deprcat rollingsum filter

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yair
2025-11-22 12:08:49 +02:00
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# GStreamer Rolling Sum Plugin - Complete Documentation
## Table of Contents
- [Overview](#overview)
- [How It Works](#how-it-works)
- [Architecture & Design](#architecture--design)
- [Plugin Properties](#plugin-properties)
- [Basic Usage](#basic-usage)
- [Debugging](#debugging)
- [CSV Analysis](#csv-analysis)
- [Recommended Thresholds](#recommended-thresholds)
- [Troubleshooting](#troubleshooting)
- [Performance](#performance)
- [Integration Examples](#integration-examples)
- [Developer Guide](#developer-guide)
- [References](#references)
## Overview
The `rollingsum` plugin analyzes video frames in real-time by tracking the mean pixel intensity of a specific column across frames. It maintains a rolling window of these values and can drop frames that deviate significantly from the rolling mean, useful for detecting and filtering unstable or anomalous frames.
**Element Name:** `rollingsum` - Transform element that analyzes pixel values and selectively drops frames
**Purpose:** Monitor a vertical column of pixels in video frames, calculate the rolling mean over a time window, and drop frames when the current frame's column mean deviates significantly from the rolling mean baseline.
## How It Works
1. **Column Analysis**: Extracts mean pixel intensity from a specified vertical column
2. **Rolling Window**: Maintains a circular buffer of recent column means
3. **Deviation Detection**: Calculates how much each frame deviates from the rolling mean
4. **Frame Filtering**: Optionally drops frames exceeding the deviation threshold
5. **CSV Logging**: Records all frame statistics for analysis
### Data Flow
```mermaid
graph TD
A[Video Frame] --> B[Extract Column]
B --> C[Calculate Column Mean]
C --> D[Store in Ring Buffer]
D --> E[Update Rolling Mean]
E --> F{Deviation > Threshold?}
F -->|Yes| G[DROP Frame]
F -->|No| H[PASS Frame]
C --> E
style G fill:#ff6b6b
style H fill:#51cf66
```
### Ring Buffer Operation
```mermaid
graph LR
subgraph Ring Buffer
A[0] --> B[1]
B --> C[2]
C --> D[...]
D --> E[N-1]
E ---|wrap| A
end
F[New Frame Mean] --> G[ring_index]
G --> A
H[Rolling Mean] --> I[Sum all values]
I --> J[Divide by count]
style G fill:#ffd43b
```
## Architecture & Design
### Base Class
- Inherits from `GstBaseTransform` (similar to [`select`](gst/select/gstselect.c))
- In-place transform (analysis only, no frame modification)
- Returns `GST_BASE_TRANSFORM_FLOW_DROPPED` to drop frames
- Returns `GST_FLOW_OK` to pass frames
### Element Structure
```c
struct _GstRollingSum
{
GstBaseTransform element;
/* Properties */
gint window_size; // Number of frames in rolling window (default: 1000)
gint column_index; // Which column to analyze (default: 1, second column)
gint stride; // Row sampling stride (default: 1, every row)
gdouble threshold; // Deviation threshold for dropping (default: 0.5)
gchar *csv_file; // CSV output file path (default: NULL)
/* State */
gdouble *ring_buffer; // Circular buffer of column means
gint ring_index; // Current position in ring buffer
gint frame_count; // Total frames processed
gdouble rolling_mean; // Current rolling mean
FILE *csv_fp; // CSV file pointer
};
```
### Algorithm (Simplified from wissotsky's cli.py)
**Per Frame Processing:**
1. **Extract column data:**
- Select column at column_index
- Sample every stride rows
- Calculate mean of sampled pixels: frame_mean
2. **Update ring buffer:**
- Store frame_mean in ring_buffer[ring_index]
- Increment ring_index (wrap around)
3. **Calculate rolling mean:**
- Sum values in ring buffer (up to window_size or frame_count)
- Divide by actual window size
4. **Calculate deviation:**
- deviation = abs(frame_mean - rolling_mean)
- normalized_deviation = deviation / 255.0 (for 8-bit video)
5. **Decision:**
- If normalized_deviation > threshold: DROP frame
- Else: PASS frame
**Key Simplifications from cli.py:**
- **No EMA tracking**: Use simple rolling mean instead of exponential moving average
- **No variance tracking**: Use fixed threshold instead of dynamic variance-based detection
- **No recording logic**: Just drop/pass, no buffering for output segments
- **No patience mechanism**: Immediate decision per frame
### Video Format Support
**Initial Implementation:**
- **Primary target**: Grayscale (GRAY8, GRAY16)
- **Secondary**: Bayer formats (common in machine vision)
**Caps Filter:**
```c
static GstStaticPadTemplate sink_template =
GST_STATIC_PAD_TEMPLATE ("sink",
GST_PAD_SINK,
GST_PAD_ALWAYS,
GST_STATIC_CAPS (
"video/x-raw, format=(string){GRAY8,GRAY16_LE,GRAY16_BE}; "
"video/x-bayer, format=(string){bggr,grbg,gbrg,rggb}"
)
);
```
## Plugin Properties
| Property | Type | Default | Range | Description |
|----------|------|---------|-------|-------------|
| `window-size` | int | 1000 | 1-100000 | Number of frames in rolling window |
| `column-index` | int | 1 | 0-width | Which vertical column to analyze (0-based) |
| `stride` | int | 1 | 1-height | Row sampling stride (1 = every row) |
| `threshold` | double | 0.5 | 0.0-1.0 | Normalized deviation threshold for dropping frames |
| `csv-file` | string | NULL | - | Path to CSV file for logging (NULL = no logging) |
### Understanding Normalized Deviation
- **Range**: 0.0 to 1.0
- **Calculation**: `absolute_deviation / 255.0` (for 8-bit video)
- **Meaning**: Fraction of the full pixel range
- `0.001` = deviation of ~0.255 pixel values
- `0.01` = deviation of ~2.55 pixel values
- `0.1` = deviation of ~25.5 pixel values
## Basic Usage
### Simple Pipeline
```powershell
gst-launch-1.0 idsueyesrc config-file=config.ini ! `
videoconvert ! `
video/x-raw,format=GRAY8 ! `
rollingsum window-size=1000 column-index=1 threshold=0.0002 ! `
autovideosink
```
### With CSV Logging
```powershell
gst-launch-1.0 idsueyesrc config-file=config.ini exposure=0.5 ! `
videoconvert ! `
video/x-raw,format=GRAY8 ! `
rollingsum window-size=1000 column-index=1 threshold=0.0002 csv-file=output.csv ! `
fakesink
```
### Custom Configuration
```powershell
gst-launch-1.0 idsueyesrc config-file=config.ini ! `
rollingsum window-size=5000 column-index=320 stride=2 threshold=0.3 ! `
queue ! `
autovideosink
```
### With Format Conversion
```powershell
gst-launch-1.0 idsueyesrc ! `
videoconvert ! `
video/x-raw,format=GRAY8 ! `
rollingsum ! `
autovideosink
```
## Debugging
### Enable Debug Output
Use the `GST_DEBUG` environment variable to see detailed plugin operation:
#### Windows PowerShell
```powershell
$env:GST_DEBUG="rollingsum:5"; gst-launch-1.0 [pipeline...]
```
#### Windows CMD
```cmd
set GST_DEBUG=rollingsum:5 && gst-launch-1.0 [pipeline...]
```
#### Linux/Mac
```bash
GST_DEBUG=rollingsum:5 gst-launch-1.0 [pipeline...]
```
### Debug Levels
| Level | Output |
|-------|--------|
| `rollingsum:1` | Errors only |
| `rollingsum:2` | Warnings |
| `rollingsum:3` | Info messages (file open/close) |
| `rollingsum:4` | Debug (caps negotiation) |
| `rollingsum:5` | Log (all frame processing) |
### Example Debug Output
```
0:00:04.029432200 DEBUG rollingsum gstrollingsum.c:436: Extracted column mean: 10.07
0:00:04.032257100 DEBUG rollingsum gstrollingsum.c:466: Frame 1: mean=10.07, rolling_mean=10.07, deviation=0.00 (normalized=0.0000)
```
**Key Fields:**
- `Frame N`: Frame number
- `mean`: Current frame's column mean
- `rolling_mean`: Average of last N frames (window-size)
- `deviation`: Absolute difference
- `normalized`: Deviation as fraction of 255
### Common Debug Scenarios
#### 1. Verify Plugin Loaded
```powershell
gst-inspect-1.0 rollingsum
```
Should show plugin details. If not found, check `GST_PLUGIN_PATH`.
#### 2. Check CSV File Creation
Look for this in debug output:
```
INFO rollingsum: Opened CSV file: output.csv
```
#### 3. Monitor Frame Drops
Look for:
```
DEBUG rollingsum: Dropping frame 42: deviation 0.0005 > threshold 0.0002
```
#### 4. Verify Caps Negotiation
```
DEBUG rollingsum: set_caps
DEBUG rollingsum: Video format: GRAY8, 1224x1026
```
## CSV Analysis
### CSV Format
The output CSV contains:
```csv
frame,column_mean,rolling_mean,deviation,normalized_deviation,dropped
1,10.071150,10.071150,0.000000,0.000000,0
2,10.059454,10.065302,0.005848,0.000023,0
...
```
### Analyze Results
Use the included analysis script:
```powershell
uv run scripts/analyze_sma.py output.csv
```
**Output includes:**
- Statistical summary (min/max/mean/std)
- Threshold recommendations based on percentiles
- Standard deviation-based suggestions
- Visualization plots saved to `results/debug/`
- Archived CSV with timestamp in `results/debug/`
**Output files are automatically organized:**
- `results/debug/output_YYYYMMDD_HHMMSS.csv` - Archived CSV
- `results/debug/output_analysis_YYYYMMDD_HHMMSS.png` - Analysis plots
The `results/` directory is gitignored to keep your repository clean.
