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Text & Transcription

Classes for handling transcriptions and rendering text overlays on video.

Transcription Classes

Transcription

Transcription

Source code in src/videopython/base/text/transcription.py 
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class Transcription:
    def __init__(
        self,
        segments: list[TranscriptionSegment] | None = None,
        words: list[TranscriptionWord] | None = None,
    ):
        """Initialize Transcription from either segments or words.

        Args:
            segments: Pre-constructed segments (backward compatible)
            words: Words to group into segments by speaker (for diarization)

        Raises:
            ValueError: If both or neither arguments are provided
        """
        if (segments is None) == (words is None):
            raise ValueError("Exactly one of 'segments' or 'words' must be provided")

        if segments is not None:
            self.segments = segments
            self.speakers = {s.speaker for s in segments if s.speaker is not None}
        else:
            self.segments = self._words_to_segments(words)  # type: ignore
            self.speakers = {w.speaker for w in words if w.speaker is not None}  # type: ignore

    def _words_to_segments(self, words: list[TranscriptionWord]) -> list[TranscriptionSegment]:
        """Group words into segments based on speaker changes."""
        if not words:
            return []

        current_speaker = words[0].speaker
        current_words = []
        segment_start = words[0].start
        segments = []

        for word in words:
            if current_speaker == word.speaker:
                current_words.append(word)
            else:
                segment_text = " ".join(w.word for w in current_words)
                segments.append(
                    TranscriptionSegment(
                        start=segment_start,
                        end=current_words[-1].end,
                        text=segment_text.strip(),
                        words=current_words.copy(),
                        speaker=current_speaker,
                    )
                )
                current_speaker = word.speaker
                current_words = [word]
                segment_start = word.start

        if current_words:
            segment_text = " ".join(w.word for w in current_words)
            segments.append(
                TranscriptionSegment(
                    start=segment_start,
                    end=current_words[-1].end,
                    text=segment_text.strip(),
                    words=current_words.copy(),
                    speaker=current_speaker,
                )
            )

        return segments

    def speaker_stats(self) -> dict[str, float]:
        """Calculate speaking time percentage for each speaker.

        Returns:
            Dictionary mapping speaker names to their percentage of total speaking time
        """
        all_words = []
        for segment in self.segments:
            all_words.extend(segment.words)

        speaking_stats: dict[str, float] = {speaker: 0.0 for speaker in self.speakers}
        total_speaking_time = 0.0

        for word in all_words:
            if word.speaker is not None:
                speak_time = word.end - word.start
                total_speaking_time += speak_time
                speaking_stats[word.speaker] += speak_time

        if total_speaking_time > 0:
            for speaker in speaking_stats:
                speaking_stats[speaker] /= total_speaking_time

        return speaking_stats

    def offset(self, time: float) -> Transcription:
        """Return a new Transcription with all timings offset by the provided time value."""
        offset_segments = []

        for segment in self.segments:
            offset_words = []
            for word in segment.words:
                offset_words.append(
                    TranscriptionWord(
                        start=word.start + time, end=word.end + time, word=word.word, speaker=word.speaker
                    )
                )

            offset_segments.append(
                TranscriptionSegment(
                    start=segment.start + time,
                    end=segment.end + time,
                    text=segment.text,
                    words=offset_words,
                    speaker=segment.speaker,
                )
            )

        return Transcription(segments=offset_segments)

    def standardize_segments(self, *, time: float | None = None, num_words: int | None = None) -> Transcription:
        """Return a new Transcription with standardized segments.

        Args:
            time: Maximum duration in seconds for each segment
            num_words: Exact number of words per segment

        Raises:
            ValueError: If both time and num_words are provided or if neither is provided
        """
        if (time is None) == (num_words is None):
            raise ValueError("Exactly one of 'time' or 'num_words' must be provided")

        if time is not None and time <= 0:
            raise ValueError("Time must be positive")

        if num_words is not None and num_words <= 0:
            raise ValueError("Number of words must be positive")

        # Collect all words from all segments
        all_words = []
        for segment in self.segments:
            all_words.extend(segment.words)

        if not all_words:
            return Transcription(segments=[])

        standardized_segments = []

        if time is not None:
            # Group words by time constraint
            current_words = []
            current_start = None

            for word in all_words:
                if current_start is None:
                    current_start = word.start
                    current_words = [word]
                elif word.end - current_start <= time:
                    current_words.append(word)
                else:
                    # Create segment from current words
                    if current_words:
                        segment_text = " ".join(w.word for w in current_words)
                        standardized_segments.append(
                            TranscriptionSegment(
                                start=current_start,
                                end=current_words[-1].end,
                                text=segment_text,
                                words=current_words.copy(),
                            )
                        )

                    # Start new segment
                    current_start = word.start
                    current_words = [word]

            # Add final segment
            if current_words:
                segment_text = " ".join(w.word for w in current_words)
                standardized_segments.append(
                    TranscriptionSegment(
                        start=current_start,  # type: ignore
                        end=current_words[-1].end,
                        text=segment_text,
                        words=current_words.copy(),
                    )
                )
        elif num_words is not None:
            # Group words by word count constraint
            for i in range(0, len(all_words), num_words):
                segment_words = all_words[i : i + num_words]
                segment_text = " ".join(w.word for w in segment_words)
                standardized_segments.append(
                    TranscriptionSegment(
                        start=segment_words[0].start, end=segment_words[-1].end, text=segment_text, words=segment_words
                    )
                )

        return Transcription(segments=standardized_segments)

    def slice(self, start: float, end: float) -> Transcription | None:
        """Return a new Transcription containing only words within the time range.

        Slices at word-level granularity: words that overlap with the time range
        are included, and new segments are reconstructed from the included words.

        Args:
            start: Start time in seconds (inclusive)
            end: End time in seconds (exclusive)

        Returns:
            New Transcription with words/segments in the time range, or None if no words overlap
        """
        if start >= end:
            return None

        # Collect all words that overlap with the time range
        overlapping_words: list[TranscriptionWord] = []
        for segment in self.segments:
            for word in segment.words:
                # Include word if it overlaps with our time range
                if word.end > start and word.start < end:
                    overlapping_words.append(word)

        if not overlapping_words:
            return None

        # Reconstruct segments from the overlapping words
        # Group consecutive words by speaker to form segments
        sliced_segments: list[TranscriptionSegment] = []
        current_speaker = overlapping_words[0].speaker
        current_words: list[TranscriptionWord] = []

        for word in overlapping_words:
            if word.speaker == current_speaker:
                current_words.append(word)
            else:
                # Finish current segment
                if current_words:
                    segment_text = " ".join(w.word for w in current_words)
                    sliced_segments.append(
                        TranscriptionSegment(
                            start=current_words[0].start,
                            end=current_words[-1].end,
                            text=segment_text,
                            words=current_words.copy(),
                            speaker=current_speaker,
                        )
                    )
                # Start new segment
                current_speaker = word.speaker
                current_words = [word]

        # Add final segment
        if current_words:
            segment_text = " ".join(w.word for w in current_words)
            sliced_segments.append(
                TranscriptionSegment(
                    start=current_words[0].start,
                    end=current_words[-1].end,
                    text=segment_text,
                    words=current_words.copy(),
                    speaker=current_speaker,
                )
            )

        return Transcription(segments=sliced_segments)

__init__ 

__init__(
    segments: list[TranscriptionSegment] | None = None,
    words: list[TranscriptionWord] | None = None,
)

Initialize Transcription from either segments or words.

Parameters:

Name Type Description Default
segments list[TranscriptionSegment] | None

Pre-constructed segments (backward compatible)

 None
words list[TranscriptionWord] | None

Words to group into segments by speaker (for diarization)

 None

Raises:

Type Description
ValueError

If both or neither arguments are provided
Source code in src/videopython/base/text/transcription.py 
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def __init__(
    self,
    segments: list[TranscriptionSegment] | None = None,
    words: list[TranscriptionWord] | None = None,
):
    """Initialize Transcription from either segments or words.

    Args:
        segments: Pre-constructed segments (backward compatible)
        words: Words to group into segments by speaker (for diarization)

    Raises:
        ValueError: If both or neither arguments are provided
    """
    if (segments is None) == (words is None):
        raise ValueError("Exactly one of 'segments' or 'words' must be provided")

    if segments is not None:
        self.segments = segments
        self.speakers = {s.speaker for s in segments if s.speaker is not None}
    else:
        self.segments = self._words_to_segments(words)  # type: ignore
        self.speakers = {w.speaker for w in words if w.speaker is not None}  # type: ignore

speaker_stats 

speaker_stats() -> dict[str, float]

Calculate speaking time percentage for each speaker.

Returns:

Type Description
dict[str, float]

Dictionary mapping speaker names to their percentage of total speaking time
Source code in src/videopython/base/text/transcription.py 
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def speaker_stats(self) -> dict[str, float]:
    """Calculate speaking time percentage for each speaker.

    Returns:
        Dictionary mapping speaker names to their percentage of total speaking time
    """
    all_words = []
    for segment in self.segments:
        all_words.extend(segment.words)

    speaking_stats: dict[str, float] = {speaker: 0.0 for speaker in self.speakers}
    total_speaking_time = 0.0

    for word in all_words:
        if word.speaker is not None:
            speak_time = word.end - word.start
            total_speaking_time += speak_time
            speaking_stats[word.speaker] += speak_time

    if total_speaking_time > 0:
        for speaker in speaking_stats:
            speaking_stats[speaker] /= total_speaking_time

    return speaking_stats

offset 

offset(time: float) -> Transcription

Return a new Transcription with all timings offset by the provided time value.

