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dissolve

Classes:

DissolveEffect

DissolveEffect(
    *, grain_strength: float = 0.3, **kwargs: Any
)

Bases: BaseEffect

grain_strength: >
    Strength of the grain noise (0.0 to 1.0).
    Default is 0.3.

Methods:

Source code in src/movfx/effects/dissolve.py
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def __init__(self, *, grain_strength: float = 0.3, **kwargs: Any) -> None:
    """
    title: Initialize dissolve effect
    parameters:
        grain_strength: >
            Strength of the grain noise (0.0 to 1.0).
            Default is 0.3.
    """
    super().__init__(**kwargs)
    self.grain_strength = max(0.0, min(1.0, grain_strength))

build_clip

build_clip(
    img_from: ndarray,
    img_to: ndarray,
    duration: float,
    fps: int = 30,
) -> VideoClip
Source code in src/movfx/effects/base.py
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def build_clip(
    self,
    img_from: np.ndarray,
    img_to: np.ndarray,
    duration: float,
    fps: int = 30,
) -> VideoClip:
    """
    title: Build a moviepy VideoClip from the effect
    parameters:
        img_from: Source image as a NumPy RGB array
        img_to: Destination image as a NumPy RGB array
        duration: Duration of the clip in seconds
        fps: Frames per second
    returns: A moviepy VideoClip with the transition
    """

    def make_frame(t: float) -> np.ndarray:
        progress = t / duration if duration > 0 else 1.0
        progress = max(0.0, min(1.0, progress))
        return self.render_frame(img_from, img_to, progress)

    clip = VideoClip(make_frame, duration=duration)
    clip = clip.with_fps(fps)
    return clip

render_frame

render_frame(
    img_from: ndarray, img_to: ndarray, progress: float
) -> ndarray
Source code in src/movfx/effects/dissolve.py
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def render_frame(
    self,
    img_from: np.ndarray,
    img_to: np.ndarray,
    progress: float,
) -> np.ndarray:
    """
    title: Render a dissolve frame
    parameters:
        img_from: Source image array
        img_to: Destination image array
        progress: Blend factor from 0.0 to 1.0
    returns: Grainy blended frame as uint8 array
    """
    h, w = img_from.shape[:2]
    noise = np.random.default_rng().uniform(
        -self.grain_strength, self.grain_strength, (h, w, 1)
    )
    per_pixel_progress = np.clip(progress + noise, 0.0, 1.0)
    blended = (1.0 - per_pixel_progress) * img_from + (
        per_pixel_progress * img_to
    )
    return blended.astype(np.uint8)