title: Illegal instruction (core dumped) while running Dlib Face detection
body: <p>I am trying to run face_landmark_detection.py example sample which was provided by the <code>Dlib</code> library.</p>
  
  <p>But when i am trying to run the command via ubuntu terminal I am getting error: </p>
  
  <pre><code>Illegal instruction (core dumped)
  </code></pre>
  
  <p>I debug it so i get to know it is because of this line :                        </p>
  
  <pre><code>win=dlib.image_window()
  </code></pre>
  
  <p>I guess something is wrong with this line</p>
  
  <p>I am running code via this command:</p>
  
  <p>./face_landmark_detection.py /home/abhishek/openCV/shape_predictor_68_face_landmarks.dat ../examples/faces</p>
  
  <p>As done in the sample code.
  my code</p>
  
  <pre><code>        import sys
          import os
          import dlib
          import glob
          from skimage import io
  
          if len(sys.argv) != 3:
          print(
      "Give the path to the trained shape predictor model as the first "
      "argument and then the directory containing the facial images.\n"
      "For example, if you are in the python_examples folder then "
      "execute this program by running:\n"
      "    ./face_landmark_detection.py shape_predictor_68_face_landmarks.dat ../examples/faces\n"
      "You can download a trained facial shape predictor from:\n"
      "    http://sourceforge.net/projects/dclib/files/dlib/v18.10/shape_predictor_68_face_landmarks.dat.bz2")
              exit()
  
          predictor_path = sys.argv[1]
          faces_folder_path = sys.argv[2]
  
          print predictor_path
          print faces_folder_path
  
          detector = dlib.get_frontal_face_detector()
          predictor = dlib.shape_predictor(predictor_path)
          win = dlib.image_window()
  
  
          for f in glob.glob(os.path.join(faces_folder_path, "*.jpg")):
  
               print("Processing file: {}".format(f))
               img = io.imread(f)
  
               print "img",img
               win.clear_overlay()
               win.set_image(img)
  
  # Ask the detector to find the bounding boxes of each face. The 1 in the
  # second argument indicates that we should upsample the image 1 time. This
  # will make everything bigger and allow us to detect more faces.
          dets = detector(img, 1)
          print("Number of faces detected: {}".format(len(dets)))
          for k, d in enumerate(dets):
                 print("Detection {}: Left: {} Top: {} Right: {} Bottom:             {}".format(
          k, d.left(), d.top(), d.right(), d.bottom()))
      # Get the landmarks/parts for the face in box d.
              shape = predictor(img, d)
              print("Part 0: {}, Part 1: {} ...".format(shape.part(0),
                                                shape.part(1)))
      # Draw the face landmarks on the screen.
             win.add_overlay(shape)
  
      win.add_overlay(dets)
      dlib.hit_enter_to_continue()
  </code></pre>