oslo: add a zea process pipeline.yaml per sub-dataset

oslo/A_cardiac/README.md

@@ -56,7 +56,17 @@ Generalized reconstruction and adaptive beamforming of cardiac ultrasound (RFP t
 
 ## Processing the Dataset
 
-The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/oslo/reconstruct.py) at the root of this collection, as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF), together with the `pipeline*.yaml` definitions at the collection root and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub; `parameters.yaml` picks the pipeline, display window and dynamic range per acquisition (see the [collection card](../README.md#processing-the-dataset)).
+The example acquisition can be processed with the `pipeline.yaml` definition in this folder and the [zea library](https://github.com/tue-bmd/zea).
+
+`zea` streams the data from the Hugging Face Hub and processes it according to the pipeline. You can try it out with the following command:
+
+```bash
+zea process \
+  --dataset hf://nvidia/OpenH-RF/oslo/A_cardiac/Verasonics_P2-4_parasternal_long_subject_1.hdf5 \
+  --config hf://nvidia/OpenH-RF/oslo/A_cardiac/pipeline.yaml
+```
+
+This `pipeline.yaml` holds the pipeline, display window and sector limits of that acquisition. Alternatively, all acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/oslo/reconstruct.py) at the root of this collection, as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF), together with the `pipeline*.yaml` definitions at the collection root and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub; `parameters.yaml` picks the pipeline, display window and dynamic range per acquisition (see the [collection card](../README.md#processing-the-dataset)).
 
 ## Dataset Format
 

oslo/A_cardiac/pipeline.yaml

@@ -0,0 +1,34 @@
+# zea pipeline for A_cardiac/Verasonics_P2-4_parasternal_long_subject_1.hdf5, the example acquisition of this sub-dataset.
+# The shared ../pipeline_sector.yaml plus the per-acquisition settings that
+# ../reconstruct.py takes from ../parameters.yaml (display window in metres,
+# dynamic range, polar_limits), so `zea process` renders it standalone.
+
+parameters:
+  f_number: 0
+  grid_type: polar
+  grid_size_x: 400
+  grid_size_z: 600
+  dynamic_range: [-60, 0]
+  selected_transmits: all
+  focal_region_length: 0.002
+  pfield_kwargs:
+    interpolation: bilinear
+    percentile: 70
+    alpha: 15
+  zlims: [0.001, 0.11]
+  polar_limits: [-0.6552720665931702, 0.6552720665931702]
+pipeline:
+  operations:
+  - name: keras.ops.cast
+    params:
+      dtype: float32
+  - name: apply_window
+  - name: demodulate
+  - name: beamform
+    params:
+      beamformer: delay_and_sum
+      enable_pfield: true
+  - name: envelope_detect
+  - name: normalize
+  - name: log_compress
+  - name: scan_convert

oslo/B_carotid/README.md

@@ -57,7 +57,17 @@ Generalized reconstruction and adaptive beamforming of vascular ultrasound (RFP
 
 ## Processing the Dataset
 
-The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/oslo/reconstruct.py) at the root of this collection, as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF), together with the `pipeline*.yaml` definitions at the collection root and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub; `parameters.yaml` picks the pipeline, display window and dynamic range per acquisition (see the [collection card](../README.md#processing-the-dataset)).
+The example acquisition can be processed with the `pipeline.yaml` definition in this folder and the [zea library](https://github.com/tue-bmd/zea).
+
+`zea` streams the data from the Hugging Face Hub and processes it according to the pipeline. You can try it out with the following command:
+
+```bash
+zea process \
+  --dataset hf://nvidia/OpenH-RF/oslo/B_carotid/L7_FI_carotid_cross_1.hdf5 \
+  --config hf://nvidia/OpenH-RF/oslo/B_carotid/pipeline.yaml
+```
+
+This `pipeline.yaml` holds the pipeline and display window of that acquisition. Alternatively, all acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/oslo/reconstruct.py) at the root of this collection, as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF), together with the `pipeline*.yaml` definitions at the collection root and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub; `parameters.yaml` picks the pipeline, display window and dynamic range per acquisition (see the [collection card](../README.md#processing-the-dataset)).
 
