text: import colorsys
  import copy
  import os
  from timeit import default_timer as timer
  import cv2
  import face_recognition
  import numpy as np
  import tensorflow as tf
  import time
  from PIL import Image, ImageDraw, ImageFont
  from tensorflow.compat.v1.keras import backend as K
  from tensorflow.keras.layers import Input, Lambda
  from tensorflow.keras.models import Model, load_model
  
  from nets.yolo4 import yolo_body, yolo_eval
  from utils.utils import letterbox_image
  
  
  #--------------------------------------------#
  #   使用自己训练好的模型预测需要修改2个参数
  #   model_path和classes_path都需要修改！
  #   如果出现shape不匹配，一定要注意
  #   训练时的model_path和classes_path参数的修改
  #--------------------------------------------#
  class YOLO(object):
      _defaults = {
          "model_path"        : 'logs/ep048-loss2.171-val_loss1.956.h5',
          "anchors_path"      : 'model_data/yolo_anchors.txt',
          "classes_path"      : 'model_data/new_classes.txt',
          "score"             : 0.5,
          "iou"               : 0.3,
          "eager"             : True,
          "max_boxes"         : 100,
          # 显存比较小可以使用416x416
          # 显存比较大可以使用608x608
          "model_image_size"  : (416, 416)
      }
  
      @classmethod
      def get_defaults(cls, n):
          if n in cls._defaults:
              return cls._defaults[n]
          else:
              return "Unrecognized attribute name '" + n + "'"
  
      #---------------------------------------------------#
      #   初始化yolo
      #---------------------------------------------------#
      def __init__(self, **kwargs):
          self.__dict__.update(self._defaults)
          if not self.eager:
              tf.compat.v1.disable_eager_execution()
              self.sess = K.get_session()
          self.class_names = self._get_class()
          self.anchors = self._get_anchors()
          self.generate()
  
      #---------------------------------------------------#
      #   获得所有的分类
      #---------------------------------------------------#
      def _get_class(self):
          classes_path = os.path.expanduser(self.classes_path)
          with open(classes_path) as f:
              class_names = f.readlines()
          class_names = [c.strip() for c in class_names]
          return class_names
  
      #---------------------------------------------------#
      #   获得所有的先验框
      #---------------------------------------------------#
      def _get_anchors(self):
          anchors_path = os.path.expanduser(self.anchors_path)
          with open(anchors_path) as f:
              anchors = f.readline()
          anchors = [float(x) for x in anchors.split(',')]
          return np.array(anchors).reshape(-1, 2)
  
      #---------------------------------------------------#
      #   载入模型
      #---------------------------------------------------#
      def generate(self):
          model_path = os.path.expanduser(self.model_path)
          assert model_path.endswith('.h5'), 'Keras model or weights must be a .h5 file.'
          
          #---------------------------------------------------#
          #   计算先验框的数量和种类的数量
          #---------------------------------------------------#
          num_anchors = len(self.anchors)
          num_classes = len(self.class_names)
  
          #---------------------------------------------------------#
          #   载入模型
          #---------------------------------------------------------#
          self.yolo_model = yolo_body(Input(shape=(None, None, 3)), num_anchors//3, num_classes)
          self.yolo_model.load_weights(self.model_path)
  
          print('{} model, anchors, and classes loaded.'.format(model_path))
  
          # 画框设置不同的颜色
          hsv_tuples = [(x / len(self.class_names), 1., 1.)
                        for x in range(len(self.class_names))]
          self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
          self.colors = list(
              map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)),
                  self.colors))
  
          # 打乱颜色
          np.random.seed(10101)
          np.random.shuffle(self.colors)
          np.random.seed(None)
  
          #---------------------------------------------------------#
          #   在yolo_eval函数中，我们会对预测结果进行后处理
          #   后处理的内容包括，解码、非极大抑制、门限筛选等
          #---------------------------------------------------------#
          if self.eager:
              self.input_image_shape = Input([2, ], batch_size=1)
              inputs = [*self.yolo_model.output, self.input_image_shape]
              outputs = Lambda(yolo_eval, output_shape=(1,), name='yolo_eval',
                  arguments={'anchors': self.anchors, 'num_classes': len(self.class_names), 'image_shape': self.model_image_size, 
                  'score_threshold': self.score, 'eager': True, 'max_boxes': self.max_boxes})(inputs)
              self.yolo_model = Model([self.yolo_model.input, self.input_image_shape], outputs)
          else:
              self.input_image_shape = K.placeholder(shape=(2, ))
              
              self.boxes, self.scores, self.classes = yolo_eval(self.yolo_model.output, self.anchors,
                      num_classes, self.input_image_shape, max_boxes=self.max_boxes,
                      score_threshold=self.score, iou_threshold=self.iou)
   
