How to use the dlib.simple_object_detector function in dlib

# python test_detector.py --detector output/stop_sign_detector.svm --testing stop_sign_testing

# import the necessary packages
from imutils import paths
import argparse
import dlib
import cv2

# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-d", "--detector", required=True, help="Path to trained object detector")
ap.add_argument("-t", "--testing", required=True, help="Path to directory of testing images")
args = vars(ap.parse_args())

# load the detector
detector = dlib.simple_object_detector(args["detector"])

# loop over the testing images
for testingPath in paths.list_images(args["testing"]):
	# load the image and make predictions
	image = cv2.imread(testingPath)
	boxes = detector(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))

	# loop over the bounding boxes and draw them
	for b in boxes:
		(x, y, w, h) = (b.left(), b.top(), b.right(), b.bottom())
		cv2.rectangle(image, (x, y), (w, h), (0, 255, 0), 2)

	# show the image
	cv2.imshow("Image", image)
	cv2.waitKey(0)

import os,sys,Image,dlib,random
from skimage import io
import numpy as np

# print "\nTest1 accuracy: ", dlib.test_simple_object_detector('/home/jyotiska/Dropbox/Computer Vision/cupdataset_2_test.xml',"cupdetector_2.svm")
# print "\nTraining accuracy: ", dlib.test_simple_object_detector('/home/jyotiska/Dropbox/Computer Vision/cupdataset_3.xml',"cupdetector_3.svm")

detector = dlib.simple_object_detector("cupdetector_4.svm")

# win_det = dlib.image_window()
# win_det.set_image(detector)

# win = dlib.image_window()
# test_dir = '/home/jyotiska/Dropbox/Computer Vision/Cups_test'
# convert_dir = '/home/jyotiska/Dropbox/Computer Vision/Cups_test_convert'
assorted_dir = 'ItemBucket/'

items =os.listdir(assorted_dir)

def classify(img):
  dets = detector(img)
  background = Image.fromarray(np.array(img))
  for d in dets:
    x = d.left()

print("")  # Print blank line to create gap from previous output
print("Training accuracy: {}".format(
    dlib.test_simple_object_detector(training_xml_path, "detector.svm")))
# However, to get an idea if it really worked without overfitting we need to
# run it on images it wasn't trained on.  The next line does this.  Happily, we
# see that the object detector works perfectly on the testing images.
print("Testing accuracy: {}".format(
    dlib.test_simple_object_detector(testing_xml_path, "detector.svm")))





# Now let's use the detector as you would in a normal application.  First we
# will load it from disk.
detector = dlib.simple_object_detector("detector.svm")

# We can look at the HOG filter we learned.  It should look like a face.  Neat!
win_det = dlib.image_window()
win_det.set_image(detector)

# Now let's run the detector over the images in the faces folder and display the
# results.
print("Showing detections 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)
    dets = detector(img)
    print("Number of faces detected: {}".format(len(dets)))
    for k, d in enumerate(dets):
        print("Detection {}: Left: {} Top: {} Right: {} Bottom: {}".format(

def view_object_detector(self):
        detector = dlib.simple_object_detector(DETECTOR_SVM)
        win_det = dlib.image_window()
        win_det.set_image(detector)
        dlib.hit_enter_to_continue()

def __init__(self, model):
        if isinstance(model, STRING_TYPES) or isinstance(model, Path):
            m_path = Path(model)
            if not Path(m_path).exists():
                raise ValueError('Model {} does not exist.'.format(m_path))
            # There are two different kinds of object detector, the
            # simple_object_detector and the fhog_object_detector, but we
            # can't tell which is which from the file name. Therefore, try one
            # and then the other. Unfortunately, it throws a runtime error,
            # which we have to catch.
            try:
                model = dlib.simple_object_detector(str(m_path))
            except RuntimeError:
                model = dlib.fhog_object_detector(str(m_path))
        self._dlib_model = model