Make quantitative charts data-auditable

app.py

@@ -384,57 +384,150 @@ def render_ddf_views(fixed_xyz: np.ndarray, ddf_xyz: np.ndarray) -> None:
             )
 
 
+METRIC_SPECS = [
+    ("NCC", "before_ncc", "after_ncc", "越高越好", True),
+    ("MSE", "before_mse", "after_mse", "越低越好", False),
+    ("MAE", "before_mae", "after_mae", "越低越好", False),
+]
+
+
+def metric_improvement(before: float, after: float, higher_is_better: bool) -> float:
+    return after - before if higher_is_better else before - after
+
+
+def format_metric(value: float) -> str:
+    return f"{float(value):.6f}"
+
+
+def build_metric_table(metrics: Dict[str, float]) -> List[Dict[str, str]]:
+    rows = []
+    for label, before_key, after_key, direction, higher_is_better in METRIC_SPECS:
+        before = float(metrics[before_key])
+        after = float(metrics[after_key])
+        improvement = metric_improvement(before, after, higher_is_better)
+        improvement_rate = improvement / (abs(before) + 1e-8) * 100.0
+        rows.append(
+            {
+                "指标": label,
+                "方向": direction,
+                "配准前": format_metric(before),
+                "配准后": format_metric(after),
+                "改善量": f"{improvement:+.6f}",
+                "改善率": f"{improvement_rate:+.4f}%",
+            }
+        )
+    return rows
+
+
+def render_metric_value_panels(metrics: Dict[str, float]) -> None:
+    fig, axes = plt.subplots(1, 3, figsize=(11.5, 3.1), dpi=130)
+    for ax, (label, before_key, after_key, direction, higher_is_better) in zip(axes, METRIC_SPECS):
+        before = float(metrics[before_key])
+        after = float(metrics[after_key])
+        improvement = metric_improvement(before, after, higher_is_better)
+        values = [before, after]
+        x = np.arange(2)
+
+        ax.bar(x, values, width=0.5, color=["#64748b", "#0f766e"])
+        ax.plot(x, values, color="#111827", linewidth=1.2, marker="o", markersize=4)
+        ax.set_xticks(x, ["配准前", "配准后"])
+        ax.set_title(f"{label}（{direction}）")
+        ymin = min(0.0, min(values) * 1.18)
+        ymax = max(0.0, max(values) * 1.18, 1e-5)
+        ax.set_ylim(ymin, ymax)
+        ax.grid(axis="y", alpha=0.22)
+
+        for index, value in enumerate(values):
+            ax.text(index, value, format_metric(value), ha="center", va="bottom", fontsize=8)
+        ax.text(0.5, max(values) * 1.06, f"改善 {improvement:+.6f}", ha="center", fontsize=8, color="#374151")
+
+    fig.tight_layout()
+    st.pyplot(fig, width="stretch")
+    plt.close(fig)
+
+
 def render_metric_charts(fixed_xyz: np.ndarray, moving_xyz: np.ndarray, warped_xyz: np.ndarray, ddf_xyz: np.ndarray | None) -> None:
     fixed_xyz, moving_xyz, warped_xyz = crop_to_common_shape(fixed_xyz, moving_xyz, warped_xyz)
     metrics = registration_metrics(fixed_xyz, moving_xyz, warped_xyz)
+    metric_rows = build_metric_table(metrics)
 
     c1, c2, c3 = st.columns(3)
-    c1.metric("NCC", f"{metrics['after_ncc']:.4f}", delta=f"{metrics['ncc_improvement']:+.4f}")
+    c1.metric("NCC", format_metric(metrics["after_ncc"]), delta=f"{metrics['ncc_improvement']:+.6f}")
     c2.metric(
         "MSE",
-        f"{metrics['after_mse']:.5f}",
+        format_metric(metrics["after_mse"]),
-        delta=f"{metrics['after_mse'] - metrics['before_mse']:+.5f}",
+        delta=f"{metrics['after_mse'] - metrics['before_mse']:+.6f}",
         delta_color="inverse",
     )
     c3.metric(
         "MAE",
-        f"{metrics['after_mae']:.5f}",
+        format_metric(metrics["after_mae"]),
-        delta=f"{metrics['after_mae'] - metrics['before_mae']:+.5f}",
+        delta=f"{metrics['after_mae'] - metrics['before_mae']:+.6f}",
         delta_color="inverse",
     )
 
-    labels = ["NCC", "MSE", "MAE"]
+    max_improvement_rate = max(abs(float(row["改善率"].rstrip("%"))) for row in metric_rows)
-    before = [metrics["before_ncc"], metrics["before_mse"], metrics["before_mae"]]
+    if max_improvement_rate < 0.1:
-    after = [metrics["after_ncc"], metrics["after_mse"], metrics["after_mae"]]
+        st.info("当前配准前后指标变化低于 0.1%，曲线和柱子高度接近是正常的；这更像是 smoke 权重的验证结果，不是充分训练后的配准效果。")
 
