收工

实验六/main.py

@@ -1 +1,58 @@
 import cv2 as cv
+import joblib
+from matplotlib import pyplot as plt, rcParams
+
+# 导入模型
+classifier = joblib.load('models/classifier.pkl')
+
+# 导入测试图片
+img=cv.imread('test.png',cv.IMREAD_GRAYSCALE)
+
+# 设置中文字体
+rcParams['font.sans-serif'] = ['SimHei']
+rcParams['axes.unicode_minus'] = False
+
+# 预处理图像
+_,img_classifier=cv.threshold(img, 0, 255, cv.THRESH_BINARY + cv.THRESH_OTSU)
+img_classifier=cv.blur(img,(3,3))
+
+# 特征提取
+def extract_contour_features(img):
+    contours, _ = cv.findContours(img, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
+    contour = contours[0]
+    area = cv.contourArea(contour)
+    perimeter = cv.arcLength(contour, True)
+    return [area, perimeter]
+
+def extract_shape_features(contour):
+    x, y, w, h = cv.boundingRect(contour)
+    aspect_ratio = float(w) / h
+    rect_area = w * h
+    shape_factor = cv.contourArea(contour) / rect_area
+    return [aspect_ratio, shape_factor]
+
+def extract_hu_moments(contour):
+    moments = cv.moments(contour)
+    hu_moments = cv.HuMoments(moments)
+    return hu_moments.flatten()
+
+def extract_features(img):
+    contours, _ = cv.findContours(img, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
+    contour = contours[0]
+    contour_features = extract_contour_features(img)
+    shape_features = extract_shape_features(contour)
+    hu_moments = extract_hu_moments(contour)
+    feature_vector = contour_features + shape_features + hu_moments.tolist()
+    return feature_vector
+
+feature_vector = extract_features(img)
+
+# 预测
+predicted_label=classifier.predict([feature_vector])
+
+plt.figure()
+plt.imshow(img,cmap='gray')
+plt.title('预测结果：'+str(predicted_label[0]))
+plt.axis('off')
+plt.tight_layout()
+plt.show()

实验六/train.py

@@ -75,11 +75,10 @@ Y_test = np.array(test_labels)
 
 # 训练分类器
 classifier = SVC(kernel="linear")
-with parallel_backend('threading',n_jobs=-1):
+with parallel_backend('threading', n_jobs=-1):
-    start_time=time.time()
+    start_time = time.time()
     classifier.fit(X_train, Y_train)
-    elapsed_time = time.time()-start_time
+    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