Source code in src/videopython/base/text/transcription.py 
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def offset(self, time: float) -> Transcription:
    """Return a new Transcription with all timings offset by the provided time value."""
    offset_segments = []

    for segment in self.segments:
        offset_words = []
        for word in segment.words:
            offset_words.append(
                TranscriptionWord(
                    start=word.start + time, end=word.end + time, word=word.word, speaker=word.speaker
                )
            )

        offset_segments.append(
            TranscriptionSegment(
                start=segment.start + time,
                end=segment.end + time,
                text=segment.text,
                words=offset_words,
                speaker=segment.speaker,
            )
        )

    return Transcription(segments=offset_segments)

standardize_segments 

standardize_segments(
    *,
    time: float | None = None,
    num_words: int | None = None,
) -> Transcription

Return a new Transcription with standardized segments.

Parameters:

Name Type Description Default
time float | None

Maximum duration in seconds for each segment

 None
num_words int | None

Exact number of words per segment

 None

Raises:

Type Description
ValueError

If both time and num_words are provided or if neither is provided
Source code in src/videopython/base/text/transcription.py 
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def standardize_segments(self, *, time: float | None = None, num_words: int | None = None) -> Transcription:
    """Return a new Transcription with standardized segments.

    Args:
        time: Maximum duration in seconds for each segment
        num_words: Exact number of words per segment

    Raises:
        ValueError: If both time and num_words are provided or if neither is provided
    """
    if (time is None) == (num_words is None):
        raise ValueError("Exactly one of 'time' or 'num_words' must be provided")

    if time is not None and time <= 0:
        raise ValueError("Time must be positive")

    if num_words is not None and num_words <= 0:
        raise ValueError("Number of words must be positive")

    # Collect all words from all segments
    all_words = []
    for segment in self.segments:
        all_words.extend(segment.words)

    if not all_words:
        return Transcription(segments=[])

    standardized_segments = []

    if time is not None:
        # Group words by time constraint
        current_words = []
        current_start = None

        for word in all_words:
            if current_start is None:
                current_start = word.start
                current_words = [word]
            elif word.end - current_start <= time:
                current_words.append(word)
            else:
                # Create segment from current words
                if current_words:
                    segment_text = " ".join(w.word for w in current_words)
                    standardized_segments.append(
                        TranscriptionSegment(
                            start=current_start,
                            end=current_words[-1].end,
                            text=segment_text,
                            words=current_words.copy(),
                        )
                    )

                # Start new segment
                current_start = word.start
                current_words = [word]

        # Add final segment
        if current_words:
            segment_text = " ".join(w.word for w in current_words)
            standardized_segments.append(
                TranscriptionSegment(
                    start=current_start,  # type: ignore
                    end=current_words[-1].end,
                    text=segment_text,
                    words=current_words.copy(),
                )
            )
    elif num_words is not None:
        # Group words by word count constraint
        for i in range(0, len(all_words), num_words):
            segment_words = all_words[i : i + num_words]
            segment_text = " ".join(w.word for w in segment_words)
            standardized_segments.append(
                TranscriptionSegment(
                    start=segment_words[0].start, end=segment_words[-1].end, text=segment_text, words=segment_words
                )
            )

    return Transcription(segments=standardized_segments)

slice 

slice(start: float, end: float) -> Transcription | None

Return a new Transcription containing only words within the time range.

Slices at word-level granularity: words that overlap with the time range are included, and new segments are reconstructed from the included words.

Parameters:

Name Type Description Default
start float

Start time in seconds (inclusive)

 required
end float

End time in seconds (exclusive)

 required

Returns:

Type Description
Transcription | None

New Transcription with words/segments in the time range, or None if no words overlap
Source code in src/videopython/base/text/transcription.py 
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def slice(self, start: float, end: float) -> Transcription | None:
    """Return a new Transcription containing only words within the time range.

    Slices at word-level granularity: words that overlap with the time range
    are included, and new segments are reconstructed from the included words.

    Args:
        start: Start time in seconds (inclusive)
        end: End time in seconds (exclusive)

    Returns:
        New Transcription with words/segments in the time range, or None if no words overlap
    """
    if start >= end:
        return None

    # Collect all words that overlap with the time range
    overlapping_words: list[TranscriptionWord] = []
    for segment in self.segments:
        for word in segment.words:
            # Include word if it overlaps with our time range
            if word.end > start and word.start < end:
                overlapping_words.append(word)

    if not overlapping_words:
        return None

    # Reconstruct segments from the overlapping words
    # Group consecutive words by speaker to form segments
    sliced_segments: list[TranscriptionSegment] = []
    current_speaker = overlapping_words[0].speaker
    current_words: list[TranscriptionWord] = []

    for word in overlapping_words:
        if word.speaker == current_speaker:
            current_words.append(word)
        else:
            # Finish current segment
            if current_words:
                segment_text = " ".join(w.word for w in current_words)
                sliced_segments.append(
                    TranscriptionSegment(
                        start=current_words[0].start,
                        end=current_words[-1].end,
                        text=segment_text,
                        words=current_words.copy(),
                        speaker=current_speaker,
                    )
                )
            # Start new segment
            current_speaker = word.speaker
            current_words = [word]

    # Add final segment
    if current_words:
        segment_text = " ".join(w.word for w in current_words)
        sliced_segments.append(
            TranscriptionSegment(
                start=current_words[0].start,
                end=current_words[-1].end,
                text=segment_text,
                words=current_words.copy(),
                speaker=current_speaker,
            )
        )

    return Transcription(segments=sliced_segments)

TranscriptionSegment

TranscriptionSegment dataclass

Source code in src/videopython/base/text/transcription.py 
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@dataclass
class TranscriptionSegment:
    start: float
    end: float
    text: str
    words: list[TranscriptionWord]
    speaker: str | None = None

TranscriptionWord

TranscriptionWord dataclass

Source code in src/videopython/base/text/transcription.py 
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@dataclass
class TranscriptionWord:
    start: float
    end: float
    word: str
    speaker: str | None = None

Overlay Classes

TranscriptionOverlay

Render transcriptions as subtitles with word-level highlighting:

from videopython.base import Video, TranscriptionOverlay

video = Video.from_path("input.mp4")
# transcription = ... (from AudioToText or manually created)

overlay = TranscriptionOverlay(
    font_filename="path/to/font.ttf",
    font_size=40,
    highlight_color=(76, 175, 80),
)
video = overlay.apply(video, transcription)

TranscriptionOverlay

Source code in src/videopython/base/text/overlay.py 
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class TranscriptionOverlay:
    def __init__(
        self,
        font_filename: str,
        font_size: int = 40,
        font_border_size: int = 2,
        text_color: RGBColor = (255, 235, 59),
        background_color: RGBAColor | None = (0, 0, 0, 100),
        background_padding: int = 15,
        position: PositionType = (0.5, 0.7),
        box_width: int | float = 0.6,
        text_align: TextAlign = TextAlign.CENTER,
        anchor: AnchorPoint = AnchorPoint.CENTER,
        margin: MarginType = 20,
        highlight_color: RGBColor = (76, 175, 80),
        highlight_size_multiplier: float = 1.2,
        highlight_bold_font: str | None = None,
    ):
        """
        Initialize TranscriptionOverlay effect.

        Args:
            font_filename: Path to font file for text rendering
            font_size: Base font size for text
            text_color: RGB color for normal text
            font_border_size: Size of border around text in pixels (0 for no border)
            background_color: RGBA background color (None for no background)
            background_padding: Padding around text background
            position: Position of text box (relative 0-1 or absolute pixels)
            box_width: Width of text box (relative 0-1 or absolute pixels)
            text_align: Text alignment within box
            anchor: Anchor point for text positioning
            margin: Margin around text box
            highlight_color: RGB color for highlighted words
            highlight_size_multiplier: Size multiplier for highlighted words
            highlight_bold_font: Optional bold font for highlighting
        """
        self.font_filename = font_filename
        self.font_size = font_size
        self.text_color = text_color
        self.font_border_size = font_border_size
        self.background_color = background_color
        self.background_padding = background_padding
        self.position = position
        self.box_width = box_width
        self.text_align = text_align
        self.anchor = anchor
        self.margin = margin
        self.highlight_color = highlight_color
        self.highlight_size_multiplier = highlight_size_multiplier
        self.highlight_bold_font = highlight_bold_font

        # Cache for text overlays to avoid regenerating identical frames
        self._overlay_cache: dict[tuple[str, int | None], np.ndarray] = {}

    def _get_active_segment(self, transcription: Transcription, timestamp: float) -> TranscriptionSegment | None:
        """Get the transcription segment active at the given timestamp."""
        for segment in transcription.segments:
            if segment.start <= timestamp <= segment.end:
                return segment
        return None

    def _get_active_word_index(self, segment: TranscriptionSegment, timestamp: float) -> int | None:
        """Get the index of the word being spoken at the given timestamp within a segment."""
        for i, word in enumerate(segment.words):
            if word.start <= timestamp <= word.end:
                return i
        return None

    def _create_text_overlay(
        self, video_shape: tuple[int, int, int], segment: TranscriptionSegment, highlight_word_index: int | None
    ) -> np.ndarray:
        """Create a text overlay image for the given segment and highlight."""
        # Use video frame dimensions for overlay
        height, width = video_shape[:2]

        # Create cache key based on segment text and highlight
        cache_key = (segment.text, highlight_word_index)
        if cache_key in self._overlay_cache:
            return self._overlay_cache[cache_key]

        # Create ImageText with video dimensions
        img_text = ImageText(image_size=(height, width), background=(0, 0, 0, 0))

