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Effects

Effects are Operation subclasses that preserve video shape and frame count. Each carries an optional window: TimeRange | None field that limits the effect to a sub-range of the video. See Operations for the base contract.

Usage

Effects are not applied to a Video directly. They run only through the streaming engine: add the operation(s) to a VideoEdit and render with run_to_file. Each effect carries an optional window that limits it to a sub-range of the segment.

from videopython.base import BoundingBox
from videopython.editing import (
    VideoEdit, SegmentConfig,
    Blur, Zoom, ColorGrading, Vignette, KenBurns,
    Fade, VolumeAdjust, TextOverlay, TimeRange,
)

edit = VideoEdit(segments=[SegmentConfig(source="input.mp4", start=0, end=5, operations=[
    # Effect across the full segment:
    Blur(mode="constant", iterations=50),
    # Effect on a sub-range via the `window` field:
    Blur(mode="constant", iterations=50, window=TimeRange(start=0.0, stop=2.0)),
])])
edit.run_to_file("output.mp4")

The constructors below produce the operation objects to drop into a segment's operations list (as above) and render with run_to_file:

video_op = Blur(mode="constant", iterations=50)
video_op = Blur(mode="constant", iterations=50, window=TimeRange(start=0.0, stop=2.0))
video_op = Zoom(zoom_factor=1.5, mode="in")
video_op = ColorGrading(brightness=0.1, contrast=1.2, saturation=1.1)
video_op = Vignette(strength=0.5, radius=0.8)

start_region = BoundingBox(x=0.0, y=0.0, width=0.5, height=0.5)
end_region = BoundingBox(x=0.5, y=0.5, width=0.5, height=0.5)
video_op = KenBurns(start_region=start_region, end_region=end_region,
                    easing="ease_in_out")

video_op = Fade(mode="in", duration=1.0)
video_op = Fade(mode="out", duration=0.5)
video_op = VolumeAdjust(volume=0.0, window=TimeRange(stop=2.0))  # mute first 2s
video_op = TextOverlay(text="Hello World", position=(0.5, 0.9), font_size=48)

# YouTube / experimental effects:
from videopython.editing import (
    Shake, PunchIn, Flash, ChromaticAberration, Glitch,
    FilmGrain, Sharpen, Pixelate, MirrorFlip, Kaleidoscope,
)

video_op = Shake(intensity_px=6, mode="rhythmic", frequency_hz=4)
video_op = PunchIn(zoom_factor=1.5, attack_frames=3, release_frames=0)
video_op = Flash(color=(255, 255, 255), peak_alpha=1.0,
                 attack_frames=2, decay_frames=4,
                 window=TimeRange(start=1.0, stop=1.3))
video_op = ChromaticAberration(shift_px=4, mode="radial")
video_op = Glitch(intensity=0.4, slice_count=12, seed=42)
video_op = FilmGrain(intensity=0.08, monochrome=True)
video_op = Sharpen(amount=1.0, kernel_size=5)
video_op = Pixelate(block_size=24,
                    region=BoundingBox(x=0.4, y=0.2, width=0.2, height=0.2))
video_op = MirrorFlip(mode="mirror_left")
video_op = Kaleidoscope(segments=6)

A SegmentConfig's operations list also accepts the inline dict form; the window field travels as a nested object:

{"op": "blur_effect", "mode": "constant", "iterations": 50,
 "window": {"start": 0.0, "stop": 2.0}}

The subtitles effect (add_subtitles) requires a transcription context, passed to the runner: run_to_file(..., context={"transcription": ...}).

Available Effects

Every effect is streamable (compatible with VideoEdit.run_to_file() for constant-memory processing). The two text-rendering effects compile to a native ffmpeg filter at plan-compile time (no per-frame Python): add_subtitles (libass subtitles=) and text_overlay (drawtext). Every other effect runs per-frame Python via process_frame — vectorised numpy/cv2. Benchmarks showed compiling pixel effects to ffmpeg filters bought at best ~1.1–1.4x (the gain was avoiding the rawvideo round-trip, not faster compute) and in some cases lost (gblur), so the engine keeps ffmpeg only for what numpy can't do well: geometry/timing transforms and text rendering. Context-requiring ops (add_subtitles) stream too: pass context= to run_to_file and the runner re-bases it onto each segment's local timeline.

op Class Description
blur_effect Blur Gaussian blur, constant or ramping
zoom_effect Zoom Time-varying zoom in/out
color_adjust ColorGrading Brightness / contrast / saturation / temperature
vignette Vignette Radial darkening from the edges
ken_burns KenBurns Pan-and-zoom between two bounding boxes
full_image_overlay FullImageOverlay Composite a full-frame image
image_overlay ImageOverlay Scaled, positioned raster/SVG image (logo / watermark)
fade Fade Audio + video fade in/out/in_out
volume_adjust VolumeAdjust Audio-only effect
text_overlay TextOverlay Rendered text on top of frames
add_subtitles TranscriptionOverlay Word-level subtitles via libass; requires transcription context
shake Shake Per-frame jitter (random / rhythmic / decay)
punch_in PunchIn Snap-zoom emphasis with optional release
flash Flash Solid-color frame flash with attack/decay
chromatic_aberration ChromaticAberration R/B channel split (horizontal / vertical / radial)
glitch Glitch Random horizontal slice displacement + channel offsets
film_grain FilmGrain Additive seeded noise (mono or RGB)
sharpen Sharpen Unsharp-mask sharpening
pixelate Pixelate Mosaic blocks, full frame or region
mirror_flip MirrorFlip Flip or reflect one half onto the other
kaleidoscope Kaleidoscope N-way radial mirror around the center

API Reference

Effect

Effect

Bases: Operation

Operation that preserves shape and frame count, driven by per-frame streaming.

Subclasses implement the streaming contract -- :meth:process_frame (and :meth:streaming_init for any precomputed per-stream state) -- which is the single source of truth for the effect's pixel logic. The streaming engine in editing/streaming.py drives that contract for bounded-memory execution, resolving :attr:window against the segment timeline so frames outside the window pass through untouched.

Effects that compile to a native ffmpeg filter instead set :attr:compiles_to_filter and implement :meth:to_ffmpeg_filter (and, for audio-coupled effects like Fade/VolumeAdjust, :meth:to_ffmpeg_audio_filter) so the window stays coherent across the decode/encode graph.

Source code in src/videopython/editing/operation.py 
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class Effect(Operation):
    """Operation that preserves shape and frame count, driven by per-frame streaming.

    Subclasses implement the streaming contract -- :meth:`process_frame` (and
    :meth:`streaming_init` for any precomputed per-stream state) -- which is the
    single source of truth for the effect's pixel logic. The streaming engine
    in ``editing/streaming.py`` drives that contract for bounded-memory
    execution, resolving :attr:`window` against the segment timeline so frames
    outside the window pass through untouched.

    Effects that compile to a native ffmpeg filter instead set
    :attr:`compiles_to_filter` and implement :meth:`to_ffmpeg_filter` (and, for
    audio-coupled effects like ``Fade``/``VolumeAdjust``,
    :meth:`to_ffmpeg_audio_filter`) so the window stays coherent across the
    decode/encode graph.
    """

    category: ClassVar[OpCategory] = OpCategory.EFFECT
    audio_coupled: ClassVar[bool] = False
    """Whether the effect mutates audio alongside pixels (``afade``/``volume``)."""

    window: TimeRange | None = Field(
        None,
        description="Time window for the effect in seconds. Omit to apply across the full duration.",
    )

    @property
    def compiles_to_filter(self) -> bool:
        """Whether this effect joins the decode filter chain instead of scheduling per-frame Python.

        When True, the streaming plan builder calls :meth:`to_ffmpeg_filter`
        (with the segment's resolved context on the :class:`FilterCtx`) and, if
        it compiles, appends the result to the vf chain at this op's plan
        position -- the Filter class of the streaming contract. Instance-level
        rather than a ClassVar because it may depend on field values (e.g.
        ``add_subtitles``'s ``renderer``). False by default: effects normally
        stream via ``streaming_init``/``process_frame``.
        """
        return False

    def predict_metadata(self, meta: VideoMetadata, **_context: Any) -> VideoMetadata:
        """Effects preserve shape and frame count, so the prediction is identity.

        Accepts ``**_context`` so requires-aware effects (``TranscriptionOverlay``)
        validate without subclasses needing to override just to widen the
        signature. Mirrors :meth:`Effect.streaming_init`'s ``**_context`` accept-all.
        """
        return meta

    def streaming_init(self, total_frames: int, fps: float, width: int, height: int, **_context: Any) -> None:
        """Hook for per-stream precomputation (per-frame alphas, sigma curves...).

        ``_context`` carries resolved ``requires`` values for context-aware
        effects (e.g. ``transcription=...`` for ``TranscriptionOverlay``),
        already re-based onto the local timeline by the runner. Effects that
        declare no ``requires`` are always called without context kwargs.

        Default: no-op. Override in subclasses that need it.
        """

    def process_frame(self, frame: np.ndarray, frame_index: int) -> np.ndarray:
        """Process one ``(H, W, 3) uint8`` frame in streaming mode.

        ``frame_index`` is 0-based within this effect's active window.
        """
        raise NotImplementedError(f"{type(self).__name__} does not support streaming")

    def streams(self) -> bool:
        """An effect streams via per-frame Python (``process_frame``) or a filter.

        Frame effects override :meth:`process_frame`; filter effects
        (``add_subtitles``, ``vignette``, ...) instead set
        :attr:`compiles_to_filter` and implement :meth:`to_ffmpeg_filter`.
        ``add_subtitles`` streams *only* via the filter path (it does not override
        ``process_frame``), so ``compiles_to_filter`` is consulted per-instance.
        """
        return type(self).process_frame is not Effect.process_frame or self.compiles_to_filter

audio_coupled class-attribute 

audio_coupled: bool = False

Whether the effect mutates audio alongside pixels (afade/volume).

compiles_to_filter property 

compiles_to_filter: bool

Whether this effect joins the decode filter chain instead of scheduling per-frame Python.

When True, the streaming plan builder calls :meth:to_ffmpeg_filter (with the segment's resolved context on the :class:FilterCtx) and, if it compiles, appends the result to the vf chain at this op's plan position -- the Filter class of the streaming contract. Instance-level rather than a ClassVar because it may depend on field values (e.g. add_subtitles's renderer). False by default: effects normally stream via streaming_init/process_frame.

predict_metadata 

predict_metadata(
    meta: VideoMetadata, **_context: Any
) -> VideoMetadata

Effects preserve shape and frame count, so the prediction is identity.

Accepts **_context so requires-aware effects (TranscriptionOverlay) validate without subclasses needing to override just to widen the signature. Mirrors :meth:Effect.streaming_init's **_context accept-all.

Source code in src/videopython/editing/operation.py 
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def predict_metadata(self, meta: VideoMetadata, **_context: Any) -> VideoMetadata:
    """Effects preserve shape and frame count, so the prediction is identity.

    Accepts ``**_context`` so requires-aware effects (``TranscriptionOverlay``)
    validate without subclasses needing to override just to widen the
    signature. Mirrors :meth:`Effect.streaming_init`'s ``**_context`` accept-all.
    """
    return meta

streaming_init 

streaming_init(
    total_frames: int,
    fps: float,
    width: int,
    height: int,
    **_context: Any,
) -> None

Hook for per-stream precomputation (per-frame alphas, sigma curves...).

