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ef7d8cca9f
slowrx is a GTK GUI app that doesn't support CLI usage, so the SSTV decoder was silently failing. This replaces it with a pure Python implementation using numpy and Pillow that supports Robot36/72, Martin1/2, Scottie1/2, and PD120/180 modes via VIS header auto-detection. Key implementation details: - Generalized Goertzel (DTFT) for exact-frequency tone detection - Vectorized batch Goertzel for real-time pixel decoding performance - Overlapping analysis windows for short-window frequency estimation - VIS header detection state machine with parity validation - Per-line sync re-synchronization for drift tolerance Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
454 lines
16 KiB
Python
454 lines
16 KiB
Python
"""SSTV scanline-by-scanline image decoder.
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Decodes raw audio samples into a PIL Image for all supported SSTV modes.
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Handles sync pulse re-synchronization on each line for robust decoding
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under weak-signal or drifting conditions.
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"""
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from __future__ import annotations
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from typing import Callable
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import numpy as np
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from .constants import (
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FREQ_BLACK,
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FREQ_PIXEL_HIGH,
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FREQ_PIXEL_LOW,
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FREQ_SYNC,
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SAMPLE_RATE,
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)
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from .dsp import (
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goertzel,
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goertzel_batch,
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samples_for_duration,
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)
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from .modes import (
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ColorModel,
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SSTVMode,
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SyncPosition,
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)
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# Pillow is imported lazily to keep the module importable when Pillow
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# is not installed (is_sstv_available() just returns True, but actual
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# decoding would fail gracefully).
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try:
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from PIL import Image
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except ImportError:
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Image = None # type: ignore[assignment,misc]
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# Type alias for progress callback: (current_line, total_lines)
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ProgressCallback = Callable[[int, int], None]
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class SSTVImageDecoder:
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"""Decode an SSTV image from a stream of audio samples.
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Usage::
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decoder = SSTVImageDecoder(mode)
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decoder.feed(samples)
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...
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if decoder.is_complete:
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image = decoder.get_image()
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"""
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def __init__(self, mode: SSTVMode, sample_rate: int = SAMPLE_RATE,
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progress_cb: ProgressCallback | None = None):
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self._mode = mode
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self._sample_rate = sample_rate
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self._progress_cb = progress_cb
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self._buffer = np.array([], dtype=np.float64)
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self._current_line = 0
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self._complete = False
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# Pre-calculate sample counts
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self._sync_samples = samples_for_duration(
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mode.sync_duration_ms / 1000.0, sample_rate)
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self._porch_samples = samples_for_duration(
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mode.sync_porch_ms / 1000.0, sample_rate)
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self._line_samples = samples_for_duration(
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mode.line_duration_ms / 1000.0, sample_rate)
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self._separator_samples = (
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samples_for_duration(mode.channel_separator_ms / 1000.0, sample_rate)
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if mode.channel_separator_ms > 0 else 0
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)
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self._channel_samples = [
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samples_for_duration(ch.duration_ms / 1000.0, sample_rate)
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for ch in mode.channels
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]
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# For PD modes, each "line" of audio produces 2 image lines
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if mode.color_model == ColorModel.YCRCB_DUAL:
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self._total_audio_lines = mode.height // 2
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else:
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self._total_audio_lines = mode.height
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# Initialize pixel data arrays per channel
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self._channel_data: list[np.ndarray] = []
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for _i, _ch_spec in enumerate(mode.channels):
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if mode.color_model == ColorModel.YCRCB_DUAL:
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# Y1, Cr, Cb, Y2 - all are width-wide
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self._channel_data.append(
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np.zeros((self._total_audio_lines, mode.width), dtype=np.uint8))
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else:
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self._channel_data.append(
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np.zeros((mode.height, mode.width), dtype=np.uint8))
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# Pre-compute candidate frequencies for batch pixel decoding (5 Hz step)
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self._freq_candidates = np.arange(
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FREQ_PIXEL_LOW - 100, FREQ_PIXEL_HIGH + 105, 5.0)
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# Track sync position for re-synchronization
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self._expected_line_start = 0 # Sample offset within buffer
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self._synced = False
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@property
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def is_complete(self) -> bool:
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return self._complete
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@property
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def current_line(self) -> int:
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return self._current_line
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@property
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def total_lines(self) -> int:
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return self._total_audio_lines
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@property
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def progress_percent(self) -> int:
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if self._total_audio_lines == 0:
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return 0
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return min(100, int(100 * self._current_line / self._total_audio_lines))
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def feed(self, samples: np.ndarray) -> bool:
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"""Feed audio samples into the decoder.
