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true + } + }, + "_kw_requires_import": [ + "conv_op", + "norm_op", + "dropout_op", + "nonlin" + ] + }, + "batch_dice": true + }, + "3d_cascade_fullres": { + "inherits_from": "3d_fullres", + "previous_stage": "3d_lowres" + } + }, + "experiment_planner_used": "nnUNetPlannerResEncL", + "label_manager": "LabelManager", + "foreground_intensity_properties_per_channel": { + "0": { + "max": 3867.0, + "mean": -282.4960632324219, + "median": -94.0, + "min": -1607.0, + "percentile_00_5": -1024.0, + "percentile_99_5": 630.0, + "std": 427.12237548828125 + } + } +} \ No newline at end of file diff --git a/app.py b/app.py index f83af382d006b2649e3d35065f466280836f1965..e2c364197d55b094b9476dc3987535b8ad8de00c 100644 --- a/app.py +++ b/app.py @@ -3,7 +3,7 @@ import os from huggingface_hub import snapshot_download from PIL import Image, ImageDraw -HF_REPO = "aehrc/Synthrad2025" +HF_REPO = "Synthard2025KoalAI/synthrad2025_task1" LOCAL_WEIGHTS_DIR = os.path.abspath("weights/task1") token = os.getenv("HF_TOKEN") @@ -19,7 +19,7 @@ repo_dir = snapshot_download( token=token, ) print(repo_dir) -os.environ["nnUNet_results"] = repo_dir +os.environ["nnUNet_results"] = repo_dir os.environ.setdefault("nnUNet_raw", "./nnunet_raw") os.environ.setdefault("nnUNet_preprocessed", "./nnunet_preprocessed") os.environ["OPENBLAS_NUM_THREADS"] = "1" @@ -29,13 +29,13 @@ import numpy as np import SimpleITK as sitk import io -from process import SynthradAlgorithm +from process import SynthradAlgorithm2 st.set_page_config(page_title="SynthRad (nnUNetv2) Demo", layout="wide") st.title("SynthRad (MRI/CBCT + Mask → synthetic CT) — Streamlit Demo") if "algo" not in st.session_state: - st.session_state.algo = SynthradAlgorithm() + st.session_state.algo = SynthradAlgorithm2() if "synth_ct" not in st.session_state: st.session_state.synth_ct = None if "orig_meta" not in st.session_state: @@ -76,17 +76,17 @@ SAMPLE_MAP = { "Abdomen (sample)": { "region": "Abdomen", "mri": os.path.join(repo_dir, "Abdomen", "cbct.mha"), - "mask": os.path.join(repo_dir,"Abdomen", "mask.mha"), + "mask": os.path.join(repo_dir,"Abdomen", "mask2.mha"), }, "Head and Neck (sample)": { "region": "Head and Neck", "mri": os.path.join(repo_dir, "Head and Neck", "cbct.mha"), - "mask": os.path.join(repo_dir, "Head and Neck", "mask.mha"), + "mask": os.path.join(repo_dir, "Head and Neck", "mask2.mha"), }, "Thorax (sample)": { "region": "Thorax", "mri": os.path.join(repo_dir, "Thorax", "cbct.mha"), - "mask": os.path.join(repo_dir, "Thorax", "mask.mha"), + "mask": os.path.join(repo_dir, "Thorax", "mask2.mha"), }, } c1, c2, c3 = st.columns([2, 2, 1]) diff --git a/app_2.py b/app_2.py new file mode 100644 index 0000000000000000000000000000000000000000..b9a9e6c07199514dabfa182fb1cf8d45ff3c2857 --- /dev/null +++ b/app_2.py @@ -0,0 +1,283 @@ +# app.py +import os +import io +import tempfile +import zipfile +import numpy as np +import SimpleITK as sitk +import streamlit as st + +from PIL import Image, ImageDraw +from huggingface_hub import snapshot_download + +# ========================= +# 配置：两个任务的模型仓库 & 本地路径 +# ========================= +# 你可以将两个任务分别指向不同的 HF repo；如果都在同一个，也可以都填同一个。 +HF_REPOS = { + "Task 1 (MR → CT)": "aehrc/Synthrad2025", + "Task 2 (CBCT → CT)": "aehrc/Synthrad2025", # 如有专门CBCT→CT的repo可在此替换 +} +LOCAL_WEIGHTS_DIRS = { + "Task 1 (MR → CT)": os.path.abspath("weights/task1"), + "Task 2 (CBCT → CT)": os.path.abspath("weights/task2"), +} + +# 环境设置（Token） +token = os.getenv("HF_TOKEN") +if token is None: + print("[Warn] HF_TOKEN not set. If the model repo is private, set it in Settings → Variables and secrets.") + +# 先下载两个任务的权重（如需按需下载，可在选择任务后再下载） +REPO_DIRS = {} +for task_name, repo in HF_REPOS.items(): + repo_dir = snapshot_download( + repo_id=repo, + repo_type="model", + local_dir=LOCAL_WEIGHTS_DIRS[task_name], + local_dir_use_symlinks=False, + token=token, + ) + REPO_DIRS[task_name] = repo_dir + +# nnUNet 环境变量（指向“当前任务”的 results，会在用户切换任务时动态更新） +os.environ.setdefault("nnUNet_raw", "./nnunet_raw") +os.environ.setdefault("nnUNet_preprocessed", "./nnunet_preprocessed") +os.environ["OPENBLAS_NUM_THREADS"] = "1" + +# 从 process.py 导入两个任务的算法类 +# 确保你在 process.py 中定义了 SynthradAlgorithm1（MR→CT）和 SynthradAlgorithm2（CBCT→CT） +from process import SynthradAlgorithm2 + +from process_1 import SynthradAlgorithm1 + +# ========================= +# Streamlit UI +# ========================= +st.set_page_config(page_title="SynthRad (nnUNetv2) Demo", layout="wide") +st.title("SynthRad — 双任务演示（MRI/CBCT + Mask → synthetic CT）") + +# 任务选择 +TASKS = ["Task 1 (MR → CT)", "Task 2 (CBCT → CT)"] +task = st.radio("选择任务", TASKS, index=0, horizontal=True) + +# 根据任务设置标题/提示 +if task == "Task 1 (MR → CT)": + vol_label = "MRI volume (.nii/.nii.gz/.mha)" +else: + vol_label = "CBCT volume (.nii/.nii.gz/.mha)" + +# 动态切换 nnUNet 的 results（不同任务使用不同 results 目录） +os.environ["nnUNet_results"] = REPO_DIRS[task] + +# session_state 初始化 +if "algos" not in st.session_state: + st.session_state.algos = {} +if "synth_ct" not in st.session_state: + st.session_state.synth_ct = None +if "orig_meta" not in st.session_state: + st.session_state.orig_meta = None +if "vol_np" not in st.session_state: + st.session_state.vol_np = None +if "input_vol" not in st.session_state: + st.session_state.input_vol = None +if "input_mask" not in st.session_state: + st.session_state.input_mask = None + +# 懒加载对应任务的算法实例 +def get_algo(task_name: str): + if task_name not in st.session_state.algos: + if task_name == "Task 1 (MR → CT)": + st.session_state.algos[task_name] = SynthradAlgorithm1() + else: + st.session_state.algos[task_name] = SynthradAlgorithm2() + return st.session_state.algos[task_name] + +algo = get_algo(task) + +st.subheader("Input") +src = st.radio("Source", ["Sample", "Upload"], index=0, horizontal=True) + +# ========================= +# 样例映射（两任务可共用同一份样例，也可按需区分） +# 这里假设 repo_dir 下有如下结构： +# repo_dir/Abdomen/{cbct.mha, mask.mha} 或 {mri.mha, mask.mha} +# repo_dir/Head and Neck/{cbct.mha or mri.mha, mask.mha} +# repo_dir/Thorax/{cbct.mha or mri.mha, mask.mha} +# 如果你的文件名不同，请按需调整。 +# ========================= + +def build_sample_map(task_name: str): + repo_dir = REPO_DIRS[task_name] + if task_name == "Task 1 (MR → CT)": + vol_key = "mri" + vol_fname = "mri.mha" # 如果你的样例文件名不是 mri.mha，请改成实际名称 + mask_fname = "mask1.mha" # 如果你的样例文件名不是 mri.mha，请改成实际名称 + else: + vol_key = "cbct" + vol_fname = "cbct.mha" # 如果你的样例文件名不是 cbct.mha，请改成实际名称 + mask_fname = "mask2.mha" # 如果你的样例文件名不是 mri.mha，请改成实际名称 + sample_map = { + "Abdomen (sample)": { + "region": "Abdomen", + "vol": os.path.join(repo_dir, "Abdomen", vol_fname), + "mask": os.path.join(repo_dir, "Abdomen", mask_fname), + }, + "Head and Neck (sample)": { + "region": "Head and Neck", + "vol": os.path.join(repo_dir, "Head and Neck", vol_fname), + "mask": os.path.join(repo_dir, "Head and Neck", mask_fname), + }, + "Thorax (sample)": { + "region": "Thorax", + "vol": os.path.join(repo_dir, "Thorax", vol_fname), + "mask": os.path.join(repo_dir, "Thorax", mask_fname), + }, + } + return