Metadata-Version: 2.4
Name: qrstream
Version: 0.7.5
Summary: Encode and decode files via QR code video streams using LT fountain codes
Project-URL: Homepage, https://github.com/ddddavid-he/qrstream-enhanced
Project-URL: Repository, https://github.com/ddddavid-he/qrstream-enhanced.git
Project-URL: Issues, https://github.com/ddddavid-he/qrstream-enhanced/issues
Author-email: David He <hewei201031l@outlook.com>
License: MIT License
        
        Copyright (c) 2026 David He
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
        SOFTWARE.
License-File: LICENSE
Keywords: erasure-code,fountain-code,lt-code,luby-transform,opencv,qr-code,video-transfer
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Topic :: Multimedia :: Video
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Requires-Python: >=3.10
Requires-Dist: numpy>=1.20.0
Requires-Dist: opencv-contrib-python>=4.5.0
Requires-Dist: qrcode[pil]>=7.0
Requires-Dist: tqdm>=4.60.0
Description-Content-Type: text/markdown

# QRStream

[中文文档](https://github.com/ddddavid-he/qrstream-enhanced/blob/main/README-zh.md)

Transfer arbitrary files through QR code video streams. Built on **LT Fountain Codes (Luby Transform)** for reliable, feedback-free data transmission — the original file can be fully recovered even if some frames are lost.

## How It Works

```
Encoder                                     Decoder
┌──────────┐   LT Fountain    ┌──────────┐   Screen cap   ┌──────────┐   QR detect    ┌──────────┐
│   File    │ ────────────── → │ QR Video │ ──────────── → │  Video   │ ────────────→ │ Recovered│
└──────────┘   zlib + base45  └──────────┘                └──────────┘   LT decode    │   File   │
                                                                                       └──────────┘
```

1. **Encode**: Split the file (optionally zlib-compressed) into blocks, generate redundant coded blocks via LT fountain codes, serialize each into a V3 protocol frame, base45-encode into QR alphanumeric-mode symbols, and output an MP4 video.
2. **Decode**: Extract QR codes from video using WeChatQRCode (highly robust), base45-decode, CRC32-validate to discard corrupted frames, feed into the LT decoder for belief propagation (peeling), and reconstruct the original file. Legacy base64 and COBS videos (pre-v0.6) continue to decode via a fallback path.

**Key Features**:
- **LT Fountain Codes**: Rateless erasure codes — naturally tolerant of frame loss, blur, and occlusion
- **Base45 + QR Alphanumeric Mode**: RFC 9285 base45 packs data into QR alphanumeric mode (5.5 bits/char vs 8 for byte mode); smaller and faster than base64 at the same QR version
- **WeChatQRCode Detector**: Far more robust than standard QR detectors for phone-captured screens (perspective, moire, lighting)
- **Adaptive Sample Rate**: Automatically selects optimal sampling strategy based on detection rate and frame repetition
- **Targeted Recovery**: After initial scan, precisely re-scans video segments where missing blocks are expected
- **Low-Memory Paths**: mmap-backed encoding and streaming decode-to-file for large inputs

## Installation

### From PyPI with pip

```bash
pip install qrstream
```

Use either command after installation:

```bash
qrstream <command> [options]
# or
qrs <command> [options]
```

You can also run it as a module:

```bash
python -m qrstream <command> [options]
```

### From PyPI with uv

```bash
uv tool install qrstream
```

Then run:

```bash
qrstream <command> [options]
```

For one-off execution without a persistent install:

```bash
uvx qrstream <command> [options]
```

### Development Install

```bash
git clone https://github.com/ddddavid-he/qrstream-enhanced.git && cd qrstream-enhanced
uv sync --dev
```

### Requirements

- Python >= 3.10
- Dependencies: `opencv-contrib-python`, `numpy`, `tqdm`, `qrcode[pil]`

## Usage

```bash
qrstream <command> [options]
qrstream -V
qrstream --version
```

`qrs` is kept as a short alias, and `python -m qrstream` works as well.