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Args:
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samples: Float64 audio samples.
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Returns:
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True when image is complete.
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"""
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if self._complete:
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return True
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self._buffer = np.concatenate([self._buffer, samples])
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# Process complete lines
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while not self._complete and len(self._buffer) >= self._line_samples:
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self._decode_line()
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# Prevent unbounded buffer growth - keep at most 2 lines worth
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max_buffer = self._line_samples * 2
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if len(self._buffer) > max_buffer and not self._complete:
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self._buffer = self._buffer[-max_buffer:]
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return self._complete
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def _find_sync(self, search_region: np.ndarray) -> int | None:
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"""Find the 1200 Hz sync pulse within a search region.
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Scans through the region looking for a stretch of 1200 Hz
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tone of approximately the right duration.
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Args:
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search_region: Audio samples to search within.
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Returns:
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Sample offset of the sync pulse start, or None if not found.
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"""
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window_size = min(self._sync_samples, 200)
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if len(search_region) < window_size:
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return None
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best_pos = None
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best_energy = 0.0
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step = window_size // 2
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for pos in range(0, len(search_region) - window_size, step):
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chunk = search_region[pos:pos + window_size]
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sync_energy = goertzel(chunk, FREQ_SYNC, self._sample_rate)
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# Check it's actually sync, not data at 1200 Hz area
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black_energy = goertzel(chunk, FREQ_BLACK, self._sample_rate)
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if sync_energy > best_energy and sync_energy > black_energy * 2:
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best_energy = sync_energy
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best_pos = pos
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return best_pos
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def _decode_line(self) -> None:
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"""Decode one scanline from the buffer."""
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if self._current_line >= self._total_audio_lines:
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self._complete = True
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return
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# Try to find sync pulse for re-synchronization
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# Search within +/-10% of expected line start
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search_margin = max(100, self._line_samples // 10)
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line_start = 0
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if self._mode.sync_position in (SyncPosition.FRONT, SyncPosition.FRONT_PD):
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# Sync is at the beginning of each line
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search_start = 0
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search_end = min(len(self._buffer), self._sync_samples + search_margin)
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search_region = self._buffer[search_start:search_end]
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sync_pos = self._find_sync(search_region)
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if sync_pos is not None:
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line_start = sync_pos
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# Skip sync + porch to get to pixel data
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pixel_start = line_start + self._sync_samples + self._porch_samples
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elif self._mode.sync_position == SyncPosition.MIDDLE:
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# Scottie: sep(1.5ms) -> G -> sep(1.5ms) -> B -> sync(9ms) -> porch(1.5ms) -> R
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# Skip initial separator (same duration as porch)
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pixel_start = self._porch_samples
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line_start = 0
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else:
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pixel_start = self._sync_samples + self._porch_samples
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# Decode each channel
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pos = pixel_start
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for ch_idx, ch_samples in enumerate(self._channel_samples):
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if pos + ch_samples > len(self._buffer):
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# Not enough data yet - put the data back and wait
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return
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channel_audio = self._buffer[pos:pos + ch_samples]
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pixels = self._decode_channel_pixels(channel_audio)
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self._channel_data[ch_idx][self._current_line, :] = pixels
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pos += ch_samples
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# Add inter-channel gaps based on mode family
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if ch_idx < len(self._channel_samples) - 1:
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if self._mode.sync_position == SyncPosition.MIDDLE:
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if ch_idx == 0:
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# Scottie: separator between G and B
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pos += self._porch_samples
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else:
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# Scottie: sync + porch between B and R
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pos += self._sync_samples + self._porch_samples
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elif self._separator_samples > 0:
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# Robot: separator + porch between channels
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pos += self._separator_samples
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elif (self._mode.sync_position == SyncPosition.FRONT
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and self._mode.color_model == ColorModel.RGB):
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# Martin: porch between channels
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pos += self._porch_samples
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# Advance buffer past this line
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consumed = max(pos, self._line_samples)
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self._buffer = self._buffer[consumed:]
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self._current_line += 1
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if self._progress_cb:
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self._progress_cb(self._current_line, self._total_audio_lines)
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if self._current_line >= self._total_audio_lines:
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self._complete = True
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# Minimum analysis window for meaningful Goertzel frequency estimation.
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# With 96 samples (2ms at 48kHz), frequency accuracy is within ~25 Hz,
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# giving pixel-level accuracy of ~8/255 levels.
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_MIN_ANALYSIS_WINDOW = 96
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def _decode_channel_pixels(self, audio: np.ndarray) -> np.ndarray:
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"""Decode pixel values from a channel's audio data.
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Uses batch Goertzel to estimate frequencies for all pixels
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simultaneously, then maps to luminance values. When pixels have
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fewer samples than ``_MIN_ANALYSIS_WINDOW``, overlapping analysis
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windows are used to maintain frequency estimation accuracy.
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Args:
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audio: Audio samples for one channel of one scanline.
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Returns:
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Array of pixel values (0-255), shape (width,).
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"""
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width = self._mode.width
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samples_per_pixel = max(1, len(audio) // width)
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if len(audio) < width or samples_per_pixel < 2:
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return np.zeros(width, dtype=np.uint8)
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window_size = max(samples_per_pixel, self._MIN_ANALYSIS_WINDOW)
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if window_size > samples_per_pixel and len(audio) >= window_size:
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# Use overlapping windows centered on each pixel position
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windows = np.lib.stride_tricks.sliding_window_view(
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audio, window_size)
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# Pixel centers, clamped to valid window indices
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centers = np.arange(width) * samples_per_pixel
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indices = np.minimum(centers, len(windows) - 1)
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audio_matrix = np.ascontiguousarray(windows[indices])
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else:
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# Non-overlapping: each pixel has enough samples
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usable = width * samples_per_pixel
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audio_matrix = audio[:usable].reshape(width, samples_per_pixel)
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# Batch Goertzel at all candidate frequencies
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energies = goertzel_batch(
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audio_matrix, self._freq_candidates, self._sample_rate)
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# Find peak frequency per pixel
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best_idx = np.argmax(energies, axis=1)
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best_freqs = self._freq_candidates[best_idx]
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# Map frequencies to pixel values (1500 Hz = 0, 2300 Hz = 255)
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normalized = (best_freqs - FREQ_PIXEL_LOW) / (FREQ_PIXEL_HIGH - FREQ_PIXEL_LOW)
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return np.clip(normalized * 255 + 0.5, 0, 255).astype(np.uint8)
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def get_image(self) -> Image.Image | None:
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"""Convert decoded channel data to a PIL Image.
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Returns:
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PIL Image in RGB mode, or None if Pillow is not available
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or decoding is incomplete.
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"""
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if Image is None:
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return None
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mode = self._mode
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if mode.color_model == ColorModel.RGB:
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return self._assemble_rgb()
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elif mode.color_model == ColorModel.YCRCB:
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return self._assemble_ycrcb()
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elif mode.color_model == ColorModel.YCRCB_DUAL:
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return self._assemble_ycrcb_dual()
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return None
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def _assemble_rgb(self) -> Image.Image:
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"""Assemble RGB image from sequential R, G, B channel data.
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Martin/Scottie channel order: G, B, R.
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"""
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height = self._mode.height
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# Channel order for Martin/Scottie: [0]=G, [1]=B, [2]=R
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g_data = self._channel_data[0][:height]
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b_data = self._channel_data[1][:height]
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r_data = self._channel_data[2][:height]
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rgb = np.stack([r_data, g_data, b_data], axis=-1)
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return Image.fromarray(rgb, 'RGB')
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def _assemble_ycrcb(self) -> Image.Image:
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"""Assemble image from YCrCb data (Robot modes).
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Robot36: Y every line, Cr/Cb alternating (half-rate chroma).
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Robot72: Y, Cr, Cb every line (full-rate chroma).
