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refactor: change ClientPublisher to accept camera streams instead of local USB

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BREAKING CHANGE: ClientPublisher::open() signature changed from
  open(sl::InputType input, Trigger* ref, int sdk_gpu_id)
to
  open(const std::string& ip, int port, Trigger* ref, int sdk_gpu_id)

The ClientPublisher now receives camera streams over network using
setFromStream() instead of opening local USB cameras. This allows the
host to receive video from edge devices running enableStreaming().

Changes:
- Use init_parameters.input.setFromStream(ip, port) for stream input
- Keep body tracking and Fusion publishing (INTRA_PROCESS) unchanged
- Add getSerialNumber() getter for logging/debugging
- Improve error messages with context (ip:port)

Co-authored-by: factory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com>
This commit is contained in:
crosstyan
2026-02-04 03:25:21 +00:00
parent 53a443d120
commit 435ea09451
5 changed files with 484 additions and 8 deletions
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138
AGENTS.md Normal file
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# Agent Context & Reminders
## ZED SDK Architecture
### Streaming API vs Fusion API
The ZED SDK provides two distinct network APIs that are often confused:
| Feature | Streaming API | Fusion API |
|---------|---------------|------------|
| **Data Transmitted** | Compressed video (H264/H265) | Metadata only (bodies, objects, poses) |
| **Bandwidth** | 10-40 Mbps | <100 Kbps |
| **Edge Compute** | Video encoding only | Full depth NN + tracking + detection |
| **Host Compute** | Full depth + tracking + detection | Lightweight fusion only |
| **API Methods** | `enableStreaming()` / `setFromStream()` | `startPublishing()` / `subscribe()` |
### Key Insight
**There is NO built-in mode for streaming computed depth maps or point clouds.** The architecture forces a choice:
1. **Streaming API**: Edge sends video → Host computes everything (depth, tracking, detection)
2. **Fusion API**: Edge computes everything → Sends only metadata (bodies/poses)
### Code Patterns
#### Streaming Sender (Edge)
```cpp
sl::StreamingParameters stream_params;
stream_params.codec = sl::STREAMING_CODEC::H265;
stream_params.port = 30000;
stream_params.bitrate = 12000;
zed.enableStreaming(stream_params);
```
#### Streaming Receiver (Host)
```cpp
sl::InitParameters init_params;
init_params.input.setFromStream("192.168.1.100", 30000);
zed.open(init_params);
// Full ZED SDK available - depth, tracking, etc.
```
#### Fusion Publisher (Edge or Host)
```cpp
sl::CommunicationParameters comm_params;
comm_params.setForLocalNetwork(30000);
// or comm_params.setForIntraProcess(); for same-machine
zed.startPublishing(comm_params);
```
#### Fusion Subscriber (Host)
```cpp
sl::Fusion fusion;
fusion.init(init_params);
sl::CameraIdentifier cam(serial_number);
fusion.subscribe(cam, comm_params, pose);
```
## Project: Multi-Camera Body Tracking
### Location
`/workspaces/zed-playground/playground/body tracking/multi-camera/cpp/`
### Architecture
- **ClientPublisher**: Receives camera streams, runs body tracking, publishes to Fusion
- **Fusion**: Subscribes to multiple ClientPublishers, fuses body data from all cameras
- **GLViewer**: 3D visualization of fused bodies
### Camera Configuration (Hard-coded)
From `inside_network.json`:
| Serial | IP | Streaming Port |
|--------|-----|----------------|
| 44289123 | 192.168.128.2 | 30000 |
| 44435674 | 192.168.128.2 | 30002 |
| 41831756 | 192.168.128.2 | 30004 |
| 46195029 | 192.168.128.2 | 30006 |
### Data Flow
```
Edge Camera (enableStreaming) → Network Stream
↓
ClientPublisher (setFromStream) → Body Tracking (host)
↓
startPublishing() → Fusion (INTRA_PROCESS)
↓
Fused Bodies → GLViewer
```
### Build
```bash
cd "/workspaces/zed-playground/playground/body tracking/multi-camera/cpp/build"
cmake ..
