RapidShot

RapidShot captures the Windows desktop and turns it into model-ready GPU tensors without the pixels ever reaching the CPU — and still hands you a NumPy array when that is what you want.

Windows desktop → native GPU frame → fused preprocessing → Torch / CuPy / DLPack

It is built on DXGI Desktop Duplication, began as a merge of several DXcam forks, and keeps a broadly familiar API. The capture path, the colour pipeline and the GPU interop have since been rewritten.

Why this instead of DXcam

If you want a screenshot as fast as possible and nothing else, use DXcam or BetterCam. They are good, they are smaller, and RapidShot does not win the capture-only frame-rate column in most cells (Performance).

RapidShot is for the case where the frame has somewhere to go. It owns the whole path from the duplicated surface to a (1, 3, 640, 640) FP16 tensor your model can consume, in one GPU dispatch, with no readback and no ctypes.

What changed in 2.6

Four things, each with the measurement behind it:

• Memory: grab() uses 58% less than RapidShot 2.5, and grab_frame() 63% less. Read that as against RapidShot 2.5, not against DXcam — the cross-library figure is in Performance, and it is a much smaller gap than 58%.
• Fused GPU preprocessing with native FP16 — crop, resize, colour conversion, normalisation and layout in a single D3D12 dispatch, emitting float16 as well as float32 and uint8. Multi-ROI batches in one dispatch too.
• One-call framework export — tensor.to_torch(), .to_cupy(), .to_dlpack(), zero-copy. This replaces roughly sixty lines of ctypes that the 2.5 README asked you to copy.
• Convert-before-transfer on hybrid GPUs — preprocess on the capture adapter, then move the finished tensor rather than the whole frame: 2.46 MB instead of 16.38 MB at 2560×1600. Verified end to end on real Optimus hardware (Intel UHD → RTX 4060), CUDA importing the transferred buffer — see Hybrid GPU laptops. Measured: 162 fps against DXcam's 96, pixels 10 ms younger, at under a fifth of the CPU — see Desktop to model.

Upgrading from 2.5? See Migrating from 2.5 — the one breaking item is a new minimum native-extension version.

Quick start

A screenshot, as a NumPy array. No GPU knowledge, no native extension:

import rapidshot

camera = rapidshot.create()          # primary output, RGB
frame = camera.grab()                # (H, W, 3) uint8, or None if nothing changed
if frame is not None:
    print(frame.shape)
    frame.release()                  # return the pooled buffer
camera.release()

A model-ready FP16 tensor on the GPU, never touching the CPU. Needs rapidshot[native]:

import rapidshot

camera = rapidshot.create()
stream = rapidshot.TensorStream(camera, size=(640, 640),
                                dtype="float16", layout="NCHW")

for tensor in stream:
    prediction = model(tensor.to_torch())

Use TensorStream rather than writing that loop by hand. Its docstring lists the four ways a hand-written version goes quietly wrong — a held frame stalls capture, the reused output buffer races the model, None means both "nothing changed" and "capture has died", and a rebuilt capture can land on a new adapter. It closes all four.

On a hybrid laptop, to_torch() above raises CrossAdapterRequired. That is not a bug. On an Optimus-style machine the display — and therefore capture — lives on the integrated GPU, while CUDA only sees the discrete one, so there is nothing for CUDA to import until the tensor has crossed. Add one object: Hybrid GPU laptops. rapidshot.diagnose() tells you which kind of machine you are on before you write any of this.

For explicit control, drive the converter yourself:

camera = rapidshot.create()

with camera.grab_frame() as frame:            # GPU texture, no CPU copy
    converter = rapidshot.GpuConverter(       # build once, reuse every frame
        frame, (640, 640), dtype="float16", layout="NCHW",
    )
    tensor = converter.process(frame)
    x = tensor.to_torch()                     # zero-copy
    prediction = model(x)

GpuConverter takes the frame first: it needs the source surface to size its resources. Build it once outside your loop and call process() per frame — the output buffer is reused, which is why TensorStream synchronises before overwriting it.

to_cupy() and to_dlpack() are the same tensor by other routes:

array   = tensor.to_cupy()     # cupy.ndarray, zero-copy
capsule = tensor.to_dlpack()   # any framework implementing DLPack

All three alias the converter's buffer, so the next process() overwrites them. Clone if you need one to outlive the next frame.

Choose your path

Installation

pip install rapidshot

That is the whole library for CPU and D3D11 capture — grab(), grab_frame(), regions, colour conversion, dirty rects, video recording. No toolchain, no compiler, no GPU requirement.