      @tf.function
      def get_pred(self, image_data, input_image_shape):
          out_boxes, out_scores, out_classes = self.yolo_model([image_data, input_image_shape], training=False)
          return out_boxes, out_scores, out_classes
  
      #---------------------------------------------------#
      #   检测图片
      #---------------------------------------------------#
      def detect_image(self, image):
          start = timer()
  
          #---------------------------------------------------------#
          #   给图像增加灰条，实现不失真的resize
          #---------------------------------------------------------#
          new_image_size = (self.model_image_size[1], self.model_image_size[0])
          boxed_image = letterbox_image(image, new_image_size)
          image_data = np.array(boxed_image, dtype='float32')
          image_data /= 255.
          #---------------------------------------------------------#
          #   添加上batch_size维度
          #---------------------------------------------------------#
          image_data = np.expand_dims(image_data, 0)  # Add batch dimension.
  
          #---------------------------------------------------------#
          #   将图像输入网络当中进行预测！
          #---------------------------------------------------------#
          if self.eager:
              # 预测结果
              input_image_shape = np.expand_dims(np.array([image.size[1], image.size[0]], dtype='float32'), 0)
              out_boxes, out_scores, out_classes = self.get_pred(image_data, input_image_shape) 
          else:
              # 预测结果
              out_boxes, out_scores, out_classes = self.sess.run(
                  [self.boxes, self.scores, self.classes],
                  feed_dict={
                      self.yolo_model.input: image_data,
                      self.input_image_shape: [image.size[1], image.size[0]],
                      K.learning_phase(): 0
                  })
          
          print('Found {} boxes for {}'.format(len(out_boxes), 'img'))
  
          # --------------------------------------------------------- #
          #   设置字体
          # --------------------------------------------------------- #
          font = ImageFont.truetype(font='font/simhei.ttf',
                      size=np.floor(3e-2 * image.size[1] + 0.5).astype('int32'))
          thickness = max((image.size[0] + image.size[1]) // 300, 1)
          person_face = image
          for i, c in list(enumerate(out_classes)):
              predicted_class = self.class_names[c]
              box = out_boxes[i]
              score = out_scores[i]
              top, left, bottom, right = box
              top = top - 5
              left = left - 5
              bottom = bottom + 5
              right = right + 5
              top = max(0, np.floor(top + 0.5).astype('int32'))
              left = max(0, np.floor(left + 0.5).astype('int32'))
              bottom = min(image.size[1], np.floor(bottom + 0.5).astype('int32'))
              right = min(image.size[0], np.floor(right + 0.5).astype('int32'))
              filename = 'temp/'+str(int(round((time.time()) * 1000)))+'.jpg'
              if predicted_class == 'person':
                  person_face = np.array(person_face)
                  person_face = person_face[top+10:bottom+10, left+10:right+10]
                  person_face = cv2.cvtColor(person_face, cv2.COLOR_RGB2BGR)
                  cv2.imwrite(filename, person_face)
  
              # 人脸识别
              # face_image = image
              # face_image = np.array(face_image.convert('RGB'))
              #
              # face_image = face_recognition.face_encodings(face_image[top+10:bottom+10, left+10:right+10])
              # images = os.listdir('img')
              # name = ''
              # if face_image:
              #     face_image = face_image[0]
              #     for img in images:
              #         current_image = face_recognition.face_encodings(face_recognition.load_image_file('img/' + img))[0]
              #         result = face_recognition.compare_faces([face_image], current_image, 0.6)
              #
              #         if result[0]:
              #             print("匹配: " + img)
              #             name = img.split('.')[0]
              # 画框框
              label = '{} {:.2f} '.format(predicted_class, score)
              draw = ImageDraw.Draw(image)
              label_size = draw.textsize(label, font)
              label = label.encode('utf-8')
              print(label, top, left, bottom, right)
  
              # pil_image = Image.fromarray(face_image)
              # pil_image.show()
  
              if top - label_size[1] >= 0:
                  text_origin = np.array([left, top - label_size[1]])
              else:
                  text_origin = np.array([left, top + 1])
  
              # 标记框
              for i in range(thickness):
                  draw.rectangle(
                      [left + i, top + i, right - i, bottom - i],
                      outline=self.colors[c])
  
              # 标记框上面的一个背景框
              draw.rectangle(
                  [tuple(text_origin), tuple(text_origin + label_size)],
                  fill=self.colors[c])
              draw.text(text_origin, str(label, 'UTF-8'), fill=(0, 0, 0), font=font)
              del draw
  
          end = timer()
          print(end - start)
          return image
  
      def close_session(self):
          self.sess.close()
  