make -j4
```
### Run
```bash
./ZED_BodyFusion <config_file.json>
```
## Related Samples
### Camera Streaming Receiver
`/workspaces/zed-playground/playground/camera streaming/receiver/cpp/`
- Simple streaming receiver sample
- Shows basic `setFromStream()` usage with OpenCV display
## Important Paths
### ZED SDK Installation
- **Root**: `/usr/local/zed/`
- **Headers**: `/usr/local/zed/include/sl/`
- `Camera.hpp` - Main camera API
- `Fusion.hpp` - Fusion module API
- `CameraOne.hpp` - Single camera utilities
- **Libraries**: `/usr/local/zed/lib/`
- **CMake config**: `/usr/local/zed/zed-config.cmake`
- **Tools**: `/usr/local/zed/tools/`
- **Settings**: `/usr/local/zed/settings/`
### Project Structure
```
/workspaces/zed-playground/
├── playground/
│ ├── body tracking/multi-camera/cpp/ # This project
│ │ ├── src/ClientPublisher.cpp
│ │ ├── src/main.cpp
│ │ ├── include/ClientPublisher.hpp
│ │ └── build/
│ └── camera streaming/receiver/cpp/ # Streaming receiver sample
└── zed_settings/
└── inside_network.json # Camera network config
```
### Configuration Files
- **Camera network config**: `/workspaces/zed-playground/zed_settings/inside_network.json`
- **Fusion config** (for ZED360 tool): Generated JSON with camera poses and communication params

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# CUDA-OpenGL Interop: Jetson vs Desktop GPU Differences
## Summary
The CUDA-OpenGL interop issue where `cudaGraphicsMapResources` is required every frame on modern desktop GPUs (RTX 3090/5070Ti with CUDA 12.8) but works with permanent mapping on Jetson (JetPack 5.1.2) is rooted in fundamental architectural differences between integrated GPUs (iGPU) and discrete GPUs (dGPU).
## Root Cause: Memory Architecture Differences
### Jetson (Tegra/iGPU) - Unified Memory Architecture
```
┌─────────────────────────────────────────┐
│ Jetson SoC │
│ ┌─────────┐ ┌─────────┐ │
│ │ CPU │◄────►│ GPU │ │
│ │ (ARM) │ │ (iGPU) │ │
│ └────┬────┘ └────┬────┘ │
│ │ │ │
│ └───────┬────────┘ │
│ ▼ │
│ ┌──────────┐ │
│ │ Shared │ Unified Memory │
│ │ DRAM │ (Zero Copy) │
│ └──────────┘ │
└─────────────────────────────────────────┘
```
**Characteristics:**
- CPU and GPU share the **same physical memory** (SoC DRAM)
- **Unified Memory** - same pointer value works for both CPU and GPU
- **Zero-copy** possible - no data transfer needed
- OpenGL and CUDA can access the same memory without mapping/unmapping
- Memory is always "mapped" because there's only one memory space
### Desktop GPU (dGPU) - Separate Memory Architecture
```
┌─────────────┐ PCIe Bus ┌─────────────┐
│ Host PC │◄───────────────────────►│ Discrete │
│ │ (High bandwidth but │ GPU │
│ ┌───────┐ │ with latency) │ ┌───────┐ │
│ │ CPU │ │ │ │ GPU │ │
│ └───┬───┘ │ │ └───┬───┘ │
│ │ │ │ │ │
│ ┌───▼───┐ │ │ ┌───▼───┐ │
│ │ RAM │ │ │ │ VRAM │ │
│ │(Host) │ │ │ │(Device│ │
│ └───────┘ │ │ │Memory)│ │
└─────────────┘ │ └───────┘ │
└─────────────┘
```
**Characteristics:**
- CPU and GPU have **separate physical memories** (Host RAM vs VRAM)
- Data must be **transferred** between host and device memory
- OpenGL texture/buffer lives in GPU memory
- CUDA must **explicitly map** the resource to get a device pointer
- **Mapping/Unmapping** is required to synchronize access between OpenGL and CUDA
## Why the Fix is Required on Desktop GPUs
### The Problem with Permanent Mapping on dGPU
When a resource is permanently mapped on desktop GPUs:
1. **CUDA holds the mapping** - thinks it owns the memory
2. **OpenGL tries to use the texture/buffer** for rendering
3. **Memory coherency issues** - CUDA writes may not be visible to OpenGL
4. **Synchronization race conditions** - undefined behavior, corruption, or crashes
### Why It Works on Jetson
On Jetson with unified memory:
1. **Same physical memory** - both CUDA and OpenGL access the same location
2. **No data transfer needed** - writes are immediately visible
3. **Cache coherency handled by hardware** - no explicit synchronization required
4. **Mapping is essentially a no-op** - just gives a pointer to shared memory
## The Correct Pattern (Applied in Our Fix)
```cpp
// Every frame:
// 1. Map resource for CUDA access
// - On dGPU: Sets up memory mapping, ensures coherency
