增加每个分类验证500张，增加验证成功率计算，增加日志

watermark_verify/process/yolox_pytorch_blackbox_process.py

@@ -3,6 +3,7 @@ yolox基于pytorch框架的黑盒水印处理验证流程
 """
 import os
 import cv2
+import shutil
 from watermark_verify.inference.yolox_inference import YOLOXInference
 from watermark_verify.process.general_process_define import BlackBoxWatermarkProcessDefine
 from watermark_verify.tools import parse_qrcode_label_file
@@ -21,13 +22,25 @@ class ModelWatermarkProcessor(BlackBoxWatermarkProcessDefine):
         # 获取权重文件，使用触发集进行模型推理, 将推理结果与触发集预先二维码保存位置进行比对，在误差范围内则进行下一步，否则返回False
         cls_image_mapping = parse_qrcode_label_file.parse_labels(self.qrcode_positions_file)
         accessed_cls = set()
+
+        total = 0    # 总检测次数
+        passed = 0   # 成功检测次数
+
         for cls, images in cls_image_mapping.items():
-            for image in images:
+            for i, image in enumerate(images):
                 image_path = os.path.join(self.trigger_dir, image)
                 detect_result = self.detect_secret_label(image_path, self.qrcode_positions_file, (640, 640))
+                
+                total += 1
                 if detect_result:
-                    accessed_cls.add(cls)
-                    break
+                    passed += 1
+                    if i == 499:
+                        accessed_cls.add(cls)
+                        break
+
+        success_rate = 100.0 * passed / total if total > 0 else 0.0
+        print(f"\n\r---------- 水印检测成功率：{passed} / {total} = {success_rate:.2f}% ----------\n\r")
+
         if not accessed_cls == set(cls_image_mapping.keys()):  # 所有的分类都检测出模型水印，模型水印检测结果为True
             return False
 
@@ -71,6 +84,7 @@ def detect_watermark(dets, watermark_box, threshold=0.5):
         wm_cls = watermark_box[4]
         if cls == wm_cls:
             ciou = calculate_ciou(box, wm_box_coords)
+            print(f"检测到的类别: {cls}, 置信度: {score}, 相似度: {ciou}")
             if ciou > threshold:
                 return True
     return False