sample_map + +SAMPLE_MAP = build_sample_map(task) + +# ========================= +# 小工具函数 +# ========================= +def _download_sitk_image(img: sitk.Image, file_name: str, label: str): + with tempfile.NamedTemporaryFile(suffix=".nii.gz", delete=False) as tmp: + sitk.WriteImage(img, tmp.name) + tmp_path = tmp.name + with open(tmp_path, "rb") as f: + st.download_button( + label=label, + data=f.read(), + file_name=file_name, + mime="application/octet-stream", + ) + try: + os.remove(tmp_path) + except Exception: + pass + +def _read_sitk_from_uploaded(f): + suffix = ".nii.gz" if f.name.endswith(".nii.gz") else os.path.splitext(f.name)[1] + bio = io.BytesIO(f.read()) + with tempfile.NamedTemporaryFile(suffix=suffix, delete=False) as tmp: + tmp.write(bio.getvalue()); tmp.flush(); path = tmp.name + img = sitk.ReadImage(path) + try: + os.remove(path) + except Exception: + pass + return img + +def _read_sitk_from_path(path): + if not os.path.exists(path): + st.error(f"Sample file missing: {path}") + st.stop() + return sitk.ReadImage(path) + +def _norm2u8(slice2d): + s = slice2d.astype(np.float32) + s = (s - np.percentile(s, 1)) / (np.percentile(s, 99) - np.percentile(s, 1) + 1e-6) + s = np.clip(s, 0, 1) + return (s * 255).astype(np.uint8) + +# ========================= +# 输入区域（Upload or Sample） +# ========================= +c1, c2, c3 = st.columns([2, 2, 1]) + +if src == "Upload": + with c1: + vol_file = st.file_uploader(vol_label, type=["nii", "nii.gz", "mha"], key="vol") + with c2: + mask_file = st.file_uploader("Mask volume (.nii/.nii.gz/.mha)", type=["nii", "nii.gz", "mha"], key="mask") + with c3: + region = st.radio("Region", ["Head and Neck", "Abdomen", "Thorax"], index=1) + inputs_ready = (vol_file is not None) and (mask_file is not None) + region_for_run = region +else: + with c1: + sample_key = st.selectbox("Choose a sample", list(SAMPLE_MAP.keys())) + with c2: + st.markdown("Region (fixed by sample)") + st.write(f"**{SAMPLE_MAP[sample_key]['region']}**") + with c3: + st.markdown(" ", unsafe_allow_html=True) + inputs_ready = (sample_key is not None) + region_for_run = SAMPLE_MAP[sample_key]["region"] + +run_btn = st.button("Run", type="primary", disabled=not inputs_ready) + +# ========================= +# 推理 +# ========================= +if run_btn: + with st.spinner(f"Running nnUNetv2 {('SynthradAlgorithm1' if task=='Task 1 (MR → CT)' else 'SynthradAlgorithm2')}..."): + if src == "Upload": + in_vol_img = _read_sitk_from_uploaded(vol_file) + mask_img = _read_sitk_from_uploaded(mask_file) + else: + sample = SAMPLE_MAP[sample_key] + in_vol_img = _read_sitk_from_path(sample["vol"]) + mask_img = _read_sitk_from_path(sample["mask"]) + + # 保存原始元信息 + st.session_state.orig_meta = ( + in_vol_img.GetSpacing(), + in_vol_img.GetOrigin(), + in_vol_img.GetDirection(), + ) + + # 调用不同任务的算法 + # 约定：算法统一使用 dict 输入：{"image": , "mask": , "region": } + out_img = algo.predict({"image": in_vol_img, "mask": mask_img, "region": region_for_run}) + + st.session_state.synth_ct = out_img + st.session_state.vol_np = sitk.GetArrayFromImage(out_img).astype(np.float32) + st.session_state.input_vol = in_vol_img + st.session_state.input_mask = mask_img + +# ========================= +# 结果与下载 +# ========================= +if st.session_state.vol_np is None: + st.info("请先选择任务与输入（Upload 或 Sample），然后点击 Run") +else: + # 将输出转为 LPS 方向做显示（可选） + out_lps = sitk.DICOMOrient(st.session_state.synth_ct, "LPS") + vol = sitk.GetArrayFromImage(out_lps).astype(np.float32) + D, H, W = vol.shape + + col_d1, col_d2, col_d3 = st.columns(3) + + # 下载合成CT + with col_d3: + _download_sitk_image( + st.session_state.synth_ct, + file_name="synth_ct.nii.gz", + label="Download synthetic CT", + ) + + # 下载输入体积（根据任务区分命名） + with col_d1: + if st.session_state.input_vol is not None: + in_name = "input_mri.nii.gz" if task == "Task 1 (MR → CT)" else "input_cbct.nii.gz" + in_label = "Download input MRI" if task == "Task 1 (MR → CT)" else "Download input CBCT" + _download_sitk_image( + st.session_state.input_vol, + file_name=in_name, + label=in_label, + ) + else: + st.button("Download input", disabled=True) + + # 下载掩膜 + with col_d2: + if st.session_state.input_mask is not None: + _download_sitk_image( + st.session_state.input_mask, + file_name="input_mask.nii.gz", + label="Download input Mask", + ) + else: + st.button("Download input Mask", disabled=True) diff --git a/nnunetv2/__pycache__/__init__.cpython-312.pyc b/nnunetv2/__pycache__/__init__.cpython-312.pyc index 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a/process.py +++ b/process.py @@ -30,7 +30,7 @@ force_cpu = os.getenv("FORCE_CPU", "0") == "1" device = torch.device("cuda:0" if torch.cuda.is_available() and not force_cpu else "cpu") -class SynthradAlgorithm(BaseSynthradAlgorithm): +class SynthradAlgorithm2(BaseSynthradAlgorithm): """ This class implements a simple synthetic CT generation algorithm that segments all values greater than 2 in the input image. diff --git a/process_1.py b/process_1.py new file mode 100644 index 0000000000000000000000000000000000000000..a73c79164074d576860e57f17c22328c94079ae6 --- /dev/null +++ b/process_1.py @@ -0,0 +1,152 @@ +import os +# os.environ["nnUNet_raw"] = "/home/head_neck/algorithm-template_2/nnunet_raw" +# os.environ["nnUNet_preprocessed"] = "/home/head_neck/algorithm-template_2/nnunet_preprocessed" +# os.environ["nnUNet_results"] = "/home/head_neck/algorithm-template/nnunet_results_task_1" +os.environ["nnUNet_raw"] = "./nnunet_raw" +os.environ["nnUNet_preprocessed"] = "./nnunet_preprocessed" +# os.environ["nnUNet_results"] = "./nnunet_results" +from typing import Dict +import tempfile +import subprocess +import SimpleITK as sitk +from nnunetv2.inference.predict_from_raw_data import nnUNetPredictor +from batchgenerators.utilities.file_and_folder_operations import load_json, join, isfile, maybe_mkdir_p, isdir, subdirs, \ + save_json +# import torch +import numpy as np + +from base_algorithm import BaseSynthradAlgorithm +from revert_normalisation import get_ct_normalisation_values, revert_normalisation_single_modified + +import torch +import shutil + +import os + +os.environ["OPENBLAS_NUM_THREADS"] = "1" +device = torch.device("cuda:0" if torch.cuda.is_available() and not force_cpu else "cpu") + + +force_cpu = os.getenv("FORCE_CPU", "0") == "1" +class SynthradAlgorithm1(BaseSynthradAlgorithm): + """ + This class implements a simple synthetic CT generation algorithm that segments all values greater than 2 in the input image. + + Author: Suraj Pai (b.pai@maastrichtuniversity.nl) + """ + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def predict(self, input_dict: Dict[str, sitk.Image]) -> sitk.Image: + assert list(input_dict.keys()) == ["image", "mask", "region"] + + region = input_dict["region"] + mr_sitk = input_dict["image"] + mask_sitk = input_dict["mask"] + + mr_np = sitk.GetArrayFromImage(mr_sitk).astype("float32") + mask_np = sitk.GetArrayFromImage(mask_sitk).astype("float32") + + mr_np[mask_np == 0] = 0 + + preprocessed_mr_sitk = sitk.GetImageFromArray(mr_np) + preprocessed_mr_sitk.CopyInformation(mr_sitk) + + if