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"""
Implements the full pipeline from raw images to line matches.
"""
import time
import cv2
import numpy as np
import torch
import torch.nn.functional as F
from torch.nn.functional import softmax
from .model_util import get_model
from .loss import get_loss_and_weights
from .metrics import super_nms
from .line_detection import LineSegmentDetectionModule
from .line_matching import WunschLineMatcher
from ..train import convert_junc_predictions
from ..misc.train_utils import adapt_checkpoint
from .line_detector import line_map_to_segments
class LineMatcher(object):
"""Full line matcher including line detection and matching
with the Needleman-Wunsch algorithm."""
def __init__(
self,
model_cfg,
ckpt_path,
device,
line_detector_cfg,
line_matcher_cfg,
multiscale=False,
scales=[1.0, 2.0],
):
# Get loss weights if dynamic weighting
_, loss_weights = get_loss_and_weights(model_cfg, device)
self.device = device
# Initialize the cnn backbone
self.model = get_model(model_cfg, loss_weights)
checkpoint = torch.load(ckpt_path, map_location=self.device)
checkpoint = adapt_checkpoint(checkpoint["model_state_dict"])
self.model.load_state_dict(checkpoint)
self.model = self.model.to(self.device)
self.model = self.model.eval()
self.grid_size = model_cfg["grid_size"]
self.junc_detect_thresh = model_cfg["detection_thresh"]
self.max_num_junctions = model_cfg.get("max_num_junctions", 300)
# Initialize the line detector
self.line_detector = LineSegmentDetectionModule(**line_detector_cfg)
self.multiscale = multiscale
self.scales = scales
# Initialize the line matcher
self.line_matcher = WunschLineMatcher(**line_matcher_cfg)
# Print some debug messages
for key, val in line_detector_cfg.items():
print(f"[Debug] {key}: {val}")
# print("[Debug] detect_thresh: %f" % (line_detector_cfg["detect_thresh"]))
# print("[Debug] num_samples: %d" % (line_detector_cfg["num_samples"]))
# Perform line detection and descriptor inference on a single image
def line_detection(
self, input_image, valid_mask=None, desc_only=False, profile=False
):
# Restrict input_image to 4D torch tensor
if (not len(input_image.shape) == 4) or (
not isinstance(input_image, torch.Tensor)
):
raise ValueError("[Error] the input image should be a 4D torch tensor")
# Move the input to corresponding device
input_image = input_image.to(self.device)
# Forward of the CNN backbone
start_time = time.time()
with torch.no_grad():
net_outputs = self.model(input_image)
outputs = {"descriptor": net_outputs["descriptors"]}
if not desc_only:
junc_np = convert_junc_predictions(
net_outputs["junctions"],
self.grid_size,
self.junc_detect_thresh,
self.max_num_junctions,
)
if valid_mask is None:
junctions = np.where(junc_np["junc_pred_nms"].squeeze())
else:
junctions = np.where(junc_np["junc_pred_nms"].squeeze() * valid_mask)
junctions = np.concatenate(
[junctions[0][..., None], junctions[1][..., None]], axis=-1
)
if net_outputs["heatmap"].shape[1] == 2:
# Convert to single channel directly from here
heatmap = (
softmax(net_outputs["heatmap"], dim=1)[:, 1:, :, :]
.cpu()
.numpy()
.transpose(0, 2, 3, 1)
)
else:
heatmap = (
torch.sigmoid(net_outputs["heatmap"])
.cpu()
.numpy()
.transpose(0, 2, 3, 1)
)
heatmap = heatmap[0, :, :, 0]
# Run the line detector.
line_map, junctions, heatmap = self.line_detector.detect(
junctions, heatmap, device=self.device
)
if isinstance(line_map, torch.Tensor):
line_map = line_map.cpu().numpy()
if isinstance(junctions, torch.Tensor):
junctions = junctions.cpu().numpy()
outputs["heatmap"] = heatmap.cpu().numpy()
outputs["junctions"] = junctions
# If it's a line map with multiple detect_thresh and inlier_thresh
if len(line_map.shape) > 2:
num_detect_thresh = line_map.shape[0]
num_inlier_thresh = line_map.shape[1]
line_segments = []
for detect_idx in range(num_detect_thresh):
line_segments_inlier = []
for inlier_idx in range(num_inlier_thresh):
line_map_tmp = line_map[detect_idx, inlier_idx, :, :]
line_segments_tmp = line_map_to_segments(
junctions, line_map_tmp
)
line_segments_inlier.append(line_segments_tmp)
line_segments.append(line_segments_inlier)
else:
line_segments = line_map_to_segments(junctions, line_map)
outputs["line_segments"] = line_segments
end_time = time.time()
if profile:
outputs["time"] = end_time - start_time
return outputs
# Perform line detection and descriptor inference at multiple scales
def multiscale_line_detection(
self,
input_image,
valid_mask=None,
desc_only=False,
profile=False,
scales=[1.0, 2.0],
aggregation="mean",
):
# Restrict input_image to 4D torch tensor
if (not len(input_image.shape) == 4) or (
not isinstance(input_image, torch.Tensor)
):
raise ValueError("[Error] the input image should be a 4D torch tensor")
# Move the input to corresponding device
input_image = input_image.to(self.device)
img_size = input_image.shape[2:4]
desc_size = tuple(np.array(img_size) // 4)
# Run the inference at multiple image scales
start_time = time.time()
junctions, heatmaps, descriptors = [], [], []
for s in scales:
# Resize the image
resized_img = F.interpolate(input_image, scale_factor=s, mode="bilinear")
# Forward of the CNN backbone
with torch.no_grad():
net_outputs = self.model(resized_img)
descriptors.append(
F.interpolate(
net_outputs["descriptors"], size=desc_size, mode="bilinear"
)
)
if not desc_only:
junc_prob = convert_junc_predictions(
net_outputs["junctions"], self.grid_size
)["junc_pred"]
junctions.append(
cv2.resize(
junc_prob.squeeze(),
(img_size[1], img_size[0]),
interpolation=cv2.INTER_LINEAR,
)
)
if net_outputs["heatmap"].shape[1] == 2:
# Convert to single channel directly from here
heatmap = softmax(net_outputs["heatmap"], dim=1)[:, 1:, :, :]
else:
heatmap = torch.sigmoid(net_outputs["heatmap"])
heatmaps.append(F.interpolate(heatmap, size=img_size, mode="bilinear"))
# Aggregate the results
if aggregation == "mean":
# Aggregation through the mean activation
descriptors = torch.stack(descriptors, dim=0).mean(0)
else:
# Aggregation through the max activation
descriptors = torch.stack(descriptors, dim=0).max(0)[0]
outputs = {"descriptor": descriptors}
if not desc_only:
if aggregation == "mean":
junctions = np.stack(junctions, axis=0).mean(0)[None]
heatmap = torch.stack(heatmaps, dim=0).mean(0)[0, 0, :, :]
heatmap = heatmap.cpu().numpy()
else:
junctions = np.stack(junctions, axis=0).max(0)[None]
heatmap = torch.stack(heatmaps, dim=0).max(0)[0][0, 0, :, :]
heatmap = heatmap.cpu().numpy()
# Extract junctions
junc_pred_nms = super_nms(
junctions[..., None],
self.grid_size,
self.junc_detect_thresh,
self.max_num_junctions,
)
if valid_mask is None:
junctions = np.where(junc_pred_nms.squeeze())
else:
junctions = np.where(junc_pred_nms.squeeze() * valid_mask)
junctions = np.concatenate(
[junctions[0][..., None], junctions[1][..., None]], axis=-1
)
# Run the line detector.
line_map, junctions, heatmap = self.line_detector.detect(
junctions, heatmap, device=self.device
)
if isinstance(line_map, torch.Tensor):
line_map = line_map.cpu().numpy()
if isinstance(junctions, torch.Tensor):
junctions = junctions.cpu().numpy()
outputs["heatmap"] = heatmap.cpu().numpy()
outputs["junctions"] = junctions
# If it's a line map with multiple detect_thresh and inlier_thresh
if len(line_map.shape) > 2:
num_detect_thresh = line_map.shape[0]
num_inlier_thresh = line_map.shape[1]
line_segments = []
for detect_idx in range(num_detect_thresh):
line_segments_inlier = []
for inlier_idx in range(num_inlier_thresh):
line_map_tmp = line_map[detect_idx, inlier_idx, :, :]
line_segments_tmp = line_map_to_segments(
junctions, line_map_tmp
)
line_segments_inlier.append(line_segments_tmp)
line_segments.append(line_segments_inlier)
else:
line_segments = line_map_to_segments(junctions, line_map)
outputs["line_segments"] = line_segments
end_time = time.time()
if profile:
outputs["time"] = end_time - start_time
return outputs
def __call__(self, images, valid_masks=[None, None], profile=False):
# Line detection and descriptor inference on both images
if self.multiscale:
forward_outputs = [
self.multiscale_line_detection(
images[0], valid_masks[0], profile=profile, scales=self.scales
),
self.multiscale_line_detection(
images[1], valid_masks[1], profile=profile, scales=self.scales
),
]
else:
forward_outputs = [
self.line_detection(images[0], valid_masks[0], profile=profile),
self.line_detection(images[1], valid_masks[1], profile=profile),
]
line_seg1 = forward_outputs[0]["line_segments"]
line_seg2 = forward_outputs[1]["line_segments"]
desc1 = forward_outputs[0]["descriptor"]
desc2 = forward_outputs[1]["descriptor"]
# Match the lines in both images
start_time = time.time()
matches = self.line_matcher.forward(line_seg1, line_seg2, desc1, desc2)
end_time = time.time()
outputs = {"line_segments": [line_seg1, line_seg2], "matches": matches}
if profile:
outputs["line_detection_time"] = (
forward_outputs[0]["time"] + forward_outputs[1]["time"]
)
outputs["line_matching_time"] = end_time - start_time
return outputs
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