### Interpreting Results
The analysis provides threshold recommendations:
| Percentile | Description | Use Case |
|------------|-------------|----------|
| 99th | Drops top 1% | Very conservative, catch only extreme outliers |
| 95th | Drops top 5% | Conservative, good for quality control |
| 90th | Drops top 10% | Balanced, moderate filtering |
| 75th | Drops top 25% | Aggressive, maximum quality |
## Recommended Thresholds
Based on analysis of stable camera footage:
### For General Use
```powershell
# Conservative (1-2% frame drop)
threshold=0.0003
# Moderate (5-10% frame drop)
threshold=0.0002
# Aggressive (20-25% frame drop)
threshold=0.0001
```
### For Specific Scenarios
**High-speed acquisition** (minimal processing):
```powershell
window-size=100 threshold=0.0005
```
**Quality-focused** (stable scenes):
```powershell
window-size=1000 threshold=0.0001
```
**Real-time monitoring** (fast response):
```powershell
window-size=50 threshold=0.0002
```
## Troubleshooting
### No frames being dropped (threshold too high)
**Symptom**: `dropped` column always 0 in CSV
**Solution**:
1. Run with CSV logging
2. Analyze with `uv run scripts/analyze_sma.py output.csv`
3. Use recommended threshold from 90th-99th percentile
### Too many frames dropped (threshold too low)
**Symptom**: Most frames have `dropped=1`, choppy video
**Solution**:
1. Increase threshold (try doubling current value)
2. Check if column_index is appropriate
3. Verify video is stable (not shaking/moving)
### CSV file not created
**Check**:
1. File path is writable
2. Look for "Opened CSV file" in debug output (`GST_DEBUG=rollingsum:3`)
3. Verify csv-file property is set correctly
### Column index out of range
**Symptom**:
```
WARNING rollingsum: Column index 1000 >= width 1224, using column 0
```
**Solution**: Set `column-index` to value < video width
### Inconsistent results
**Possible causes**:
1. Window size too small (< 50 frames)
2. Sampling moving/dynamic content
3. Column contains edge/artifact data
**Solutions**:
- Increase `window-size` to 500-1000
- Choose different `column-index` (avoid edges)
- Use `stride=2` or higher for faster processing
## Performance
### Performance Tips
1. **Larger window = more stable** but slower to adapt to scene changes
2. **Stride > 1** reduces computation but less accurate column mean
3. **CSV logging** has minimal performance impact
4. **Debug level 5** can produce massive logs, use only when needed
### Memory Usage
- Ring buffer: `window_size * sizeof(double)` = ~8KB for default 1000 frames
- Minimal per-frame allocation
### CPU Usage
- Column extraction: O(height/stride)
- Rolling mean update: O(1) using incremental sum
- Very lightweight compared to full-frame processing
### Optimization Opportunities
1. **Incremental mean**: Track sum instead of recalculating
2. **SIMD**: Vectorize column summation
3. **Skip calculation**: Only recalc every N frames if baseline is stable
## Integration Examples
### Python Script Control
```python
import subprocess
# Run pipeline with CSV logging
subprocess.run([
'gst-launch-1.0',
'idsueyesrc', 'config-file=config.ini',
'!', 'videoconvert',
'!', 'video/x-raw,format=GRAY8',
'!', 'rollingsum',
'window-size=1000',
'column-index=1',
'threshold=0.0002',
'csv-file=output.csv',
'!', 'fakesink'
])
# Analyze results
subprocess.run(['uv', 'run', 'scripts/analyze_sma.py', 'output.csv'])
```
### Adaptive Threshold
Use analysis results to set optimal threshold for next run:
```python
import pandas as pd
# Analyze previous run
df = pd.read_csv('output.csv')
recommended_threshold = df['normalized_deviation'].quantile(0.95)
print(f"Recommended threshold: {recommended_threshold:.6f}")
```
## Developer Guide
### Implementation Files
**Directory Structure:**
```
gst/rollingsum/
├── CMakeLists.txt
├── gstrollingsum.c
└── gstrollingsum.h
```
**gstrollingsum.h:**
- Element type definitions
- Structure declarations
- Property enums
- Function prototypes
**gstrollingsum.c:**
- GObject methods (init, dispose, get/set properties)
- GstBaseTransform methods (transform_ip)
- Helper functions (extract_column_mean, update_rolling_mean)
- Plugin registration
**CMakeLists.txt:**
- Build configuration (copy from [`gst/select/CMakeLists.txt`](gst/select/CMakeLists.txt))
- Link GStreamer base and video libraries
### Adding New Features
Key files:
- [`gst/rollingsum/gstrollingsum.c`](gst/rollingsum/gstrollingsum.c) - Main implementation
- [`gst/rollingsum/gstrollingsum.h`](gst/rollingsum/gstrollingsum.h) - Header/structures
- [`gst/rollingsum/CMakeLists.txt`](gst/rollingsum/CMakeLists.txt) - Build config
### Rebuild After Changes
```powershell
.\build.ps1 # Windows
```
```bash
./build.sh # Linux
```
### Testing
```powershell
# Quick test
gst-inspect-1.0 rollingsum
# Full pipeline test with debug
$env:GST_DEBUG="rollingsum:5"
gst-launch-1.0 videotestsrc ! rollingsum ! fakesink
```
### Testing Strategy
**Unit Tests:**
- Ring buffer wrapping
- Mean calculation accuracy
- Threshold comparison logic
**Integration Tests:**
- Pipeline with videotestsrc
- Pipeline with idsueyesrc
- Frame drop verification
- Property changes during playback
**Test Cases:**
1. Static video (all frames similar) → all pass
2. Single bright frame → that frame drops
3. Gradual change → frames pass
4. Periodic pattern → pattern frames drop
### Integration with Existing Project
**Build System:**
Update [`gst/CMakeLists.txt`](gst/CMakeLists.txt):
```cmake
add_subdirectory (rollingsum)
```
**Documentation:**
Update [`README.md`](README.md):
- Add rollingsum to "Other elements" section
- Add pipeline example
### Future Enhancements
**Phase 2 (If Needed):**
- Add EMA baseline tracking (like cli.py)
- Add variance-based thresholds
- Support multiple columns or regions
- Add metadata output (tag frames with deviation values)
- RGB format support (analyze specific channel)
**Phase 3 (Advanced):**
- Full cli.py recording logic
- Buffer and output segments
- Integration with probe detection systems
### Implementation Checklist
- [x] Create gst/rollingsum directory
- [x] Implement gstrollingsum.h
- [x] Implement gstrollingsum.c
- [x] Create CMakeLists.txt
- [x] Update gst/CMakeLists.txt
- [x] Build and test basic functionality
- [x] Test with idsueyesrc
- [x] Update README.md
- [x] Create feature branch
- [x] Commit and document
## References
- Original algorithm: `cli.py` lines 64-79 (column extraction and mean comparison)
- Template element: [`gst/select/gstselect.c`](gst/select/gstselect.c)
- GStreamer base transform: [GstBaseTransform documentation](https://gstreamer.freedesktop.org/documentation/base/gstbasetransform.html)
- [scripts/analyze_sma.py](scripts/analyze_sma.py) - Analysis tool
- GStreamer documentation: https://gstreamer.freedesktop.org/documentation/
## Support
For issues or questions:
1. Enable debug output (`$env:GST_DEBUG="rollingsum:5"` in PowerShell)
2. Generate CSV log and analyze
3. Check this guide's troubleshooting section
4. Review debug output for errors/warnings

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@@ -12,6 +12,5 @@ if (ENABLE_KLV)
endif ()
add_subdirectory (misb)
add_subdirectory (rollingsum)
add_subdirectory (select)
add_subdirectory (videoadjust)

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set (SOURCES
gstrollingsum.c
)
set (HEADERS
gstrollingsum.h)
include_directories (AFTER
${ORC_INCLUDE_DIR})
set (libname gstrollingsum)
add_library (${libname} MODULE
${SOURCES}
${HEADERS})
target_link_libraries (${libname}
${ORC_LIBRARIES}
${GLIB2_LIBRARIES}
${GOBJECT_LIBRARIES}
${GSTREAMER_LIBRARY}
${GSTREAMER_BASE_LIBRARY}
${GSTREAMER_VIDEO_LIBRARY})
if (WIN32)
install (FILES $<TARGET_PDB_FILE:${libname}> DESTINATION ${PDB_INSTALL_DIR} COMPONENT pdb OPTIONAL)
endif ()
install(TARGETS ${libname} LIBRARY DESTINATION ${PLUGIN_INSTALL_DIR})

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@@ -1,535 +0,0 @@
/* GStreamer
* Copyright (C) 2024 <your-name@your-email.com>
*
* This library is free software; you can redistribute it and/or
* modify it under the terms of the GNU Library General Public
* License as published by the Free Software Foundation; either
* version 2 of the License, or (at your option) any later version.
*
* This library is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* Library General Public License for more details.
*
* You should have received a copy of the GNU Library General Public
* License along with this library; if not, write to the
* Free Software Foundation, Inc., 59 Temple Place - Suite 330,
* Boston, MA 02111-1307, USA.
*/
/**
* SECTION:element-rollingsum
*
* Drops frames based on rolling mean analysis of a single column.
* Inspired by cli.py detection algorithm, simplified for real-time streaming.