### Encode (File → QR Video)

```bash
qrstream encode <file> -o output.mp4 [options]
```

| Option | Default | Description |
|--------|---------|-------------|
| `<file>` | - | Input file path |
| `-o, --output` | `<filename>.mp4` | Output video path |
| `--overhead` | `2.0` | Encoding redundancy ratio (multiple of source block count) |
| `--fps` | `10` | Output video frame rate |
| `--ec-level` | `1` | QR error correction: 0=L(7%), 1=M(15%), 2=Q(25%), 3=H(30%) |
| `--qr-version` | `25` | QR code version 1-40 (higher = denser) |
| `--border` | standard 4-module quiet zone | Quiet-zone width as a percentage of QR content width (`--border 10` = 10%, `--border 0` disables it) |
| `--lead-in-seconds` | `0.0` | Insert white lead-in frames before the first QR frame |
| `--no-compress` | - | Disable zlib compression |
| `--force-compress` | - | Force compression for large V3 inputs (higher memory usage) |
| `--qr-mode` | `alphanumeric` | QR payload encoding: `alphanumeric` (base45, default, denser) or `base64` (byte mode, fallback) |
| `--legacy-qr` | - | Accepted but ignored (kept for CLI backward compatibility) |
| `--codec` | `mp4v` | Video codec: `mp4v` or `mjpeg` (faster but larger files) |
| `-w, --workers` | `min(CPU count, 4)` | Parallel worker threads for QR generation. The auto-picked default is capped at 4 because `qrcode.make()` is pure-Python (GIL-bound), so more than ~4 worker threads mostly contend on the GIL without adding real parallelism. Pass a larger value explicitly to override the cap on CPU-rich machines if profiling shows benefit. |
| `-v, --verbose` | - | Print extra detail (progress bars always shown) |

### Decode (QR Video → File)

```bash
qrstream decode <video> -o output_file [options]
```

| Option | Default | Description |
|--------|---------|-------------|
| `<video>` | - | Input video path (MP4, MOV, etc.) |
| `-o, --output` | `decoded_output` | Output file path |
| `-s, --sample-rate` | `0` (auto) | Sample every Nth frame (0 = adaptive probing) |
| `-w, --workers` | All CPU cores | Parallel worker threads for QR detection. `WeChatQRCode` is implemented in C++ and releases the GIL during detection, so more threads scale close to linearly on multi-core machines. |
| `-v, --verbose` | - | Print detailed progress |

### Examples

```bash
# Encode a PDF (default: base45 alphanumeric mode, 2x redundancy)
qrstream encode report.pdf -o report.mp4 --overhead 2.0 -v

# Decode video (adaptive sample rate + targeted recovery)
qrstream decode report.mp4 -o report_recovered.pdf -v

# Encode with high error correction (for phone screen capture)
qrstream encode data.bin -o data.mp4 --ec-level 3 --qr-version 15

# Add a larger quiet zone and white lead-in before recording
qrstream encode slides.zip -o slides.mp4 --border 10 --lead-in-seconds 1.5
```

### Python API

```python
from qrstream.encoder import encode_to_video
from qrstream.decoder import extract_qr_from_video, decode_blocks, decode_blocks_to_file

# Encode (default: base45 alphanumeric mode)
encode_to_video("input.bin", "output.mp4", overhead=2.0, verbose=True)

# Add recording-friendly quiet zone and white lead-in
encode_to_video("input.bin", "output.mp4", border=10.0, lead_in_seconds=1.5)

# Decode to memory
blocks = extract_qr_from_video("output.mp4", verbose=True)
result = decode_blocks(blocks, verbose=True)

# Better for large files: stream directly to file with incremental decompression
written = decode_blocks_to_file(blocks, "recovered.bin", verbose=True)
print(f"wrote {written} bytes")
```

