实验七完成

2024-12-01 17:46:49 +08:00
parent 9b7ceadf8e
commit 7a915e901c
6 changed files with 235 additions and 11 deletions
@@ -0,0 +1,39 @@
+import cv2 as cv
+import os
+import tqdm
+
+# 处理图像
+def pretrain(img_path,output_path):
+    img=cv.imread(img_path, cv.IMREAD_GRAYSCALE)
+    img = cv.resize(img, (50, 50))
+    _,img=cv.threshold(img,127,255,cv.THRESH_BINARY)
+    img=cv.blur(img,(3,3))
+    os.makedirs(os.path.dirname(output_path), exist_ok=True)
+    cv.imwrite(output_path, img)
+
+# 获取文件路径
+def get_img_name(directory, extensions=None):
+    if extensions is None:
+        extensions = ['.png', '.jpg','.jpeg']
+    files = []
+    for root, dirs, file_names in os.walk(directory):
+        for file_name in file_names:
+            if any(file_name.lower().endswith(ext) for ext in extensions):
+                files.append(os.path.join(root, file_name))
+    return files
+
+# 处理非人脸数据集
+os.makedirs('cache/pretrained/non_face/', exist_ok=True)
+non_face_files=get_img_name('cache/dataset/non_face/')
+print('预处理非人脸数据中：')
+for img_path in tqdm.tqdm(non_face_files):
+    pretrain(img_path, os.path.join('cache/pretrained/non_face', os.path.basename(img_path)))
+
+# 处理人脸数据集
+os.makedirs('cache/pretrained/face/', exist_ok=True)
+face_files=get_img_name('cache/dataset/face/')
+print('预处理人脸数据中：')
+for img_path in tqdm.tqdm(face_files):
+    relative_path=os.path.relpath(img_path, 'cache/dataset/face/')
+    output_path=os.path.join('cache/pretrained/face', relative_path)
+    pretrain(img_path, output_path)
@@ -0,0 +1,88 @@
+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")
@@ -1,16 +1,111 @@
-# 这是一个示例 Python 脚本。
+import cv2 as cv
+import numpy as np
+from joblib import load

-# 按 Shift+F10 执行或将其替换为您的代码。
-# 按 双击 Shift 在所有地方搜索类、文件、工具窗口、操作和设置。
+classifier = load('models/classifier.pkl')
+
+# 预处理图像
+def pre_detect(img):
+    pre_img = img.copy()
+    pre_img = cv.resize(pre_img, (50, 50))
+    pre_img = cv.cvtColor(pre_img, cv.COLOR_BGR2GRAY)
+    _, pre_img = cv.threshold(pre_img, 127, 255, cv.THRESH_BINARY)
+    pre_img = cv.blur(pre_img, (3, 3))
+    return pre_img
+
+# 特征提取
+def pre_detect(img):
+    pre_img = img.copy()
+    pre_img = cv.resize(pre_img, (50, 50))
+    pre_img = cv.cvtColor(pre_img, cv.COLOR_BGR2GRAY)
+    _, pre_img = cv.threshold(pre_img, 127, 255, cv.THRESH_BINARY)
+    pre_img = cv.blur(pre_img, (3, 3))
+    return pre_img
+
+# 特征提取
+def extract_contour_features(img):
+    contours, _ = cv.findContours(img, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
+    if len(contours) == 0:
+        return [0, 0]  # 如果没有轮廓，返回默认值
+    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 print_hi(name):
-    # 在下面的代码行中使用断点来调试脚本。
-    print(f'Hi, {name}')  # 按 Ctrl+F8 切换断点。
+def extract_features(img):
+    contours, _ = cv.findContours(img, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
+    if len(contours) == 0:
+        # 如果没有找到轮廓，返回 11 个零，保持特征数量一致
+        return [0] * 11
+    contour = contours[0]
+
+    # 提取轮廓特征
+    contour_features = extract_contour_features(img)
+
+    # 提取形状特征
+    shape_features = extract_shape_features(contour)
+
+    # 提取 Hu 矩
+    hu_moments = extract_hu_moments(contour)
+
+    # 合并所有特征为一个特征向量，确保总共有 11 个特征
+    feature_vector = contour_features + shape_features + hu_moments.tolist()
+    return feature_vector


-# 按装订区域中的绿色按钮以运行脚本。
-if __name__ == '__main__':
-    print_hi('PyCharm')
+# 读取图像并进行预处理
+img = cv.imread('test.jpg')
+pre_img = pre_detect(img)

-# 访问 https://www.jetbrains.com/help/pycharm/ 获取 PyCharm 帮助
+features = extract_features(pre_img)
+
+features = np.array(features).reshape(1, -1)
+
+predict_label = classifier.predict(features)
+
+cv.imshow('face? ' + str(predict_label[0]), img)
+cv.waitKey(0)
+cv.destroyAllWindows()
+
+# 读取视频并进行检测
+cap = cv.VideoCapture(1)
+
+while True:
+    ret, frame = cap.read()
+    if not ret:
+        break
+
+    # 预处理
+    pre_img = pre_detect(frame)
+
+    # 提取特征
+    features = extract_features(pre_img)
+    features=np.array(features).reshape(1, -1)
+
+    predict_label = classifier.predict(features)
+
+    # 在帧上显示预测结果
+    label_text = 'Face' if predict_label[0] else 'Non-Face'
+    cv.putText(frame, label_text, (50, 50), cv.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2, cv.LINE_AA)
+
+    # 展示
+    cv.imshow('video', frame)
+
+    if cv.waitKey(1) & 0xFF == ord('q'):
+        break
+
+cap.release()
+cv.destroyAllWindows()