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gen_ood_lesion.py
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gen_ood_lesion.py
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### Tumor Generateion
import random
import cv2
import torch
import elasticdeform
from PIL import Image
import numpy as np
from scipy.ndimage import gaussian_filter
def to_PIL(tensor):
tensor = np.stack([tensor, tensor, tensor], axis=-1)
return Image.fromarray((tensor*255.0).astype(np.uint8))
def to_PIL_raw(tensor):
return Image.fromarray((tensor*255.0).astype(np.uint8))
def generate_prob_function(mask_shape):
sigma = np.random.uniform(3,15)
# uniform noise generate
a = np.random.uniform(0, 1, size=(mask_shape[0],mask_shape[1],mask_shape[2]))
# Gaussian filter
# this taks some time
a_2 = gaussian_filter(a, sigma=sigma)
scale = np.random.uniform(0.19, 0.21)
base = np.random.uniform(0.04, 0.06)
a = scale * (a_2 - np.min(a_2)) / (np.max(a_2) - np.min(a_2)) + base
return a
# first generate 5*200*200*200
def get_texture(mask_shape):
# get the prob function
a = generate_prob_function(mask_shape)
# sample once
random_sample = np.random.uniform(0, 1, size=(mask_shape[0],mask_shape[1],mask_shape[2]))
# if a(x) > random_sample(x), set b(x) = 1
b = (a > random_sample).astype(float) # int type can't do Gaussian filter
# Gaussian filter
if np.random.uniform() < 0.7:
sigma_b = np.random.uniform(3, 5)
else:
sigma_b = np.random.uniform(5, 8)
# this takes some time
b2 = gaussian_filter(b, sigma_b)
# Scaling and clipping
u_0 = np.random.uniform(0.5, 0.55)
threshold_mask = b2 > 0.12 # this is for calculte the mean_0.2(b2)
beta = u_0 / (np.sum(b2 * threshold_mask) / threshold_mask.sum())
Bj = np.clip(beta*b2, 0, 1) # 目前是0-1区间
return Bj
# here we want to get predefined texutre:
def get_predefined_texture(mask_shape, sigma_a, sigma_b):
# uniform noise generate
a = np.random.uniform(0, 1, size=(mask_shape[0],mask_shape[1],mask_shape[2]))
a_2 = gaussian_filter(a, sigma=sigma_a)
scale = np.random.uniform(0.19, 0.21)
base = np.random.uniform(0.04, 0.06)
a = scale * (a_2 - np.min(a_2)) / (np.max(a_2) - np.min(a_2)) + base
# sample once
random_sample = np.random.uniform(0, 1, size=(mask_shape[0],mask_shape[1],mask_shape[2]))
b = (a > random_sample).astype(float) # int type can't do Gaussian filter
b = gaussian_filter(b, sigma_b)
# Scaling and clipping
u_0 = np.random.uniform(0.5, 0.55)
threshold_mask = b > 0.12 # this is for calculte the mean_0.2(b2)
beta = u_0 / (np.sum(b * threshold_mask) / threshold_mask.sum())
Bj = np.clip(beta*b, 0, 1) # 目前是0-1区间
return Bj
# Step 1: Random select (numbers) location for tumor.
def random_select(mask_scan):
# we first find z index and then sample point with z slice
z_start, z_end = np.where(np.any(mask_scan, axis=(0, 1)))[0][[0, -1]]
# we need to strict number z's position (0.3 - 0.7 in the middle of liver)
z = int(0.5 * (z_end - z_start)) + z_start
liver_mask = mask_scan[..., z]
# erode the mask (we don't want the edge points)
kernel = np.ones((5,5), dtype=np.uint8)
liver_mask = cv2.erode(liver_mask, kernel, iterations=1)
coordinates = np.argwhere(liver_mask == 1)
random_index = np.random.randint(0, len(coordinates))
xyz = coordinates[random_index].tolist() # get x,y
xyz.append(z)
potential_points = xyz
return potential_points
# Step 2 : generate the ellipsoid
def get_ellipsoid(x, y, z):
""""
x, y, z is the radius of this ellipsoid in x, y, z direction respectly.