_context carries resolved requires values for context-aware effects (e.g. transcription=... for TranscriptionOverlay), already re-based onto the local timeline by the runner. Effects that declare no requires are always called without context kwargs.

Default: no-op. Override in subclasses that need it.

Source code in src/videopython/editing/operation.py 
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def streaming_init(self, total_frames: int, fps: float, width: int, height: int, **_context: Any) -> None:
    """Hook for per-stream precomputation (per-frame alphas, sigma curves...).

    ``_context`` carries resolved ``requires`` values for context-aware
    effects (e.g. ``transcription=...`` for ``TranscriptionOverlay``),
    already re-based onto the local timeline by the runner. Effects that
    declare no ``requires`` are always called without context kwargs.

    Default: no-op. Override in subclasses that need it.
    """

process_frame 

process_frame(
    frame: ndarray, frame_index: int
) -> np.ndarray

Process one (H, W, 3) uint8 frame in streaming mode.

frame_index is 0-based within this effect's active window.

Source code in src/videopython/editing/operation.py 
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def process_frame(self, frame: np.ndarray, frame_index: int) -> np.ndarray:
    """Process one ``(H, W, 3) uint8`` frame in streaming mode.

    ``frame_index`` is 0-based within this effect's active window.
    """
    raise NotImplementedError(f"{type(self).__name__} does not support streaming")

streams 

streams() -> bool

An effect streams via per-frame Python (process_frame) or a filter.

Frame effects override :meth:process_frame; filter effects (add_subtitles, vignette, ...) instead set :attr:compiles_to_filter and implement :meth:to_ffmpeg_filter. add_subtitles streams only via the filter path (it does not override process_frame), so compiles_to_filter is consulted per-instance.

Source code in src/videopython/editing/operation.py 
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def streams(self) -> bool:
    """An effect streams via per-frame Python (``process_frame``) or a filter.

    Frame effects override :meth:`process_frame`; filter effects
    (``add_subtitles``, ``vignette``, ...) instead set
    :attr:`compiles_to_filter` and implement :meth:`to_ffmpeg_filter`.
    ``add_subtitles`` streams *only* via the filter path (it does not override
    ``process_frame``), so ``compiles_to_filter`` is consulted per-instance.
    """
    return type(self).process_frame is not Effect.process_frame or self.compiles_to_filter

Blur

Blur

Bases: Effect

Applies Gaussian blur that can stay constant or ramp up/down over the clip.

Source code in src/videopython/editing/effects.py 
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class Blur(Effect):
    """Applies Gaussian blur that can stay constant or ramp up/down over the clip."""

    op: Literal["blur_effect"] = "blur_effect"

    mode: Literal["constant", "ascending", "descending"] = Field(
        description=(
            '"constant" applies uniform blur, "ascending" ramps from sharp to blurry, '
            '"descending" ramps from blurry to sharp.'
        ),
    )
    iterations: int = Field(
        ge=1,
        description="Blur strength. Higher values produce a stronger blur (e.g. 5 for subtle, 50+ for heavy).",
    )
    kernel_size: tuple[int, int] = Field(
        (5, 5),
        description=(
            "Gaussian kernel [width, height] in pixels. Must be odd numbers. Larger kernels spread the blur wider."
        ),
    )

    _stream_sigmas: np.ndarray | None = PrivateAttr(default=None)

    def _blur_frame(self, frame: np.ndarray, sigma: float) -> np.ndarray:
        return cv2.GaussianBlur(frame, self.kernel_size, sigma)

    def _compute_sigmas(self, n_frames: int) -> np.ndarray:
        base_sigma = 0.3 * ((self.kernel_size[0] - 1) * 0.5 - 1) + 0.8
        max_sigma = base_sigma * np.sqrt(self.iterations)

        if self.mode == "constant":
            return np.full(n_frames, max_sigma)
        if self.mode == "ascending":
            ratios = np.linspace(1 / n_frames, 1.0, n_frames)
        else:  # descending
            ratios = np.linspace(1.0, 1 / n_frames, n_frames)
        return base_sigma * np.sqrt(np.maximum(1, np.round(ratios * self.iterations)))

    def streaming_init(self, total_frames: int, fps: float, width: int, height: int, **_context: Any) -> None:
        self._stream_sigmas = self._compute_sigmas(total_frames)

    def process_frame(self, frame: np.ndarray, frame_index: int) -> np.ndarray:
        assert self._stream_sigmas is not None
        idx = min(frame_index, len(self._stream_sigmas) - 1)
        return self._blur_frame(frame, self._stream_sigmas[idx])

Zoom

Zoom

Bases: Effect

Progressively zooms into or out of the frame center over the clip duration.

Source code in src/videopython/editing/effects.py 
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class Zoom(Effect):
    """Progressively zooms into or out of the frame center over the clip duration."""

    op: Literal["zoom_effect"] = "zoom_effect"

    zoom_factor: float = Field(
        gt=1,
        description="How far to zoom. 1.5 is a subtle push, 2.0 is moderate, 3.0+ is dramatic. Must be greater than 1.",
    )
    mode: Literal["in", "out"] = Field(
        description='"in" starts wide and pushes into the center, "out" starts tight and pulls back.',
    )

    _stream_crops: np.ndarray | None = PrivateAttr(default=None)
    _stream_width: int = PrivateAttr(default=0)
    _stream_height: int = PrivateAttr(default=0)

    def _crop_sizes(self, n_frames: int, width: int, height: int) -> np.ndarray:
        crop_w = np.linspace(width // self.zoom_factor, width, n_frames)
        crop_h = np.linspace(height // self.zoom_factor, height, n_frames)
        if self.mode == "in":
            crop_w, crop_h = crop_w[::-1], crop_h[::-1]
        return np.stack([crop_w, crop_h], axis=1)

    def streaming_init(self, total_frames: int, fps: float, width: int, height: int, **_context: Any) -> None:
        self._stream_crops = self._crop_sizes(total_frames, width, height)
        self._stream_width = width
        self._stream_height = height

    def process_frame(self, frame: np.ndarray, frame_index: int) -> np.ndarray:
        assert self._stream_crops is not None
        idx = min(frame_index, len(self._stream_crops) - 1)
        w, h = self._stream_crops[idx]
        width, height = self._stream_width, self._stream_height
        x = width / 2 - w / 2
        y = height / 2 - h / 2
        cropped = frame[round(y) : round(y + h), round(x) : round(x + w)]
        return cv2.resize(cropped, (width, height))

FullImageOverlay

FullImageOverlay

Bases: Effect

Composites a full-frame image on top of every video frame.

Useful for watermarks, logos, or static graphic overlays. Supports transparency via RGBA images and an overall opacity control. The overlay is loaded just-in-time from source so the op stays JSON-serialisable.

Source code in src/videopython/editing/effects.py 
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class FullImageOverlay(Effect):
    """Composites a full-frame image on top of every video frame.

    Useful for watermarks, logos, or static graphic overlays. Supports
    transparency via RGBA images and an overall opacity control. The overlay
    is loaded just-in-time from ``source`` so the op stays JSON-serialisable.
    """

    op: Literal["full_image_overlay"] = "full_image_overlay"
    llm_exposed: ClassVar[bool] = False

    source: Path = Field(
        description=(
            "Path to an RGB or RGBA image file. Loaded at apply time; "
            "the image must match the video's width and height."
        ),
    )
    alpha: float = Field(1.0, ge=0, le=1, description="Overall opacity. 0 = fully transparent, 1 = fully opaque.")
    fade_time: float = Field(
        0.0,
        ge=0,
        description="Seconds to fade the overlay in at the start and out at the end of its time range.",
    )

    _overlay_rgba: np.ndarray | None = PrivateAttr(default=None)
    _stream_total: int = PrivateAttr(default=0)
    _stream_fade_frames: int = PrivateAttr(default=0)

    def _load_overlay(self) -> np.ndarray:
        if self._overlay_rgba is not None:
            return self._overlay_rgba
        img = Image.open(self.source).convert("RGBA")
        self._overlay_rgba = np.array(img, dtype=np.uint8)
        return self._overlay_rgba

    def _overlay_frame(self, img: np.ndarray, alpha: float = 1.0) -> np.ndarray:
        overlay = self._load_overlay().copy()
        overlay[:, :, 3] = (overlay[:, :, 3].astype(np.float32) * (self.alpha * alpha)).astype(np.uint8)
        img_pil = Image.fromarray(img)
        overlay_pil = Image.fromarray(overlay)
        img_pil.paste(overlay_pil, (0, 0), overlay_pil)
        return np.array(img_pil)

    def streaming_init(self, total_frames: int, fps: float, width: int, height: int, **_context: Any) -> None:
        self._load_overlay()
        self._stream_total = total_frames
        self._stream_fade_frames = round(self.fade_time * fps) if self.fade_time > 0 else 0

    def process_frame(self, frame: np.ndarray, frame_index: int) -> np.ndarray:
        if self._stream_fade_frames == 0:
            return self._overlay_frame(frame)
        dist_from_end = min(frame_index, self._stream_total - 1 - frame_index)
        fade_alpha = 1.0 if dist_from_end >= self._stream_fade_frames else dist_from_end / self._stream_fade_frames
        return self._overlay_frame(frame, fade_alpha)

    def predict_metadata(self, meta: VideoMetadata, **_context: Any) -> VideoMetadata:
        """Reject an overlay that cannot composite onto this video at run time.

        Two failures ``run_to_file()`` cannot survive are caught at ``validate()``
        time instead of mid-stream: (a) the overlay's pixel dimensions must match
        the video frame exactly (this op is full-frame, unlike
        :class:`ImageOverlay`), and (b) the combined fade-in + fade-out cannot
        exceed the clip length. Both checks come from the deleted eager path; the
        overlay header is read once here (no per-frame work).
        """
        overlay = self._load_overlay()
        frame_shape = (meta.height, meta.width, 3)
        if frame_shape != overlay[:, :, :3].shape:
            raise ValueError(f"Mismatch of overlay shape `{overlay.shape}` with video shape: `{frame_shape}`!")
        if not (0 <= 2 * self.fade_time <= meta.total_seconds):
            raise ValueError(f"Video is only {meta.total_seconds}s long, but fade time is {self.fade_time}s!")
        return meta

predict_metadata 

predict_metadata(
    meta: VideoMetadata, **_context: Any
) -> VideoMetadata

Reject an overlay that cannot composite onto this video at run time.

Two failures run_to_file() cannot survive are caught at validate() time instead of mid-stream: (a) the overlay's pixel dimensions must match the video frame exactly (this op is full-frame, unlike :class:ImageOverlay), and (b) the combined fade-in + fade-out cannot exceed the clip length. Both checks come from the deleted eager path; the overlay header is read once here (no per-frame work).

Source code in src/videopython/editing/effects.py 
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def predict_metadata(self, meta: VideoMetadata, **_context: Any) -> VideoMetadata:
    """Reject an overlay that cannot composite onto this video at run time.

    Two failures ``run_to_file()`` cannot survive are caught at ``validate()``
    time instead of mid-stream: (a) the overlay's pixel dimensions must match
    the video frame exactly (this op is full-frame, unlike
    :class:`ImageOverlay`), and (b) the combined fade-in + fade-out cannot
    exceed the clip length. Both checks come from the deleted eager path; the
    overlay header is read once here (no per-frame work).
    """
    overlay = self._load_overlay()
    frame_shape = (meta.height, meta.width, 3)
    if frame_shape != overlay[:, :, :3].shape:
        raise ValueError(f"Mismatch of overlay shape `{overlay.shape}` with video shape: `{frame_shape}`!")
    if not (0 <= 2 * self.fade_time <= meta.total_seconds):
        raise ValueError(f"Video is only {meta.total_seconds}s long, but fade time is {self.fade_time}s!")
    return meta

ImageOverlay

ImageOverlay

Bases: _AnchoredOverlay

Composites a scaled image at an anchored position on every frame in the window.