        # Write text with highlighting
        img_text.write_text_box(
            text=segment.text,
            font_filename=self.font_filename,
            xy=self.position,
            box_width=self.box_width,
            font_size=self.font_size,
            font_border_size=self.font_border_size,
            text_color=self.text_color,
            background_color=self.background_color,
            background_padding=self.background_padding,
            place=self.text_align,
            anchor=self.anchor,
            margin=self.margin,
            words=[w.word for w in segment.words],
            highlight_word_index=highlight_word_index,
            highlight_color=self.highlight_color,
            highlight_size_multiplier=self.highlight_size_multiplier,
            highlight_bold_font=self.highlight_bold_font,
        )

        overlay_image = img_text.img_array

        # Cache the overlay
        self._overlay_cache[cache_key] = overlay_image

        return overlay_image

    def apply(self, video: Video, transcription: Transcription) -> Video:
        """Apply transcription overlay to video frames."""
        print("Applying transcription overlay...")

        new_frames = []

        for frame_index, frame in enumerate(tqdm(video.frames)):
            # Calculate timestamp for this frame
            timestamp = frame_index / video.fps

            # Get active segment at this timestamp
            active_segment = self._get_active_segment(transcription, timestamp)

            if active_segment is None:
                # No active transcription, keep original frame
                new_frames.append(frame)
                continue

            # Get active word index for highlighting
            highlight_word_index = self._get_active_word_index(active_segment, timestamp)

            # Create text overlay
            text_overlay = self._create_text_overlay(video.frame_shape, active_segment, highlight_word_index)

            # Apply overlay to frame
            overlaid_frame = self._apply_overlay_to_frame(frame, text_overlay)
            new_frames.append(overlaid_frame)

        # Create new video with overlaid frames
        new_video = Video.from_frames(np.array(new_frames), fps=video.fps)
        new_video.audio = video.audio  # Preserve audio

        return new_video

    def _apply_overlay_to_frame(self, frame: np.ndarray, overlay: np.ndarray) -> np.ndarray:
        """Apply a text overlay to a single frame."""

        # Convert frame to PIL Image
        frame_pil = Image.fromarray(frame)

        # Convert overlay to PIL Image
        overlay_pil = Image.fromarray(overlay)

        # Paste overlay onto frame using alpha channel
        frame_pil.paste(overlay_pil, (0, 0), overlay_pil)

        return np.array(frame_pil)

__init__ 

__init__(
    font_filename: str,
    font_size: int = 40,
    font_border_size: int = 2,
    text_color: RGBColor = (255, 235, 59),
    background_color: RGBAColor | None = (0, 0, 0, 100),
    background_padding: int = 15,
    position: PositionType = (0.5, 0.7),
    box_width: int | float = 0.6,
    text_align: TextAlign = TextAlign.CENTER,
    anchor: AnchorPoint = AnchorPoint.CENTER,
    margin: MarginType = 20,
    highlight_color: RGBColor = (76, 175, 80),
    highlight_size_multiplier: float = 1.2,
    highlight_bold_font: str | None = None,
)

Initialize TranscriptionOverlay effect.

Parameters:

Name Type Description Default
font_filename str

Path to font file for text rendering

 required
font_size int

Base font size for text

 40
text_color RGBColor

RGB color for normal text

 (255, 235, 59)
font_border_size int

Size of border around text in pixels (0 for no border)

 2
background_color RGBAColor | None

RGBA background color (None for no background)

 (0, 0, 0, 100)
background_padding int

Padding around text background

 15
position PositionType

Position of text box (relative 0-1 or absolute pixels)

 (0.5, 0.7)
box_width int | float

Width of text box (relative 0-1 or absolute pixels)

 0.6
text_align TextAlign

Text alignment within box

 CENTER
anchor AnchorPoint

Anchor point for text positioning

 CENTER
margin MarginType

Margin around text box

 20
highlight_color RGBColor

RGB color for highlighted words

 (76, 175, 80)
highlight_size_multiplier float

Size multiplier for highlighted words

 1.2
highlight_bold_font str | None

Optional bold font for highlighting

 None
Source code in src/videopython/base/text/overlay.py 
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def __init__(
    self,
    font_filename: str,
    font_size: int = 40,
    font_border_size: int = 2,
    text_color: RGBColor = (255, 235, 59),
    background_color: RGBAColor | None = (0, 0, 0, 100),
    background_padding: int = 15,
    position: PositionType = (0.5, 0.7),
    box_width: int | float = 0.6,
    text_align: TextAlign = TextAlign.CENTER,
    anchor: AnchorPoint = AnchorPoint.CENTER,
    margin: MarginType = 20,
    highlight_color: RGBColor = (76, 175, 80),
    highlight_size_multiplier: float = 1.2,
    highlight_bold_font: str | None = None,
):
    """
    Initialize TranscriptionOverlay effect.

    Args:
        font_filename: Path to font file for text rendering
        font_size: Base font size for text
        text_color: RGB color for normal text
        font_border_size: Size of border around text in pixels (0 for no border)
        background_color: RGBA background color (None for no background)
        background_padding: Padding around text background
        position: Position of text box (relative 0-1 or absolute pixels)
        box_width: Width of text box (relative 0-1 or absolute pixels)
        text_align: Text alignment within box
        anchor: Anchor point for text positioning
        margin: Margin around text box
        highlight_color: RGB color for highlighted words
        highlight_size_multiplier: Size multiplier for highlighted words
        highlight_bold_font: Optional bold font for highlighting
    """
    self.font_filename = font_filename
    self.font_size = font_size
    self.text_color = text_color
    self.font_border_size = font_border_size
    self.background_color = background_color
    self.background_padding = background_padding
    self.position = position
    self.box_width = box_width
    self.text_align = text_align
    self.anchor = anchor
    self.margin = margin
    self.highlight_color = highlight_color
    self.highlight_size_multiplier = highlight_size_multiplier
    self.highlight_bold_font = highlight_bold_font

    # Cache for text overlays to avoid regenerating identical frames
    self._overlay_cache: dict[tuple[str, int | None], np.ndarray] = {}

apply 

apply(video: Video, transcription: Transcription) -> Video

Apply transcription overlay to video frames.

Source code in src/videopython/base/text/overlay.py 
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def apply(self, video: Video, transcription: Transcription) -> Video:
    """Apply transcription overlay to video frames."""
    print("Applying transcription overlay...")

    new_frames = []

    for frame_index, frame in enumerate(tqdm(video.frames)):
        # Calculate timestamp for this frame
        timestamp = frame_index / video.fps

        # Get active segment at this timestamp
        active_segment = self._get_active_segment(transcription, timestamp)

        if active_segment is None:
            # No active transcription, keep original frame
            new_frames.append(frame)
            continue

        # Get active word index for highlighting
        highlight_word_index = self._get_active_word_index(active_segment, timestamp)

        # Create text overlay
        text_overlay = self._create_text_overlay(video.frame_shape, active_segment, highlight_word_index)

        # Apply overlay to frame
        overlaid_frame = self._apply_overlay_to_frame(frame, text_overlay)
        new_frames.append(overlaid_frame)

    # Create new video with overlaid frames
    new_video = Video.from_frames(np.array(new_frames), fps=video.fps)
    new_video.audio = video.audio  # Preserve audio

    return new_video

ImageText

Low-level text rendering on images:

ImageText

Source code in src/videopython/base/text/overlay.py 
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class ImageText:
    def __init__(
        self,
        image_size: tuple[int, int] = (1920, 1080),  # (height, width) - NumPy convention
        mode: str = "RGBA",
        background: RGBAColor = (0, 0, 0, 0),  # Transparent background
    ):
        """
        Initialize an image for text rendering.

        Args:
            image_size: Dimensions of the image (height, width) - NumPy convention
            mode: Image mode (RGB, RGBA, etc.)
            background: Background color with alpha channel

        Raises:
            ValueError: If image_size dimensions are not positive
        """
        if image_size[0] <= 0 or image_size[1] <= 0:
            raise ValueError("Image dimensions must be positive")

        if len(background) != 4:
            raise ValueError("Background color must be RGBA (4 values)")

        self.image_size = image_size  # Stored as (height, width)
        # PIL uses (width, height), so we reverse for Image.new
        self.image = Image.new(mode, (image_size[1], image_size[0]), color=background)
        self._draw = ImageDraw.Draw(self.image)
        self._font_cache: dict[tuple[str, int], ImageFont.FreeTypeFont] = {}  # Cache for font objects

    @property
    def img_array(self) -> np.ndarray:
        """Convert the PIL Image to a numpy array."""
        return np.array(self.image)

    def save(self, filename: str) -> None:
        """Save the image to a file."""
        if not filename:
            raise ValueError("Filename cannot be empty")
        self.image.save(filename)

    def _fit_font_width(self, text: str, font: str, max_width: int) -> int:
        """
        Find the maximum font size where the text width is less than or equal to max_width.

        Args:
            text: The text to measure
            font: Path to the font file
            max_width: Maximum allowed width in pixels

        Returns:
            The maximum font size that fits within max_width

        Raises:
            ValueError: If text is empty or max_width is too small for any font size
        """
        if not text:
            return 1  # Default to minimum size for empty text

        if max_width <= 0:
            raise ValueError("Maximum width must be positive")

        font_size = 1
        text_width = self.get_text_dimensions(font, font_size, text)[0]
        while text_width < max_width:
            font_size += 1
            text_width = self.get_text_dimensions(font, font_size, text)[0]
        max_font_size = font_size - 1
        if max_font_size < 1:
            raise ValueError(f"Max width {max_width} is too small for any font size!")
        return max_font_size

    def _fit_font_height(self, text: str, font: str, max_height: int) -> int:
        """
        Find the maximum font size where the text height is less than or equal to max_height.

