修改测试

.gitignore

@@ -166,3 +166,5 @@ pnnx*
 /ultralytics/assets/
 confusion_gift_cls4_0.45/
 *.jpg
+*.png
+*.txt

demo.py

@@ -3,8 +3,9 @@ import numpy as np
 # model = YOLOv10.from_pretrained('jameslahm/yolov10{n/s/m/b/l/x}')
 # or
 # wget https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10{n/s/m/b/l/x}.pt
-model = YOLOv10('ckpts/20250514/best_gift_v10n.pt')
+model = YOLOv10('ckpts/20250630/best_gift_v10n.pt')
 
-result = model.predict('./data/bandage.jpg', save=False, imgsz=[224, 224], conf=0.1)
+# result = model.predict('./data/bandage.jpg', save=True, imgsz=[224, 224], conf=0.1)
+result = model.predict('/home/lc/data_center/gift/trace_subimgs/predict_actual_test/gift', save=True, imgsz=[224, 224], conf=0.1)
 print(result)
-print(result[0].boxes.conf)
+# print(result[0].boxes.conf)

gift_demo.py

@@ -43,7 +43,7 @@ def get_image_list(path):
 
 
 def _init():
-    model = YOLOv10('ckpts/20250620/best_gift_v10n.pt')
+    model = YOLOv10('ckpts/20250701/best_gift_v10n.pt')
     return model
 
 
@@ -94,8 +94,5 @@ def main(path):
 
 
 if __name__ == "__main__":
-    # path = '../data_center/gift/trace_subimgs/d50'  # 间距为50时
-    # path = '../data_center/gift/trace_subimgs/actual_test'  # 永辉超市实测
     path = '../data_center/gift/gift_test'  #yolov10单图测试
-    # path = '../data_center/gift/trace_subimgs/tracluster'  # tracluster方法过滤
     main(path)

trace_demo.py

@@ -43,7 +43,7 @@ def get_image_list(path):
 
 
 def _init():
-    model = YOLOv10('ckpts/20250620/best_gift_v10n.pt')
+    model = YOLOv10('ckpts/20250701/best_gift_v10n.pt')
     return model
 
 

ultralytics/engine/predictor.py

@@ -253,6 +253,8 @@ class BasePredictor:
                 # Postprocess
                 with profilers[2]:
                     self.results = self.postprocess(preds, im, im0s)
+                if len(self.results) == 0:
+                    continue
                 self.run_callbacks("on_predict_postprocess_end")
 
                 # Visualize, save, write results

ultralytics/models/yolov10/predict.py

@@ -16,7 +16,7 @@ class YOLOv10DetectionPredictor(DetectionPredictor):
             pass
         else:
             preds = preds.transpose(-1, -2)
-            bboxes, scores, labels = ops.v10postprocess(preds, self.args.max_det, preds.shape[-1]-4)
+            bboxes, scores, labels = ops.v10postprocess(preds, self.args.max_det, preds.shape[-1] - 4)
             bboxes = ops.xywh2xyxy(bboxes)
             preds = torch.cat([bboxes, scores.unsqueeze(-1), labels.unsqueeze(-1)], dim=-1)
 
@@ -31,6 +31,18 @@ class YOLOv10DetectionPredictor(DetectionPredictor):
 
         results = []
         for i, pred in enumerate(preds):
+            #### 不保存负样本predict的结果#######
+            # if pred.numel() == 0:
+            #     continue
+            # print('pred >>> {}'.format(pred[:, 4]))
+            # if float(pred[:, 4][0]) < 0.1:
+            #     continue
+            ##################################
+            #####保存正样本predict漏检的结果######
+            # if pred.numel() != 0:
+            #     if float(pred[:, 4][-1]) > 0.1:
+            #         continue
+            ##################################
             orig_img = orig_imgs[i]
             pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
             img_path = self.batch[0][i]

ultralytics/utils/show_trace_pr.py

@@ -26,6 +26,17 @@ class ShowPR:
             values.append(value)
         return values
 
+    def calculate_mena(self, ratio=0.5):
+        values = []
+        for data in self.prec_value:
+            thres_num = int(len(data)*ratio)
+            sorted_data = sorted(data, reverse=True)
+            value = sorted_data[:thres_num]
+            if len(value) == 0:
+                value = sorted_data[:1]
+            values.append(sum(value)/len(value))
+        return values
+
     def _calculate_pr(self, prec_value):
         FN, FP, TN, TP = 0, 0, 0, 0
         for output, target in zip(prec_value, self.tags):
@@ -53,7 +64,7 @@ class ShowPR:
             # print("TP>>{}, FP>>{}, TN>>{}, FN>>{}".format(TP, FP, TN, FN))
         return prec, recall, tn_prec, tn_recall
 
-    def calculate_multiple(self, ratio=0.2):
+    def calculate_multiple_1(self, ratio=0.2):  # 方案1 计算满足阈值判断的占比(ratio)
         recall, recall_TN, PrecisePos, PreciseNeg = [], [], [], []
         for thre in self.thres:
             prec_value = []
@@ -73,6 +84,21 @@ class ShowPR:
             recall_TN.append(tn_recall)
         return recall, recall_TN, PrecisePos, PreciseNeg
 
+    def calculate_multiple_2(self, ratio=0.2):  # 方案2 计算前ratio的预测试值的平均值大于thre为赠品小于为非赠品
+        recall, recall_TN, PrecisePos, PreciseNeg = [], [], [], []
+        event_value = self.calculate_mena(ratio)
+        for thre in self.thres:
+            prec_value = [1 if num >= thre else 0 for num in event_value]
+            prec, recall_pos, tn_prec, tn_recall = self._calculate_pr(prec_value)
+            print(
+                f"thre>>{ratio:.2f},  recall>>{recall_pos:.4f},  precise_pos>>{prec:.4f},  recall_tn>>{tn_recall:.4f},  precise_neg>>{tn_prec:4f}")
+            PrecisePos.append(prec)
+            recall.append(recall_pos)
+            PreciseNeg.append(tn_prec)
+            recall_TN.append(tn_recall)
+        return recall, recall_TN, PrecisePos, PreciseNeg
+
+
     def write_results_to_file(self, recall, recall_TN, PrecisePos, PreciseNeg, ratio):
         file_path = os.sep.join(['./ckpts/tracePR', self.title_name + f"_{ratio:.2f}" + '.txt'])
         with open(file_path, 'w') as file:
@@ -112,5 +138,6 @@ class ShowPR:
             # ratio = 0.5
             if ratio < 0.1 or ratio > 0.95:
                 continue
-            recall, recall_TN, PrecisePos, PreciseNeg = self.calculate_multiple(ratio)
+            recall, recall_TN, PrecisePos, PreciseNeg = self.calculate_multiple_1(ratio)
+            # recall, recall_TN, PrecisePos, PreciseNeg = self.calculate_multiple_2(ratio)
             self.show_pr(recall, recall_TN, PrecisePos, PreciseNeg, ratio)

val.py

@@ -1,4 +1,4 @@
 from ultralytics import YOLOv10
 
-model = YOLOv10('/home/lc/ieemoo-ai-gift/runs/detect/train/weights/best_gift_v10n.pt')
+model = YOLOv10('./ckpts/20250701/best_gift_v10n.pt')
 metrics = model.val(batch=1, data='gift.yaml', imgsz=224)