A resolution-independent watermark / logo / brand mark. Unlike :class:FullImageOverlay (full-frame only, raises on size mismatch), the image is scaled to a fraction of the frame width and placed at an anchored normalized position, so one config works across 1080p / 4k / vertical / square. Loaded just-in-time from source so the op stays JSON-serialisable. Off-frame or oversized placement clips to a partial paste or a no-op -- the same contract as :class:TextOverlay, never an error; only an unreadable source is rejected (in predict_metadata).

source may be a raster image (PNG/JPEG/WebP) or an SVG (detected by the .svg extension). An SVG is rasterised by resvg at the exact target pixel width -- crisp at any frame size, not a blurry upscale of a fixed-size bitmap -- with a transparent background and no remote-resource fetching (the local path only; no SSRF). SVGs containing text depend on the fonts available at render time.

Source code in src/videopython/editing/effects.py 
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class ImageOverlay(_AnchoredOverlay):
    """Composites a scaled image at an anchored position on every frame in the window.

    A resolution-independent watermark / logo / brand mark. Unlike
    :class:`FullImageOverlay` (full-frame only, raises on size mismatch), the
    image is scaled to a fraction of the frame *width* and placed at an
    anchored normalized position, so one config works across 1080p / 4k /
    vertical / square. Loaded just-in-time from ``source`` so the op stays
    JSON-serialisable. Off-frame or oversized placement clips to a partial
    paste or a no-op -- the same contract as :class:`TextOverlay`, never an
    error; only an unreadable ``source`` is rejected (in ``predict_metadata``).

    ``source`` may be a raster image (PNG/JPEG/WebP) or an SVG (detected by the
    ``.svg`` extension). An SVG is rasterised by ``resvg`` *at the exact target
    pixel width* -- crisp at any frame size, not a blurry upscale of a
    fixed-size bitmap -- with a transparent background and no remote-resource
    fetching (the local path only; no SSRF). SVGs containing text depend on the
    fonts available at render time.
    """

    op: Literal["image_overlay"] = "image_overlay"
    llm_exposed: ClassVar[bool] = False

    source: Path = Field(
        description=(
            "Path to an image file: a raster RGB/RGBA image (PNG/JPEG/WebP) or "
            "an SVG (`.svg`, rasterised at the target resolution). Loaded at "
            "apply time; kept JSON-serialisable as a path."
        ),
    )
    scale: float = Field(
        0.15,
        gt=0,
        le=1,
        description=(
            "Overlay width as a fraction of frame width (0-1). Height follows "
            "the image's aspect ratio. Resolution-independent."
        ),
    )
    opacity: float = Field(
        1.0,
        ge=0,
        le=1,
        description="Multiplies the image's own alpha. 0 = fully transparent, 1 = use the image alpha unchanged.",
    )
    position: tuple[float, float] = Field(
        (0.95, 0.95),
        description=(
            "Where to place the overlay as normalized (x, y) coordinates. "
            "(0, 0) = top-left corner, (1, 1) = bottom-right corner."
        ),
    )
    anchor: Literal["center", "top_left", "top_center", "bottom_center", "bottom_left", "bottom_right"] = Field(
        "bottom_right",
        description="Which point of the overlay box sits at the position coordinate.",
    )

    _overlay_rgba: np.ndarray | None = PrivateAttr(default=None)
    _svg_cache: dict[int, np.ndarray] = PrivateAttr(default_factory=dict)

    def _is_svg(self) -> bool:
        return self.source.suffix.lower() == ".svg"

    def predict_metadata(self, meta: VideoMetadata, **_context: Any) -> VideoMetadata:
        """Reject only a missing/unreadable ``source`` (see :meth:`Operation.predict_metadata`).

        An unreadable source is the one failure ``run_to_file()`` cannot survive -- it
        would raise mid-stream after expensive frame decode -- so it is caught
        at ``validate()`` time, symmetric with ``TranscriptionOverlay``.
        Geometry (oversized / off-frame) is deliberately *not* checked here: it
        clips to a valid no-op like :class:`TextOverlay`, so rejecting it would
        break that contract and the parity with the op this is modeled on. Both
        checks are cheap (a header ``verify()`` / a 1px SVG parse, no full
        decode), so ``validate()`` stays frame-free.
        """
        try:
            if self._is_svg():
                import resvg_py

                resvg_py.svg_to_bytes(svg_path=str(self.source), width=1)
            else:
                with Image.open(self.source) as im:
                    im.verify()
        except (OSError, ValueError) as exc:
            message = f"image_overlay source {str(self.source)!r} is not a readable image: {exc}"
            raise PlanValidationError(
                message,
                [PlanError(code=PlanErrorCode.SOURCE_UNREADABLE, op=self.op, field="source")],
            ) from exc
        return meta

    def _rasterize_svg(self, target_w: int) -> np.ndarray:
        cached = self._svg_cache.get(target_w)
        if cached is not None:
            return cached
        # Lazy import: only when an SVG source is actually used. resvg renders
        # at the exact target width (height proportional to the viewBox) with a
        # transparent background and never fetches remote resources.
        import resvg_py

        png = resvg_py.svg_to_bytes(svg_path=str(self.source), width=target_w)
        arr = np.array(Image.open(BytesIO(bytes(png))).convert("RGBA"), dtype=np.uint8)
        self._svg_cache[target_w] = arr
        return arr

    def _load_overlay(self) -> np.ndarray:
        if self._overlay_rgba is not None:
            return self._overlay_rgba
        img = Image.open(self.source).convert("RGBA")
        self._overlay_rgba = np.array(img, dtype=np.uint8)
        return self._overlay_rgba

    def _resized_overlay(self, frame_w: int) -> np.ndarray:
        target_w = max(1, round(self.scale * frame_w))
        if self._is_svg():
            # Rasterise the vector at the target size (crisp) rather than
            # upscaling a fixed bitmap. resvg derives height from the viewBox.
            return self._rasterize_svg(target_w)
        overlay = self._load_overlay()
        src_h, src_w = overlay.shape[:2]
        target_h = max(1, round(target_w * src_h / src_w))
        if (target_w, target_h) == (src_w, src_h):
            return overlay
        resized = Image.fromarray(overlay).resize((target_w, target_h), Image.LANCZOS)
        return np.array(resized, dtype=np.uint8)

    def _overlay_for_frame(self, frame_width: int, frame_height: int) -> np.ndarray:
        return self._resized_overlay(frame_width)

    def _overlay_opacity(self) -> float:
        return self.opacity

predict_metadata 

predict_metadata(
    meta: VideoMetadata, **_context: Any
) -> VideoMetadata

Reject only a missing/unreadable source (see :meth:Operation.predict_metadata).

An unreadable source is the one failure run_to_file() cannot survive -- it would raise mid-stream after expensive frame decode -- so it is caught at validate() time, symmetric with TranscriptionOverlay. Geometry (oversized / off-frame) is deliberately not checked here: it clips to a valid no-op like :class:TextOverlay, so rejecting it would break that contract and the parity with the op this is modeled on. Both checks are cheap (a header verify() / a 1px SVG parse, no full decode), so validate() stays frame-free.

Source code in src/videopython/editing/effects.py 
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def predict_metadata(self, meta: VideoMetadata, **_context: Any) -> VideoMetadata:
    """Reject only a missing/unreadable ``source`` (see :meth:`Operation.predict_metadata`).

    An unreadable source is the one failure ``run_to_file()`` cannot survive -- it
    would raise mid-stream after expensive frame decode -- so it is caught
    at ``validate()`` time, symmetric with ``TranscriptionOverlay``.
    Geometry (oversized / off-frame) is deliberately *not* checked here: it
    clips to a valid no-op like :class:`TextOverlay`, so rejecting it would
    break that contract and the parity with the op this is modeled on. Both
    checks are cheap (a header ``verify()`` / a 1px SVG parse, no full
    decode), so ``validate()`` stays frame-free.
    """
    try:
        if self._is_svg():
            import resvg_py

            resvg_py.svg_to_bytes(svg_path=str(self.source), width=1)
        else:
            with Image.open(self.source) as im:
                im.verify()
    except (OSError, ValueError) as exc:
        message = f"image_overlay source {str(self.source)!r} is not a readable image: {exc}"
        raise PlanValidationError(
            message,
            [PlanError(code=PlanErrorCode.SOURCE_UNREADABLE, op=self.op, field="source")],
        ) from exc
    return meta

ColorGrading

ColorGrading

Bases: Effect

Adjusts color properties: brightness, contrast, saturation, and temperature.

Source code in src/videopython/editing/effects.py 
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class ColorGrading(Effect):
    """Adjusts color properties: brightness, contrast, saturation, and temperature."""

    op: Literal["color_adjust"] = "color_adjust"

    brightness: float = Field(
        0.0,
        ge=-1.0,
        le=1.0,
        description="Shift brightness. -1.0 = much darker, 0 = unchanged, 1.0 = much brighter.",
    )
    contrast: float = Field(
        1.0,
        ge=0.5,
        le=2.0,
        description="Scale contrast. 0.5 = flat/washed out, 1.0 = unchanged, 2.0 = high contrast.",
    )
    saturation: float = Field(
        1.0,
        ge=0.0,
        le=2.0,
        description="Scale color intensity. 0.0 = grayscale, 1.0 = unchanged, 2.0 = vivid/oversaturated.",
    )
    temperature: float = Field(
        0.0,
        ge=-1.0,
        le=1.0,
        description="Shift color temperature. -1.0 = cool/blue tint, 0 = neutral, 1.0 = warm/orange tint.",
    )

    def _grade_frame(self, frame: np.ndarray) -> np.ndarray:
        img = frame.astype(np.float32) / 255.0

        if self.brightness != 0:
            img = img + self.brightness
        if self.contrast != 1.0:
            img = (img - 0.5) * self.contrast + 0.5
        if self.saturation != 1.0:
            hsv = cv2.cvtColor(np.clip(img, 0, 1).astype(np.float32), cv2.COLOR_RGB2HSV)
            hsv[:, :, 1] = np.clip(hsv[:, :, 1] * self.saturation, 0, 1)
            img = cv2.cvtColor(hsv, cv2.COLOR_HSV2RGB).astype(np.float32)
        if self.temperature != 0:
            temp_shift = self.temperature * 0.1
            img[:, :, 0] = img[:, :, 0] + temp_shift
            img[:, :, 2] = img[:, :, 2] - temp_shift

        return np.clip(img * 255, 0, 255).astype(np.uint8)

    def process_frame(self, frame: np.ndarray, frame_index: int) -> np.ndarray:
        return self._grade_frame(frame)

Vignette

Vignette

Bases: Effect

Darkens the edges of the frame, drawing attention to the center.

