File size: 5,471 Bytes
9223079 ab830e5 9223079 60ad158 ab830e5 9223079 ab830e5 9223079 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 |
import sys
from pathlib import Path
from ..utils.base_model import BaseModel
import torch
from ..utils.base_model import BaseModel
from .. import logger
import subprocess
sold2_path = Path(__file__).parent / "../../third_party/SOLD2"
sys.path.append(str(sold2_path))
from sold2.model.line_matcher import LineMatcher
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",
]
weight_urls = {
"sold2_wireframe.tar": "https://www.polybox.ethz.ch/index.php/s/blOrW89gqSLoHOk/download",
}
# Initialize the line matcher
def _init(self, conf):
checkpoint_path = conf["checkpoint_dir"] / conf["weights"]
# Download the model.
if not checkpoint_path.exists():
checkpoint_path.parent.mkdir(exist_ok=True)
link = self.weight_urls[conf["weights"]]
cmd = ["wget", link, "-O", str(checkpoint_path)]
logger.info(f"Downloading the SOLD2 model with `{cmd}`.")
subprocess.run(cmd, check=True)
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
|