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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

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

video = Video.from_path("input.mp4")

# Effect across the full duration:
video = Blur(mode="constant", iterations=50).apply(video)

# Effect on a sub-range via the `window` field:
video = Blur(
    mode="constant",
    iterations=50,
    window=TimeRange(start=0.0, stop=2.0),
).apply(video)

video = Zoom(zoom_factor=1.5, mode="in").apply(video)
video = ColorGrading(brightness=0.1, contrast=1.2, saturation=1.1).apply(video)
video = Vignette(strength=0.5, radius=0.8).apply(video)

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 = KenBurns(start_region=start_region, end_region=end_region,
                 easing="ease_in_out").apply(video)

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

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

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

Inside a VideoEdit plan, effects go in the segment's operations list. The window field travels inline as a nested object:

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

Available Effects

Every effect except add_subtitles is streamable (compatible with VideoEdit.run_to_file() for constant-memory processing).

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 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, with optional streaming.

Subclasses override :meth:_apply for in-memory execution and may additionally override :meth:streaming_init / :meth:process_frame for bounded-memory streaming via editing/streaming.py. The base :meth:apply resolves :attr:window, slices the video, runs _apply on the slice, splices the result back, and asserts the shape-preserving invariant.

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

    Subclasses override :meth:`_apply` for in-memory execution and may
    additionally override :meth:`streaming_init` / :meth:`process_frame` for
    bounded-memory streaming via ``editing/streaming.py``. The base
    :meth:`apply` resolves :attr:`window`, slices the video, runs
    ``_apply`` on the slice, splices the result back, and asserts the
    shape-preserving invariant.
    """

    category: ClassVar[OpCategory] = OpCategory.EFFECT

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

    def apply(self, video: Video, **context: Any) -> Video:
        from videopython.base.video import Video as _Video

        original_shape = video.video_shape

        if self.window is None or (self.window.start is None and self.window.stop is None):
            result = self._apply(video)
        else:
            start_s, stop_s = self._resolved_window(video.total_seconds)
            start_f = round(start_s * video.fps)
            end_f = round(stop_s * video.fps)
            inner = self._apply(video[start_f:end_f])
            old_audio = video.audio
            result = _Video.from_frames(
                np.r_["0,2", video.frames[:start_f], inner.frames, video.frames[end_f:]],
                fps=video.fps,
            )
            result.audio = old_audio

        if result.video_shape != original_shape:
            raise RuntimeError(
                f"{type(self).__name__} changed video shape from {original_shape} "
                f"to {result.video_shape}; effects must preserve shape and frame count."
            )
        return result

    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.apply`'s ``**context`` accept-all.
        """
        return meta

    def _resolved_window(self, total_seconds: float) -> tuple[float, float]:
        win = self.window or TimeRange()
        start_s = 0.0 if win.start is None else float(win.start)
        stop_s = total_seconds if win.stop is None else float(win.stop)
        start_s = min(start_s, total_seconds)
        stop_s = min(stop_s, total_seconds)
        if stop_s < start_s:
            raise ValueError(f"Effect stop ({stop_s}) must be >= start ({start_s})")
        return start_s, stop_s

    def _apply(self, video: Video) -> Video:
        """Apply the effect to ``video`` in memory. Override in subclasses."""
        raise NotImplementedError(f"{type(self).__name__}._apply not implemented")

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

        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")

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.apply'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.apply`'s ``**context`` accept-all.
    """
    return meta

streaming_init 

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

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

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) -> None:
    """Hook for per-stream precomputation (per-frame alphas, sigma curves...).

