Improve registration comparison viewer

app.py

@@ -119,6 +119,28 @@ def configure_matplotlib_cjk_font() -> None:
 configure_matplotlib_cjk_font()
 
 
+OVERLAY_MANUAL_DEFAULTS = {
+    "overlay_manual_enabled": False,
+    "overlay_shift_x": 0.0,
+    "overlay_shift_y": 0.0,
+    "overlay_shift_z": 0.0,
+    "overlay_scale": 1.0,
+    "overlay_axial_rotation": 0.0,
+    "overlay_coronal_rotation": 0.0,
+    "overlay_sagittal_rotation": 0.0,
+}
+
+
+def ensure_overlay_manual_defaults() -> None:
+    for key, value in OVERLAY_MANUAL_DEFAULTS.items():
+        st.session_state.setdefault(key, value)
+
+
+def reset_overlay_manual_controls() -> None:
+    for key, value in OVERLAY_MANUAL_DEFAULTS.items():
+        st.session_state[key] = value
+
+
 def _require_nibabel():
     try:
         import nibabel as nib  # type: ignore
@@ -293,10 +315,22 @@ def color_difference_overlay(fixed_slice: np.ndarray, warped_slice: np.ndarray,
     return np.clip(rgb, 0.0, 1.0)
 
 
-def signed_difference(fixed_slice: np.ndarray, warped_slice: np.ndarray) -> np.ndarray:
-    fixed_norm = normalize_slice(fixed_slice)
-    warped_norm = normalize_slice(warped_slice)
-    return warped_norm - fixed_norm
+def signed_difference(base_slice: np.ndarray, target_slice: np.ndarray) -> np.ndarray:
+    """计算模型强度差，输入体数据本身已在预处理阶段归一化到 [0, 1]。"""
+
+    return target_slice.astype(np.float32, copy=False) - base_slice.astype(np.float32, copy=False)
+
+
+def difference_summary(diff: np.ndarray) -> Dict[str, float]:
+    finite = diff[np.isfinite(diff)]
+    if finite.size == 0:
+        return {"abs_mean": 0.0, "abs_p95": 0.0, "abs_p99": 0.0}
+    abs_diff = np.abs(finite)
+    return {
+        "abs_mean": float(np.mean(abs_diff)),
+        "abs_p95": float(np.percentile(abs_diff, 95.0)),
+        "abs_p99": float(np.percentile(abs_diff, 99.0)),
+    }
 
 
 def ddf_magnitude(ddf_xyz: np.ndarray) -> np.ndarray:
@@ -351,7 +385,12 @@ def render_rgb(image: np.ndarray, caption: str, plane: str = "Axial") -> None:
     plt.close(fig)
 
 
-def render_signed_difference(image: np.ndarray, caption: str, plane: str = "Axial") -> None:
+def render_signed_difference(
+    image: np.ndarray,
+    caption: str,
+    plane: str = "Axial",
+    colorbar_label: str = "归一化强度差",
+) -> None:
     fig, ax = plt.subplots(figsize=(4.8, 4.8), dpi=120)
     diff = orient_for_display(image, plane)
     finite = diff[np.isfinite(diff)]
@@ -360,7 +399,8 @@ def render_signed_difference(image: np.ndarray, caption: str, plane: str = "Axia
     im = ax.imshow(diff, cmap="coolwarm", vmin=-vmax, vmax=vmax, interpolation="nearest")
     ax.set_title(caption, fontsize=10)
     ax.axis("off")
-    fig.colorbar(im, ax=ax, fraction=0.046, pad=0.02)
+    cbar = fig.colorbar(im, ax=ax, fraction=0.046, pad=0.02)
+    cbar.set_label(colorbar_label)
     st.pyplot(fig, width="stretch")
     plt.close(fig)
 
