text: import colorsys
  import imghdr
  import os
  import random
  import tensorflow as tf
  from tensorflow.keras import backend as K
  
  import numpy as np
  from PIL import Image, ImageDraw, ImageFont
  
  def read_classes(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 read_anchors(anchors_path):
      with open(anchors_path) as f:
          anchors = f.readline()
          anchors = [float(x) for x in anchors.split(',')]
          anchors = np.array(anchors).reshape(-1, 2)
      return anchors
  
  def generate_colors(class_names):
      hsv_tuples = [(x / len(class_names), 1., 1.) for x in range(len(class_names))]
      colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
      colors = list(map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)), colors))
      random.seed(10101)  # Fixed seed for consistent colors across runs.
      random.shuffle(colors)  # Shuffle colors to decorrelate adjacent classes.
      random.seed(None)  # Reset seed to default.
      return colors
  
  def scale_boxes(boxes, image_shape):
      """ Scales the predicted boxes in order to be drawable on the image"""
      height = image_shape[0]
      width = image_shape[1]
      image_dims = K.stack([height, width, height, width])
      image_dims = K.reshape(image_dims, [1, 4])
      boxes = boxes * image_dims
      return boxes
  
  def preprocess_image(img_path, model_image_size):
      image_type = imghdr.what(img_path)
      image = Image.open(img_path)
      resized_image = image.resize(tuple(reversed(model_image_size)), Image.BICUBIC)
      image_data = np.array(resized_image, dtype='float32')
      image_data /= 255.
      image_data = np.expand_dims(image_data, 0)  # Add batch dimension.
      return image, image_data
  
  def draw_boxes(image, out_scores, out_boxes, out_classes, class_names, colors):
      
      font = ImageFont.truetype(font='font/FiraMono-Medium.otf',size=np.floor(3e-2 * image.size[1] + 0.5).astype('int32'))
      thickness = (image.size[0] + image.size[1]) // 300
  
      for i, c in reversed(list(enumerate(out_classes))):
          predicted_class = class_names[c]
          box = out_boxes[i]
          score = out_scores[i]
  
          label = '{} {:.2f}'.format(predicted_class, score)
  
          draw = ImageDraw.Draw(image)
          label_size = draw.textsize(label, font)
  
          top, left, bottom, right = box
          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'))
          print(label, (left, top), (right, bottom))
  
          if top - label_size[1] >= 0:
              text_origin = np.array([left, top - label_size[1]])
          else:
              text_origin = np.array([left, top + 1])
  
          # My kingdom for a good redistributable image drawing library.
          for i in range(thickness):
              draw.rectangle([left + i, top + i, right - i, bottom - i], outline=colors[c])
          draw.rectangle([tuple(text_origin), tuple(text_origin + label_size)], fill=colors[c])
          draw.text(text_origin, label, fill=(0, 0, 0), font=font)
          del draw