diff --git a/main.py b/main.py
index b030988..d5bf503 100644
--- a/main.py
+++ b/main.py
@@ -28,6 +28,9 @@
 XGBOOST_ENABLED = os.getenv("XGBOOST_ENABLED", "True").lower() == "true"
 LIGHTGBM_ENABLED = os.getenv("LIGHTGBM_ENABLED", "True").lower() == "true"
 
+# Get normalization setting
+NORMALIZE_ENABLED = os.getenv("NORMALIZE_ENABLED", "False").lower() == "true"
+
 
 def load_model(model_path, model_type="lgb"):
     with open(model_path, "rb") as model_file:
@@ -104,6 +107,7 @@
     )
 
     rows = []
+    normalized_rows = []
     for image_file_name in png_files:
         image_path = os.path.join(base_dir, image_file_name)
         frame = cv2.imread(image_path)
@@ -135,6 +139,32 @@
         }
         rows.append(row)
 
+        if NORMALIZE_ENABLED:
+            source_points = np.array(
+                [
+                    [float(row[f"{pos}_x"]), float(row[f"{pos}_y"])]
+                    for pos in ["left_shoulder", "right_shoulder", "left_hip", "right_hip"]
+                ],
+                dtype=np.float32,
+            )
+            stethoscope_point = np.array([float(row["stethoscope_x"]), float(row["stethoscope_y"])])
+            normalized_points = normalize_quadrilateral_with_point(source_points.flatten(), stethoscope_point)
+
+            normalized_row = {
+                "image_file_name": image_file_name,
+                "left_shoulder_x": normalized_points[0, 0],
+                "left_shoulder_y": normalized_points[0, 1],
+                "right_shoulder_x": normalized_points[1, 0],
+                "right_shoulder_y": normalized_points[1, 1],
+                "left_hip_x": normalized_points[2, 0],
+                "left_hip_y": normalized_points[2, 1],
+                "right_hip_x": normalized_points[3, 0],
+                "right_hip_y": normalized_points[3, 1],
+                "stethoscope_x": normalized_points[4, 0],
+                "stethoscope_y": normalized_points[4, 1],
+            }
+            normalized_rows.append(normalized_row)
+
     if rows:
         fieldnames = list(rows[0].keys())
         if CONV_ENABLED:
@@ -163,7 +193,7 @@
             if XGBOOST_ENABLED:
                 prev_values["Xgboost"] = (180, 180)
 
-            for row in rows:
+            for i, row in enumerate(rows):
                 source_points = np.array(
                     [
                         [float(row[f"{pos}_x"]), float(row[f"{pos}_y"])]
@@ -204,9 +234,28 @@
                     for key in prev_values:
                         prev_values[key] = (row[f"{key}_stethoscope_x"], row[f"{key}_stethoscope_y"])
 
+                # 正規化されたデータにも予測結果を追加
+                if NORMALIZE_ENABLED:
+                    for key in prev_values:
+                        normalized_rows[i][f"{key}_stethoscope_x"] = row[f"{key}_stethoscope_x"]
+                        normalized_rows[i][f"{key}_stethoscope_y"] = row[f"{key}_stethoscope_y"]
+
                 writer.writerow(row)
 
         print(f"Processed and saved results to: {csv_path}")
+
+        # Write normalized results-convert.csv if enabled
+        if NORMALIZE_ENABLED and normalized_rows:
+            normalized_csv_path = os.path.join(results_dir, "results-convert.csv")
+            normalized_fieldnames = list(normalized_rows[0].keys())
+
+            with open(normalized_csv_path, "w", newline="") as csvfile:
+                writer = csv.DictWriter(csvfile, fieldnames=normalized_fieldnames)
+                writer.writeheader()
+                writer.writerows(normalized_rows)
+
+            print(f"Processed and saved normalized results to: {normalized_csv_path}")
+
         generate_visualizations(csv_path, base_dir, output_dir)
     else:
         print("No data to write to CSV.")
@@ -214,7 +263,7 @@
 
 def generate_visualizations(csv_path, original_images_dir, output_dir):
     df = pd.read_csv(csv_path)
-    body_image = cv2.imread("./images/body/BodyF.png")  # オリジナルのパスを使用
+    body_image = cv2.imread("./images/body/BodyF.png")
     results_dir = os.path.join(output_dir, "results")
     os.makedirs(results_dir, exist_ok=True)
 
@@ -320,4 +369,3 @@
     frames_dir = os.path.join(args.output_dir, "frames")
     video_to_frames(args.video_path, frames_dir)
     process_images(frames_dir, args.output_dir)
-    generate_visualizations(os.path.join(args.output_dir, "results", "results.csv"), frames_dir, args.output_dir)