
self.ckpt_path = self.config["Weights"]["SALAD"]
self.image_size = self.config["Loop"]["SALAD"]["image_size"]
self.batch_size = self.config["Loop"]["SALAD"]["batch_size"]
self.similarity_threshold = self.config["Loop"]["SALAD"]["similarity_threshold"]
self.top_k = self.config["Loop"]["SALAD"]["top_k"]
self.use_nms = self.config["Loop"]["SALAD"]["use_nms"]
self.nms_threshold = self.config["Loop"]["SALAD"]["nms_threshold"]
self.output = output
self.model = None
self.device = None
self.image_paths = None
self.descriptors = None
self.loop_closures = None
def _input_transform(self, image_size=None):
"""Create image transformation function"""
MEAN = [0.485, 0.456, 0.406]
STD = [0.229, 0.224, 0.225]
if image_size:
return T.Compose(
[
T.Resize(image_size, interpolation=T.InterpolationMode.BILINEAR),
T.ToTensor(),
T.Normalize(mean=MEAN, std=STD),
]
)
else:
return T.Compose([T.ToTensor(), T.Normalize(mean=MEAN, std=STD)])
def load_model(self):
"""Load model"""
model = VPRModel(
backbone_arch="dinov2_vitb14",
backbone_config={
"num_trainable_blocks": 4,
"return_token": True,
"norm_layer": True,
},
agg_arch="SALAD",
agg_config={
"num_channels": 768,
"num_clusters": 64,
"cluster_dim": 128,
"token_dim": 256,
},
)
model.load_state_dict(torch.load(self.ckpt_path))
model = model.eval()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)
print(f"Model loaded: {self.ckpt_path}")
self.model = model
self.device = device
return model, device
def get_image_paths(self):
"""Get paths of all image files in directory"""
image_extensions = [".jpg", ".jpeg", ".png"]
image_paths = []
for ext in image_extensions:
image_paths.extend(list(Path(self.image_dir).glob(f"*{ext}")))
image_paths.extend(list(Path(self.image_dir).glob(f"*{ext.upper()}")))
image_paths = sorted(image_paths)
self.image_paths = image_paths
return image_paths
def extract_descriptors(self):
"""Extract image feature descriptors"""
if self.model is None or self.device is None:
self.load_model()
if self.image_paths is None:
self.get_image_paths()
transform = self._input_transform(self.image_size)
descriptors = []
for i in tqdm(
range(0, len(self.image_paths), self.batch_size), desc="Extracting features"
):
batch_paths = self.image_paths[i : i + self.batch_size]
batch_imgs = []
for path in batch_paths:
try:
img = Image.open(path).convert("RGB")
img = transform(img)
batch_imgs.append(img)
except Exception as e:
print(f"Error processing image {path}: {e}")
img = (
torch.zeros(3, 224, 224)
if self.image_size is None
else torch.zeros(3, self.image_size[0], self.image_size[1])
)
batch_imgs.append(img)
batch_tensor = torch.stack(batch_imgs).to(self.device)
with torch.no_grad():
with torch.autocast(
device_type="cuda" if torch.cuda.is_available() else "cpu", dtype=torch.float16
):
batch_descriptors = self.model(batch_tensor).cpu()
descriptors.append(batch_descriptors)
self.descriptors = torch.cat(descriptors)
return self.descriptors
def _apply_nms_filter(self, loop_closures, nms_threshold):
"""Apply Non-Maximum Suppression (NMS) filtering to loop pairs"""
if not loop_closures or nms_threshold <= 0:
return loop_closures
sorted_loops = sorted(loop_closures, key=lambda x: x[2], reverse=True)
filtered_loops = []
suppressed = set()
max_frame = max(max(idx1, idx2) for idx1, idx2, _ in loop_closures)
for idx1, idx2, sim in sorted_loops:
if idx1 in suppressed or idx2 in suppressed:
continue
filtered_loops.append((idx1, idx2, sim))
suppress_range = set()
start1 = max(0, idx1 - nms_threshold)
end1 = min(idx1 + nms_threshold + 1, idx2)
suppress_range.update(range(start1, end1))
start2 = max(idx1 + 1, idx2 - nms_threshold)
end2 = min(idx2 + nms_threshold + 1, max_frame + 1)
suppress_range.update(range(start2, end2))
suppressed.update(suppress_range)
return filtered_loops
def _ensure_decending_order(self, tuples_list):
return [(max(a, b), min(a, b), score) for a, b, score in tuples_list]
def find_loop_closures(self):
"""Find loop closures"""
if self.descriptors is None:
self.extract_descriptors()
embed_size = self.descriptors.shape[1]
faiss_index = faiss.IndexFlatIP(embed_size)
normalized_descriptors = self.descriptors.numpy()
faiss_index.add(normalized_descriptors)
similarities, indices = faiss_index.search(
normalized_descriptors, self.top_k + 1
) # +1 because self is most similar
loop_closures = []
for i in range(len(self.descriptors)):
# Skip first result (self)
for j in range(1, self.top_k + 1):
neighbor_idx = indices[i, j]
similarity = similarities[i, j]
if similarity > self.similarity_threshold and abs(i - neighbor_idx) > 10:
if i < neighbor_idx:
loop_closures.append((i, neighbor_idx, similarity))
else:
loop_closures.append((neighbor_idx, i, similarity))
loop_closures = list(set(loop_closures))
loop_closures.sort(key=lambda x: x[2], reverse=True)
if self.use_nms and self.nms_threshold > 0:
loop_closures = self._apply_nms_filter(loop_closures, self.nms_threshold)
self.loop_closures = self._ensure_decending_order(loop_closures)
return self.loop_closures
def save_results(self):
"""Save loop detection results to file"""
if self.loop_closures is None:
self.find_loop_closures()
with open(self.output, "w") as f:
f.write("# Loop Detection Results (index1, index2, similarity)\n")
if self.use_nms:
f.write(f"# NMS filtering applied, threshold: {self.nms_threshold}\n")
f.write("\n# Loop pairs:\n")
for i, j, sim in self.loop_closures:
f.write(f"{i}, {j}, {sim:.4f}\n")
f.write("\n# Image path list:\n")
for i, path in enumerate(self.image_paths):
f.write(f"# {i}: {path}\n")
print(f"Found {len(self.loop_closures)} loop pairs, results saved to {self.output}")
if self.use_nms:
print(f"NMS filtering applied, threshold: {self.nms_threshold}")
if self.loop_closures:
print("\nTop 10 loop pairs:")
for i, (idx1, idx2, sim) in enumerate(self.loop_closures[:10]):
print(f"{idx1}, {idx2}, similarity: {sim:.4f}")
if i >= 9:
break
def get_loop_list(self):
return [(idx1, idx2) for idx1, idx2, _ in self.loop_closures]
def run(self):
"""Run complete loop detection pipeline"""
print("Loading model...")
if self.model is None:
self.load_model()
self.get_image_paths()
if not self.image_paths:
print(f"No image files found in {self.image_dir}")
return
print(f"Found {len(self.image_paths)} image files")
self.extract_descriptors()
self.find_loop_closures()
self.save_results()
return self.loop_closures
def main():
parser = argparse.ArgumentParser(description="Loop detection using SALAD model")
parser.add_argument(
"--image_dir",
type=str,
default="/media/deng/Data/KITTIdataset/data_odometry_color/dataset/sequences/00/image_2",
help="Directory path containing images",
)
parser.add_argument(
"--ckpt_path", type=str, default="./weights/dino_salad.ckpt", help="Model checkpoint path"
)
parser.add_argument(
"--image_size",
nargs=2,
type=int,
default=[336, 336],
help="Image resize dimensions [height width]",
)
parser.add_argument("--batch_size", type=int, default=32, help="Batch size for processing")
parser.add_argument(
"--similarity_threshold",
type=float,
default=0.7,
help="Similarity threshold for loop closure",
)
parser.add_argument(
"--top_k", type=int, default=5, help="Number of nearest neighbors to check for each image"
)
parser.add_argument("--output", type=str, default="loop_closures.txt", help="Output file path")
parser.add_argument(
"--use_nms",
action="store_true",
default=True,
help="Whether to use Non-Maximum Suppression (NMS) filtering",
)
parser.add_argument(
"--nms_threshold",
type=int,
default=25,
help="NMS threshold for minimum frame difference between loop pairs",
)
args = parser.parse_args()
detector = LoopDetector(
image_dir=args.image_dir,
ckpt_path=args.ckpt_path,
image_size=args.image_size,
batch_size=args.batch_size,
similarity_threshold=args.similarity_threshold,
top_k=args.top_k,
use_nms=args.use_nms,
nms_threshold=args.nms_threshold,
output=args.output,
)
detector.run()
if __name__ == "__main__":
main()
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