"""
sh = (4*x, 4*y, 4*z)
out = np.zeros(sh, int)
aux = np.zeros(sh)
radii = np.array([x, y, z])
com = np.array([2*x, 2*y, 2*z]) # center point
# calculate the ellipsoid
bboxl = np.floor(com-radii).clip(0,None).astype(int)
bboxh = (np.ceil(com+radii)+1).clip(None, sh).astype(int)
roi = out[tuple(map(slice,bboxl,bboxh))]
roiaux = aux[tuple(map(slice,bboxl,bboxh))]
logrid = *map(np.square,np.ogrid[tuple(
map(slice,(bboxl-com)/radii,(bboxh-com-1)/radii,1j*(bboxh-bboxl)))]),
dst = (1-sum(logrid)).clip(0,None)
mask = dst>roiaux
roi[mask] = 1
np.copyto(roiaux,dst,where=mask)
return out
def get_fixed_geo(mask_scan, tumor_type, num_tumor):
enlarge_x, enlarge_y, enlarge_z = 160, 160, 160
geo_mask = np.zeros((mask_scan.shape[0] + enlarge_x, mask_scan.shape[1] + enlarge_y, mask_scan.shape[2] + enlarge_z), dtype=np.int8)
# texture_map = np.zeros((mask_scan.shape[0] + enlarge_x, mask_scan.shape[1] + enlarge_y, mask_scan.shape[2] + enlarge_z), dtype=np.float16)
tiny_radius, small_radius, medium_radius, large_radius = 4, 8, 16, 32
if tumor_type == 'tiny':
for _ in range(num_tumor):
# Tiny tumor
x = random.randint(int(0.75*tiny_radius), int(1.25*tiny_radius))
y = random.randint(int(0.75*tiny_radius), int(1.25*tiny_radius))
z = random.randint(int(0.75*tiny_radius), int(1.25*tiny_radius))
sigma = random.uniform(0.5,1)
geo = get_ellipsoid(x, y, z)
geo = elasticdeform.deform_random_grid(geo, sigma=sigma, points=3, order=0, axis=(0,1))
geo = elasticdeform.deform_random_grid(geo, sigma=sigma, points=3, order=0, axis=(1,2))
geo = elasticdeform.deform_random_grid(geo, sigma=sigma, points=3, order=0, axis=(0,2))
point = random_select(mask_scan)
new_point = [point[0] + enlarge_x//2, point[1] + enlarge_y//2, point[2] + enlarge_z//2]
x_low, x_high = new_point[0] - geo.shape[0]//2, new_point[0] + geo.shape[0]//2
y_low, y_high = new_point[1] - geo.shape[1]//2, new_point[1] + geo.shape[1]//2
z_low, z_high = new_point[2] - geo.shape[2]//2, new_point[2] + geo.shape[2]//2
# paste small tumor geo into test sample
geo_mask[x_low:x_high, y_low:y_high, z_low:z_high] += geo
if tumor_type == 'small':
for _ in range(num_tumor):
# Small tumor
x = random.randint(int(0.75*small_radius), int(1.25*small_radius))
y = random.randint(int(0.75*small_radius), int(1.25*small_radius))
z = random.randint(int(0.75*small_radius), int(1.25*small_radius))
sigma = random.randint(1, 2)
geo = get_ellipsoid(x, y, z)
geo = elasticdeform.deform_random_grid(geo, sigma=sigma, points=3, order=0, axis=(0,1))
geo = elasticdeform.deform_random_grid(geo, sigma=sigma, points=3, order=0, axis=(1,2))
geo = elasticdeform.deform_random_grid(geo, sigma=sigma, points=3, order=0, axis=(0,2))
# texture = get_texture((4*x, 4*y, 4*z))
point = random_select(mask_scan)
new_point = [point[0] + enlarge_x//2, point[1] + enlarge_y//2, point[2] + enlarge_z//2]
x_low, x_high = new_point[0] - geo.shape[0]//2, new_point[0] + geo.shape[0]//2