Source code in src/videopython/editing/effects.py 
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class Vignette(Effect):
    """Darkens the edges of the frame, drawing attention to the center."""

    op: Literal["vignette"] = "vignette"

    strength: float = Field(
        0.5,
        ge=0.0,
        le=1.0,
        description="Edge darkness amount. 0.0 = no darkening, 0.5 = moderate, 1.0 = fully black edges.",
    )
    radius: float = Field(
        1.0,
        ge=0.5,
        le=2.0,
        description=(
            "Size of the bright center area. Smaller values (0.5) create a tight spotlight, "
            "larger values (2.0) keep more of the frame lit."
        ),
    )

    _mask: np.ndarray | None = PrivateAttr(default=None)
    _stream_mask_3d: np.ndarray | None = PrivateAttr(default=None)

    def _create_mask(self, height: int, width: int) -> np.ndarray:
        y = np.linspace(-1, 1, height)
        x = np.linspace(-1, 1, width)
        X, Y = np.meshgrid(x, y)
        distance = np.sqrt(X**2 + Y**2) / self.radius
        mask = 1.0 - np.clip(distance - 0.5, 0, 1) * 2 * self.strength
        return mask.astype(np.float32)

    def streaming_init(self, total_frames: int, fps: float, width: int, height: int, **_context: Any) -> None:
        if self._mask is None or self._mask.shape != (height, width):
            self._mask = self._create_mask(height, width)
        self._stream_mask_3d = self._mask[:, :, np.newaxis]

    def process_frame(self, frame: np.ndarray, frame_index: int) -> np.ndarray:
        assert self._stream_mask_3d is not None
        return (frame.astype(np.float32) * self._stream_mask_3d).astype(np.uint8)

KenBurns

KenBurns

Bases: Effect

Cinematic pan-and-zoom that smoothly animates between two crop regions.

Creates movement by transitioning from a start region to an end region over the clip. Use it to add motion to still images or to guide the viewer's eye across a scene.

Source code in src/videopython/editing/effects.py 
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class KenBurns(Effect):
    """Cinematic pan-and-zoom that smoothly animates between two crop regions.

    Creates movement by transitioning from a start region to an end region over
    the clip. Use it to add motion to still images or to guide the viewer's eye
    across a scene.
    """

    op: Literal["ken_burns"] = "ken_burns"

    start_region: BoundingBox = Field(
        description="Starting crop region as a BoundingBox with normalized 0-1 coordinates."
    )
    end_region: BoundingBox = Field(description="Ending crop region as a BoundingBox with normalized 0-1 coordinates.")
    easing: Literal["linear", "ease_in", "ease_out", "ease_in_out"] = Field(
        "linear",
        description=(
            'Animation curve. "linear" moves at constant speed, "ease_in" starts slow, '
            '"ease_out" ends slow, "ease_in_out" starts and ends slow.'
        ),
    )

    _stream_regions: np.ndarray | None = PrivateAttr(default=None)
    _stream_target_w: int = PrivateAttr(default=0)
    _stream_target_h: int = PrivateAttr(default=0)

    @model_validator(mode="after")
    def _validate_regions(self) -> KenBurns:
        for name, region in [("start_region", self.start_region), ("end_region", self.end_region)]:
            if not (0 <= region.x <= 1 and 0 <= region.y <= 1):
                raise ValueError(f"{name} position must be in range [0, 1]!")
            if not (0 < region.width <= 1 and 0 < region.height <= 1):
                raise ValueError(f"{name} dimensions must be in range (0, 1]!")
            if region.x + region.width > 1 or region.y + region.height > 1:
                raise ValueError(f"{name} extends beyond image bounds!")
        return self

    def _crop_and_scale_frame(
        self, frame: np.ndarray, x: int, y: int, crop_w: int, crop_h: int, target_w: int, target_h: int
    ) -> np.ndarray:
        cropped = frame[y : y + crop_h, x : x + crop_w]
        return cv2.resize(cropped, (target_w, target_h), interpolation=cv2.INTER_LINEAR)

    def _precompute_regions(self, n_frames: int, width: int, height: int) -> np.ndarray:
        sx = int(self.start_region.x * width)
        sy = int(self.start_region.y * height)
        sw = int(self.start_region.width * width)
        sh = int(self.start_region.height * height)
        ex = int(self.end_region.x * width)
        ey = int(self.end_region.y * height)
        ew = int(self.end_region.width * width)
        eh = int(self.end_region.height * height)

        regions = np.empty((n_frames, 4), dtype=np.int32)
        eased = ease(np.arange(n_frames, dtype=np.float64) / max(1, n_frames - 1), self.easing)
        for i in range(n_frames):
            et = float(eased[i])
            crop_w = int(sw + (ew - sw) * et)
            crop_h = int(sh + (eh - sh) * et)
            x = max(0, min(int(sx + (ex - sx) * et), width - crop_w))
            y = max(0, min(int(sy + (ey - sy) * et), height - crop_h))
            regions[i] = (x, y, crop_w, crop_h)
        return regions

    def streaming_init(self, total_frames: int, fps: float, width: int, height: int, **_context: Any) -> None:
        self._stream_regions = self._precompute_regions(total_frames, width, height)
        self._stream_target_w = width
        self._stream_target_h = height

    def process_frame(self, frame: np.ndarray, frame_index: int) -> np.ndarray:
        assert self._stream_regions is not None
        idx = min(frame_index, len(self._stream_regions) - 1)
        x, y, cw, ch = self._stream_regions[idx]
        return self._crop_and_scale_frame(frame, x, y, cw, ch, self._stream_target_w, self._stream_target_h)

Fade

Fade

Bases: Effect

Fades video and audio to or from black.

Source code in src/videopython/editing/effects.py 
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class Fade(Effect):
    """Fades video and audio to or from black."""

    op: Literal["fade"] = "fade"
    audio_coupled: ClassVar[bool] = True

    mode: Literal["in", "out", "in_out"] = Field(
        description=('"in" fades from black at the start, "out" fades to black at the end, "in_out" does both.'),
    )
    duration: float = Field(1.0, gt=0, description="Length of each fade in seconds.")
    curve: Literal["sqrt", "linear", "exponential"] = Field(
        "sqrt",
        description=(
            'Brightness ramp shape. "sqrt" feels perceptually even (recommended), '
            '"linear" is mathematically even, "exponential" starts slow and finishes fast.'
        ),
    )

    _stream_alpha: np.ndarray | None = PrivateAttr(default=None)

    def _fade_envelope(self, length: int, rate: float) -> np.ndarray:
        """Build the per-sample (or per-frame) alpha envelope for ``length`` ticks.

        ``rate`` is fps for video frames or sample_rate for audio samples;
        ``self.duration`` is converted to a tick count via ``rate`` and clipped
        to ``length``. The ramp shape follows ``self.curve``.
        """
        ramp = min(round(self.duration * rate), length)
        alpha = np.ones(length, dtype=np.float32)
        if self.mode in ("in", "in_out"):
            t = np.linspace(0, 1, ramp, dtype=np.float32)
            alpha[:ramp] = _compute_curve(t, self.curve)
        if self.mode in ("out", "in_out"):
            t = np.linspace(1, 0, ramp, dtype=np.float32)
            alpha[-ramp:] = np.minimum(alpha[-ramp:], _compute_curve(t, self.curve))
        return alpha

    def streaming_init(self, total_frames: int, fps: float, width: int, height: int, **_context: Any) -> None:
        self._stream_alpha = self._fade_envelope(total_frames, fps)

    def process_frame(self, frame: np.ndarray, frame_index: int) -> np.ndarray:
        assert self._stream_alpha is not None
        idx = min(frame_index, len(self._stream_alpha) - 1)
        a = self._stream_alpha[idx]
        if a == 1.0:
            return frame
        return (frame.astype(np.float32) * a).astype(np.uint8)

    def _curve_expr(self, progress: str) -> str:
        """ffmpeg gain sub-expression for a 0->1 ramp ``progress``, per ``self.curve``.

        Mirrors :func:`_compute_curve` (``sqrt`` -> ``sqrt(p)``, ``exponential``
        -> ``p*p``, ``linear`` -> ``p``). Native shape, not bit-identical to the
        numpy ramp -- audio has no pixel sign-off (the libass precedent).
        """
        if self.curve == "sqrt":
            return f"sqrt({progress})"
        if self.curve == "exponential":
            return f"({progress})*({progress})"
        return f"({progress})"

    def to_ffmpeg_audio_filter(self, ctx: FilterCtx) -> str | None:
        """Express the fade's gain envelope as a windowed ``volume`` expression.

        The audio twin of the video fade (which scales pixels by the same alpha
        envelope in :meth:`process_frame`). It must mirror
        :func:`_fade_envelope`: the ramp applies only WITHIN ``[start, stop]``
        and the gain is 1.0
        everywhere else -- so a *windowed* fade-out returns to full volume after
        the window (matching the video, which resumes full brightness), instead
        of staying muted. Native ``afade`` cannot express this (it holds 0
        outside the ramp), so the gain is a piecewise ``volume=...:eval=frame``
        expression, like :class:`VolumeAdjust`. ``None`` when the window is
        degenerate.
        """
        if ctx.fps <= 0 or ctx.frame_count <= 0:
            return None
        total_seconds = ctx.frame_count / ctx.fps
        win = self.window
        start_s = 0.0 if win is None or win.start is None else min(float(win.start), total_seconds)
        stop_s = total_seconds if win is None or win.stop is None else min(float(win.stop), total_seconds)
        if stop_s <= start_s:
            return None
        window_len = stop_s - start_s
        # Halve the ramp for in_out so the lead and trailing ramps cannot
        # overlap; a clamped half-window keeps them disjoint.
        both = self.mode == "in_out"
        ramp = min(self.duration, window_len / 2 if both else window_len)
        if ramp <= 0:
            return None
        # Build nested if()s: each ramp region uses its curve; gain is 1 outside.
        terms: list[tuple[str, str]] = []
        if self.mode in ("in", "in_out"):
            in_end = start_s + ramp
            terms.append((f"between(t,{start_s:.6f},{in_end:.6f})", self._curve_expr(f"(t-{start_s:.6f})/{ramp:.6f}")))
        if self.mode in ("out", "in_out"):
            out_start = stop_s - ramp
            terms.append((f"between(t,{out_start:.6f},{stop_s:.6f})", self._curve_expr(f"({stop_s:.6f}-t)/{ramp:.6f}")))
        return volume_envelope(terms)

to_ffmpeg_audio_filter 

to_ffmpeg_audio_filter(ctx: FilterCtx) -> str | None

Express the fade's gain envelope as a windowed volume expression.

The audio twin of the video fade (which scales pixels by the same alpha envelope in :meth:process_frame). It must mirror :func:_fade_envelope: the ramp applies only WITHIN [start, stop] and the gain is 1.0 everywhere else -- so a windowed fade-out returns to full volume after the window (matching the video, which resumes full brightness), instead of staying muted. Native afade cannot express this (it holds 0 outside the ramp), so the gain is a piecewise volume=...:eval=frame expression, like :class:VolumeAdjust. None when the window is degenerate.

Source code in src/videopython/editing/effects.py 
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def to_ffmpeg_audio_filter(self, ctx: FilterCtx) -> str | None:
    """Express the fade's gain envelope as a windowed ``volume`` expression.

