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import sys
from pathlib import Path
from ..utils.base_model import BaseModel
import torch
from ..utils.base_model import BaseModel
sold2_path = Path(__file__).parent / "../../third_party/SOLD2"
sys.path.append(str(sold2_path))
from sold2.model.line_matcher import LineMatcher
from sold2.misc.visualize_util import (
plot_images,
plot_lines,
plot_line_matches,
plot_color_line_matches,
plot_keypoints,
)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
class SOLD2(BaseModel):
default_conf = {
"weights": "sold2_wireframe.tar",
"match_threshold": 0.2,
"checkpoint_dir": sold2_path / "pretrained",
"detect_thresh": 0.25,
"multiscale": False,
"valid_thresh": 1e-3,
"num_blocks": 20,
"overlap_ratio": 0.5,
}
required_inputs = [
"image0",
"image1",
]
# Initialize the line matcher
def _init(self, conf):
checkpoint_path = conf["checkpoint_dir"] / conf["weights"]
mode = "dynamic" # 'dynamic' or 'static'
match_config = {
"model_cfg": {
"model_name": "lcnn_simple",
"model_architecture": "simple",
# Backbone related config
"backbone": "lcnn",
"backbone_cfg": {
"input_channel": 1, # Use RGB images or grayscale images.
"depth": 4,
"num_stacks": 2,
"num_blocks": 1,
"num_classes": 5,
},
# Junction decoder related config
"junction_decoder": "superpoint_decoder",
"junc_decoder_cfg": {},
# Heatmap decoder related config
"heatmap_decoder": "pixel_shuffle",
"heatmap_decoder_cfg": {},
# Descriptor decoder related config
"descriptor_decoder": "superpoint_descriptor",
"descriptor_decoder_cfg": {},
# Shared configurations
"grid_size": 8,
"keep_border_valid": True,
# Threshold of junction detection
"detection_thresh": 0.0153846, # 1/65
"max_num_junctions": 300,
# Threshold of heatmap detection
"prob_thresh": 0.5,
# Weighting related parameters
"weighting_policy": mode,
# [Heatmap loss]
"w_heatmap": 0.0,
"w_heatmap_class": 1,
"heatmap_loss_func": "cross_entropy",
"heatmap_loss_cfg": {"policy": mode},
# [Heatmap consistency loss]
# [Junction loss]
"w_junc": 0.0,
"junction_loss_func": "superpoint",
"junction_loss_cfg": {"policy": mode},
# [Descriptor loss]
"w_desc": 0.0,
"descriptor_loss_func": "regular_sampling",
"descriptor_loss_cfg": {
"dist_threshold": 8,
"grid_size": 4,
"margin": 1,
"policy": mode,
},
},
"line_detector_cfg": {
"detect_thresh": 0.25, # depending on your images, you might need to tune this parameter
"num_samples": 64,
"sampling_method": "local_max",
"inlier_thresh": 0.9,
"use_candidate_suppression": True,
"nms_dist_tolerance": 3.0,
"use_heatmap_refinement": True,
"heatmap_refine_cfg": {
"mode": "local",
"ratio": 0.2,
"valid_thresh": 1e-3,
"num_blocks": 20,
"overlap_ratio": 0.5,
},
},
"multiscale": False,
"line_matcher_cfg": {
"cross_check": True,
"num_samples": 5,
"min_dist_pts": 8,
"top_k_candidates": 10,
"grid_size": 4,
},
}
self.net = LineMatcher(
match_config["model_cfg"],
checkpoint_path,
device,
match_config["line_detector_cfg"],
match_config["line_matcher_cfg"],
match_config["multiscale"],
)
def _forward(self, data):
img0 = data["image0"]
img1 = data["image1"]
pred = self.net([img0, img1])
line_seg1 = pred["line_segments"][0]
line_seg2 = pred["line_segments"][1]
matches = pred["matches"]
valid_matches = matches != -1
match_indices = matches[valid_matches]
matched_lines1 = line_seg1[valid_matches][:, :, ::-1]
matched_lines2 = line_seg2[match_indices][:, :, ::-1]
pred["raw_lines0"], pred["raw_lines1"] = line_seg1, line_seg2
pred["lines0"], pred["lines1"] = matched_lines1, matched_lines2
pred = {**pred, **data}
return pred
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