499 lines
21 KiB
Python
Executable File
499 lines
21 KiB
Python
Executable File
#Originally by Trix
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#Contributors: R1chterScale, Yiss and Kosaka
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from math import ceil
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from pathlib import Path
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import json
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import os
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import subprocess
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import re
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import argparse
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import psutil
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import shutil
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import platform
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import vapoursynth as vs
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core = vs.core
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core.max_cache_size = 1024
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IS_WINDOWS = platform.system() == 'Windows'
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NULL_DEVICE = 'NUL' if IS_WINDOWS else '/dev/null'
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if shutil.which("av1an") is None:
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raise FileNotFoundError("av1an not found, exiting")
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if shutil.which("turbo-metrics") is None:
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print("turbo-metrics not found, defaulting to vs-zip")
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ssimu2zig = True
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default_skip = 3
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else:
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ssimu2zig = False
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default_skip = 1
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parser = argparse.ArgumentParser()
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parser.add_argument("-s", "--stage", help = "Select stage: 1 = encode, 2 = calculate metrics, 3 = generate zones | Default: all", default=0)
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parser.add_argument("-i", "--input", required=True, help = "Video input filepath (original source file)")
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parser.add_argument("-q", "--quality", help = "Base quality (CRF) | Default: 30", default=30)
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parser.add_argument("-d", "--deviation", help = "Maximum CRF change from original | Default: 10", default=10)
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parser.add_argument("-p", "--preset", help = "Fast encode preset | Default: 9", default=9)
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parser.add_argument("-w", "--workers", help = "Number of av1an workers | Default: amount of physical cores", default=psutil.cpu_count(logical=False))
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parser.add_argument("-m", "--metrics", help = "Select metrics: 1 = SSIMU2, 2 = XPSNR, 3 = Both | Default: 1", default=1)
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parser.add_argument("-S", "--skip", help = "SSIMU2 skip value, every nth frame's SSIMU2 is calculated | Default: 1 for turbo-metrics, 3 for vs-zip")
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parser.add_argument("-z", "--zones", help = "Zones calculation method: 1 = SSIMU2, 2 = XPSNR, 3 = Multiplication, 4 = Lowest Result | Default: 1", default=1)
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parser.add_argument("-a", "--aggressive", action='store_true', help = "More aggressive boosting | Default: not active")
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args = parser.parse_args()
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stage = int(args.stage)
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src_file = Path(args.input).resolve()
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output_dir = src_file.parent
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tmp_dir = output_dir / "temp"
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output_file = output_dir / f"{src_file.stem}_fastpass.mkv"
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scenes_file = tmp_dir / "scenes.json"
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br = float(args.deviation)
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skip = args.skip if args.skip is not None else default_skip
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aggressive = args.aggressive
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def get_ranges(scenes: str) -> list[int]:
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"""
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Reads a scene file and returns a list of frame numbers for each scene change.
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:param scenes: path to scene file
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:type scenes: str
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:return: list of frame numbers
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:rtype: list[int]
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"""
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ranges = [0]
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with scenes.open("r") as file:
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content = json.load(file)
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for scene in content['scenes']:
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ranges.append(scene['end_frame'])
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return ranges
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def fast_pass(
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input_file: str, output_file: str, tmp_dir: str, preset: int, crf: float, workers: int
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):
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"""
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Quick fast-pass using Av1an
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:param input_file: path to input file
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:type input_file: str
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:param output_file: path to output file
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:type output_file: str
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:param tmp_dir: path to temporary directory
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:type tmp_dir: str
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:param preset: encoder preset
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:type preset: int
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:param crf: target CRF
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:type crf: float
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:param workers: number of workers
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:type workers: int
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"""
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fast_av1an_command = [
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'av1an',
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'-i', input_file,
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'--temp', tmp_dir,
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'-y',
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'--verbose',
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'--keep',
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'-m', 'lsmash',
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'-c', 'mkvmerge',
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'--min-scene-len', '24',
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'--sc-downscale-height', '720',
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'--set-thread-affinity', '2',
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'-e', 'svt-av1',
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'--force',
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'-v', f'--preset {preset} --crf {crf:.2f} --lp 2 --scm 0 --keyint 0 --fast-decode 1 --color-primaries 1 --transfer-characteristics 1 --matrix-coefficients 1',
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'-w', str(workers),
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'-o', output_file
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]
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try:
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subprocess.run(fast_av1an_command, text=True, check=True)
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except subprocess.CalledProcessError as e:
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print(f"Av1an encountered an error:\n{e}")
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exit(1)
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def turbo_metrics(
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source: str, distorted: str, every: int
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) -> subprocess.CompletedProcess:
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"""
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Compare two files with SSIMULACRA2 using turbo-metrics.