    The audio twin of the video fade (which scales pixels by the same alpha
    envelope in :meth:`process_frame`). It must mirror
    :func:`_fade_envelope`: the ramp applies only WITHIN ``[start, stop]``
    and the gain is 1.0
    everywhere else -- so a *windowed* fade-out returns to full volume after
    the window (matching the video, which resumes full brightness), instead
    of staying muted. Native ``afade`` cannot express this (it holds 0
    outside the ramp), so the gain is a piecewise ``volume=...:eval=frame``
    expression, like :class:`VolumeAdjust`. ``None`` when the window is
    degenerate.
    """
    if ctx.fps <= 0 or ctx.frame_count <= 0:
        return None
    total_seconds = ctx.frame_count / ctx.fps
    win = self.window
    start_s = 0.0 if win is None or win.start is None else min(float(win.start), total_seconds)
    stop_s = total_seconds if win is None or win.stop is None else min(float(win.stop), total_seconds)
    if stop_s <= start_s:
        return None
    window_len = stop_s - start_s
    # Halve the ramp for in_out so the lead and trailing ramps cannot
    # overlap; a clamped half-window keeps them disjoint.
    both = self.mode == "in_out"
    ramp = min(self.duration, window_len / 2 if both else window_len)
    if ramp <= 0:
        return None
    # Build nested if()s: each ramp region uses its curve; gain is 1 outside.
    terms: list[tuple[str, str]] = []
    if self.mode in ("in", "in_out"):
        in_end = start_s + ramp
        terms.append((f"between(t,{start_s:.6f},{in_end:.6f})", self._curve_expr(f"(t-{start_s:.6f})/{ramp:.6f}")))
    if self.mode in ("out", "in_out"):
        out_start = stop_s - ramp
        terms.append((f"between(t,{out_start:.6f},{stop_s:.6f})", self._curve_expr(f"({stop_s:.6f}-t)/{ramp:.6f}")))
    return volume_envelope(terms)

VolumeAdjust

VolumeAdjust

Bases: Effect

Changes audio volume within a time range without affecting video frames.

Source code in src/videopython/editing/effects.py 
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class VolumeAdjust(Effect):
    """Changes audio volume within a time range without affecting video frames."""

    op: Literal["volume_adjust"] = "volume_adjust"
    audio_coupled: ClassVar[bool] = True

    volume: float = Field(
        1.0,
        ge=0,
        description="Volume multiplier. 0.0 = silence, 1.0 = original level, 2.0 = twice as loud (may clip).",
    )
    ramp_duration: float = Field(
        0.0,
        ge=0,
        description="Seconds to smoothly ramp volume at the start and end of the window, preventing audible clicks.",
    )

    def process_frame(self, frame: np.ndarray, frame_index: int) -> np.ndarray:
        return frame

    def to_ffmpeg_audio_filter(self, ctx: FilterCtx) -> str | None:
        """Apply the volume change over the window via the ``volume`` filter.

        The audio twin of the (pixel-passthrough) volume effect. A flat
        multiplier compiles to ``volume=<v>:enable='between(t,start,stop)'``.
        When ``ramp_duration>0`` the gain is a time-piecewise expression
        (``volume=eval=frame``) that ramps ``1 -> volume`` over the first
        ``ramp_duration`` of the window and back over the last, following the
        ``1 + (volume-1)*sqrt(t)`` edge-ramp shape. The window resolves
        against the segment duration (``ctx.frame_count / ctx.fps``); ``None``
        for a degenerate window or a no-op (``volume == 1`` with no ramp).
        """
        if ctx.fps <= 0 or ctx.frame_count <= 0:
            return None
        total_seconds = ctx.frame_count / ctx.fps
        win = self.window
        start_s = 0.0 if win is None or win.start is None else min(float(win.start), total_seconds)
        stop_s = total_seconds if win is None or win.stop is None else min(float(win.stop), total_seconds)
        if stop_s <= start_s:
            return None
        window_len = stop_s - start_s
        ramp = min(self.ramp_duration, window_len / 2)
        if ramp <= 0:
            if abs(self.volume - 1.0) < 1e-9:
                return None
            return f"volume={self.volume:.6f}:enable='between(t,{start_s:.6f},{stop_s:.6f})'"
        # Piecewise gain: 1 -> volume (sqrt ease) over the leading ramp, hold,
        # then volume -> 1 over the trailing ramp; 1 (no change) outside the
        # window. `eval=frame` re-evaluates the expression per sample-frame.
        v = self.volume
        up_end = start_s + ramp
        down_start = stop_s - ramp
        up = f"(1+({v:.6f}-1)*sqrt((t-{start_s:.6f})/{ramp:.6f}))"
        down = f"(1+({v:.6f}-1)*sqrt(({stop_s:.6f}-t)/{ramp:.6f}))"
        terms = [
            (f"between(t,{start_s:.6f},{up_end:.6f})", up),
            (f"between(t,{up_end:.6f},{down_start:.6f})", f"{v:.6f}"),
            (f"between(t,{down_start:.6f},{stop_s:.6f})", down),
        ]
        return volume_envelope(terms)

to_ffmpeg_audio_filter 

to_ffmpeg_audio_filter(ctx: FilterCtx) -> str | None

Apply the volume change over the window via the volume filter.

The audio twin of the (pixel-passthrough) volume effect. A flat multiplier compiles to volume=<v>:enable='between(t,start,stop)'. When ramp_duration>0 the gain is a time-piecewise expression (volume=eval=frame) that ramps 1 -> volume over the first ramp_duration of the window and back over the last, following the 1 + (volume-1)*sqrt(t) edge-ramp shape. The window resolves against the segment duration (ctx.frame_count / ctx.fps); None for a degenerate window or a no-op (volume == 1 with no ramp).

Source code in src/videopython/editing/effects.py 
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def to_ffmpeg_audio_filter(self, ctx: FilterCtx) -> str | None:
    """Apply the volume change over the window via the ``volume`` filter.

    The audio twin of the (pixel-passthrough) volume effect. A flat
    multiplier compiles to ``volume=<v>:enable='between(t,start,stop)'``.
    When ``ramp_duration>0`` the gain is a time-piecewise expression
    (``volume=eval=frame``) that ramps ``1 -> volume`` over the first
    ``ramp_duration`` of the window and back over the last, following the
    ``1 + (volume-1)*sqrt(t)`` edge-ramp shape. The window resolves
    against the segment duration (``ctx.frame_count / ctx.fps``); ``None``
    for a degenerate window or a no-op (``volume == 1`` with no ramp).
    """
    if ctx.fps <= 0 or ctx.frame_count <= 0:
        return None
    total_seconds = ctx.frame_count / ctx.fps
    win = self.window
    start_s = 0.0 if win is None or win.start is None else min(float(win.start), total_seconds)
    stop_s = total_seconds if win is None or win.stop is None else min(float(win.stop), total_seconds)
    if stop_s <= start_s:
        return None
    window_len = stop_s - start_s
    ramp = min(self.ramp_duration, window_len / 2)
    if ramp <= 0:
        if abs(self.volume - 1.0) < 1e-9:
            return None
        return f"volume={self.volume:.6f}:enable='between(t,{start_s:.6f},{stop_s:.6f})'"
    # Piecewise gain: 1 -> volume (sqrt ease) over the leading ramp, hold,
    # then volume -> 1 over the trailing ramp; 1 (no change) outside the
    # window. `eval=frame` re-evaluates the expression per sample-frame.
    v = self.volume
    up_end = start_s + ramp
    down_start = stop_s - ramp
    up = f"(1+({v:.6f}-1)*sqrt((t-{start_s:.6f})/{ramp:.6f}))"
    down = f"(1+({v:.6f}-1)*sqrt(({stop_s:.6f}-t)/{ramp:.6f}))"
    terms = [
        (f"between(t,{start_s:.6f},{up_end:.6f})", up),
        (f"between(t,{up_end:.6f},{down_start:.6f})", f"{v:.6f}"),
        (f"between(t,{down_start:.6f},{stop_s:.6f})", down),
    ]
    return volume_envelope(terms)

TextOverlay

TextOverlay

Bases: _AnchoredOverlay

Draws text on video frames, with auto word-wrap and optional background box.

Source code in src/videopython/editing/effects.py 
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class TextOverlay(_AnchoredOverlay):
    """Draws text on video frames, with auto word-wrap and optional background box."""

    op: Literal["text_overlay"] = "text_overlay"

    text: str = Field(min_length=1, description=r"The string to display. Use \n for line breaks.")
    position: tuple[float, float] = Field(
        (0.5, 0.9),
        description=(
            "Where to place the text as normalized (x, y) coordinates. "
            "(0, 0) = top-left corner, (1, 1) = bottom-right corner."
        ),
    )
    font_size: int = Field(48, ge=1, description="Font size in pixels.")
    text_color: tuple[int, int, int] = Field((255, 255, 255), description="Text color as [R, G, B], each 0-255.")
    background_color: tuple[int, int, int, int] | None = Field(
        (0, 0, 0, 160),
        description="Background box color as [R, G, B, A] (0-255), or null to disable the background.",
    )
    background_padding: int = Field(12, ge=0, description="Padding in pixels between text and background edge.")
    max_width: float = Field(
        0.8,
        gt=0.0,
        le=1.0,
        description=(
            "Maximum text width as a fraction of frame width (0-1). Text longer than this wraps to the next line."
        ),
    )
    anchor: Literal["center", "top_left", "top_center", "bottom_center", "bottom_left", "bottom_right"] = Field(
        "center",
        description="Which point of the text box sits at the position coordinate.",
    )
    font: Literal["anton", "bebas-neue", "lato-bold", "poppins-bold"] | None = Field(
        None,
        description=(
            "Bundled font to render with, or null for the default. "
            "'poppins-bold': clean geometric sans, general purpose. "
            "'lato-bold': humanist sans, very readable. "
            "'anton': tall condensed display, ideal for short-form vertical. "
            "'bebas-neue': bold condensed display, dramatic alternative."
        ),
    )
    font_filename: str | None = Field(
        None,
        description=(
            "Advanced override: path to a .ttf font file. Takes precedence over `font`; None for the default font."
        ),
        json_schema_extra={"llm_hidden": True},
    )

    _rendered: np.ndarray | None = PrivateAttr(default=None)

    def _get_font(self) -> ImageFont.FreeTypeFont | ImageFont.ImageFont:
        return load_font(self.font_filename or self.font, self.font_size)

    def _wrap_text(self, text: str, font: ImageFont.FreeTypeFont | ImageFont.ImageFont, max_px: int) -> str:
        lines: list[str] = []
        for paragraph in text.split("\n"):
            words = paragraph.split()
            if not words:
                lines.append("")
                continue
            current = words[0]
            for word in words[1:]:
                test = current + " " + word
                bbox = font.getbbox(test)
                if bbox[2] - bbox[0] <= max_px:
                    current = test
                else:
                    lines.append(current)
                    current = word
            lines.append(current)
        return "\n".join(lines)

    def _render_text_image(self, frame_width: int, frame_height: int) -> np.ndarray:
        font = self._get_font()
        max_px = int(self.max_width * frame_width)
        wrapped = self._wrap_text(self.text, font, max_px)

        temp_img = Image.new("RGBA", (1, 1))
        temp_draw = ImageDraw.Draw(temp_img)
        bbox = temp_draw.multiline_textbbox((0, 0), wrapped, font=font)
        text_w = bbox[2] - bbox[0]
        text_h = bbox[3] - bbox[1]

        pad = self.background_padding
        img_w = text_w + 2 * pad
        img_h = text_h + 2 * pad

        if self.background_color is not None:
            img = Image.new("RGBA", (img_w, img_h), self.background_color)
        else:
            img = Image.new("RGBA", (img_w, img_h), (0, 0, 0, 0))

        draw = ImageDraw.Draw(img)
        draw.multiline_text((pad - bbox[0], pad - bbox[1]), wrapped, font=font, fill=(*self.text_color, 255))

        return np.array(img, dtype=np.uint8)

    def _overlay_for_frame(self, frame_width: int, frame_height: int) -> np.ndarray:
        if self._rendered is None:
            self._rendered = self._render_text_image(frame_width, frame_height)
        return self._rendered

    def _resolve_fontfile(self) -> Path | None:
        """Filesystem path to the .ttf ``drawtext`` should render with, or ``None``.