@@ -389,7 +429,8 @@ def render_heatmap_overlay(
     )
     ax.set_title(caption, fontsize=10)
     ax.axis("off")
-    fig.colorbar(im, ax=ax, fraction=0.046, pad=0.02)
+    cbar = fig.colorbar(im, ax=ax, fraction=0.046, pad=0.02)
+    cbar.set_label("位移强度 mm")
     st.pyplot(fig, width="stretch")
     plt.close(fig)
 
@@ -435,13 +476,40 @@ def render_three_way_views(fixed_xyz: np.ndarray, moving_xyz: np.ndarray, warped
                     render_image(get_slice(volume, plane, index), f"{label} #{index}", plane=plane)
 
 
-def render_overlay_views(fixed_xyz: np.ndarray, moving_xyz: np.ndarray, warped_xyz: np.ndarray) -> None:
+def render_overlay_views(
+    fixed_xyz: np.ndarray,
+    moving_xyz: np.ndarray,
+    warped_xyz: np.ndarray,
+    display_stride: int = 1,
+) -> None:
     fixed_xyz, moving_xyz, warped_xyz = crop_to_common_shape(fixed_xyz, moving_xyz, warped_xyz)
-    target_label = st.radio("叠加对象", ["配准后图像", "移动图像"], horizontal=True)
-    target_xyz = warped_xyz if target_label == "配准后图像" else moving_xyz
+    comparison = st.radio(
+        "对比组合",
+        ["配准后图像 vs 移动图像", "配准后图像 vs 固定图像", "移动图像 vs 固定图像"],
+        horizontal=True,
+    )
+    if comparison == "配准后图像 vs 移动图像":
+        base_label = "移动图像"
+        target_label = "配准后图像"
+        base_xyz = moving_xyz
+        target_xyz = warped_xyz
+        diff_label = "配准后 - 移动"
+    elif comparison == "配准后图像 vs 固定图像":
+        base_label = "固定图像"
+        target_label = "配准后图像"
+        base_xyz = fixed_xyz
+        target_xyz = warped_xyz
+        diff_label = "配准后 - 固定"
+    else:
+        base_label = "固定图像"
+        target_label = "移动图像"
+        base_xyz = fixed_xyz
+        target_xyz = moving_xyz
+        diff_label = "移动 - 固定"
+
     mode = st.radio("对比模式", ["灰度融合", "红蓝差异", "差异热图", "棋盘格"], horizontal=True)
     if mode in {"灰度融合", "红蓝差异"}:
-        alpha = st.slider("被叠加图像权重", 0.0, 1.0, 0.5, 0.05)
+        alpha = st.slider(f"{target_label}权重", 0.0, 1.0, 0.5, 0.05)
     else:
         alpha = 0.5
     if mode == "棋盘格":
@@ -450,28 +518,30 @@ def render_overlay_views(fixed_xyz: np.ndarray, moving_xyz: np.ndarray, warped_x
         tile = 24
 
     if mode == "红蓝差异":
-        st.caption("红/金色偏向被叠加图像，蓝色偏向固定图像；白灰区域表示两者灰度接近。")
+        st.caption(f"红/金色偏向{target_label}，蓝色偏向{base_label}；白灰区域表示两者灰度接近。")
     elif mode == "差异热图":
-        st.caption("差异热图显示被叠加图像减固定图像的灰度差，色条以当前切片的 99% 绝对差自动缩放。")
+        st.caption(f"差异热图显示 {diff_label} 的模型归一化强度差；色条以当前切片的 99% 绝对差自动缩放。")
 
