code: import numpy as np
  import cv2
  
  # A motion detecetor, yup... lol.
  # Remember to use an old python version < 3.6
  
  
  image_path = 'room_people.jpg' # Photo
  
  # The model was already formulated, just need to loaad it into the system.
  
  prototxt_path = 'models/MobileNetSSD_deploy.prototxt' # Load Model
  model_path = 'models/MobileNetSSD_deploy.caffemodel'
  min_confidence = 0.2
  
  # Things it can identify
  classes = ["background","aeroplane","bicycle","bird","boat","bottle","bus","car","cat","chair","cow","diningtable","dog","horse",
             "motorbike","person","pottedplant","sheep","sofa","train","tvmonitor"]
  
  np.random.seed(543210) # Same Colors
  colors = np.random.uniform(0, 255, size=(len(classes), 3))
  
  net = cv2.dnn.readNetFromCaffe(prototxt_path, model_path)
  
  # img = cv2.imread(image_path)
  
  cap = cv2.VideoCapture(0)
  
  while True:
  
      _, img = cap.read()
  
      height, width = img.shape[0], img.shape[1]
  
      blob = cv2.dnn.blobFromImage(cv2.resize(img, (300, 300)), 0.007, (300,300), 130)
  
      net.setInput(blob)
  
      detected_objects = net.forward()
  
      for i in range(detected_objects.shape[2]):
  
          confidence = detected_objects[0][0][i][2]
  
          if confidence > min_confidence:
              class_index = int(detected_objects[0,0,i,1])
  
              upper_left_x = int(detected_objects[0, 0, i, 3] * width)
              upper_left_y = int(detected_objects[0, 0, i, 3] * height)
              lower_right_x = int(detected_objects[0, 0, i, 5] * width)
              lower_right_y = int(detected_objects[0, 0, i, 6] * height)
  
              prediction_text = f"{classes[class_index]}: {confidence:.2f}%"
              cv2.rectangle(img, (upper_left_x, upper_left_y), (lower_right_x, lower_right_y), colors[class_index], 3)
              cv2.putText(img, prediction_text, (upper_left_x,
                          upper_left_y- 15 if upper_left_y > 30 else upper_left_y + 15),
                          cv2.FONT_HERSHEY_SIMPLEX, 0.6, colors[class_index], 2)
  
      cv2.imshow("Detected Objects", img)
      cv2.waitKey(5)
  
  cv2.destroyAllWindows()
  cap.release()
  
apis: numpy.random.seed