实验七完成

实验七/3.train.py

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+import numpy as np
+import cv2 as cv
+import os
+import tqdm
+from sklearn.metrics import accuracy_score
+from sklearn.model_selection import train_test_split
+from sklearn.svm import SVC
+from joblib import dump
+
+
+# 加载图像
+def load_dataset(directory):
+    files = []
+    for root, dirs, file_list in os.walk(directory):
+        for file in file_list:
+            files.append(os.path.join(root, file))
+    return files
+
+# 提取特征
+## 提取轮廓特征
+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]
+
+## 提取形状特征
+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]
+
+## 计算HU矩
+def extract_hu_moments(contour):
+    moments = cv.moments(contour)
+    hu_moments = cv.HuMoments(moments)
+    return hu_moments.flatten()
+
+## 特征向量构建
+def extract_features(img_path):
+    img = cv.imread(img_path, cv.IMREAD_GRAYSCALE)
+    if img is None:
+        raise FileNotFoundError(f"无法加载图像: {img_path}")
+
+    _, img_bin = cv.threshold(img, 128, 255, cv.THRESH_BINARY)
+    contours, _ = cv.findContours(img_bin, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
+    if len(contours) == 0:
+        return [0] * 11
+    contour = contours[0]
+    contour_features = extract_contour_features(img_bin)
+    shape_features = extract_shape_features(contour)
+    hu_moments = extract_hu_moments(contour)
+    feature_vector = contour_features + shape_features + hu_moments.tolist()
+    return feature_vector + [0] * (11 - len(feature_vector))
+
+# 加载数据集和标签
+face_paths = load_dataset("cache/pretrained/face/")
+face_train_data = [(path, True) for path in face_paths]
+non_face_paths = load_dataset("cache/pretrained/non_face/")
+non_face_train_data = [(path, False) for path in non_face_paths]
+
+train_test_data = face_train_data + non_face_train_data
+train_data, test_data = train_test_split(train_test_data, test_size=0.3)
+
+# 提取特征和标签
+X_train = np.vstack([extract_features(train_path) for train_path, _ in tqdm.tqdm(train_data, desc="数据集特征提取中：")])
+X_test = np.vstack([extract_features(test_path) for test_path, _ in tqdm.tqdm(test_data, desc="测试集特征提取中：")])
+
+Y_train = np.array([label for _, label in train_data])
+Y_test = np.array([label for _, label in test_data])
+
+
+# 训练分类器
+classifier = SVC(kernel='linear', max_iter=10000)
+classifier.fit(X_train, Y_train)
+
+# 评估
+Y_pred = classifier.predict(X_test)
+
+accuracy = accuracy_score(Y_test, Y_pred)
+print(f"准确率: {accuracy * 100:.2f}%")
+
+# 保存模型
+os.makedirs("models", exist_ok=True)
+dump(classifier, "models/classifier.pkl")