vidstats 0.1.2

Extract per-frame colour and luminosity metrics from video via ffprobe signalstats.

vidstats

Extract per-frame colour and luminosity metrics from video using ffprobe's signalstats filter. Output goes to Parquet, CSV, IPC/Feather, or NDJSON.

Designed for research pipelines that need a fast, dependency-light way to turn a video into a tabular time series — for example, non-contact cardiac monitoring in animals via colour/motion video analysis.

Installation

pixi (recommended — pulls in ffmpeg automatically):

pixi global install vidstats

uv (global install):

uv tool install vidstats

conda:

conda install -c conda-forge vidstats

pip (requires ffmpeg on PATH separately):

pip install vidstats

Usage

# Extract all metrics → Parquet
vidstats input.mp4 output.parquet

# Extract only luminance and saturation averages → CSV
vidstats input.mp4 output.csv --metrics YAVG,SATAVG

# Include both frame number and timestamp in seconds
vidstats input.mp4 output.parquet --timestamps both

# Override FPS when stream metadata is missing or wrong
vidstats input.mp4 output.parquet --timestamps both --fps 30

# Apply a crop region (X Y W H) before extraction
vidstats input.mp4 output.parquet --crop "10 20 180 180"

# Force format regardless of extension
vidstats input.mp4 output.dat --format csv

# Disable hardware acceleration
vidstats input.mp4 output.parquet --no-hwaccel

# List all available metric names and their output column names
vidstats --list-metrics

Python API

vidstats can also be used directly from Python. The simplest path is the extract() convenience function:

import vidstats

df = vidstats.extract("input.mp4")

df is a Polars DataFrame with the same schema as the CLI output. Optional arguments mirror the CLI:

df = vidstats.extract(
    "input.mp4",
    metrics=["YAVG", "SATAVG", "HUEMED"],  # default: all metrics
    crop=(10, 20, 180, 180),               # (x, y, w, h) in pixels
    include_time=True,                     # adds time_s column
    fps=25.0,                              # override FPS for time_s
    hwaccel="cuda",                        # hardware decode acceleration
)

Write the result with any Polars method:

df.write_parquet("output.parquet")
df.write_csv("output.csv")

Supported output formats

Extension(s)	Format
.parquet	Parquet
.csv	CSV
.tsv	TSV
.ipc, .arrow, .feather	Arrow IPC
.ndjson, .jsonl	NDJSON

Available metrics

All 25 signalstats metrics are extracted by default. Run vidstats --list-metrics for the full table of ffprobe names, output column names, and descriptions. They cover:

• Luminance: YMIN, YLOW, YAVG, YHIGH, YMAX
• Cb chrominance (U): same set of five
• Cr chrominance (V): same set of five
• Saturation: SATMIN, SATLOW, SATAVG, SATHIGH, SATMAX
• Hue: HUEMED, HUEAVG
• Quality flags: TOUT, VREP, BRNG

The --metrics flag accepts ffprobe names (e.g. YAVG,SATAVG). Output columns use descriptive snake_case names (e.g. luminance_mean, saturation_mean) — see --list-metrics for the full mapping.

Output schema

Column	Type	Condition
frame	UInt32	always
time_s	Float64	--timestamps seconds or both
metrics…	Float32	selected metrics

time_s is computed as frame / fps. FPS is read from stream metadata automatically; use --fps to override it or supply it when metadata is absent.

Hardware acceleration

vidstats auto-detects CUDA and passes -hwaccel cuda to ffprobe if available. This accelerates the decode stage only — signalstats itself always runs on CPU. For small (e.g. 200×200) videos the gain is negligible, but the detection is there for larger inputs. Suppress with --no-hwaccel.

No special CUDA packages or drivers are required beyond what you already have. vidstats uses ffprobe's built-in NVDEC hardware decoding, which talks directly to the NVIDIA driver on your system — there is no cudatoolkit, no pytorch-cuda, and no GPU-specific installation step.

License

MIT