Filter System

class Graph:
    """Filter graph for audio/video processing."""
    
    # Properties
    configured: bool            # True if graph is configured
    
    def __init__(self):
        """Create new filter graph."""
    
    def add(self, filter_name, *args, **kwargs) -> FilterContext:
        """
        Add filter to graph.
        
        Parameters:
        - filter_name: str - Filter name
        - *args: Positional filter arguments
        - **kwargs: Named filter arguments
        
        Returns:
        FilterContext for the added filter
        """
    
    def add_buffer(self, template=None, width=None, height=None, format=None,
                   name=None, **kwargs) -> FilterContext:
        """
        Add video buffer source.
        
        Parameters:
        - template: VideoStream - Template stream for properties
        - width: int - Frame width
        - height: int - Frame height
        - format: str - Pixel format
        - name: str - Buffer name
        
        Returns:
        Video buffer FilterContext
        """
    
    def add_abuffer(self, template=None, format=None, layout=None, rate=None,
                    name=None, **kwargs) -> FilterContext:
        """
        Add audio buffer source.
        
        Parameters:
        - template: AudioStream - Template stream for properties
        - format: str - Audio format
        - layout: str - Channel layout
        - rate: int - Sample rate
        - name: str - Buffer name
        
        Returns:
        Audio buffer FilterContext
        """
    
    def configure(self) -> None:
        """Configure the filter graph for processing."""
    
    def link_nodes(self, output_ctx, input_ctx, output_idx=0, input_idx=0) -> None:
        """
        Link filter contexts.
        
        Parameters:
        - output_ctx: FilterContext - Source context
        - input_ctx: FilterContext - Destination context
        - output_idx: int - Output pad index
        - input_idx: int - Input pad index
        """
    
    def set_audio_frame_size(self, nb_samples) -> None:
        """Set audio frame size for the graph."""
    
    def push(self, frame) -> None:
        """Push frame to graph input."""
    
    def pull(self) -> Frame | None:
        """Pull frame from graph output."""
    
    def vpush(self, frame) -> None:
        """Push video frame to graph."""
    
    def vpull(self) -> VideoFrame | None:
        """Pull video frame from graph."""

# Available filters
filters_available: set[str]  # Set of available filter names

class Filter:
    """Filter information."""
    
    # Properties
    name: str                   # Filter name
    description: str            # Filter description
    descriptor: Descriptor      # Filter descriptor with options
    options: tuple[Option, ...] # Available options
    flags: int                 # Filter flags
    
    def __init__(self, name):
        """
        Get filter by name.
        
        Parameters:
        - name: str - Filter name
        """

class FilterContext:
    """Filter instance in a graph."""
    
    # Properties
    name: str | None           # Filter context name
    graph: Graph              # Parent graph
    
    def init(self, args=None, **kwargs) -> None:
        """
        Initialize filter context.
        
        Parameters:
        - args: str - Filter argument string
        - **kwargs: Named arguments
        """
    
    def link_to(self, target, output_idx=0, input_idx=0) -> None:
        """
        Link this context to another.
        
        Parameters:
        - target: FilterContext - Target context
        - output_idx: int - Output pad index
        - input_idx: int - Input pad index
        """
    
    def push(self, frame) -> None:
        """
        Push frame to this filter.
        
        Parameters:
        - frame: Frame - Input frame
        """
    
    def pull(self) -> Frame | None:
        """
        Pull processed frame.
        
        Returns:
        Processed frame or None
        """

def stats(loudnorm_args: str, stream: AudioStream) -> bytes:
    """
    Generate loudness normalization statistics.
    
    Parameters:
    - loudnorm_args: str - Loudnorm filter arguments
    - stream: AudioStream - Input audio stream
    
    Returns:
    Statistics data for second-pass normalization
    """

import av

# Open input video
input_container = av.open('input.mp4')
input_stream = input_container.streams.video[0]

# Create output
output_container = av.open('filtered.mp4', 'w')
output_stream = output_container.add_stream('h264', rate=input_stream.framerate)
output_stream.width = input_stream.width
output_stream.height = input_stream.height
output_stream.pix_fmt = 'yuv420p'

# Create filter graph
graph = av.filter.Graph()

# Add input buffer
buffer_ctx = graph.add_buffer(template=input_stream)

# Add scale filter (resize)
scale_ctx = graph.add('scale', width=1280, height=720)

# Add color adjustment filter
colorbalance_ctx = graph.add('colorbalance', 
                            rs=0.1,  # Red shadows
                            gs=-0.1, # Green shadows  
                            bs=0.05) # Blue shadows

