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lossless_video_encoding_testing.jl
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lossless_video_encoding_testing.jl
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# A tool for testing lossless video encoding
using VideoIO, ColorTypes, FixedPointNumbers, DataFrames
function collectexecoutput(exec::Cmd)
out = Pipe()
err = Pipe()
p = Base.open(pipeline(ignorestatus(exec), stdout = out, stderr = err))
close(out.in)
close(err.in)
err_s = readlines(err)
out_s = readlines(out)
return (length(out_s) > length(err_s)) ? out_s : err_s
end
function createtestvideo(;
filename::String = "$(tempname()).mp4",
duration::Real = 5,
width::Int64 = 1280,
height::Int64 = 720,
framerate::Real = 30,
testtype::String = "testsrc2",
encoder::String = "libx264rgb",
)
withenv(VideoIO.execenv) do
return collectexecoutput(`$(VideoIO.ffmpeg) -y -f lavfi -i
$testtype=duration=$duration:size=$(width)x$(height):rate=$framerate
-c:v $encoder -preset slow -crf 0 -c:a copy $filename`)
end
return filename
end
function testvideocomp!(df, preset, imgstack_gray)
t = @elapsed VideoIO.save(
"video.mp4",
imgstack_gray,
framerate = 30,
codec_name = "libx264",
encoder_options = (color_range = 2, crf = 0, "preset" = preset),
)
fs = filesize("video.mp4")
f = openvideo("video.mp4", target_format = VideoIO.AV_PIX_FMT_GRAY8)
imgstack_gray_copy = []
while !eof(f)
push!(imgstack_gray_copy, read(f))
end
identical = !any(.!(imgstack_gray .== imgstack_gray_copy))
return push!(df, [preset, fs, t, identical])
end
imgstack_gray_noise = map(x -> rand(Gray{N0f8}, 1280, 720), 1:1000)
f = openvideo(createtestvideo())
imgstack = []
while !eof(f)
push!(imgstack, read(f))
end
imgstack_gray_testvid = map(x -> convert.(Gray{N0f8}, x), imgstack)
f = openvideo("videos/ladybird.mp4")
imgstack = []
while !eof(f)
push!(imgstack, read(f))
end
imgstack_gray_ladybird = map(x -> convert.(Gray{N0f8}, x), imgstack)
df_noise = DataFrame(preset = [], filesize = [], time = [], identical = [])
df_testvid = DataFrame(preset = [], filesize = [], time = [], identical = [])
df_ladybird = DataFrame(preset = [], filesize = [], time = [], identical = [])
for preset in ["ultrafast", "superfast", "veryfast", "faster", "fast", "medium", "slow", "slower", "veryslow"]
@show preset
for rep in 1:3
@show rep
testvideocomp!(df_noise, preset, imgstack_gray_noise)
testvideocomp!(df_testvid, preset, imgstack_gray_testvid)
testvideocomp!(df_ladybird, preset, imgstack_gray_ladybird)
end
end
noise_raw_size = size(imgstack_gray_noise[1], 1) * size(imgstack_gray_noise[1], 2) * length(imgstack_gray_noise)
testvid_raw_size = size(imgstack_gray_testvid[1], 1) * size(imgstack_gray_testvid[1], 2) * length(imgstack_gray_testvid)
ladybird_raw_size =
size(imgstack_gray_ladybird[1], 1) * size(imgstack_gray_ladybird[1], 2) * length(imgstack_gray_ladybird)
df_noise[:filesize_perc] = 100 * (df_noise[:filesize] ./ noise_raw_size)
df_testvid[:filesize_perc] = 100 * (df_testvid[:filesize] ./ testvid_raw_size)
df_ladybird[:filesize_perc] = 100 * (df_ladybird[:filesize] ./ ladybird_raw_size)
df_noise[:fps] = length(imgstack_gray_noise) ./ df_noise[:time]
df_testvid[:fps] = length(imgstack_gray_testvid) ./ df_testvid[:time]
df_ladybird[:fps] = length(imgstack_gray_ladybird) ./ df_ladybird[:time]
using Statistics
df_noise_summary = by(
df_noise,
:preset,
identical = :identical => minimum,
fps_mean = :fps => mean,
