def test_progress_iter():
with capture_output(display=False) as captured:
for i in display.ProgressBar(5):
out = captured.stdout
nt.assert_in('{0}/5'.format(i), out)
out = captured.stdout
nt.assert_in('5/5', out)
def progress(n, width=50):
"""Display progress bar for long running operations.
:param n: total number of steps to completion
:param width: width of the progress bar (only for the text version)
>>> import arlpy
>>> progress = arlpy.utils.progress(100)
>>> for j in range(100):
next(progress)
"""
if _notebook:
import IPython.display as _ipyd
p = _ipyd.ProgressBar(total=n)
did = str(_uuid.uuid4())
_ipyd.display(p, display_id=did)
for j in range(1, n):
p.progress = j
_ipyd.update_display(p, display_id=did)
yield j
_ipyd.update_display(_ipyd.HTML(''), display_id=did)
yield None
else:
_sys.stdout.write('%s|\n' % ('-' * width))
_sys.stdout.flush()
c = 0
for j in range(n):
c1 = int(width * (j + 1) / n)
if c1 > c:
_sys.stdout.write('>' * (c1 - c))
c = c1
if c == width:
_sys.stdout.write('\n')
_sys.stdout.flush()
yield j
def test_progress_iter():
with capture_output(display=False) as captured:
for i in display.ProgressBar(5):
out = captured.stdout
assert "{0}/5".format(i) in out
out = captured.stdout
assert "5/5" in out
def test_progress():
p = display.ProgressBar(10)
assert "0/10" in repr(p)
p.html_width = "100%"
p.progress = 5
assert (p._repr_html_() ==
"<progress style='width:100%' max='10' value='5'></progress>")
def test_progress():
p = display.ProgressBar(10)
nt.assert_in('0/10', repr(p))
p.html_width = '100%'
p.progress = 5
nt.assert_equal(
p._repr_html_(),
"<progress style='width:100%' max='10' value='5'></progress>")
def face_aligner(face_image_paths,
images_directory_output=None,
output_size=256,
limit_num_faces_=None,
limit_num_files_=None):
def write_faces_to_disk(directory, faces):
print("writing faces to disk...")
if os.path.exists(directory):
shutil.rmtree(directory)
print('creating output directory: %s' % (directory))
os.mkdir(directory)
for i in range(faces.shape[0]):
cv2.imwrite(''.join([directory, "%03d.jpg" % i]),
faces[i, :, :, ::-1])
print("wrote %d faces" % (faces.shape[0]))
if images_directory_output and images_directory_output[-1] != '/':
images_directory_output += '/'
faces = []
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(
'/Users/ost437/Documents/OneDrive/workspace/WorkspaceLiClipse/semi-adversarial-networks/shape_predictor_68_face_landmarks.dat'
)
max_val = len(
face_image_paths) if limit_num_files_ is None else limit_num_files_
pb = display.ProgressBar(max_val)
pb.display()
face_counter = 0
for img_idx, img_file_path in enumerate(face_image_paths):
# load the input image, resize it, and convert it to grayscale
image = cv2.imread(img_file_path)
if image is None:
continue
image = image[:, :, ::-1] # BGR to RGB
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# detect faces in the grayscale image
rects = detector(gray, 1)
if len(rects) > 0:
# loop over the face detections
for (i, rect) in enumerate(rects):
######### Align with facial features detector #########
shape = predictor(gray, rect) # get facial features
shape = np.array([(shape.part(j).x, shape.part(j).y)
for j in range(shape.num_parts)])
# center and scale face around mid point between eyes
center_eyes = shape[27].astype(np.int)
eyes_d = np.linalg.norm(shape[36] - shape[45])
face_size_x = int(eyes_d * 2.)
if face_size_x < 50: continue
# rotate to normalized angle
d = (shape[45] - shape[36]
) / eyes_d # normalized eyes-differnce vector (direction)
a = np.rad2deg(np.arctan2(d[1], d[0])) # angle
###scale_factor = float(output_size) / float(face_size_x * 2.) # scale to fit in output_size
scale_factor = float(output_size) / float(
face_size_x) # scale to fit in output_size
# rotation (around center_eyes) + scale transform
M = np.append(cv2.getRotationMatrix2D(
(center_eyes[0], center_eyes[1]), a, scale_factor),
[[0, 0, 1]],
axis=0)
# apply shift from center_eyes to middle of output_size
M1 = np.array([[1., 0., -center_eyes[0] + output_size / 2.],
[0., 1., -center_eyes[1] + output_size / 2.],
[0, 0, 1.]])
# concatenate transforms (rotation-scale + translation)
M = M1.dot(M)[:2]
# warp
try:
face = cv2.warpAffine(image,
M, (output_size, output_size),
borderMode=cv2.BORDER_REPLICATE)
except:
continue
face_counter += 1
face = cv2.resize(face, (output_size, output_size))
faces.append(face)
pb.progress = img_idx + 1
if limit_num_faces_ is not None and faces.shape[0] > limit_num_faces_:
break
if limit_num_files_ is not None and img_idx >= limit_num_files_:
break
faces = np.asarray(faces)
if images_directory_output:
write_faces_to_disk(images_directory_output, faces)
return faces
