memoir-capture 0.1.4

Windows-native capture/replay module with Python bindings

memoir-capture

Windows-native screen capture module with Python bindings for real-time frame analysis and deterministic replay recording.

Memoir captures frames from a window or monitor using Windows Graphics Capture (WGC), delivers them to Python as NumPy arrays, and optionally records them to HEVC video with per-frame metadata — all without GPU-to-CPU roundtrips in the recording path.

Features

• WGC capture — continuous frame capture from any window or monitor
• NumPy delivery — BGRA frames as (H, W, 4) uint8 arrays via bounded queue
• NVENC recording — GPU-accelerated HEVC encoding via FFmpeg, lossless YUV444 by default
• Binary metadata — .meta sidecar with per-frame keyboard state, timestamps, and frame IDs
• Dynamic recording — start/stop recording without restarting capture
• Frame-accurate keyboard — key state snapshot at the exact moment each frame is accepted
• Typed Python API — full type annotations, dataclasses, context managers

Requirements

• Windows 10 1903+ (for WGC CreateFreeThreaded)
• NVIDIA GPU with NVENC support
• Python 3.10+
• Visual Studio 2022 (for building from source)

Installation

From PyPI (prebuilt)

pip install memoir-capture

From source

# Clone with vcpkg
git clone https://github.com/lunavod/memoir-capture.git
cd Memoir
git clone https://github.com/microsoft/vcpkg.git vcpkg --depth 1
.\vcpkg\bootstrap-vcpkg.bat -disableMetrics

# Build
pip install build numpy
python -m build --wheel

# Install
pip install dist\memoir_capture-*.whl

The first build takes ~15 minutes (vcpkg builds FFmpeg). Subsequent builds use cached binaries.

For local development without installing:

.\scripts\build.ps1
# memoir_capture/ package is ready to import from the project root

Quick Start

Capture frames

import memoir_capture

engine = memoir_capture.CaptureEngine(
    memoir_capture.MonitorTarget(0),    # primary monitor
    max_fps=10.0,
)
engine.start()

for packet in engine.frames():
    with packet:
        img = packet.cpu_bgra           # numpy (H, W, 4) uint8
        print(f"Frame {packet.frame_id}: {img.shape}")
        print(f"Keys: 0x{packet.keyboard_mask:016x}")
    break

engine.stop()

Capture a specific window

engine = memoir_capture.CaptureEngine(
    memoir_capture.WindowTitleTarget(r"(?i)notepad"),
    max_fps=30.0,
)

Record to MP4

engine = memoir_capture.CaptureEngine(
    memoir_capture.MonitorTarget(0),
    max_fps=10.0,
    record_width=1920,
    record_height=1080,
    record_gop=1,           # 1 = all-intra (frame-accurate seeking)
)
engine.start()

info = engine.start_recording("session_001")
print(f"Recording to {info.video_path}")  # session_001.mp4

for i, packet in enumerate(engine.frames()):
    packet.release()
    if i >= 99:
        break

engine.stop_recording()     # finalizes .mp4 + .meta
engine.stop()

Read metadata

meta = memoir_capture.MetaReader.read("session_001.meta")

print(f"Keys tracked: {[k.name for k in meta.keys]}")

for row in meta.rows:
    print(f"Frame {row.frame_id}: keyboard=0x{row.keyboard_mask:016x}")

Write metadata (for synthetic replays)

from memoir_capture import MetaWriter, MetaKeyEntry, MetaRow

keys = [MetaKeyEntry(0, 0x57, "W"), MetaKeyEntry(1, 0x41, "A")]

with MetaWriter("synthetic.meta", keys) as w:
    w.write_row(MetaRow(
        frame_id=0, record_frame_index=0,
        capture_qpc=0, host_accept_qpc=0,
        keyboard_mask=0b01,
        width=1920, height=1080, analysis_stride=7680,
    ))

Context manager

with memoir_capture.CaptureEngine(memoir_capture.MonitorTarget(0)) as engine:
    packet = engine.get_next_frame(timeout_ms=2000)
    if packet:
        with packet:
            process(packet.cpu_bgra)

API Reference

CaptureEngine(target, *, max_fps, ...)

Parameter	Default	Description
target	required	MonitorTarget(index), WindowTitleTarget(regex), or WindowExeTarget(regex)
max_fps	10.0	Maximum accepted frame rate
analysis_queue_capacity	1	Bounded queue size for Python delivery
capture_cursor	False	Include cursor in capture
record_width	1920	Recording output width
record_height	1080	Recording output height
record_gop	1	GOP size (1 = all-intra, higher = smaller files)

Methods: start(), stop(), get_next_frame(timeout_ms), frames(), start_recording(base_path), stop_recording(), is_recording(), stats()

FramePacket

Property	Type	Description
frame_id	int	Monotonic ID (only accepted frames get IDs)
cpu_bgra	np.ndarray	(H, W, 4) uint8 BGRA pixel data
keyboard_mask	int	64-bit bitmask of tracked keys
capture_qpc	int	WGC capture timestamp (100ns units)
width, height, stride	int	Frame dimensions

Supports with packet: (auto-release) and explicit packet.release().

Recording Quality

Default encoding: lossless HEVC (QP=0), YUV 4:4:4, full color range.

Setting	File size (10s, 1080p)
GOP=1 (all-intra)	~52 MB
GOP=10	~17 MB
GOP=30	~14 MB

Architecture

WGC FrameArrived (thread pool)
  │
  ├─ FPS limiter → drop if too soon
  ├─ Queue check → drop if full (drop-new policy)
  │
  ├─ Accept: assign frame_id, snapshot keyboard
  ├─ GPU→CPU: CopyResource → staging → Map → memcpy
  ├─ Recording: swscale (BGRA→YUV444) → hevc_nvenc → MP4
  └─ Enqueue → Python consumer

• Single ID3D11DeviceContext + mutex for all GPU ops
• WGC CreateFreeThreaded — no DispatcherQueue needed
• Recording runs on the callback thread (well within budget at 10fps)
• FramePacket owns its pixel buffer; release() frees it

Testing

# Full suite (requires display + NVIDIA GPU)
pytest -v

# Headless (CI-safe)
pytest --headless -v

License

MIT. Links against FFmpeg (LGPL 2.1+).