Update app.py

app.py

@@ -4,7 +4,7 @@ import torch
 import torch.nn as nn
 import gradio as gr
 from torchvision.models import efficientnet_v2_m, EfficientNet_V2_M_Weights
-from torchvision.ops import nms
+from torchvision.ops import nms, box_iou
 import torch.nn.functional as F
 from torchvision import transforms
 from PIL import Image, ImageDraw, ImageFont, ImageFilter
@@ -250,7 +250,7 @@ async def detect_multiple_dogs(image, conf_threshold=0.25, iou_threshold=0.4, me
     confidences = []
     
     for box in results.boxes:
-        if box.cls == 16:  # COCO dataset class for dog is 16
+        if box.cls == 16:  # COCO 數據集中狗的類別是 16
             xyxy = box.xyxy[0].tolist()
             confidence = box.conf.item()
             boxes.append(torch.tensor(xyxy))
@@ -260,7 +260,7 @@ async def detect_multiple_dogs(image, conf_threshold=0.25, iou_threshold=0.4, me
         boxes = torch.stack(boxes)
         confidences = torch.tensor(confidences)
         
-        # Apply NMS
+        # 應用非極大值抑制 (NMS)
         keep = nms(boxes, confidences, iou_threshold)
         
         for i in keep:
@@ -269,7 +269,7 @@ async def detect_multiple_dogs(image, conf_threshold=0.25, iou_threshold=0.4, me
             cropped_image = image.crop((xyxy[0], xyxy[1], xyxy[2], xyxy[3]))
             dogs.append((cropped_image, confidence, xyxy))
         
-        # Merge nearby boxes
+        # 合併鄰近的邊界框
         merged_dogs = []
         while dogs:
             base_dog = dogs.pop(0)