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"""
Export line detections and descriptors given a list of input images.
"""
import os
import argparse
import cv2
import numpy as np
import torch
from tqdm import tqdm
from .experiment import load_config
from .model.line_matcher import LineMatcher
def export_descriptors(
images_list, ckpt_path, config, device, extension, output_folder, multiscale=False
):
# Extract the image paths
with open(images_list, "r") as f:
image_files = f.readlines()
image_files = [path.strip("\n") for path in image_files]
# Initialize the line matcher
line_matcher = LineMatcher(
config["model_cfg"],
ckpt_path,
device,
config["line_detector_cfg"],
config["line_matcher_cfg"],
multiscale,
)
print("\t Successfully initialized model")
# Run the inference on each image and write the output on disk
for img_path in tqdm(image_files):
img = cv2.imread(img_path, 0)
img = torch.tensor(img[None, None] / 255.0, dtype=torch.float, device=device)
# Run the line detection and description
ref_detection = line_matcher.line_detection(img)
ref_line_seg = ref_detection["line_segments"]
ref_descriptors = ref_detection["descriptor"][0].cpu().numpy()
# Write the output on disk
img_name = os.path.splitext(os.path.basename(img_path))[0]
output_file = os.path.join(output_folder, img_name + extension)
np.savez_compressed(
output_file, line_seg=ref_line_seg, descriptors=ref_descriptors
)
if __name__ == "__main__":
# Parse input arguments
parser = argparse.ArgumentParser()
parser.add_argument(
"--img_list",
type=str,
required=True,
help="List of input images in a text file.",
)
parser.add_argument(
"--output_folder", type=str, required=True, help="Path to the output folder."
)
parser.add_argument(
"--config", type=str, default="config/export_line_features.yaml"
)
parser.add_argument(
"--checkpoint_path", type=str, default="pretrained_models/sold2_wireframe.tar"
)
parser.add_argument("--multiscale", action="store_true", default=False)
parser.add_argument("--extension", type=str, default=None)
args = parser.parse_args()
# Get the device
if torch.cuda.is_available():
device = torch.device("cuda")
else:
device = torch.device("cpu")
# Get the model config, extension and checkpoint path
config = load_config(args.config)
ckpt_path = os.path.abspath(args.checkpoint_path)
extension = "sold2" if args.extension is None else args.extension
extension = "." + extension
export_descriptors(
args.img_list,
ckpt_path,
config,
device,
extension,
args.output_folder,
args.multiscale,
)
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