i m so sad

test/2024,12,03/main.py

@@ -1,13 +1,12 @@
 import cv2 as cv
 import dlib
-from matplotlib import pyplot as plot
 def draw_rect(img, faces):
     for face in faces:
         cv.rectangle(img,(face.left(),face.top()),(face.right(),face.bottom()),(0,255,0),2)
     return img
 
 if __name__ == '__main__':
-    cap = cv.VideoCapture(1)
+    cap = cv.VideoCapture(0)
     while True:
         ret, frame = cap.read()
         if not ret:

实验八/1.get_dataset.py

@@ -75,3 +75,10 @@ download(face_label_url,face_label_path)
 # 解压数据集
 decompress(os.path.join(face_dataset_path,face_dataset_url.split('/')[-1]),face_dataset_path)
 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_url2='https://github.com/davisking/dlib-models/raw/master/dlib_face_recognition_resnet_model_v1.dat'
+model_path='models/'
+download(model_url1,model_path)
+download(model_url2,model_path)

实验八/2.pretrain.py

@@ -0,0 +1,31 @@
+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.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)

实验八/3.train.py

@@ -0,0 +1,31 @@
+import dlib
+import cv2 as cv
+import numpy as np
+import os
+
+# 提取人脸特征
+def extract_face_features(image_path):
+    img=cv.imread(image_path)
+    detections=detector(img,1)
+    face_features=[]
+    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
+
+# 获取图片路径
+def get_img_path(directory,extension=None):
+    if extension==None:
+        extension=['.jpg','.jpeg','.png']
+    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 extension):
+                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')