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:param source: path to source file
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:type source: str
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:param distorted: path to distorted file
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:type distorted: str
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:param every: compare every X frames
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:type every: int
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:return: completed process
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:rtype: subprocess.CompletedProcess
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"""
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turbo_cmd = [
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"turbo-metrics",
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"-m",
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"ssimulacra2",
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"--output",
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"csv",
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]
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if every > 1:
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turbo_cmd.append("--every")
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turbo_cmd.append(str(every))
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turbo_cmd.append(source)
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turbo_cmd.append(distorted)
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return subprocess.run(
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turbo_cmd,
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capture_output=True,
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text=True,
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)
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def calculate_ssimu2(src_file, enc_file, ssimu2_txt_path, ranges, skip):
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if not ssimu2zig: # Try turbo-metrics first if ssimu2zig is False
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turbo_metrics_run = turbo_metrics(src_file, enc_file, skip)
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if turbo_metrics_run.returncode == 0: # If turbo-metrics succeeds
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with ssimu2_txt_path.open("w") as file:
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file.write(f"skip: {skip}\n")
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frame = 0
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# for whatever reason, turbo-metrics in csv mode dumps the entire scores to stdout at the end even though it prints them live to stdout.
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# so we need to see if we've seen ``ssimulacra2`` before and if we have, ignore anything after the second one.
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ignore_end_barf = False
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for line in turbo_metrics_run.stdout.splitlines():
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# set ignore_end_barf to true as this is the first "ssimulacra2" line
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if line == "ssimulacra2" and not ignore_end_barf:
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ignore_end_barf = True
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# break the loop as we've encountered the second "ssimulacra2" line so we don't get a dupe of the scores.
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elif line == "ssimulacra2" and ignore_end_barf:
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break
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# assume everything not "ssimulacra2" is a score.
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if line != "ssimulacra2":
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frame += 1
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with ssimu2_txt_path.open("a") as file:
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file.write(f"{frame}: {float(line)}\n")
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return # Exit if turbo-metrics succeeded
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else:
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print(f"Turbo Metrics exited with code: {turbo_metrics_run.returncode}")
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print(turbo_metrics_run.stdout)
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print(turbo_metrics_run.stderr)
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print("Falling back to vs-zip")
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skip = args.skip if args.skip is not None else '3'
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# If ssimu2zig is True or turbo-metrics failed, use vs-zip
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source_clip = core.lsmas.LWLibavSource(source=src_file, cache=0)
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encoded_clip = core.lsmas.LWLibavSource(source=enc_file, cache=0)
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#source_clip = source_clip.resize.Bicubic(format=vs.RGBS, matrix_in_s='709').fmtc.transfer(transs="srgb", transd="linear", bits=32)
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#encoded_clip = encoded_clip.resize.Bicubic(format=vs.RGBS, matrix_in_s='709').fmtc.transfer(transs="srgb", transd="linear", bits=32)
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print(f"source: {len(source_clip)} frames")
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print(f"encode: {len(encoded_clip)} frames")
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with ssimu2_txt_path.open("w") as file:
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file.write(f"skip: {skip}\n")
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iter = 0
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for i in range(len(ranges) - 1):
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cut_source_clip = source_clip[ranges[i]:ranges[i+1]].std.SelectEvery(cycle=skip, offsets=1)
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cut_encoded_clip = encoded_clip[ranges[i]:ranges[i+1]].std.SelectEvery(cycle=skip, offsets=1)
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result = core.vszip.Metrics(cut_source_clip, cut_encoded_clip, mode=0)
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for index, frame in enumerate(result.frames()):