The 2.6 GPU tensor features need the prebuilt native extension:

pip install "rapidshot[native]"

This installs rapidshot-native >= 0.2.0, which is the minimum that exports GpuConverter and TensorTransfer — both were added after rapidshot-native 0.1.0. If you somehow end up with an older extension, RapidShot says so:

RuntimeError: GpuConverter12 requires rapidshot-native >= 0.2.0; the installed
extension is version 0.1.0, from rapidshot-native wheel.

Upgrade the prebuilt wheel:
    pip install --upgrade 'rapidshot-native>=0.2.0'

Other extras: rapidshot[gpu_cuda12] or [gpu_cuda13] for CuPy (pick one — the cupy-cudaNNx wheels are mutually exclusive), and rapidshot[all] for everything that is not CUDA-version-specific.

Check what your machine actually has:

import rapidshot
print(rapidshot.diagnose())

It reports the adapters, outputs, whether the native extension loaded and from where, and which optional dependencies are present — before you write any capture code.

The 2.6 pipeline

                              ┌── NumPy / OpenCV        grab()
                              │
   DXGI ──→ D3D11 Frame ──────┼── raw GPU texture       grab_frame()
                              │
                              └── GpuConverter
                                       │
                          crop · resize · RGB/BGR
                          normalize · FP16 · layout
                              (one GPU dispatch)
                                       │
                          ┌────────────┼────────────┐
                          ↓            ↓            ↓
                       Torch         CuPy        DLPack

On a hybrid laptop the tensor crosses adapters after preprocessing, so what moves is a small tensor rather than a full frame — see Hybrid GPU laptops.

Capturing to NumPy

camera = rapidshot.create(output_color="BGR")          # RGB, BGR, RGBA, BGRA, GRAY
frame  = camera.grab(region=(0, 0, 1920, 1080))        # left, top, right, bottom

grab() returns None when nothing has changed — Desktop Duplication reports only changed content, so an idle desktop legitimately produces no frames.

Continuous capture runs on a background thread:

camera.start(target_fps=60, video_mode=True)
for _ in range(120):
    frame = camera.get_latest_frame()
    ...
camera.stop()

One camera per output; create several for several monitors. rapidshot.create() returns the same camera for the same output if you ask twice.

Frame buffers and who owns them

grab() returns a PooledBuffer — a reused buffer, not a fresh array. Allocating per frame costs about 1.6 ms on a 1080p RGB frame, because first-touch page faults cost more than the conversion.

frame = camera.grab()
if frame is not None:
    frame.shape, frame.dtype, frame.ndim
    pixel = frame[y, x]
    arr = np.asarray(frame)             # zero-copy, for cv2 / PIL / a model
    frame.release()                     # the one new line

Release when you are done. The buffer goes back to the pool and on to the next capture. Reading it after release raises BufferReleasedError rather than returning stale pixels. To keep the data, use frame.copy() or np.array(frame, copy=True).

Forgetting to release is never corrupting — the pool will not hand out a buffer someone is still reading. With a conversion (RGB, BGR, RGBA, GRAY) an exhausted pool falls back to allocating. BGRA converts nothing, so the frame you hold is the staging buffer and there is nothing to fall back to: grab() returns None until one comes back.

Prefer plain arrays? rapidshot.create(pool_output=False).

GPU-resident frames

grab_frame() hands back the Direct3D texture itself. Nothing is copied to system memory.

with camera.grab_frame() as frame:      # the context manager is the safe form
    tex = frame.d3d11_texture           # ID3D11Texture2D, valid until release
    print(frame.width, frame.height, frame.timestamp_qpc)

A held frame blocks capture. DXGI will not produce the next frame while a reference is outstanding, so release promptly — with does it for you.

Frames also carry the compositor's metadata: dirty_rects and move_rects in frame coordinates, changed_fraction, accumulated_frames, cursor, rotation_angle, protected_content, and timestamp_qpc from the present itself.

Model-ready tensors

GpuConverter is one compute dispatch that does all of it:

Multi-ROI in a single dispatch:

converter = rapidshot.GpuConverter(frame, (224, 224), batch=4)
tensor = converter.process(frame, regions=[
    (0, 0, 400, 300), (800, 40, 1000, 240),
])                                          # (2, 3, 224, 224)

Payload sizes at 640², which is what crosses a bus if anything has to:

against 8.29 MB for a 1080p frame or 16.38 MB at 2560×1600.

Hybrid GPU laptops

On an Optimus-style laptop the display is driven by the integrated GPU, so capture happens there, while CUDA lives on the discrete GPU. Something has to cross.