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"""
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height = self._mode.height
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width = self._mode.width
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if not self._mode.has_half_rate_chroma:
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# Full-rate chroma (Robot72): Y, Cr, Cb as separate channels
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y_data = self._channel_data[0][:height].astype(np.float64)
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cr = self._channel_data[1][:height].astype(np.float64)
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cb = self._channel_data[2][:height].astype(np.float64)
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return self._ycrcb_to_rgb(y_data, cr, cb, height, width)
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# Half-rate chroma (Robot36): Y + alternating Cr/Cb
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y_data = self._channel_data[0][:height].astype(np.float64)
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chroma_data = self._channel_data[1][:height].astype(np.float64)
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# Separate Cr (even lines) and Cb (odd lines), then interpolate
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cr = np.zeros((height, width), dtype=np.float64)
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cb = np.zeros((height, width), dtype=np.float64)
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for line in range(height):
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if line % 2 == 0:
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cr[line] = chroma_data[line]
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else:
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cb[line] = chroma_data[line]
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# Interpolate missing chroma lines
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for line in range(height):
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if line % 2 == 1:
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# Missing Cr - interpolate from neighbors
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prev_cr = line - 1 if line > 0 else line + 1
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next_cr = line + 1 if line + 1 < height else line - 1
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cr[line] = (cr[prev_cr] + cr[next_cr]) / 2
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else:
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# Missing Cb - interpolate from neighbors
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prev_cb = line - 1 if line > 0 else line + 1
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next_cb = line + 1 if line + 1 < height else line - 1
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if prev_cb >= 0 and next_cb < height:
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cb[line] = (cb[prev_cb] + cb[next_cb]) / 2
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elif prev_cb >= 0:
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cb[line] = cb[prev_cb]
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else:
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cb[line] = cb[next_cb]
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return self._ycrcb_to_rgb(y_data, cr, cb, height, width)
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def _assemble_ycrcb_dual(self) -> Image.Image:
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"""Assemble image from dual-luminance YCrCb data (PD modes).
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PD modes send Y1, Cr, Cb, Y2 per audio line, producing 2 image lines.
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"""
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audio_lines = self._total_audio_lines
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width = self._mode.width
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height = self._mode.height
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y1_data = self._channel_data[0][:audio_lines].astype(np.float64)
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cr_data = self._channel_data[1][:audio_lines].astype(np.float64)
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cb_data = self._channel_data[2][:audio_lines].astype(np.float64)
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y2_data = self._channel_data[3][:audio_lines].astype(np.float64)
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# Interleave Y1 and Y2 to produce full-height luminance
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y_full = np.zeros((height, width), dtype=np.float64)
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cr_full = np.zeros((height, width), dtype=np.float64)
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cb_full = np.zeros((height, width), dtype=np.float64)
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for i in range(audio_lines):
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even_line = i * 2
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odd_line = i * 2 + 1
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if even_line < height:
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y_full[even_line] = y1_data[i]
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cr_full[even_line] = cr_data[i]
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cb_full[even_line] = cb_data[i]
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if odd_line < height:
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y_full[odd_line] = y2_data[i]
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cr_full[odd_line] = cr_data[i]
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cb_full[odd_line] = cb_data[i]
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return self._ycrcb_to_rgb(y_full, cr_full, cb_full, height, width)
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@staticmethod
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def _ycrcb_to_rgb(y: np.ndarray, cr: np.ndarray, cb: np.ndarray,
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height: int, width: int) -> Image.Image:
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"""Convert YCrCb pixel data to an RGB PIL Image.
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Uses the SSTV convention where pixel values 0-255 map to the
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standard Y'CbCr color space used by JPEG/SSTV.
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"""
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# Normalize from 0-255 pixel range to standard ranges
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# Y: 0-255, Cr/Cb: 0-255 centered at 128
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y_norm = y
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cr_norm = cr - 128.0
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cb_norm = cb - 128.0
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# ITU-R BT.601 conversion
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r = y_norm + 1.402 * cr_norm
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g = y_norm - 0.344136 * cb_norm - 0.714136 * cr_norm
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b = y_norm + 1.772 * cb_norm
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# Clip and convert
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r = np.clip(r, 0, 255).astype(np.uint8)
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g = np.clip(g, 0, 255).astype(np.uint8)
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b = np.clip(b, 0, 255).astype(np.uint8)
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rgb = np.stack([r, g, b], axis=-1)
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return Image.fromarray(rgb, 'RGB')
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