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iter += 1
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score = frame.props['_SSIMULACRA2']
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with ssimu2_txt_path.open("a") as file:
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file.write(f"{iter}: {score}\n")
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def calculate_xpsnr(src_file, enc_path, xpsnr_txt_path):
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if IS_WINDOWS:
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xpsnr_txt_path = f"{src_file.stem}_xpsnr.log"
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src_file_dir = src_file.parent
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os.chdir(src_file_dir)
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xpsnr_command = [
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"ffmpeg",
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"-i", src_file,
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"-i", enc_path,
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"-lavfi", f"xpsnr=stats_file={xpsnr_txt_path}",
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"-f", "null", NULL_DEVICE
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]
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try:
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subprocess.run(xpsnr_command, text=True, check=True)
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except subprocess.CalledProcessError as e:
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print(f"XPSNR encountered an error:\n{e}")
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exit(-2)
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def get_xpsnr(xpsnr_txt_path):
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count=0
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sum_weighted = 0
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values_weighted: list[int] = []
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with xpsnr_txt_path.open("r") as file:
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for line in file:
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match = re.search(r"XPSNR [yY]: ([0-9]+\.[0-9]+|inf) XPSNR [uU]: ([0-9]+\.[0-9]+|inf) XPSNR [vV]: ([0-9]+\.[0-9]+|inf)", line)
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if match:
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Y = float(match.group(1)) if match.group(1) != 'inf' else 100.0
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U = float(match.group(2)) if match.group(2) != 'inf' else 100.0
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V = float(match.group(3)) if match.group(3) != 'inf' else 100.0
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W = (4 * Y + U + V) / 6
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sum_weighted += W
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values_weighted.append(W)
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count += 1
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else:
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print(line)
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avg_weighted = sum_weighted / count
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for i in range(len(values_weighted)):
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values_weighted[i] /= avg_weighted
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return values_weighted
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def get_ssimu2(ssimu2_txt_path):
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ssimu2_scores: list[int] = []
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with ssimu2_txt_path.open("r") as file:
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skipmatch = re.search(r"skip: ([0-9]+)", file.readline())
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if skipmatch:
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skip = int(skipmatch.group(1))
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else:
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print("Skip value not detected in SSIMU2 file, exiting.")
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exit(-2)
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for line in file:
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match = re.search(r"([0-9]+): ([0-9]+\.[0-9]+)", line)
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if match:
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score = float(match.group(2))
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ssimu2_scores.append(score)
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else:
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print(line)
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return ssimu2_scores, skip
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def calculate_std_dev(score_list: list[int]):
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"""
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Takes a list of metrics scores and returns the associated arithmetic mean,
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5th percentile and 95th percentile scores.
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:param score_list: list of SSIMU2 scores
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:type score_list: list
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"""
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filtered_score_list = [score if score >= 0 else 0.0 for score in score_list]
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sorted_score_list = sorted(filtered_score_list)
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average = sum(filtered_score_list)/len(filtered_score_list)
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percentile_5 = sorted_score_list[len(filtered_score_list)//20]
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percentile_95 = sorted_score_list[int (len(filtered_score_list)//(20/19))]
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return (average, percentile_5, percentile_95)
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def generate_zones(ranges: list, percentile_5_total: list, average: int, crf: float, zones_txt_path: str):
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"""
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Appends a scene change to the ``zones_txt_path`` file in Av1an zones format.
