train.py

import os
import sys
import glob

import dlib

faces_folder = r'C:\Users\gmplk\Desktop\TrainHOG\TrainHOG\TrainHOG'
#faces_folder = ""

options = dlib.simple_object_detector_training_options()
options.C = 5
options.num_threads = 4
options.be_verbose = True


training_xml_path = os.path.join(faces_folder, "train\\training01.xml")
testing_xml_path = os.path.join(faces_folder, "test\\test.xml")

dlib.train_simple_object_detector(training_xml_path, "detector.svm", options)

print("")  # Print blank line to create gap from previous output
print("Training accuracy: {}".format(
    dlib.test_simple_object_detector(training_xml_path, "detector.svm")))

print("Testing accuracy: {}".format(
    dlib.test_simple_object_detector(testing_xml_path, "detector.svm")))

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

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

print("Showing detections on the images in the faces folder...")
win = dlib.image_window()
for f in glob.glob(os.path.join(faces_folder+"\\test", "*.jpg")):
    print("Processing file: {}".format(f))
    img = dlib.load_rgb_image(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(
            k, d.left(), d.top(), d.right(), d.bottom()))

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

detector1 = dlib.fhog_object_detector("detector.svm")

detector2 = dlib.fhog_object_detector("detector.svm") 

detectors = [detector1, detector2]
image = dlib.load_rgb_image(faces_folder + '/2008_002506.jpg')
[boxes, confidences, detector_idxs] = dlib.fhog_object_detector.run_multiple(detectors, image, upsample_num_times=1, adjust_threshold=0.0)
for i in range(len(boxes)):
    print("detector {} found box {} with confidence {}.".format(detector_idxs[i], boxes[i], confidences[i]))

images = [dlib.load_rgb_image(faces_folder + '/2008_002506.jpg'),
          dlib.load_rgb_image(faces_folder + '/2009_004587.jpg')]

boxes_img1 = ([dlib.rectangle(left=329, top=78, right=437, bottom=186),
               dlib.rectangle(left=224, top=95, right=314, bottom=185),
               dlib.rectangle(left=125, top=65, right=214, bottom=155)])
boxes_img2 = ([dlib.rectangle(left=154, top=46, right=228, bottom=121),
               dlib.rectangle(left=266, top=280, right=328, bottom=342)])

boxes = [boxes_img1, boxes_img2]

detector2 = dlib.train_simple_object_detector(images, boxes, options)
# We could save this detector to disk by uncommenting the following.
#detector2.save('detector2.svm')

# Now let's look at its HOG filter!
win_det.set_image(detector2)
dlib.hit_enter_to_continue()

# Note that you don't have to use the XML based input to
# test_simple_object_detector().  If you have already loaded your training
# images and bounding boxes for the objects then you can call it as shown
# below.
print("\nTraining accuracy: {}".format(
    dlib.test_simple_object_detector(images, boxes, detector2)))