Add All Folders
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soorena62
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from dagster import job, graph, op
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from pathlib import Path
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from .ops import (
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read_image_and_mask,
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read_image_and_mask_from_case,
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filter_image,
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extract_features,
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write_outputs,
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enumerate_cases_auto
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)
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# Extract image file name from config for single file mode
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@op(config_schema={"image_path": str})
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def get_case_name_from_config(context) -> str:
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image_path = context.op_config["image_path"]
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return Path(image_path).stem
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# Extract case_name from case dictionary for batch mode
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@op
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def get_case_name_from_case(context, case: dict) -> str:
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return case["case_name"]
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# Single-case workflow job
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@job(config={
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"ops": {
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"read_image_and_mask": {
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"config": {
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"image_path": "C:/Users/Omen16/Documents/Radiuma_Mini/data/images/CT_pitch.nii.gz",
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"mask_path": "C:/Users/Omen16/Documents/Radiuma_Mini/data/masks/CT_pitch_mask.nii.gz",
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}
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},
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"get_case_name_from_config": {
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"config": {
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"image_path": "C:/Users/Omen16/Documents/Radiuma_Mini/data/images/CT_pitch.nii.gz"
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}
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}
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}
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})
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def radiomics_job():
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image, mask = read_image_and_mask()
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filtered = filter_image(image)
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feats = extract_features(image=filtered, mask=mask)
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case_name = get_case_name_from_config()
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write_outputs(features=feats, case_name=case_name)
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# Graph that processes one case dict (used for batch mapping)
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@graph
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def process_case(case):
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image, mask = read_image_and_mask_from_case(case)
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filtered = filter_image(image)
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feats = extract_features(image=filtered, mask=mask)
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case_name = get_case_name_from_case(case)
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write_outputs(features=feats, case_name=case_name)
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# Batch workflow job (auto-discovery + dynamic mapping)
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@job
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def radiomics_batch_job():
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cases = enumerate_cases_auto()
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cases.map(process_case)
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# Preprocessing-only workflow job
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@job(config={
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"ops": {
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"read_image_and_mask": {
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"config": {
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"image_path": "C:/Users/Omen16/Documents/Radiuma_Mini/data/images/CT_pitch.nii.gz",
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"mask_path": "C:/Users/Omen16/Documents/Radiuma_Mini/data/masks/CT_pitch_mask.nii.gz",
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}
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},
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"get_case_name_from_config": {
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"config": {
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"image_path": "C:/Users/Omen16/Documents/Radiuma_Mini/data/images/CT_pitch.nii.gz"
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}
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}
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}
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})
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def preprocessing_job():
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image, mask = read_image_and_mask()
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filtered = filter_image(image)
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feats = extract_features(image=filtered, mask=mask)
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case_name = get_case_name_from_config()
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write_outputs(features=feats, case_name=case_name)
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# Feature-extraction-only workflow job
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@job(config={
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"ops": {
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"read_image_and_mask": {
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"config": {
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"image_path": "C:/Users/Omen16/Documents/Radiuma_Mini/data/images/CT_pitch.nii.gz",
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"mask_path": "C:/Users/Omen16/Documents/Radiuma_Mini/data/masks/CT_pitch_mask.nii.gz",
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}
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},
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"get_case_name_from_config": {
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"config": {
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"image_path": "C:/Users/Omen16/Documents/Radiuma_Mini/data/images/CT_pitch.nii.gz"
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}
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}
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}
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})
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def feature_extraction_job():
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image, mask = read_image_and_mask()
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feats = extract_features(image=image, mask=mask)
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case_name = get_case_name_from_config()
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write_outputs(features=feats, case_name=case_name)
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import os
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import numpy as np
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import pandas as pd
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from pathlib import Path
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import SimpleITK as sitk
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from dagster import op, In, Out, DynamicOut, DynamicOutput
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# Utility functions
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def remove_ext(filename: str, exts):
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fname = filename
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for ext in exts:
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if fname.lower().endswith(ext.lower()):
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return fname[:-len(ext)]
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return fname
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def strip_suffixes(name_no_ext: str, suffixes):
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for s in suffixes:
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if name_no_ext.endswith(s):
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return name_no_ext[:-len(s)]
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return name_no_ext
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def levenshtein(a: str, b: str) -> int:
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if a == b:
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return 0
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m, n = len(a), len(b)
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dp = list(range(n + 1))
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for i in range(1, m + 1):
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prev, dp[0] = dp[0], i
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for j in range(1, n + 1):
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cur = dp[j]
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cost = 0 if a[i - 1] == b[j - 1] else 1
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dp[j] = min(dp[j] + 1, dp[j - 1] + 1, prev + cost)
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prev = cur
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return dp[n]
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def list_files(root: Path, exts):
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files = []
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for p in Path(root).rglob("*"):
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if p.is_file() and any(str(p).lower().endswith(ext.lower()) for ext in exts):
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files.append(p)
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return sorted(files)
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# Single-case readers
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@op(
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config_schema={
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"image_path": str,
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"mask_path": str,
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},
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out={"image": Out(), "mask": Out()}
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)
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def read_image_and_mask(context):
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image_path = context.op_config["image_path"]
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mask_path = context.op_config["mask_path"]
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if not os.path.exists(image_path):
