做完了，但是是大粪

实验八/train.py

@@ -8,88 +8,94 @@ from sklearn.model_selection import train_test_split
 from sklearn.svm import SVC
 from tqdm import tqdm
 
+
 # 方法区
-## 转换为二值图和GRB图
+## 转换为二值图和RGB图
 def img_cvt(img):
     img_gray = cv.cvtColor(img, cv2.COLOR_BGR2GRAY)
-    _,img_gray=cv.threshold(img_gray,127,255,cv.THRESH_BINARY)
-    img_gray = cv.blur(img_gray,(3,3))
+    _, img_gray = cv.threshold(img_gray, 127, 255, cv.THRESH_BINARY)
+    img_gray = cv.blur(img_gray, (3, 3))
     img_rgb = cv.cvtColor(img, cv.COLOR_BGR2RGB)
-    return img_gray,img_rgb
+    return img_gray, img_rgb
+
+
 ## 提取人脸特征
 def extract_face_feature(img):
-    faces=detector(img)
-    if len(faces)>0:
-        face=faces[0]
-        landmarks=predictor(img,face)
-        face_descriptor=face_rec_model.compute_face_descriptor(img,landmarks)
+    faces = detector(img)
+    if len(faces) > 0:
+        face = faces[0]
+        landmarks = predictor(img, face)
+        face_descriptor = face_rec_model.compute_face_descriptor(img, landmarks)
         return np.array(face_descriptor)
     else:
         return None
+
+
 ## 生成伪样品
 def gen_negative_samples(features_dim=128):
-    return np.random.uniform(-1,1,features_dim)
+    return np.random.uniform(-1, 1, features_dim)
+
 
 # 变量区
-dataset_video=cv.VideoCapture('cache/dataset.mp4')
-datasets=[]
-datasets_rgb=[]
-features=[]
-negative_samples=[]
+dataset_video = cv.VideoCapture('cache/dataset.mp4')
+datasets = []
+datasets_rgb = []
+features = []
+negative_samples = []
 
 # 收集数据集
 ## 采用视频的格式收集图片
-while(True):
-    ret,frame=dataset_video.read()
+while (True):
+    ret, frame = dataset_video.read()
     if not ret:
         break
     datasets.append(frame)
 
 # 加载人脸检测模型
-detector=dlib.get_frontal_face_detector()
-predictor=dlib.shape_predictor("models/shape_predictor_68_face_landmarks_GTX.dat")
-face_rec_model=dlib.face_recognition_model_v1('models/dlib_face_recognition_resnet_model_v1.dat')
+detector = dlib.get_frontal_face_detector()
+predictor = dlib.shape_predictor("models/shape_predictor_68_face_landmarks_GTX.dat")
+face_rec_model = dlib.face_recognition_model_v1('models/dlib_face_recognition_resnet_model_v1.dat')
 
 # 预处理数据集
-for dataset in tqdm(datasets,desc='正在预处理数据集'):
-    _,dataset_rgb = img_cvt(dataset)
+for dataset in tqdm(datasets, desc='正在预处理数据集'):
+    _, dataset_rgb = img_cvt(dataset)
     datasets_rgb.append(dataset_rgb)
 
 # 求人脸特征
-for dataset in tqdm(datasets_rgb,desc='正在求人脸特征'):
-    face_descriptor=extract_face_feature(dataset)
+for dataset in tqdm(datasets_rgb, desc='正在求人脸特征'):
+    face_descriptor = extract_face_feature(dataset)
     if face_descriptor is not None:
         features.append(face_descriptor)
 
 # 生成负样本
-for i in tqdm(range(len(features)*15),desc='正在生成负样本'):
-    negative_sample=gen_negative_samples()
+for i in tqdm(range(len(features) * 2048), desc='正在生成负样本'):
+    negative_sample = gen_negative_samples()
     negative_samples.append(negative_sample)
 
 # 编码数据集
-positive_labels=[1]*len(features)
-negative_labels=[0]*len(negative_samples)
+positive_labels = [1] * len(features)
+negative_labels = [0] * len(negative_samples)
 
-samples=features+negative_samples
-labels=positive_labels+negative_labels
+samples = features + negative_samples
+labels = positive_labels + negative_labels
 
-X_train=np.array(samples)
-Y_train=np.array(labels)
+X_train = np.array(samples)
+Y_train = np.array(labels)
 
 # 分离数据集和训练集
-X_train,X_test,Y_train,Y_test=train_test_split(X_train,Y_train,test_size=0.2,random_state=42,stratify=Y_train)
+X_train, X_test, Y_train, Y_test = train_test_split(X_train, Y_train, test_size=0.2, random_state=42, stratify=Y_train)
 
 # 训练
 print('正在训练分类器')
-classifier=SVC(kernel='linear',C=1.0,random_state=42)
-classifier.fit(X_train,Y_train)
+classifier = SVC(kernel='linear', C=1.0, random_state=42)
+classifier.fit(X_train, Y_train)
 
 # 评估分类器性能
-Y_pred=[]
-for test_sample in tqdm(X_test,desc='评估分类器'):
-    Y_pred.append(classifier.predict(test_sample.reshape(1,-1)))
-accuracy=accuracy_score(Y_test,Y_pred)
-print(f'准确率：{accuracy*100:.2f}%')
+Y_pred = []
+for test_sample in tqdm(X_test, desc='评估分类器'):
+    Y_pred.append(classifier.predict(test_sample.reshape(1, -1)))
+accuracy = accuracy_score(Y_test, Y_pred)
+print(f'准确率：{accuracy * 100:.5f}%')
 
 # 保存模型
-joblib.dump(classifier,'models/classifier.pkl')
+joblib.dump(classifier, 'models/classifier.pkl')