def chen_get_bbx(txt_path):
coordinate = np.loadtxt(txt_path, comments='\n', delimiter=',')
y, x = coordinate.T
max_x = max(x)
min_x = min(x)
max_y = max(y)
min_y = min(y) - 18
points = np.array([[min_x, min_y], [min_x, max_y], [max_x, max_y],
[min_x, max_y]])
graph = PointDirectedGraph(points, adjacency_matrix)
bbx = graph.bounding_box()
return bbx, coordinate
def get_bbx(txt_path):
coordinate = get_coordinate(txt_path)
y, x = coordinate.T
max_x = max(x)
min_x = min(x)
max_y = max(y)
min_y = min(y) - 18
points = np.array([[min_x, min_y], [min_x, max_y], [max_x, max_y],
[min_x, max_y]])
adjacency_matrix = np.array([[0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1],
[1, 0, 0, 0]])
graph = PointDirectedGraph(points, adjacency_matrix)
bbx = graph.bounding_box()
return bbx
def fit(path_to_images, path_to_test, c, r, w):
training_images = []
for img in print_progress(mio.import_images(path_to_images, verbose=True)):
# convert to greyscale
if img.n_channels == 3:
img = img.as_greyscale()
# crop to landmarks bounding box with an extra 20% padding
img = img.crop_to_landmarks_proportion(0.2)
# rescale image if its diagonal is bigger than 400 pixels
d = img.diagonal()
if d > 1000:
img = img.rescale(1000.0 / d)
# define a TriMesh which will be useful for Piecewise Affine Warp of HolisticAAM
# labeller(img, 'PTS', face_ibug_68_to_face_ibug_68_trimesh)
# append to list
training_images.append(img)
# ## Training ribcage - Patch
# from menpofit.aam import PatchAAM
# from menpo.feature import fast_dsift
#
# patch_aam = PatchAAM(training_images, group='PTS', patch_shape=[(15, 15), (23, 23)],
# diagonal=500, scales=(0.5, 1.0), holistic_features=fast_dsift,
# max_shape_components=20, max_appearance_components=150,
# verbose=True)
## Training ribcage - Holistic
patch_aam = HolisticAAM(training_images,
group='PTS',
diagonal=500,
scales=(0.5, 1.0),
holistic_features=fast_dsift,
verbose=True,
max_shape_components=20,
max_appearance_components=150)
## Prediction
fitter = LucasKanadeAAMFitter(patch_aam,
lk_algorithm_cls=WibergInverseCompositional,
n_shape=[5, 20],
n_appearance=[30, 150])
image = mio.import_image(path_to_test)
#initialize box
adjacency_matrix = np.array([
[0, 1, 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1],
[1, 0, 0, 0],
])
# points = np.array([[0,0], [0,2020], [2020, 2020], [2020, 0]])
points = np.array([[r - w / 2, c - w / 2], [r - w / 2, c + w / 2],
[r + w / 2, c + w / 2], [r + w / 2, c - w / 2]])
graph = PointDirectedGraph(points, adjacency_matrix)
box = graph.bounding_box()
# initial bbox
initial_bbox = box
# fit image
result = fitter.fit_from_bb(image, initial_bbox, max_iters=[15, 5])
pts = result.final_shape.points
return pts