// - On Jetson: Lightweight operation (already unified)
cudaGraphicsMapResources(1, &resource, stream);
// 2. Get pointer/array for CUDA to use
cudaGraphicsResourceGetMappedPointer(&ptr, &size, resource);
// or
cudaGraphicsSubResourceGetMappedArray(&arr, resource, 0, 0);
// 3. Copy/process data with CUDA
cudaMemcpy(ptr, data, size, cudaMemcpyHostToDevice);
// 4. Synchronize to ensure copy completes
cudaStreamSynchronize(stream);
// 5. Unmap so OpenGL can use it
// - On dGPU: Releases mapping, makes data available to OpenGL
// - On Jetson: Still good practice, minimal overhead
cudaGraphicsUnmapResources(1, &resource, stream);
// 6. OpenGL rendering can now safely use the resource
// glBindTexture(GL_TEXTURE_2D, texture);
// glDrawArrays(...);
```
## CUDA Version and Driver Changes
### CUDA 12.8 Behavior Changes
According to NVIDIA Developer Forums (2025):
> "CUDA randomly freezes when using cudaStreamSynchronize with OpenGL interop... `cudaGraphicsMapResources` needs to be called every frame"
This suggests that newer CUDA versions and drivers have **tightened synchronization requirements**:
- **Older CUDA/drivers** may have been more lenient with permanent mapping
- **CUDA 12.8+** enforces proper map/unmap cycles for correctness
- **Stricter memory ordering** between CUDA and OpenGL contexts
### Why JetPack 5.1.2 Still Works
- **Older driver branch** - Jetson drivers lag behind desktop
- **Unified memory hides the issue** - no actual data movement needed
- **Different driver implementation** - Tegra drivers optimized for unified memory path
## Performance Considerations
### Desktop GPU (dGPU)
| Approach | Overhead | Correctness |
|----------|----------|-------------|
| Permanent mapping | Low | **Broken on CUDA 12.8+** |
| Map/unmap per frame | Higher | **Correct** |
**Optimization for dGPU:**
- Use `cudaStreamSynchronize(0)` or explicit events
- Batch multiple copies if possible
- Consider using CUDA arrays directly if no OpenGL needed
### Jetson (iGPU)
| Approach | Overhead | Correctness |
|----------|----------|-------------|
| Permanent mapping | Minimal | Works (unified memory) |
| Map/unmap per frame | Minimal | Works (good practice) |
**Recommendation:** Use the same map/unmap pattern on both platforms for code portability, even though Jetson is more forgiving.
## References
### NVIDIA Documentation
1. **CUDA for Tegra** - https://docs.nvidia.com/cuda/cuda-for-tegra-appnote/index.html
> "Tegra's integrated GPU (iGPU) shares the same SoC DRAM with CPU... contrasting with dGPUs that have separate memory"
2. **CUDA Runtime API - OpenGL Interop** - https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__OPENGL.html
3. **Unified Memory on Jetson** - https://forums.developer.nvidia.com/t/unified-memory-on-jetson-platforms/187448
### Community Discussions
1. **NVIDIA Developer Forums** (2023-2025)
- "cudaGraphicsMapResources each frame or just once"
- Users report CUDA 12.8 requires per-frame mapping
- Jetson works with permanent mapping due to unified memory
2. **CUDA Random Freezes with OpenGL** (2025)
- https://forums.developer.nvidia.com/t/cuda-randomly-freezes-when-using-cudastreamsynchronize-with-opengl-interop/318514
- Confirms stricter synchronization in newer CUDA versions
## Conclusion
The fix applied to `GLViewer.cpp` (map/unmap every frame) is **required for correctness on desktop GPUs** and is **good practice on Jetson**:
1. **Desktop RTX GPUs** - Separate VRAM requires explicit synchronization between CUDA and OpenGL
2. **Jetson iGPU** - Unified memory is more forgiving, but same pattern works
3. **CUDA 12.8+** - Stricter driver requirements enforce proper resource management
4. **Code portability** - Same code works correctly on both platforms
The architectural difference explains why the original code worked on Jetson but failed on your RTX 3090/5070Ti with CUDA 12.8.

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# GLViewer CUDA-OpenGL Interop Fix
## Problem
The GLViewer had CUDA-OpenGL interop issues where the application would crash or display corrupted graphics when rendering point clouds and camera views. This was caused by improper resource mapping between CUDA and OpenGL.