# Add output buffersink
buffersink_ctx = graph.add('buffersink')

# Link filters: buffer -> scale -> colorbalance -> buffersink
buffer_ctx.link_to(scale_ctx)
scale_ctx.link_to(colorbalance_ctx)
colorbalance_ctx.link_to(buffersink_ctx)

# Configure graph
graph.configure()

# Process frames
for frame in input_container.decode(input_stream):
    # Push frame to filter graph
    buffer_ctx.push(frame)
    
    # Pull filtered frames
    while True:
        try:
            filtered_frame = buffersink_ctx.pull()
            if filtered_frame is None:
                break
                
            # Set timing for output
            filtered_frame.pts = frame.pts
            filtered_frame.time_base = input_stream.time_base
            
            # Encode and write
            for packet in output_stream.encode(filtered_frame):
                output_container.mux(packet)
                
        except av.BlockingIOError:
            break

# Flush
for packet in output_stream.encode():
    output_container.mux(packet)

input_container.close()
output_container.close()

import av

# Open input audio
input_container = av.open('input.wav')
input_stream = input_container.streams.audio[0]

# Create output
output_container = av.open('filtered.wav', 'w')
output_stream = output_container.add_stream('pcm_s16le', rate=input_stream.sample_rate)
output_stream.channels = input_stream.channels
output_stream.layout = input_stream.layout

# Create audio filter graph
graph = av.filter.Graph()

# Add audio input buffer
abuffer_ctx = graph.add_abuffer(template=input_stream)

# Add volume filter
volume_ctx = graph.add('volume', volume=1.5)  # Increase volume by 50%

# Add high-pass filter
highpass_ctx = graph.add('highpass', frequency=80, poles=2)

# Add low-pass filter for noise reduction
lowpass_ctx = graph.add('lowpass', frequency=15000, poles=2)

# Add audio output
abuffersink_ctx = graph.add('abuffersink')

# Link audio filters
abuffer_ctx.link_to(volume_ctx)
volume_ctx.link_to(highpass_ctx)
highpass_ctx.link_to(lowpass_ctx)
lowpass_ctx.link_to(abuffersink_ctx)

# Configure graph
graph.configure()

# Process audio frames
for frame in input_container.decode(input_stream):
    abuffer_ctx.push(frame)
    
    while True:
        try:
            filtered_frame = abuffersink_ctx.pull()
            if filtered_frame is None:
                break
                
            # Maintain timing
            filtered_frame.pts = frame.pts
            filtered_frame.time_base = input_stream.time_base
            
            # Encode filtered audio
            for packet in output_stream.encode(filtered_frame):
                output_container.mux(packet)
                
        except av.BlockingIOError:
            break

# Flush encoder
for packet in output_stream.encode():
    output_container.mux(packet)

input_container.close()
output_container.close()

import av

def create_complex_video_filter(input_stream):
    """Create complex video processing filter chain."""
    
    graph = av.filter.Graph()
    
    # Input buffer
    buffer_ctx = graph.add_buffer(template=input_stream)
    
    # 1. Deinterlace if needed
    deinterlace_ctx = graph.add('yadif', mode=0, parity=-1)
    
    # 2. Denoise
    denoise_ctx = graph.add('hqdn3d', luma_temporal=4.0, chroma_temporal=3.0)
    
    # 3. Color correction
    curves_ctx = graph.add('curves', 
                          red='0/0 0.5/0.58 1/1',      # Slight red lift
                          green='0/0 0.5/0.5 1/1',     # No green change
                          blue='0/0 0.5/0.42 1/1')     # Slight blue reduction
    
    # 4. Sharpen
    sharpen_ctx = graph.add('unsharp', 
                           luma_msize_x=5, luma_msize_y=5,
                           luma_amount=1.2,
                           chroma_msize_x=3, chroma_msize_y=3,
                           chroma_amount=0.8)
    
    # 5. Scale to target resolution
    scale_ctx = graph.add('scale', width=1920, height=1080)
    
    # 6. Add subtle vignette
    vignette_ctx = graph.add('vignette', angle='PI/4', x0='w/2', y0='h/2')
    
    # Output
    buffersink_ctx = graph.add('buffersink')
    
    # Link all filters
    buffer_ctx.link_to(deinterlace_ctx)
    deinterlace_ctx.link_to(denoise_ctx)
    denoise_ctx.link_to(curves_ctx)
    curves_ctx.link_to(sharpen_ctx)
    sharpen_ctx.link_to(scale_ctx)
    scale_ctx.link_to(vignette_ctx)
    vignette_ctx.link_to(buffersink_ctx)
    
    graph.configure()
    
    return graph, buffer_ctx, buffersink_ctx

# Use complex filter
input_container = av.open('input.mov')
input_stream = input_container.streams.video[0]

output_container = av.open('processed.mp4', 'w')
output_stream = output_container.add_stream('h264', rate=input_stream.framerate)
output_stream.width = 1920
output_stream.height = 1080
output_stream.pix_fmt = 'yuv420p'

