做完了，但是是大粪

实验八/detect.py

@@ -0,0 +1,78 @@
+import cv2 as cv
+import numpy as np
+import dlib
+import joblib
+
+
+# 画框函数
+def draw_rect(img, faces):
+    for face in faces:
+        cv.rectangle(img, (face.left(), face.top()), (face.right(), face.bottom()), (0, 0, 255), 32)
+        return img
+
+
+# 检测人脸并画框
+def detect_face(img):
+    img_gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
+    result = detector(img_gray, 1)
+    img_result = draw_rect(img, result)
+    return img_result
+
+
+# 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)
+        return np.array(face_descriptor)
+    else:
+        return None
+
+
+# 加载分类器
+classifier = joblib.load('models/classifier.pkl')
+
+# 读取测试图片
+positive_sample = cv.imread('cache/positive_test.jpg')
+positive_sample_rgb = cv.cvtColor(positive_sample, cv.COLOR_BGR2RGB)
+
+negative_sample = cv.imread('cache/negative_test.jpg')
+negative_sample_rgb = cv.cvtColor(negative_sample, cv.COLOR_BGR2RGB)
+
+# 加载dlib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')
+
+# 检测人脸
+positive_result = detect_face(positive_sample)
+negative_result = detect_face(negative_sample)
+
+# 计算人脸特征
+positive_feature = extract_face_feature(positive_sample_rgb)
+positive_feature = np.array(positive_feature).reshape(1, -1)
+
+negative_feature = extract_face_feature(negative_sample_rgb)
+negative_feature = np.array(negative_feature).reshape(1, -1)
+
+# 通过分类器预测
+positive_predict = classifier.predict(positive_feature)
+negative_predict = classifier.predict(negative_feature)
+
+# 展示
+positive_result_resized = cv.resize(positive_result,
+                                    (int(positive_result.shape[1] * 0.1), int(positive_result.shape[0] * 0.1)))
+positive_result_resized = cv.putText(positive_result_resized, str(positive_predict), (50, 50), cv.FONT_HERSHEY_SIMPLEX,
+                                     1, (0, 0, 255), 2)
+
+negative_result_resized = cv.resize(negative_result,
+                                    (int(negative_result.shape[1] * 0.1), int(negative_result.shape[0] * 0.1)))
+negative_result_resized = cv.putText(negative_result_resized, str(negative_predict), (50, 50), cv.FONT_HERSHEY_SIMPLEX,
+                                     1, (0, 0, 255), 2)
+
+cv.imshow('positive_result', positive_result_resized)
+cv.imshow('negative_result', negative_result_resized)
+cv.waitKey(0)
+cv.destroyAllWindows()

实验八/train.py

@@ -8,14 +8,17 @@ 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_rgb = cv.cvtColor(img, cv.COLOR_BGR2RGB)
     return img_gray, img_rgb
+
+
 ## 提取人脸特征
 def extract_face_feature(img):
     faces = detector(img)
@@ -26,10 +29,13 @@ def extract_face_feature(img):
         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)
 
+
 # 变量区
 dataset_video = cv.VideoCapture('cache/dataset.mp4')
 datasets = []
@@ -62,7 +68,7 @@ for dataset in tqdm(datasets_rgb,desc='正在求人脸特征'):
         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'):
+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)
 
@@ -89,7 +95,7 @@ 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}%')
+print(f'准确率：{accuracy * 100:.5f}%')
 
 # 保存模型
 joblib.dump(classifier, 'models/classifier.pkl')