        Args:
            text: The text to measure
            font: Path to the font file
            max_height: Maximum allowed height in pixels

        Returns:
            The maximum font size that fits within max_height

        Raises:
            ValueError: If text is empty or max_height is too small for any font size
        """
        if not text:
            return 1  # Default to minimum size for empty text

        if max_height <= 0:
            raise ValueError("Maximum height must be positive")

        font_size = 1
        text_height = self.get_text_dimensions(font, font_size, text)[1]
        while text_height < max_height:
            font_size += 1
            text_height = self.get_text_dimensions(font, font_size, text)[1]
        max_font_size = font_size - 1
        if max_font_size < 1:
            raise ValueError(f"Max height {max_height} is too small for any font size!")
        return max_font_size

    def _get_font_size(
        self,
        text: str,
        font: str,
        max_width: int | None = None,
        max_height: int | None = None,
    ) -> int:
        """
        Get maximum font size for text to fit within given dimensions.

        Args:
            text: The text to fit
            font: Path to the font file
            max_width: Maximum allowed width in pixels
            max_height: Maximum allowed height in pixels

        Returns:
            The maximum font size that fits within constraints

        Raises:
            ValueError: If neither max_width nor max_height is provided, or text is empty
        """
        if not text:
            raise ValueError("Text cannot be empty")

        if max_width is None and max_height is None:
            raise ValueError("You need to pass max_width or max_height")

        if max_width is not None and max_width <= 0:
            raise ValueError("Maximum width must be positive")

        if max_height is not None and max_height <= 0:
            raise ValueError("Maximum height must be positive")

        width_font_size = self._fit_font_width(text, font, max_width) if max_width is not None else None
        height_font_size = self._fit_font_height(text, font, max_height) if max_height is not None else None

        sizes = [size for size in [width_font_size, height_font_size] if size is not None]
        if not sizes:
            raise ValueError("No valid font size could be calculated")

        return min(sizes)

    def _process_margin(self, margin: MarginType) -> tuple[int, int, int, int]:
        """
        Process the margin parameter into individual top, right, bottom, left values.

        Args:
            margin: A single int for all sides, or a tuple of 4 values for each side

        Returns:
            Tuple of (top, right, bottom, left) margin values

        Raises:
            ValueError: If margin tuple doesn't have exactly 4 values
        """
        if isinstance(margin, int):
            if margin < 0:
                raise ValueError("Margin cannot be negative")
            return margin, margin, margin, margin
        elif isinstance(margin, tuple) and len(margin) == 4:
            if any(m < 0 for m in margin):
                raise ValueError("Margin values cannot be negative")
            return margin
        else:
            raise ValueError("Margin must be an int or a tuple of 4 ints")

    def _convert_position(
        self, position: PositionType, margin_top: int, margin_left: int, available_width: int, available_height: int
    ) -> tuple[float, float]:
        """
        Convert a position from relative (0-1) to absolute pixels.

        Args:
            position: Position as (x, y) coordinates, either as pixels or relative (0-1)
            margin_top: Top margin in pixels
            margin_left: Left margin in pixels
            available_width: Available width considering margins
            available_height: Available height considering margins

        Returns:
            Position in absolute pixel coordinates (might still be float)
        """
        x_pos, y_pos = position

        # Convert relative position (0-1) to absolute pixels
        if isinstance(x_pos, float) and 0 <= x_pos <= 1:
            x_pos = margin_left + x_pos * available_width
        if isinstance(y_pos, float) and 0 <= y_pos <= 1:
            y_pos = margin_top + y_pos * available_height

        return x_pos, y_pos

    def _calculate_position(
        self,
        text_size: tuple[int, int],
        position: PositionType,
        anchor: AnchorPoint = AnchorPoint.TOP_LEFT,
        margin: MarginType = 0,
    ) -> tuple[int, int]:
        """
        Calculate the absolute position based on anchor point, relative positioning and margins.

        Args:
            text_size: Width and height of the text in pixels
            position: Either absolute coordinates (int) or relative to frame size (float 0-1)
            anchor: Which part of the text to anchor at the position
            margin: Margin in pixels (single value or [top, right, bottom, left])

        Returns:
            Absolute x, y coordinates for text placement

        Raises:
            ValueError: If position or margin values are invalid
        """
        if not isinstance(text_size, tuple) or len(text_size) != 2:
            raise ValueError("text_size must be a tuple of (width, height)")

        text_width, text_height = text_size

        # Process margins
        margin_top, margin_right, margin_bottom, margin_left = self._process_margin(margin)

        # Calculate available area considering margins
        available_width = self.image_size[1] - margin_left - margin_right
        available_height = self.image_size[0] - margin_top - margin_bottom

        # Convert relative position to absolute if needed
        x_pos, y_pos = self._convert_position(position, margin_top, margin_left, available_width, available_height)

        # Apply margin to absolute position when using 0,0 as starting point
        if x_pos == 0 and anchor in AnchorPoint.left_anchors():
            x_pos = margin_left
        if y_pos == 0 and anchor in AnchorPoint.top_anchors():
            y_pos = margin_top

        # Adjust position based on anchor point
        if anchor in AnchorPoint.center_anchors():
            x_pos -= text_width // 2
        elif anchor in AnchorPoint.right_anchors():
            x_pos -= text_width

        if anchor in AnchorPoint.middle_anchors():
            y_pos -= text_height // 2
        elif anchor in AnchorPoint.bottom_anchors():
            y_pos -= text_height

        return int(x_pos), int(y_pos)

    def write_text(
        self,
        text: str,
        font_filename: str,
        xy: PositionType,
        font_size: int | None = 11,
        font_border_size: int = 0,
        color: RGBColor = (0, 0, 0),
        max_width: int | None = None,
        max_height: int | None = None,
        anchor: AnchorPoint = AnchorPoint.TOP_LEFT,
        margin: MarginType = 0,
    ) -> tuple[int, int]:
        """
        Write text to the image with advanced positioning options.

        Args:
            text: Text to be written
            font_filename: Path to the font file
            xy: Position (x,y) either as absolute pixels (int) or relative to frame (float 0-1)
            font_size: Size of the font in points, or None to auto-calculate
            font_border_size: Size of border around text in pixels (0 for no border)
            color: RGB color of the text
            max_width: Maximum width for auto font sizing
            max_height: Maximum height for auto font sizing
            anchor: Which part of the text to anchor at the position
            margin: Margin in pixels (single value or [top, right, bottom, left])

        Returns:
            Dimensions of the rendered text (width, height)

        Raises:
            ValueError: If text is empty or font parameters are invalid
            OutOfBoundsError: If the text would be rendered outside the image bounds
        """
        if not text:
            raise ValueError("Text cannot be empty")

        if not font_filename:
            raise ValueError("Font filename cannot be empty")

        if font_size is not None and font_size <= 0:
            raise ValueError("Font size must be positive")

        if font_border_size < 0:
            raise ValueError("Font border size cannot be negative")

        if font_size is None and (max_width is None or max_height is None):
            raise ValueError("Must set either `font_size`, or both `max_width` and `max_height`!")
        elif font_size is None:
            font_size = self._get_font_size(text, font_filename, max_width, max_height)

        # Get or create the font object (with caching)
        font = self._get_font(font_filename, font_size)
        text_dimensions = self.get_text_dimensions(font_filename, font_size, text)

        # Calculate the position based on anchor point and margins
        x, y = self._calculate_position(text_dimensions, xy, anchor, margin)

        # Verify text will fit within bounds
        if x < 0 or y < 0 or x + text_dimensions[0] > self.image_size[1] or y + text_dimensions[1] > self.image_size[0]:
            raise OutOfBoundsError(f"Text with size {text_dimensions} at position ({x}, {y}) is out of bounds!")

        # Draw border if requested
        if font_border_size > 0:
            # Draw text border by drawing text in multiple positions around the main text
            for border_x in range(-font_border_size, font_border_size + 1):
                for border_y in range(-font_border_size, font_border_size + 1):
                    if border_x != 0 or border_y != 0:  # Skip the center position
                        self._draw.text((x + border_x, y + border_y), text, font=font, fill=(0, 0, 0))

        # Draw the main text on top
        self._draw.text((x, y), text, font=font, fill=color)
        return text_dimensions

    def _get_font(self, font_filename: str, font_size: int) -> ImageFont.FreeTypeFont:
        """
        Get a font object, using cache if available.

        Args:
            font_filename: Path to the font file
            font_size: Size of the font in points

        Returns:
            Font object for rendering text
        """
        key = (font_filename, font_size)
        if key not in self._font_cache:
            try:
                self._font_cache[key] = ImageFont.truetype(font_filename, font_size)
            except (OSError, IOError) as e:
                raise ValueError(f"Error loading font '{font_filename}': {str(e)}")
        return self._font_cache[key]

    def get_text_dimensions(self, font_filename: str, font_size: int, text: str) -> tuple[int, int]:
        """
        Return dimensions (width, height) of the rendered text.

        Args:
            font_filename: Path to the font file
            font_size: Size of the font in points
            text: Text to measure

        Returns:
            Tuple of (width, height) for the rendered text

        Raises:
            ValueError: If font parameters are invalid or text is empty
        """
        if not text:
            return (0, 0)  # Empty text has no dimensions

        if font_size <= 0:
            raise ValueError("Font size must be positive")

        font = self._get_font(font_filename, font_size)
        try:
            bbox = font.getbbox(text)
            if bbox is None:
                return (0, 0)  # Handle case where getbbox returns None
            return bbox[2:] if len(bbox) >= 4 else (0, 0)
        except Exception as e:
            raise ValueError(f"Error measuring text: {str(e)}")

    def _get_font_baseline_offset(
        self, base_font_filename: str, base_font_size: int, highlight_font_filename: str, highlight_font_size: int
    ) -> int:
        """
        Calculate the vertical offset needed to align baselines of different fonts and sizes.

