def facial_landmarks_torch(alpha, delta, w, t, LM=True):
"""
Construct facial landmarks from facial geometry latent parameters alpha, delta and object transformation w, t.
:param alpha: array, 30dim
:param delta: array, 20dim
:param w: rotation angles around x,y, z. Given as list [theta_x, theta_y, theta_z].
:param t: translation in x,y,z space. Given as list [translation_x, translation_y, translation_z]
:return:
"""
landmarks_idx = np.loadtxt("Landmarks68_model2017-1_face12_nomouth.anl",
dtype=int)
pca = read_pca_model()
if LM:
G = get_face_point_cloud_torch(pca, alpha, delta)[landmarks_idx].t()
else:
G = get_face_point_cloud_torch(pca, alpha, delta).t()
G_h = [G, torch.ones(G.shape[1]).view((1, -1))]
G_h = torch.cat(G_h, dim=0)
# get T matrix
T = torch.eye(4)
T[:3, :3] = rotation_matrix(
w
) #rotation_tensor(w, 1)#get_rotation_matrix_torch(w) #torch.tensor([[1, 0, 0], [0, 1, 0], [0, 0, 1]])#
T[:3, 3] = t
# Get V and P matrices
W = 172
H = 162
image_aspect_ratio = W / H
angle = 10
near = .1
far = 10
right, left, top, bottom = get_perspective(image_aspect_ratio, angle, near,
far)
V = get_V(right, left, top, bottom)
[V] = list(map(torch.from_numpy, [V]))
V = V.to(dtype=torch.float32)
n, f, t, b = near, far, top, bottom
P = torch.Tensor([[(2 * n) / (t - b), 0, 0, 0],
[0, (2 * n) / (t - b), 0, 0],
[0, 0, -(f + n) / (f - n), -(2 * f * n) / (f - n)],
[0, 0, -1, 0]])
i = V @ P @ T @ G_h
# h**o to cartesian
i = i / i[3, :].clone()
# two-dimensional
return i[:2, :].t()
def facial_landmarks(alpha, delta, w, t):
"""
Construct facial landmarks from facial geometry latent parameters alpha, delta and object transformation w, t.
:param alpha: array, 30dim
:param delta: array, 20dim
:param w: rotation angles around x,y, z. Given as list [theta_x, theta_y, theta_z].
:param t: translation in x,y,z space. Given as list [translation_x, translation_y, translation_z]
:return:
"""
landmarks_idx = np.loadtxt("Landmarks68_model2017-1_face12_nomouth.anl",
dtype=int)
pca = read_pca_model()
G = get_face_point_cloud(pca, alpha, delta)[landmarks_idx].T
G_h = np.append(G, np.ones(G.shape[1]).reshape((1, -1)), axis=0)
# get T matrix
T = np.eye(4)
T[:3, :3] = get_rotation_matrix(w)
T[:3, 3] = t
# Get V and P matrices
W = 255
H = 255
image_aspect_ratio = W / H
angle = 10
near = 300
far = 2000
right, left, top, bottom = get_perspective(image_aspect_ratio, angle, near,
far)
V = get_V(right, left, top, bottom)
P = get_P(near, far, right, left, top, bottom)
i = V @ P @ T @ G_h
# cartesian
i /= i[3, :]
# two-dimensional
return i[:2, :]
def main():
## def texturing():
im = Image.open('faces_sparser_sampling.gif')
frames = np.array([
np.array(frame.copy().convert('RGB').getdata(),
dtype=np.uint8).reshape(frame.size[1], frame.size[0], 3)
for frame in ImageSequence.Iterator(im)
])
image = frames[0] #this is the image
target_landmarks = torch.from_numpy(
detect_landmark(image)).to(dtype=torch.float)
alpha, delta, w, t = [
i for i in np.load("best_params.npy", allow_pickle=True)
]
estimated_all = get_final_landmarks(alpha, delta, w, t, target_landmarks)
#print(estimated_landmarks.shape)
print(estimated_all.shape)
plt.imshow(image)
plt.show()
plt.scatter(estimated_all.T[0], estimated_all.T[1])
plt.imshow(image)
plt.show()
plt.scatter(estimated_landmarks.T[0], estimated_landmarks.T[1])
plt.imshow(image)
plt.show()
tex = find_corresponding_texture(estimated_all, image)
# get 3D point cloud
p = read_pca_model()
G = get_face_point_cloud(p, alpha.detach().numpy(), delta.detach().numpy())
mesh = trimesh.base.Trimesh(vertices=G, faces=triangles, vertex_colors=tex)
mesh_to_png("mesh.png", mesh)
t = mesh_to_png("mesh.png", mesh)
return t
def rotate_face(angles):
"""
Task 3.1
:param angles: list of angles. each angle has three entries [theta_x, theta_y, theta_z]
:return:
"""
# sample face
pca = read_pca_model()
G = random_face_point_cloud(pca).T
# transform to homogeneous coordinates
G_h = np.append(G, np.ones(G.shape[1]).reshape((1, -1)), axis=0)
for w in angles:
w = np.array(w)
# get T matrix for only rotation
T = np.eye(4)
T[:3, :3] = rotation_matrix(w, is_numpy=True)
# save resulting rotated face
mesh = Mesh(vertices=(T @ G_h)[:3].T, colors=mean_tex, triangles=triangles)
mesh_to_png("./results/rotation/"+str(w)+".png", mesh)
return
def facial_landmarks(alpha, delta, w, t):
"""
Construct facial landmarks from facial geometry latent parameters alpha, delta and object transformation w, t.
:param alpha: array, 30dim
:param delta: array, 20dim
:param w: rotation angles around x,y, z. Given as list [theta_x, theta_y, theta_z].
:param t: translation in x,y,z space. Given as list [translation_x, translation_y, translation_z]
:return:
"""
landmarks_idx = np.loadtxt("./models/Landmarks68_model2017-1_face12_nomouth.anl", dtype=int)
pca = read_pca_model()
G = get_face_point_cloud(pca, alpha, delta).reshape((-1, 3))[landmarks_idx].T
G_h = np.append(G, np.ones(G.shape[1]).reshape((1, -1)), axis=0)
# get T matrix
T = np.eye(4)
T[:3, :3] = rotation_matrix(w, is_numpy=True)
T[:3, 3] = t
# Get V and P matrices
W = H = 255
# angle 10
P = perspective_projection_matrix(W, H, 300, 2000, is_numpy=True)
V = viewport_matrix(right=W, left=0, top=H, bottom=0, is_numpy=True)
i = V @ P @ T @ G_h
# cartesian
i /= i[3,:]
# two-dimensional
return i[:2, :]
def read_pca_model_torch():
p = read_pca_model()
for i in p:
p[i] = torch.from_numpy(p[i])
return p