*
* <refsect2>
* <title>Example launch line</title>
* |[
* gst-launch-1.0 idsueyesrc config-file=config.ini ! rollingsum window-size=1000 column-index=1 threshold=0.5 ! autovideosink
* ]|
* </refsect2>
*/
#ifdef HAVE_CONFIG_H
#include "config.h"
#endif
#include "gstrollingsum.h"
#include <string.h>
#include <math.h>
#include <stdio.h>
enum
{
PROP_0,
PROP_WINDOW_SIZE,
PROP_COLUMN_INDEX,
PROP_STRIDE,
PROP_THRESHOLD,
PROP_CSV_FILENAME,
PROP_LAST
};
#define DEFAULT_PROP_WINDOW_SIZE 1000
#define DEFAULT_PROP_COLUMN_INDEX 1
#define DEFAULT_PROP_STRIDE 1
#define DEFAULT_PROP_THRESHOLD 0.5
#define DEFAULT_PROP_CSV_FILENAME NULL
/* Supported video formats */
#define SUPPORTED_CAPS \
GST_VIDEO_CAPS_MAKE("{ GRAY8, GRAY16_LE, GRAY16_BE }") ";" \
"video/x-bayer, format=(string){bggr,grbg,gbrg,rggb}"
static GstStaticPadTemplate gst_rolling_sum_sink_template =
GST_STATIC_PAD_TEMPLATE ("sink",
GST_PAD_SINK,
GST_PAD_ALWAYS,
GST_STATIC_CAPS (SUPPORTED_CAPS)
);
static GstStaticPadTemplate gst_rolling_sum_src_template =
GST_STATIC_PAD_TEMPLATE ("src",
GST_PAD_SRC,
GST_PAD_ALWAYS,
GST_STATIC_CAPS (SUPPORTED_CAPS)
);
/* GObject vmethod declarations */
static void gst_rolling_sum_set_property (GObject * object, guint prop_id,
const GValue * value, GParamSpec * pspec);
static void gst_rolling_sum_get_property (GObject * object, guint prop_id,
GValue * value, GParamSpec * pspec);
static void gst_rolling_sum_dispose (GObject * object);
/* GstBaseTransform vmethod declarations */
static gboolean gst_rolling_sum_set_caps (GstBaseTransform * trans,
GstCaps * incaps, GstCaps * outcaps);
static GstFlowReturn gst_rolling_sum_transform_ip (GstBaseTransform * trans,
GstBuffer * buf);
/* GstRollingSum method declarations */
static void gst_rolling_sum_reset (GstRollingSum * filter);
static gdouble gst_rolling_sum_extract_column_mean (GstRollingSum * filter,
GstBuffer * buf);
/* setup debug */
GST_DEBUG_CATEGORY_STATIC (rolling_sum_debug);
#define GST_CAT_DEFAULT rolling_sum_debug
G_DEFINE_TYPE (GstRollingSum, gst_rolling_sum, GST_TYPE_BASE_TRANSFORM);
/************************************************************************/
/* GObject vmethod implementations */
/************************************************************************/
static void
gst_rolling_sum_dispose (GObject * object)
{
GstRollingSum *filter = GST_ROLLING_SUM (object);
GST_DEBUG ("dispose");
/* Close CSV file if open */
if (filter->csv_file) {
fclose (filter->csv_file);
filter->csv_file = NULL;
}
/* Free CSV filename */
if (filter->csv_filename) {
g_free (filter->csv_filename);
filter->csv_filename = NULL;
}
gst_rolling_sum_reset (filter);
/* chain up to the parent class */
G_OBJECT_CLASS (gst_rolling_sum_parent_class)->dispose (object);
}
static void
gst_rolling_sum_class_init (GstRollingSumClass * klass)
{
GObjectClass *gobject_class = G_OBJECT_CLASS (klass);
GstElementClass *gstelement_class = GST_ELEMENT_CLASS (klass);
GstBaseTransformClass *gstbasetransform_class =
GST_BASE_TRANSFORM_CLASS (klass);
GST_DEBUG_CATEGORY_INIT (rolling_sum_debug, "rollingsum", 0,
"Rolling Sum Filter");
GST_DEBUG ("class init");
/* Register GObject vmethods */
gobject_class->dispose = GST_DEBUG_FUNCPTR (gst_rolling_sum_dispose);
gobject_class->set_property = GST_DEBUG_FUNCPTR (gst_rolling_sum_set_property);
gobject_class->get_property = GST_DEBUG_FUNCPTR (gst_rolling_sum_get_property);
/* Install GObject properties */
g_object_class_install_property (gobject_class, PROP_WINDOW_SIZE,
g_param_spec_int ("window-size", "Window Size",
"Number of frames in rolling window", 1, 100000,
DEFAULT_PROP_WINDOW_SIZE,
G_PARAM_STATIC_STRINGS | G_PARAM_READWRITE |
GST_PARAM_MUTABLE_PLAYING));
g_object_class_install_property (gobject_class, PROP_COLUMN_INDEX,
g_param_spec_int ("column-index", "Column Index",
"Which vertical column to analyze (0-based)", 0, G_MAXINT,
DEFAULT_PROP_COLUMN_INDEX,
G_PARAM_STATIC_STRINGS | G_PARAM_READWRITE |
GST_PARAM_MUTABLE_PLAYING));
g_object_class_install_property (gobject_class, PROP_STRIDE,
g_param_spec_int ("stride", "Row Stride",
"Row sampling stride (1 = every row)", 1, G_MAXINT,
DEFAULT_PROP_STRIDE,