y_low, y_high = new_point[1] - geo.shape[1]//2, new_point[1] + geo.shape[1]//2
z_low, z_high = new_point[2] - geo.shape[2]//2, new_point[2] + geo.shape[2]//2
# paste small tumor geo into test sample
geo_mask[x_low:x_high, y_low:y_high, z_low:z_high] += geo
# texture_map[x_low:x_high, y_low:y_high, z_low:z_high] = texture
if tumor_type == 'medium':
for _ in range(num_tumor):
# medium tumor
x = random.randint(int(0.75*medium_radius), int(1.25*medium_radius))
y = random.randint(int(0.75*medium_radius), int(1.25*medium_radius))
z = random.randint(int(0.75*medium_radius), int(1.25*medium_radius))
sigma = random.randint(3, 6)
geo = get_ellipsoid(x, y, z)
geo = elasticdeform.deform_random_grid(geo, sigma=sigma, points=3, order=0, axis=(0,1))
geo = elasticdeform.deform_random_grid(geo, sigma=sigma, points=3, order=0, axis=(1,2))
geo = elasticdeform.deform_random_grid(geo, sigma=sigma, points=3, order=0, axis=(0,2))
# texture = get_texture((4*x, 4*y, 4*z))
point = random_select(mask_scan)
new_point = [point[0] + enlarge_x//2, point[1] + enlarge_y//2, point[2] + enlarge_z//2]
x_low, x_high = new_point[0] - geo.shape[0]//2, new_point[0] + geo.shape[0]//2
y_low, y_high = new_point[1] - geo.shape[1]//2, new_point[1] + geo.shape[1]//2
z_low, z_high = new_point[2] - geo.shape[2]//2, new_point[2] + geo.shape[2]//2
# paste medium tumor geo into test sample
geo_mask[x_low:x_high, y_low:y_high, z_low:z_high] += geo
# texture_map[x_low:x_high, y_low:y_high, z_low:z_high] = texture
if tumor_type == 'large':
for _ in range(num_tumor):
# Large tumor
x = random.randint(int(0.75*large_radius), int(1.25*large_radius))
y = random.randint(int(0.75*large_radius), int(1.25*large_radius))
z = random.randint(int(0.75*large_radius), int(1.25*large_radius))
sigma = random.randint(5, 10)
geo = get_ellipsoid(x, y, z)
geo = elasticdeform.deform_random_grid(geo, sigma=sigma, points=3, order=0, axis=(0,1))
geo = elasticdeform.deform_random_grid(geo, sigma=sigma, points=3, order=0, axis=(1,2))
geo = elasticdeform.deform_random_grid(geo, sigma=sigma, points=3, order=0, axis=(0,2))
# texture = get_texture((4*x, 4*y, 4*z))
point = random_select(mask_scan)
new_point = [point[0] + enlarge_x//2, point[1] + enlarge_y//2, point[2] + enlarge_z//2]
x_low, x_high = new_point[0] - geo.shape[0]//2, new_point[0] + geo.shape[0]//2
y_low, y_high = new_point[1] - geo.shape[1]//2, new_point[1] + geo.shape[1]//2
z_low, z_high = new_point[2] - geo.shape[2]//2, new_point[2] + geo.shape[2]//2
# paste small tumor geo into test sample
geo_mask[x_low:x_high, y_low:y_high, z_low:z_high] += geo
# texture_map[x_low:x_high, y_low:y_high, z_low:z_high] = texture
if tumor_type == "mix":
# tiny
num_tumor = random.randint(3,10)
for _ in range(num_tumor):
# Tiny tumor
x = random.randint(int(0.75*tiny_radius), int(1.25*tiny_radius))
y = random.randint(int(0.75*tiny_radius), int(1.25*tiny_radius))
z = random.randint(int(0.75*tiny_radius), int(1.25*tiny_radius))
