text: import tensorflow as tf
  tf.get_logger().setLevel('ERROR')           # Suppress TensorFlow logging (2)
  import os
  os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'    # Suppress TensorFlow logging (1)
  import numpy as np
  import cv2
  from activity_recognition_class import ActivityRecognition
  
  # classes and variables for activity recognition in video
  # load the contents of the class labels file, then define the sample
  # duration (i.e., # of frames for classification) and sample size
  # (i.e., the spatial dimensions of the frame)
  CLASSES = open("C:\\Users\\catal\\Facultate\\an4\\licenta\\ActivityRecognition\\action_recognition_kinetics.txt").read().strip().split("\n")
  SAMPLE_SIZE = 112
  
  # load the human activity recognition model
  print("[INFO] loading human activity recognition model...")
  net = cv2.dnn.readNet("C:\\Users\\catal\\Facultate\\an4\\licenta\\ActivityRecognition\\resnet-34_kinetics.onnx")
  
  def get_activity_recognition(frames):
      #construct blob for video classification
      # now that our frames array is filled we can construct our blob
      blob = cv2.dnn.blobFromImages(np.array(frames, dtype='uint8'), 1.0,
          (SAMPLE_SIZE, SAMPLE_SIZE), (114.7748, 107.7354, 99.4750),
          swapRB=True, crop=True)
      blob = np.transpose(blob, (1, 0, 2, 3))
      blob = np.expand_dims(blob, axis=0)
  
      # pass the blob through the network to obtain our human activity recognition predictions
      net.setInput(blob)
      outputs = net.forward()
      label = CLASSES[np.argmax(outputs)] 
  
      #calculate score out of outputs values knowing that sum of all predictions for all classes is 0
      min_value = min(outputs[0])
      abs_value = abs(min_value)
      outputs[0] = list(np.asarray(outputs[0]) + abs_value)
      max_value = max(outputs[0])
      sum_of_scores = sum(outputs[0])
      score = int(max_value/sum_of_scores*100*100)
      if score > 100: score = 100
      print("Score is {}%".format(score))
  
      post_process = ActivityRecognition(label, score)
      return post_process