    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")

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"
    streamable: ClassVar[bool] = True

    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) -> 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])

    def _apply(self, video: Video) -> Video:
        n_frames = len(video.frames)
        sigmas = self._compute_sigmas(n_frames)
        logger.info(f"Applying {self.mode} blur...")
        for i in tqdm(range(n_frames), desc="Blurring"):
            video.frames[i] = self._blur_frame(video.frames[i], sigmas[i])
        return video

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"
    streamable: ClassVar[bool] = True

    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) -> 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))

    def _apply(self, video: Video) -> Video:
        n_frames = len(video.frames)
        width = video.metadata.width
        height = video.metadata.height
        crops = self._crop_sizes(n_frames, width, height)
        for i in tqdm(range(n_frames), desc="Zooming"):
            w, h = crops[i]
            x = width / 2 - w / 2
            y = height / 2 - h / 2
            cropped = video.frames[i][round(y) : round(y + h), round(x) : round(x + w)]
            video.frames[i] = cv2.resize(cropped, (width, height))
        return video

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"
    streamable: ClassVar[bool] = True

    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) -> 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 _apply(self, video: Video) -> Video:
        overlay = self._load_overlay()
        if video.frame_shape != overlay[:, :, :3].shape:
            raise ValueError(f"Mismatch of overlay shape `{overlay.shape}` with video shape: `{video.frame_shape}`!")
        if not (0 <= 2 * self.fade_time <= video.total_seconds):
            raise ValueError(f"Video is only {video.total_seconds}s long, but fade time is {self.fade_time}s!")

        logger.info("Overlaying video...")
        n = len(video.frames)
        num_fade_frames = round(self.fade_time * video.fps) if self.fade_time > 0 else 0
        for i in tqdm(range(n), desc="Overlaying frames"):
            if num_fade_frames == 0:
                video.frames[i] = self._overlay_frame(video.frames[i])
            else:
                dist_from_end = min(i, n - i)
                fade_alpha = 1.0 if dist_from_end >= num_fade_frames else dist_from_end / num_fade_frames
                video.frames[i] = self._overlay_frame(video.frames[i], fade_alpha)
        return video

ImageOverlay

ImageOverlay

Bases: Effect

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(Effect):
    """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"
    streamable: ClassVar[bool] = True

    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)
    _stream_noop: bool = PrivateAttr(default=False)
    _stream_alpha: np.ndarray | None = PrivateAttr(default=None)
    _stream_rgb: np.ndarray | None = PrivateAttr(default=None)
    _stream_dst: tuple[int, int, int, int] = PrivateAttr(default=(0, 0, 0, 0))

    @model_validator(mode="after")
    def _validate_position(self) -> ImageOverlay:
        if not (0.0 <= self.position[0] <= 1.0 and 0.0 <= self.position[1] <= 1.0):
            raise ValueError("position values must be in range [0, 1]")
        return self

    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()`` 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:
            raise ValueError(f"image_overlay source {str(self.source)!r} is not a readable image: {exc}") 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 _compute_position(self, frame_width: int, frame_height: int, img_w: int, img_h: int) -> tuple[int, int]:
        # Copied verbatim from TextOverlay: ImageOverlay's anchor Literal is
        # deliberately the same set, so the geometry is shared by construction.
        px = int(self.position[0] * frame_width)
        py = int(self.position[1] * frame_height)

        if self.anchor == "center":
            return px - img_w // 2, py - img_h // 2
        if self.anchor == "top_left":
            return px, py
        if self.anchor == "top_center":
            return px - img_w // 2, py
        if self.anchor == "bottom_center":
            return px - img_w // 2, py - img_h
        if self.anchor == "bottom_left":
            return px, py - img_h
        # bottom_right
        return px - img_w, py - img_h

    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 _blend_params(
        self, frame_w: int, frame_h: int
    ) -> tuple[np.ndarray, np.ndarray, tuple[int, int, int, int]] | None:
        """Placement + blend inputs shared by the eager and streaming paths.

