text: import os
  import cv2
  import math
  import paddle
  import numpy as np
  import paddle.nn as nn
  import paddlehub as hub
  from Photo2Cartoon.model import ResnetGenerator
  from paddlehub.module.module import moduleinfo
  
  
  @moduleinfo(
      name="Photo2Cartoon",  # 模型名称
      type="CV",  # 模型类型
      author="jm12138",  # 作者名称
      author_email="jm12138@qq.com",  # 作者邮箱
      summary="Photo2Cartoon",  # 模型介绍
      version="1.0.0"  # 版本号
  )
  class Photo2Cartoon(nn.Layer):
      def __init__(self):
          super(Photo2Cartoon, self).__init__()
          # 加载人脸关键点检测模型
          self.face_detector = hub.Module(name="face_landmark_localization")
  
          # 加载人脸分割模型
          self.seg = hub.Module(name='FCN_HRNet_W18_Face_Seg')
  
          # 加载人脸动漫化模型
          self.net = ResnetGenerator(ngf=32, img_size=256, light=True)
  
          # 加载人脸动漫化模型参数
          state_dict = paddle.load(os.path.join(self.directory, 'photo2cartoon_weights.pdparams'))
          self.net.set_state_dict(state_dict['genA2B'])
  
          # 将人脸动漫化模型设为评估模式
          self.net.eval()
  
      # 读取数据函数
      @staticmethod
      def load_datas(paths, images):
          datas = []
  
          # 读取数据列表
          if paths is not None:
              for im_path in paths:
                  assert os.path.isfile(im_path), "The {} isn't a valid file path.".format(im_path)
                  im = cv2.imread(im_path)
                  datas.append(im)
  
          if images is not None:
              datas = images
  
          # 返回数据列表
          return datas
  
      # 数据预处理函数
      def preprocess(self, images, batch_size, use_gpu):
          # 获取人脸关键点
          outputs = self.face_detector.keypoint_detection(images=images, batch_size=batch_size, use_gpu=use_gpu)
  
          crops = []
          for output, image in zip(outputs, images):
              for landmarks in output['data']:
                  landmarks = np.array(landmarks)
  
                  # rotation angle
                  left_eye_corner = landmarks[36]
                  right_eye_corner = landmarks[45]
                  radian = np.arctan(
                      (left_eye_corner[1] - right_eye_corner[1]) / (left_eye_corner[0] - right_eye_corner[0]))
  
                  # image size after rotating
                  height, width, _ = image.shape
                  cos = math.cos(radian)
                  sin = math.sin(radian)
                  new_w = int(width * abs(cos) + height * abs(sin))
                  new_h = int(width * abs(sin) + height * abs(cos))
  
                  # translation
                  Tx = new_w // 2 - width // 2
                  Ty = new_h // 2 - height // 2
  
                  # affine matrix
                  M = np.array([[cos, sin, (1 - cos) * width / 2. - sin * height / 2. + Tx],
                                [-sin, cos, sin * width / 2. + (1 - cos) * height / 2. + Ty]])
  
                  image = cv2.warpAffine(image, M, (new_w, new_h), borderValue=(255, 255, 255))
  
                  landmarks = np.concatenate([landmarks, np.ones((landmarks.shape[0], 1))], axis=1)
                  landmarks = np.dot(M, landmarks.T).T
                  landmarks_top = np.min(landmarks[:, 1])
                  landmarks_bottom = np.max(landmarks[:, 1])
                  landmarks_left = np.min(landmarks[:, 0])
                  landmarks_right = np.max(landmarks[:, 0])
  
                  # expand bbox
                  top = int(landmarks_top - 0.8 * (landmarks_bottom - landmarks_top))
                  bottom = int(landmarks_bottom + 0.3 * (landmarks_bottom - landmarks_top))
                  left = int(landmarks_left - 0.3 * (landmarks_right - landmarks_left))
                  right = int(landmarks_right + 0.3 * (landmarks_right - landmarks_left))
  
