Update app.py

app.py

@@ -167,105 +167,43 @@ async def predict_single_dog(image):
     return top1_prob, topk_breeds, topk_probs_percent
     
 
-# async def detect_multiple_dogs(image, conf_threshold=0.25, iou_threshold=0.4):
-#     results = model_yolo(image, conf=conf_threshold, iou=iou_threshold)[0]
-#     dogs = []
-#     boxes = []
-#     for box in results.boxes:
-#         if box.cls == 16:  # COCO dataset class for dog is 16
-#             xyxy = box.xyxy[0].tolist()
-#             confidence = box.conf.item()
-#             boxes.append((xyxy, confidence))
-    
-#     if not boxes:
-#         dogs.append((image, 1.0, [0, 0, image.width, image.height]))
-#     else:
-#         nms_boxes = non_max_suppression(boxes, iou_threshold)
-        
-#         for box, confidence in nms_boxes:
-#             x1, y1, x2, y2 = box
-#             w, h = x2 - x1, y2 - y1
-#             x1 = max(0, x1 - w * 0.05)
-#             y1 = max(0, y1 - h * 0.05)
-#             x2 = min(image.width, x2 + w * 0.05)
-#             y2 = min(image.height, y2 + h * 0.05)
-#             cropped_image = image.crop((x1, y1, x2, y2))
-#             dogs.append((cropped_image, confidence, [x1, y1, x2, y2]))
-    
-#     return dogs
-
-
-# def non_max_suppression(boxes, iou_threshold):
-#     keep = []
-#     boxes = sorted(boxes, key=lambda x: x[1], reverse=True)
-#     while boxes:
-#         current = boxes.pop(0)
-#         keep.append(current)
-#         boxes = [box for box in boxes if calculate_iou(current[0], box[0]) < iou_threshold]
-#     return keep
-
-# def calculate_iou(box1, box2):
-#     x1 = max(box1[0], box2[0])
-#     y1 = max(box1[1], box2[1])
-#     x2 = min(box1[2], box2[2])
-#     y2 = min(box1[3], box2[3])
-    
-#     intersection = max(0, x2 - x1) * max(0, y2 - y1)
-#     area1 = (box1[2] - box1[0]) * (box1[3] - box1[1])
-#     area2 = (box2[2] - box2[0]) * (box2[3] - box2[1])
-    
-#     iou = intersection / float(area1 + area2 - intersection)
-#     return iou
-
-
-async def detect_multiple_dogs(image, conf_threshold=0.1, iou_threshold=0.3):
+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 = []
     boxes = []
-    
     for box in results.boxes:
         if box.cls == 16:  # COCO dataset class for dog is 16
             xyxy = box.xyxy[0].tolist()
             confidence = box.conf.item()
             boxes.append((xyxy, confidence))
-
+    
     if not boxes:
         dogs.append((image, 1.0, [0, 0, image.width, image.height]))
     else:
-        # 新增框的面積過濾條件，避免太小的框
-        valid_boxes = [box for box in boxes if (box[0][2] - box[0][0]) * (box[0][3] - box[0][1]) > 0.1 * image.width * image.height]
-        
-        nms_boxes = non_max_suppression(valid_boxes, iou_threshold)
+        nms_boxes = non_max_suppression(boxes, iou_threshold)
         
         for box, confidence in nms_boxes:
             x1, y1, x2, y2 = box
             w, h = x2 - x1, y2 - y1
-            
-            # 調整框的位置，處理重疊框問題
-            if w * h < 0.2 * image.width * image.height and confidence < 0.2:
-                continue  # 跳過信心分數過低的框
-
-            # 根據框的大小動態調整信心門檻
-            if w * h < 0.05 * image.width * image.height:
-                continue  # 過小的框直接跳過
-            
+            x1 = max(0, x1 - w * 0.05)
+            y1 = max(0, y1 - h * 0.05)
+            x2 = min(image.width, x2 + w * 0.05)
+            y2 = min(image.height, y2 + h * 0.05)
             cropped_image = image.crop((x1, y1, x2, y2))
             dogs.append((cropped_image, confidence, [x1, y1, x2, y2]))
-
+    
     return dogs
 
 
-def non_max_suppression(boxes, iou_threshold=0.3):
+def non_max_suppression(boxes, iou_threshold):
     keep = []
-    boxes = sorted(boxes, key=lambda x: x[1], reverse=True)  # 按信心分數排序
-    
+    boxes = sorted(boxes, key=lambda x: x[1], reverse=True)
     while boxes:
         current = boxes.pop(0)
         keep.append(current)
         boxes = [box for box in boxes if calculate_iou(current[0], box[0]) < iou_threshold]
     return keep
 
-
 def calculate_iou(box1, box2):
     x1 = max(box1[0], box2[0])
     y1 = max(box1[1], box2[1])
@@ -280,8 +218,6 @@ def calculate_iou(box1, box2):
     return iou
 
 
-
-
 async def process_single_dog(image):
     top1_prob, topk_breeds, topk_probs_percent = await predict_single_dog(image)
     if top1_prob < 0.2: