Update app.py

app.py

@@ -194,7 +194,7 @@ async def predict_single_dog(image):
     return top1_prob, topk_breeds, topk_probs_percent
     
 
-async def detect_multiple_dogs(image, conf_threshold=0.2, iou_threshold=0.3):
+async def detect_multiple_dogs(image, conf_threshold=0.3, iou_threshold=0.5):
     results = model_yolo(image, conf=conf_threshold, iou=iou_threshold)[0]
     dogs = []
     boxes = []
@@ -207,22 +207,17 @@ async def detect_multiple_dogs(image, conf_threshold=0.2, iou_threshold=0.3):
     if not boxes:
         dogs.append((image, 1.0, [0, 0, image.width, image.height]))
     else:
-        sorted_boxes = sorted(boxes, key=lambda x: x[1], reverse=True)
-        nms_boxes = non_max_suppression(sorted_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
-            x1 = max(0, x1 - w * 0.1)
-            y1 = max(0, y1 - h * 0.1)
-            x2 = min(image.width, x2 + w * 0.1)
-            y2 = min(image.height, y2 + h * 0.1)
+            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]))
-        
-        # 如果只檢測到一隻狗，但置信度較低，添加整張圖片作為備選
-        if len(dogs) == 1 and dogs[0][1] < 0.5:
-            dogs.append((image, 1.0, [0, 0, image.width, image.height]))
     
     return dogs
 
@@ -470,21 +465,20 @@ async def predict(image):
             draw.rectangle(box, outline=color, width=3)
             draw.text((box[0], box[1]), f"Dog {i+1}", fill=color, font=font)
 
-            # 結合 YOLO 檢測置信度和品種識別置信度
             combined_confidence = detection_confidence * top1_prob
 
-            if combined_confidence >= 0.4:
+            if top1_prob >= 0.5:
                 breed = topk_breeds[0]
                 description = get_dog_description(breed)
                 formatted_description = format_description(description, breed)
-                explanations.append(f"Dog {i+1}: {formatted_description}")
+                explanations.append(f"Dog {i+1}: Breed: {breed}\n{formatted_description}")
             elif combined_confidence >= 0.2:
                 dog_explanation = f"Dog {i+1}: Top 3 possible breeds:\n"
                 dog_explanation += "\n".join([f"{j+1}. **{breed}** ({prob} confidence)" for j, (breed, prob) in enumerate(zip(topk_breeds[:3], topk_probs_percent[:3]))])
                 explanations.append(dog_explanation)
                 buttons.extend([f"Dog {i+1}: More about {breed}" for breed in topk_breeds[:3]])
             else:
-                explanations.append(f"Dog {i+1}: The image is unclear or the breed is not in the dataset.")
+                explanations.append(f"Dog {i+1}: The image is unclear or the breed is not in the dataset. Please upload a clearer image.")
 
         final_explanation = "\n\n".join(explanations)
         if buttons: