def main():
"""
Main function.
"""
label = None
show = False
classifier = None
settings = opt.map_settings()
key = opt.default_settings()
# Parse command-line arguments
try:
short_opts = ['']
long_opts = ['help', 'classifier=', 'label=', 'settings=', 'show']
opts, args = getopt.getopt(sys.argv[1:], short_opts, long_opts)
except getopt.GetoptError as error:
print('Invalid argument: \'' + str(error) + '\'\n')
print_usage()
if len(opts) == 0:
print_usage()
for o, a in opts:
if o == '--help':
print_usage()
elif o == '--classifier':
classifier = opt.validate_file(a)
elif o == '--label':
label = opt.validate_raw_dataset(sys.path[1], a)
elif o == '--settings':
key = a
elif o == '--show':
show = True
if not label:
print('\n Label not specified!\n')
print_usage()
elif key not in settings.keys():
print('\n Settings not specified!\n')
print_usage()
# Initialize variables
configuration = config.Config(settings[key])
recognizer = configuration.recognizer()
width = int(recognizer['width'])
height = int(recognizer['height'])
stream = camera.Camera(0, configuration)
detector_obj = detector.Detector(classifier, configuration)
raw_path = sys.path[1] + '/data/faces/' + label + '/raw/'
training_path = sys.path[1] + '/data/faces/' + label + '/training/'
image_paths = []
os.makedirs(training_path, exist_ok=True)
# Get the absolute path of each image
print('Collecting raw images... ', end='')
for entry in os.listdir(raw_path):
image_paths.append(os.path.join(raw_path, entry))
print('DONE')
# Preprocess each image
l = len(image_paths)
for i, path in enumerate(image_paths):
print('\rPreprocessing raw images... (' + str(i+1) + '/' + str(l) + ')', end='')
cont = False
image_pil = Image.open(path)
image_org = numpy.array(image_pil)
image_rgb = cv2.cvtColor(image_org, cv2.COLOR_BGR2RGB)
(iwidth, iheight) = image_pil.size
aspect_ratio = iwidth / iheight
image = cv2.resize(image_rgb, (stream.get_width(), int(stream.get_width() / aspect_ratio)))
(x, y, w, h) = (0, 0, 0, 0)
try:
(x, y, w, h) = detector_obj.detect(image)[0]
except IndexError:
print('\nNo faces detected in:', path)
cont = True
if show:
cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 255), 2)
cv2.imshow('process_raw_images.py', image)
cv2.waitKey(1)
if cont:
continue
face = imgproc.preprocess(image, width, height, x, y, w, h)
if i < 10:
cv2.imwrite(training_path + label + '.0' + str(i) + '.png', face)
else:
cv2.imwrite(training_path + label + '.' + str(i) + '.png', face)
print('\rPreprocessing raw images... DONE ')
def main():
"""
Main function.
"""
label1 = None
label2 = None
classifier = None
settings = opt.map_settings()
key = opt.default_settings()
# Parse command-line arguments
try:
short_opts = ['']
long_opts = [
'help',
'classifier=',
'label1=',
'label2=',
'settings=',
'show'
]
opts, args = getopt.getopt(sys.argv[1:], short_opts, long_opts)
except getopt.GetoptError as error:
print('Invalid argument: \'' + str(error) + '\'\n')
print_usage()
if len(opts) == 0:
print_usage()
for o, a in opts:
if o == '--help':
print_usage()
elif o == '--classifier':
classifier = opt.validate_file(a)
elif o == '--label1':
label1 = opt.validate_raw_dataset(sys.path[1], a)
elif o == '--label2':
label2 = opt.validate_raw_dataset(sys.path[1], a)
elif o == '--settings':
key = a
if not label1 or not label2:
print('\n Label not specified!\n')
print_usage()
elif key not in settings.keys():
print('\n Settings not specified!\n')
print_usage()
# Initialize variables
configuration = config.Config(settings[key])
recognizer_obj = recognizer.Recognizer(classifier, label1, configuration)
raw_path = sys.path[1] + '/data/faces/' + label2 + '/raw/'
image_paths = []
all_confidences = []
all_widths = []
all_heights = []
percent = 0
# Get the absolute path of each image
print('Collecting images of ' + label2 + '... ', end='')
for entry in os.listdir(raw_path):
image_paths.append(os.path.join(raw_path, entry))
print('DONE')
# Preprocess each image
for i, path in enumerate(image_paths):
percent = ((i+1) / len(image_paths)) * 100
print("\rCalculating confidence statistics... {:.1f}%".format(percent), end='')
sys.stdout.flush()
skip = False
image, objects, labels, confidences = recognizer_obj.recognize_from_file(path)
try:
if len(confidences) > 1:
all_confidences.append(confidences[1])
all_widths.append(objects[1][2])
all_heights.append(objects[1][3])
else:
all_confidences.append(confidences[0])
all_widths.append(objects[0][2])
all_heights.append(objects[0][3])
except IndexError:
skip = True
if skip:
continue
print('\rCalculating confidence statistics... DONE ')
print('Five Number Summary:')
print(' Max:\t {}'.format(numpy.max(all_confidences)))
print(' Min:\t {}'.format(numpy.min(all_confidences)))
print(' Median: {}'.format(numpy.median(all_confidences)))
print(' Mean:\t {}'.format(numpy.mean(all_confidences)))
print(' StdDev: {}'.format(numpy.std(all_confidences)))
print('Mean face size: %dx%d' % (numpy.mean(all_widths), numpy.mean(all_heights)))