sigma = random.uniform(0.5,1)
geo = get_ellipsoid(x, y, z)
geo = elasticdeform.deform_random_grid(geo, sigma=sigma, points=3, order=0, axis=(0,1))
geo = elasticdeform.deform_random_grid(geo, sigma=sigma, points=3, order=0, axis=(1,2))
geo = elasticdeform.deform_random_grid(geo, sigma=sigma, points=3, order=0, axis=(0,2))
point = random_select(mask_scan)
new_point = [point[0] + enlarge_x//2, point[1] + enlarge_y//2, point[2] + enlarge_z//2]
x_low, x_high = new_point[0] - geo.shape[0]//2, new_point[0] + geo.shape[0]//2
y_low, y_high = new_point[1] - geo.shape[1]//2, new_point[1] + geo.shape[1]//2
z_low, z_high = new_point[2] - geo.shape[2]//2, new_point[2] + geo.shape[2]//2
# paste small tumor geo into test sample
geo_mask[x_low:x_high, y_low:y_high, z_low:z_high] += geo
# small
num_tumor = random.randint(5,10)
for _ in range(num_tumor):
# Small tumor
x = random.randint(int(0.75*small_radius), int(1.25*small_radius))
y = random.randint(int(0.75*small_radius), int(1.25*small_radius))
z = random.randint(int(0.75*small_radius), int(1.25*small_radius))
sigma = random.randint(1, 2)
geo = get_ellipsoid(x, y, z)
geo = elasticdeform.deform_random_grid(geo, sigma=sigma, points=3, order=0, axis=(0,1))
geo = elasticdeform.deform_random_grid(geo, sigma=sigma, points=3, order=0, axis=(1,2))
geo = elasticdeform.deform_random_grid(geo, sigma=sigma, points=3, order=0, axis=(0,2))
# texture = get_texture((4*x, 4*y, 4*z))
point = random_select(mask_scan)
new_point = [point[0] + enlarge_x//2, point[1] + enlarge_y//2, point[2] + enlarge_z//2]
x_low, x_high = new_point[0] - geo.shape[0]//2, new_point[0] + geo.shape[0]//2
y_low, y_high = new_point[1] - geo.shape[1]//2, new_point[1] + geo.shape[1]//2
z_low, z_high = new_point[2] - geo.shape[2]//2, new_point[2] + geo.shape[2]//2
# paste small tumor geo into test sample
geo_mask[x_low:x_high, y_low:y_high, z_low:z_high] += geo
# texture_map[x_low:x_high, y_low:y_high, z_low:z_high] = texture
# medium
num_tumor = random.randint(2, 5)
for _ in range(num_tumor):
# medium tumor
x = random.randint(int(0.75*medium_radius), int(1.25*medium_radius))
y = random.randint(int(0.75*medium_radius), int(1.25*medium_radius))
z = random.randint(int(0.75*medium_radius), int(1.25*medium_radius))
sigma = random.randint(3, 6)
geo = get_ellipsoid(x, y, z)
geo = elasticdeform.deform_random_grid(geo, sigma=sigma, points=3, order=0, axis=(0,1))
geo = elasticdeform.deform_random_grid(geo, sigma=sigma, points=3, order=0, axis=(1,2))
geo = elasticdeform.deform_random_grid(geo, sigma=sigma, points=3, order=0, axis=(0,2))
# texture = get_texture((4*x, 4*y, 4*z))
point = random_select(mask_scan)
new_point = [point[0] + enlarge_x//2, point[1] + enlarge_y//2, point[2] + enlarge_z//2]
x_low, x_high = new_point[0] - geo.shape[0]//2, new_point[0] + geo.shape[0]//2
y_low, y_high = new_point[1] - geo.shape[1]//2, new_point[1] + geo.shape[1]//2
z_low, z_high = new_point[2] - geo.shape[2]//2, new_point[2] + geo.shape[2]//2
# paste medium tumor geo into test sample
geo_mask[x_low:x_high, y_low:y_high, z_low:z_high] += geo
# texture_map[x_low:x_high, y_low:y_high, z_low:z_high] = texture
# large
num_tumor = random.randint(1,3)
for _ in range(num_tumor):
# Large tumor
x = random.randint(int(0.75*large_radius), int(1.25*large_radius))
y = random.randint(int(0.75*large_radius), int(1.25*large_radius))
z = random.randint(int(0.75*large_radius), int(1.25*large_radius))
sigma = random.randint(5, 10)
geo = get_ellipsoid(x, y, z)
geo = elasticdeform.deform_random_grid(geo, sigma=sigma, points=3, order=0, axis=(0,1))
geo = elasticdeform.deform_random_grid(geo, sigma=sigma, points=3, order=0, axis=(1,2))
geo = elasticdeform.deform_random_grid(geo, sigma=sigma, points=3, order=0, axis=(0,2))
# texture = get_texture((4*x, 4*y, 4*z))
point = random_select(mask_scan)
new_point = [point[0] + enlarge_x//2, point[1] + enlarge_y//2, point[2] + enlarge_z//2]
x_low, x_high = new_point[0] - geo.shape[0]//2, new_point[0] + geo.shape[0]//2
y_low, y_high = new_point[1] - geo.shape[1]//2, new_point[1] + geo.shape[1]//2
z_low, z_high = new_point[2] - geo.shape[2]//2, new_point[2] + geo.shape[2]//2
# paste small tumor geo into test sample
geo_mask[x_low:x_high, y_low:y_high, z_low:z_high] += geo
# texture_map[x_low:x_high, y_low:y_high, z_low:z_high] = texture
geo_mask = geo_mask[enlarge_x//2:-enlarge_x//2, enlarge_y//2:-enlarge_y//2, enlarge_z//2:-enlarge_z//2]
# texture_map = texture_map[enlarge_x//2:-enlarge_x//2, enlarge_y//2:-enlarge_y//2, enlarge_z//2:-enlarge_z//2]
geo_mask = (geo_mask * mask_scan) >=1
return geo_mask, new_point[2] - enlarge_z//2
def get_tumor(volume_scan, mask_scan, tumor_type, texture, num_tumor):
geo_mask, z = get_fixed_geo(mask_scan, tumor_type, num_tumor)
sigma = np.random.uniform(1, 2)
difference = np.random.uniform(30, 185) / 255
# blur the boundary
geo_blur = gaussian_filter(geo_mask*1.0, sigma)
abnormally = (volume_scan + texture * geo_blur * difference) * mask_scan
# abnormally = (volume_scan - texture * geo_mask * difference) * mask_scan
abnormally_full = volume_scan * (1 - mask_scan) + abnormally
abnormally_mask = mask_scan + geo_mask
return abnormally_full, geo_blur, z
def SynthesisTumor(volume_scan, mask_scan, tumor_type, texture, num_tumor):
# for speed_generate_tumor, we only send the liver part into the generate program
x_start, x_end = np.where(np.any(mask_scan, axis=(1, 2)))[0][[0, -1]]
y_start, y_end = np.where(np.any(mask_scan, axis=(0, 2)))[0][[0, -1]]
z_start, z_end = np.where(np.any(mask_scan, axis=(0, 1)))[0][[0, -1]]
# shrink the boundary
x_start, x_end = max(0, x_start+1), min(mask_scan.shape[0], x_end-1)
y_start, y_end = max(0, y_start+1), min(mask_scan.shape[1], y_end-1)
z_start, z_end = max(0, z_start+1), min(mask_scan.shape[2], z_end-1)
liver_volume = volume_scan[x_start:x_end, y_start:y_end, z_start:z_end]
liver_mask = mask_scan[x_start:x_end, y_start:y_end, z_start:z_end]
# input texture shape: 420, 300, 320
# we need to cut it into the shape of liver_mask
# for examples, the liver_mask.shape = 286, 173, 46; we should change the texture shape
x_length, y_length, z_length = x_end - x_start, y_end - y_start, z_end - z_start
start_x = random.randint(0, texture.shape[0] - x_length - 1) # random select the start point, -1 is to avoid boundary check
start_y = random.randint(0, texture.shape[1] - y_length - 1)
start_z = random.randint(0, texture.shape[2] - z_length - 1)
cut_texture = texture[start_x:start_x+x_length, start_y:start_y+y_length, start_z:start_z+z_length]
liver_volume, tumor_mask_blur, z = get_tumor(liver_volume, liver_mask, tumor_type, cut_texture, num_tumor)
tumor_mask = np.zeros_like(volume_scan)
volume_scan[x_start:x_end, y_start:y_end, z_start:z_end] = liver_volume
tumor_mask[x_start:x_end, y_start:y_end, z_start:z_end] = tumor_mask_blur
return volume_scan, tumor_mask, z
from tqdm import tqdm
tumor_types = ['tiny', 'small', 'medium', 'large']
textures = []
sigma_as = [3, 6, 9, 12, 15]
# sigma_as = [3]
sigma_bs = [4, 7]
# sigma_bs = [4]
predefined_texture_shape = (420, 300, 320)
all_data = np.memmap('data.mmap', dtype=np.float32, mode='w+', shape=(10, 420, 300, 320))
for sigma_a in sigma_as:
for sigma_b in sigma_bs:
texture = get_predefined_texture(predefined_texture_shape, sigma_a, sigma_b)
all_data[index] = texture
print(sigma_a, sigma_b)
# np.save(f"texture/{sigma_a}_{sigma_b}.npy", texture)
# from tqdm import tqdm
# from copy import deepcopy
# # all_data = np.empty((10, 420, 300, 320), dtype=np.float32)
# index = 0
# for sigma_a in sigma_as:
# for sigma_b in sigma_bs:
# # texture = np.load(f"texture/{sigma_a}_{sigma_b}.npy")
# # for i in tqdm(range(1000)):
# # texture = np.load(f"texture/{sigma_a}_{sigma_b}.npz")['data']
# texture = np.load(f"texture/{sigma_a}_{sigma_b}.npz", mmap_mode='r')['data']
# # np.savez(f"texture/{sigma_a}_{sigma_b}.npz", data=texture, allow_pickle=True, pickle_kwargs={'protocol': 4})
# # import ipdb; ipdb.set_trace()
# all_data[index] = texture
# print(sigma_a, sigma_b, 3)
# index += 1
def run_synthesis(input, tumor_type, num_tumor=1, dataset):
input = input.cpu().squeeze().detach().numpy()
texture = all_data[random.randint(0, 9)]
image = np.zeros([256, 256, 64])
label = np.ones_like(image)
syn_image, syn_mask, z = SynthesisTumor(image, label, tumor_type, texture, num_tumor)
syn_image = syn_image[..., z]
syn_mask = syn_mask[..., z]
syn_image = np.stack([syn_image, syn_image, syn_image], axis=0)
syn_mask = np.stack([syn_mask, syn_mask, syn_mask], axis=0)
if dataset == 'CXR':
ret = input * (1) + syn_image * syn_mask
elif dataset == 'Fundoscopy':
ret = input * (1) - syn_image * syn_mask
else:
raise NotImplementedError
return torch.from_numpy(ret).cuda().unsqueeze(0).float()