        Mirrors :func:`load_font`'s resolution down to a real file: a bundled
        NAME -> its bundled .ttf; an explicit ``font_filename`` path that exists;
        else the bundled DejaVu Sans default. ``None`` only when even DejaVu is
        unreachable -- then ``load_font`` falls back to PIL's built-in bitmap
        font, which ``drawtext`` cannot consume, so the op stays frame-only.
        """
        from videopython.base.fonts import BUNDLED_FONTS, DEFAULT_FONT_FILENAME, bundled_fonts_dir

        name = self.font_filename or self.font
        fonts_dir = bundled_fonts_dir()
        if name:
            bundled = BUNDLED_FONTS.get(name)
            if bundled is not None:
                candidate = fonts_dir / bundled
                if candidate.is_file():
                    return candidate
            candidate = Path(name)
            if candidate.is_file():
                return candidate
        default = fonts_dir / DEFAULT_FONT_FILENAME
        return default if default.is_file() else None

    @property
    def compiles_to_filter(self) -> bool:
        """Compile to ``drawtext`` when a real TrueType file resolves, else frame-only."""
        return self._resolve_fontfile() is not None

    def to_ffmpeg_filter(self, ctx: FilterCtx) -> str | None:
        """Compile the anchored, word-wrapped text overlay to one ``drawtext`` entry.

        The text is pre-wrapped with the SAME PIL metrics (:meth:`_wrap_text`)
        the numpy twin uses and written to a ``textfile`` (registered on
        ``ctx.owned_files`` for the runner to delete), so line breaks match
        exactly -- drawtext has no pixel word-wrap. The box is
        ``box=1:boxcolor=...:boxborderw=<padding>`` placed at the anchored
        position :meth:`_compute_position` computes. Faithful *visual* twin
        (freetype vs PIL metrics diverge a few px), re-baselined to ffmpeg.
        Comma-join safe: every comma-bearing value is wrapped via
        :func:`escape_filter_value`. yuv-safe (drawtext needs no rgb24).
        """
        fontfile = self._resolve_fontfile()
        if fontfile is None:
            return None

        font = self._get_font()
        max_px = int(self.max_width * ctx.width)
        wrapped = self._wrap_text(self.text, font, max_px)

        bbox = ImageDraw.Draw(Image.new("RGBA", (1, 1))).multiline_textbbox((0, 0), wrapped, font=font)
        text_w = bbox[2] - bbox[0]
        text_h = bbox[3] - bbox[1]
        pad = self.background_padding
        box_x, box_y = self._compute_position(ctx.width, ctx.height, text_w + 2 * pad, text_h + 2 * pad)

        tmp = tempfile.NamedTemporaryFile("w", suffix=".txt", delete=False, encoding="utf-8")
        try:
            tmp.write(wrapped)
        finally:
            tmp.close()
        textfile = Path(tmp.name)
        ctx.owned_files.append(textfile)

        r, g, b = self.text_color
        parts = [
            f"textfile={escape_filter_value(str(textfile))}",
            f"fontfile={escape_filter_value(str(fontfile))}",
            f"fontsize={self.font_size}",
            f"fontcolor={escape_filter_value(f'0x{r:02x}{g:02x}{b:02x}')}",
            f"x={box_x + pad}",
            f"y={box_y + pad}",
        ]
        if self.background_color is not None:
            br, bg, bb, ba = self.background_color
            parts.append("box=1")
            parts.append(f"boxcolor={escape_filter_value(f'0x{br:02x}{bg:02x}{bb:02x}@{ba / 255:.4f}')}")
            parts.append(f"boxborderw={pad}")
        if self.window is not None:
            start = 0.0 if self.window.start is None else float(self.window.start)
            stop = ctx.frame_count / ctx.fps if self.window.stop is None else float(self.window.stop)
            parts.append(f"enable={escape_filter_value(f'between(t,{start:g},{stop:g})')}")
        return "drawtext=" + ":".join(parts)

compiles_to_filter property 

compiles_to_filter: bool

Compile to drawtext when a real TrueType file resolves, else frame-only.

to_ffmpeg_filter 

to_ffmpeg_filter(ctx: FilterCtx) -> str | None

Compile the anchored, word-wrapped text overlay to one drawtext entry.

The text is pre-wrapped with the SAME PIL metrics (:meth:_wrap_text) the numpy twin uses and written to a textfile (registered on ctx.owned_files for the runner to delete), so line breaks match exactly -- drawtext has no pixel word-wrap. The box is box=1:boxcolor=...:boxborderw=<padding> placed at the anchored position :meth:_compute_position computes. Faithful visual twin (freetype vs PIL metrics diverge a few px), re-baselined to ffmpeg. Comma-join safe: every comma-bearing value is wrapped via :func:escape_filter_value. yuv-safe (drawtext needs no rgb24).

Source code in src/videopython/editing/effects.py 
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def to_ffmpeg_filter(self, ctx: FilterCtx) -> str | None:
    """Compile the anchored, word-wrapped text overlay to one ``drawtext`` entry.

    The text is pre-wrapped with the SAME PIL metrics (:meth:`_wrap_text`)
    the numpy twin uses and written to a ``textfile`` (registered on
    ``ctx.owned_files`` for the runner to delete), so line breaks match
    exactly -- drawtext has no pixel word-wrap. The box is
    ``box=1:boxcolor=...:boxborderw=<padding>`` placed at the anchored
    position :meth:`_compute_position` computes. Faithful *visual* twin
    (freetype vs PIL metrics diverge a few px), re-baselined to ffmpeg.
    Comma-join safe: every comma-bearing value is wrapped via
    :func:`escape_filter_value`. yuv-safe (drawtext needs no rgb24).
    """
    fontfile = self._resolve_fontfile()
    if fontfile is None:
        return None

    font = self._get_font()
    max_px = int(self.max_width * ctx.width)
    wrapped = self._wrap_text(self.text, font, max_px)

    bbox = ImageDraw.Draw(Image.new("RGBA", (1, 1))).multiline_textbbox((0, 0), wrapped, font=font)
    text_w = bbox[2] - bbox[0]
    text_h = bbox[3] - bbox[1]
    pad = self.background_padding
    box_x, box_y = self._compute_position(ctx.width, ctx.height, text_w + 2 * pad, text_h + 2 * pad)

    tmp = tempfile.NamedTemporaryFile("w", suffix=".txt", delete=False, encoding="utf-8")
    try:
        tmp.write(wrapped)
    finally:
        tmp.close()
    textfile = Path(tmp.name)
    ctx.owned_files.append(textfile)

    r, g, b = self.text_color
    parts = [
        f"textfile={escape_filter_value(str(textfile))}",
        f"fontfile={escape_filter_value(str(fontfile))}",
        f"fontsize={self.font_size}",
        f"fontcolor={escape_filter_value(f'0x{r:02x}{g:02x}{b:02x}')}",
        f"x={box_x + pad}",
        f"y={box_y + pad}",
    ]
    if self.background_color is not None:
        br, bg, bb, ba = self.background_color
        parts.append("box=1")
        parts.append(f"boxcolor={escape_filter_value(f'0x{br:02x}{bg:02x}{bb:02x}@{ba / 255:.4f}')}")
        parts.append(f"boxborderw={pad}")
    if self.window is not None:
        start = 0.0 if self.window.start is None else float(self.window.start)
        stop = ctx.frame_count / ctx.fps if self.window.stop is None else float(self.window.stop)
        parts.append(f"enable={escape_filter_value(f'between(t,{start:g},{stop:g})')}")
    return "drawtext=" + ":".join(parts)

Shake

Shake

Bases: Effect

Per-frame camera shake: jitters every frame by a random or rhythmic offset.

The frame is translated by (dx, dy) and cropped back to the original canvas, so the visible area shrinks slightly at the edges. Useful for reaction emphasis, impact moments, or music-synced vibration.

Source code in src/videopython/editing/effects.py 
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class Shake(Effect):
    """Per-frame camera shake: jitters every frame by a random or rhythmic offset.

    The frame is translated by ``(dx, dy)`` and cropped back to the original
    canvas, so the visible area shrinks slightly at the edges. Useful for
    reaction emphasis, impact moments, or music-synced vibration.
    """

    op: Literal["shake"] = "shake"

    intensity_px: float = Field(
        gt=0,
        description="Maximum displacement in pixels at peak intensity (e.g. 5 = subtle, 20 = heavy).",
    )
    mode: Literal["random", "rhythmic", "decay"] = Field(
        "random",
        description=(
            '"random" jitters independently each frame, "rhythmic" oscillates as a sine wave, '
            '"decay" starts at full intensity and fades to zero.'
        ),
    )
    frequency_hz: float = Field(
        8.0,
        gt=0,
        description='Oscillation frequency for "rhythmic" mode. Ignored for other modes.',
    )
    seed: int = Field(
        0,
        description='Seed for the random number generator (used in "random" mode). Same seed = reproducible.',
    )

    _stream_offsets: np.ndarray | None = PrivateAttr(default=None)

    def _compute_offsets(self, n_frames: int, fps: float) -> np.ndarray:
        offsets = np.zeros((n_frames, 2), dtype=np.float32)
        if self.mode == "random":
            rng = np.random.default_rng(self.seed)
            offsets[:] = rng.uniform(-self.intensity_px, self.intensity_px, size=(n_frames, 2))
        elif self.mode == "rhythmic":
            t = np.arange(n_frames, dtype=np.float32) / max(fps, 1e-6)
            phase = 2 * np.pi * self.frequency_hz * t
            offsets[:, 0] = self.intensity_px * np.sin(phase)
            offsets[:, 1] = self.intensity_px * np.cos(phase)
        else:  # decay
            rng = np.random.default_rng(self.seed)
            envelope = np.linspace(1.0, 0.0, n_frames, dtype=np.float32)
            jitter = rng.uniform(-1.0, 1.0, size=(n_frames, 2)).astype(np.float32)
            offsets[:] = jitter * envelope[:, np.newaxis] * self.intensity_px
        return offsets

    def _shake_frame(self, frame: np.ndarray, dx: float, dy: float) -> np.ndarray:
        h, w = frame.shape[:2]
        M = np.array([[1.0, 0.0, dx], [0.0, 1.0, dy]], dtype=np.float32)
        return cv2.warpAffine(frame, M, (w, h), borderMode=cv2.BORDER_REFLECT)

    def streaming_init(self, total_frames: int, fps: float, width: int, height: int, **_context: Any) -> None:
        self._stream_offsets = self._compute_offsets(total_frames, fps)

    def process_frame(self, frame: np.ndarray, frame_index: int) -> np.ndarray:
        assert self._stream_offsets is not None
        idx = min(frame_index, len(self._stream_offsets) - 1)
        dx, dy = self._stream_offsets[idx]
        return self._shake_frame(frame, float(dx), float(dy))

PunchIn

PunchIn

Bases: Effect

Snap-zoom emphasis: rapidly zooms into the center, holds, optionally releases.