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creates ``zones_txt_path`` if it does not exist. If it does exist, the line is
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appended to the end of the file.
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:param ranges: Scene changes list
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:type ranges: list
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:param percentile_5_total: List containing all 5th percentile scores
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:type percentile_5_total: list
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:param average: Full clip average score
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:type average: int
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:param crf: CRF setting to use for the zone
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:type crf: int
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:param zones_txt_path: Path to the zones.txt file
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:type zones_txt_path: str
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"""
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zones_iter = 0
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for i in range(len(ranges)-1):
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zones_iter += 1
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if aggressive:
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new_crf = crf - ceil((1.0 - (percentile_5_total[i] / average)) * 40 * 4) / 4
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else:
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new_crf = crf - ceil((1.0 - (percentile_5_total[i] / average)) * 20 * 4) / 4
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if new_crf < crf - br: # set lowest allowed crf
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new_crf = crf - br
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if new_crf > crf + br: # set highest allowed crf
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new_crf = crf + br
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print(f'Enc: [{ranges[i]}:{ranges[i+1]}]\n'
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f'Chunk 5th percentile: {percentile_5_total[i]}\n'
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f'Adjusted CRF: {new_crf:.2f}\n')
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with zones_txt_path.open("w" if zones_iter == 1 else "a") as file:
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file.write(f"{ranges[i]} {ranges[i+1]} svt-av1 --crf {new_crf:.2f}\n")
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def calculate_metrics(src_file, output_file, tmp_dir, ranges, skip, metrics):
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match metrics:
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case 1:
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ssimu2_txt_path = output_dir / f"{src_file.stem}_ssimu2.log"
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calculate_ssimu2(src_file, output_file, ssimu2_txt_path, ranges, skip)
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case 2:
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xpsnr_txt_path = output_dir / f"{src_file.stem}_xpsnr.log"
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calculate_xpsnr(src_file, output_file, xpsnr_txt_path)
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case 3:
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xpsnr_txt_path = output_dir / f"{src_file.stem}_xpsnr.log"
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ssimu2_txt_path = output_dir / f"{src_file.stem}_ssimu2.log"
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calculate_xpsnr(src_file, output_file, xpsnr_txt_path)
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calculate_ssimu2(src_file, output_file, ssimu2_txt_path, ranges, skip)
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def calculate_zones(tmp_dir, ranges, zones, cq):
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match zones:
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case 1:
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ssimu2_txt_path = output_dir / f"{src_file.stem}_ssimu2.log"
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(ssimu2_scores, skip) = get_ssimu2(ssimu2_txt_path)
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ssimu2_zones_txt_path = tmp_dir / "ssimu2_zones.txt"
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ssimu2_total_scores: list[int] = []
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ssimu2_percentile_5_total = []
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ssimu2_iter = 0
