Reading Render Output

Reading Render Output

When to Use

Use this skill when the user asks to get pixels, read an image, save a PNG, display rendered output, or access render var data.

Inputs

Resolve inputs in this order: existing repository files and referenced snippets, explicit user request, then broader agent context.

• Target API surface: Python, C/C++, USD, or a combination.
• RenderProduct path, RenderVar/output name, and whether the output is single-tensor or composite.
• Mapping target: CPU pixels, linear CUDA tensor, or C CUDA array mapping.
• Image/tensor shape, dtype, channel order, synchronization requirements, and whether the data must outlive the mapping.
• Repository source snippets referenced below. Treat these snippets as the API source of truth.

Prerequisites

• Use an ovrtx checkout that contains the referenced examples and docs tests.
• Read the relevant > **Source:** snippet before writing or explaining API usage.
• Confirm the RenderProduct already requests the desired RenderVar; use camera-outputs-rt2 when the question is which camera output to add.
• Use reading-sensor-pointclouds for lidar/radar PointCloud composite tensors.

Instructions

1. Identify the RenderProduct path, RenderVar name, target device, and whether the caller needs CPU pixels, CUDA tensors, or CUDA arrays.
2. Read the matching map/unmap snippet before choosing Python frame.render_vars[...] access or C ovrtx_map_render_var_output().
3. Preserve dtype, shape, channel order, and ownership rules for the selected output.
4. Always unmap C outputs, and keep CUDA synchronization aligned with the CUDA interop skill when reading on GPU.
5. When changing code, run the camera sensor or minimal example snippet that owns the readback path whenever practical.

Output Format

• For explanations, cite the relevant API names, source snippets, and caveats.
• For code changes, summarize the files changed, snippets affected, and validation run.

Scripts

This skill has no scripts.

Limitations

• The referenced snippets remain the source of truth; update or add tested snippets before documenting new API usage.

Overview

This skill is the hands-on counterpart to the conceptual reference at docs/sensors/sensor_outputs.rst, which describes what a render variable output is and how its tensors and params are laid out. Use this skill for how to map and read one; use the conceptual page when the question is about what the structure carries or why it is shaped the way it is.

After stepping the renderer and fetching results, each RenderVarOutput (e.g., LdrColor, HdrColor, depth) must be mapped to access its data.

A render variable carries one or more named tensors and zero or more named params. For a single-tensor render variable, consume the mapping directly with DLPack (np.from_dlpack(rv)); for a multi-tensor render variable, address tensors by name (rv["TensorName"]) and reach params through rv.params["paramName"]. Both tensors and params expose the DLPack protocol uniformly, so np.from_dlpack(rv["TensorName"]) and np.from_dlpack(rv.params["paramName"]) both yield zero-copy NumPy/Warp/etc. arrays.

• In Python, render-var mapping supports device=Device.CPU and device=Device.CUDA (from from ovrtx import Device).
• In C, ovrtx_map_render_var_output also supports OVRTX_MAP_DEVICE_TYPE_CUDA_ARRAY for zero-copy workflows.

After reading/processing, unmap to release the mapped buffer.

Python

Map to CPU and read as NumPy

Source: examples/python/minimal/main.py snippet read-render-output

Save as PNG with Pillow

Source: tests/docs/python/test_camera_sensors.py snippet doc-step-and-map-camera-outputs

Map to CUDA for GPU processing

Source: tests/docs/python/test_camera_sensors.py snippet doc-map-render-output-cuda

C

Map to CPU

Source: examples/c/minimal/main.cpp snippet map-rendered-output-cpu

Map to linear CUDA memory

Source: tests/docs/c/test_camera_sensors.cpp snippet doc-map-render-output-cuda-c

Map to CUDA array (zero-copy)

Source: tests/docs/c/test_camera_sensors.cpp snippet doc-map-render-output-cuda-array-c

Find a specific render var in C

This helper is not part of the ovrtx API; define it in your own code (see examples/c/minimal/main.cpp):

Source: examples/c/minimal/main.cpp snippet find-output-helper

Key Types / Functions

Device types (Python: from ovrtx import Device):

• Device.CPU -- read back to host memory (sync + copy)
• Device.CUDA -- linear CUDA device memory (may copy)
• C also supports: OVRTX_MAP_DEVICE_TYPE_CUDA_ARRAY (zero-copy for image outputs) and OVRTX_MAP_DEVICE_TYPE_DEFAULT (auto-selects the most efficient format; returns a CUDA array for image outputs, avoiding an extra copy).

Common render variables:

• LdrColor -- single-tensor, RGBA uint8, sRGB color space
• HdrColor -- single-tensor, RGBA float16, linear color space
• Render variables may also carry multiple tensors plus params (e.g. a point-cloud render variable producing Coordinates + Intensity tensors alongside frameId / hitCount params); address those by name.

Troubleshooting

• Consumer owns lifetime. A mapping's C buffer stays alive for exactly as long as DLPack consumers (NumPy, Warp, etc.) reference it via np.from_dlpack / wp.from_dlpack. Drop those views (via del, rebind, or scope exit) to free the resource. If you need data beyond that, take an independent copy: np.from_dlpack(var).copy().
• Mind Python's deferred reclamation. A MappedRenderVar and any RenderVarTensor / RenderVarParam / DLPack array minted from it stays alive in its local-variable slot until that name is rebound or the enclosing scope ends. A with block releases interest in the mapping (the C unmap fires once the last view is dropped), but the Python names can outlive the with — most commonly the loop variable bound inside a for loop survives until the next iteration rebinds it, or until the loop exits. When you're done with a mapping but the owning name will outlive that interest, del it explicitly so renderer resources are reclaimed promptly instead of waiting on garbage collection.
• Mapping lifetime is independent of the owning products object. Dropping products (or its C-level ovrtx_destroy_results) does not invalidate live mappings — consumer references are what keep their buffers alive.
• For CUDA_ARRAY mapping, you must wait on rendered_output.cuda_sync.wait_event before accessing the data.
• For explicit CUDA sync in Python, call var.unmap(stream=...) or var.unmap(event=...) — these record the sync hint used when ovrtx reclaims the buffer.

References

• Use the > **Source:** directives in this skill to locate tested snippets before reusing API patterns.
• Keep related skills, docs, and snippets synchronized when changing the workflow.