 ## Dataset Format
 

oslo/B_carotid/pipeline.yaml

@@ -0,0 +1,41 @@
+# zea pipeline for B_carotid/L7_FI_carotid_cross_1.hdf5, the example acquisition of this sub-dataset.
+# The shared ../pipeline_scanline.yaml plus the per-acquisition settings that
+# ../reconstruct.py takes from ../parameters.yaml (display window in metres,
+# dynamic range), so `zea process` renders it standalone.
+# zea process writes the pixel array as-is, so the grid is sized for square
+# pixels (scanline: resized after log compression).
+
+parameters:
+  f_number: 1.75
+  dynamic_range: [-60, 0]
+  enable_scanline: true
+  grid_type: cartesian
+  selected_transmits: all
+  grid_size_z: 700
+  zlims: [0.001, 0.06]
+  xlims: [-0.019, 0.019]
+pipeline:
+  operations:
+  - name: keras.ops.cast
+    params:
+      dtype: float32
+  - name: apply_window
+  - name: demodulate
+  - name: beamform
+    params:
+      beamformer: delay_and_sum
+      enable_aligned_apodization: true
+      enable_pfield: false
+  - name: envelope_detect
+  - name: normalize
+  - name: log_compress
+  - name: keras.ops.expand_dims
+    params:
+      axis: -1
+  - name: keras.ops.image.resize
+    params:
+      size: [700, 449]
+      interpolation: bilinear
+  - name: keras.ops.squeeze
+    params:
+      axis: -1

oslo/C_verasonics_phantom/README.md

@@ -56,7 +56,17 @@ Generalized reconstruction, image-quality assessment (resolution, contrast, dyna
 
 ## Processing the Dataset
 
-The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/oslo/reconstruct.py) at the root of this collection, as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF), together with the `pipeline*.yaml` definitions at the collection root and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub; `parameters.yaml` picks the pipeline, display window and dynamic range per acquisition (see the [collection card](../README.md#processing-the-dataset)).
+The example acquisition can be processed with the `pipeline.yaml` definition in this folder and the [zea library](https://github.com/tue-bmd/zea).
+
+`zea` streams the data from the Hugging Face Hub and processes it according to the pipeline. You can try it out with the following command:
+
+```bash
+zea process \
+  --dataset hf://nvidia/OpenH-RF/oslo/C_verasonics_phantom/FI_P4_cysts_center.hdf5 \
+  --config hf://nvidia/OpenH-RF/oslo/C_verasonics_phantom/pipeline.yaml
+```
+
+This `pipeline.yaml` holds the pipeline, display window and sector limits of that acquisition. Alternatively, all acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/oslo/reconstruct.py) at the root of this collection, as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF), together with the `pipeline*.yaml` definitions at the collection root and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub; `parameters.yaml` picks the pipeline, display window and dynamic range per acquisition (see the [collection card](../README.md#processing-the-dataset)).
 
 ## Dataset Format
 

oslo/C_verasonics_phantom/pipeline.yaml

@@ -0,0 +1,34 @@
+# zea pipeline for C_verasonics_phantom/FI_P4_cysts_center.hdf5, the example acquisition of this sub-dataset.
+# The shared ../pipeline_sector.yaml plus the per-acquisition settings that
+# ../reconstruct.py takes from ../parameters.yaml (display window in metres,
+# dynamic range, polar_limits), so `zea process` renders it standalone.
+
+parameters:
+  f_number: 0
+  grid_type: polar
+  grid_size_x: 400
+  grid_size_z: 600
+  dynamic_range: [-60, 0]
+  selected_transmits: all
+  focal_region_length: 0.002
+  pfield_kwargs:
+    interpolation: bilinear
+    percentile: 70
+    alpha: 15
+  zlims: [0.001, 0.115]
+  polar_limits: [-0.7853981852531433, 0.7853981852531433]
+pipeline:
+  operations:
+  - name: keras.ops.cast
+    params:
+      dtype: float32
+  - name: apply_window
+  - name: demodulate
+  - name: beamform
+    params:
+      beamformer: delay_and_sum
+      enable_pfield: true
+  - name: envelope_detect
+  - name: normalize
+  - name: log_compress
+  - name: scan_convert

oslo/D_alpinion_phantom/README.md

@@ -56,7 +56,17 @@ Generalized reconstruction and image-quality assessment on a second hardware pla
 
