assert os.path.isfile(training_xml_path)

assert not os.path.isfile(model_file)

# Now let's do the training. The train_simple_object_detector() function has a

# bunch of options, all of which come with reasonable default values. The next

# few lines goes over some of these options.

options = dlib.simple_object_detector_training_options()

# Since faces are left/right symmetric we can tell the trainer to train a

# symmetric detector. This helps it get the most value out of the training

# data.

options.add_left_right_image_flips = True

# The trainer is a kind of support vector machine and therefore has the usual

# SVM C parameter. In general, a bigger C encourages it to fit the training

# data better but might lead to overfitting. You must find the best C value

# empirically by checking how well the trained detector works on a test set of

# images you haven't trained on. Don't just leave the value set at 5. Try a

# few different C values and see what works best for your data.

options.C = 10

# Tell the code how many CPU cores your computer has for the fastest training.

options.num_threads = 6

options.epsilon = 0.001

options.be_verbose = True

options.detection_window_size = 4096 #(32, 32)

# options.upsample_limit = 8

# This function does the actual training. It will save the final detector to

# detector.svm. The input is an XML file that lists the images in the training

# dataset and also contains the positions of the face boxes. To create your

# own XML files you can use the imglab tool which can be found in the

# tools/imglab folder. It is a simple graphical tool for labeling objects in

# images with boxes. To see how to use it read the tools/imglab/README.txt

# file. But for this example, we just use the training.xml file included with

# dlib.

print("Goingt to train ...")

dlib.train_simple_object_detector(training_xml_path, model_file, options)

# Now that we have a face detector we can test it. The first statement tests

# it on the training data. It will print(the precision, recall, and then)

# average precision.

print("") # Print blank line to create gap from previous output

print("Training accuracy: {}".format(

from utils.preprocess import generate_all_abs_filenames

# 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(model_file)

# 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 testing folder...")

win = dlib.image_window()

files = generate_all_abs_filenames(test_folder)

for f in files:

# print("Processing file: {}".format(f))

img = cv2.imread(f)

img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

# img = cv2.resize(img, None, fx=0.5, fy=0.5)

# img = io.imread(f)

# img = cv2.resize(img, (640, 640))

# if np.mean(img) > 2.0:

# print("Going to dividie 255")

# img = img/255.0

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()

# import time

# 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)

import time

start_time = time.clock()

img = cv2.resize(img, None, fx=0.5, fy=0.5)

img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

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()))

rect = [[d.left(), d.top()], [d.right(), d.top()], [d.right(), d.bottom()], d.left(), d.bottom()]

print(rect)

img = cv2.rectangle(img, (d.left(), d.top()), (d.right(), d.bottom()), (0,0,255))

end_time = time.clock()

# compute the tracking fps

current_fps = 1.0 / (end_time - start_time)

cv2.putText(img, "FPS:{:5.2f}".format(current_fps), (5, 15),

cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (255, 255, 255))

img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)

global detector

img = show_with_cv_one(img)

cv2.imshow("Dlib detection result", img)

cv2.waitKey(1)

output = img

# merge dlib file together

# ./imglab --add /home/miao/dataset/armer_video/v001/dlib_conf/manual_label_armer_half_size.xml /home/miao/dataset/armer_video/v002/dlib_conf/manual_label_armer_half_size.xml

# --add will output merge.xml, and the name cannot be easily changed

# shuffle

# ./imglab merged.xml --shuffle

import shutil

import subprocess

from subprocess import TimeoutExpired, PIPE

for f in files:

assert os.path.isfile(f)

tmp_dir = '/tmp/combine_dlib_xml/'

if not os.path.isdir(tmp_dir):

os.makedirs(tmp_dir)

file1 = tmp_dir+'/file1.xml'

file2 = tmp_dir+'/file2.xml'

dlib_imglab_dir = '/home/miao/icra2018_dji/dlib_198/tools/imglab/build/'

shutil.copyfile(files[0], file1)

for f in files[1:]:

shutil.copyfile(f, file2)

proc = subprocess.Popen( [dlib_imglab_dir+'/imglab', '--add', file1, file2 ], stdout=PIPE )

try:

outs, errs = proc.communicate(timeout=15)

except TimeoutExpired:

proc.kill()

outs, errs = proc.communicate()

shutil.copyfile('merged.xml', file1)

shutil.copyfile('merged.xml', combine_f)

proc = subprocess.Popen([dlib_imglab_dir + '/imglab', '--shuffle', combine_f], stdout=PIPE)

try:

outs, errs = proc.communicate(timeout=15)

except TimeoutExpired:

proc.kill()