--- a
+++ b/test_docker.py
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+"""script to predict the segmentation results and calculate surrogate biomarkers of a given testing dataset using the docker image.
+
+This script allows users to execute the whole pipeline using the docker image.
+"""
+
+import os
+import sys
+import warnings
+warnings.filterwarnings('ignore')
+
+from src.LFBNet.utilities import train_valid_paths
+from src.LFBNet.preprocessing import preprocessing
+from src.run import trainer, parse_argument
+from src.LFBNet.utilities.compute_surrogate_features import ComputesTMTVsDmaxFromNii
+
+
+def main():
+    """ Predicts tumor segmentation results and calculates associated quantitative metrics on a given testing dataset.
+
+
+
+    This function receives the path directory to the testing dataset that contains the PET images. It predicts the
+
+    segmentation results and saves them as .nii files. It then calculates the surrogate metabolic tumor volume (sTMTV) and
+
+    surrogate dissemination feature (sDmax) and saves it as CSV or Xls file.
+
+    Acronyms:
+        PET: Nifti format of [18]F-FDG PET images in SUV unit.
+        GT: Ground truth mask from the expert if available.
+
+    [directory_path_to_raw 3D nifti data with SUV values] should have the following structure as:
+    main_dir:
+        -- patient_id_1:
+            -- PET
+                --give_name.nii or give_name.nii.gz
+            -- GT (if available) (Ground truth mask from the expert if available)
+                -- give_name.nii or give_name.nii.gz
+
+         -- patient_id_2:
+            -- PET
+                --give_name.nii or give_name.nii.gz
+            -- GT (if available)
+                -- give_name.nii or give_name.nii.gz
+
+    It reads the .nii files, resize, crop, and save the 3D data, then from these data it generates the sagittal and
+    coronal PET MIPs and the ground truth (mask from the expert) if available in the folder.
+
+    Get the latest trained model weight from './weight' directory and use that weight to predict the segmentation.
+
+    Returns:
+        save segmented images and computed surrogate biomarker features using the last weight saved in the ./weight
+        folder.
+    """
+
+    # Path to the parent/main directory. Please read readme.md for how to organize your files.
+    input_dir = "/input"
+
+    # parameters to set
+    dataset_name = 'data'
+    desired_spacing = [4.0, 4.0, 4.0]
+
+    # path to the preprocessed data
+    preprocessing_data_dir = "/output"
+
+    preprocessing_params = dict(
+        data_path=input_dir, data_name=dataset_name, saving_dir=preprocessing_data_dir, save_3D=True,
+        output_resolution=[128, 128, 256], desired_spacing=desired_spacing, generate_mip=True
+        )
+    mip_data_dir = preprocessing.read_pet_gt_resize_crop_save_as_3d_andor_mip(**preprocessing_params)
+
+    # get list of all patient names from the generated mip directory
+    patient_id_list = os.listdir(mip_data_dir)
+    print('There are %d cases to evaluate \n' % len(patient_id_list))
+
+    # prediction on the given testing dataset
+    test_params = dict(
+        preprocessed_dir=mip_data_dir, data_list=patient_id_list, predicted_dir=preprocessing_data_dir
+        )
+    network_run = trainer.ModelTesting(**test_params)
+    network_run.test()
+
+    print("\n\n Computing the surrogate biomarkers ... \n\n")
+    for identifier, data_path in zip(
+            ["predicted", "ground_truth"], [os.path.join(preprocessing_data_dir, "predicted_data"),
+                            os.path.join(preprocessing_data_dir, "data_default_MIP_dir")]
+            ):
+        try:
+            csv_file = ComputesTMTVsDmaxFromNii(data_path=data_path, get_identifier=identifier)
+            csv_file.compute_and_save_surrogate_features()
+        except:
+            continue
+
+
+# check
+if __name__ == '__main__':
+    print("\n Running the integrated framework for testing use case... \n\n")
+    main()