# Create filter chain
graph, buffer_input, buffer_output = create_complex_video_filter(input_stream)

print("Processing with complex filter chain:")
print("- Deinterlacing")
print("- Noise reduction")
print("- Color correction")
print("- Sharpening")
print("- Scaling to 1080p")
print("- Vignette effect")

# Process video
frame_count = 0
for frame in input_container.decode(input_stream):
    buffer_input.push(frame)
    
    while True:
        try:
            processed_frame = buffer_output.pull()
            if processed_frame is None:
                break
                
            processed_frame.pts = frame_count
            processed_frame.time_base = output_stream.time_base
            
            for packet in output_stream.encode(processed_frame):
                output_container.mux(packet)
                
            frame_count += 1
            
        except av.BlockingIOError:
            break

# Flush
for packet in output_stream.encode():
    output_container.mux(packet)

print(f"Processed {frame_count} frames")
input_container.close()
output_container.close()

import av

def normalize_audio_loudness(input_file, output_file, target_lufs=-23.0):
    """Normalize audio to target loudness using two-pass process."""
    
    # First pass: analyze audio
    print("First pass: analyzing audio...")
    
    input_container = av.open(input_file)
    input_stream = input_container.streams.audio[0]
    
    # Create analysis filter
    graph = av.filter.Graph()
    abuffer = graph.add_abuffer(template=input_stream)
    
    # Loudnorm filter for analysis
    loudnorm = graph.add('loudnorm', 
                        I=target_lufs,      # Target integrated loudness
                        TP=-2.0,            # Target true peak
                        LRA=11.0,           # Target loudness range
                        print_format='json')
    
    abuffersink = graph.add('abuffersink')
    
    abuffer.link_to(loudnorm)
    loudnorm.link_to(abuffersink)
    graph.configure()
    
    # Process for analysis (discard output)
    for frame in input_container.decode(input_stream):
        abuffer.push(frame)
        
        while True:
            try:
                abuffersink.pull()  # Discard frame, just analyze
            except av.BlockingIOError:
                break
    
    input_container.close()
    
    # Get analysis results (in real implementation, this would be extracted from filter)
    # For this example, we'll simulate the stats
    analysis_stats = {
        'input_i': -16.0,     # Input integrated loudness
        'input_tp': -1.5,     # Input true peak
        'input_lra': 15.2,    # Input loudness range
        'input_thresh': -26.8, # Input threshold
        'target_offset': -7.0  # Calculated offset
    }
    
    print(f"Analysis complete:")
    print(f"  Input integrated loudness: {analysis_stats['input_i']} LUFS")
    print(f"  Target offset: {analysis_stats['target_offset']} dB")
    
    # Second pass: apply normalization
    print("Second pass: applying normalization...")
    
    input_container = av.open(input_file)
    input_stream = input_container.streams.audio[0]
    
    output_container = av.open(output_file, 'w')
    output_stream = output_container.add_stream('aac', rate=input_stream.sample_rate)
    output_stream.channels = input_stream.channels
    output_stream.layout = input_stream.layout
    
    # Create normalization filter with analysis results
    graph = av.filter.Graph()
    abuffer = graph.add_abuffer(template=input_stream)
    
    loudnorm = graph.add('loudnorm',
                        I=target_lufs,
                        TP=-2.0,
                        LRA=11.0,
                        measured_I=analysis_stats['input_i'],
                        measured_TP=analysis_stats['input_tp'],
                        measured_LRA=analysis_stats['input_lra'],
                        measured_thresh=analysis_stats['input_thresh'],
                        offset=analysis_stats['target_offset'],
                        linear=True)
    
    abuffersink = graph.add('abuffersink')
    
    abuffer.link_to(loudnorm)
    loudnorm.link_to(abuffersink)
    graph.configure()
    
    # Process and encode normalized audio
    for frame in input_container.decode(input_stream):
        abuffer.push(frame)
        
        while True:
            try:
                normalized_frame = abuffersink.pull()
                if normalized_frame is None:
                    break
                    
                for packet in output_stream.encode(normalized_frame):
                    output_container.mux(packet)
                    
            except av.BlockingIOError:
                break
    
    # Flush
    for packet in output_stream.encode():
        output_container.mux(packet)
    
    input_container.close()
    output_container.close()
    
    print(f"Normalization complete: {output_file}")

# Normalize audio file
normalize_audio_loudness('input.wav', 'normalized.aac', target_lufs=-16.0)

import av

class FilterChainBuilder:
    """Builder for creating filter chains."""
    
    def __init__(self):
        self.graph = av.filter.Graph()
        self.contexts = []
        self.last_context = None
        
    def add_input_buffer(self, stream):
        """Add input buffer for audio or video stream."""
        if stream.type == 'video':
            ctx = self.graph.add_buffer(template=stream)
        elif stream.type == 'audio':
            ctx = self.graph.add_abuffer(template=stream)
        else:
            raise ValueError(f"Unsupported stream type: {stream.type}")
            
        self.contexts.append(ctx)
        self.last_context = ctx
        return self
    
    def add_filter(self, filter_name, **kwargs):
        """Add filter to chain."""
        ctx = self.graph.add(filter_name, **kwargs)
        
        if self.last_context:
            self.last_context.link_to(ctx)
            
        self.contexts.append(ctx)
        self.last_context = ctx
        return self
    
    def add_output_buffer(self):
        """Add output buffer sink."""
        if hasattr(self.contexts[0], 'template'):
            # Determine type from first context
            template = self.contexts[0].template
            if template and hasattr(template, 'width'):
                ctx = self.graph.add('buffersink')
            else:
                ctx = self.graph.add('abuffersink')
        else:
            ctx = self.graph.add('buffersink')  # Default to video
            
        if self.last_context:
            self.last_context.link_to(ctx)
            
        self.contexts.append(ctx)
        self.last_context = ctx
        return self
    
    def build(self):
        """Configure and return the filter graph."""
        self.graph.configure()
        return self.graph, self.contexts[0], self.contexts[-1]

# Example usage
def process_with_builder(input_file, output_file):
    """Process video using filter chain builder."""
    
    input_container = av.open(input_file)
    input_stream = input_container.streams.video[0]
    
    output_container = av.open(output_file, 'w')
    output_stream = output_container.add_stream('h264', rate=input_stream.framerate)
    output_stream.width = 1280
    output_stream.height = 720
    output_stream.pix_fmt = 'yuv420p'
    
    # Build filter chain
    builder = FilterChainBuilder()
    graph, input_ctx, output_ctx = (builder
        .add_input_buffer(input_stream)
        .add_filter('scale', width=1280, height=720)
        .add_filter('eq', brightness=0.1, contrast=1.1, saturation=1.2)
        .add_filter('unsharp', luma_amount=0.8)
        .add_output_buffer()
        .build())
    
    print("Created filter chain: input -> scale -> eq -> unsharp -> output")
    
    # Process frames
    for frame in input_container.decode(input_stream):
        input_ctx.push(frame)
        
        while True:
            try:
                filtered_frame = output_ctx.pull()
                if filtered_frame is None:
                    break
                    
                filtered_frame.pts = frame.pts
                filtered_frame.time_base = input_stream.time_base
                
                for packet in output_stream.encode(filtered_frame):
                    output_container.mux(packet)
                    
            except av.BlockingIOError:
                break
    
    # Flush
    for packet in output_stream.encode():
        output_container.mux(packet)
    
    input_container.close()
    output_container.close()

# Use builder
process_with_builder('input.mp4', 'enhanced.mp4')

import av

def list_available_filters():
    """List all available filters by category."""
    
    print(f"Total available filters: {len(av.filters_available)}")
    
    # Categorize filters
    video_filters = []
    audio_filters = []
    other_filters = []
    
    for filter_name in sorted(av.filters_available):
        try:
            filter_obj = av.Filter(filter_name)
            description = filter_obj.description
            
            if 'video' in description.lower():
                video_filters.append((filter_name, description))
            elif 'audio' in description.lower():
                audio_filters.append((filter_name, description))
            else:
                other_filters.append((filter_name, description))
        except:
            other_filters.append((filter_name, "No description"))
    
    print(f"\nVideo filters ({len(video_filters)}):")
    for name, desc in video_filters[:10]:  # Show first 10
        print(f"  {name:20} - {desc[:60]}...")
    
    print(f"\nAudio filters ({len(audio_filters)}):")
    for name, desc in audio_filters[:10]:  # Show first 10
        print(f"  {name:20} - {desc[:60]}...")
    
    print(f"\nOther filters ({len(other_filters)}):")
    for name, desc in other_filters[:5]:  # Show first 5
        print(f"  {name:20} - {desc[:60]}...")

# List filters
list_available_filters()

# Get detailed info about specific filter
scale_filter = av.Filter('scale')
print(f"\nScale filter details:")
print(f"  Name: {scale_filter.name}")
print(f"  Description: {scale_filter.description}")
print(f"  Options: {len(scale_filter.options)} available")