code: import colorsys
  import os
  
  import numpy as np
  from keras import backend as K
  from keras.layers import Input
  from keras.models import load_model
  from PIL import Image, ImageDraw, ImageFont
  
  from nets.yolo3 import yolo_body, yolo_eval
  from utils.utils import letterbox_image
  
  class YOLO(object):
      _defaults = {
          "model_path"        : '../model/trained_weights.h5',
          "anchors_path"      : '../model/yolo_anchors.txt',
          "classes_path"      : '../model/yolo_classes.txt',
          "score"             : 0.5,
          "iou"               : 0.3,
          "max_boxes"         : 100,
          "model_image_size"  : (416, 416),
          "letterbox_image"   : False,
      }
  
      def get_defaults(cls, n):
          if n in cls._defaults:
              return cls._defaults[n]
          else:
              return "Unrecognized attribute name '" + n + "'"
  
      def __init__(self, **kwargs):
          self.__dict__.update(self._defaults)
          self.class_names = self._get_class()
          self.anchors = self._get_anchors()
          self.sess = K.get_session()
          self.boxes, self.scores, self.classes = self.generate()
  
      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
  
      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)
  
      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.'
  
          num_anchors = len(self.anchors)
          num_classes = len(self.class_names)
  
          try:
              self.yolo_model = load_model(model_path, compile=False)
          except:
              self.yolo_model = yolo_body(Input(shape=(None,None,3)), num_anchors//3, num_classes)
              self.yolo_model.load_weights(self.model_path)
          else:
              assert self.yolo_model.layers[-1].output_shape[-1] == \
                  num_anchors/len(self.yolo_model.output) * (num_classes + 5), \
                  'Mismatch between model and given anchor and class sizes'
  
          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))
  
          np.random.seed(10101)
          np.random.shuffle(self.colors)
          np.random.seed(None)
  
          self.input_image_shape = K.placeholder(shape=(2, ))
  
          boxes, scores, 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, letterbox_image = self.letterbox_image)
          return boxes, scores, classes
  
      def detect_image(self, image):
          if self.letterbox_image:
              boxed_image = letterbox_image(image, (self.model_image_size[1],self.model_image_size[0]))
          else:
              boxed_image = image.convert('RGB')
              boxed_image = boxed_image.resize((self.model_image_size[1],self.model_image_size[0]), Image.BICUBIC)
          image_data = np.array(boxed_image, dtype='float32')
          image_data /= 255.
          image_data = np.expand_dims(image_data, 0)
          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})
  
          font = ImageFont.truetype(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)
  
          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'))
  
              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)
              
              if top - label_size[1] >= 0:
                  text_origin = np.array([left, top - label_size[1]])
              else:
                  text_origin = np.array([left, top + 1])
  
              for i in range(thickness):
                  draw.rectangle(
                      [left + i, top + i, right - i, bottom - i],
                      outline=self.colors[c])
              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
  
          return image
  
      def close_session(self):
          self.sess.close()
  
apis: numpy.random.shuffle numpy.floor numpy.random.seed numpy.expand_dims numpy.array