update

app_0.py

@@ -1,115 +0,0 @@
-# app.py
-import os
-from huggingface_hub import snapshot_download
-
-HF_REPO = "Synthard2025KoalAI/synthrad2025_task1"    # 你的模型仓库
-LOCAL_WEIGHTS_DIR = os.path.abspath("weights/task1") # 下载到 Space 工作目录
-repo_dir = snapshot_download(
-    HF_REPO,
-    repo_type="model",
-    local_dir=LOCAL_WEIGHTS_DIR,
-    local_dir_use_symlinks=False,
-    token=os.getenv("HF_TOKEN"),   # 私有模型需要
-)
-
-
-os.environ["nnUNet_results"] = repo_dir
-os.environ["nnUNet_raw"] = "./nnunet_raw"
-os.environ["nnUNet_preprocessed"] = "./nnunet_preprocessed"
-# os.environ["nnUNet_results"] = "./nnunet_results"
-os.environ["OPENBLAS_NUM_THREADS"] = "1"
-
-import streamlit as st
-import numpy as np
-import SimpleITK as sitk
-import io
-
-from process import SynthradAlgorithm
-
-st.set_page_config(page_title="SynthRad (nnUNetv2) Demo", layout="wide")
-st.title("SynthRad (MRI+Mask → synthetic CT) — Streamlit Demo")
-
-# ---- 全局算法（避免重复加载模型）----
-if "algo" not in st.session_state:
-    st.session_state.algo = SynthradAlgorithm()
-if "synth_ct" not in st.session_state:
-    st.session_state.synth_ct = None  # SimpleITK.Image
-if "orig_meta" not in st.session_state:
-    st.session_state.orig_meta = None  # (spacing, origin, direction)
-if "vol_np" not in st.session_state:
-    st.session_state.vol_np = None     # numpy (D,H,W)
-
-# ---- 左上：输入区（保留“框架”风格）----
-c1, c2, c3 = st.columns([2, 2, 1])
-with c1:
-    mri_file = st.file_uploader("MRI volume (.nii/.nii.gz/.mha)", type=["nii", "nii.gz", "mha"], key="mri")
-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)
-
-run_btn = st.button("Run", type="primary", disabled=not (mri_file and mask_file))
-
-def _read_sitk_from_uploaded(f):
-    # 把上传文件读到 SimpleITK
-    suffix = ".nii.gz" if f.name.endswith(".nii.gz") else os.path.splitext(f.name)[1]
-    bio = io.BytesIO(f.read())
-    # SimpleITK 不能直接读 Bytes，需要写临时文件
-    import tempfile
-    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 _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)
-
-if run_btn:
-    if not (mri_file and mask_file):
-        st.warning("请同时上传 MRI 和 Mask")
-    else:
-        with st.spinner("Running nnUNetv2 SynthradAlgorithm..."):
-            mr_img = _read_sitk_from_uploaded(mri_file)
-            mask_img = _read_sitk_from_uploaded(mask_file)
-
-            st.session_state.orig_meta = (mr_img.GetSpacing(), mr_img.GetOrigin(), mr_img.GetDirection())
-            out_img = st.session_state.algo.predict({"image": mr_img, "mask": mask_img, "region": region})
-            st.session_state.synth_ct = out_img
-            st.session_state.vol_np = sitk.GetArrayFromImage(out_img).astype(np.float32)  # (D,H,W)
-
-if st.session_state.vol_np is None:
-    st.info("请上传 MRI + Mask 并点击 Run")
-else:
-    vol = st.session_state.vol_np
-    D, H, W = vol.shape
-
-    colL, colR = st.columns(2)
-    with colL:
-        z_idx = st.slider("Axial (Z)", 0, D - 1, D // 2, key="z_idx")
-        axial = _norm2u8(vol[z_idx, :, :])
-        st.image(axial, caption=f"Axial slice z={z_idx}", use_column_width=True)
-
-    with colR:
-        y_idx = st.slider("Coronal (Y)", 0, H - 1, H // 2, key="y_idx")
-        coronal = _norm2u8(vol[:, y_idx, :])
-        st.image(coronal, caption=f"Coronal slice y={y_idx}", use_column_width=True)
-
-    spacing, origin, direction = st.session_state.orig_meta
-    out_path = "synth_ct.nii.gz"
-    sitk.WriteImage(st.session_state.synth_ct, out_path)
-    with open(out_path, "rb") as f:
-        st.download_button(
-            label="Download synthetic CT (.nii.gz)",
-            data=f.read(),
-            file_name="synth_ct.nii.gz",
-            mime="application/octet-stream",
-        )

