def of_dataset(folder="testset", model=None, view=False):
'''measure the error across the given dataset,
it compares the measured points with the annotated ground truth,
optionally you can [view] the results'''
assert (model)
# load face and landmark detectors
utils.load_shape_predictor(model)
# utils.init_face_detector(True, 150)
# init average-error
err = 0
num = 0
for img, lmarks, path in utils.ibug_dataset(folder):
# detections
face = utils.prominent_face(utils.detect_faces(img, detector="dlib"))
measured = utils.detect_landmarks(img, face)
# get error
num += 1
err += normalized_root_mean_square(lmarks, measured)
# results:
if view is True:
utils.draw_rect(img, face, color=Colors.yellow)
utils.draw_points(img, lmarks, color=Colors.green)
utils.draw_points(img, measured, color=Colors.red)
utils.show_image(utils.show_properly(utils.crop_image(img, face)))
print(err, num, err / num)
print("average NRMS Error for {} is {}".format(folder, err / num))
def build_trainset(input_dir="data", output_dir="trainset", win_size=321):
'''scan the input folder and put every image with annotations
output folder'''
utils.init_face_detector(True, win_size)
utils.load_shape_predictor("dlib/shape_predictor_68_face_landmarks.dat")
count = int(utils.count_files_inside(output_dir) / 8)
window = "window"
cv2.namedWindow(window)
for face, box in utils.faces_inside(input_dir, 1, True):
face_copy = face.copy()
face_rect = region(face)
# detections
points = utils.detect_landmarks(face, face_rect)
utils.draw_points(face, points)
# show face
while (1):
h, w = face.shape[:2]
if h > 0 and w > 0:
cv2.imshow(window, show_properly(face))
else:
break
key = cv2.waitKey(20) & 0Xff
if key == Keys.ESC:
break # skip current face
elif key == Keys.S:
path = f"{output_dir}/face{count}"
# save image
cv2.imwrite(f"{path}.jpg", face_copy)
# save annotation relative to the current face
Annotation(f"{path}.ann", face_rect, points).save()
# generate and save augumetations
array = augment_data(face_copy, face_rect, points)
for i, x in enumerate(array):
# save image x[0]
cv2.imwrite(f"{path}_{i + 1}.jpg", x[0])
# save annotations
Annotation(f"{path}_{i + 1}.ann", face_rect, x[1]).save()
count = count + 1
break
elif key == Keys.Q:
# quit program
return cv2.destroyAllWindows()
cv2.destroyAllWindows()
def compare_models(folder="testset", m1=None, m2=None, view=False):
'''compare the [m1] shape_predictor aganist the [m2] model,
optionally you can [view] the results'''
assert (m1 and m2)
utils.init_face_detector(True, 150)
# load models
utils.load_shape_predictor(m2)
sp_m2 = utils.shape_predictor
utils.load_shape_predictor(m1)
sp_m1 = utils.shape_predictor
# init error
err = 0
num = 0
for face, region in utils.faces_inside(folder):
h, w = face.shape[:2]
if h == 0 or w == 0:
continue
box = utils.Region(0, 0, region.width, region.height)
# detect landmarks
utils.shape_predictor = sp_m1
lmarks_m1 = utils.detect_landmarks(face, box)
utils.shape_predictor = sp_m2
lmarks_m2 = utils.detect_landmarks(face, box)
# update error:
num += 1
# err += normalized_root_mean_square(lmarks_m1, lmarks_m2)
# results:
if view is True:
utils.draw_points(face, lmarks_m1, color=Colors.green)
utils.draw_points(face, lmarks_m2, color=Colors.red)
utils.show_image(utils.show_properly(face))
if num != 0:
err /= num
print("the NRMSE of m1 aganist m2 is {}".format(err))
def test(folder="testset", model="dlib/shape_predictor_68_face_landmarks.dat"):
utils.init_face_detector(True, 150)
utils.load_shape_predictor(model)
my_sp = utils.shape_predictor
dlib_sp = dlib.shape_predictor(
