text: import os
  import numpy as np
  import matplotlib.pyplot as plt
  import cv2
  import json
  import glob
  
  from Mask.config import Config
  import Mask.utils as utils
  import Mask.model as modellib
  import Mask.visualize as visualize
  import tensorflow as tf
  
  np.set_printoptions(threshold=np.inf)
  tf.logging.set_verbosity(tf.logging.ERROR)
  # path of the trained model
  dir_path = os.path.dirname(os.path.realpath(__file__))
  MODEL_DIR = dir_path + "/models/"
  MODEL_PATH = "models/MAKRCNN/mask_rcnn_moles_0033.h5"
  
  
  class CocoConfig(Config):
      ''' 
      MolesConfig:
          Contain the configuration for the dataset + those in Config
      '''
      NAME = "moles"
      NUM_CLASSES = 1 + 2 # background + (malignant , benign)
      IMAGE_MIN_DIM = 128
      IMAGE_MAX_DIM = 128
      GPU_COUNT = 1
      IMAGES_PER_GPU = 1
      DETECTION_MAX_INSTANCES = 3
  
  # create and instance of config
  config = CocoConfig()
  class_names = ["BG", "malign", "benign"]
  # take the trained model
  model = modellib.MaskRCNN(mode="inference", model_dir=MODEL_DIR, config=config)
  model.load_weights(MODEL_PATH, by_name=True)
  img = open("Data/Images/1_malign.jpg", 'rb').read()
  nparr = np.fromstring(img, np.uint8)
  img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
  # background + (malignant , benign)
  
  height, width = img.shape[0], img.shape[1]
  r = model.detect([img])[0]
  
  image_mask = visualize.display_instances(img, r['rois'], r['masks'], r['class_ids'],
                              class_names, r['scores'])
  image_mask = cv2.resize(image_mask, (height, width))
  if len(r['class_ids']) > 0:
      class_name = class_names[r['class_ids'][0]]
  else:
      class_name = "unsure"
  print(class_name)
  
  