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for i in range(len(ranges)-1):
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ssimu2_chunk_scores: list[int] = []
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xpsnr_chunk_scores: list[int] = []
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ssimu2_frames = (ranges[i+1] - ranges[i]) // skip
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for frames in range(ssimu2_frames):
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ssimu2_score = ssimu2_scores[ssimu2_iter]
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ssimu2_chunk_scores.append(ssimu2_score)
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ssimu2_total_scores.append(ssimu2_score)
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ssimu2_iter += 1
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(ssimu2_average, ssimu2_percentile_5, ssimu2_percentile_95) = calculate_std_dev(ssimu2_chunk_scores)
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ssimu2_percentile_5_total.append(ssimu2_percentile_5)
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#print(f'5th Percentile: {ssimu2_percentile_5}')
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(ssimu2_average, ssimu2_percentile_5, ssimu2_percentile_95) = calculate_std_dev(ssimu2_total_scores)
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print(f'SSIMU2:')
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print(f'Median score: {ssimu2_average}')
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print(f'5th Percentile: {ssimu2_percentile_5}')
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print(f'95th Percentile: {ssimu2_percentile_95}\n')
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generate_zones(ranges, ssimu2_percentile_5_total, ssimu2_average, cq, ssimu2_zones_txt_path)
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case 2:
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xpsnr_txt_path = output_dir / f"{src_file.stem}_xpsnr.log"
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xpsnr_scores: list[int] = get_xpsnr(xpsnr_txt_path)
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xpsnr_zones_txt_path = tmp_dir / "xpsnr_zones.txt"
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xpsnr_total_scores: list[int] = []
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xpsnr_percentile_5_total = []
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xpsnr_iter = 0
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for i in range(len(ranges)-1):
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xpsnr_chunk_scores: list[int] = []
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xpsnr_frames = (ranges[i+1] - ranges[i])
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for frames in range(xpsnr_frames):
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xpsnr_score = xpsnr_scores[xpsnr_iter]
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xpsnr_chunk_scores.append(xpsnr_score)
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xpsnr_total_scores.append(xpsnr_score)
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xpsnr_iter += 1
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(xpsnr_average, xpsnr_percentile_5, xpsnr_percentile_95) = calculate_std_dev(xpsnr_chunk_scores)
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xpsnr_percentile_5_total.append(xpsnr_percentile_5)
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(xpsnr_average, xpsnr_percentile_5, xpsnr_percentile_95) = calculate_std_dev(xpsnr_total_scores)
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print(f'XPSNR:')
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print(f'Median score: {xpsnr_average}')
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print(f'5th Percentile: {xpsnr_percentile_5}')
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print(f'95th Percentile: {xpsnr_percentile_95}\n')
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generate_zones(ranges, xpsnr_percentile_5_total, xpsnr_average, cq, xpsnr_zones_txt_path)
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case 3:
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ssimu2_txt_path = output_dir / f"{src_file.stem}_ssimu2.log"
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(ssimu2_scores, skip) = get_ssimu2(ssimu2_txt_path)
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xpsnr_txt_path = output_dir / f"{src_file.stem}_xpsnr.log"
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xpsnr_scores: list[int] = get_xpsnr(xpsnr_txt_path)
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multiplied_zones_txt_path = tmp_dir / "multiplied_zones.txt"