 ## Processing the Dataset
 
-The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/oslo/reconstruct.py) at the root of this collection, as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF), together with the `pipeline*.yaml` definitions at the collection root and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub; `parameters.yaml` picks the pipeline, display window and dynamic range per acquisition (see the [collection card](../README.md#processing-the-dataset)).
+The example acquisition can be processed with the `pipeline.yaml` definition in this folder and the [zea library](https://github.com/tue-bmd/zea).
+
+`zea` streams the data from the Hugging Face Hub and processes it according to the pipeline. You can try it out with the following command:
+
+```bash
+zea process \
+  --dataset hf://nvidia/OpenH-RF/oslo/D_alpinion_phantom/Alpinion_L3-8_CPWC_hypoechoic.hdf5 \
+  --config hf://nvidia/OpenH-RF/oslo/D_alpinion_phantom/pipeline.yaml
+```
+
+This `pipeline.yaml` holds the pipeline and display window of that acquisition. Alternatively, all acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/oslo/reconstruct.py) at the root of this collection, as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF), together with the `pipeline*.yaml` definitions at the collection root and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub; `parameters.yaml` picks the pipeline, display window and dynamic range per acquisition (see the [collection card](../README.md#processing-the-dataset)).
 
 ## Dataset Format
 

oslo/D_alpinion_phantom/pipeline.yaml

@@ -0,0 +1,29 @@
+# zea pipeline for D_alpinion_phantom/Alpinion_L3-8_CPWC_hypoechoic.hdf5, the example acquisition of this sub-dataset.
+# The shared ../pipeline.yaml plus the per-acquisition settings that
+# ../reconstruct.py takes from ../parameters.yaml (display window in metres,
+# dynamic range), so `zea process` renders it standalone.
+# zea process writes the pixel array as-is, so the grid is sized for square
+# pixels.
+
+parameters:
+  f_number: 1.75
+  grid_size_x: 507
+  grid_size_z: 600
+  dynamic_range: [-60, 0]
+  selected_transmits: all
+  zlims: [0.005, 0.05]
+  xlims: [-0.019, 0.019]
+pipeline:
+  operations:
+  - name: keras.ops.cast
+    params:
+      dtype: float32
+  - name: apply_window
+  - name: demodulate
+  - name: beamform
+    params:
+      beamformer: delay_and_sum
+      enable_pfield: false
+  - name: envelope_detect
+  - name: normalize
+  - name: log_compress

oslo/E_simulation/README.md

@@ -57,7 +57,17 @@ Generalized reconstruction, beamformer development and validation with known gro
 
 ## Processing the Dataset
 
-The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/oslo/reconstruct.py) at the root of this collection, as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF), together with the `pipeline*.yaml` definitions at the collection root and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub; `parameters.yaml` picks the pipeline, display window and dynamic range per acquisition (see the [collection card](../README.md#processing-the-dataset)).
+The example acquisition can be processed with the `pipeline.yaml` definition in this folder and the [zea library](https://github.com/tue-bmd/zea).
+
+`zea` streams the data from the Hugging Face Hub and processes it according to the pipeline. You can try it out with the following command:
+
+```bash
+zea process \
+  --dataset hf://nvidia/OpenH-RF/oslo/E_simulation/PICMUS_numerical_calib_v2.hdf5 \
+  --config hf://nvidia/OpenH-RF/oslo/E_simulation/pipeline.yaml
+```
+
+This `pipeline.yaml` holds the pipeline and display window of that acquisition. Alternatively, all acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/oslo/reconstruct.py) at the root of this collection, as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF), together with the `pipeline*.yaml` definitions at the collection root and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub; `parameters.yaml` picks the pipeline, display window and dynamic range per acquisition (see the [collection card](../README.md#processing-the-dataset)).
 
 ## Dataset Format
 

oslo/E_simulation/pipeline.yaml

@@ -0,0 +1,28 @@
+# zea pipeline for E_simulation/PICMUS_numerical_calib_v2.hdf5, the example acquisition of this sub-dataset.
+# The shared ../pipeline_iq.yaml plus the per-acquisition settings that
+# ../reconstruct.py takes from ../parameters.yaml (display window in metres,
+# dynamic range), so `zea process` renders it standalone.
+# zea process writes the pixel array as-is, so the grid is sized for square
+# pixels.
+
+parameters:
+  f_number: 1.75
+  grid_size_x: 507
+  grid_size_z: 600
+  dynamic_range: [-60, 0]
+  selected_transmits: all
+  zlims: [0.005, 0.05]
+  xlims: [-0.019, 0.019]
+pipeline:
+  operations:
+  - name: keras.ops.cast
+    params:
+      dtype: float32
+  - name: apply_window
+  - name: beamform
+    params:
+      beamformer: delay_and_sum
+      enable_pfield: false
+  - name: envelope_detect
+  - name: normalize
+  - name: log_compress

oslo/F_motion/README.md

@@ -57,7 +57,17 @@ Motion estimation (RFP task 6.4): shear-wave velocity estimation, ARFI displacem
 
 ## Processing the Dataset
 
-The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/oslo/reconstruct.py) at the root of this collection, as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF), together with the `pipeline*.yaml` definitions at the collection root and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub; `parameters.yaml` picks the pipeline, display window and dynamic range per acquisition (see the [collection card](../README.md#processing-the-dataset)). These plane-wave tracking acquisitions use the non-focused `compound` pipeline, and the reference reconstruction shows a single tracking frame.
+The example acquisition can be processed with the `pipeline.yaml` definition in this folder and the [zea library](https://github.com/tue-bmd/zea).
+
+`zea` streams the data from the Hugging Face Hub and processes it according to the pipeline. You can try it out with the following command:
+
+```bash
+zea process \
+  --dataset hf://nvidia/OpenH-RF/oslo/F_motion/SWE_L7_type_III.hdf5 \
+  --config hf://nvidia/OpenH-RF/oslo/F_motion/pipeline.yaml
+```
+
+This `pipeline.yaml` holds the pipeline and display window of that acquisition. Alternatively, all acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/oslo/reconstruct.py) at the root of this collection, as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF), together with the `pipeline*.yaml` definitions at the collection root and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub; `parameters.yaml` picks the pipeline, display window and dynamic range per acquisition (see the [collection card](../README.md#processing-the-dataset)). These plane-wave tracking acquisitions use the non-focused `compound` pipeline, and the reference reconstruction shows a single tracking frame.
 
 ## Dataset Format
 

oslo/F_motion/pipeline.yaml

@@ -0,0 +1,29 @@
+# zea pipeline for F_motion/SWE_L7_type_III.hdf5, the example acquisition of this sub-dataset.
+# The shared ../pipeline.yaml plus the per-acquisition settings that
+# ../reconstruct.py takes from ../parameters.yaml (display window in metres,
+# dynamic range), so `zea process` renders it standalone.
+# zea process writes the pixel array as-is, so the grid is sized for square
+# pixels.
+
+parameters:
+  f_number: 1.75
+  grid_size_x: 585
+  grid_size_z: 600
+  dynamic_range: [-60, 0]
+  selected_transmits: all
+  zlims: [0.001, 0.04]
+  xlims: [-0.019, 0.019]
+pipeline:
+  operations:
+  - name: keras.ops.cast
+    params:
+      dtype: float32
+  - name: apply_window
+  - name: demodulate
+  - name: beamform
+    params:
+      beamformer: delay_and_sum
+      enable_pfield: false
+  - name: envelope_detect
+  - name: normalize
+  - name: log_compress

oslo/README.md

@@ -57,7 +57,7 @@ Total: **39 acquisitions**, 8.61 GB of stored HDF5 data.
 
 ## Processing the Dataset
 
-Every acquisition is reconstructable from the file alone — all acquisition parameters live in the zea `/scan` and `/probe` groups. The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/oslo/reconstruct.py), as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF), together with the `pipeline*.yaml` and `parameters.yaml` definitions in this folder and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub: set `PATHS` to the acquisition to reconstruct (default: the apical four-chamber cardiac scan above) and `REFOCUS = True` to also emit the REFoCUS variant. See [Reconstruction Details](#reconstruction-details) for how each acquisition's pipeline is chosen.
+Every acquisition is reconstructable from the file alone — all acquisition parameters live in the zea `/scan` and `/probe` groups. The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/oslo/reconstruct.py), as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF), together with the `pipeline*.yaml` and `parameters.yaml` definitions in this folder and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub: set `PATHS` to the acquisition to reconstruct (default: the apical four-chamber cardiac scan above) and `REFOCUS = True` to also emit the REFoCUS variant. See [Reconstruction Details](#reconstruction-details) for how each acquisition's pipeline is chosen. Each sub-dataset folder also holds a `pipeline.yaml` for its example acquisition, so it can be rendered with a single `zea process` command (see the sub-dataset cards).
 
 ## Dataset Format