                  # crop
                  if bottom - top > right - left:
                      left -= ((bottom - top) - (right - left)) // 2
                      right = left + (bottom - top)
                  else:
                      top -= ((right - left) - (bottom - top)) // 2
                      bottom = top + (right - left)
  
                  image_crop = np.ones((bottom - top + 1, right - left + 1, 3), np.uint8) * 255
  
                  h, w = image.shape[:2]
                  left_white = max(0, -left)
                  left = max(0, left)
                  right = min(right, w - 1)
                  right_white = left_white + (right - left)
                  top_white = max(0, -top)
                  top = max(0, top)
                  bottom = min(bottom, h - 1)
                  bottom_white = top_white + (bottom - top)
  
                  image_crop[top_white:bottom_white + 1, left_white:right_white + 1] = image[top:bottom + 1, left:right +
                                                                                             1].copy()
                  crops.append(image_crop)
  
          # 获取人像分割的输出
          results = self.seg.Segmentation(images=crops, batch_size=batch_size)
  
          faces = []
          masks = []
  
          for result in results:
              # 提取MASK和输出图像
              face = result['face']
              mask = result['mask']
  
              # 图像格式转换
              face = cv2.cvtColor(face, cv2.COLOR_BGR2RGB)
  
              # 图像拼接
              face_rgba = np.dstack((face, mask))
  
              # 图像缩放
              face_rgba = cv2.resize(face_rgba, (256, 256), interpolation=cv2.INTER_AREA)
  
              # 拆分图像
              face = face_rgba[:, :, :3].copy()
              mask = face_rgba[:, :, 3][:, :, np.newaxis].copy() / 255.
  
              # 数据格式转换
              face = np.transpose(face[np.newaxis, :, :, :], (0, 3, 1, 2)).astype(np.float32)
  
              faces.append(face)
              masks.append(mask)
  
          input_datas = np.concatenate(faces, 0)
  
          # 切分数据
          datas_num = input_datas.shape[0]
          split_num = datas_num // batch_size + 1 if datas_num % batch_size != 0 else datas_num // batch_size
          input_datas = np.array_split(input_datas, split_num)
  
          return input_datas, masks
  
      # 模型预测函数
      def predict(self, input_datas):
          outputs = []
  
          for data in input_datas:
              # 转换数据为Tensor
              data = paddle.to_tensor(data)
  
              # 模型前向计算
              cartoon = self.net(data)
  
              outputs.append(cartoon[0].numpy())
  
          outputs = np.concatenate(outputs, 0)
  
          return outputs
  
      # 结果后处理函数
      @staticmethod
      def postprocess(outputs, masks, visualization, output_dir):
          # 检查输出目录
          if visualization:
              if not os.path.exists(output_dir):
                  os.mkdir(output_dir)
  
          cartoons = []
  
          for cartoon, mask, i in zip(outputs, masks, range(len(masks))):
              # 格式转换
              cartoon = np.transpose(cartoon, (1, 2, 0))
              cartoon = (cartoon + 1) * 127.5
  
              # 计算输出图像
              cartoon = (cartoon * mask + 255 * (1 - mask)).astype(np.uint8)
              cartoon = cv2.cvtColor(cartoon, cv2.COLOR_RGB2BGR)
  
              # 可视化结果保存
              if visualization:
                  cv2.imwrite(os.path.join(output_dir, 'result_%d.png' % i), cartoon)
  
              cartoons.append(cartoon)
  
          return cartoons
  
      def Cartoon_GEN(self,
                      images=None,
                      paths=None,
                      batch_size=1,
                      output_dir='output',
                      visualization=False,
                      use_gpu=False):
  
          # 获取输入数据
          images = self.load_datas(paths, images)
  
          # 数据预处理
          input_datas, masks = self.preprocess(images, batch_size, use_gpu)
  
          # 模型预测
          outputs = self.predict(input_datas)
  
          # 结果后处理
          cartoons = self.postprocess(outputs, masks, visualization, output_dir)
  
          return cartoons
  