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multiplied_total_scores: list[int] = []
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multiplied_percentile_5_total = []
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multiplied_iter = 0
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for i in range(len(ranges)-1):
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multiplied_chunk_scores: list[int] = []
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ssimu2_frames = (ranges[i+1] - ranges[i]) // skip
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for frames in range(ssimu2_frames):
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ssimu2_score = ssimu2_scores[multiplied_iter]
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xpsnr_index = (skip*frames) + ranges[i] + 1
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xpsnr_scores_averaged = 0
|
|
for avg_index in range(skip):
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|
xpsnr_scores_averaged += xpsnr_scores[xpsnr_index + avg_index - 1]
|
|
xpsnr_scores_averaged /= skip
|
|
multiplied_score = xpsnr_scores_averaged * ssimu2_score
|
|
multiplied_chunk_scores.append(multiplied_score)
|
|
multiplied_total_scores.append(multiplied_score)
|
|
multiplied_iter += 1
|
|
(multiplied_average, multiplied_percentile_5, multiplied_percentile_95) = calculate_std_dev(multiplied_chunk_scores)
|
|
multiplied_percentile_5_total.append(multiplied_percentile_5)
|
|
(multiplied_average, multiplied_percentile_5, multiplied_percentile_95) = calculate_std_dev(multiplied_total_scores)
|
|
|
|
print(f'Multiplied:')
|
|
print(f'Median score: {multiplied_average}')
|
|
print(f'5th Percentile: {multiplied_percentile_5}')
|
|
print(f'95th Percentile: {multiplied_percentile_95}\n')
|
|
generate_zones(ranges, multiplied_percentile_5_total, multiplied_average, cq, multiplied_zones_txt_path)
|
|
|
|
|
|
case 4:
|
|
ssimu2_txt_path = output_dir / f"{src_file.stem}_ssimu2.log"
|
|
(ssimu2_scores, skip) = get_ssimu2(ssimu2_txt_path)
|
|
xpsnr_txt_path = output_dir / f"{src_file.stem}_xpsnr.log"
|
|
xpsnr_scores: list[int] = get_xpsnr(xpsnr_txt_path)
|
|
|
|
minimum_zones_txt_path = tmp_dir / "minimum_zones.txt"
|
|
minimum_total_scores: list[int] = []
|
|
minimum_percentile_5_total = []
|
|
minimum_iter = 0
|
|
ssimu2_total_scores: list[int] = []
|
|
for ssimu2_iter in range(len(ssimu2_scores)-1):
|
|
ssimu2_total_scores.append(ssimu2_scores[ssimu2_iter])
|
|
(ssimu2_average, ssimu2_percentile_5, ssimu2_percentile_95) = calculate_std_dev(ssimu2_total_scores)
|
|
|
|
for i in range(len(ranges)-1):
|
|
minimum_chunk_scores: list[int] = []
|
|
ssimu2_frames = (ranges[i+1] - ranges[i]) // skip
|
|
for frames in range(ssimu2_frames):
|
|
ssimu2_score = ssimu2_scores[minimum_iter]
|
|
xpsnr_index = (skip*frames) + ranges[i] + 1
|
|
xpsnr_scores_averaged = 0
|
|
for avg_index in range(skip):
|
|
xpsnr_scores_averaged += xpsnr_scores[xpsnr_index + avg_index - 1]
|
|
xpsnr_scores_averaged /= skip
|
|
xpsnr_scores_averaged *= ssimu2_average
|
|
minimum_score = min(ssimu2_score, xpsnr_scores_averaged)
|
|
minimum_chunk_scores.append(minimum_score)
|
|
minimum_total_scores.append(minimum_score)
|
|
minimum_iter += 1
|
|
(minimum_average, minimum_percentile_5, minimum_percentile_95) = calculate_std_dev(minimum_chunk_scores)
|
|
minimum_percentile_5_total.append(minimum_percentile_5)
|
|
(minimum_average, minimum_percentile_5, minimum_percentile_95) = calculate_std_dev(minimum_total_scores)
|
|
|
|
print(f'Minimum:')
|
|
print(f'Median score: {minimum_average}')
|
|
print(f'5th Percentile: {minimum_percentile_5}')
|
|
print(f'95th Percentile: {minimum_percentile_95}\n')
|
|
generate_zones(ranges, minimum_percentile_5_total, minimum_average, cq, minimum_zones_txt_path)
|
|
|
|
match stage:
|
|
case 0:
|
|
workers = args.workers
|
|
crf = float(args.quality)
|
|
preset = args.preset
|
|
fast_pass(src_file, output_file, tmp_dir, preset, crf, workers)
|
|
ranges = get_ranges(scenes_file)
|
|
metrics = int(args.metrics)
|
|
calculate_metrics(src_file, output_file, tmp_dir, ranges, skip, metrics)
|
|
zones = int(args.zones)
|
|
calculate_zones(tmp_dir, ranges, zones, crf)
|
|
case 1:
|
|
workers = args.workers
|
|
crf = float(args.quality)
|
|
preset = args.preset
|
|
fast_pass(src_file, output_file, tmp_dir, preset, crf, workers)
|
|
case 2:
|
|
ranges = get_ranges(scenes_file)
|
|
metrics = int(args.metrics)
|
|
calculate_metrics(src_file, output_file, tmp_dir, ranges, skip, metrics)
|
|
case 3:
|
|
ranges = get_ranges(scenes_file)
|
|
zones = int(args.zones)
|
|
crf = float(args.quality)
|
|
calculate_zones(tmp_dir, ranges, zones, crf)
|
|
case _:
|
|
print(f"Stage argument invalid, exiting.")
|
|
exit(-2)
|