Update app.py

app.py

@@ -283,94 +283,18 @@ def _predict_single_dog(image):
         topk_probs_percent = [f"{prob.item() * 100:.2f}%" for prob in topk_probs[0]]
     return top1_prob, topk_breeds, topk_probs_percent
 
-# async def detect_multiple_dogs(image, conf_threshold=0.3):
-#     # 調整 YOLO 模型的置信度閾值
-#     return await asyncio.to_thread(_detect_multiple_dogs, image, conf_threshold)
-
-# def _detect_multiple_dogs(image, conf_threshold):
-#     results = model_yolo(image, conf=conf_threshold)
-#     dogs = []
-#     for result in results:
-#         for box in result.boxes:
-#             if box.cls == 16:  # COCO 資料集中狗的類別是 16
-#                 xyxy = box.xyxy[0].tolist()
-#                 confidence = box.conf.item()
-#                 if confidence >= conf_threshold:  # 只保留置信度高於閾值的框
-#                     cropped_image = image.crop((xyxy[0], xyxy[1], xyxy[2], xyxy[3]))
-#                     dogs.append((cropped_image, confidence, xyxy))
-#     return dogs
-
-# async def detect_multiple_dogs(image, conf_threshold=0.2, iou_threshold=0.5):
-#     results = model_yolo(image, conf=conf_threshold, iou=iou_threshold)[0]
-#     dogs = []
-#     for box in results.boxes:
-#         if box.cls == 16:  # COCO 資料集中狗的類別是 16
-#             xyxy = box.xyxy[0].tolist()
-#             confidence = box.conf.item()
-#             cropped_image = image.crop((xyxy[0], xyxy[1], xyxy[2], xyxy[3]))
-#             dogs.append((cropped_image, confidence, xyxy))
-#     return dogs
-# 此為如果後面調不好 使用的版本
-
-async def detect_multiple_dogs(image, conf_threshold=0.3, iou_threshold=0.5):
+
+async def detect_multiple_dogs(image, conf_threshold=0.2, iou_threshold=0.5):
     results = model_yolo(image, conf=conf_threshold, iou=iou_threshold)[0]
     dogs = []
     for box in results.boxes:
         if box.cls == 16:  # COCO 資料集中狗的類別是 16
             xyxy = box.xyxy[0].tolist()
             confidence = box.conf.item()
-            area = (xyxy[2] - xyxy[0]) * (xyxy[3] - xyxy[1])
-            if area > 1000:  # 過濾掉太小的檢測框
-                cropped_image = image.crop((xyxy[0], xyxy[1], xyxy[2], xyxy[3]))
-                dogs.append((cropped_image, confidence, xyxy))
-    
-    # 合併重疊的檢測框
-    dogs = merge_overlapping_boxes(dogs, iou_threshold=0.6)
-    
+            cropped_image = image.crop((xyxy[0], xyxy[1], xyxy[2], xyxy[3]))
+            dogs.append((cropped_image, confidence, xyxy))
     return dogs
 
-def merge_overlapping_boxes(dogs, iou_threshold=0.6):
-    merged_dogs = []
-    while dogs:
-        base = dogs.pop(0)
-        i = 0
-        while i < len(dogs):
-            if calculate_iou(base[2], dogs[i][2]) > iou_threshold:
-                # 合併重疊的框
-                base = merge_boxes(base, dogs.pop(i))
-            else:
-                i += 1
-        merged_dogs.append(base)
-    return merged_dogs
-
-def merge_boxes(box1, box2):
-    xyxy1, conf1, _ = box1
-    xyxy2, conf2, _ = box2
-    merged_xyxy = [
-        min(xyxy1[0], xyxy2[0]),
-        min(xyxy1[1], xyxy2[1]),
-        max(xyxy1[2], xyxy2[2]),
-        max(xyxy1[3], xyxy2[3])
-    ]
-    merged_conf = max(conf1, conf2)
-    merged_image = Image.new('RGB', (int(merged_xyxy[2] - merged_xyxy[0]), int(merged_xyxy[3] - merged_xyxy[1])))
-    merged_image.paste(box1[0], (0, 0))
-    return (merged_image, merged_conf, merged_xyxy)
-
-def calculate_iou(box1, box2):
-    # 計算兩個邊界框的交集面積
-    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 predict(image):
@@ -484,97 +408,6 @@ def calculate_iou(box1, box2):
 # if __name__ == "__main__":
 #     iface.launch()
 