## Project Structure

```
project-root/
├── pyproject.toml             # Project config & dependencies
├── src/qrstream/
│   ├── cli.py                 # CLI entry (encode/decode subcommands)
│   ├── encoder.py             # LT encode → QR frame generation → MP4 video
│   ├── decoder.py             # Video frame extraction → QR detect → LT decode → file rebuild
│   ├── lt_codec.py            # LT fountain code primitives (PRNG, RSD, BlockGraph)
│   ├── protocol.py            # V3 protocol serialization + base45 codec (legacy base64/COBS decode supported)
│   └── qr_utils.py            # QR generation (OpenCV) + detection (WeChatQRCode)
├── tests/
│   ├── test_lt_codec.py       # LT codec unit tests
│   ├── test_protocol.py       # V3 protocol + base45 tests
│   ├── test_decoder.py        # Decoder validation + probe strategy tests
│   ├── test_roundtrip.py      # End-to-end roundtrip tests
│   └── test_optimizations.py  # Perf optimizations + WeChatQR + legacy-fallback tests
└── benchmarks/
    └── benchmark.py           # Performance benchmarks
```

## Technical Details

### V3 Protocol Format (24-byte header + 4-byte trailing CRC)

```
Offset  Size  Field
  0      1    version      0x03
  1      1    flags        bit0=zlib compressed, bit1=high-density mode (base45 alphanumeric)
  2      8    filesize     uint64 BE (encoded payload size; compressed size when zlib is on)
 10      2    blocksize    uint16 BE
 12      4    block_count  uint32 BE  K = ceil(filesize / blocksize)
 16      4    seed         uint32 BE  PRNG seed
 20      2    block_seq    uint16 BE  monotonically increasing sequence number
 22      2    reserved     reserved (currently 0)
 24      ...  data         blocksize bytes of encoded data
 ...     4    crc32        CRC32(header[0:24] + data)
```

- Default encoding uses **V3 + base45 alphanumeric QR**.
- The decoder tries base45 → base64 → COBS in order, preserving compatibility with pre-v0.6 videos.
- V3 extends `filesize` to `uint64` and `block_count` to `uint32`, supporting larger files and block counts.

### Encoding Modes

| Mode | QR Content | QR Mode | Overhead | Default |
|------|-----------|---------|----------|---------|
| Base45 alphanumeric | raw bytes → base45 → `0-9A-Z $%*+-./:` | Alphanumeric (5.5 bits/char) | ~67% (but uses denser QR mode → **net denser** than byte mode) | Yes |
| Base64 | raw bytes → base64 string | Byte (8 bits/char) | ~33% | No (`--qr-mode base64`) |
| COBS (legacy) | raw bytes → COBS → latin-1 string | Byte | ~0.4% | **Removed** in v0.6; decode-only fallback for old videos |

Base45 (RFC 9285) is the default because QR's alphanumeric mode is denser per character than byte mode — at V25/M the base45 payload per frame is ~30% larger than base64, and in practice produces 20–25% smaller videos and 10–20% faster encode/decode.

### Large Files & Low-Memory Paths

- For large **V3** inputs, the encoder uses `mmap` for random access, avoiding loading the entire file into memory.
- When the input is large enough, V3 encoding automatically disables `zlib` compression to preserve the low-memory path; use `--force-compress` to override.
- The decoder supports streaming writes with incremental decompression, reducing memory overhead.
- Large file decoding shows **LT block decoding progress** and **output write progress** bars.

### Decoding Pipeline

1. **Probe phase**: Sample 3 spread-out windows in the video (120 frames each by default), measure detection rate and repetition per window, pick the most conservative `sample_rate`
2. **Main scan**: Detect QR codes in parallel at the adaptive sample rate, feeding into the LT decoder in real time
3. **Targeted recovery**: If the first pass didn't recover all blocks, use linear regression on observed (seed, frame) pairs to locate missing seeds and re-scan those segments precisely
4. **LT decode**: Belief propagation (peeling) to recover all source blocks
5. **Output writeback**: Write recovered blocks sequentially; incremental decompression in compressed mode

### LT Fountain Code Parameters

| Parameter | Value | Notes |
|-----------|-------|-------|
| Degree distribution | Robust Soliton Distribution | c=0.1, delta=0.5 |
| PRNG | LCG (a=16807, m=2^31-1) | 5 warmup rounds to eliminate sequential seed bias |
| XOR | numpy vectorized + in-place | 10-50x faster than pure Python |
| Decoding | Belief Propagation (Peeling) | Iterative elimination on bipartite graph |

## Testing

```bash
uv run pytest tests/ -v
```

## License

MIT
