enhance train and adapt main to train only

Former-commit-id: dd0f5b88b7a401eb41006cf357eff993ca661dd7

Picture_Train/main.py

@@ -4,8 +4,8 @@ from pathlib import Path
 from train_2 import train_model,prepare_data,predict
 import joblib
 
-inp = Path("data/train")
-val = Path("data/val")
+inp = Path("data/train_new")
+val = Path("data/val_new")
 proc = Path("data/proc")
 proc.mkdir(exist_ok=True)
 
@@ -33,36 +33,37 @@ def preproc(dir:Path):
         h = round(np.sum(hsv[:,:,0])/cnt)
         s = round(np.sum(hsv[:,:,1])/cnt)
         v = round(np.sum(hsv[:,:,2])/cnt)
-        name = f"{h}_{s}_{v}.jpg"
+        name = f"{cl}_{h}_{s}_{v}.jpg"
         d[cl].append((h,s,v))
         # (proc/cl).mkdir(exist_ok=True)
-        # cv2.imwrite(proc/cl/name,cv2.cvtColor(hsv,cv2.COLOR_HSV2BGR))
+        # cv2.imwrite(proc/name,cv2.cvtColor(hsv,cv2.COLOR_HSV2BGR))
     return d
 
-d:dict[str,list[tuple[int,int,int]]] = preproc(inp)
+# d:dict[str,list[tuple[int,int,int]]] = preproc(inp)
 val:dict[str,list[tuple[int,int,int]]] = preproc(val)
 
 print("数据预处理完成")
 
-model, label_map = train_model(d)
-print("训练完成")
-joblib.dump(model, "model.pkl")
+model, label_map = train_model(val)
+# print("训练完成")
+# joblib.dump(model, "model.pkl")
 
 # model = joblib.load("model.pkl")
-X_train, y_train = prepare_data(d)
-print(predict(model, label_map, d))
-print(predict(model, label_map, val))
+X_train, y_train = prepare_data(val)
+rate,failed = predict(model, label_map, val)
+print(rate)
+# print(predict(model, label_map, val))
 # print(f"\n训练集准确率: {model.score(X_train, y_train):.5f}")
 # X_train, y_train = prepare_data(val)
 # print(f"\n训练集准确率: {model.score(X_train, y_train):.5f}")
 # model.predict()
-from src_predict import predictor
-suc = cnt = 0
-for file in Path("data/train").glob("*/*.jpg"):
-    cnt+=1
-    pcl,_=predictor(file)
-    acl = file.parents[0].name
-    if acl == pcl:
-        suc+=1
+# from src_predict import predictor
+# suc = cnt = 0
+# for file in Path("data/val").glob("*/*.jpg"):
+#     cnt+=1
+#     pcl,_=predictor(file)
+#     acl = file.parents[0].name
+#     if acl == pcl:
+#         suc+=1
 
-print(f"预测准确率: {suc/cnt:.4f}")
+# print(f"预测准确率: {suc/cnt:.4f}")

Picture_Train/train_2.py

@@ -71,7 +71,7 @@ def predict(model, label_map: Dict[int, str], val_data: Dict[str, List[np.ndarra
     返回:
     预测结果字典，格式为 dict[str, list[list[str]]]，表示每个输入数组中样本的预测类别
     """
-    results = {}
+    failed = []
     
     suc = 0
     cnt = 0
@@ -87,14 +87,15 @@ def predict(model, label_map: Dict[int, str], val_data: Dict[str, List[np.ndarra
             # 进行预测并转换为类别名
             pred_labels = model.predict(arr)
             pred_classes = [label_map[label] for label in pred_labels]
-            if len(pred_classes) > 1:continue
-            if class_name==pred_classes[0]:
+            if len(pred_classes) == 1 and class_name==pred_classes[0]:
                 suc+=1
+            else:
+                failed.append(arrays_list)
             # class_predictions.append(pred_classes)
         
-        results[class_name] = class_predictions
+        # results[class_name] = class_predictions
     
-    return suc/cnt
+    return suc/cnt,failed
 
 if __name__ == "__main__":
     exit()