G_PARAM_STATIC_STRINGS | G_PARAM_READWRITE |
GST_PARAM_MUTABLE_PLAYING));
g_object_class_install_property (gobject_class, PROP_THRESHOLD,
g_param_spec_double ("threshold", "Threshold",
"Normalized deviation threshold for dropping frames", 0.0, 1.0,
DEFAULT_PROP_THRESHOLD,
G_PARAM_STATIC_STRINGS | G_PARAM_READWRITE |
GST_PARAM_MUTABLE_PLAYING));
g_object_class_install_property (gobject_class, PROP_CSV_FILENAME,
g_param_spec_string ("csv-file", "CSV File",
"Path to CSV file for logging frame data (NULL = no logging)",
DEFAULT_PROP_CSV_FILENAME,
G_PARAM_STATIC_STRINGS | G_PARAM_READWRITE |
GST_PARAM_MUTABLE_READY));
gst_element_class_add_pad_template (gstelement_class,
gst_static_pad_template_get (&gst_rolling_sum_sink_template));
gst_element_class_add_pad_template (gstelement_class,
gst_static_pad_template_get (&gst_rolling_sum_src_template));
gst_element_class_set_static_metadata (gstelement_class,
"Rolling sum filter", "Filter/Effect/Video",
"Drops frames based on rolling mean analysis of a single column",
"Your Name <your-email@example.com>");
/* Register GstBaseTransform vmethods */
gstbasetransform_class->set_caps =
GST_DEBUG_FUNCPTR (gst_rolling_sum_set_caps);
gstbasetransform_class->transform_ip =
GST_DEBUG_FUNCPTR (gst_rolling_sum_transform_ip);
}
static void
gst_rolling_sum_init (GstRollingSum * filter)
{
GST_DEBUG_OBJECT (filter, "init class instance");
filter->window_size = DEFAULT_PROP_WINDOW_SIZE;
filter->column_index = DEFAULT_PROP_COLUMN_INDEX;
filter->stride = DEFAULT_PROP_STRIDE;
filter->threshold = DEFAULT_PROP_THRESHOLD;
filter->csv_filename = NULL;
filter->ring_buffer = NULL;
filter->ring_index = 0;
filter->frame_count = 0;
filter->rolling_mean = 0.0;
filter->rolling_sum = 0.0;
filter->info_set = FALSE;
filter->csv_file = NULL;
gst_base_transform_set_in_place (GST_BASE_TRANSFORM (filter), TRUE);
gst_rolling_sum_reset (filter);
}
static void
gst_rolling_sum_set_property (GObject * object, guint prop_id,
const GValue * value, GParamSpec * pspec)
{
GstRollingSum *filter = GST_ROLLING_SUM (object);
GST_DEBUG_OBJECT (filter, "setting property %s", pspec->name);
switch (prop_id) {
case PROP_WINDOW_SIZE:
{
gint new_size = g_value_get_int (value);
if (new_size != filter->window_size) {
filter->window_size = new_size;
/* Reallocate ring buffer */
gst_rolling_sum_reset (filter);
}
break;
}
case PROP_COLUMN_INDEX:
filter->column_index = g_value_get_int (value);
break;
case PROP_STRIDE:
filter->stride = g_value_get_int (value);
break;
case PROP_THRESHOLD:
filter->threshold = g_value_get_double (value);
break;
case PROP_CSV_FILENAME:
{
const gchar *filename = g_value_get_string (value);
/* Close old file if open */
if (filter->csv_file) {
fclose (filter->csv_file);
filter->csv_file = NULL;
}
/* Free old filename */
if (filter->csv_filename) {
g_free (filter->csv_filename);
filter->csv_filename = NULL;
}
/* Set new filename and open file */
if (filename && filename[0] != '\0') {
filter->csv_filename = g_strdup (filename);
filter->csv_file = fopen (filter->csv_filename, "w");
if (filter->csv_file) {
/* Write CSV header */
fprintf (filter->csv_file, "frame,column_mean,rolling_mean,deviation,normalized_deviation,dropped\n");
fflush (filter->csv_file);
GST_INFO_OBJECT (filter, "Opened CSV file: %s", filter->csv_filename);
} else {
GST_ERROR_OBJECT (filter, "Failed to open CSV file: %s", filter->csv_filename);
g_free (filter->csv_filename);
filter->csv_filename = NULL;
}
}
break;
}
default:
G_OBJECT_WARN_INVALID_PROPERTY_ID (object, prop_id, pspec);
break;
}
}
static void
gst_rolling_sum_get_property (GObject * object, guint prop_id, GValue * value,
GParamSpec * pspec)
{
GstRollingSum *filter = GST_ROLLING_SUM (object);
GST_DEBUG_OBJECT (filter, "getting property %s", pspec->name);
switch (prop_id) {
case PROP_WINDOW_SIZE:
g_value_set_int (value, filter->window_size);
break;
case PROP_COLUMN_INDEX:
g_value_set_int (value, filter->column_index);
break;
case PROP_STRIDE:
g_value_set_int (value, filter->stride);
break;
case PROP_THRESHOLD:
g_value_set_double (value, filter->threshold);
break;
case PROP_CSV_FILENAME:
g_value_set_string (value, filter->csv_filename);
break;
default:
G_OBJECT_WARN_INVALID_PROPERTY_ID (object, prop_id, pspec);
break;
}
}
static gboolean
gst_rolling_sum_set_caps (GstBaseTransform * trans, GstCaps * incaps,
GstCaps * outcaps)
{
GstRollingSum *filter = GST_ROLLING_SUM (trans);
GST_DEBUG_OBJECT (filter, "set_caps");
if (!gst_video_info_from_caps (&filter->video_info, incaps)) {
GST_ERROR_OBJECT (filter, "Failed to parse caps");
return FALSE;
}
filter->info_set = TRUE;
GST_DEBUG_OBJECT (filter, "Video format: %s, %dx%d",
gst_video_format_to_string (GST_VIDEO_INFO_FORMAT (&filter->video_info)),
GST_VIDEO_INFO_WIDTH (&filter->video_info),
GST_VIDEO_INFO_HEIGHT (&filter->video_info));
return TRUE;
}
static gdouble
gst_rolling_sum_extract_column_mean (GstRollingSum * filter, GstBuffer * buf)
{
GstMapInfo map;
gdouble sum = 0.0;
gint count = 0;
gint width, height, stride_bytes;
gint row, col_offset;
guint8 *data;
GstVideoFormat format;
if (!filter->info_set) {
GST_WARNING_OBJECT (filter, "Video info not set yet");
return 0.0;
}
if (!gst_buffer_map (buf, &map, GST_MAP_READ)) {
GST_ERROR_OBJECT (filter, "Failed to map buffer");
return 0.0;
}
data = map.data;
width = GST_VIDEO_INFO_WIDTH (&filter->video_info);
height = GST_VIDEO_INFO_HEIGHT (&filter->video_info);
stride_bytes = GST_VIDEO_INFO_PLANE_STRIDE (&filter->video_info, 0);
format = GST_VIDEO_INFO_FORMAT (&filter->video_info);
/* Check column index is valid */
if (filter->column_index >= width) {
GST_WARNING_OBJECT (filter, "Column index %d >= width %d, using column 0",
filter->column_index, width);
filter->column_index = 0;
}
/* Calculate column offset based on format */
if (format == GST_VIDEO_FORMAT_GRAY8) {
col_offset = filter->column_index;
/* Sum column values with stride */
for (row = 0; row < height; row += filter->stride) {
sum += data[row * stride_bytes + col_offset];
count++;
}
} else if (format == GST_VIDEO_FORMAT_GRAY16_LE ||
format == GST_VIDEO_FORMAT_GRAY16_BE) {
col_offset = filter->column_index * 2;
/* Sum column values with stride */
for (row = 0; row < height; row += filter->stride) {
guint16 pixel_value;
guint8 *pixel_ptr = &data[row * stride_bytes + col_offset];
if (format == GST_VIDEO_FORMAT_GRAY16_LE) {
pixel_value = pixel_ptr[0] | (pixel_ptr[1] << 8);
} else {
pixel_value = (pixel_ptr[0] << 8) | pixel_ptr[1];
}
sum += pixel_value;
count++;
}
} else {
/* For Bayer formats, treat as GRAY8 */
col_offset = filter->column_index;
for (row = 0; row < height; row += filter->stride) {
sum += data[row * stride_bytes + col_offset];
count++;
}
}
gst_buffer_unmap (buf, &map);
return count > 0 ? sum / count : 0.0;
}
static GstFlowReturn
gst_rolling_sum_transform_ip (GstBaseTransform * trans, GstBuffer * buf)
{
GstRollingSum *filter = GST_ROLLING_SUM (trans);
gdouble frame_mean, deviation, old_value;
gint effective_window_size;
GST_DEBUG_OBJECT (filter, "transform_ip called, frame_count=%d", filter->frame_count);
/* Extract column mean from current frame */
frame_mean = gst_rolling_sum_extract_column_mean (filter, buf);
GST_DEBUG_OBJECT (filter, "Extracted column mean: %.2f", frame_mean);
/* Store in ring buffer */
old_value = filter->ring_buffer[filter->ring_index];
filter->ring_buffer[filter->ring_index] = frame_mean;
/* Update rolling sum efficiently */
if (filter->frame_count < filter->window_size) {
/* Still filling the buffer */
filter->rolling_sum += frame_mean;
filter->frame_count++;
effective_window_size = filter->frame_count;
} else {
/* Buffer is full, replace old value */
filter->rolling_sum = filter->rolling_sum - old_value + frame_mean;
effective_window_size = filter->window_size;
}
/* Update rolling mean */
filter->rolling_mean = filter->rolling_sum / effective_window_size;
/* Calculate deviation */
deviation = fabs(frame_mean - filter->rolling_mean);
/* Normalize deviation (assuming 8-bit equivalent range) */
gdouble normalized_deviation = deviation / 255.0;
GST_DEBUG_OBJECT (filter,
"Frame %d: mean=%.2f, rolling_mean=%.2f, deviation=%.2f (normalized=%.4f)",
filter->frame_count, frame_mean, filter->rolling_mean, deviation,
normalized_deviation);
/* Advance ring buffer index */
filter->ring_index = (filter->ring_index + 1) % filter->window_size;
/* Decision: drop or pass frame */
gboolean dropped = FALSE;
if (normalized_deviation > filter->threshold) {
GST_DEBUG_OBJECT (filter,
"Dropping frame %d: deviation %.4f > threshold %.4f",
filter->frame_count, normalized_deviation, filter->threshold);
dropped = TRUE;
}
/* Write to CSV if file is open */
if (filter->csv_file) {
fprintf (filter->csv_file, "%d,%.6f,%.6f,%.6f,%.6f,%d\n",
filter->frame_count, frame_mean, filter->rolling_mean,
deviation, normalized_deviation, dropped ? 1 : 0);
fflush (filter->csv_file);
}
if (dropped) {
return GST_BASE_TRANSFORM_FLOW_DROPPED;
}
return GST_FLOW_OK;
}
static void
gst_rolling_sum_reset (GstRollingSum * filter)
{
GST_DEBUG_OBJECT (filter, "reset");
/* Free old ring buffer if exists */
if (filter->ring_buffer) {
g_free (filter->ring_buffer);
}
/* Allocate new ring buffer */
filter->ring_buffer = g_new0 (gdouble, filter->window_size);
filter->ring_index = 0;
filter->frame_count = 0;
filter->rolling_mean = 0.0;
filter->rolling_sum = 0.0;
}
static gboolean
plugin_init (GstPlugin * plugin)
{
GST_DEBUG_CATEGORY_INIT (GST_CAT_DEFAULT, "rollingsum", 0, "rollingsum");
GST_DEBUG ("plugin_init");
GST_CAT_INFO (GST_CAT_DEFAULT, "registering rollingsum element");
if (!gst_element_register (plugin, "rollingsum", GST_RANK_NONE,
GST_TYPE_ROLLING_SUM)) {
return FALSE;
}
return TRUE;
}
GST_PLUGIN_DEFINE (GST_VERSION_MAJOR,
GST_VERSION_MINOR,
rollingsum,
"Filter that drops frames based on rolling mean analysis",
plugin_init, GST_PACKAGE_VERSION, GST_PACKAGE_LICENSE, GST_PACKAGE_NAME,
GST_PACKAGE_ORIGIN);

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@@ -1,82 +0,0 @@
/* GStreamer
* Copyright (C) 2024 <your-name@your-email.com>
*
* This library is free software; you can redistribute it and/or
* modify it under the terms of the GNU Library General Public
* License as published by the Free Software Foundation; either
* version 2 of the License, or (at your option) any later version.
*
* This library is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* Library General Public License for more details.
*
* You should have received a copy of the GNU Library General Public
* License along with this library; if not, write to the
* Free Software Foundation, Inc., 59 Temple Place - Suite 330,
* Boston, MA 02111-1307, USA.
*/
#ifndef __GST_ROLLING_SUM_H__
#define __GST_ROLLING_SUM_H__
#include <gst/base/gstbasetransform.h>
#include <gst/video/video.h>
#include <stdio.h>
G_BEGIN_DECLS
#define GST_TYPE_ROLLING_SUM \
(gst_rolling_sum_get_type())
#define GST_ROLLING_SUM(obj) \
(G_TYPE_CHECK_INSTANCE_CAST((obj),GST_TYPE_ROLLING_SUM,GstRollingSum))
#define GST_ROLLING_SUM_CLASS(klass) \
(G_TYPE_CHECK_CLASS_CAST((klass),GST_TYPE_ROLLING_SUM,GstRollingSumClass))
#define GST_IS_ROLLING_SUM(obj) \
(G_TYPE_CHECK_INSTANCE_TYPE((obj),GST_TYPE_ROLLING_SUM))
#define GST_IS_ROLLING_SUM_CLASS(klass) \
(G_TYPE_CHECK_CLASS_TYPE((klass),GST_TYPE_ROLLING_SUM))
typedef struct _GstRollingSum GstRollingSum;
typedef struct _GstRollingSumClass GstRollingSumClass;
/**
* GstRollingSum:
* @element: the parent element.
*
* The opaque GstRollingSum data structure.
*/
struct _GstRollingSum
{
GstBaseTransform element;
/* Properties */
gint window_size; /* Number of frames in rolling window */
gint column_index; /* Which column to analyze (0-based) */
gint stride; /* Row sampling stride */
gdouble threshold; /* Deviation threshold for dropping frames */
gchar *csv_filename; /* CSV output filename (NULL = no CSV) */
/* State */
gdouble *ring_buffer; /* Circular buffer of column means */
gint ring_index; /* Current position in ring buffer */
gint frame_count; /* Total frames processed */
gdouble rolling_mean; /* Current rolling mean */
gdouble rolling_sum; /* Current rolling sum for efficient mean update */
FILE *csv_file; /* CSV file handle */
/* Video format info */
GstVideoInfo video_info;
gboolean info_set;
};
struct _GstRollingSumClass
{
GstBaseTransformClass parent_class;
};
GType gst_rolling_sum_get_type(void);
G_END_DECLS
#endif /* __GST_ROLLING_SUM_H__ */