def extract_patches_multifiles_interior(data_dir, names, target_label,
patch_size, patch_step, save_dir):
patches = []
labeles = []
for name in names:
if name is not None and not name.endswith(target_label):
continue
cur_data_dir = os.path.join(data_dir, name)
print 'extract patches from ', cur_data_dir, ' at ', str(os.getpid())
pv_mask_image, pv_mhd_image = read_from_dir(cur_data_dir)
pv_mask_image = image_erode(pv_mask_image, size=5)
if np.sum(pv_mask_image == 1) < 30:
continue
[x_min, x_max, y_min, y_max] = get_boundingbox(pv_mask_image)
r = patch_size / 2
cur_patches = []
for i in range(x_min, x_max, patch_step):
for j in range(y_min, y_max, patch_step):
cur_patch = pv_mhd_image[i - r:i + r + 1, j - r:j + r + 1]
cur_mask_patch = pv_mhd_image[i - r:i + r + 1, j - r:j + r + 1]
if ((1.0 * np.sum(cur_mask_patch)) /
(1.0 * patch_size * patch_size)) < 0.1:
continue
cur_patches.append(np.array(cur_patch).flatten())
if save_dir is None:
# if len(cur_patches) == 0:
# continue
patches.append(cur_patches)
labeles.append(int(target_label))
print len(patches), len(labeles)
return patches, labeles
def extract_patch_npy_for_JI(dir_name, suffix_name, save_path, patch_size, patch_step=1, erode_size=1):
'''
提取指定类型病灶的ｐａｔｃｈ 保存原始像素值，存成ｎｐｙ的格式
:param patch_size: 提取ｐａｔｃｈ的大小
:param dir_name: 目前所有病例的存储路径
:param suffix_name: 指定的病灶类型的后缀，比如说cyst 就是０
:param save_path: 提取得到的ｐａｔｃｈ的存储路径
:param patch_step: 提取ｐａｔｃｈ的步长
:param erode_size: 向内缩的距离，因为我们需要确定内部区域,所以为了得到内部区域，我们就将原区域向内缩以得到内部区域
:return: None
'''
count = 0
names = os.listdir(dir_name)
patches = []
for name in names:
if name.endswith(suffix_name):
# 只提取指定类型病灶的ｐａｔｃｈ
mask_images = []
mhd_images = []
flag = True
for phasename in phasenames:
image_path = glob(os.path.join(dir_name, name, phasename + '_Image*.mhd'))[0]
mask_path = os.path.join(dir_name, name, phasename + '_Registration.mhd')
mhd_image = read_mhd_image(image_path)
mhd_image = np.squeeze(mhd_image)
# show_image(mhd_image)
mask_image = read_mhd_image(mask_path)
mask_image = np.squeeze(mask_image)
[xmin, xmax, ymin, ymax] = get_boundingbox(mask_image)
if (xmax - xmin) <= erode_size or (ymax - ymin) <= erode_size:
flag = False
continue
mask_image = image_erode(mask_image, erode_size)
[xmin, xmax, ymin, ymax] = get_boundingbox(mask_image)
mask_image = mask_image[xmin: xmax, ymin: ymax]
mhd_image = mhd_image[xmin: xmax, ymin: ymax]
# mhd_image[mask_image != 1] = 0
mask_images.append(mask_image)
mhd_images.append(mhd_image)
# show_image(mhd_image)
if not flag:
continue
mask_images = convert2depthlaster(mask_images)
mhd_images = convert2depthlaster(mhd_images)
count += 1
[width, height, depth] = list(np.shape(mhd_images))
patch_count = 1
# if width * height >= 900:
# patch_step = int(math.sqrt(width * height / 100))
for i in range(patch_size / 2, width - patch_size / 2, patch_step):
for j in range(patch_size / 2, height - patch_size / 2, patch_step):
cur_patch = mhd_images[i - patch_size / 2:i + patch_size / 2 + 1,
j - patch_size / 2: j + patch_size / 2 + 1, :]