-async def predict(image):
-    if image is None:
-        return "Please upload an image to start.", None, gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
-
-    try:
-        if isinstance(image, np.ndarray):
-            image = Image.fromarray(image)
-
-        # 嘗試檢測多隻狗，進一步降低閾值以提高檢測率
-        dogs = await detect_multiple_dogs(image, conf_threshold=0.05)  # 降低閾值以檢測更多狗
-        
-        if len(dogs) <= 1:
-            # 單狗情境
-            return await process_single_dog(image)
-
-        # 多狗情境
-        color_list = ['#FF0000', '#00FF00', '#0000FF', '#FFFF00', '#00FFFF', '#FF00FF', '#800080', '#FFA500']
-        explanations = []
-        buttons = []
-        annotated_image = image.copy()
-        draw = ImageDraw.Draw(annotated_image)
-        font = ImageFont.load_default()
-
-        for i, (cropped_image, _, box) in enumerate(dogs):
-            top1_prob, topk_breeds, topk_probs_percent = await predict_single_dog(cropped_image)
-            color = color_list[i % len(color_list)]
-            draw.rectangle(box, outline=color, width=3)
-            draw.text((box[0], box[1]), f"Dog {i+1}", fill=color, font=font)
-
-            breed = topk_breeds[0]
-            if top1_prob >= 0.5:
-                description = get_dog_description(breed)
-                formatted_description = format_description(description, breed)
-                explanations.append(f"Dog {i+1}: {formatted_description}")
-            elif top1_prob >= 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([gr.update(visible=True, value=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.")
-
-        final_explanation = "\n\n".join(explanations)
-        if buttons:
-            final_explanation += "\n\nClick on a button to view more information about the breed."
-            return (final_explanation, annotated_image, 
-                    buttons[0] if len(buttons) > 0 else gr.update(visible=False),
-                    buttons[1] if len(buttons) > 1 else gr.update(visible=False),
-                    buttons[2] if len(buttons) > 2 else gr.update(visible=False),
-                    gr.update(visible=True))  # 顯示 back 按鈕
-        else:
-            return final_explanation, annotated_image, gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
-
-    except Exception as e:
-        return f"An error occurred: {str(e)}", None, gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
-
-async def process_single_dog(image):
-    top1_prob, topk_breeds, topk_probs_percent = await predict_single_dog(image)
-    if top1_prob < 0.2:
-        return "The image is unclear or the breed is not in the dataset. Please upload a clearer image of a dog.", None, gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
-
-    breed = topk_breeds[0]
-    description = get_dog_description(breed)
-
-    if top1_prob >= 0.5:
-        formatted_description = format_description(description, breed)
-        return formatted_description, image, gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
-    else:
-        explanation = (
-            f"The model couldn't confidently identify the breed. Here are the top 3 possible breeds:\n\n"
-            f"1. **{topk_breeds[0]}** ({topk_probs_percent[0]} confidence)\n"
-            f"2. **{topk_breeds[1]}** ({topk_probs_percent[1]} confidence)\n"
-            f"3. **{topk_breeds[2]}** ({topk_probs_percent[2]} confidence)\n\n"
-            "Click on a button to view more information about the breed."
-        )
-        return (explanation, image, 
-                gr.update(visible=True, value=f"More about {topk_breeds[0]}"),
-                gr.update(visible=True, value=f"More about {topk_breeds[1]}"),
-                gr.update(visible=True, value=f"More about {topk_breeds[2]}"),
-                gr.update(visible=True))  # 顯示 back 按鈕
-
-def show_details(choice, previous_output):
-    if not choice:
-        return previous_output, gr.update(visible=True)
-
-    try:
-        breed = choice.split("More about ")[-1]
-        description = get_dog_description(breed)
-        return format_description(description, breed), gr.update(visible=True)
-    except Exception as e:
-        return f"An error occurred while showing details: {e}", gr.update(visible=True)
 
 
 # 介面部分