f6fd5e50c8
:Release Notes: - :Detailed Notes: - :Testing Performed: - :QA Notes: - :Issues Addressed: -
114 lines
3.5 KiB
Python
114 lines
3.5 KiB
Python
#!/usr/bin/python3
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import subprocess
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import pandas as pd
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def run_psnr_check(original, encoded, video_info):
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out = ""
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options = f"-f rawvideo {video_info} -i {original} -i {encoded} -filter_complex psnr -f null /dev/null"
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with open("ffmpeg-log.txt", "w") as f:
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proc = subprocess.run(["ffmpeg", *options.split()], stdout=f, stderr=subprocess.STDOUT, text=True)
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print(f"Return code: {proc.returncode}")
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with open("ffmpeg-log.txt", "r") as f:
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out = f.read()
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return out
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def run_ssim_check(original, encoded, video_info):
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options = f"-f rawvideo {video_info} -i {original} -i {encoded} -filter_complex ssim -f null /dev/null"
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with open("ffmpeg-log.txt", "w") as f:
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proc = subprocess.run(["ffmpeg", *options.split()], stdout=f, stderr=subprocess.STDOUT, text=True)
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print(f"Return code: {proc.returncode}")
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with open("ffmpeg-log.txt", "r") as f:
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out = f.read()
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return out
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def parse_psnr_output(output):
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for line in output.splitlines():
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if "[Parsed_psnr" in line and "PSNR" in line:
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parts = line.split()
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y = parts[4].split(":")[1]
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u = parts[5].split(":")[1]
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v = parts[6].split(":")[1]
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avg = parts[7].split(":")[1]
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minYUV = parts[8].split(":")[1]
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maxYUV = parts[9].split(":")[1]
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return {
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"Y": y,
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"U": u,
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"V": v,
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"Average": avg,
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"MinYUV": minYUV,
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"MaxYUV": maxYUV
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}
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return {}
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def parse_ssim_output(output):
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for line in output.splitlines():
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if "[Parsed_ssim" in line and "SSIM" in line:
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parts = line.split()
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all_value = parts[10].split(":")[1]
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y = parts[4].split(":")[1]
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u = parts[6].split(":")[1]
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v = parts[8].split(":")[1]
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return {
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"Y": y,
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"U": u,
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"V": v,
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"Average": all_value
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}
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return {}
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def run_quality_check(original, encoded, option):
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psnr_result = run_psnr_check(original, encoded, option)
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ssim_result = run_ssim_check(original, encoded, option)
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psnr_metrics = parse_psnr_output(psnr_result)
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ssim_metrics = parse_ssim_output(ssim_result)
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print ("PSNR Metrics:", psnr_metrics)
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print ("SSIM Metrics:", ssim_metrics)
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return psnr_metrics, ssim_metrics
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def parse_quality_report(psnr_metrics, ssim_metrics):
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psnrSeries = pd.Series(psnr_metrics)
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ssimSeries = pd.Series(ssim_metrics)
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combined = pd.concat([psnrSeries, ssimSeries], axis=1)
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combined.columns = ["PSNR", "SSIM"]
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combined = combined.fillna(0)
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return combined
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# psnr, ssim = run_quality_check(
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# "base-x264enc-kpop-test-10.yuv",
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# "encoded-x264enc-kpop-test-10.mp4",
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# "-pixel_format yuv420p -color_range tv -video_size 1920x1080 -framerate 23.98 "
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# )
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# combined = parse_quality_report(
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# psnr,
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# ssim
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# )
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# encoder = "x264enc"
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# profile = "main"
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# params = "bitrate=5000"
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# columns = pd.MultiIndex.from_tuples(
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# [(encoder, profile, params, col) for col in combined.columns]
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# )
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# combined.columns = columns
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# main_df = combined
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# profile = "baseline"
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# combined2 = parse_quality_report(
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# psnr,
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# ssim
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# )
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# columns = pd.MultiIndex.from_tuples(
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# [(encoder, profile, params, col) for col in combined2.columns]
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# )
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# combined2.columns = columns
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# main_df = pd.concat([main_df, combined2], axis=1)
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# print(main_df)
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# main_df.to_csv("quality_report.csv")
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