     with st.expander("人工微调重叠", expanded=True):
-        enabled = st.toggle("启用人工微调", value=False)
+        ensure_overlay_manual_defaults()
+        st.button("恢复原始状态", on_click=reset_overlay_manual_controls)
+        enabled = st.toggle("启用人工微调", key="overlay_manual_enabled")
         c1, c2, c3, c4 = st.columns(4)
         with c1:
-            shift_x = st.slider("X 平移", -80.0, 80.0, 0.0, 0.5)
+            shift_x = st.slider("X 平移", -80.0, 80.0, step=0.5, key="overlay_shift_x")
         with c2:
-            shift_y = st.slider("Y 平移", -80.0, 80.0, 0.0, 0.5)
+            shift_y = st.slider("Y 平移", -80.0, 80.0, step=0.5, key="overlay_shift_y")
         with c3:
-            shift_z = st.slider("Z 平移", -80.0, 80.0, 0.0, 0.5)
+            shift_z = st.slider("Z 平移", -80.0, 80.0, step=0.5, key="overlay_shift_z")
         with c4:
-            scale = st.slider("缩放", 0.85, 1.15, 1.0, 0.005)
+            scale = st.slider("缩放", 0.85, 1.15, step=0.005, key="overlay_scale")
         r1, r2, r3 = st.columns(3)
         with r1:
-            axial_rotation = st.slider("轴状面旋转", -15.0, 15.0, 0.0, 0.5)
+            axial_rotation = st.slider("轴状面旋转", -15.0, 15.0, step=0.5, key="overlay_axial_rotation")
         with r2:
-            coronal_rotation = st.slider("冠状面旋转", -15.0, 15.0, 0.0, 0.5)
+            coronal_rotation = st.slider("冠状面旋转", -15.0, 15.0, step=0.5, key="overlay_coronal_rotation")
         with r3:
-            sagittal_rotation = st.slider("矢状面旋转", -15.0, 15.0, 0.0, 0.5)
+            sagittal_rotation = st.slider("矢状面旋转", -15.0, 15.0, step=0.5, key="overlay_sagittal_rotation")
         if enabled:
             st.caption(
                 f"当前人工微调：X={shift_x:+.1f}, Y={shift_y:+.1f}, Z={shift_z:+.1f}, "
@@ -492,7 +562,7 @@ def render_overlay_views(fixed_xyz: np.ndarray, moving_xyz: np.ndarray, warped_x
         axis = plane_axis(plane)
         with col:
             index = st.slider(f"{cn_name}切片", 0, fixed_xyz.shape[axis] - 1, fixed_xyz.shape[axis] // 2, key=f"overlay-{plane}")
-            fixed_slice = get_slice(fixed_xyz, plane, index)
+            base_slice = get_slice(base_xyz, plane, index)
             target_slice = get_slice(target_xyz, plane, index)
             target_slice = apply_manual_slice_transform(
                 target_slice,
@@ -501,14 +571,28 @@ def render_overlay_views(fixed_xyz: np.ndarray, moving_xyz: np.ndarray, warped_x
                 scale=scale_value,
                 rotation_deg=rotation_by_plane[plane],
             )
+            slice_label = f"{cn_name} #{index}"
+            if display_stride > 1:
+                slice_label = f"{slice_label} / 原始 #{index * display_stride}"
             if mode == "棋盘格":
-                render_image(checkerboard(fixed_slice, target_slice, tile=tile), f"{cn_name} 棋盘格 #{index}", plane=plane)
+                render_image(checkerboard(base_slice, target_slice, tile=tile), f"{slice_label} 棋盘格", plane=plane)
             elif mode == "红蓝差异":
-                render_rgb(color_difference_overlay(fixed_slice, target_slice, alpha=alpha), f"{cn_name} 红蓝差异 #{index}", plane=plane)
+                render_rgb(color_difference_overlay(base_slice, target_slice, alpha=alpha), f"{slice_label} 红蓝差异", plane=plane)
             elif mode == "差异热图":
-                render_signed_difference(signed_difference(fixed_slice, target_slice), f"{cn_name} 差异热图 #{index}", plane=plane)
+                diff = signed_difference(base_slice, target_slice)
+                render_signed_difference(
+                    diff,
+                    f"{slice_label} 差异热图",
+                    plane=plane,
+                    colorbar_label=f"{diff_label}，归一化强度",
+                )
+                stats = difference_summary(diff)
+                st.caption(
+                    f"|差|均值={stats['abs_mean']:.4f}，"
+                    f"P95={stats['abs_p95']:.4f}，P99={stats['abs_p99']:.4f}"
+                )
             else:
-                render_image(gray_fusion(fixed_slice, target_slice, alpha=alpha), f"{cn_name} 灰度融合 #{index}", plane=plane)
+                render_image(gray_fusion(base_slice, target_slice, alpha=alpha), f"{slice_label} 灰度融合", plane=plane)
 