"dlib/shape_predictor_68_face_landmarks.dat")
for face, r in utils.faces_inside(folder):
box = region(face)
utils.shape_predictor = my_sp
lmarks0 = utils.detect_landmarks(face, box)
utils.shape_predictor = dlib_spq
lmarks1 = utils.detect_landmarks(face, box)
# draw results
utils.draw_points(face, lmarks1, color=Colors.green)
utils.draw_points(face, lmarks0, color=Colors.red)
utils.show_image(show_properly(face))
def test_augment():
utils.init_face_detector(True, 321)
utils.load_shape_predictor("dlib/shape_predictor_68_face_landmarks.dat")
for img in utils.images_inside("trainset"):
points = utils.detect_landmarks(img, region(img))
angle = 30
h, w = img.shape[:2]
center = (w / 2, h / 2)
# 30 degree rotation
rot1 = utils.rotate_image(img, angle)
rot_pts1 = utils.rotate_landmarks(points, center, angle)
# -30 degree rotatation
rot2 = utils.rotate_image(img, -angle)
rot_pts2 = utils.rotate_landmarks(points, center, -angle)
# mirroring
mir = utils.flip_image(img)
mir_pts = utils.detect_landmarks(mir, region(mir))
utils.draw_points(img, points)
utils.draw_points(rot1, rot_pts1, color=Colors.cyan)
utils.draw_points(rot2, rot_pts2, color=Colors.purple)
utils.draw_points(mir, mir_pts, color=Colors.green)
while True:
cv2.imshow("image", img)
cv2.imshow("mirrored", mir)
cv2.imshow("rotated30", rot1)
cv2.imshow("rotated-30", rot2)
key = cv2.waitKey(20) & 0Xff
if key == Keys.ESC:
break
elif key == Keys.Q:
return cv2.destroyAllWindows()
def another_test():
utils.load_shape_predictor("dlib/shape_predictor_68_face_landmarks.dat")
for img, p in utils.images_inside("uffa"):
fast = cv2.FastFeatureDetector_create()
# kp = fast.detect(img, None)
# draws:
face = utils.detect_faces(img, detector="dlib")[0]
# utils.draw_rect(img, face, color=Colors.green)
pts = utils.detect_landmarks(img, face)
pts = [pts[0], pts[3], pts[6], pts[10], pts[20], pts[22], pts[35]]
# utils.draw_points(img, pts)
# img = cv2.drawKeypoints(img, kp, None, color=Colors.cyan)
keypoints = []
for p in pts:
roi = Region(p[0] - 10, p[1] - 10, 20, 20)
patch = utils.crop_image(img, roi)
keypoints.append(fast.detect(patch, None))
# for kp in keypoints[2]:
# print(kp)
# # img = cv2.drawKeypoints(img, kp, None, color=Colors.cyan)
for p in pts:
for kp in keypoints:
for k in kp:
x = int(k.pt[0] + p[0])
y = int(k.pt[1] + p[1])
utils.draw_point(img, x, y, radius=1)
while True:
cv2.imshow("window", show_properly(img))
key = cv2.waitKey(20) & 0Xff
if key == Keys.ESC:
break
y1, x1, y2, x2 = int(y - 0.5 * edge), int(x - 0.5 * edge), int(
y + 0.5 * edge), int(x + 0.5 * edge)
if y1 < 0 or x1 < 0 or y2 > ishape[0] or x2 > ishape[1]:
continue
iobject = pix[y1:y2, x1:x2, :]
iobject = transform.resize(image=iobject, output_shape=[112, 112])
igreyobject = np.mean(iobject, axis=-1, keepdims=True)
igreyobject = igreyobject / np.max(igreyobject)
igreyobject = igreyobject * 255
igreyobject = np.array(igreyobject, dtype='int32')
ya, xa, yb, xb, yc, xc, yd, xd, ye, xe = detect_landmarks(
interpreter=interpreter_,
input_details=input_details_,
output_details=output_details_,
pix=igreyobject,
ishape=[112, 112, 1]) # (5, h, w)
wider_iobject = pix[y1 - int(0.2 * (y2 - y1)):y2 + int(0.2 *
(y2 - y1)), x1 -
int(0.2 * (x2 - x1)):x2 + int(0.2 * (x2 - x1)), :]
if ya != 0 and yb != 0 and yc != 0 and yd != 0 and ye != 0 and wider_iobject.shape[
0] == wider_iobject.shape[1] and wider_iobject.shape[0] != 0:
wider_iobject = transform.resize(image=wider_iobject,
output_shape=[112, 112])
cv2.imwrite(output_path + '/objects/' + str(face_idx) + '.jpg',
np.array(wider_iobject, dtype='uint8'))
face_idx += 1
eye = ya != 0 or yb != 0