python实现自行训练模型并进行人脸识别,要求输出人脸识别的置信度,完整代码实现
时间: 2024-03-19 20:43:52 浏览: 20
下面是完整的Python代码实现,包括数据集准备、模型训练、人脸识别和输出置信度:
```
import os
import cv2
import face_recognition
# 定义训练数据集路径和测试数据集路径
TRAIN_DIR = "train"
TEST_DIR = "test"
# 定义用于存储训练数据集和测试数据集的列表
train_encodings = []
train_labels = []
test_encodings = []
test_labels = []
# 加载训练数据集
for person_name in os.listdir(TRAIN_DIR):
person_dir = os.path.join(TRAIN_DIR, person_name)
if not os.path.isdir(person_dir):
continue
for image_name in os.listdir(person_dir):
image_path = os.path.join(person_dir, image_name)
image = cv2.imread(image_path)
rgb_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
face_locations = face_recognition.face_locations(rgb_image)
face_encodings = face_recognition.face_encodings(rgb_image, face_locations)
if len(face_encodings) == 0:
continue
train_encodings.append(face_encodings[0])
train_labels.append(person_name)
# 加载测试数据集
for person_name in os.listdir(TEST_DIR):
person_dir = os.path.join(TEST_DIR, person_name)
if not os.path.isdir(person_dir):
continue
for image_name in os.listdir(person_dir):
image_path = os.path.join(person_dir, image_name)
image = cv2.imread(image_path)
rgb_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
face_locations = face_recognition.face_locations(rgb_image)
face_encodings = face_recognition.face_encodings(rgb_image, face_locations)
if len(face_encodings) == 0:
continue
test_encodings.append(face_encodings[0])
test_labels.append(person_name)
# 训练人脸识别模型
face_recognition.train(train_encodings, train_labels)
# 进行人脸识别并输出置信度
for i in range(len(test_encodings)):
unknown_face_encoding = test_encodings[i]
results = face_recognition.compare_faces(train_encodings, unknown_face_encoding)
confidence = face_recognition.face_distance(train_encodings, unknown_face_encoding)
print("Test image {} is a match with {} with a confidence of {}".format(i+1, train_labels[results.index(True)], 1-confidence[results.index(True)]))
```
需要注意的是,该代码仅为示例代码,需要根据具体的数据集和需求进行修改和优化。
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