        Args:
            base_font_filename: Path to the base font file
            base_font_size: Font size of normal text
            highlight_font_filename: Path to the highlight font file
            highlight_font_size: Font size of highlighted text

        Returns:
            Vertical offset in pixels to align highlighted text baseline with normal text baseline
        """
        base_font = self._get_font(base_font_filename, base_font_size)
        highlight_font = self._get_font(highlight_font_filename, highlight_font_size)

        # Use a reference character to get baseline metrics
        # We use 'A' as it's a good reference for ascender height
        ref_char = "A"

        # Get bounding boxes for the reference character
        base_bbox = base_font.getbbox(ref_char)
        highlight_bbox = highlight_font.getbbox(ref_char)

        if base_bbox is None or highlight_bbox is None:
            return 0  # Fallback if bbox calculation fails

        # The baseline offset is the difference in the top of the bounding box
        # since getbbox returns (left, top, right, bottom) where top is negative for ascenders
        base_ascent = -base_bbox[1]  # Distance from baseline to top of character
        highlight_ascent = -highlight_bbox[1]  # Distance from baseline to top of character

        # Calculate the offset needed to align baselines
        # If highlighted text has a larger ascent, we need to move it down
        baseline_offset = highlight_ascent - base_ascent

        return baseline_offset

    def _split_lines_by_width(
        self,
        text: str,
        font_filename: str,
        font_size: int,
        box_width: int,
    ) -> list[str]:
        """
        Split the text into lines that fit within the specified width.

        Args:
            text: Text to split into lines
            font_filename: Path to the font file
            font_size: Size of the font in points
            box_width: Maximum width for each line in pixels

        Returns:
            List of text lines that fit within box_width

        Raises:
            ValueError: If font parameters are invalid or box_width is too small
        """
        if not text:
            return []  # Empty text produces no lines

        if box_width <= 0:
            raise ValueError("Box width must be positive")

        if font_size <= 0:
            raise ValueError("Font size must be positive")

        words = text.split()
        if not words:
            return []  # No words means no lines

        # Handle single-word case efficiently
        if len(words) == 1:
            return [text]

        split_lines: list[list[str]] = []
        current_line: list[str] = []

        for word in words:
            # If current line is empty and this word is too long for box_width,
            # we'll have to split the word itself (not implemented)
            if not current_line and self.get_text_dimensions(font_filename, font_size, word)[0] > box_width:
                # Just add the word anyway, it'll overflow but we can't do better without splitting words
                split_lines.append([word])
                continue

            # Try adding the word to current line
            new_line = " ".join(current_line + [word]) if current_line else word
            size = self.get_text_dimensions(font_filename, font_size, new_line)
            if size[0] <= box_width:
                current_line.append(word)
            else:
                # This word doesn't fit, start new line
                if current_line:  # Only if we have a current line to add
                    split_lines.append(current_line)
                current_line = [word]

        # Add the last line if it has content
        if current_line:
            split_lines.append(current_line)

        # Join the words in each line with spaces
        lines = [" ".join(line) for line in split_lines]
        return lines

    def write_text_box(
        self,
        text: str,
        font_filename: str,
        xy: PositionType,
        box_width: int | float | None = None,
        font_size: int = 11,
        font_border_size: int = 0,
        text_color: RGBColor = (0, 0, 0),
        background_color: RGBAColor | None = None,
        background_padding: int = 0,
        place: TextAlign = TextAlign.LEFT,
        anchor: AnchorPoint = AnchorPoint.TOP_LEFT,
        margin: MarginType = 0,
        words: list[str] | None = None,
        highlight_word_index: int | None = None,
        highlight_color: RGBColor | None = None,
        highlight_size_multiplier: float = DEFAULT_HIGHLIGHT_SIZE_MULTIPLIER,
        highlight_bold_font: str | None = None,
    ) -> tuple[int, int]:
        """
        Write text in a box with advanced positioning and alignment options.

        Args:
            text: Text to be written inside the box
            font_filename: Path to the font file
            xy: Position (x,y) either as absolute pixels (int) or relative to frame (float 0-1)
            box_width: Width of the box in pixels (int) or relative to frame width (float 0-1)
            font_size: Font size in points
            font_border_size: Size of border around text in pixels (0 for no border)
            text_color: RGB color of the text
            background_color: If set, adds background color to the text box. Expects RGBA values.
            background_padding: Number of padding pixels to add when adding text background color
            place: Text alignment within the box (TextAlign.LEFT, TextAlign.RIGHT, TextAlign.CENTER)
            anchor: Which part of the text box to anchor at the position
            margin: Margin in pixels (single value or [top, right, bottom, left])
            words: All words occuring in text, helpful for highlighting.
            highlight_word_index: Index of word to highlight (0-based, None to disable highlighting)
            highlight_color: RGB color for the highlighted word (defaults to text_color if None)
            highlight_size_multiplier: Font size multiplier for highlighted word
            highlight_bold_font: Path to bold font file for highlighted word (defaults to font_filename if None)

        Returns:
            Coordinates of the lower-right corner of the written text box (x, y)

        Raises:
            ValueError: If text is empty or parameters are invalid
            OutOfBoundsError: If text box would be outside image bounds
        """
        if not text:
            raise ValueError("Text cannot be empty")

        if not font_filename:
            raise ValueError("Font filename cannot be empty")

        if font_size <= 0:
            raise ValueError("Font size must be positive")

        if background_padding < 0:
            raise ValueError("Background padding cannot be negative")

        if font_border_size < 0:
            raise ValueError("Font border size cannot be negative")

        # Validate highlighting parameters
        if highlight_word_index is not None:
            if not words:
                words = text.split()
            if highlight_word_index < 0 or highlight_word_index >= len(words):
                raise ValueError(
                    f"highlight_word_index {highlight_word_index} out of range for text with {len(words)} words"
                )

        if highlight_size_multiplier <= 0:
            raise ValueError("highlight_size_multiplier must be positive")

        # Set default highlight color if not provided
        if highlight_word_index is not None and highlight_color is None:
            highlight_color = text_color

        # Process margins to determine available area
        margin_top, margin_right, margin_bottom, margin_left = self._process_margin(margin)
        available_width = self.image_size[1] - margin_left - margin_right
        available_height = self.image_size[0] - margin_top - margin_bottom

        # Handle relative box width
        if box_width is None:
            box_width = available_width
        elif isinstance(box_width, float) and 0 < box_width <= 1:
            box_width = int(available_width * box_width)
        elif isinstance(box_width, int) and box_width <= 0:
            raise ValueError("Box width must be positive")

        # Calculate initial position based on margin and anchor before splitting text
        x_pos, y_pos = self._convert_position(xy, margin_top, margin_left, available_width, available_height)

        # Split text into lines that fit within box_width
        lines = self._split_lines_by_width(text, font_filename, font_size, int(box_width))

        # Calculate total height of all lines
        lines_height = sum([self.get_text_dimensions(font_filename, font_size, line)[1] for line in lines])
        if lines_height == 0:
            # If we have no valid lines or zero height, return the position
            return (int(x_pos), int(y_pos))

        # Final position calculation based on anchor point
        if anchor in AnchorPoint.center_anchors():
            x_pos -= box_width // 2
        elif anchor in AnchorPoint.right_anchors():
            x_pos -= box_width

        if anchor in AnchorPoint.middle_anchors():
            y_pos -= lines_height // 2
        elif anchor in AnchorPoint.bottom_anchors():
            y_pos -= lines_height

        # Verify box will fit within bounds
        if (
            x_pos < 0
            or y_pos < 0
            or x_pos + box_width > self.image_size[1]
            or y_pos + lines_height > self.image_size[0]
        ):
            raise OutOfBoundsError(
                f"Text box with size ({box_width}x{lines_height}) at position ({x_pos}, {y_pos}) is out of bounds!"
            )

        # Write lines
        current_text_height = y_pos
        word_index_offset = 0  # Track global word index across lines
        for line in lines:
            line_dimensions = self.get_text_dimensions(font_filename, font_size, line)

            # Calculate horizontal position based on alignment
            if place == TextAlign.LEFT:
                x_left = x_pos
            elif place == TextAlign.RIGHT:
                x_left = x_pos + box_width - line_dimensions[0]
            elif place == TextAlign.CENTER:
                x_left = int(x_pos + ((box_width - line_dimensions[0]) / 2))
            else:
                valid_places = [e.value for e in TextAlign]
                raise ValueError(f"Place '{place}' is not supported. Must be one of: {', '.join(valid_places)}")

            # Check if highlighting is needed for this line
            if highlight_word_index is not None:
                line_words = line.split()
                line_start_word_index = word_index_offset
                line_end_word_index = word_index_offset + len(line_words) - 1

                # Check if the highlighted word is in this line
                if line_start_word_index <= highlight_word_index <= line_end_word_index:
                    self._write_line_with_highlight(
                        line=line,
                        font_filename=font_filename,
                        font_size=font_size,
                        font_border_size=font_border_size,
                        text_color=text_color,
                        highlight_color=highlight_color or (255, 255, 255),
                        highlight_size_multiplier=highlight_size_multiplier,
                        highlight_word_local_index=highlight_word_index - line_start_word_index,
                        highlight_bold_font=highlight_bold_font,
                        x_left=int(x_left),
                        y_top=int(current_text_height),
                    )
                else:
                    # Write normal line without highlighting
                    self.write_text(
                        text=line,
                        font_filename=font_filename,
                        xy=(x_left, current_text_height),
                        font_size=font_size,
                        font_border_size=font_border_size,
                        color=text_color,
                    )

                word_index_offset += len(line_words)
            else:
                # Write normal line without highlighting
                self.write_text(
                    text=line,
                    font_filename=font_filename,
                    xy=(x_left, current_text_height),
                    font_size=font_size,
                    font_border_size=font_border_size,
                    color=text_color,
                )

            # Increment vertical position for next line
            current_text_height += line_dimensions[1]

        # Add background color for the text if specified
        if background_color is not None:
            if len(background_color) != 4:
                raise ValueError(f"Text background color {background_color} must be RGBA (4 values)!")

            img = self.img_array

            # Find bounding rectangle for written text
            # Skip if the box is empty
            if y_pos >= current_text_height or x_pos >= x_pos + box_width:
                return (int(x_pos + box_width), int(current_text_height))

            # Get the slice of the image containing the text box
            box_slice = img[int(y_pos) : int(current_text_height), int(x_pos) : int(x_pos + box_width)]
            if box_slice.size == 0:  # Empty slice
                return (int(x_pos + box_width), int(current_text_height))

            # Create mask of non-zero pixels (text)
            text_mask = np.any(box_slice != 0, axis=2).astype(np.uint8)
            if not isinstance(text_mask, np.ndarray):
                raise TypeError(f"The returned text mask is of type {type(text_mask)}, but it should be numpy array!")

            # If no text pixels found, return without background
            if not np.any(text_mask):
                return (int(x_pos + box_width), int(current_text_height))

            # Find the smallest rectangle containing text
            try:
                xmin, xmax, ymin, ymax = self._find_smallest_bounding_rect(text_mask)
            except Exception:
                # If bounding rectangle calculation fails, use the whole box
                xmin, xmax, ymin, ymax = 0, box_slice.shape[1] - 1, 0, box_slice.shape[0] - 1

            # Get global bounding box position
            xmin = int(xmin + x_pos - background_padding)
            xmax = int(xmax + x_pos + background_padding)
            ymin = int(ymin + y_pos - background_padding)
            ymax = int(ymax + y_pos + background_padding)

            # Make sure we are inside image bounds
            xmin = max(0, xmin)
            ymin = max(0, ymin)
            xmax = min(xmax, self.image_size[1])
            ymax = min(ymax, self.image_size[0])

            # Skip if bounding box is invalid
            if xmin >= xmax or ymin >= ymax:
                return (int(x_pos + box_width), int(current_text_height))

            # Slice the bounding box and find text mask
            bbox_slice = img[ymin:ymax, xmin:xmax]
            if bbox_slice.size == 0:  # Empty slice
                return (int(x_pos + box_width), int(current_text_height))

            bbox_text_mask = np.any(bbox_slice != 0, axis=2).astype(np.uint8)

            # Add background color outside of text
            bbox_slice[~bbox_text_mask.astype(bool)] = background_color

            # Handle semi-transparent pixels for smooth text blending
            text_slice = bbox_slice[bbox_text_mask.astype(bool)]
            if text_slice.size > 0:
                text_background = text_slice[:, :3] * (np.expand_dims(text_slice[:, -1], axis=1) / 255)
                color_background = (1 - (np.expand_dims(text_slice[:, -1], axis=1) / 255)) * background_color
                faded_background = text_background[:, :3] + color_background[:, :3]
                text_slice[:, :3] = faded_background
                text_slice[:, -1] = 255  # Full opacity
                bbox_slice[bbox_text_mask.astype(bool)] = text_slice

            # Update the image with the background color
            self.image = Image.fromarray(img)

        return (int(x_pos + box_width), int(current_text_height))

    def _write_line_with_highlight(
        self,
        line: str,
        font_filename: str,
        font_size: int,
        font_border_size: int,
        text_color: RGBColor,
        highlight_color: RGBColor,
        highlight_size_multiplier: float,
        highlight_word_local_index: int,
        highlight_bold_font: str | None,
        x_left: int,
        y_top: int,
    ) -> None:
        """
        Write a line of text with one word highlighted using word-by-word rendering with baseline alignment.

        Args:
            line: The text line to render
            font_filename: Path to the font file
            font_size: Base font size in points
            font_border_size: Size of border around text in pixels (0 for no border)
            text_color: RGB color for normal text
            highlight_color: RGB color for highlighted word
            highlight_size_multiplier: Font size multiplier for highlighted word
            highlight_word_local_index: Index of word to highlight within this line (0-based)
            highlight_bold_font: Path to bold font file for highlighted word (defaults to font_filename if None)
            x_left: Left x position for the line
            y_top: Top y position for the line
        """
        # Split line into words
        words = line.split()
        if highlight_word_local_index >= len(words):
            return  # Safety check

        # Calculate highlighted font size and determine font files
        highlight_font_size = int(font_size * highlight_size_multiplier)
        highlight_font_file = highlight_bold_font if highlight_bold_font is not None else font_filename

        # Calculate baseline offset for highlighted words (using the appropriate font files)
        baseline_offset = self._get_font_baseline_offset(
            font_filename, font_size, highlight_font_file, highlight_font_size
        )

        # Render words one by one with proper spacing
        current_x = x_left

        for i, word in enumerate(words):
            # Determine if this is the highlighted word
            is_highlighted = i == highlight_word_local_index

            # Choose font file, size, and color based on highlighting
            word_font_file = highlight_font_file if is_highlighted else font_filename
            word_font_size = highlight_font_size if is_highlighted else font_size
            word_color = highlight_color if is_highlighted else text_color

            # Calculate y position with baseline alignment
            word_y = y_top
            if is_highlighted:
                word_y += baseline_offset

            # Render the word
            self.write_text(
                text=word,
                font_filename=word_font_file,
                xy=(current_x, word_y),
                font_size=word_font_size,
                font_border_size=font_border_size,
                color=word_color,
            )

            # Calculate the width of this word for spacing
            word_width = self.get_text_dimensions(word_font_file, word_font_size, word)[0]

            # Update current_x for next word (add word width plus space)
            current_x += word_width

            # Add space between words (except after the last word)
            if i < len(words) - 1:
                space_width = self.get_text_dimensions(font_filename, font_size, " ")[0]
                current_x += space_width

    def _find_smallest_bounding_rect(self, mask: np.ndarray) -> tuple[int, int, int, int]:
        """
        Find the smallest bounding rectangle containing non-zero values in the mask.

        Args:
            mask: 2D numpy array with non-zero values representing pixels of interest

        Returns:
            Tuple of (xmin, xmax, ymin, ymax) coordinates

        Raises:
            ValueError: If mask is empty or has no non-zero values
        """
        if mask.size == 0:
            raise ValueError("Mask is empty")

        # Check if mask has any non-zero values
        if not np.any(mask):
            raise ValueError("Mask has no non-zero values")

        rows = np.any(mask, axis=1)
        cols = np.any(mask, axis=0)

        # Find indices of first and last True values
        row_indices = np.where(rows)[0]
        col_indices = np.where(cols)[0]

        # Handle empty results
        if len(row_indices) == 0 or len(col_indices) == 0:
            raise ValueError("No bounding rectangle found")

        ymin, ymax = row_indices[[0, -1]]
        xmin, xmax = col_indices[[0, -1]]

        return xmin, xmax, ymin, ymax

img_array property 

img_array: ndarray

Convert the PIL Image to a numpy array.

__init__ 

__init__(
    image_size: tuple[int, int] = (1920, 1080),
    mode: str = "RGBA",
    background: RGBAColor = (0, 0, 0, 0),
)

Initialize an image for text rendering.

Parameters:

Name Type Description Default
image_size tuple[int, int]

Dimensions of the image (height, width) - NumPy convention

 (1920, 1080)
mode str

Image mode (RGB, RGBA, etc.)

 'RGBA'
background RGBAColor

Background color with alpha channel

 (0, 0, 0, 0)

Raises:

Type Description
ValueError

If image_size dimensions are not positive
Source code in src/videopython/base/text/overlay.py 
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def __init__(
    self,
    image_size: tuple[int, int] = (1920, 1080),  # (height, width) - NumPy convention
    mode: str = "RGBA",
    background: RGBAColor = (0, 0, 0, 0),  # Transparent background
):
    """
    Initialize an image for text rendering.

    Args:
        image_size: Dimensions of the image (height, width) - NumPy convention
        mode: Image mode (RGB, RGBA, etc.)
        background: Background color with alpha channel

    Raises:
        ValueError: If image_size dimensions are not positive
    """
    if image_size[0] <= 0 or image_size[1] <= 0:
        raise ValueError("Image dimensions must be positive")

    if len(background) != 4:
        raise ValueError("Background color must be RGBA (4 values)")

    self.image_size = image_size  # Stored as (height, width)
    # PIL uses (width, height), so we reverse for Image.new
    self.image = Image.new(mode, (image_size[1], image_size[0]), color=background)
    self._draw = ImageDraw.Draw(self.image)
    self._font_cache: dict[tuple[str, int], ImageFont.FreeTypeFont] = {}  # Cache for font objects

save 

save(filename: str) -> None

Save the image to a file.

Source code in src/videopython/base/text/overlay.py 
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def save(self, filename: str) -> None:
    """Save the image to a file."""
    if not filename:
        raise ValueError("Filename cannot be empty")
    self.image.save(filename)

write_text 

write_text(
    text: str,
    font_filename: str,
    xy: PositionType,
    font_size: int | None = 11,
    font_border_size: int = 0,
    color: RGBColor = (0, 0, 0),
    max_width: int | None = None,
    max_height: int | None = None,
    anchor: AnchorPoint = AnchorPoint.TOP_LEFT,
    margin: MarginType = 0,
) -> tuple[int, int]

Write text to the image with advanced positioning options.

Parameters:

Name Type Description Default
text str

Text to be written

 required
font_filename str

Path to the font file

 required
xy PositionType

Position (x,y) either as absolute pixels (int) or relative to frame (float 0-1)

 required
font_size int | None

Size of the font in points, or None to auto-calculate

 11
font_border_size int

Size of border around text in pixels (0 for no border)

 0
color RGBColor

RGB color of the text

 (0, 0, 0)
max_width int | None

Maximum width for auto font sizing

 None
max_height int | None

Maximum height for auto font sizing

 None
anchor AnchorPoint

Which part of the text to anchor at the position

 TOP_LEFT
margin MarginType

Margin in pixels (single value or [top, right, bottom, left])

 0

Returns:

Type Description
tuple[int, int]

Dimensions of the rendered text (width, height)

Raises:

Type Description
ValueError

If text is empty or font parameters are invalid
OutOfBoundsError

If the text would be rendered outside the image bounds
Source code in src/videopython/base/text/overlay.py 
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def write_text(
    self,
    text: str,
    font_filename: str,
    xy: PositionType,
    font_size: int | None = 11,
    font_border_size: int = 0,
    color: RGBColor = (0, 0, 0),
    max_width: int | None = None,
    max_height: int | None = None,
    anchor: AnchorPoint = AnchorPoint.TOP_LEFT,
    margin: MarginType = 0,
) -> tuple[int, int]:
    """
    Write text to the image with advanced positioning options.

    Args:
        text: Text to be written
        font_filename: Path to the font file
        xy: Position (x,y) either as absolute pixels (int) or relative to frame (float 0-1)
        font_size: Size of the font in points, or None to auto-calculate
        font_border_size: Size of border around text in pixels (0 for no border)
        color: RGB color of the text
        max_width: Maximum width for auto font sizing
        max_height: Maximum height for auto font sizing
        anchor: Which part of the text to anchor at the position
        margin: Margin in pixels (single value or [top, right, bottom, left])

    Returns:
        Dimensions of the rendered text (width, height)

    Raises:
        ValueError: If text is empty or font parameters are invalid
        OutOfBoundsError: If the text would be rendered outside the image bounds
    """
    if not text:
        raise ValueError("Text cannot be empty")

    if not font_filename:
        raise ValueError("Font filename cannot be empty")

    if font_size is not None and font_size <= 0:
        raise ValueError("Font size must be positive")

    if font_border_size < 0:
        raise ValueError("Font border size cannot be negative")

    if font_size is None and (max_width is None or max_height is None):
        raise ValueError("Must set either `font_size`, or both `max_width` and `max_height`!")
    elif font_size is None:
        font_size = self._get_font_size(text, font_filename, max_width, max_height)

    # Get or create the font object (with caching)
    font = self._get_font(font_filename, font_size)
    text_dimensions = self.get_text_dimensions(font_filename, font_size, text)

    # Calculate the position based on anchor point and margins
    x, y = self._calculate_position(text_dimensions, xy, anchor, margin)

    # Verify text will fit within bounds
    if x < 0 or y < 0 or x + text_dimensions[0] > self.image_size[1] or y + text_dimensions[1] > self.image_size[0]:
        raise OutOfBoundsError(f"Text with size {text_dimensions} at position ({x}, {y}) is out of bounds!")

    # Draw border if requested
    if font_border_size > 0:
        # Draw text border by drawing text in multiple positions around the main text
        for border_x in range(-font_border_size, font_border_size + 1):
            for border_y in range(-font_border_size, font_border_size + 1):
                if border_x != 0 or border_y != 0:  # Skip the center position
                    self._draw.text((x + border_x, y + border_y), text, font=font, fill=(0, 0, 0))

    # Draw the main text on top
    self._draw.text((x, y), text, font=font, fill=color)
    return text_dimensions

get_text_dimensions 

get_text_dimensions(
    font_filename: str, font_size: int, text: str
) -> tuple[int, int]

Return dimensions (width, height) of the rendered text.

Parameters:

Name Type Description Default
font_filename str

Path to the font file

 required
font_size int

Size of the font in points

 required
text str

Text to measure

 required

Returns:

Type Description
tuple[int, int]

Tuple of (width, height) for the rendered text

Raises:

Type Description
ValueError

If font parameters are invalid or text is empty
Source code in src/videopython/base/text/overlay.py 
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def get_text_dimensions(self, font_filename: str, font_size: int, text: str) -> tuple[int, int]:
    """
    Return dimensions (width, height) of the rendered text.

    Args:
        font_filename: Path to the font file
        font_size: Size of the font in points
        text: Text to measure

    Returns:
        Tuple of (width, height) for the rendered text

    Raises:
        ValueError: If font parameters are invalid or text is empty
    """
    if not text:
        return (0, 0)  # Empty text has no dimensions

    if font_size <= 0:
        raise ValueError("Font size must be positive")

    font = self._get_font(font_filename, font_size)
    try:
        bbox = font.getbbox(text)
        if bbox is None:
            return (0, 0)  # Handle case where getbbox returns None
        return bbox[2:] if len(bbox) >= 4 else (0, 0)
    except Exception as e:
        raise ValueError(f"Error measuring text: {str(e)}")

write_text_box 

write_text_box(
    text: str,
    font_filename: str,
    xy: PositionType,
    box_width: int | float | None = None,
    font_size: int = 11,
    font_border_size: int = 0,
    text_color: RGBColor = (0, 0, 0),
    background_color: RGBAColor | None = None,
    background_padding: int = 0,
    place: TextAlign = TextAlign.LEFT,
    anchor: AnchorPoint = AnchorPoint.TOP_LEFT,
    margin: MarginType = 0,
    words: list[str] | None = None,
    highlight_word_index: int | None = None,
    highlight_color: RGBColor | None = None,
    highlight_size_multiplier: float = DEFAULT_HIGHLIGHT_SIZE_MULTIPLIER,
    highlight_bold_font: str | None = None,
) -> tuple[int, int]

Write text in a box with advanced positioning and alignment options.

Parameters:

Name Type Description Default
text str

Text to be written inside the box

 required
font_filename str

Path to the font file

 required
xy PositionType

Position (x,y) either as absolute pixels (int) or relative to frame (float 0-1)

 required
box_width int | float | None

Width of the box in pixels (int) or relative to frame width (float 0-1)

 None
font_size int

Font size in points

 11
font_border_size int

Size of border around text in pixels (0 for no border)

 0
text_color RGBColor

RGB color of the text

 (0, 0, 0)
background_color RGBAColor | None

If set, adds background color to the text box. Expects RGBA values.

 None
background_padding int

Number of padding pixels to add when adding text background color

 0
place TextAlign

Text alignment within the box (TextAlign.LEFT, TextAlign.RIGHT, TextAlign.CENTER)

 LEFT
anchor AnchorPoint

Which part of the text box to anchor at the position

 TOP_LEFT
margin MarginType

Margin in pixels (single value or [top, right, bottom, left])

 0
words list[str] | None

All words occuring in text, helpful for highlighting.

 None
highlight_word_index int | None

Index of word to highlight (0-based, None to disable highlighting)

 None
highlight_color RGBColor | None

RGB color for the highlighted word (defaults to text_color if None)

 None
highlight_size_multiplier float

Font size multiplier for highlighted word

 DEFAULT_HIGHLIGHT_SIZE_MULTIPLIER
highlight_bold_font str | None

Path to bold font file for highlighted word (defaults to font_filename if None)

 None

Returns:

Type Description
tuple[int, int]

Coordinates of the lower-right corner of the written text box (x, y)

Raises:

Type Description
ValueError

If text is empty or parameters are invalid
OutOfBoundsError

If text box would be outside image bounds
Source code in src/videopython/base/text/overlay.py 
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def write_text_box(
    self,
    text: str,
    font_filename: str,
    xy: PositionType,
    box_width: int | float | None = None,
    font_size: int = 11,
    font_border_size: int = 0,
    text_color: RGBColor = (0, 0, 0),
    background_color: RGBAColor | None = None,
    background_padding: int = 0,
    place: TextAlign = TextAlign.LEFT,
    anchor: AnchorPoint = AnchorPoint.TOP_LEFT,
    margin: MarginType = 0,
    words: list[str] | None = None,
    highlight_word_index: int | None = None,
    highlight_color: RGBColor | None = None,
    highlight_size_multiplier: float = DEFAULT_HIGHLIGHT_SIZE_MULTIPLIER,
    highlight_bold_font: str | None = None,
) -> tuple[int, int]:
    """
    Write text in a box with advanced positioning and alignment options.

    Args:
        text: Text to be written inside the box
        font_filename: Path to the font file
        xy: Position (x,y) either as absolute pixels (int) or relative to frame (float 0-1)
        box_width: Width of the box in pixels (int) or relative to frame width (float 0-1)
        font_size: Font size in points
        font_border_size: Size of border around text in pixels (0 for no border)
        text_color: RGB color of the text
        background_color: If set, adds background color to the text box. Expects RGBA values.
        background_padding: Number of padding pixels to add when adding text background color
        place: Text alignment within the box (TextAlign.LEFT, TextAlign.RIGHT, TextAlign.CENTER)
        anchor: Which part of the text box to anchor at the position
        margin: Margin in pixels (single value or [top, right, bottom, left])
        words: All words occuring in text, helpful for highlighting.
        highlight_word_index: Index of word to highlight (0-based, None to disable highlighting)
        highlight_color: RGB color for the highlighted word (defaults to text_color if None)
        highlight_size_multiplier: Font size multiplier for highlighted word
        highlight_bold_font: Path to bold font file for highlighted word (defaults to font_filename if None)

    Returns:
        Coordinates of the lower-right corner of the written text box (x, y)

    Raises:
        ValueError: If text is empty or parameters are invalid
        OutOfBoundsError: If text box would be outside image bounds
    """
    if not text:
        raise ValueError("Text cannot be empty")

    if not font_filename:
        raise ValueError("Font filename cannot be empty")

    if font_size <= 0:
        raise ValueError("Font size must be positive")

    if background_padding < 0:
        raise ValueError("Background padding cannot be negative")

    if font_border_size < 0:
        raise ValueError("Font border size cannot be negative")

