CelestialSurveyor

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if __name__ == '__main__':
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    from multiprocessing import freeze_support
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    freeze_support()
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    import argparse
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    import os
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    import sys
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    from backend.find_asteroids import predict_asteroids, save_results, annotate_results
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    from backend.progress_bar import ProgressBarCli
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    from backend.source_data_v2 import SourceDataV2
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    from logger.logger import get_logger
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    from user_interface.main_window import start_ui
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    logger = get_logger()
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    version = "0.3.0"
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    arg_parser = argparse.ArgumentParser(
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        prog='CelestialSurveyor',
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        description='It\'s is designed to analyze astronomical images with the primary goal of identifying and '
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                    'locating asteroids and comets within the vastness of the cosmic terrain')
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    arg_parser.add_argument('-c', '--cli_mode', dest='cli_mode', action="store_true", default=False,
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                            help='Run app in command line mode')
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    arg_parser.add_argument('-s', '--source_folder', dest='source_folder', type=str, required=False,
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                            help='Path to the folder with xisf or fit or fits files to be analyzed')
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    arg_parser.add_argument('-o', '--output_folder', dest='output_folder', type=str, required=False,
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                            help='Path to the folder where results will be stored')
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    arg_parser.add_argument('--dark_folder', dest='dark_folder', type=str, required=False, default="",
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                            help='Path to the folder with Dark frames (Optional)')
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    arg_parser.add_argument('--flat_folder', dest='flat_folder', type=str, required=False, default="",
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                            help='Path to the folder with Flat frames (Optional). Work only if DarkFlat '
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                                 'folder is provided')
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    arg_parser.add_argument('--dark_flat_folder', dest='dark_flat_folder', type=str, required=False,
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                            default="",
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                            help='Path to the folder with DarkFlat frames (Optional). Work only if DarkFlat '
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                                 'folder is provided')
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    arg_parser.add_argument('-m', '--model_path', dest='model_path',
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                            type=str, default="",
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                            required=False,
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                            help='Path to the AI model file')
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    arg_parser.add_argument('-n', '--non_linear', dest='non_linear', action="store_true", required=False,
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                            help='Provide this key if the images are not in linear state')
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    arg_parser.add_argument('-d', '--debayer', dest='debayer', action="store_true", required=False,
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                            default=False, help='Debayer color FIT images if required')
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    arg_parser.add_argument('-a', '--align', dest='align_images', action="store_true", required=False,
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                            default=False, help='Do image alignment when loading')
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    arg_parser.add_argument('-v', '--version', dest='version', action="store_true", required=False,
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                            help='Display version of this app.')
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    arg_parser.add_argument('-l', '--magnitude_limit', dest='magnitude_limit', type=float, required=False,
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                            default='18.0')
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    provided_args = arg_parser.parse_args()
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    if provided_args.version:
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        print(f"CelestialSurveyor v{version}")
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    if not provided_args.cli_mode:
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        start_ui()
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    else:
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        if not os.path.exists(provided_args.source_folder):
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            logger.log.error(f"Provided source folder {provided_args.source_folder} does not exist")
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            sys.exit(0)
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        if not os.path.exists(provided_args.output_folder):
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            try:
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                os.makedirs(provided_args.output_folder)
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            except Exception as e:
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                logger.log.error(f"Unable to create output folder {provided_args.output_folder} due to {str(e)}")
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                sys.exit(0)
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        if provided_args.magnitude_limit >= 25.0:
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            logger.log.error(
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                f"Provided magnitude limit {provided_args.magnitude_limit} is too high. Maximum value is 25.0")
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            sys.exit(0)
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        if provided_args.model_path and not os.path.exists(provided_args.model_path):
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            logger.log.error(f"Provided model path {provided_args.model_path} does not exist")
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            sys.exit(0)
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        if provided_args.flat_folder and not os.path.exists(provided_args.flat_folder):
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            logger.log.error(f"Provided flat folder {provided_args.flat_folder} does not exist")
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            sys.exit(0)
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        if provided_args.dark_folder and not os.path.exists(provided_args.dark_folder):
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            logger.log.error(f"Provided dark folder {provided_args.dark_folder} does not exist")
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            sys.exit(0)
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        if provided_args.dark_flat_folder and not os.path.exists(provided_args.dark_flat_folder):
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            logger.log.error(f"Provided dark flat folder {provided_args.dark_flat_folder} does not exist")
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            sys.exit(0)
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        source_data = SourceDataV2(provided_args.debayer)
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        img_paths = source_data.make_file_paths(provided_args.source_folder)
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        source_data.extend_headers(file_list=img_paths)
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        source_data.load_images(progress_bar=ProgressBarCli())
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        dark_files = source_data.make_file_paths(provided_args.dark_folder) if provided_args.dark_folder else None
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        flat_files = source_data.make_file_paths(provided_args.flat_folder) if provided_args.flat_folder else None
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        dark_flat_files = source_data.make_file_paths(
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            provided_args.dark_flat_folder) if provided_args.dark_flat_folder else None
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        source_data.calibrate_images(
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            dark_files=dark_files,
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            flat_files=flat_files,
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            dark_flat_files=dark_flat_files,
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            progress_bar=ProgressBarCli())
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        if provided_args.align_images:
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            source_data.plate_solve_all(progress_bar=ProgressBarCli())
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            source_data.align_images_wcs(progress_bar=ProgressBarCli())
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        source_data.crop_images()
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        if not provided_args.non_linear:
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            source_data.stretch_images(progress_bar=ProgressBarCli())
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        source_data.images_from_buffer()
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        results = predict_asteroids(source_data, model_path=provided_args.model_path, progress_bar=ProgressBarCli())
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        image_to_annotate = save_results(source_data=source_data, results=results,
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                                         output_folder=provided_args.output_folder)
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        magnitude_limit = float(provided_args.magnitude_limit)
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        annotate_results(source_data, image_to_annotate, provided_args.output_folder, magnitude_limit=magnitude_limit)
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        logger.log.info(f"Results are saved in {provided_args.output_folder}")
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