train!

实验八/1.get_dataset.py

@@ -1,4 +1,7 @@
+import bz2
 import os
+import tempfile
+
 import requests
 import tqdm
 import tarfile
@@ -62,6 +65,27 @@ def decompress(file_path, output_dir):
                 tar.extract(member, path=output_dir)
                 bar.update(1)
 
+
+def decompress_bz2(file_path, output_dir):
+    # 确保输出目录存在
+    os.makedirs(output_dir, exist_ok=True)
+    print('解压 ' + file_path.split('/')[-1], ' 到 ', output_dir)
+
+    # 获取.bz2文件的名称，不包含扩展名
+    output_filename = os.path.basename(file_path).rsplit('.bz2', 1)[0]
+
+    # 构建输出文件的完整路径
+    output_file_path = os.path.join(output_dir, output_filename)
+
+    # 检查输出文件是否已存在，如果存在则跳过解压
+    if os.path.exists(output_file_path):
+        print(output_filename, ' 已存在，跳过解压')
+        return
+
+    # 解压.bz2文件
+    with bz2.BZ2File(file_path, 'rb') as bz2_file, open(output_file_path, 'wb') as output_file:
+        output_file.write(bz2_file.read())
+
 # 下载人脸数据集
 face_dataset_url='http://vis-www.cs.umass.edu/fddb/originalPics.tar.gz'
 face_dataset_path='cache/dataset/face/'
@@ -77,8 +101,11 @@ decompress(os.path.join(face_dataset_path,face_dataset_url.split('/')[-1]),face_
 decompress(os.path.join(face_label_path,face_label_url.split('/')[-1]),face_label_path)
 
 # 下载ResNet模型
-model_url1='https://github.com/davisking/dlib-models/raw/master/shape_predictor_68_face_landmarks.dat'
+model_url1='https://github.com/davisking/dlib-models/raw/refs/heads/master/shape_predictor_68_face_landmarks.dat.bz2'
-model_url2='https://github.com/davisking/dlib-models/raw/master/dlib_face_recognition_resnet_model_v1.dat'
+model_url2='https://github.com/davisking/dlib-models/raw/refs/heads/master/dlib_face_recognition_resnet_model_v1.dat.bz2'
 model_path='models/'
 download(model_url1,model_path)
 download(model_url2,model_path)
+
+decompress_bz2(os.path.join(model_path,model_url1.split('/')[-1]),model_path)
+decompress_bz2(os.path.join(model_path,model_url2.split('/')[-1]),model_path)

实验八/3.train.py

@@ -1,22 +1,31 @@
 import dlib
 import cv2 as cv
+import joblib
 import numpy as np
 import os
 
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.model_selection import train_test_split
+from sklearn.multioutput import MultiOutputClassifier
+from tqdm import tqdm
+
 # 提取人脸特征
 def extract_face_features(image_path):
     img = cv.imread(image_path)
     detections = detector(img, 1)
     face_features = []
+    face_bboxes = []
     for rect in detections:
         shape = predictor(img, rect)
         face_descriptor = face_rec_model.compute_face_descriptor(img, shape)
         face_features.append(face_descriptor)
-    return face_features
+        bbox = (rect.left(), rect.top(), rect.right(), rect.bottom())
+        face_bboxes.append(bbox)
+    return face_features, face_bboxes
 
 # 获取图片路径
 def get_img_path(directory, extension=None):
-    if extension==None:
+    if extension is None:
         extension = ['.jpg', '.jpeg', '.png']
     files = []
     for root, dirs, file_names in os.walk(directory):
@@ -25,7 +34,41 @@ def get_img_path(directory,extension=None):
                 files.append(os.path.join(root, file_name))
     return files
 
+
 # 加载dlib模型
 detector = dlib.get_frontal_face_detector()
 predictor = dlib.shape_predictor('models/shape_predictor_68_face_landmarks.dat')
-face_rec_model=dlib.face_recognition_model_v1('实验八/models/dlib_face_recognition_resnet_model_v1.dat')
+face_rec_model = dlib.face_recognition_model_v1('models/dlib_face_recognition_resnet_model_v1.dat')
+
+# 获取图片
+img_directory = 'cache/pretrained/'
+images = get_img_path(img_directory)
+
+# 提取特征
+features = []
+labels = []
+
+for image in tqdm(images, desc='提取图片特征中：'):
+    extracted_features, face_bboxes = extract_face_features(image)
+    for feature,bbox in zip(extracted_features, face_bboxes):
+        features.append(feature)
+        labels.append(bbox)
+
+X_train = np.array(features)
+Y_train = np.array(labels)
+
+# 分割测试集
+X_train, X_test, Y_train, Y_test = train_test_split(X_train, Y_train, test_size=0.2, random_state=42)
+
+# 训练SVM模型
+print('训练模型中')
+clf=MultiOutputClassifier(RandomForestClassifier(n_estimators=100,random_state=42))
+clf.fit(X_train, Y_train)
+
+# 评估训练数据
+predictions = clf.predict(X_test)
+accuracy=(predictions==Y_test).mean()
+print(f'分类器准确度：{accuracy * 100:.2f}%')
+
+os.makedirs('models/', exist_ok=True)
+joblib.dump(clf, 'models/my_classifier.pkl')