测试文件提交

yolo_test.py

@@ -161,6 +161,9 @@ def yolo_shibie(im_PIL, detections, model, enhance_enabled=False, enhance_params
 
         # ✅ 提取检测信息
         if result.boxes is not None and len(result.boxes.xyxy) > 0:
+            # 用于存储多个候选npc4（如果检测到多个）
+            npc4_candidates = []
+            
             for i in range(len(result.boxes.xyxy)):
                 try:
                     left = float(result.boxes.xyxy[i][0])
@@ -169,11 +172,37 @@ def yolo_shibie(im_PIL, detections, model, enhance_enabled=False, enhance_params
                     bottom = float(result.boxes.xyxy[i][3])
                     cls_id = int(result.boxes.cls[i])
                     label = result.names[cls_id]
+                    
+                    # 获取置信度（如果可用）
+                    confidence = float(result.boxes.conf[i]) if hasattr(result.boxes, 'conf') and len(result.boxes.conf) > i else 1.0
 
-                    if label in ['center', 'next', 'npc1', 'npc2', 'npc3', 'npc4', 'boss', 'zhaozi']:
+                    # npc1-npc4 使用底部位置（与main.py保持一致）
+                    if label in ['npc1', 'npc2', 'npc3', 'npc4']:
+                        player_x = int(left + (right - left) / 2)
+                        player_y = int(bottom) + 30  # 使用底部位置，与main.py保持一致
+                        position = [player_x, player_y]
+                        
+                        # 特殊处理npc4：如果检测到多个，收集所有候选
+                        if label == 'npc4':
+                            npc4_candidates.append({
+                                'position': position,
+                                'confidence': confidence,
+                                'box': [left, top, right, bottom],
+                                'area': (right - left) * (bottom - top)  # 检测框面积
+                            })
+                        else:
+                            # npc1-npc3直接赋值（如果已经有值，保留置信度更高的）
+                            if detections[label] is None or (hasattr(result.boxes, 'conf') and 
+                                                              confidence > 0.5):
+                                detections[label] = position
+                    
+                    # 其他目标使用中心点
+                    elif label in ['center', 'next', 'boss', 'zhaozi']:
                         player_x = int(left + (right - left) / 2) + 3
                         player_y = int(top + (bottom - top) / 2) + 40
                         detections[label] = [player_x, player_y]
+                    
+                    # 道具和怪物可以多个
                     elif label in ['daojv', 'gw']:
                         player_x = int(left + (right - left) / 2) + 3
                         player_y = int(top + (bottom - top) / 2) + 40
@@ -181,9 +210,34 @@ def yolo_shibie(im_PIL, detections, model, enhance_enabled=False, enhance_params
                         if label not in detections:
                             detections[label] = []
                         detections[label].append([player_x, player_y])
+                        
                 except Exception as e:
                     print(f"⚠️ 处理检测框时出错: {e}")
                     continue
+            
+            # 处理npc4：如果检测到多个，选择最合适的
+            if npc4_candidates:
+                # 按置信度排序，选择置信度最高的
+                npc4_candidates.sort(key=lambda x: x['confidence'], reverse=True)
+                
+                # 选择最佳候选（置信度最高且面积合理）
+                best_npc4 = None
+                for candidate in npc4_candidates:
+                    # 置信度阈值：至少0.3（可根据实际情况调整）
+                    if candidate['confidence'] >= 0.3:
+                        # 检查检测框面积是否合理（避免过小的误检）
+                        area = candidate['area']
+                        if area > 100:  # 最小面积阈值
+                            best_npc4 = candidate
+                            break
+                
+                if best_npc4:
+                    detections['npc4'] = best_npc4['position']
+                    # 可选：输出调试信息
+                    # print(f"✅ 检测到npc4: 位置={best_npc4['position']}, 置信度={best_npc4['confidence']:.2f}")
+                elif len(npc4_candidates) == 1:
+                    # 如果只有一个候选，即使置信度较低也使用
+                    detections['npc4'] = npc4_candidates[0]['position']
 
     except Exception as e:
         print(f"⚠️ YOLO检测出错: {e}")