## Root Cause
The original code kept CUDA graphics resources permanently mapped:
- Resources were mapped once during initialization
- Never unmapped until cleanup/resize
- CUDA and OpenGL were competing for the same memory resources
This violates CUDA-OpenGL interop best practices which require:
1. **Map** resource before CUDA operations
2. **Use** the mapped pointer/array
3. **Unmap** resource before OpenGL uses it
## Solution Applied
### 1. PointCloud::pushNewPC() Fix
**Before:**
```cpp
void PointCloud::pushNewPC() {
// ... initialization code ...
// Resource mapped once, never unmapped
cudaMemcpy(xyzrgbaMappedBuf_, refMat.getPtr<sl::float4>(sl::MEM::CPU),
numBytes_, cudaMemcpyHostToDevice);
}
```
**After:**
```cpp
void PointCloud::pushNewPC() {
// ... initialization code ...
// Map the resource for CUDA access
cudaError_t err = cudaGraphicsMapResources(1, &bufferCudaID_, 0);
err = cudaGraphicsResourceGetMappedPointer((void**)&xyzrgbaMappedBuf_,
&numBytes_, bufferCudaID_);
// Copy data from ZED SDK to mapped OpenGL buffer
cudaMemcpy(xyzrgbaMappedBuf_, refMat.getPtr<sl::float4>(sl::MEM::CPU),
numBytes_, cudaMemcpyHostToDevice);
// Synchronize to ensure copy completes
cudaStreamSynchronize(0);
// Unmap so OpenGL can use it
err = cudaGraphicsUnmapResources(1, &bufferCudaID_, 0);
}
```
### 2. CameraViewer::pushNewImage() Fix
**Before:**
```cpp
void CameraViewer::pushNewImage() {
if (!ref.isInit()) return;
// ArrIm was permanently mapped, direct copy
cudaMemcpy2DToArray(ArrIm, 0, 0, ref.getPtr<sl::uchar1>(sl::MEM::CPU),
ref.getStepBytes(sl::MEM::CPU),
ref.getPixelBytes() * ref.getWidth(),
ref.getHeight(), cudaMemcpyHostToDevice);
}
```
**After:**
```cpp
void CameraViewer::pushNewImage() {
if (!ref.isInit()) return;
// Map the resource for CUDA access
auto err = cudaGraphicsMapResources(1, &cuda_gl_ressource, 0);
err = cudaGraphicsSubResourceGetMappedArray(&ArrIm, cuda_gl_ressource, 0, 0);
// Copy data to mapped array
err = cudaMemcpy2DToArray(ArrIm, 0, 0, ref.getPtr<sl::uchar1>(sl::MEM::CPU),
ref.getStepBytes(sl::MEM::CPU),
ref.getPixelBytes() * ref.getWidth(),
ref.getHeight(), cudaMemcpyHostToDevice);
// Synchronize to ensure copy completes
cudaStreamSynchronize(0);
// Unmap so OpenGL can use it
err = cudaGraphicsUnmapResources(1, &cuda_gl_ressource, 0);
}
```
### 3. CameraViewer Initialization Cleanup
Removed permanent mapping from initialization:
**Before:**
```cpp
// In CameraViewer::initialize():
cudaGraphicsGLRegisterImage(&cuda_gl_ressource, texture, GL_TEXTURE_2D,
cudaGraphicsRegisterFlagsWriteDiscard);
cudaGraphicsMapResources(1, &cuda_gl_ressource, 0); // REMOVED
cudaGraphicsSubResourceGetMappedArray(&ArrIm, cuda_gl_ressource, 0, 0); // REMOVED
```
**After:**
```cpp
// In CameraViewer::initialize():
cudaGraphicsGLRegisterImage(&cuda_gl_ressource, texture, GL_TEXTURE_2D,
cudaGraphicsRegisterFlagsWriteDiscard);
// Mapping now done in pushNewImage()
```
## Key CUDA-OpenGL Interop Rules
1. **Register once** - `cudaGraphicsGLRegisterBuffer/RegisterImage`
2. **Map before CUDA** - `cudaGraphicsMapResources`
3. **Get pointer/array** - `cudaGraphicsResourceGetMappedPointer/SubResourceGetMappedArray`
4. **Copy data** - `cudaMemcpy/cudaMemcpy2DToArray`
5. **Synchronize** - `cudaStreamSynchronize` (ensures copy completes)
6. **Unmap before GL** - `cudaGraphicsUnmapResources`
7. **Unregister on cleanup** - `cudaGraphicsUnregisterResource`
## Reference
This fix is based on commit `45fcb88e9dcc7a1b1fb86b928b884bfcdc1da0f2` in the zed-sdk repository which applied the same fix to the depth sensing sample.

2
src/ClientPublisher.cpp
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@ -12,7 +12,7 @@ bool ClientPublisher::open(const std::string& ip, int port, Trigger* ref, int sd
p_trigger = ref;
sl::InitParameters init_parameters;
init_parameters.depth_mode = sl::DEPTH_MODE::NEURAL_PLUS;
init_parameters.depth_mode = sl::DEPTH_MODE::NEURAL;
// Use stream input instead of local camera
init_parameters.input.setFromStream(sl::String(ip.c_str()), port);
init_parameters.coordinate_units = sl::UNIT::METER;

40
src/GLViewer.cpp
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@ -827,7 +827,22 @@ void PointCloud::pushNewPC() {
if (err) std::cerr << "Error CUDA " << cudaGetErrorString(err) << std::endl;
}
// Map the resource for CUDA access
cudaError_t err = cudaGraphicsMapResources(1, &bufferCudaID_, 0);
if (err) std::cerr << "Error CUDA map " << cudaGetErrorString(err) << std::endl;
err = cudaGraphicsResourceGetMappedPointer((void**)&xyzrgbaMappedBuf_, &numBytes_, bufferCudaID_);
if (err) std::cerr << "Error CUDA get pointer " << cudaGetErrorString(err) << std::endl;
// Copy data from ZED SDK to mapped OpenGL buffer
cudaMemcpy(xyzrgbaMappedBuf_, refMat.getPtr<sl::float4>(sl::MEM::CPU), numBytes_, cudaMemcpyHostToDevice);
// Synchronize to ensure copy completes
cudaStreamSynchronize(0);
// Unmap so OpenGL can use it
err = cudaGraphicsUnmapResources(1, &bufferCudaID_, 0);
if (err) std::cerr << "Error CUDA unmap " << cudaGetErrorString(err) << std::endl;
}
}
@ -987,18 +1002,29 @@ bool CameraViewer::initialize(sl::Mat &im, sl::float3 clr) {
if (err) std::cout << "err alloc " << err << " " << cudaGetErrorString(err) << "\n";
glDisable(GL_TEXTURE_2D);
err = cudaGraphicsMapResources(1, &cuda_gl_ressource, 0);
if (err) std::cout << "err 0 " << err << " " << cudaGetErrorString(err) << "\n";
err = cudaGraphicsSubResourceGetMappedArray(&ArrIm, cuda_gl_ressource, 0, 0);
if (err) std::cout << "err 1 " << err << " " << cudaGetErrorString(err) << "\n";
return (err == cudaSuccess);
}
void CameraViewer::pushNewImage() {
if (!ref.isInit()) return;
auto err = cudaMemcpy2DToArray(ArrIm, 0, 0, ref.getPtr<sl::uchar1>(sl::MEM::CPU), ref.getStepBytes(sl::MEM::CPU), ref.getPixelBytes() * ref.getWidth(), ref.getHeight(), cudaMemcpyHostToDevice);
if (err) std::cout << "err 2 " << err << " " << cudaGetErrorString(err) << "\n";
// Map the resource for CUDA access
auto err = cudaGraphicsMapResources(1, &cuda_gl_ressource, 0);
if (err) std::cout << "err map " << err << " " << cudaGetErrorString(err) << "\n";
err = cudaGraphicsSubResourceGetMappedArray(&ArrIm, cuda_gl_ressource, 0, 0);
if (err) std::cout << "err get array " << err << " " << cudaGetErrorString(err) << "\n";
// Copy data to mapped array
err = cudaMemcpy2DToArray(ArrIm, 0, 0, ref.getPtr<sl::uchar1>(sl::MEM::CPU), ref.getStepBytes(sl::MEM::CPU), ref.getPixelBytes() * ref.getWidth(), ref.getHeight(), cudaMemcpyHostToDevice);
if (err) std::cout << "err copy " << err << " " << cudaGetErrorString(err) << "\n";
// Synchronize to ensure copy completes
cudaStreamSynchronize(0);
// Unmap so OpenGL can use it
err = cudaGraphicsUnmapResources(1, &cuda_gl_ressource, 0);
if (err) std::cout << "err unmap " << err << " " << cudaGetErrorString(err) << "\n";
}
void CameraViewer::draw(sl::Transform vpMatrix) {