def augmentation_pair(params):
"""
Augmenting pair of images
"""
source_image_path, target_image_path, size, source_out_path, target_out_path, augment, verbose = params
source_image = extract_image(source_image_path)
target_image = extract_image(target_image_path)
filename = os.path.basename(os.path.normpath(source_image_path))
if verbose:
print("Augmenting image %r ..." % filename)
augmentImagePairPhantom(source_image, target_image, size, source_out_path,
target_out_path, augment)
if verbose:
print("Augmented image %r ..." % filename)
def image_teach():
image = utils.extract_image(request)
name, id = utils.extract_name_or_id(request)
face = dlib_api.teach_person(np.array(image),
name=name,
id=id,
weight=request.json.get('weight', 1.0))
return jsonify(utils.face_to_dict(face))
def main(data_dir):
"""
Extracts faces from FaceForensics++ dataset.
Args:
data_dir: Base directory of the FaceForensics++ dataset.
"""
extract_count = 0
try:
# Validate argument. Exit if invalid.
if not os.path.isdir(data_dir):
print('"{}" is not a directory'.format(data_dir), file=stderr)
exit(1)
# Create directory for output images, if it does not already exist.
output_dir = '{}/original_sequences_images/{}/images'.format(
data_dir, COMPRESSION_LEVEL)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
print("Extracting images...")
seqs = get_orig_sequences(data_dir, COMPRESSION_LEVEL)
for s in seqs:
output_fn = '{}/{}.png'.format(output_dir, s.seq_id)
if os.path.exists(output_fn):
# Do not recreate an image if it already exists.
# If the user wants to recreated an image,
# the old image must be deleted first.
continue
print('Extracting image for sequence {}...'.format(s.seq_id))
img, _ = extract_image(s)
if img is None:
print(" No frame with a face found")
else:
# Write image to disk.
try:
cv2.imwrite(output_fn, img)
extract_count += 1
except KeyboardInterrupt as e:
# Safely handle premature termination.
# Remove unfinished file.
if os.exists(output_fn):
os.remove(output_fn)
raise e
except KeyboardInterrupt:
print('Program terminated prematurely')
finally:
if extract_count == 0:
print('No images extracted')
else:
print('{} images extracted'.format(extract_count))
def hack(img_name):
"""
HACK Recognize a CAPTCHA image
:param img_name: filename of image
:return: digits: 1x5 matrix, 5 digits in the input CAPTCHA image.
"""
data = sio.loadmat('hack_data.mat')
X = data['X']
y = data['y']
digits = extract_image(img_name)
show_image(digits)
digits = knn(digits, X, y, 20).reshape(-1)
print('The digits in this CAPTCHA is %d %d %d %d %d' % (digits[0], digits[1], digits[2], digits[3], digits[4]))
def extract_face(seq):
"""
Extracts a single face image from a video sequence.
Args:
seq: FFVideoSeq representing video sequence.
Returns:
An image of a face on success. None on failure.
"""
img, locations = extract_image(seq)
if img is None:
# No frame with a face was found.
return None
else:
# We found a frame with a face.
# If there are multiple faces, choose the largest.
loc = get_largest_face(locations)
cropped = crop_face(img, loc, ZOOMOUT_FACTOR)
return cropped
def image_identify():
image = utils.extract_image(request)
faces = dlib_api.detect_and_identify_faces(np.array(image))
return jsonify(utils.faces_to_list(faces))