def particles(image_id,
npart,
coord,
center_arr,
img,
sigma,
Nx,
Ny,
rad=0.3,
edgy=0):
all_part_mask = np.zeros((Ny, Nx))
#global sub_mask_details
#global annotation_id
for j in range(npart):
center = center_arr[j]
a = get_random_points(n=4, scale=30) + center
s, c = get_bezier_curve(a, rad=rad, edgy=edgy)
Density = rtnorm(0, 1, 0.92, 0.3)
p = path.Path(c)
particle_pixl = p.contains_points(coord)
particle_pixl = np.reshape(particle_pixl, (Ny, Nx)) - 0.0
particle = particle_pixl * Density
img = np.maximum(img, particle)
all_part_mask = np.maximum(all_part_mask, particle_pixl)
"""
sub_mask_details['image_id'].append(image_id+1)
sub_mask_details['category_id'].append(2)
sub_mask_details['pixl_arr'].append(particle_pixl)
sub_mask_details['id'].append(annotation_id)
annotation_id += 1
"""
return img, all_part_mask
def fiber(self, nfiber, center_arr):
alpha = np.zeros((nfiber, 1))
L = np.zeros((nfiber, 1))
W = np.zeros((nfiber, 1))
Density = np.zeros((nfiber, 1))
all_fiber_mask = np.zeros((self.Ny, self.Nx))
for i in range(nfiber):
alpha[i] = np.random.uniform(-math.pi / 2, math.pi / 2)
L[i] = np.random.normal(40, 5) # fiber length mu=40, sd = 5
W[i] = 4 # constant fiber width 4
Density[i] = rtnorm(0, 1, 0.92, 0.3)
for i in range(nfiber):
#four vertices of a single fiber
p1 = np.vstack((-W[i] / 2, -L[i] / 2))
p2 = np.vstack((W[i] / 2, -L[i] / 2))
p3 = np.vstack((W[i] / 2, L[i] / 2))
p4 = np.vstack((-W[i] / 2, L[i] / 2))
theta = alpha[i]
R = np.array([[math.cos(theta), -math.sin(theta)],
[math.sin(theta), math.cos(theta)]])
center = np.expand_dims(center_arr[i], axis=1)
# rotation of vertices and move to center
q1 = np.dot(R, p1) + center
q2 = np.dot(R, p2) + center
q3 = np.dot(R, p3) + center
q4 = np.dot(R, p4) + center
V_xy = np.hstack((q1, q2, q3, q4))
p = path.Path(np.transpose(V_xy))
fiber_pixl = p.contains_points(self.coord)
fiber_pixl = np.reshape(fiber_pixl, (self.Ny, self.Nx)) - 0.0
fiber = fiber_pixl * Density[i]
self.img = np.maximum(self.img, fiber)
all_fiber_mask = np.maximum(
all_fiber_mask,
fiber_pixl) # mask of all fibers in a single image
self.sub_mask_details['category_id'].append(
1) # 1 is for fiber category
self.sub_mask_details['pixl_arr'].append(fiber_pixl)
return self.img, all_fiber_mask # at this point self.img contains all the fibers per image
def fiber(image_id, nfiber, coord, center_arr, img, sigma, Nx,
Ny): # tag changed to image_id
#global sub_mask_details
#global annotation_id
alpha = np.zeros((nfiber, 1))
L = np.zeros((nfiber, 1))
W = np.zeros((nfiber, 1))
Density = np.zeros((nfiber, 1))
for i in range(nfiber):
alpha[i] = np.random.uniform(-math.pi / 2, math.pi / 2)
L[i] = np.random.normal(40, 5)
W[i] = 2 * 2
Density[i] = rtnorm(0, 1, 0.92, 0.3)
all_fiber_mask = np.zeros((Ny, Nx))
for i in range(nfiber):
p1 = np.vstack((-W[i] / 2, -L[i] / 2))
p2 = np.vstack((W[i] / 2, -L[i] / 2))
p3 = np.vstack((W[i] / 2, L[i] / 2))
p4 = np.vstack((-W[i] / 2, L[i] / 2))
theta = alpha[i]
R = np.array([[math.cos(theta), -math.sin(theta)],
[math.sin(theta), math.cos(theta)]])
center = np.expand_dims(center_arr[i], axis=1)
q1 = np.dot(R, p1) + center
q2 = np.dot(R, p2) + center
q3 = np.dot(R, p3) + center
q4 = np.dot(R, p4) + center
V_xy = np.hstack((q1, q2, q3, q4))
p = path.Path(np.transpose(V_xy))
fiber_pixl = p.contains_points(coord)
fiber_pixl = np.reshape(fiber_pixl, (Ny, Nx)) - 0.0
fiber = fiber_pixl * Density[i]
img = np.maximum(img, fiber)
all_fiber_mask = np.maximum(all_fiber_mask, fiber_pixl)
"""
sub_mask_details['image_id'].append(image_id+1)
sub_mask_details['category_id'].append(1)
sub_mask_details['pixl_arr'].append(fiber_pixl)
sub_mask_details['id'].append(annotation_id)
annotation_id += 1
"""
return img, all_fiber_mask
def particles(self, npart, center_arr, rad=0.3, edgy=0):
all_part_mask = np.zeros((self.Ny, self.Nx))
for j in range(npart):
center = center_arr[j]
a = get_random_points(n=4, scale=30) + center
s, c = get_bezier_curve(a, rad=rad, edgy=edgy)
Density = rtnorm(0, 1, 0.92, 0.3)
p = path.Path(c)
particle_pixl = p.contains_points(self.coord)
particle_pixl = np.reshape(particle_pixl, (self.Ny, self.Nx)) - 0.0
particle = particle_pixl * Density
self.img = np.maximum(self.img, particle)
all_part_mask = np.maximum(all_part_mask, particle_pixl)
self.sub_mask_details['category_id'].append(
2) # 2 is for particle category
self.sub_mask_details['pixl_arr'].append(particle_pixl)
return self.img, all_part_mask, self.sub_mask_details # at this point self.img contains all fibers & particles per image