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raise FileNotFoundError(f"Image not found: {image_path}")
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if not os.path.exists(mask_path):
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raise FileNotFoundError(f"Mask not found: {mask_path}")
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image = sitk.ReadImage(image_path)
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mask = sitk.ReadImage(mask_path)
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img_arr = sitk.GetArrayFromImage(image)
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msk_arr = sitk.GetArrayFromImage(mask)
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context.log.info(f"[single] image:{img_arr.shape} mask:{msk_arr.shape}")
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return img_arr, msk_arr
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@op(
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ins={"case": In()},
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out={"image": Out(), "mask": Out()}
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)
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def read_image_and_mask_from_case(context, case: dict):
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image_path = case["image_path"]
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mask_path = case["mask_path"]
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if not os.path.exists(image_path):
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raise FileNotFoundError(f"Image not found: {image_path}")
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if not os.path.exists(mask_path):
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raise FileNotFoundError(f"Mask not found: {mask_path}")
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image = sitk.ReadImage(image_path)
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mask = sitk.ReadImage(mask_path)
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img_arr = sitk.GetArrayFromImage(image)
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msk_arr = sitk.GetArrayFromImage(mask)
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context.log.info(f"[case] image:{img_arr.shape} mask:{msk_arr.shape}")
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return img_arr, msk_arr
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# Preprocess, features, writer
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@op(out=Out())
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def filter_image(context, image: np.ndarray):
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context.log.info(f"[filter] image:{image.shape}")
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return image
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@op(ins={"image": In(), "mask": In()}, out=Out())
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def extract_features(context, image: np.ndarray, mask: np.ndarray):
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masked = image[mask > 0]
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mean_val = float(masked.mean()) if masked.size > 0 else 0.0
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features = {"mean_intensity": mean_val}
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context.log.info(f"[features] {features}")
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return features
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RESULTS_DIR = Path("results")
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RESULTS_DIR.mkdir(exist_ok=True)
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@op(ins={"features": In(), "case_name": In()}, out=Out())
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def write_outputs(context, features: dict, case_name: str):
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# Define output CSV file path
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csv_path = RESULTS_DIR / "features.csv"
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# Prepare row with case name and features
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row = {"case_name": case_name}
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row.update(features)
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# If CSV does not exist, create new file with header
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if not csv_path.exists():
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df = pd.DataFrame([row])
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df.to_csv(csv_path, index=False)
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else:
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# Append new row to existing CSV
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df = pd.DataFrame([row])
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df.to_csv(csv_path, mode="a", header=False, index=False)
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context.log.info(f"[write] features appended to {csv_path}")
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# Optional Excel export (currently disabled)
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# excel_path = RESULTS_DIR / "features.xlsx"
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# df.to_excel(excel_path, index=False)
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return str(csv_path)
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# Batch case discovery
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@op(
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config_schema={
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"images_dir": str,
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"masks_dir": str,
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"image_exts": list,
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"mask_exts": list,
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"mask_suffixes": list,
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"allow_fuzzy_match": bool,
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"fuzzy_max_distance": int,
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"verbose": bool,
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},
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out=DynamicOut()
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)
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def enumerate_cases_auto(context):
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cfg = context.op_config
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images_dir = Path(cfg["images_dir"]).expanduser().resolve()
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masks_dir = Path(cfg["masks_dir"]).expanduser().resolve()
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image_exts = [ext.lower() for ext in cfg["image_exts"]]
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mask_exts = [ext.lower() for ext in cfg["mask_exts"]]
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mask_suffixes = cfg["mask_suffixes"]
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allow_fuzzy = cfg["allow_fuzzy_match"]
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max_dist = int(cfg["fuzzy_max_distance"])
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verbose = bool(cfg.get("verbose", True))
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img_files = list_files(images_dir, image_exts)
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msk_files = list_files(masks_dir, mask_exts)
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context.log.info(f"[discover] images_dir={images_dir} masks_dir={masks_dir}")
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context.log.info(f"[discover] found images: {len(img_files)}")
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for p in img_files:
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context.log.info(f" - image: {p.name}")
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context.log.info(f"[discover] found masks: {len(msk_files)}")
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for p in msk_files:
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context.log.info(f" - mask: {p.name}")
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msk_index = {}
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for m in msk_files:
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base = strip_suffixes(remove_ext(m.name, mask_exts), mask_suffixes).lower()
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msk_index.setdefault(base, []).append(m)
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yielded = 0
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skipped = 0
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for img in img_files:
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base = remove_ext(img.name, image_exts).lower()
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candidates = msk_index.get(base, [])
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chosen = None
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reason = None
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if candidates:
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chosen = candidates[0]
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else:
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if allow_fuzzy and msk_files:
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best = None
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best_d = 10**9
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for m in msk_files:
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mbase = strip_suffixes(remove_ext(m.name, mask_exts), mask_suffixes).lower()
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d = levenshtein(base, mbase)
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if d < best_d:
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best, best_d = m, d
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if best is not None and best_d <= max_dist:
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chosen = best
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else:
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reason = f"no exact or fuzzy match (best distance {best_d})"
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if chosen is None:
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skipped += 1
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context.log.warning(f"[skip] image:{img.name} → no mask found ({reason}) | base(image)='{base}'")
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continue
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case = {
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"image_path": str(img),
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"mask_path": str(chosen),
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"case_name": remove_ext(img.name, image_exts),
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}
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context.log.info(f"[pair] {case['case_name']} → image:{img.name} mask:{Path(chosen).name}")
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yield DynamicOutput(case, mapping_key=case["case_name"])
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yielded += 1
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context.log.info(f"[summary] yielded={yielded} skipped={skipped}")
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if yielded == 0:
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context.log.error("No valid cases discovered. See [skip] messages above for reasons.")
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raise Exception("No valid cases discovered. See logs for details.")
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