def extract_patch(dir_name, suffix_name, patch_size, patch_step=1, flatten=False):
'''
提取指定类型病灶的ｐａｔｃｈ
: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 patch_step: 提取ｐａｔｃｈ的步长
:return: patch_arr (图像的个数, patch的个数)
'''
count = 0
names = os.listdir(dir_name)
patches_arr = []
paths = []
for name in names:
if name.endswith(suffix_name):
# 只提取指定类型病灶的ｐａｔｃｈ
mask_images = []
mhd_images = []
paths.append(os.path.join(dir_name, name))
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, rejust=True)
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)
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)
mask_images = convert2depthlaster(mask_images)
mhd_images = convert2depthlaster(mhd_images)
# show_image(mhd_images)
count += 1
[width, height, depth] = list(np.shape(mhd_images))
patch_count = 1
# if width * height >= 400:
# patch_step = int(math.sqrt(width * height / 100))
patches = []
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)) < 0.9:
continue
patch_count += 1
if flatten:
patches.append(np.array(cur_patch).flatten())
else:
patches.append(cur_patch)
if patch_count == 1:
continue
patches_arr.append(patches)
return np.array(patches_arr)
def extract_patch_npy(dir_name, suffix_name, save_dir, patch_size, patch_step=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_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 = []
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)
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)
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)) < 0.9:
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
def load_patch(dir_path, return_roi=False, parent_dir=None):
phasenames = ['NC', 'ART', 'PV']
mhd_images = []
mask_images = []
for phasename in phasenames:
# print os.path.join(parent_dir, basename, phasename + '_Image*.mhd')
# print os.path.join(dir_path, phasename + '_Image*.mhd')
image_path = glob(os.path.join(dir_path, phasename + '_Image*.mhd'))[0]
mask_path = os.path.join(dir_path, phasename + '_Registration.mhd')
mhd_image = read_mhd_image(image_path,
rejust=False) # 因为存储的是ｎｐｙ格式，所以不进行窗宽窗位的调整
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)
# xmin -= 15
# xmax += 15
# ymin -= 15
# ymax += 15
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)
mhd_images = convert2depthlaster(mhd_images)
return mhd_images
def visulization(data_dir):
img = None
for phasename in ['NC', 'ART', 'PV']:
mhd_path = glob(os.path.join(data_dir, phasename + '_Image*.mhd'))[0]
mask_path = glob(
os.path.join(data_dir, phasename + '_Registration*.mhd'))[0]
mhd_image = read_mhd_image(mhd_path)
mask_image = read_mhd_image(mask_path)
mhd_image = np.squeeze(mhd_image)
mask_image = np.squeeze(mask_image)
x_min, x_max, y_min, y_max = get_boundingbox(mask_image)
ROI = mhd_image[x_min:x_max, y_min:y_max]
print np.shape(ROI)
if img is None:
img = []
img.append(ROI)
img = convert2depthlaster(img)
print np.shape(img)
img = Image.fromarray(np.asarray(img, np.uint8))
img.show()
img.save('./multi-phase_ROI.png')
def load_patch(patch_path, return_roi=False, parent_dir=None):
if not return_roi:
if patch_path.endswith('.jpg'):
return Image.open(patch_path)
if patch_path.endswith('.npy'):
return np.load(patch_path)
else:
phasenames = ['NC', 'ART', 'PV']
if patch_path.endswith('.jpg'):
basename = os.path.basename(patch_path)
basename = basename[:basename.rfind('_')]
mask_images = []
mhd_images = []
for phasename in phasenames:
image_path = glob(
os.path.join(parent_dir, basename,
phasename + '_Image*.mhd'))[0]
mask_path = os.path.join(parent_dir, basename,
phasename + '_Registration.mhd')
mhd_image = read_mhd_image(image_path, rejust=True)
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)
# xmin -= 15
# xmax += 15
# ymin -= 15
# ymax += 15
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)
mhd_images = convert2depthlaster(mhd_images)
return mhd_images
if patch_path.endswith('.npy'):
basename = os.path.basename(patch_path)
basename = basename[:basename.rfind('_')]
mask_images = []
mhd_images = []
for phasename in phasenames:
image_path = glob(
os.path.join(parent_dir, basename,
phasename + '_Image*.mhd'))[0]
mask_path = os.path.join(parent_dir, basename,
phasename + '_Registration.mhd')
mhd_image = read_mhd_image(
image_path, rejust=False) # 因为存储的是ｎｐｙ格式，所以不进行窗宽窗位的调整
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)
# xmin -= 15
# xmax += 15
# ymin -= 15
# ymax += 15
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)
mhd_images = convert2depthlaster(mhd_images)
return mhd_images