        Single source of truth so the two paths cannot drift -- the
        eager/stream parity-hole class of bug fixed in 0.34.1. Returns ``None``
        when the overlay lands fully off-frame (the effect is a no-op).
        """
        overlay = self._resized_overlay(frame_w)
        oh, ow = overlay.shape[:2]
        x, y = self._compute_position(frame_w, frame_h, ow, oh)

        src_x = max(0, -x)
        src_y = max(0, -y)
        dst_x = max(0, x)
        dst_y = max(0, y)
        paste_w = min(ow - src_x, frame_w - dst_x)
        paste_h = min(oh - src_y, frame_h - dst_y)

        if paste_w <= 0 or paste_h <= 0:
            return None

        region = overlay[src_y : src_y + paste_h, src_x : src_x + paste_w]
        alpha = (region[:, :, 3:4].astype(np.float32) / 255.0) * self.opacity
        rgb = region[:, :, :3].astype(np.float32)
        return alpha, rgb, (dst_y, dst_x, paste_h, paste_w)

    def streaming_init(self, total_frames: int, fps: float, width: int, height: int) -> None:
        params = self._blend_params(width, height)
        if params is None:
            self._stream_noop = True
            return
        self._stream_noop = False
        self._stream_alpha, self._stream_rgb, self._stream_dst = params

    def process_frame(self, frame: np.ndarray, frame_index: int) -> np.ndarray:
        if self._stream_noop:
            return frame
        assert self._stream_alpha is not None and self._stream_rgb is not None
        dy, dx, ph, pw = self._stream_dst
        region = frame[dy : dy + ph, dx : dx + pw]
        blended = (
            self._stream_rgb * self._stream_alpha + region.astype(np.float32) * (1.0 - self._stream_alpha)
        ).astype(np.uint8)
        frame[dy : dy + ph, dx : dx + pw] = blended
        return frame

    def _apply(self, video: Video) -> Video:
        frame_h, frame_w = video.frame_shape[:2]
        params = self._blend_params(frame_w, frame_h)
        if params is None:
            return video
        alpha, rgb, (dy, dx, ph, pw) = params

        logger.info("Applying image overlay...")
        for frame in tqdm(video.frames, desc="Image overlay"):
            region = frame[dy : dy + ph, dx : dx + pw]
            blended = (rgb * alpha + region.astype(np.float32) * (1.0 - alpha)).astype(np.uint8)
            frame[dy : dy + ph, dx : dx + pw] = blended
        return video

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() 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()`` 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:
        raise ValueError(f"image_overlay source {str(self.source)!r} is not a readable image: {exc}") 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"
    streamable: ClassVar[bool] = True

    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)

    def _apply(self, video: Video) -> Video:
        logger.info("Applying color grading...")
        for i in tqdm(range(len(video.frames)), desc="Color grading"):
            video.frames[i] = self._grade_frame(video.frames[i])
        return video

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"
    streamable: ClassVar[bool] = True

    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) -> 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)

    def _apply(self, video: Video) -> Video:
        logger.info("Applying vignette effect...")
        height, width = video.frame_shape[:2]
        if self._mask is None or self._mask.shape != (height, width):
            self._mask = self._create_mask(height, width)
        mask_3d = self._mask[:, :, np.newaxis]
        batch_size = 64
        for start in range(0, len(video.frames), batch_size):
            end = min(start + batch_size, len(video.frames))
            video.frames[start:end] = (video.frames[start:end].astype(np.float32) * mask_3d).astype(np.uint8)
        return video

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"
    streamable: ClassVar[bool] = True

    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 _ease(self, t: float) -> float:
        if self.easing == "linear":
            return t
        if self.easing == "ease_in":
            return t * t
        if self.easing == "ease_out":
            return 1 - (1 - t) * (1 - t)
        # ease_in_out
        if t < 0.5:
            return 2 * t * t
        return 1 - 2 * (1 - t) * (1 - t)

    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)
        for i in range(n_frames):
            t = i / max(1, n_frames - 1)
            et = self._ease(t)
            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) -> 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)

    def _apply(self, video: Video) -> Video:
        n_frames = len(video.frames)
        height, width = video.frame_shape[:2]
        regions = self._precompute_regions(n_frames, width, height)
        logger.info("Applying Ken Burns effect...")
        for i in tqdm(range(n_frames), desc="Ken Burns"):
            x, y, cw, ch = regions[i]
            video.frames[i] = self._crop_and_scale_frame(video.frames[i], x, y, cw, ch, width, height)
        return video

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"
    streamable: 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) -> 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 apply(self, video: Video, **context: Any) -> Video:
        start_s, stop_s = self._resolved_window(video.total_seconds)
        start_f = round(start_s * video.fps)
        end_f = round(stop_s * video.fps)
        n_effect = end_f - start_f
        alpha = self._fade_envelope(n_effect, video.fps)

        batch_size = 64
        for batch_start in range(0, n_effect, batch_size):
            batch_end = min(batch_start + batch_size, n_effect)
            batch_alpha = alpha[batch_start:batch_end, np.newaxis, np.newaxis, np.newaxis]
            if np.all(batch_alpha == 1.0):
                continue
            abs_start = start_f + batch_start
            abs_end = start_f + batch_end
            video.frames[abs_start:abs_end] = (video.frames[abs_start:abs_end].astype(np.float32) * batch_alpha).astype(
                np.uint8
            )

        if video.audio is not None and not video.audio.is_silent:
            self._apply_audio(video.audio, start_s, stop_s)
        return video

    def _apply_audio(self, audio: Audio, start_s: float, stop_s: float) -> None:
        sample_rate = audio.metadata.sample_rate
        audio_start = round(start_s * sample_rate)
        audio_end = min(round(stop_s * sample_rate), len(audio.data))
        alpha = self._fade_envelope(audio_end - audio_start, sample_rate)

        if audio.data.ndim == 1:
            audio.data[audio_start:audio_end] *= alpha
        else:
            audio.data[audio_start:audio_end] *= alpha[:, np.newaxis]
        np.clip(audio.data, -1.0, 1.0, out=audio.data)

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"
    streamable: 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 apply(self, video: Video, **context: Any) -> Video:
        start_s, stop_s = self._resolved_window(video.total_seconds)
        if video.audio is not None and not video.audio.is_silent:
            self._apply_audio(video.audio, start_s, stop_s)
        return video

    def _apply_audio(self, audio: Audio, start_s: float, stop_s: float) -> None:
        sample_rate = audio.metadata.sample_rate
        start_sample = round(start_s * sample_rate)
        end_sample = min(round(stop_s * sample_rate), len(audio.data))
        n_samples = end_sample - start_sample
        envelope = np.full(n_samples, self.volume, dtype=np.float32)

        if self.ramp_duration > 0:
            ramp_samples = min(round(self.ramp_duration * sample_rate), n_samples // 2)
            if ramp_samples > 0:
                t = np.linspace(0, 1, ramp_samples, dtype=np.float32)
                envelope[:ramp_samples] = 1.0 + (self.volume - 1.0) * np.sqrt(t)
                t = np.linspace(1, 0, ramp_samples, dtype=np.float32)
                envelope[-ramp_samples:] = 1.0 + (self.volume - 1.0) * np.sqrt(t)

        if audio.data.ndim == 1:
            audio.data[start_sample:end_sample] *= envelope
        else:
            audio.data[start_sample:end_sample] *= envelope[:, np.newaxis]
        np.clip(audio.data, -1.0, 1.0, out=audio.data)

TextOverlay

TextOverlay

Bases: Effect

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(Effect):
    """Draws text on video frames, with auto word-wrap and optional background box."""

    op: Literal["text_overlay"] = "text_overlay"
    streamable: ClassVar[bool] = True

    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_filename: str | None = Field(None, description="Path to a .ttf font file, or None for the default font.")

