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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, | |
| ) | |