在Linux上部署AI换脸通常需要使用一些特定的库，如OpenCV、dlib或者深度学习框架如TensorFlow或PyTorch。以下是一个基于Python和深度学习框架TensorFlow的简单示例。
- 安装必要的库：
pip install tensorflow-gpu opencv-python-headless dlib
- 准备模型，可以使用已经训练好的模型如“face-recognition”库中的模型，或者自己训练。
- 编写换脸代码：
import cv2
import dlib
import numpy as np
import tensorflow as tf
import tensorflow_hub as hub
# 加载TensorFlow Hub模型
hub_model = 'https://tfhub.dev/deepmind/deeppixel-celeba/1'
# 初始化模型
module = hub.Module(hub_model)
# 加载dlib人脸检测器和68点标记点检测器
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat')
# 读取图片
def load_image_file(file_path, max_dimension=1024):
image = cv2.imread(file_path)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image_shape = image.shape
image_dims = []
for dim in image_shape:
if dim > max_dimension:
image_dims.append(max_dimension)
else:
image_dims.append(dim)
image = cv2.resize(image, tuple(image_dims), interpolation=cv2.INTER_AREA)
return image
# 换脸
def swap_faces(source_image_path, target_image_path, output_image_path):
source_image = load_image_file(source_image_path)
target_image = load_image_file(target_image_path)
# 检测人脸
source_faces = detector(source_image, 1)
target_faces = detector(target_image, 1)
if len(source_faces) < 1 or len(target_faces) < 1:
raise Exception('No faces found.')
# 获取人脸的68点标记
source_landmarks = predictor(source_image, source_faces[0])
target_landmarks = predictor(target_image, target_faces[0])
# 获取变换矩阵
source_points = np.matrix([[p.x, p.y] for p in source_landmarks.parts()])
target_points = np.matrix([[p.x, p.y] for p in target_landmarks.parts()])
transformation_matrix = _get_transformation_matrix(source_points, target_points)
# 应用变换
source_image_warped = cv2.warpAffine(source_image, transformation_matrix, target_image.shape[:2])
# 融合图像
alpha = 0.6
output_image = (1 - alpha) * target_image + alpha * source_image_warped
# 保存结果
cv2.imwrite(output_image_path, output_image)
# 获取变换矩阵的辅助函数
def _get_transformation_matrix(source_points, target_points):
# 此处应该是用于计算变换矩阵的代码，可以使用OpenCV的函数cv2.estimateRigidTransform()
pass
# 使用模型进行换脸
def transform_image(sour