测试文件提交

yolo_test.py

@@ -125,16 +125,14 @@ def set_camera_properties(cap, brightness=None, contrast=None, saturation=None,
                 logger.warning(f"  ⚠️ 设置参数 {prop} 失败: {e}")
 
 
-def yolo_shibie(im_PIL, im_opencv_rgb, raw_frame_bgr, detections, model, show_original=True, 
+def yolo_shibie(im_PIL, detections, model, enhance_enabled=False, enhance_params=None):
-                enhance_enabled=False, enhance_params=None):
     """
     YOLO识别函数
     :param im_PIL: PIL图像对象
-    :param im_opencv_rgb: RGB格式的OpenCV图像（裁剪后）
-    :param raw_frame_bgr: 原始BGR格式的OpenCV图像（未裁剪，与raw_frame.jpg一致）
     :param detections: 检测结果字典
     :param model: YOLO模型
-    :param show_original: 是否同时显示原始帧
+    :param enhance_enabled: 是否启用图像增强
+    :param enhance_params: 图像增强参数
     :return: 更新后的detections字典，如果用户退出则返回None
     """
     if im_PIL is None:
@@ -158,29 +156,8 @@ def yolo_shibie(im_PIL, im_opencv_rgb, raw_frame_bgr, detections, model, show_or
             except Exception as e:
                 print(f"⚠️ 图像增强失败: {e}")
         
-        # 显示画面
+        # 显示YOLO检测结果
-        if show_original and raw_frame_bgr is not None:
+        cv2.imshow("YOLO Real-time Detection", display_frame)
-            # 同时显示原始帧和检测结果（并排显示）
-            # 调整原始帧大小以匹配裁剪后的检测结果
-            h, w = display_frame.shape[:2]
-            # 裁剪原始帧（与get_frame的处理一致：30:30+720, 0:1280）
-            raw_height, raw_width = raw_frame_bgr.shape[:2]
-            crop_top = 30
-            crop_bottom = min(crop_top + h, raw_height)
-            crop_right = min(w, raw_width)
-            raw_cropped = raw_frame_bgr[crop_top:crop_bottom, 0:crop_right]
-            
-            # 如果尺寸不匹配，调整原始帧大小
-            if raw_cropped.shape[:2] != (h, w):
-                raw_cropped = cv2.resize(raw_cropped, (w, h))
-            
-            # 并排显示：原始帧(左) | 检测结果(右)
-            # 原始帧已经是BGR格式，检测结果也是BGR格式，可以直接拼接
-            combined = cv2.hconcat([raw_cropped, display_frame])
-            cv2.imshow("Original BGR (Left) | YOLO Detection (Right)", combined)
-        else:
-            # 只显示检测结果
-            cv2.imshow("YOLO Real-time Detection", display_frame)
 
         # ✅ 提取检测信息
         if result.boxes is not None and len(result.boxes.xyxy) > 0:
@@ -271,7 +248,6 @@ def main():
     print("✅ 采集卡初始化成功")
     print("\n快捷键:")
     print("  'q' 或 ESC - 退出")
-    print("  'o' - 切换原始帧对比模式")
     print("  'e' - 切换图像增强")
     print("  '1'/'2' - 调整锐化强度 (+/-0.1)")
     print("  '3'/'4' - 调整对比度 (+/-0.1)")
@@ -279,7 +255,6 @@ def main():
     
     try:
         frame_count = 0
-        show_original = True  # 默认同时显示原始帧和检测结果
         enhance_enabled = False  # 默认关闭图像增强
         
         # 图像增强参数
@@ -309,11 +284,6 @@ def main():
                 print("⚠️ PIL图像为空，跳过...")
                 continue
             
-            # 获取原始BGR帧（与test_capture_card.py保存的raw_frame.jpg一致）
-            raw_frame_bgr = None
-            if get_image.cap is not None and get_image.frame is not None:
-                raw_frame_bgr = get_image.frame.copy()  # 原始BGR格式，未裁剪
-            
             # 初始化检测结果字典
             detections = {
                 'center': None, 'next': None,
@@ -323,17 +293,13 @@ def main():
             }
             
             # 执行YOLO检测
-            detections = yolo_shibie(im_PIL, im_opencv_rgb, raw_frame_bgr, detections, model, 
+            detections = yolo_shibie(im_PIL, detections, model, enhance_enabled, enhance_params)
-                                    show_original, enhance_enabled, enhance_params)
             
             # 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('o') or key == ord('O'):
-                show_original = not show_original
-                print(f"切换显示模式: {'原始帧对比' if show_original else '仅检测结果'}")
             elif key == ord('e') or key == ord('E'):
                 enhance_enabled = not enhance_enabled
                 status = "开启" if enhance_enabled else "关闭"