    _rendered: np.ndarray | None = PrivateAttr(default=None)
    _stream_noop: bool = PrivateAttr(default=False)
    _stream_alpha: np.ndarray | None = PrivateAttr(default=None)
    _stream_rgb: np.ndarray | None = PrivateAttr(default=None)
    _stream_dst: tuple[int, int, int, int] = PrivateAttr(default=(0, 0, 0, 0))

    @model_validator(mode="after")
    def _validate_position(self) -> TextOverlay:
        if not (0.0 <= self.position[0] <= 1.0 and 0.0 <= self.position[1] <= 1.0):
            raise ValueError("position values must be in range [0, 1]")
        return self

    def _get_font(self) -> ImageFont.FreeTypeFont | ImageFont.ImageFont:
        return load_font(self.font_filename, 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 _compute_position(self, frame_width: int, frame_height: int, img_w: int, img_h: int) -> tuple[int, int]:
        px = int(self.position[0] * frame_width)
        py = int(self.position[1] * frame_height)

        if self.anchor == "center":
            return px - img_w // 2, py - img_h // 2
        if self.anchor == "top_left":
            return px, py
        if self.anchor == "top_center":
            return px - img_w // 2, py
        if self.anchor == "bottom_center":
            return px - img_w // 2, py - img_h
        if self.anchor == "bottom_left":
            return px, py - img_h
        # bottom_right
        return px - img_w, py - img_h

    def streaming_init(self, total_frames: int, fps: float, width: int, height: int) -> None:
        if self._rendered is None:
            self._rendered = self._render_text_image(width, height)
        oh, ow = self._rendered.shape[:2]
        x, y = self._compute_position(width, height, ow, oh)
        src_x = max(0, -x)
        src_y = max(0, -y)
        dst_x = max(0, x)
        dst_y = max(0, y)
        paste_w = min(ow - src_x, width - dst_x)
        paste_h = min(oh - src_y, height - dst_y)
        if paste_w <= 0 or paste_h <= 0:
            self._stream_noop = True
            return
        self._stream_noop = False
        overlay_region = self._rendered[src_y : src_y + paste_h, src_x : src_x + paste_w]
        self._stream_alpha = overlay_region[:, :, 3:4].astype(np.float32) / 255.0
        self._stream_rgb = overlay_region[:, :, :3].astype(np.float32)
        self._stream_dst = (dst_y, dst_x, paste_h, paste_w)

    def process_frame(self, frame: np.ndarray, frame_index: int) -> np.ndarray:
        if self._stream_noop:
            return frame
        assert self._stream_alpha is not None and self._stream_rgb is not None
        dy, dx, ph, pw = self._stream_dst
        region = frame[dy : dy + ph, dx : dx + pw]
        blended = (
            self._stream_rgb * self._stream_alpha + region.astype(np.float32) * (1.0 - self._stream_alpha)
        ).astype(np.uint8)
        frame[dy : dy + ph, dx : dx + pw] = blended
        return frame

    def _apply(self, video: Video) -> Video:
        frame_h, frame_w = video.frame_shape[:2]
        if self._rendered is None:
            self._rendered = self._render_text_image(frame_w, frame_h)

        overlay_rgba = self._rendered
        oh, ow = overlay_rgba.shape[:2]
        x, y = self._compute_position(frame_w, frame_h, ow, oh)

        src_x = max(0, -x)
        src_y = max(0, -y)
        dst_x = max(0, x)
        dst_y = max(0, y)
        paste_w = min(ow - src_x, frame_w - dst_x)
        paste_h = min(oh - src_y, frame_h - dst_y)

        if paste_w <= 0 or paste_h <= 0:
            return video

        overlay_region = overlay_rgba[src_y : src_y + paste_h, src_x : src_x + paste_w]
        alpha = overlay_region[:, :, 3:4].astype(np.float32) / 255.0
        overlay_rgb = overlay_region[:, :, :3].astype(np.float32)

        logger.info("Applying text overlay...")
        for frame in tqdm(video.frames, desc="Text overlay"):
            region = frame[dst_y : dst_y + paste_h, dst_x : dst_x + paste_w]
            blended = (overlay_rgb * alpha + region.astype(np.float32) * (1.0 - alpha)).astype(np.uint8)
            frame[dst_y : dst_y + paste_h, dst_x : dst_x + paste_w] = blended

        return video

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"
    streamable: ClassVar[bool] = True

    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) -> 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))

    def _apply(self, video: Video) -> Video:
        offsets = self._compute_offsets(len(video.frames), video.fps)
        for i in tqdm(range(len(video.frames)), desc="Shaking"):
            dx, dy = offsets[i]
            video.frames[i] = self._shake_frame(video.frames[i], float(dx), float(dy))
        return video

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"
    streamable: ClassVar[bool] = True

    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)
            ease = 1.0 - (1.0 - t) * (1.0 - t)  # ease_out
            zooms[:attack] = 1.0 + (self.zoom_factor - 1.0) * ease
        release = min(self.release_frames, n_frames - attack)
        if release > 0:
            t = np.linspace(1.0, 0.0, release, dtype=np.float32)
            ease = 1.0 - (1.0 - t) * (1.0 - t)
            zooms[-release:] = 1.0 + (self.zoom_factor - 1.0) * ease
        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) -> 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)

    def _apply(self, video: Video) -> Video:
        n = len(video.frames)
        height, width = video.frame_shape[:2]
        zooms = self._zoom_envelope(n)
        for i in tqdm(range(n), desc="Punching in"):
            video.frames[i] = self._zoom_frame(video.frames[i], float(zooms[i]), width, height)
        return video

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"
    streamable: ClassVar[bool] = True

    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) -> 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)

    def _apply(self, video: Video) -> Video:
        n = len(video.frames)
        alpha = self._alpha_envelope(n)
        color = np.array(self.color, dtype=np.float32)
        for i in tqdm(range(n), desc="Flashing"):
            a = float(alpha[i])
            if a <= 0:
                continue
            video.frames[i] = (video.frames[i].astype(np.float32) * (1.0 - a) + color * a).astype(np.uint8)
        return video

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"
    streamable: ClassVar[bool] = True

    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) -> 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)

    def _apply(self, video: Video) -> Video:
        h, w = video.frame_shape[:2]
        if self.mode == "radial":
            self._stream_maps = self._build_radial_maps(w, h)
        for i in tqdm(range(len(video.frames)), desc="Chromatic aberration"):
            video.frames[i] = self._aberrate(video.frames[i])
        return video

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"
    streamable: ClassVar[bool] = True

    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) -> None:
        return None

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

    def _apply(self, video: Video) -> Video:
        for i in tqdm(range(len(video.frames)), desc="Glitching"):
            video.frames[i] = self._glitch_frame(video.frames[i], i)
        return video

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"
    streamable: ClassVar[bool] = True

    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) -> None:
        return None

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

    def _apply(self, video: Video) -> Video:
        for i in tqdm(range(len(video.frames)), desc="Adding grain"):
            video.frames[i] = self._grain_frame(video.frames[i], i)
        return video

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"
    streamable: ClassVar[bool] = True

    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) -> None:
        return None

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

    def _apply(self, video: Video) -> Video:
        for i in tqdm(range(len(video.frames)), desc="Sharpening"):
            video.frames[i] = self._sharpen_frame(video.frames[i])
        return video

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"
    streamable: ClassVar[bool] = True

    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) -> 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)

    def _apply(self, video: Video) -> Video:
        h, w = video.frame_shape[:2]
        region = self._resolve_region(w, h)
        for i in tqdm(range(len(video.frames)), desc="Pixelating"):
            video.frames[i] = self._pixelate_frame(video.frames[i], region)
        return video

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"
    streamable: ClassVar[bool] = True

    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) -> None:
        return None

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

    def _apply(self, video: Video) -> Video:
        for i in tqdm(range(len(video.frames)), desc="Mirroring"):
            video.frames[i] = self._flip_frame(video.frames[i])
        return video

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"
    streamable: ClassVar[bool] = True

    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) -> 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)

    def _apply(self, video: Video) -> Video:
        h, w = video.frame_shape[:2]
        self._stream_map_x, self._stream_map_y = self._build_maps(w, h)
        for i in tqdm(range(len(video.frames)), desc="Kaleidoscope"):
            video.frames[i] = self._kaleidoscope_frame(video.frames[i])
        return video