if (np.sum(mask_images[i - patch_size / 2:i + patch_size / 2,
j - patch_size / 2: j + patch_size / 2, :]) / (
(patch_size - 1) * (patch_size - 1) * 3)) < 0.5:
continue
# save_path = os.path.join(save_dir, name + '_' + str(patch_count) + '.npy')
# print save_path
# np.save(save_path, np.array(cur_patch))
cur_patch = np.reshape(cur_patch, [patch_size*patch_size*3])
patches.append(cur_patch)
patch_count += 1
if patch_count == 1:
continue
print np.shape(patches)
patches = np.concatenate([patches, np.expand_dims([int(suffix_name)] * len(patches), axis=1)], axis=1)
print np.shape(patches)
np.save(save_path, patches)
print count
def extract_interior_boundary_patch_npy(dir_name, suffix_name, save_dir, patch_size, patch_step):
'''
提取指定类型病灶的ｐａｔｃｈ 保存原始像素值，存成ｎｐｙ的格式
:param patch_size: 提取ｐａｔｃｈ的大小
:param dir_name: 目前所有病例的存储路径
:param suffix_name: 指定的病灶类型的后缀，比如说cyst 就是０
:param save_dir: 提取得到的ｐａｔｃｈ的存储路径
:param patch_step: 提取ｐａｔｃｈ的步长
:return: None
'''
count = 0
names = os.listdir(dir_name)
for name in names:
if name.endswith(suffix_name):
# 只提取指定类型病灶的ｐａｔｃｈ
mask_images = []
mhd_images = []
flag = True
for phasename in phasenames:
image_path = glob(os.path.join(dir_name, name, phasename + '_Image*.mhd'))[0]
mask_path = os.path.join(dir_name, name, phasename + '_Registration.mhd')
mhd_image = read_mhd_image(image_path)
mhd_image = np.squeeze(mhd_image)
# show_image(mhd_image)
mask_image = read_mhd_image(mask_path)
mask_image = np.squeeze(mask_image)
[xmin, xmax, ymin, ymax] = get_boundingbox(mask_image)
if (xmax - xmin) <= 5 or (ymax - ymin) <= 5:
flag = False
continue
interior_boundary = image_erode(mask_image, 5)
expand_boundary = image_expand(mask_image, 10)
mask_image = np.asarray(np.logical_and(interior_boundary == 0, expand_boundary == 1), np.uint8)
mask_image = mask_image[xmin: xmax, ymin: ymax]
mhd_image = mhd_image[xmin: xmax, ymin: ymax]
mhd_image[mask_image != 1] = 0
mask_images.append(mask_image)
mhd_images.append(mhd_image)
# show_image(mhd_image)
if not flag:
continue
mask_images = convert2depthlaster(mask_images)
mhd_images = convert2depthlaster(mhd_images)
count += 1
[width, height, depth] = list(np.shape(mhd_images))
patch_count = 1
# if width * height >= 900:
# patch_step = int(math.sqrt(width * height / 100))
for i in range(patch_size / 2, width - patch_size / 2, patch_step):
for j in range(patch_size / 2, height - patch_size / 2, patch_step):
cur_patch = mhd_images[i - patch_size / 2:i + patch_size / 2,
j - patch_size / 2: j + patch_size / 2, :]
if (np.sum(mask_images[i - patch_size / 2:i + patch_size / 2,
j - patch_size / 2: j + patch_size / 2, :]) / (
(patch_size - 1) * (patch_size - 1) * 3)) < 0.7:
continue
save_path = os.path.join(save_dir, name + '_' + str(patch_count) + '.npy')
# print save_path
np.save(save_path, np.array(cur_patch))
patch_count += 1
if patch_count == 1:
continue
# save_path = os.path.join(save_dir, name + '_' + str(patch_count) + '.png')
# roi_image = Image.fromarray(np.asarray(mhd_images, np.uint8))
# roi_image.save(save_path)
print count