-    fig, axes = plt.subplots(1, 2, figsize=(10, 3.6), dpi=130)
+    st.caption("量化结果由当前加载的 Fixed、Moving、Warped 体数据现场计算；逐切片曲线未平滑、未随机采样。")
-    x = np.arange(len(labels))
+    st.table(metric_rows)
-    width = 0.36
+    render_metric_value_panels(metrics)
-    axes[0].bar(x - width / 2, before, width, label="配准前", color="#64748b")
+
-    axes[0].bar(x + width / 2, after, width, label="配准后", color="#0f766e")
+    controls = st.columns(2)
-    axes[0].set_xticks(x, labels)
+    with controls[0]:
-    axes[0].set_title("配准前后指标对比")
+        plane_label = st.selectbox("逐切片平面", ["轴状面", "冠状面", "矢状面"])
+    with controls[1]:
+        metric_label = st.selectbox("逐切片指标", ["MSE", "MAE", "NCC"])
+
+    axis = {"矢状面": 0, "冠状面": 1, "轴状面": 2}[plane_label]
+    metric_key = metric_label.lower()
+    higher_is_better = metric_key == "ncc"
+    curve = slice_metric_curve(fixed_xyz, moving_xyz, warped_xyz, axis=axis, metric=metric_key)
+    slice_index = np.asarray(curve["slice_index"], dtype=np.int32)
+    before_curve = np.asarray(curve[f"before_{metric_key}"], dtype=np.float32)
+    after_curve = np.asarray(curve[f"after_{metric_key}"], dtype=np.float32)
+    improvement_curve = after_curve - before_curve if higher_is_better else before_curve - after_curve
+
+    fig, axes = plt.subplots(
+        2,
+        1,
+        figsize=(10.5, 5.4),
+        dpi=130,
+        sharex=True,
+        gridspec_kw={"height_ratios": [2.0, 1.0]},
+    )
+    axes[0].plot(slice_index, before_curve, label=f"配准前 {metric_label}", color="#64748b", linewidth=1.5)
+    axes[0].plot(slice_index, after_curve, label=f"配准后 {metric_label}", color="#0f766e", linewidth=1.5)
+    axes[0].set_title(f"{plane_label}逐切片{metric_label}")
+    axes[0].set_ylabel(metric_label)
+    axes[0].grid(alpha=0.22)
     axes[0].legend(frameon=False)
-    axes[0].grid(axis="y", alpha=0.22)
 
-    curve = slice_metric_curve(fixed_xyz, moving_xyz, warped_xyz, axis=2)
+    axes[1].axhline(0.0, color="#9ca3af", linewidth=0.9)
-    axes[1].plot(curve["slice_index"], curve["before_mse"], label="配准前 MSE", color="#64748b", linewidth=1.8)
+    axes[1].plot(slice_index, improvement_curve, color="#b45309", linewidth=1.4)
-    axes[1].plot(curve["slice_index"], curve["after_mse"], label="配准后 MSE", color="#b45309", linewidth=1.8)
+    axes[1].set_title(f"{metric_label}逐切片改善量")
-    axes[1].set_title("轴状面逐切片误差")
+    axes[1].set_xlabel("切片序号")
-    axes[1].set_xlabel("Slice")
+    axes[1].set_ylabel("改善量")
     axes[1].grid(alpha=0.22)
-    axes[1].legend(frameon=False)
     st.pyplot(fig, width="stretch")
     plt.close(fig)
 
     if ddf_xyz is not None:
         mag = ddf_magnitude(ddf_xyz)
+        values = mag[np.isfinite(mag)]
+        if values.size == 0:
+            return
         fig2, ax = plt.subplots(figsize=(10, 3.2), dpi=130)
-        ax.hist(mag.ravel(), bins=80, color="#7c3f00", alpha=0.82)
+        ax.hist(values, bins=80, color="#7c3f00", alpha=0.82)
+        for percentile, color in [(50, "#111827"), (95, "#0f766e"), (99, "#dc2626")]:
+            marker = float(np.percentile(values, percentile))
+            ax.axvline(marker, color=color, linewidth=1.2, label=f"P{percentile}={format_mm(marker)} mm")
         ax.set_title("DDF 位移强度分布")
         ax.set_xlabel("mm")
-        ax.set_ylabel("Voxel count")
+        ax.set_ylabel("体素数")
         ax.grid(axis="y", alpha=0.2)
+        ax.legend(frameon=False)
         st.pyplot(fig2, width="stretch")
         plt.close(fig2)
 

metrics.py

@@ -86,10 +86,20 @@ def slice_metric_curve(
     moving: np.ndarray,
     warped: np.ndarray,
     axis: int = 2,
+    metric: str = "mse",
 ) -> Dict[str, Iterable[float]]:
-    """逐切片计算 MSE，适合生成“配准前后误差曲线”。"""
+    """逐切片计算配准前后指标曲线。"""
 
     fixed, moving, warped = crop_to_common_shape(fixed, moving, warped)
+    metric_funcs = {
+        "mse": mse,
+        "mae": mae,
+        "ncc": global_ncc,
+    }
+    if metric not in metric_funcs:
+        raise ValueError(f"不支持的逐切片指标: {metric}")
+    metric_fn = metric_funcs[metric]
+
     before = []
     after = []
 
@@ -97,13 +107,13 @@ def slice_metric_curve(
         selector = [slice(None)] * 3
         selector[axis] = index
         selector = tuple(selector)
-        before.append(mse(fixed[selector], moving[selector]))
+        before.append(metric_fn(fixed[selector], moving[selector]))
-        after.append(mse(fixed[selector], warped[selector]))
+        after.append(metric_fn(fixed[selector], warped[selector]))
 
     return {
         "slice_index": list(range(fixed.shape[axis])),
-        "before_mse": before,
+        f"before_{metric}": before,
-        "after_mse": after,
+        f"after_{metric}": after,
     }
 
 
@@ -120,4 +130,3 @@ def ddf_summary(ddf_xyz: np.ndarray) -> Dict[str, float]:
         "ddf_p95": float(np.percentile(mag, 95)),
         "ddf_max": float(np.max(mag)),
     }
-