Different from Zoom (which ramps continuously over the whole clip). PunchIn reaches the target zoom in attack_frames and stays there; if release_frames > 0 it eases back out at the end.

Source code in src/videopython/editing/effects.py 
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class PunchIn(Effect):
    """Snap-zoom emphasis: rapidly zooms into the center, holds, optionally releases.

    Different from ``Zoom`` (which ramps continuously over the whole clip).
    ``PunchIn`` reaches the target zoom in ``attack_frames`` and stays there;
    if ``release_frames > 0`` it eases back out at the end.
    """

    op: Literal["punch_in"] = "punch_in"

    zoom_factor: float = Field(
        gt=1.0,
        description="Target zoom level. 1.2 is subtle emphasis, 1.5 is moderate, 2.0+ is dramatic.",
    )
    attack_frames: int = Field(
        3,
        ge=0,
        description="Frames to reach full zoom from 1.0. 0 = instant snap, 3 = ~one beat at 30fps.",
    )
    release_frames: int = Field(
        0,
        ge=0,
        description="Frames at the end to ease zoom back to 1.0. 0 = stays zoomed.",
    )

    _stream_zooms: np.ndarray | None = PrivateAttr(default=None)
    _stream_width: int = PrivateAttr(default=0)
    _stream_height: int = PrivateAttr(default=0)

    def _zoom_envelope(self, n_frames: int) -> np.ndarray:
        zooms = np.full(n_frames, self.zoom_factor, dtype=np.float32)
        attack = min(self.attack_frames, n_frames)
        if attack > 0:
            t = np.linspace(0.0, 1.0, attack, dtype=np.float32)
            zooms[:attack] = 1.0 + (self.zoom_factor - 1.0) * ease_out(t)
        release = min(self.release_frames, n_frames - attack)
        if release > 0:
            t = np.linspace(1.0, 0.0, release, dtype=np.float32)
            zooms[-release:] = 1.0 + (self.zoom_factor - 1.0) * ease_out(t)
        return zooms

    def _zoom_frame(self, frame: np.ndarray, zoom: float, width: int, height: int) -> np.ndarray:
        if zoom <= 1.0 + 1e-6:
            return frame
        crop_w = max(1, int(width / zoom))
        crop_h = max(1, int(height / zoom))
        x = (width - crop_w) // 2
        y = (height - crop_h) // 2
        cropped = frame[y : y + crop_h, x : x + crop_w]
        return cv2.resize(cropped, (width, height), interpolation=cv2.INTER_LINEAR)

    def streaming_init(self, total_frames: int, fps: float, width: int, height: int, **_context: Any) -> None:
        self._stream_zooms = self._zoom_envelope(total_frames)
        self._stream_width = width
        self._stream_height = height

    def process_frame(self, frame: np.ndarray, frame_index: int) -> np.ndarray:
        assert self._stream_zooms is not None
        idx = min(frame_index, len(self._stream_zooms) - 1)
        return self._zoom_frame(frame, float(self._stream_zooms[idx]), self._stream_width, self._stream_height)

Flash

Flash

Bases: Effect

Solid-color frame flash that fades in over attack_frames and out over decay_frames.

Commonly used between hard cuts, on impact moments, or as a strobe. The flash color is blended over the source using an alpha curve that peaks at peak_alpha in the middle of the window.

Source code in src/videopython/editing/effects.py 
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class Flash(Effect):
    """Solid-color frame flash that fades in over ``attack_frames`` and out over ``decay_frames``.

    Commonly used between hard cuts, on impact moments, or as a strobe. The
    flash color is blended over the source using an alpha curve that peaks
    at ``peak_alpha`` in the middle of the window.
    """

    op: Literal["flash"] = "flash"

    color: tuple[int, int, int] = Field(
        (255, 255, 255),
        description="Flash color as [R, G, B] each 0-255. (255,255,255) is white, (0,0,0) is a blackout.",
    )
    peak_alpha: float = Field(
        1.0,
        gt=0.0,
        le=1.0,
        description="Maximum opacity of the flash. 1.0 fully replaces the frame at peak, 0.5 is a half-blend.",
    )
    attack_frames: int = Field(
        2,
        ge=0,
        description="Frames to ramp from 0 alpha to peak. 0 = instant cut to color.",
    )
    decay_frames: int = Field(
        4,
        ge=0,
        description="Frames to ramp from peak back to 0. 0 = abrupt end.",
    )

    _stream_alpha: np.ndarray | None = PrivateAttr(default=None)
    _stream_color: np.ndarray | None = PrivateAttr(default=None)

    def _alpha_envelope(self, n_frames: int) -> np.ndarray:
        alpha = np.zeros(n_frames, dtype=np.float32)
        attack = min(self.attack_frames, n_frames)
        if attack > 0:
            alpha[:attack] = np.linspace(0.0, self.peak_alpha, attack, dtype=np.float32)
        else:
            alpha[: max(1, attack)] = self.peak_alpha
        decay = min(self.decay_frames, n_frames - attack)
        if decay > 0:
            alpha[attack : attack + decay] = np.linspace(self.peak_alpha, 0.0, decay, dtype=np.float32)
        if attack + decay < n_frames:
            alpha[attack + decay :] = 0.0
        # If attack is 0 and decay is 0, hold peak for the whole window
        if attack == 0 and decay == 0:
            alpha[:] = self.peak_alpha
        return alpha

    def streaming_init(self, total_frames: int, fps: float, width: int, height: int, **_context: Any) -> None:
        self._stream_alpha = self._alpha_envelope(total_frames)
        self._stream_color = np.array(self.color, dtype=np.float32)

    def process_frame(self, frame: np.ndarray, frame_index: int) -> np.ndarray:
        assert self._stream_alpha is not None and self._stream_color is not None
        idx = min(frame_index, len(self._stream_alpha) - 1)
        a = float(self._stream_alpha[idx])
        if a <= 0:
            return frame
        return (frame.astype(np.float32) * (1.0 - a) + self._stream_color * a).astype(np.uint8)

ChromaticAberration

ChromaticAberration

Bases: Effect

Splits R and B channels by shift_px to mimic lens chromatic aberration.

A defining look of glitch / vaporwave / experimental edits. Use a small shift (1-3 px) for a stylistic edge, larger (8+ px) for impact frames.

Source code in src/videopython/editing/effects.py 
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class ChromaticAberration(Effect):
    """Splits R and B channels by ``shift_px`` to mimic lens chromatic aberration.

    A defining look of glitch / vaporwave / experimental edits. Use a small
    shift (1-3 px) for a stylistic edge, larger (8+ px) for impact frames.
    """

    op: Literal["chromatic_aberration"] = "chromatic_aberration"

    shift_px: int = Field(
        gt=0,
        description="Channel displacement in pixels. 2 is subtle, 6 is noticeable, 12+ is dramatic.",
    )
    mode: Literal["horizontal", "vertical", "radial"] = Field(
        "horizontal",
        description=(
            '"horizontal" shifts R/B sideways, "vertical" shifts them up/down, '
            '"radial" scales R outward and B inward from the center (lens-like).'
        ),
    )

    _stream_maps: tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray] | None = PrivateAttr(default=None)

    def _build_radial_maps(self, width: int, height: int) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
        cx, cy = width / 2.0, height / 2.0
        max_d = float(max(width, height))
        scale_r = 1.0 - self.shift_px / max_d  # red sampled from slightly outward
        scale_b = 1.0 + self.shift_px / max_d  # blue sampled from slightly inward
        y, x = np.mgrid[0:height, 0:width].astype(np.float32)
        r_map_x = (x - cx) * scale_r + cx
        r_map_y = (y - cy) * scale_r + cy
        b_map_x = (x - cx) * scale_b + cx
        b_map_y = (y - cy) * scale_b + cy
        return r_map_x, r_map_y, b_map_x, b_map_y

    def _aberrate(self, frame: np.ndarray) -> np.ndarray:
        h, w = frame.shape[:2]
        out = frame.copy()
        r = frame[:, :, 0]
        b = frame[:, :, 2]
        if self.mode == "horizontal":
            M_r = np.array([[1.0, 0.0, float(self.shift_px)], [0.0, 1.0, 0.0]], dtype=np.float32)
            M_b = np.array([[1.0, 0.0, float(-self.shift_px)], [0.0, 1.0, 0.0]], dtype=np.float32)
            out[:, :, 0] = cv2.warpAffine(r, M_r, (w, h), borderMode=cv2.BORDER_REPLICATE)
            out[:, :, 2] = cv2.warpAffine(b, M_b, (w, h), borderMode=cv2.BORDER_REPLICATE)
        elif self.mode == "vertical":
            M_r = np.array([[1.0, 0.0, 0.0], [0.0, 1.0, float(self.shift_px)]], dtype=np.float32)
            M_b = np.array([[1.0, 0.0, 0.0], [0.0, 1.0, float(-self.shift_px)]], dtype=np.float32)
            out[:, :, 0] = cv2.warpAffine(r, M_r, (w, h), borderMode=cv2.BORDER_REPLICATE)
            out[:, :, 2] = cv2.warpAffine(b, M_b, (w, h), borderMode=cv2.BORDER_REPLICATE)
        else:  # radial
            if self._stream_maps is None:
                self._stream_maps = self._build_radial_maps(w, h)
            r_map_x, r_map_y, b_map_x, b_map_y = self._stream_maps
            out[:, :, 0] = cv2.remap(r, r_map_x, r_map_y, cv2.INTER_LINEAR, borderMode=cv2.BORDER_REPLICATE)
            out[:, :, 2] = cv2.remap(b, b_map_x, b_map_y, cv2.INTER_LINEAR, borderMode=cv2.BORDER_REPLICATE)
        return out

    def streaming_init(self, total_frames: int, fps: float, width: int, height: int, **_context: Any) -> None:
        if self.mode == "radial":
            self._stream_maps = self._build_radial_maps(width, height)

    def process_frame(self, frame: np.ndarray, frame_index: int) -> np.ndarray:
        return self._aberrate(frame)

Glitch

Glitch

Bases: Effect

Random horizontal slice displacement + channel offsets for a digital-corruption look.

Each frame gets a fresh set of slices shuffled left/right plus a small R/B channel shift. Deterministic given seed -- the same plan produces the same glitch every run.

Source code in src/videopython/editing/effects.py 
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class Glitch(Effect):
    """Random horizontal slice displacement + channel offsets for a digital-corruption look.

