T36 data format v2

README.md

@@ -91,7 +91,7 @@ PACFISH API. We have examples for both `Python` and `MATLAB`.
 
 ## Use case: Implement a conversion adapter
 
-    impot pacfish as pf
+    import pacfish as pf
 
     class DeviceSpecificAdapter(pf.BaseAdapter):
 

examples/python/read_imagio_file.py

@@ -0,0 +1,51 @@
+#
+# This file reads an example Laser Optic Movie (LOM) produced by the Seno (https://senomedical.com/) 
+# Imagio System and produces an IPASC-compatible HDF5 file.  In addition, if enabled, graphical 
+# representations of the data are written as .png files.
+#
+
+import os
+import numpy as np
+import cv2
+import pathlib
+from pacfish.api.adapters.Imagio_File_Converter import ImagioFileConverter
+from pacfish import write_data
+from pacfish import quality_check_pa_data
+
+# input file is a Laser Optic Movie (LOM) from a scan of a phantom using the Seno Imagio system
+# input_file = '../../examples/python/input/I0000001.dcm.lom' # Gen2 (4 OA frames, 1 US image, hair target)
+
+# other examples
+#input_file = '../../examples/python/input/2017_12_11-10_54_55_0.lom' # Gen1a (many OA frames, many US images, lesion)
+input_file = '../../examples/python/input/20211102134837219776.dcm.lom' # Gen2 (many OA frames, many US images, hair target)
+
+converter = ImagioFileConverter(input_file) 
+
+pa_data = converter.generate_pa_data()
+
+quality_check_pa_data(pa_data, verbose=True, log_file_path="")
+
+folder = "output/" + os.path.basename(input_file)
+pathlib.Path(folder).mkdir(parents=True, exist_ok=True)
+
+output_png = True # disable if needed
+if (output_png):
+    timestamps = pa_data.get_measurement_time_stamps()
+    wavelengths = pa_data.get_acquisition_wavelengths()
+    for idx, oa_frame in enumerate(pa_data.binary_time_series_data[0, 0, :]):
+        iTick = str(int(timestamps[idx] * 1E3)) + "_msec"
+        wavelength = str(int(wavelengths[idx] * 1E9)) + "_nm"
+        file = folder + "/oa_" + str(iTick) + "_" + wavelength + ".png"
+        cv2.imwrite(file, (oa_frame+32768)/256) # conversion is to ensure image is written with 16-bit values
+        print(f"INFO: Wrote file '{file}' for OA frame")
+
+    for idx, us_image in enumerate(pa_data.meta_data_acquisition['ultrasound_image_data']):
+        iTick = str(int(pa_data.meta_data_acquisition['ultrasound_image_timestamps'][idx]*1E3)) + "_msec"
+        file = folder + "/us_" + str(iTick) + ".png"
+        cv2.imwrite(file, us_image)
+        print(f"INFO: Wrote file '{file}' for US frame")
+
+file = folder + "/" + os.path.basename(input_file) + "_imagio_ipasc.hdf5"
+write_data(file, pa_data)
+print(f"INFO: Wrote file '{file}' with IPASC HDF5 data")
+

examples/python/read_ipasc_hdf5_file.py

@@ -7,7 +7,7 @@
 import numpy as np
 
 name = "kwave_2Dsim_random_array_new"
-FILE_PATH = r"..\..\data\ipasc_compatible_V1.hdf5"
+FILE_PATH = r"..\..\data\sample_ipasc_kwave_2Dsim_circular_array.hdf5"
 #FILE_PATH = f"C:/kWave-PACFISH-export/{name}.hdf5"
 # FILE_PATH = "C:/standardised-image-reconstruction/15PPMPX__ZJQLvYSdWe5CxumvueVrizFy_ipasc.hdf5"
 
@@ -21,7 +21,8 @@
 shape = np.shape(pa_data.binary_time_series_data)
 plt.figure()
 plt.title("Time Series Data")
-plt.imshow(np.squeeze(pa_data.binary_time_series_data[:, :, 0].T))
+plt.imshow(np.squeeze(pa_data.binary_time_series_data[:, :, 0].T), aspect=(pa_data.binary_time_series_data.shape[0]/
+                                                                           pa_data.binary_time_series_data.shape[1]))
 plt.xlabel("Num detector elements")
 plt.ylabel("Num time samples")
 cb = plt.colorbar()

examples/python/read_nrrd_file.py

@@ -19,11 +19,11 @@
 
 pa_data = converter.generate_pa_data()
 
-quality_check_pa_data(pa_data, verbose=True, log_file_path="")
+quality_check_pa_data(pa_data, verbose=True)
 
 write_data("demodata_ipasc.hdf5", pa_data)
 
-binary = np.rot90(pa_data.binary_time_series_data[:, 500:-2500, 0, 0], -1)
+binary = np.rot90(pa_data.binary_time_series_data[:, 500:-2500, 0], -1)
 binary = binary - np.min(binary) + 1
 binary = np.log10(binary)
 plt.imshow(binary, aspect=0.08, vmin=np.percentile(binary, 1), vmax=np.percentile(binary, 99))

examples/python/visualise_zenodo_example_data.py

@@ -19,10 +19,11 @@
 #data_url = "https://zenodo.org/record/5938838/files/sample_ipasc_kwave_2Dsim_semicircular_array.hdf5"
 
 # Downloading file
-filename = download_file(data_url)
+#filename = download_file(data_url)
 
 # Loading file from disk as a pa_data instance
-pa_data = pf.load_data(filename)
+# "D:/PACFISH/examples/python/output/2017_12_11-10_54_55_0.lom/2017_12_11-10_54_55_0.lom_imagio_ipasc.hdf5"
+pa_data = pf.load_data("D:/PACFISH/examples/python/output/20211102134837219776.dcm.lom/20211102134837219776.dcm.lom_imagio_ipasc.hdf5")
 
 # perform completeness and consistency checking on the pa_data
 pf.quality_check_pa_data(pa_data, verbose=True)
@@ -35,7 +36,7 @@
 shape = np.shape(pa_data.binary_time_series_data)
 plt.figure(figsize=(4.75, 4))
 plt.title("Time Series Data")
-plt.imshow(np.squeeze(pa_data.binary_time_series_data).T, aspect=shape[0]/shape[1])
+plt.imshow(np.squeeze(pa_data.binary_time_series_data[:, :, 0]).T, aspect=shape[0]/shape[1])
 plt.xlabel("Num detector elements")
 plt.ylabel("Num time samples")
 cb = plt.colorbar()

pacfish/api/BaseAdapter.py

@@ -47,6 +47,9 @@ def __init__(self):
         meta_data_device = self.generate_device_meta_data()
         self.pa_data.meta_data_device = meta_data_device
 
+        # We hard-set the version number when exporting.
+        self.pa_data.meta_data_acquisition[MetadataAcquisitionTags.VERSION.tag] = "V2"
+
 
     @abstractmethod
     def generate_binary_data(self) -> np.ndarray: