volume, header = load_dicom_volume_as_numpy_from_list(get_series_for_inference(study_dir))
print(f"Found series of {volume.shape[2]} axial slices")
print("HippoVolume.AI: Running inference...")
# Use the UNetInferenceAgent class and model parameter file from the previous section
inference_agent = UNetInferenceAgent(
device="cpu",
parameter_file_path=r"../../section2/out/model.pth")
# Run inference
# single_volume_inference_unpadded takes a volume of arbitrary size
# and reshapes y and z dimensions to the patch size used by the model before
# running inference. Your job is to implement it.
t_start = time.time()
pred_label = inference_agent.single_volume_inference_unpadded(np.array(volume))
pred_volumes = get_predicted_volumes(pred_label)
# Create and save the report
print("Creating and pushing report...")
report_save_path = r"../out/report.dcm"
# STAND OUT SUGGESTION: save_report_as_dcm has some suggestions if you want to expand your
# knowledge of DICOM format
report_img = create_report(pred_volumes, header, volume, pred_label)
save_report_as_dcm(header, report_img, report_save_path)
t_end = time.time()
print(f"Process time: {t_end-t_start}")
# Send report to our storage archive
# Write a command line string that will issue a DICOM C-STORE request to send our report
# TASK_COMPLETE : get_series_for_inference is not comete. Go and complete it
# study_dir = [dir for dir, subdirs, files in os.walk(path) if '13_HCropVolume' in dir]
volume, header = load_dicom_volume_as_numpy_from_list(get_series_for_inference(study_dir))
print(f"Found series of {volume.shape[2]} axial slices")
print("HippoVome.AI: Running inference...")
# TASK_COMPLETE : Use the UNetInferenceAgent class and model paramet file from the previous section
inference_agent = UNetInferenceAgent(
device="cpu",
parameter_file_path=r"")
# Run inference
# TASK_COMPLETE : single_volume_inference_unpadded takes a volume of arbitrary size
# and reshapes y and z dimensionto the patch size used by the model before
# nning inference. Your job is to implement it.
pred_label = inference_agent.single_volume_inference_unpadded(np.array(volume), patch_size=64)
# TASK_COMPLETE : get_pricted_volumes is not complete. Go and complete it
pred_volumes = get_predicted_volumes(pred_label)
# Create and savehe report
print("Creating and pushing report...")
report_save_path = r"../out/report.dcm"
# TASK_COMPLETE : create_report is not complete. Go and complete it.
# STAND OUT SUGGESTION: savreport_as_dcm has some suggestns if you want to expand your
# knowledge of DICOM format
report_img = create_report(pred_volumes, header, volume, pred_label)
save_report_as_dcm(header, report_img, report_save_path)
# Send report to our storage archive
# TASK_COMPLETE : Write a comma line string that will issue a DICOM C-STORE request to send our report
# to our Orthanserver (that runs on port 4242 of the local machine), usinstorescu tool