Update app.py

app.py

@@ -242,12 +242,21 @@ def special_process_image_yolo(risk_level, image_path, point1, point2, threshold
     # タイムスタンプを作成
     timestamp = datetime.now().strftime("%Y%m%d%H%M%S")
 
-    # リスクレベルに基づいた減衰率の計算
-    def logistic_decay(risk_level, k=0.1, r0=50):
-        return 1 / (1 + np.exp(-k * (risk_level - r0)))
+    # テキストラベルのリストとその優先順に基づいた閾値の減衰率の計算
+    tex = [
+        'text', 'poster', 'Name tag', 'License plate', 'Digital screens',
+        'signboard', 'sign', 'logo', 'manhole', 'electricity pole', 'cardboard'
+    ]
+    
+    def logistic_decay_for_label(risk_level, label_index, k=0.1, r0=50):
+        base_decay = 1 / (1 + np.exp(-k * (risk_level - r0)))
+        # ラベルの順序に応じた減衰の段階を追加
+        return max(base_decay + 0.05 * label_index, 0.01)
 
-    decay_factor = logistic_decay(risk_level)
-    adjusted_thresholds = {key: max(value - decay_factor + 0.8, 0.01) / 2 for key, value in (thresholds or {}).items()}
+    adjusted_thresholds = {}
+    for i, label in enumerate(tex):
+        decay_factor = logistic_decay_for_label(risk_level, i)
+        adjusted_thresholds[label] = max(0.01, decay_factor / 2)
 
     # 画像の読み込みとRGB変換
     image = cv2.imread(image_path)
@@ -259,21 +268,17 @@ def special_process_image_yolo(risk_level, image_path, point1, point2, threshold
     # 初期化したマスク画像
     mask = np.zeros(image.shape[:2], dtype=np.uint8)
 
-    # リスクレベルに基づいて消去対象のオブジェクトを決定
-    target_objects = decide_to_object(risk_level)
-
-    # 各検出結果に基づきマスク作成
+    # 全ての検出オブジェクトを対象としてマスク作成
     for box in results[0].boxes:
         x1, y1, x2, y2 = map(int, box.xyxy[0])
         confidence = box.conf[0]
         class_id = box.cls[0]
         object_type = model.names[int(class_id)]
 
-        # クラス名が消去対象に含まれている場合のみ処理
-        if object_type in target_objects:
-            threshold = adjusted_thresholds.get(object_type, 0.5)
-            if confidence >= threshold:
-                mask = create_mask(image, x1, y1, x2, y2)
+        # オブジェクトの閾値を確認し、マスクを適用
+        threshold = adjusted_thresholds.get(object_type, 0.5)
+        if confidence >= threshold:
+            mask = create_mask(image, x1, y1, x2, y2)
 
     # 絶対座標に変換した点の範囲を黒に設定
     p1_x, p1_y = int(point1[0] * image.shape[1]), int(point1[1] * image.shape[0])