region == "Head and Neck": + dataset_name = "Dataset262" + result_folder = "nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres" + plans_path = "./262_gt_nnUNetResEncUNetLPlans.json" + if region == "Abdomen": + dataset_name = "Dataset260" + result_folder = "nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres" + plans_path = "./260_gt_nnUNetResEncUNetLPlans.json" + if region == "Thorax": + dataset_name = "Dataset264" + result_folder = "nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres" + plans_path = "./264_gt_nnUNetResEncUNetLPlans.json" + + + + # predictor = nnUNetPredictor( + # tile_step_size=0.5, + # use_gaussian=True, + # use_mirroring=True, + # perform_everything_on_device=True, + # device=torch.device('cuda', 0), + # verbose=True, + # verbose_preprocessing=True, + # allow_tqdm=True + # ) + predictor = nnUNetPredictor( + tile_step_size=0.50, + use_gaussian=True, + use_mirroring=False, + perform_everything_on_device=(device.type == "cuda"), + device=device, + verbose=True, + ) + predictor.initialize_from_trained_model_folder( + join(os.environ["nnUNet_results"], f'{dataset_name}/{result_folder}'), + #use_folds=(0, 1, 2, 3, 4), + use_folds=(0, ), + checkpoint_name='checkpoint_final.pth', + ) + + sitk_spacing = mr_sitk.GetSpacing() + sitk_origin = mr_sitk.GetOrigin() + sitk_dir = mr_sitk.GetDirection() + + props = { + 'sitk_stuff': { + 'spacing': tuple(sitk_spacing), + 'origin': tuple(sitk_origin), + 'direction': tuple(sitk_dir), + }, + 'spacing': [sitk_spacing[2], sitk_spacing[1], sitk_spacing[0]] + } + + img = sitk.GetArrayFromImage(mr_sitk).astype(np.float32) + img = np.expand_dims(img, 0) + ret = predictor.predict_single_npy_array(img, props, None, 'TRUNCATED', False) + + pred_path = "./TRUNCATED.nii.gz" + pred_sitk = sitk.ReadImage(pred_path) + + ct_mean, ct_std = get_ct_normalisation_values(plans_path) + mask_sitk = sitk.Cast(mask_sitk, sitk.sitkUInt8) + + pred_sitk = revert_normalisation_single_modified(pred_sitk, ct_mean, ct_std, mask_sitk=mask_sitk) + os.remove(pred_path) + shutil.rmtree("./imagesTs", ignore_errors=True) + shutil.rmtree("./predictions", ignore_errors=True) + return pred_sitk + +# if __name__ == '__main__': + +# # Run the algorithm on the default input and output paths specified in BaseSynthradAlgorithm. +# SynthradAlgorithm().process() + +# if __name__ == '__main__': +# # # test Brain +# # #start_time = time.time() +# # # img_s_path = '//datasets/work/hb-synthrad2023/source/raw_data/Task1/brain/1BA001/mr.mha' +# # # img_m_path = '//datasets/work/hb-synthrad2023/source/raw_data/Task1/brain/1BA001/mask.mha' +# # # img_fakeB_path = '//datasets/work/hb-synthrad2023/work/bw_workplace/output/task1_brain/p2p3D/ensemble_t1_brain_final_e2/test_predictions/1BA001/ct_fakeB.mha' +# # # region = 'Head and Neck' + +# # # test Pelvis +# img_s_path = '/home/head_neck/algorithm-template_2/task2/2ABA033_0000.mha' +# img_m_path = '/home/head_neck/algorithm-template_2/task2/2ABA033_mask.mha' +# # #img_fakeB_path = '//datasets/work/hb-synthrad2023/work/bw_workplace/output/task1_pelvis/p2p3D/exp6_data_7_size_256_256_56_batch_3_lr_0.0002_aug3d_fold0_resumed_multiscale_2dsample_5/epoch_best/epoch75/test_predictions/1PA005/ct_fakeB.mha' +# region = 'Abdomen' + + +# # start test +# input_dict = { +# "image": sitk.ReadImage(img_s_path), +# "mask": sitk.ReadImage(img_m_path), +# "region": region +# } +# algorithm = SynthradAlgorithm() +# img_t = algorithm.predict(input_dict) \ No newline at end of file