yolotest2.py

@@ -0,0 +1,226 @@
+"""
+从main.py提取的YOLO识别测试文件
+使用与main.py相同的识别逻辑
+"""
+import cv2
+from utils.get_image import GetImage
+from ultralytics import YOLO
+from config import config_manager
+import os
+
+# 检查模型文件是否存在
+model_path = r"best.pt"
+model0_path = r"best0.pt"
+
+if not os.path.exists(model_path):
+    print(f"❌ 模型文件不存在: {model_path}")
+    exit(1)
+if not os.path.exists(model0_path):
+    print(f"❌ 模型文件不存在: {model0_path}")
+    exit(1)
+
+# 加载YOLO模型（与main.py保持一致）
+try:
+    model = YOLO(model_path).to('cuda')
+    model0 = YOLO(model0_path).to('cuda')
+    print(f"✅ 模型加载成功: {model_path}")
+    print(f"✅ 模型加载成功: {model0_path}")
+except Exception as e:
+    print(f"❌ 模型加载失败: {e}")
+    exit(1)
+
+
+def yolo_shibie(im_PIL, detections, model):
+    """
+    YOLO识别函数（与main.py中的实现完全一致）
+    :param im_PIL: PIL图像对象
+    :param detections: 检测结果字典
+    :param model: YOLO模型
+    :return: 更新后的detections字典
+    """
+    results = model(im_PIL)  # 目标检测
+    for result in results:
+        for i in range(len(result.boxes.xyxy)):
+            left, top, right, bottom = result.boxes.xyxy[i]
+            scalar_tensor = result.boxes.cls[i]
+            value = scalar_tensor.item()
+            label = result.names[int(value)]
+            if label == 'center' or label == 'next' or label == 'boss' or label == 'zhaozi':
+                player_x = int(left + (right - left) / 2)
+                player_y = int(top + (bottom - top) / 2) + 30
+                RW = [player_x, player_y]
+                detections[label] = RW
+            elif label == 'daojv' or label == 'gw':
+                player_x = int(left + (right - left) / 2)
+                player_y = int(top + (bottom - top) / 2) + 30
+                RW = [player_x, player_y]
+                detections[label].append(RW)
+            elif label == 'npc1' or label == 'npc2' or label == 'npc3' or label == 'npc4':
+                player_x = int(left + (right - left) / 2)
+                player_y = int(bottom) + 30
+                RW = [player_x, player_y]
+                detections[label] = RW
+    return detections
+
+
+def main():
+    """主函数"""
+    print("="*60)
+    print("YOLO识别测试（main.py逻辑）")
+    print("="*60)
+    
+    # 从配置加载采集卡设置
+    active_group = config_manager.get_active_group()
+    
+    if active_group is None:
+        print("⚠️ 没有活动的配置组，使用默认设置")
+        print("提示: 可以运行 python gui_config.py 设置配置")
+        cam_index = 0
+        width = 1920
+        height = 1080
+        use_model = model  # 默认使用model
+    else:
+        print(f"📋 使用配置组: {active_group['name']}")
+        cam_index = active_group['camera_index']
+        width = active_group['camera_width']
+        height = active_group['camera_height']
+        use_model = model0  # 城镇中使用model0
+        print(f"   使用模型: model0 (best0.pt) - 用于城镇识别")
+    
+    print(f"   采集卡索引: {cam_index}")
+    print(f"   分辨率: {width}x{height}")
+    print()
+    
+    # 初始化采集卡
+    print("🔧 正在初始化采集卡...")
+    get_image = GetImage(
+        cam_index=cam_index,
+        width=width,
+        height=height
+    )
+    
+    if get_image.cap is None:
+        print("❌ 采集卡初始化失败")
+        print("请检查：")
+        print("1. 采集卡是否正确连接")
+        print("2. 采集卡索引是否正确")
+        print("3. 采集卡驱动是否安装")
+        return
+    
+    print("✅ 采集卡初始化成功")
+    print("\n快捷键:")
+    print("  'q' 或 ESC - 退出")
+    print("  'm' - 切换模型 (model/model0)")
+    print("  'd' - 显示/隐藏检测信息")
+    print()
+    
+    try:
+        frame_count = 0
+        show_detections = True  # 是否显示检测信息
+        current_model = use_model  # 当前使用的模型
+        current_model_name = "model0" if use_model == model0 else "model"
+        
+        while True:
+            # 获取帧
+            frame_data = get_image.get_frame()
+            
+            if frame_data is None:
+                print("⚠️ 无法获取帧，跳过...")
+                continue