app_1.py

@@ -1,204 +0,0 @@
-# app.py
-import os
-from huggingface_hub import snapshot_download
-
-from PIL import Image, ImageDraw
-HF_REPO = "aehrc/Synthrad2025"   
-LOCAL_WEIGHTS_DIR = os.path.abspath("weights/task1")  
-
-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.")
-
-repo_dir = snapshot_download(
-    repo_id=HF_REPO,
-    repo_type="model",
-    local_dir=LOCAL_WEIGHTS_DIR,
-    local_dir_use_symlinks=False,
-    token=token,
-)
-print(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"
-
-import streamlit as st
-import numpy as np
-import SimpleITK as sitk
-import io
-
-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 = SynthradAlgorithm2()
-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_mr" not in st.session_state:
-    st.session_state.input_mr = None
-if "input_mask" not in st.session_state:
-    st.session_state.input_mask = None
-
-st.subheader("Input")
-src = st.radio("Source", ["Sample", "Upload"], index=0, horizontal=True)
-
-from huggingface_hub import snapshot_download
-import os
-import tempfile, zipfile
-
-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
-SAMPLES_REPO = "Synthard2025KoalAI/synthrad2025_task1"
-# samples_dir = os.path.join(repo_dir, "samples")
-
-SAMPLE_MAP = {
-    "Abdomen (sample)": {
-        "region": "Abdomen",
-        "mri":  os.path.join(repo_dir,  "Abdomen",  "cbct.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", "mask2.mha"),
-    },
-    "Thorax (sample)": {
-        "region": "Thorax",
-        "mri":  os.path.join(repo_dir,  "Thorax",   "cbct.mha"),
-        "mask": os.path.join(repo_dir,  "Thorax",   "mask2.mha"),
-    },
-}
-c1, c2, c3 = st.columns([2, 2, 1])
-
-if src == "Upload":
-    with c1:
-        mri_file = st.file_uploader("MRI volume (.nii/.nii.gz/.mha)", type=["nii", "nii.gz", "mha"], key="mri")
-    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 = (mri_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)
-
-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())
-    import tempfile
-    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)
-if run_btn:
-    with st.spinner("Running nnUNetv2 SynthradAlgorithm..."):
-        if src == "Upload":
-            mr_img = _read_sitk_from_uploaded(mri_file)
-            mask_img = _read_sitk_from_uploaded(mask_file)
-        else:
-            sample = SAMPLE_MAP[sample_key]
-            mr_img = _read_sitk_from_path(sample["mri"])
-            mask_img = _read_sitk_from_path(sample["mask"])
-
-        st.session_state.orig_meta = (mr_img.GetSpacing(), mr_img.GetOrigin(), mr_img.GetDirection())
-        out_img = st.session_state.algo.predict({"image": mr_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_mr = mr_img
-        st.session_state.input_mask = mask_img
-
-
-
-if st.session_state.vol_np is None:
-    st.info("Please upload MRI + Mask or Sample, then Run")
-else:
-    mr_lps = sitk.DICOMOrient(st.session_state.synth_ct, "LPS")
-    vol = sitk.GetArrayFromImage(mr_lps).astype(np.float32) 
-    # vol = st.session_state.vol_np          # shape: (D, H, W) = (Z, Y, X)
-    D, H, W = vol.shape
-
-
-    # out_path = "synth_ct.nii.gz"
-    # sitk.WriteImage(st.session_state.synth_ct, out_path)
-    # with open(out_path, "rb") as f:
-    #     st.download_button(
-    #         label="Download synthetic CT (.nii.gz)",
-    #         data=f.read(),
-    #         file_name="synth_ct.nii.gz",
-    #         mime="application/octet-stream",
-    #     )
-    col_d1, col_d2, col_d3 = st.columns(3)
-
-    with col_d3:
-        _download_sitk_image(
-            st.session_state.synth_ct,
-            file_name="synth_ct.nii.gz",
-            label="Download synthetic CT"
-        )
-
-    with col_d1:
-        if st.session_state.input_mr is not None:
-            _download_sitk_image(
-                st.session_state.input_mr,
-                file_name="input_mri.nii.gz",   # 如果你其实是 CBCT，可改成 input_cbct.nii.gz
-                label="Download input MRI/CBCT"
-            )
-        else:
-            st.button("Download input MRI/CBCT", disabled=True)
-
-    with col_d2:
-        if st.session_state.input_mask is not None:
-            # （可选）确保掩膜是整型：mask = sitk.Cast(st.session_state.input_mask, sitk.sitkUInt16)
-            _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)
-