在上文基础上改写代码使用Dlib cnn模型完成人脸的检测和对比
时间: 2024-04-10 13:33:12 浏览: 157
使用dlib实现人脸识别+活体检测
Sure! Here is an example code that uses Dlib's CNN model for face detection and face recognition:
```python
import dlib
import cv2
def detect_faces(image):
detector = dlib.cnn_face_detection_model_v1('path/to/dlib_model/mmod_human_face_detector.dat')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
faces = detector(gray, 1)
return faces
def recognize_faces(image, faces):
shape_predictor = dlib.shape_predictor('path/to/dlib_model/shape_predictor_68_face_landmarks.dat')
face_recognizer = dlib.face_recognition_model_v1('path/to/dlib_model/dlib_face_recognition_resnet_model_v1.dat')
descriptors = []
for face in faces:
shape = shape_predictor(image, face.rect)
descriptor = face_recognizer.compute_face_descriptor(image, shape)
descriptors.append(descriptor)
return descriptors
# Load the input image
image = cv2.imread('path/to/input/image.jpg')
# Perform face detection
faces = detect_faces(image)
# Perform face recognition
descriptors = recognize_faces(image, faces)
# Display the results
for face in faces:
cv2.rectangle(image, (face.rect.left(), face.rect.top()), (face.rect.right(), face.rect.bottom()), (0, 255, 0), 2)
cv2.imshow("Face Detection", image)
cv2.waitKey(0)
cv2.destroyAllWindows()
```
In this code, we first define the `detect_faces` function that uses the Dlib CNN face detection model (`mmod_human_face_detector.dat`) to detect faces in the input image. We then define the `recognize_faces` function that uses the Dlib shape predictor model (`shape_predictor_68_face_landmarks.dat`) and the Dlib face recognition model (`dlib_face_recognition_resnet_model_v1.dat`) to compute face descriptors for each detected face.
Finally, we load the input image, perform face detection, perform face recognition, and display the results by drawing rectangles around the detected faces.
Please make sure to replace `'path/to/dlib_model/'` with the actual path to the Dlib model files on your system.
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