fps_std = :fps => std,
filesize_perc_mean = :filesize_perc => mean,
filesize_perc_std = :filesize_perc => std,
)
df_testvid_summary = by(
df_testvid,
:preset,
identical = :identical => minimum,
fps_mean = :fps => mean,
fps_std = :fps => std,
filesize_perc_mean = :filesize_perc => mean,
filesize_perc_std = :filesize_perc => std,
)
df_ladybird_summary = by(
df_ladybird,
:preset,
identical = :identical => minimum,
fps_mean = :fps => mean,
fps_std = :fps => std,
filesize_perc_mean = :filesize_perc => mean,
filesize_perc_std = :filesize_perc => std,
)
@show df_noise_summary
@show df_testvid_summary
@show df_ladybird_summary
### Results (generated 2019-05-29 on a 2019 Macbook Pro)
### OUTDATED. Generated before change to VideoIO.save
#=
df_noise_summary = 9×6 DataFrame
│ Row │ preset │ identical │ fps_mean │ fps_std │ filesize_perc_mean │ filesize_perc_std │
│ │ Any │ Bool │ Float64 │ Float64 │ Float64 │ Float64 │
├─────┼───────────┼───────────┼──────────┼─────────┼────────────────────┼───────────────────┤
│ 1 │ ultrafast │ true │ 92.5769 │ 8.40224 │ 156.444 │ 0.0 │
│ 2 │ superfast │ true │ 62.3509 │ 1.19652 │ 144.019 │ 0.0 │
│ 3 │ veryfast │ true │ 59.9182 │ 1.77294 │ 144.019 │ 0.0 │
│ 4 │ faster │ true │ 60.3482 │ 2.32679 │ 144.02 │ 0.0 │
│ 5 │ fast │ true │ 149.169 │ 1.56068 │ 100.784 │ 0.0 │
│ 6 │ medium │ true │ 146.141 │ 3.41282 │ 100.784 │ 0.0 │
│ 7 │ slow │ true │ 147.214 │ 1.23929 │ 100.784 │ 0.0 │
│ 8 │ slower │ true │ 138.808 │ 2.553 │ 100.784 │ 0.0 │
│ 9 │ veryslow │ true │ 132.505 │ 3.28558 │ 100.784 │ 0.0 │
df_testvid_summary = 9×6 DataFrame
│ Row │ preset │ identical │ fps_mean │ fps_std │ filesize_perc_mean │ filesize_perc_std │
│ │ Any │ Bool │ Float64 │ Float64 │ Float64 │ Float64 │
├─────┼───────────┼───────────┼──────────┼─────────┼────────────────────┼───────────────────┤
│ 1 │ ultrafast │ true │ 228.166 │ 75.1439 │ 4.80392 │ 0.0 │
│ 2 │ superfast │ true │ 239.73 │ 54.2033 │ 3.62199 │ 0.0 │
│ 3 │ veryfast │ true │ 197.506 │ 13.1121 │ 3.59901 │ 0.0 │
│ 4 │ faster │ true │ 174.174 │ 18.0316 │ 3.60282 │ 0.0 │
│ 5 │ fast │ true │ 235.181 │ 7.40358 │ 3.44104 │ 0.0 │
│ 6 │ medium │ true │ 219.654 │ 3.27445 │ 3.40832 │ 0.0 │
│ 7 │ slow │ true │ 171.337 │ 3.92415 │ 3.33917 │ 0.0 │
│ 8 │ slower │ true │ 105.24 │ 6.59151 │ 3.25774 │ 5.43896e-16 │
│ 9 │ veryslow │ true │ 63.1136 │ 2.47291 │ 3.2219 │ 0.0 │
df_ladybird_summary = 9×6 DataFrame
│ Row │ preset │ identical │ fps_mean │ fps_std │ filesize_perc_mean │ filesize_perc_std │
│ │ Any │ Bool │ Float64 │ Float64 │ Float64 │ Float64 │
├─────┼───────────┼───────────┼──────────┼──────────┼────────────────────┼───────────────────┤
│ 1 │ ultrafast │ true │ 176.787 │ 36.5227 │ 12.2293 │ 0.0 │
│ 2 │ superfast │ true │ 135.925 │ 7.04431 │ 10.3532 │ 0.0 │
│ 3 │ veryfast │ true │ 117.115 │ 1.28102 │ 10.1954 │ 0.0 │
│ 4 │ faster │ true │ 94.39 │ 3.48494 │ 9.85604 │ 0.0 │
│ 5 │ fast │ true │ 69.657 │ 1.61004 │ 9.62724 │ 0.0 │
│ 6 │ medium │ true │ 54.9621 │ 0.568074 │ 9.51032 │ 0.0 │
│ 7 │ slow │ true │ 37.8888 │ 1.27484 │ 9.33622 │ 0.0 │
│ 8 │ slower │ true │ 20.1112 │ 1.04282 │ 9.25529 │ 0.0 │
│ 9 │ veryslow │ true │ 10.0016 │ 0.473213 │ 9.24999 │ 0.0 │
=#
# HISTOGRAM COMPARISON - useful for diagnosing range compression
# using PyPlot, ImageCore
# figure()
# hist(rawview(channelview(imgstack_gray_copy[1]))[:],0:256,label="copy")
# hist(rawview(channelview(imgstack_gray[1]))[:],0:256,label="original")
# legend()