+            
+            # frame_data 是 [im_opencv_rgb, im_PIL] 格式
+            im_opencv_rgb, im_PIL = frame_data
+            
+            if im_PIL is None:
+                print("⚠️ PIL图像为空，跳过...")
+                continue
+            
+            # 初始化检测结果字典
+            detections = {
+                'center': None, 'next': None,
+                'npc1': None, 'npc2': None, 'npc3': None, 'npc4': None,
+                'boss': None, 'zhaozi': None,
+                'daojv': [], 'gw': []
+            }
+            
+            # 执行YOLO检测（使用main.py的逻辑）
+            detections = yolo_shibie(im_PIL, detections, current_model)
+            
+            # 获取绘制好框的图像用于显示
+            try:
+                results = current_model(im_PIL)
+                result = results[0]
+                frame_with_boxes_rgb = result.plot()
+                frame_with_boxes_bgr = cv2.cvtColor(frame_with_boxes_rgb, cv2.COLOR_RGB2BGR)
+            except Exception as e:
+                print(f"⚠️ 绘制检测框失败: {e}")
+                frame_with_boxes_bgr = cv2.cvtColor(im_opencv_rgb, cv2.COLOR_RGB2BGR)
+            
+            # 在图像上显示检测信息
+            if show_detections:
+                # 显示模型名称
+                cv2.putText(frame_with_boxes_bgr, f"Model: {current_model_name}", 
+                           (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
+                
+                # 显示检测到的目标
+                y_offset = 60
+                detected_items = []
+                for key, value in detections.items():
+                    if value is not None and value != []:
+                        if key in ['daojv', 'gw']:
+                            detected_items.append(f"{key}: {len(value)}个")
+                        else:
+                            detected_items.append(f"{key}: {value}")
+                
+                if detected_items:
+                    text = f"Detected: {', '.join(detected_items[:5])}"  # 最多显示5个
+                    if len(detected_items) > 5:
+                        text += f" ... (+{len(detected_items)-5})"
+                    cv2.putText(frame_with_boxes_bgr, text, 
+                               (10, y_offset), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 0), 2)
+            
+            # 显示图像
+            cv2.imshow("YOLO Detection (main.py logic)", frame_with_boxes_bgr)
+            
+            # 检查按键
+            key = cv2.waitKey(1) & 0xFF
+            if key in [27, ord('q'), ord('Q')]:
+                print("\n用户退出")
+                break
+            elif key == ord('m') or key == ord('M'):
+                # 切换模型
+                if current_model == model:
+                    current_model = model0
+                    current_model_name = "model0"
+                else:
+                    current_model = model
+                    current_model_name = "model"
+                print(f"切换模型: {current_model_name}")
+            elif key == ord('d') or key == ord('D'):
+                show_detections = not show_detections
+                print(f"显示检测信息: {'开启' if show_detections else '关闭'}")
+            
+            frame_count += 1
+            if frame_count % 30 == 0:  # 每30帧打印一次
+                print(f"📊 已处理 {frame_count} 帧 (模型: {current_model_name})")
+                # 打印有检测到的目标
+                detected_items = {k: v for k, v in detections.items() if v is not None and v != []}
+                if detected_items:
+                    print(f"   检测到: {detected_items}")
+    
+    except KeyboardInterrupt:
+        print("\n\n用户中断测试")
+    except Exception as e:
+        print(f"\n❌ 测试过程中发生错误: {e}")
+        import traceback
+        traceback.print_exc()
+    finally:
+        # 清理资源
+        get_image.release()
+        cv2.destroyAllWindows()
+        print("🔚 测试结束")
+
+
+if __name__ == "__main__":
+    main()
+