Upload 3 files

app.py

@@ -14,6 +14,7 @@ from object_distance import (
     estimate_distances,
     estimate_focal_length,
     load_yolo,
+    metrics_table,
     run_yolo,
 )
 
@@ -120,7 +121,7 @@ def run_object_distance_task(
     inner_ratio: float,
     min_depth_value: float,
     blend_weight_pinhole: float,
-) -> Tuple[np.ndarray, np.ndarray, pd.DataFrame, Dict[str, Any]]:
+) -> Tuple[np.ndarray, np.ndarray, pd.DataFrame, pd.DataFrame]:
     img_bgr = _ensure_bgr(image_rgb)
 
     if focal_mode == "Estimate from FOV":
@@ -154,7 +155,6 @@ def run_object_distance_task(
     depth_heatmap = depth_to_heatmap(depth_map)
     det_df = _detections_df(detections)
 
-    # Add some run metadata for convenience
     metrics = dict(metrics)
     metrics.update(
         {
@@ -165,8 +165,8 @@ def run_object_distance_task(
             "focal_length_px": float(focal_length),
         }
     )
-
-    return _bgr_to_rgb(annotated), _bgr_to_rgb(depth_heatmap), det_df, metrics
+    metrics_df = pd.DataFrame(metrics_table(metrics), columns=["metric", "value"])
+    return _bgr_to_rgb(annotated), _bgr_to_rgb(depth_heatmap), det_df, metrics_df
 
 
 DESCRIPTION = """
@@ -244,7 +244,7 @@ with gr.Blocks(title="CV Project Playground", analytics_enabled=False) as demo:
                 out_depth = gr.Image(label="MiDaS depth heatmap", type="numpy")
 
             out_table = gr.Dataframe(label="Detections table", wrap=True)
-            out_metrics = gr.JSON(label="Evaluation / run metrics")
+            out_metrics = gr.Dataframe(label="Evaluation metrics (key)", wrap=True)
 
             run_btn_2.click(
                 fn=run_object_distance_task,

object_distance.py

@@ -465,6 +465,43 @@ def compute_evaluation_metrics(
     return metrics
 
 
+def metrics_table(metrics: dict) -> list[list[str]]:
+    """
+    Convert the full metrics dict into a small table (key metrics only).
+    Returns rows: [metric_name, value].
+    """
+    def fmt(v):
+        if v is None:
+            return "N/A"
+        if isinstance(v, float):
+            return f"{v:.4f}"
+        return str(v)
+
+    keys = [
+        # Coverage
+        ("num_detections", "num_detections"),
+        ("mean_confidence", "mean_confidence"),
+        ("known_height_coverage", "known_height_coverage"),
+        ("distance_coverage", "distance_coverage"),
+        ("unresolved_count", "unresolved_count"),
+        # Calibration
+        ("calibration_anchor_count", "calibration_anchor_count"),
+        ("midas_scale_factor", "midas_scale_factor"),
+        ("anchor_scale_cv", "anchor_scale_cv"),
+        # Agreement (if available)
+        ("agreement_sample_count", "agreement_sample_count"),
+        ("agreement_mae_m", "agreement_mae_m"),
+        ("agreement_rmse_m", "agreement_rmse_m"),
+        ("agreement_mean_relative_error", "agreement_mean_relative_error"),
+        ("agreement_within_20pct", "agreement_within_20pct"),
+    ]
+
+    rows = []
+    for label, k in keys:
+        rows.append([label, fmt(metrics.get(k))])
+    return rows
+
+
 def save_evaluation_outputs(
     detections: list[dict],
     metrics: dict,
@@ -493,9 +530,11 @@ def save_evaluation_outputs(
                 det.get("method", "unknown"),
             ])
 
-    metrics_path = os.path.join(eval_dir, "metrics.json")
-    with open(metrics_path, "w", encoding="utf-8") as f:
-        json.dump(metrics, f, indent=2)
+    metrics_table_path = os.path.join(eval_dir, "metrics_table.csv")
+    with open(metrics_table_path, "w", newline="", encoding="utf-8") as f:
+        writer = csv.writer(f)
+        writer.writerow(["metric", "value"])
+        writer.writerows(metrics_table(metrics))
 
     report_path = os.path.join(eval_dir, "evaluation_report.txt")
     with open(report_path, "w", encoding="utf-8") as f:
@@ -504,16 +543,10 @@ def save_evaluation_outputs(
         f.write("This report measures internal consistency only.\n")
         f.write("No ground-truth object distances are available here, so these metrics\n")
         f.write("should be interpreted as coverage / robustness diagnostics, not absolute accuracy.\n\n")
-        f.write("Key metrics\n")
-        f.write("-----------\n")
-        for key, value in metrics.items():
-            if value is None:
-                pretty = "N/A"
-            elif isinstance(value, float):
-                pretty = f"{value:.4f}"
-            else:
-                pretty = str(value)
-            f.write(f"{key}: {pretty}\n")
+        f.write("Key metrics (table)\n")
+        f.write("-------------------\n")
+        for k, v in metrics_table(metrics):
+            f.write(f"{k}: {v}\n")
 
         f.write("\nMetric sufficiency note\n")
         f.write("----------------------\n")
@@ -522,7 +555,7 @@ def save_evaluation_outputs(
         f.write("- To measure real accuracy, add ground-truth distances and report MAE/RMSE/MAPE against labels.\n")
 
     print(f"         Saved -> {csv_path}")
-    print(f"         Saved -> {metrics_path}")
+    print(f"         Saved -> {metrics_table_path}")
     print(f"         Saved -> {report_path}")