 
 def render_ddf_views(fixed_xyz: np.ndarray, ddf_xyz: np.ndarray) -> None:
@@ -551,7 +635,7 @@ def render_ddf_views(fixed_xyz: np.ndarray, ddf_xyz: np.ndarray) -> None:
                 vmin=vmin,
                 vmax=vmax,
                 show_base=show_base,
-                caption=f"{cn_name} 形变强度 #{index}",
+                caption=f"{cn_name} 位移强度 #{index}",
             )
 
 
@@ -896,19 +980,26 @@ def main() -> None:
 
     warped_xyz = None
     ddf_xyz = None
+    warped_stride = 1
+    ddf_stride = 1
     if Path(warped_path).exists():
         try:
-            warped_xyz, _, _ = load_nifti_cached(warped_path, max_voxels=display_max_voxels)
+            warped_xyz, _, warped_stride = load_nifti_cached(warped_path, max_voxels=display_max_voxels)
         except Exception as exc:
             st.warning(f"Warped 读取失败: {exc}")
     if Path(ddf_path).exists():
         try:
-            ddf_xyz, _, _ = load_nifti_cached(ddf_path, max_voxels=display_max_voxels)
+            ddf_xyz, _, ddf_stride = load_nifti_cached(ddf_path, max_voxels=display_max_voxels)
         except Exception as exc:
             st.warning(f"DDF 读取失败: {exc}")
 
-    if moving_stride > 1 or fixed_stride > 1:
-        st.info(f"网页显示已自动下采样：Moving stride={moving_stride}, Fixed stride={fixed_stride}")
+    display_stride = max(moving_stride, fixed_stride, warped_stride)
+    if display_stride > 1 or ddf_stride > 1:
+        st.info(
+            f"网页显示已自动下采样：Moving stride={moving_stride}, Fixed stride={fixed_stride}, "
+            f"Warped stride={warped_stride}, DDF stride={ddf_stride}。"
+            f"切片标题会同时显示网页索引和原始模型网格索引，例如 stride=2 时 #80 对应原始 #160。"
+        )
 
     tab_views, tab_overlay, tab_ddf, tab_metrics = st.tabs(["正交三视图", "重叠对比", "形变场", "量化图"])
     with tab_views:
@@ -918,7 +1009,7 @@ def main() -> None:
         if warped_xyz is None:
             st.warning("尚未生成配准后图像。")
         else:
-            render_overlay_views(fixed_xyz, moving_xyz, warped_xyz)
+            render_overlay_views(fixed_xyz, moving_xyz, warped_xyz, display_stride=display_stride)
 
     with tab_ddf:
         if ddf_xyz is None:

额外测试/slice080_comparison_metadata.json

@@ -0,0 +1,89 @@
+{
+  "moving_model_input_path": "outputs/inference/moving_model_input.nii.gz",
+  "warped_moving_path": "outputs/inference/warped_moving.nii.gz",
+  "moving_model_input_shape_xyz": [
+    320,
+    320,
+    352
+  ],
+  "warped_moving_shape_xyz": [
+    320,
+    320,
+    352
+  ],
+  "model_spacing_xyz_mm": [
+    1.0,
+    1.0,
+    1.0
+  ],
+  "model_index_used_for_png": 80,
+  "difference_definition": "warped_moving - moving_model_input",
+  "intensity_unit": "preprocessed normalized intensity, not HU",
+  "outputs": [
+    {
+      "plane": "axial",
+      "moving_png": "额外测试/slice080_model_axial_moving.png",
+      "warped_png": "额外测试/slice080_model_axial_warped.png",
+      "signed_heatmap_png": "额外测试/slice080_model_axial_warped_minus_moving_signed_heatmap.png",
+      "absolute_heatmap_png": "额外测试/slice080_model_axial_warped_minus_moving_abs_heatmap.png",
+      "comparison_panel_png": "额外测试/slice080_model_axial_comparison_panel.png",
+      "diff_mean": 0.00033523677848279476,
+      "diff_std": 0.05242208391427994,
+      "diff_abs_mean": 0.012141597457230091,
+      "diff_abs_p95": 0.05994440242648125,
+      "diff_abs_p99": 0.26289790868759155,
+      "diff_min": -0.8903505206108093,
+      "diff_max": 0.8546980619430542,
+      "heatmap_limit_abs_p99": 0.26289790868759155,
+      "abs_heatmap_vmax_p99": 0.26289790868759155
+    },
+    {
+      "plane": "sagittal",
+      "moving_png": "额外测试/slice080_model_sagittal_moving.png",
+      "warped_png": "额外测试/slice080_model_sagittal_warped.png",
+      "signed_heatmap_png": "额外测试/slice080_model_sagittal_warped_minus_moving_signed_heatmap.png",
+      "absolute_heatmap_png": "额外测试/slice080_model_sagittal_warped_minus_moving_abs_heatmap.png",
+      "comparison_panel_png": "额外测试/slice080_model_sagittal_comparison_panel.png",
+      "diff_mean": 0.000903263979125768,
+      "diff_std": 0.03735477849841118,
+      "diff_abs_mean": 0.007962067611515522,
+      "diff_abs_p95": 0.04058312624692917,
+      "diff_abs_p99": 0.1862204670906067,
+      "diff_min": -0.8619486093521118,
+      "diff_max": 0.5144104957580566,
+      "heatmap_limit_abs_p99": 0.1862204670906067,
+      "abs_heatmap_vmax_p99": 0.1862204670906067
+    }
+  ],
+  "raw_moving_dicom_reference": {
+    "path": "Data/患者1-仰头CT/00000080.dcm",
+    "png": "额外测试/slice080_raw_dicom_moving_reference.png",
+    "shape_rows_cols": [
+      512,
+      512
+    ],
+    "instance_number": 80,
+    "pixel_spacing_row_col_mm": [
+      0.646484375,
+      0.646484375
+    ],
+    "slice_thickness_mm": 1.0,
+    "note": "Raw DICOM is not directly subtracted because warped output is a 320x320x352 preprocessed NIfTI model grid."
+  },
+  "app_display_note": {
+    "streamlit_display_stride": 2,
+    "app_sagittal_slider_index": 80,
+    "corresponding_original_model_x_index": 160,
+    "moving_png": "额外测试/slice080_app_sagittal_moving_originalX160.png",
+    "warped_png": "额外测试/slice080_app_sagittal_warped_originalX160.png",
+    "signed_heatmap_png": "额外测试/slice080_app_sagittal_warped_minus_moving_signed_heatmap.png",
+    "absolute_heatmap_png": "额外测试/slice080_app_sagittal_warped_minus_moving_abs_heatmap.png",
+    "comparison_panel_png": "额外测试/slice080_app_sagittal_comparison_panel.png",
+    "difference_definition": "warped_moving - moving_model_input",
+    "diff_abs_mean": 0.021932723000645638,
+    "diff_abs_p95": 0.12442082911729813,
+    "diff_abs_p99": 0.30432429909706116,
+    "diff_min": -0.9248806834220886,
+    "diff_max": 0.8222805857658386
+  }
+}