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marCCD frames are widened on the device

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The marCCD reader read each frame's 16-bit scanlines into scratch and widened them to int32 on the
worker thread (marccd::ReadInto, ~20 % of the image-loop worker CPU on 4nwv), and the 4-byte image was
uploaded. ReadRawImage now reads the scanlines straight into the image's buffer and hands them over as
a new read-only CompressionAlgorithm::UINT16_WIDENED (mode Int32): two bytes a pixel cross PCIe, from
page-locked memory, and the preprocessing kernel widens them itself.

It is not CompressedImageMode::Uint16, which calls 65535 an error pixel; a marCCD's 65535 is its
saturation marker. So the kernel compares each pixel with int32's error marker and the int32 saturation
cap - the comparisons are made on the pixel's value, in int64, rather than in the storage type - and
decides every pixel exactly as for the int32 image the host would have widened it to. The host widening
(JFJochDecompressPtr) is the CPU path.

Test: ImagePreprocessorGPU_StoredEncodingsMatchCPU, with a frame carrying 65535 at a saturation of
65535, against the CPU preprocessor on the host-widened image.

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01EizBKhTqrAYago9KJG3dA3
This commit is contained in:
leonarski_f
2026-10-12 22:37:47 +02:00
co-authored by Claude Opus 5.5
parent f4252ad39d
commit dfd69fcb91
10 changed files with 94 additions and 30 deletions
Download Patch File Download Diff File Expand all files Collapse all files
image_analysis/image_preprocessing/ImagePreprocessorGPU.cu
+19 -5
View File
@@ -21,8 +21,8 @@ __global__ void preprocess_kernel(
const uint8_t *__restrict__ mask,
int32_t *__restrict__ output,
ImageStatistics *__restrict__ stats,
T saturation_limit,
T err_value,
int64_t saturation_limit,
int64_t err_value,
int npixels) {
// Shared block accumulators
__shared__ unsigned long long s_masked;
@@ -410,7 +410,8 @@ ImageStatistics ImagePreprocessorGPU::Analyze(ImagePreprocessorBuffer &processed
bool ImagePreprocessorGPU::AnalyzeCompressed(ImagePreprocessorBuffer &processed_image,
const CompressedImage &image,
ImageStatistics &stats) {
if (image.GetCompressionAlgorithm() == CompressionAlgorithm::CBF_BYTE_OFFSET) {
if (image.GetCompressionAlgorithm() == CompressionAlgorithm::CBF_BYTE_OFFSET
|| image.GetCompressionAlgorithm() == CompressionAlgorithm::UINT16_WIDENED) {
stats = AnalyzeStoredInt32(processed_image, image);
return true;
}
@@ -543,6 +544,19 @@ ImageStatistics ImagePreprocessorGPU::AnalyzeStoredInt32(ImagePreprocessorBuffer
if (!gpu_decompressed_image.get())
gpu_decompressed_image = CudaDevicePtr<uint8_t>(npixels * sizeof(uint32_t));
if (image.GetCompressionAlgorithm() == CompressionAlgorithm::UINT16_WIDENED) {
// Uploaded as stored, two bytes a pixel, and widened by the preprocessing kernel itself: with
// int32's error marker and int32's saturation, each pixel is decided exactly as the int32 image
// the host would have widened it to.
last_decode = LastDecode::None;
if (image.GetCompressedSize() != npixels * sizeof(uint16_t))
throw JFJochException(JFJochExceptionCategory::InputParameterInvalid, "Mismatch in size");
PinInputRegion(image.GetCompressed(), image.GetCompressedSize());
cuda_err(cudaMemcpyAsync(gpu_decompressed_image, image.GetCompressed(), image.GetCompressedSize(),
cudaMemcpyHostToDevice, *stream));
return AnalyzeOnDevice<uint16_t>(processed_image, INT32_MIN, INT32_MAX);
}
last_decode = LastDecode::CBF;
if (!cbf_decoder)
cbf_decoder = std::make_unique<CBFDecoderGPU>(stream);
@@ -556,9 +570,9 @@ ImageStatistics ImagePreprocessorGPU::AnalyzeStoredInt32(ImagePreprocessorBuffer
// entry points so the two cannot drift apart.
template<class T>
ImageStatistics ImagePreprocessorGPU::AnalyzeOnDevice(ImagePreprocessorBuffer &processed_image,
T err_value, T sat_value) {
int64_t err_value, int64_t sat_value) {
if (sat_value > saturation_limit)
sat_value = static_cast<T>(saturation_limit);
sat_value = saturation_limit;
cpu_stats[0] = ImageStatistics{.max_value = INT64_MIN, .min_value = INT64_MAX};
cuda_err(cudaMemcpyAsync(gpu_stats, cpu_stats.data(), sizeof(ImageStatistics), cudaMemcpyHostToDevice, *stream));