How to use the dlib.image_window function in dlib

import sys
import dlib
from PIL import Image
import numpy as np
from timeit import default_timer as timer

# (563, 511, 3)   total time: 0.433351890999802
#file_name = "/home/ekmek/intership_project/hog_tests/face_example.jpg"
file_name = '/home/ekmek/intership_project/video_parser_v1/_videos_to_test/small_dataset/input/frames/s0216.jpg'

show = True


if show:
    win = dlib.image_window()

# Create a HOG face detector using the built-in dlib class
face_detector = dlib.get_frontal_face_detector()

# Load the image into an array
#image = io.imread(file_name)
image = Image.open(file_name)

image = np.array(image)
print(image.shape)

# Run the HOG face detector on the image data.
# The result will be the bounding boxes of the faces in our image.

start = timer()
detected_faces = face_detector(image, 1)

if len(sys.argv) != 3:
    print(
        "execute this program by running: \n"
        "python sleep_detection.py  ./face_landmark_detection.py shape_predictor_68_face_landmarks.dat /path/to/image.jpg"
        "You can download a trained facial shape predictor from:\n "
        "http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2")
    exit()

predictor_path = sys.argv[1]
image_path = sys.argv[2]
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(predictor_path)

win = dlib.image_window()
img = io.imread(image_path)
win.clear_overlay()
win.set_image(img)
dets = detector(img, 1)
vec = np.empty([68, 2], dtype = int)

status="Not Sleeping"

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),

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]

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)

    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()))

def get_face(filename):
    # Create a HOG face detector using the built-in dlib class
    predictor_model = "shape_predictor_68_face_landmarks.dat"
    
    face_detector = dlib.get_frontal_face_detector()
    face_pose_predictor = dlib.shape_predictor(predictor_model)
    face_aligner = openface.AlignDlib(predictor_model)

    win = dlib.image_window()

    # Load the image into an array
    image = io.imread(filename)

    # Run the HOG face detector on the image data.
    # The result will be the bounding boxes of the faces in our image.
    detected_faces = face_detector(image, 1)

    # Open a window on the desktop showing the image
    win.set_image(image)

    # Loop through each face we found in the image
    for i, face_rect in enumerate(detected_faces):
        # Detected faces are returned as an object with the coordinates 
        # of the top, left, right and bottom edges
        face1 = image[face_rect.top():face_rect.bottom(), face_rect.left():face_rect.right()]

import sys
import dlib
from skimage import io
import openface

# You can download the required pretrained face detection model here:
# http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2
predictor_model = "shape_predictor_68_face_landmarks.dat"

# Create a HOG face detector using the built-in dlib class
face_detector = dlib.get_frontal_face_detector()
face_pose_predictor = dlib.shape_predictor(predictor_model)
face_aligner = openface.AlignDlib(predictor_model)

win = dlib.image_window()

# Load the image
#image = io.imread("images/Chandrakanth.jpg")
image = io.imread("test/2.jpg")

# Run the HOG face detector on the image data
detected_faces = face_detector(image, 1)

print("Found {} faces in the image file {}".format(len(detected_faces), "multipleimg/3.jpg"))

# Show the desktop window with the image
win.set_image(image)

# Loop through each face we found in the image
for i, face_rect in enumerate(detected_faces):

#   On Ubuntu, this can be done easily by running the command:
#       sudo apt-get install libboost-python-dev cmake
#
#   Also note that this example requires scikit-image which can be installed
#   via the command:
#       pip install -U scikit-image
#   Or downloaded from http://scikit-image.org/download.html. 

import sys

import dlib
from skimage import io


detector = dlib.get_frontal_face_detector()
win = dlib.image_window()

for f in sys.argv[1:]:
    print("Processing file: {}".format(f))
    img = io.imread(f)
    # 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 i, d in enumerate(dets):
        print("Detection {}: Left: {} Top: {} Right: {} Bottom: {}".format(
            i, d.left(), d.top(), d.right(), d.bottom()))

    win.clear_overlay()
    win.set_image(img)
    win.add_overlay(dets)

def create_window(self):
        self.window = dlib.image_window()

# face landmarking datasets you will obtain state-of-the-art results, as shown
# in the Kazemi paper.
testing_xml_path = os.path.join(faces_folder, "testing_with_face_landmarks.xml")
print("Testing accuracy: {}".format(
    dlib.test_shape_predictor(testing_xml_path, "predictor.dat")))

# Now let's use it as you would in a normal application.  First we will load it
# from disk. We also need to load a face detector to provide the initial
# estimate of the facial location.
predictor = dlib.shape_predictor("predictor.dat")
detector = dlib.get_frontal_face_detector()

# Now let's run the detector and shape_predictor over the images in the faces
# folder and display the results.
print("Showing detections and predictions on the images in the faces folder...")
win = dlib.image_window()
for f in glob.glob(os.path.join(faces_folder, "*.jpg")):
    print("Processing file: {}".format(f))
    img = io.imread(f)

    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.

# Github:   https://github.com/coneypo/Dlib_examples

import dlib
from skimage import io

# 使用 Dlib 的正面人脸检测器 frontal_face_detector
detector = dlib.get_frontal_face_detector()

# Dlib 的 68 点模型
predictor = dlib.shape_predictor("../data/data_dlib/shape_predictor_68_face_landmarks.dat")

# 图片所在路径
img = io.imread("../data/data_faces/faces_2.jpeg")

# 生成 Dlib 的图像窗口
win = dlib.image_window()
win.set_image(img)

# 使用检测器来检测图像中的人脸
faces = detector(img, 1)
print("人脸数：", len(faces))

for i, d in enumerate(faces):
    print("第", i+1, "个人脸的矩形框坐标：",
          "left:", d.left(), '\t', "right:", d.right(), '\t', "top:", d.top(),'\t',  "bottom:", d.bottom())

    # 使用 predictor 来计算面部轮廓
    shape = predictor(img, faces[i])

    # 绘制面部轮廓, Blue
    # full_object_detection
    win.add_overlay(shape)