Update app.py

app.py

@@ -196,21 +196,6 @@ async def predict_single_dog(image):
 #     return dogs
 
 
-def filter_detections(dogs, image_size):
-    filtered_dogs = []
-    image_area = image_size[0] * image_size[1]
-    
-    for dog in dogs:
-        _, _, box = dog
-        dog_area = (box[2] - box[0]) * (box[3] - box[1])
-        area_ratio = dog_area / image_area
-        
-        if 0.01 < area_ratio < 0.9:  # 過濾掉太小或太大的檢測框
-            filtered_dogs.append(dog)
-    
-    return filtered_dogs
-    
-
 async def detect_multiple_dogs(image, conf_threshold=0.2, iou_threshold=0.4):
     results = model_yolo(image, conf=conf_threshold, iou=iou_threshold)[0]
     dogs = []
@@ -230,14 +215,27 @@ async def detect_multiple_dogs(image, conf_threshold=0.2, iou_threshold=0.4):
         for box, confidence in nms_boxes:
             x1, y1, x2, y2 = [int(coord) for coord in box]
             cropped_image = image.crop((x1, y1, x2, y2))
-            
-            if is_valid_dog(cropped_image):
-                dogs.append((cropped_image, confidence, [x1, y1, x2, y2]))
-
+            dogs.append((cropped_image, confidence, [x1, y1, x2, y2]))
+    
+    # 應用過濾器來移除可能的錯誤檢測
     dogs = filter_detections(dogs, (image.width, image.height))
     
     return dogs
 
+def filter_detections(dogs, image_size):
+    filtered_dogs = []
+    image_area = image_size[0] * image_size[1]
+    
+    for dog in dogs:
+        _, _, box = dog
+        dog_area = (box[2] - box[0]) * (box[3] - box[1])
+        area_ratio = dog_area / image_area
+        
+        if 0.01 < area_ratio < 0.9:  # 過濾掉太小或太大的檢測框
+            filtered_dogs.append(dog)
+    
+    return filtered_dogs
+
 
 def merge_overlapping_boxes(boxes, overlap_threshold):
     merged = []