    Each frame gets a fresh set of slices shuffled left/right plus a small R/B
    channel shift. Deterministic given ``seed`` -- the same plan produces the
    same glitch every run.
    """

    op: Literal["glitch"] = "glitch"

    intensity: float = Field(
        0.5,
        gt=0.0,
        le=1.0,
        description="Overall glitch strength. 0.2 = subtle, 0.5 = moderate, 1.0 = chaotic.",
    )
    slice_count: int = Field(
        12,
        gt=0,
        description="Number of horizontal slices displaced per frame. More slices = more granular corruption.",
    )
    channel_shift_px: int = Field(
        4,
        ge=0,
        description="Pixels to shift R/B channels for the chromatic-aberration component. 0 disables it.",
    )
    seed: int = Field(0, description="Seed for the per-frame RNG. Same seed = reproducible glitch.")

    def _glitch_frame(self, frame: np.ndarray, frame_index: int) -> np.ndarray:
        h, w = frame.shape[:2]
        rng = np.random.default_rng(self.seed + frame_index)
        out = frame.copy()

        max_shift = int(self.intensity * w * 0.15)
        if max_shift > 0 and self.slice_count > 0:
            edges = np.sort(rng.integers(0, h, size=self.slice_count + 1))
            edges[0] = 0
            edges[-1] = h
            for i in range(self.slice_count):
                y0, y1 = int(edges[i]), int(edges[i + 1])
                if y1 <= y0:
                    continue
                shift = int(rng.integers(-max_shift, max_shift + 1))
                if shift == 0:
                    continue
                out[y0:y1] = np.roll(out[y0:y1], shift, axis=1)

        if self.channel_shift_px > 0:
            s = int(self.channel_shift_px * self.intensity) or 1
            out[:, :, 0] = np.roll(out[:, :, 0], s, axis=1)
            out[:, :, 2] = np.roll(out[:, :, 2], -s, axis=1)

        return out

    def streaming_init(self, total_frames: int, fps: float, width: int, height: int, **_context: Any) -> None:
        return None

    def process_frame(self, frame: np.ndarray, frame_index: int) -> np.ndarray:
        return self._glitch_frame(frame, frame_index)

FilmGrain

FilmGrain

Bases: Effect

Additive Gaussian noise simulating film grain.

Seeded per-frame so renders are reproducible. monochrome=True keeps the noise luma-only (cinematic), False adds independent RGB noise (digital / 8-bit look).

Source code in src/videopython/editing/effects.py 
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class FilmGrain(Effect):
    """Additive Gaussian noise simulating film grain.

    Seeded per-frame so renders are reproducible. ``monochrome=True`` keeps
    the noise luma-only (cinematic), False adds independent RGB noise
    (digital / 8-bit look).
    """

    op: Literal["film_grain"] = "film_grain"

    intensity: float = Field(
        0.1,
        gt=0.0,
        le=1.0,
        description="Noise standard deviation as a fraction of full-scale. 0.05 subtle, 0.2 heavy.",
    )
    monochrome: bool = Field(
        True,
        description="True applies the same noise to all RGB channels (luma-only). False uses per-channel noise.",
    )
    seed: int = Field(0, description="Seed for the noise RNG. Same seed = same grain pattern.")

    def _grain_frame(self, frame: np.ndarray, frame_index: int) -> np.ndarray:
        rng = np.random.default_rng(self.seed + frame_index)
        h, w = frame.shape[:2]
        amp = self.intensity * 255.0
        if self.monochrome:
            noise = rng.standard_normal((h, w, 1), dtype=np.float32) * amp
        else:
            noise = rng.standard_normal((h, w, 3), dtype=np.float32) * amp
        return np.clip(frame.astype(np.float32) + noise, 0, 255).astype(np.uint8)

    def streaming_init(self, total_frames: int, fps: float, width: int, height: int, **_context: Any) -> None:
        return None

    def process_frame(self, frame: np.ndarray, frame_index: int) -> np.ndarray:
        return self._grain_frame(frame, frame_index)

Sharpen

Sharpen

Bases: Effect

Unsharp-mask sharpening: blur the frame and subtract from itself with weight.

amount=0 returns the original frame; higher values produce a crisper look at the cost of edge halos.

Source code in src/videopython/editing/effects.py 
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class Sharpen(Effect):
    """Unsharp-mask sharpening: blur the frame and subtract from itself with weight.

    ``amount=0`` returns the original frame; higher values produce a crisper
    look at the cost of edge halos.
    """

    op: Literal["sharpen"] = "sharpen"

    amount: float = Field(
        1.0,
        ge=0.0,
        le=3.0,
        description="Sharpening strength. 0.5 is subtle, 1.0 moderate, 2.0+ aggressive (may cause halos).",
    )
    kernel_size: int = Field(
        5,
        ge=3,
        description="Gaussian blur kernel size used to build the unsharp mask. Must be odd; larger = wider halos.",
    )

    @model_validator(mode="after")
    def _validate_kernel(self) -> Sharpen:
        if self.kernel_size % 2 == 0:
            raise ValueError(f"kernel_size must be odd, got {self.kernel_size}")
        return self

    def _sharpen_frame(self, frame: np.ndarray) -> np.ndarray:
        if self.amount == 0:
            return frame
        blurred = cv2.GaussianBlur(frame, (self.kernel_size, self.kernel_size), 0)
        sharpened = cv2.addWeighted(frame, 1.0 + self.amount, blurred, -self.amount, 0)
        return sharpened

    def streaming_init(self, total_frames: int, fps: float, width: int, height: int, **_context: Any) -> None:
        return None

    def process_frame(self, frame: np.ndarray, frame_index: int) -> np.ndarray:
        return self._sharpen_frame(frame)

Pixelate

Pixelate

Bases: Effect

Mosaic blocks: downscale + nearest-neighbour upscale, optionally limited to a region.

Useful for face censoring (combine with BoundingBox from face detection) or a stylistic 8-bit look.

Source code in src/videopython/editing/effects.py 
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class Pixelate(Effect):
    """Mosaic blocks: downscale + nearest-neighbour upscale, optionally limited to a region.

    Useful for face censoring (combine with ``BoundingBox`` from face
    detection) or a stylistic 8-bit look.
    """

    op: Literal["pixelate"] = "pixelate"

    block_size: int = Field(
        gt=1,
        description="Mosaic block size in pixels. 8 is coarse, 32 is censor-grade, 64 is heavy.",
    )
    region: BoundingBox | None = Field(
        None,
        description="Optional normalized region (0-1) to pixelate. None = full frame.",
    )

    _stream_region_px: tuple[int, int, int, int] | None = PrivateAttr(default=None)

    def _resolve_region(self, width: int, height: int) -> tuple[int, int, int, int]:
        if self.region is None:
            return 0, 0, width, height
        x = max(0, int(self.region.x * width))
        y = max(0, int(self.region.y * height))
        w = max(1, min(int(self.region.width * width), width - x))
        h = max(1, min(int(self.region.height * height), height - y))
        return x, y, w, h

    def _pixelate_frame(self, frame: np.ndarray, region: tuple[int, int, int, int]) -> np.ndarray:
        x, y, w, h = region
        crop = frame[y : y + h, x : x + w]
        small_w = max(1, w // self.block_size)
        small_h = max(1, h // self.block_size)
        small = cv2.resize(crop, (small_w, small_h), interpolation=cv2.INTER_LINEAR)
        big = cv2.resize(small, (w, h), interpolation=cv2.INTER_NEAREST)
        frame[y : y + h, x : x + w] = big
        return frame

    def streaming_init(self, total_frames: int, fps: float, width: int, height: int, **_context: Any) -> None:
        self._stream_region_px = self._resolve_region(width, height)

    def process_frame(self, frame: np.ndarray, frame_index: int) -> np.ndarray:
        assert self._stream_region_px is not None
        return self._pixelate_frame(frame, self._stream_region_px)

MirrorFlip

MirrorFlip

Bases: Effect

Flip frames or reflect one half onto the other.

horizontal / vertical are plain mirror flips. The mirror_* modes reflect one half of the frame onto the opposite half, producing a symmetric image with the chosen half preserved.

Source code in src/videopython/editing/effects.py 
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class MirrorFlip(Effect):
    """Flip frames or reflect one half onto the other.

    ``horizontal`` / ``vertical`` are plain mirror flips. The ``mirror_*``
    modes reflect one half of the frame onto the opposite half, producing
    a symmetric image with the chosen half preserved.
    """

    op: Literal["mirror_flip"] = "mirror_flip"

    mode: Literal[
        "horizontal",
        "vertical",
        "mirror_left",
        "mirror_right",
        "mirror_top",
        "mirror_bottom",
    ] = Field(
        description=(
            '"horizontal" / "vertical" flip the whole frame. '
            '"mirror_left" reflects the left half onto the right (and analogously for the other mirror_ modes).'
        ),
    )

    def _flip_frame(self, frame: np.ndarray) -> np.ndarray:
        h, w = frame.shape[:2]
        if self.mode == "horizontal":
            return cv2.flip(frame, 1)
        if self.mode == "vertical":
            return cv2.flip(frame, 0)

        out = frame.copy()
        if self.mode == "mirror_left":
            half = w // 2
            out[:, w - half :] = out[:, :half][:, ::-1]
        elif self.mode == "mirror_right":
            half = w // 2
            out[:, :half] = out[:, w - half :][:, ::-1]
        elif self.mode == "mirror_top":
            half = h // 2
            out[h - half :, :] = out[:half, :][::-1, :]
        else:  # mirror_bottom
            half = h // 2
            out[:half, :] = out[h - half :, :][::-1, :]
        return out

    def streaming_init(self, total_frames: int, fps: float, width: int, height: int, **_context: Any) -> None:
        return None

    def process_frame(self, frame: np.ndarray, frame_index: int) -> np.ndarray:
        return self._flip_frame(frame)

Kaleidoscope

Kaleidoscope

Bases: Effect

N-way radial mirror around the frame center.

Samples one wedge of the frame and reflects it segments times around the center. The mapping is precomputed once per stream, so per-frame cost is a single cv2.remap.

Source code in src/videopython/editing/effects.py 
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class Kaleidoscope(Effect):
    """N-way radial mirror around the frame center.

    Samples one wedge of the frame and reflects it ``segments`` times around
    the center. The mapping is precomputed once per stream, so per-frame cost
    is a single ``cv2.remap``.
    """

    op: Literal["kaleidoscope"] = "kaleidoscope"

    segments: int = Field(
        6,
        ge=2,
        le=24,
        description="Number of mirror segments. 6 is a classic snowflake, 12 is dense, 2 is minimal.",
    )
    angle_offset: float = Field(
        0.0,
        description="Rotation of the kaleidoscope pattern in radians (e.g. pi/2 rotates by 90 degrees).",
    )

    _stream_map_x: np.ndarray | None = PrivateAttr(default=None)
    _stream_map_y: np.ndarray | None = PrivateAttr(default=None)

    def _build_maps(self, width: int, height: int) -> tuple[np.ndarray, np.ndarray]:
        cx, cy = (width - 1) / 2.0, (height - 1) / 2.0
        y, x = np.mgrid[0:height, 0:width].astype(np.float32)
        dx = x - cx
        dy = y - cy
        r = np.sqrt(dx * dx + dy * dy)
        theta = np.arctan2(dy, dx) - self.angle_offset

        wedge = 2.0 * np.pi / self.segments
        half_wedge = wedge * 0.5
        theta_mod = np.mod(theta, wedge)
        theta_folded = np.where(theta_mod > half_wedge, wedge - theta_mod, theta_mod)
        theta_final = theta_folded + self.angle_offset

        src_x = (cx + r * np.cos(theta_final)).astype(np.float32)
        src_y = (cy + r * np.sin(theta_final)).astype(np.float32)
        return src_x, src_y

    def _kaleidoscope_frame(self, frame: np.ndarray) -> np.ndarray:
        assert self._stream_map_x is not None and self._stream_map_y is not None
        return cv2.remap(
            frame,
            self._stream_map_x,
            self._stream_map_y,
            cv2.INTER_LINEAR,
            borderMode=cv2.BORDER_REFLECT,
        )

    def streaming_init(self, total_frames: int, fps: float, width: int, height: int, **_context: Any) -> None:
        self._stream_map_x, self._stream_map_y = self._build_maps(width, height)

    def process_frame(self, frame: np.ndarray, frame_index: int) -> np.ndarray:
        return self._kaleidoscope_frame(frame)