86 lines
3.8 KiB
Python
86 lines
3.8 KiB
Python
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import copy
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import os
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from . import res
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from pprint import pprint
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def start_workflow():
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res.bootstrap_folder_structure()
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aggregated_metrics_av1_from_file = res.read_dict_from_json_file(
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os.path.join(
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res.get_path_results_aggregations(),
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res.get_filename_results_aggregations_av1()
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)
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)
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aggregated_metrics_hevc_from_file = res.read_dict_from_json_file(
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os.path.join(
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res.get_path_results_aggregations(),
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res.get_filename_results_aggregations_hevc()
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)
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)
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aggregated_metrics_hevc = {}
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for preset, data_of_preset in aggregated_metrics_hevc_from_file.items():
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for crf, data_of_crf in data_of_preset.items():
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aggregated_metrics_hevc = copy.deepcopy(data_of_crf)
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del aggregated_metrics_hevc['samples']
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tolerance_filesize_in_percent = 20
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hevc_filesize_percentage_mean = aggregated_metrics_hevc["filesize_percentage"]["mean"]
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hevc_filesize_percentage_median = aggregated_metrics_hevc["filesize_percentage"]["median"]
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hevc_filesize_percentage_mean_with_tolerance = aggregated_metrics_hevc["filesize_percentage"]["mean"] * (1 + tolerance_filesize_in_percent / 100)
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hevc_filesize_percentage_median_with_tolerance = aggregated_metrics_hevc["filesize_percentage"]["median"] * (1 + tolerance_filesize_in_percent / 100)
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hevc_vmaf_score_mean = aggregated_metrics_hevc["vmaf_score"]["mean"]
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hevc_vmaf_score_median = aggregated_metrics_hevc["vmaf_score"]["median"]
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possible_candidates = {}
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possible_candidates_with_tolerance = {}
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for preset, data_of_preset in aggregated_metrics_av1_from_file.items():
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for crf, data_of_crf in data_of_preset.items():
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viable_only_with_tolerance = False
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av1_filesize_percentage_mean = data_of_crf["filesize_percentage"]["mean"]
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av1_filesize_percentage_median = data_of_crf["filesize_percentage"]["median"]
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av1_vmaf_score_mean = data_of_crf["vmaf_score"]["mean"]
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av1_vmaf_score_median = data_of_crf["vmaf_score"]["median"]
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if av1_filesize_percentage_mean > hevc_filesize_percentage_mean:
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if av1_filesize_percentage_mean > hevc_filesize_percentage_mean_with_tolerance:
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continue
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viable_only_with_tolerance = True
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if av1_filesize_percentage_median > hevc_filesize_percentage_median:
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if av1_filesize_percentage_median > hevc_filesize_percentage_median_with_tolerance:
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continue
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viable_only_with_tolerance = True
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if av1_vmaf_score_mean < hevc_vmaf_score_mean:
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continue
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if av1_vmaf_score_median < hevc_vmaf_score_median:
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continue
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if viable_only_with_tolerance:
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if not preset in possible_candidates_with_tolerance.keys():
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possible_candidates_with_tolerance[preset] = {}
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possible_candidates_with_tolerance[preset][crf] = copy.deepcopy(data_of_crf)
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del possible_candidates_with_tolerance[preset][crf]["samples"]
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continue
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if not preset in possible_candidates.keys():
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possible_candidates[preset] = {}
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possible_candidates[preset][crf] = copy.deepcopy(data_of_crf)
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del possible_candidates[preset][crf]["samples"]
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output_file = os.path.join(
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res.get_path_results_candidates(),
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res.get_filename_results_candidates_viable()
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)
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output_file_with_tolerance = os.path.join(
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res.get_path_results_candidates(),
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res.get_filename_results_candidates_viable_with_tolerance()
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)
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res.write_dict_to_json_file(output_file, possible_candidates)
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res.write_dict_to_json_file(output_file_with_tolerance, possible_candidates_with_tolerance)
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