soorena62
90 lines
3.5 KiB
Python
90 lines
3.5 KiB
Python
import os
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import time
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import json
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import luigi
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from pathlib import Path
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import pysera
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from engine.utils import json_safe
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class FeatureExtraction(luigi.Task):
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artifacts_dir = luigi.Parameter(default="artifacts")
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temp_dir = luigi.Parameter(default=r"C:\Users\Omen16\AppData\Local\ViSERA\res\memory\memmap\pysera_temp")
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def requires(self):
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from engine.tasks_filter import ImageFilter
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from engine.tasks_maskreg import MaskRegistration
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return {
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"filter": ImageFilter(artifacts_dir=self.artifacts_dir),
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"maskreg": MaskRegistration(artifacts_dir=self.artifacts_dir)
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}
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def output(self):
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return luigi.LocalTarget(os.path.join(self.artifacts_dir, "radiomics_index.json"))
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def run(self):
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#
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filt_index = os.path.join(self.artifacts_dir, "filtered_index.txt")
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mask_index = os.path.join(self.artifacts_dir, "mask_registered_index.txt")
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with open(filt_index, "r") as f:
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filtered_paths = [line.strip() for line in f if line.strip()]
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with open(mask_index, "r") as f:
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mask_paths = [line.strip() for line in f if line.strip()]
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results = []
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for img, mask in zip(filtered_paths, mask_paths):
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start = time.time()
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result = pysera.process_batch(
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image_input=img,
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mask_input=mask,
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output_path=self.artifacts_dir,
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categories="diag,morph,glcm,glrlm,glszm,ngtdm,ngldm",
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dimensions="1st,3D",
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bin_size=25,
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roi_num=2,
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roi_selection_mode="per_region",
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apply_preprocessing=True,
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feature_value_mode="REAL_VALUE",
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min_roi_volume=50,
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enable_parallelism=True,
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num_workers="4",
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report="info",
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temporary_files_path=str(self.temp_dir),
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IBSI_based_parameters={
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"radiomics_DataType": "CT",
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"radiomics_DiscType": "FBS",
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"radiomics_isScale": 0,
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"radiomics_VoxInterp": "Nearest",
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"radiomics_ROIInterp": "Nearest",
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"radiomics_isotVoxSize": 2.0,
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"radiomics_isotVoxSize2D": 2.0,
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"radiomics_isIsot2D": 0,
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"radiomics_isGLround": 0,
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"radiomics_isReSegRng": 0,
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"radiomics_isOutliers": 0,
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"radiomics_isQuntzStat": 1,
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"radiomics_ReSegIntrvl01": -1000,
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"radiomics_ReSegIntrvl02": 400,
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"radiomics_ROI_PV": 0.5,
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"radiomics_qntz": "Uniform",
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"radiomics_IVH_Type": 3,
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"radiomics_IVH_DiscCont": 1,
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"radiomics_IVH_binSize": 2.0,
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},
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)
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elapsed = round(time.time() - start, 2)
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safe_result = json_safe(result)
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case_json = os.path.join(self.artifacts_dir, f"{Path(img).name}_radiomics.json")
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with open(case_json, "w", encoding="utf-8") as f:
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json.dump(safe_result, f, indent=2, ensure_ascii=False)
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results.append({
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"image": img,
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"mask": mask,
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"elapsed_seconds": elapsed,
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"result_file": case_json,
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})
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with self.output().open("w") as f:
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json.dump({"radiomics_results": results}, f, indent=2, ensure_ascii=False)
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