2.5 — move the frame, convert on arrival
   2560×1600 BGRA (16.38 MB) → cross-adapter transfer → resize/RGB/normalize → model

2.6 — convert first, move the result
   2560×1600 capture → crop/resize/RGB/normalize/FP16 → 2.46 MB tensor
                     → cross-adapter transfer → model

converter = rapidshot.GpuConverter(frame, (640, 640), dtype="float16")
transfer  = rapidshot.TensorTransfer(converter)
converter.process(frame)
transfer.transfer()

This is also the answer to CrossAdapterRequired from the quick start: once the tensor is on the CUDA adapter, the consumer imports it there.

Verified on real hybrid hardware, 2026-09-20 — Intel UHD Graphics capture, RTX 4060 destination, 2560×1600:

• examples/verify_cross_adapter.py moved 5 frames Intel → RTX 4060, each 16,384,000 bytes, every one byte-exact against a source-side readback.
• The converted-tensor path was verified end to end with CUDA importing the transferred buffer from the shared D3D12 heap on the discrete GPU (benchmarks/ai_ingestion.py --paths rapidshot-converter-xadapter --verify), max deviation 1 RGB8 level — the documented bilinear rounding tolerance.

Why that last point is stated so specifically: TensorTransfer used to hand the consumer the source device's shared handle. Against a WARP destination that still "passed" — CUDA imported the capture GPU's own memory and the path reported a crossing that never happened. WARP is the configuration that hid the bug, so a WARP result is not evidence for this path. The check above is the one that counts, and it needs two real GPUs.

Measured against 2.6.0 on this laptop, 2026-09-25: 162 fps to a CUDA tensor, pixels 27.6 ms old at the median, 2.0 ms of CPU per frame — against DXcam's 95.6 fps, 37.7 ms and 11.2 ms. The full table, including the full-frame transfer this replaces, is in Desktop to model.

Correctness guarantees

These are the disciplines the code actually enforces, not aspirations:

• An independent reference comes before any performance claim. Conversion kernels are checked byte-for-byte against NumPy; the GPU converter against a D3D11 staging readback of the same texture (tests/test_gpu_converter.py); NV12/P010 against a CPU reference and the published inverse matrices, so the kernel and its reference cannot share a wrong constant.
• Frame lifetime is enforced, not documented. Reading a released frame or a released buffer raises rather than returning stale pixels. Generation tracking invalidates frames from a duplicator that has been rebuilt.
• GPU reads are ordered on the GPU. AcquireNextFrame returns when the copy is submitted, not complete. Every D3D12 path that reads the capture surface waits on a shared fence — without it, measured stale-frame rates ran from 7/150 to 65/150 (native/src/capture_order.rs).
• Producer and consumer both have a barrier. GpuTensor.sync() blocks until CUDA work on the buffer is done, and set_consumer_fence() stops the producer overwriting a buffer a consumer is still reading.
• WARP is not a GPU. A software adapter proves an API works and nothing about what it costs, and every table and message that uses one says so.

Performance

Three different questions, deliberately separated — DXcam can win the first while RapidShot wins the others.

A. Capture alone

RapidShot does not win this column in most cells, and says so. Full tables, methodology and caveats: docs/BENCHMARKS.md.

B. Memory

benchmarks/memory-baseline-machineB.json — Machine B (RTX 4060 laptop, Intel UHD capture, 2560×1600 at 165 Hz), still desktop, 10 s after a 2 s warm-up, three passes, working set:

Read the two comparisons separately. Against RapidShot 2.5 the reduction is 58% and 63%. Against DXcam, grab_frame() is now 1.01× its working set, where 2.5 was 2.7×; grab() remains 1.70×, because it also holds converted output buffers DXcam has no equivalent of. Throughput was unchanged across the change: 165 fps for grab_frame(), ~100 for grab(), every difference inside the run-to-run spread.

None of it came from a redesign — a lazy CuPy import (that one import was 178.8 MB resident, paid by every caller including those with no NVIDIA GPU), a staging pool sized to what a converting grab() can actually use, and pool_size_frames defaulting to 2 instead of 4.

C. Desktop to model

Pixel age, not call duration: a source encodes a frame ID into the image and records every Present(), so each library is timed on one clock by how old its pixels were when they became a (1, 3, 640, 640) FP16 tensor on CUDA. Recorded 2026-09-25 against 2.6.0 with the rapidshot-native 0.2.0 wheel from PyPI, Machine B, 2560×1600 at 165 Hz, medians across 3 passes, 8 s per path, every path verified to produce the correct tensor before being timed.

Hybrid laptop — Intel UHD captures, RTX 4060 runs CUDA (section7-ingestion-machineB-hybrid-2.6.0.json):

Converting on the capture GPU and moving only the 2.46 MB tensor gives 1.7× DXcam's frames, pixels 10 ms younger, at under a fifth of its CPU — and its worst pass beat DXcam's best on all three columns. 162 fps is the 165 Hz panel, so that column is a floor. Moving the whole frame across adapters is still slower than DXcam, at 81 fps; on a hybrid laptop, convert first.

