加了个进度条

实验六/get_dataset.py

@@ -16,5 +16,5 @@ def save_img_subset(data, save_path, num_samples):
         img.save(os.path.join(save_path, f"{i}-label-{label}.png"))
 
 # 保存前 600 张训练集图片和前 100 张测试集图片
-save_img_subset(train_data, './DataImages-Train', 600)
+save_img_subset(train_data, './DataImages-Train', 6000)
-save_img_subset(test_data, './DataImages-Test', 100)
+save_img_subset(test_data, './DataImages-Test', 1000)

实验六/train.py

@@ -1,3 +1,5 @@
+import time
+
 import cv2 as cv
 import joblib
 import numpy as np
@@ -6,6 +8,8 @@ import os
 
 from sklearn.metrics import accuracy_score
 from sklearn.svm import SVC
+from sklearn.utils import parallel_backend
+
 
 # 提取轮廓特征
 def extract_contour_features(img):
@@ -71,11 +75,19 @@ Y_test = np.array(test_labels)
 
 # 训练分类器
 classifier = SVC(kernel="linear")
+with parallel_backend('threading',n_jobs=-1):
+    start_time=time.time()
     classifier.fit(X_train, Y_train)
+    elapsed_time = time.time()-start_time
+
+print(f"模型训练耗时: {elapsed_time:.2f} 秒")
 
 # 在测试集上进行评估
-Y_pred = classifier.predict(X_test)
+Y_pred = []
+for test_sample in tqdm.tqdm(X_test, desc="测试集中预测进度"):
+    Y_pred.append(classifier.predict(test_sample.reshape(1, -1)))
 accuracy = accuracy_score(Y_test, Y_pred)
+
 print(f"性能: {accuracy * 100:.2f}%")
 
 # 保存模型