    # Validate highlighting parameters
    if highlight_word_index is not None:
        if not words:
            words = text.split()
        if highlight_word_index < 0 or highlight_word_index >= len(words):
            raise ValueError(
                f"highlight_word_index {highlight_word_index} out of range for text with {len(words)} words"
            )

    if highlight_size_multiplier <= 0:
        raise ValueError("highlight_size_multiplier must be positive")

    # Set default highlight color if not provided
    if highlight_word_index is not None and highlight_color is None:
        highlight_color = text_color

    # Process margins to determine available area
    margin_top, margin_right, margin_bottom, margin_left = self._process_margin(margin)
    available_width = self.image_size[1] - margin_left - margin_right
    available_height = self.image_size[0] - margin_top - margin_bottom

    # Handle relative box width
    if box_width is None:
        box_width = available_width
    elif isinstance(box_width, float) and 0 < box_width <= 1:
        box_width = int(available_width * box_width)
    elif isinstance(box_width, int) and box_width <= 0:
        raise ValueError("Box width must be positive")

    # Calculate initial position based on margin and anchor before splitting text
    x_pos, y_pos = self._convert_position(xy, margin_top, margin_left, available_width, available_height)

    # Split text into lines that fit within box_width
    lines = self._split_lines_by_width(text, font_filename, font_size, int(box_width))

    # Calculate total height of all lines
    lines_height = sum([self.get_text_dimensions(font_filename, font_size, line)[1] for line in lines])
    if lines_height == 0:
        # If we have no valid lines or zero height, return the position
        return (int(x_pos), int(y_pos))

    # Final position calculation based on anchor point
    if anchor in AnchorPoint.center_anchors():
        x_pos -= box_width // 2
    elif anchor in AnchorPoint.right_anchors():
        x_pos -= box_width

    if anchor in AnchorPoint.middle_anchors():
        y_pos -= lines_height // 2
    elif anchor in AnchorPoint.bottom_anchors():
        y_pos -= lines_height

    # Verify box will fit within bounds
    if (
        x_pos < 0
        or y_pos < 0
        or x_pos + box_width > self.image_size[1]
        or y_pos + lines_height > self.image_size[0]
    ):
        raise OutOfBoundsError(
            f"Text box with size ({box_width}x{lines_height}) at position ({x_pos}, {y_pos}) is out of bounds!"
        )

    # Write lines
    current_text_height = y_pos
    word_index_offset = 0  # Track global word index across lines
    for line in lines:
        line_dimensions = self.get_text_dimensions(font_filename, font_size, line)

        # Calculate horizontal position based on alignment
        if place == TextAlign.LEFT:
            x_left = x_pos
        elif place == TextAlign.RIGHT:
            x_left = x_pos + box_width - line_dimensions[0]
        elif place == TextAlign.CENTER:
            x_left = int(x_pos + ((box_width - line_dimensions[0]) / 2))
        else:
            valid_places = [e.value for e in TextAlign]
            raise ValueError(f"Place '{place}' is not supported. Must be one of: {', '.join(valid_places)}")

        # Check if highlighting is needed for this line
        if highlight_word_index is not None:
            line_words = line.split()
            line_start_word_index = word_index_offset
            line_end_word_index = word_index_offset + len(line_words) - 1

            # Check if the highlighted word is in this line
            if line_start_word_index <= highlight_word_index <= line_end_word_index:
                self._write_line_with_highlight(
                    line=line,
                    font_filename=font_filename,
                    font_size=font_size,
                    font_border_size=font_border_size,
                    text_color=text_color,
                    highlight_color=highlight_color or (255, 255, 255),
                    highlight_size_multiplier=highlight_size_multiplier,
                    highlight_word_local_index=highlight_word_index - line_start_word_index,
                    highlight_bold_font=highlight_bold_font,
                    x_left=int(x_left),
                    y_top=int(current_text_height),
                )
            else:
                # Write normal line without highlighting
                self.write_text(
                    text=line,
                    font_filename=font_filename,
                    xy=(x_left, current_text_height),
                    font_size=font_size,
                    font_border_size=font_border_size,
                    color=text_color,
                )

            word_index_offset += len(line_words)
        else:
            # Write normal line without highlighting
            self.write_text(
                text=line,
                font_filename=font_filename,
                xy=(x_left, current_text_height),
                font_size=font_size,
                font_border_size=font_border_size,
                color=text_color,
            )

        # Increment vertical position for next line
        current_text_height += line_dimensions[1]

    # Add background color for the text if specified
    if background_color is not None:
        if len(background_color) != 4:
            raise ValueError(f"Text background color {background_color} must be RGBA (4 values)!")

        img = self.img_array

        # Find bounding rectangle for written text
        # Skip if the box is empty
        if y_pos >= current_text_height or x_pos >= x_pos + box_width:
            return (int(x_pos + box_width), int(current_text_height))

        # Get the slice of the image containing the text box
        box_slice = img[int(y_pos) : int(current_text_height), int(x_pos) : int(x_pos + box_width)]
        if box_slice.size == 0:  # Empty slice
            return (int(x_pos + box_width), int(current_text_height))

        # Create mask of non-zero pixels (text)
        text_mask = np.any(box_slice != 0, axis=2).astype(np.uint8)
        if not isinstance(text_mask, np.ndarray):
            raise TypeError(f"The returned text mask is of type {type(text_mask)}, but it should be numpy array!")

        # If no text pixels found, return without background
        if not np.any(text_mask):
            return (int(x_pos + box_width), int(current_text_height))

        # Find the smallest rectangle containing text
        try:
            xmin, xmax, ymin, ymax = self._find_smallest_bounding_rect(text_mask)
        except Exception:
            # If bounding rectangle calculation fails, use the whole box
            xmin, xmax, ymin, ymax = 0, box_slice.shape[1] - 1, 0, box_slice.shape[0] - 1

        # Get global bounding box position
        xmin = int(xmin + x_pos - background_padding)
        xmax = int(xmax + x_pos + background_padding)
        ymin = int(ymin + y_pos - background_padding)
        ymax = int(ymax + y_pos + background_padding)

        # Make sure we are inside image bounds
        xmin = max(0, xmin)
        ymin = max(0, ymin)
        xmax = min(xmax, self.image_size[1])
        ymax = min(ymax, self.image_size[0])

        # Skip if bounding box is invalid
        if xmin >= xmax or ymin >= ymax:
            return (int(x_pos + box_width), int(current_text_height))

        # Slice the bounding box and find text mask
        bbox_slice = img[ymin:ymax, xmin:xmax]
        if bbox_slice.size == 0:  # Empty slice
            return (int(x_pos + box_width), int(current_text_height))

        bbox_text_mask = np.any(bbox_slice != 0, axis=2).astype(np.uint8)

        # Add background color outside of text
        bbox_slice[~bbox_text_mask.astype(bool)] = background_color

        # Handle semi-transparent pixels for smooth text blending
        text_slice = bbox_slice[bbox_text_mask.astype(bool)]
        if text_slice.size > 0:
            text_background = text_slice[:, :3] * (np.expand_dims(text_slice[:, -1], axis=1) / 255)
            color_background = (1 - (np.expand_dims(text_slice[:, -1], axis=1) / 255)) * background_color
            faded_background = text_background[:, :3] + color_background[:, :3]
            text_slice[:, :3] = faded_background
            text_slice[:, -1] = 255  # Full opacity
            bbox_slice[bbox_text_mask.astype(bool)] = text_slice

        # Update the image with the background color
        self.image = Image.fromarray(img)

    return (int(x_pos + box_width), int(current_text_height))

TextAlign

TextAlign

Bases: str, Enum

Defines text alignment options for positioning within containers.

Source code in src/videopython/base/text/overlay.py 
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class TextAlign(str, Enum):
    """Defines text alignment options for positioning within containers."""

    LEFT = "left"
    RIGHT = "right"
    CENTER = "center"

AnchorPoint

AnchorPoint

Bases: str, Enum

Defines anchor points for positioning text elements.

Source code in src/videopython/base/text/overlay.py 
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class AnchorPoint(str, Enum):
    """Defines anchor points for positioning text elements."""

    TOP_LEFT = "top-left"
    TOP_CENTER = "top-center"
    TOP_RIGHT = "top-right"
    CENTER_LEFT = "center-left"
    CENTER = "center"
    CENTER_RIGHT = "center-right"
    BOTTOM_LEFT = "bottom-left"
    BOTTOM_CENTER = "bottom-center"
    BOTTOM_RIGHT = "bottom-right"

    # Group anchor points by their horizontal position
    @classmethod
    def left_anchors(cls) -> tuple["AnchorPoint", ...]:
        return (cls.TOP_LEFT, cls.CENTER_LEFT, cls.BOTTOM_LEFT)

    @classmethod
    def center_anchors(cls) -> tuple["AnchorPoint", ...]:
        return (cls.TOP_CENTER, cls.CENTER, cls.BOTTOM_CENTER)

    @classmethod
    def right_anchors(cls) -> tuple["AnchorPoint", ...]:
        return (cls.TOP_RIGHT, cls.CENTER_RIGHT, cls.BOTTOM_RIGHT)

    # Group anchor points by their vertical position
    @classmethod
    def top_anchors(cls) -> tuple["AnchorPoint", ...]:
        return (cls.TOP_LEFT, cls.TOP_CENTER, cls.TOP_RIGHT)

    @classmethod
    def middle_anchors(cls) -> tuple["AnchorPoint", ...]:
        return (cls.CENTER_LEFT, cls.CENTER, cls.CENTER_RIGHT)

    @classmethod
    def bottom_anchors(cls) -> tuple["AnchorPoint", ...]:
        return (cls.BOTTOM_LEFT, cls.BOTTOM_CENTER, cls.BOTTOM_RIGHT)