input: Write a python function to recognize the presence of a person in a given frame of an image/video using a trained computer vision model.
  assert detect_person('frame_with_person.jpg')==True
  assert detect_person('frame_without_person.jpg')==False
  assert detect_person('frame_with_multiple_people.jpg')==True
output: def detect_person(frame_path):
      import cv2
      import numpy as np
  
      # Load a pre-trained deep learning model for person detection (e.g., MobileNet SSD)
      net = cv2.dnn.readNetFromCaffe('deploy.prototxt', 'res10_300x300_ssd_iter_140000_fp16.caffemodel')
  
      # Load the image
      image = cv2.imread(frame_path)
      (h, w) = image.shape[:2]
  
      # Prepare the image for the model
      blob = cv2.dnn.blobFromImage(cv2.resize(image, (300, 300)), 0.007843, (300, 300), 127.5)
      net.setInput(blob)
      detections = net.forward()
  
      # Iterate over the detections and check for person class (class id 15 for MobileNet SSD)
      for i in range(detections.shape[2]):
          confidence = detections[0, 0, i, 2]
  
          # Filter out weak detections
          if confidence > 0.2:
              idx = int(detections[0, 0, i, 1])
              if idx == 15:  # Class ID for person
                  return True
  
      return False