NVIDIA driving the display — the MUX in discrete-only mode, so capture and CUDA share one adapter (section7-ingestion-machineB-dgpu-2.6.0.json):

The GpuConverter row is two passes, not three, and it read frames as fast as the source presented them, so its frame rate is a floor rather than its ceiling. The two tables are different machine configurations; compare within one, not across.

Through a detector — the same clock carried to usable detections: YOLO11n on the RTX 4060 through ONNX Runtime's CUDA provider, NMS included, with a scene of signage and a keyboard panning under the marker so the model has something to find. Discrete-only mode (section7-inference-machineB-dgpu-2.6.0.json):

34% more frames through the model than DXcam, detections 6.9 ms sooner, and a third less CPU, with every pass ahead of DXcam's best. Every path found the same scene: 8.4–9.7 detections per frame. Three passes are consistent, not the five paired runs the harness itself requires before calling a difference a verdict (ROADMAP § 7.0c).

Run it on your machine

pip install "rapidshot[benchmark]"
rapidshot benchmark

The same harness as the tables above: every installed library and every RapidShot path this hardware supports, verified first, then three 8-second passes timed by pixel age. About five minutes; a test pattern fills the screen. It writes a Markdown report and a JSON file with no hostname, username or device IDs in them. Please open a benchmark issue with both. Add cupy-cuda12x or cupy-cuda13x to include the GPU paths, --full for CPU per tensor and memory as well (about ten minutes), and --check to see what would run without running it. The report also says whether HDR was on and whether frames can cross between your GPUs.

What none of this shows

One machine, one resolution, one refresh rate, one driver. No AMD part has ever run these. Live capture rates depend on what the screen is doing and are not comparable across recordings — see docs/BENCHMARKS.md for why a single live recording is not a measurement.

Tested hardware

Nothing in RapidShot branches on GPU vendor. This is what has actually been run, which is a different claim:

Verified

Not verified

Migrating from 2.5

• rapidshot-native >= 0.2.0 is now required for the GPU tensor features. pip install --upgrade "rapidshot[native]". An older extension gives a named error telling you this, not a missing-attribute traceback.
• GpuConverter replaces GpuPreprocessor12 as the recommended transform. GpuPreprocessor12 still works and is unchanged; GpuConverter adds bilinear sampling (the old path decimates with Load()), FP16, NHWC, crop, multi-ROI and NV12/P010. sampling="nearest" reproduces the old path bit for bit.
• to_torch() / to_cupy() / to_dlpack() replace the ctypes recipe. examples/gpu_tensor_to_cupy.py remains as the worked explanation of what they do.
• pool_size_frames now defaults to 2, from 4. Externally visible: with BGRA (which does no conversion) an exhausted pool returns None sooner. Pass pool_size_frames=4 to restore the old behaviour.
• Nothing was renamed or removed.

Everything else, including the bugs fixed, is in CHANGELOG.md.

Advanced: native interop

For consumers that bind GPU resources directly — DirectML, ONNX Runtime, a custom CUDA kernel — the underlying handles are reachable:

• GpuPreprocessor12 — the 2.3-era NCHW float32 preprocessor.
• converter.output_resource_address — the ID3D12Resource address, which is the vendor-neutral route for DirectML and ONNX Runtime. It is on the converter, not the tensor, and sees the whole buffer including every batch slot; converter.output_byte_size is its size.
• native.CrossAdapterTransfer — move a whole frame across adapters (ordering A), with transfer_async(), shared_fence_handle, set_consumer_fence() and wait_for_consumer().
• native.probe_d3d12_sharing(), probe_shareable_buffers(), texture_sharing_info() — capability probes.

examples/gpu_tensor_to_cupy.py and examples/verify_cross_adapter.py are the worked, commented versions. You should not need any of this to use GpuConverter.

Diagnostics and troubleshooting

import rapidshot
print(rapidshot.diagnose())                 # adapters, outputs, extension, deps
print(rapidshot.diagnose(probe_gpu=True))   # also probe D3D12 sharing

Contributing

See CONTRIBUTING.md. The one rule worth stating here: a correctness check against an independent reference comes before any performance claim. A fast wrong answer is worthless.

Plans and their reasoning are in ROADMAP.md; measurements and their methodology in docs/BENCHMARKS.md.

License

MIT — see LICENSE.

Acknowledgments

DXcam and BetterCam, whose forks RapidShot began as a merge of.