def preprocess_train(datapath, processedpath):
os.mkdir(processedpath)
os.mkdir(f'{processedpath}/image')
os.mkdir(f'{processedpath}/label')
idxs = range(len(os.listdir(f'{datapath}/image/')))
n = len(idxs)
for i, idx in enumerate(idxs):
sys.stdout.write(f'\rProcessing...{i+1}/{n}')
sys.stdout.flush()
img = imread(f'{datapath}/image/{idx}.tif')
lung_mask = preprocess_helpers.get_lung_mask(img).astype('float')
if str(idx) + '.tif' in os.listdir('data/special_train_masks'):
lung_mask += imread(f'data/special_train_masks/{idx}.tif').astype(
'float')
lung_mask = np.clip(lung_mask, 0, 1)
lung_mask = preprocess_helpers.resize(lung_mask)
if lung_mask.sum() == 0:
sys.stdout.write(
f'\rEmpty lung field returned for image {idx}. Skipping\n')
continue
img = preprocess_helpers.normalize(img)
img = preprocess_helpers.resize(img)
img = img * lung_mask
pil_im = Image.fromarray(img)
enhancer = ImageEnhance.Contrast(pil_im)
enhanced_im = enhancer.enhance(2.0)
np_im = np.array(enhanced_im)
imsave(f'{processedpath}/image/{idx}.tif', np_im)
mask = imread(f'{datapath}/label/{idx}.tif')
mask = preprocess_helpers.resize(mask)
imsave(f'{processedpath}/label/{idx}.tif', mask)
print(f'\nComplete.')
def preprocess(datapath, processedpath):
os.mkdir(processedpath)
for i in range(8):
os.mkdir(f'{processedpath}/image{i}')
os.mkdir(f'{processedpath}/label{i}')
idxs = os.listdir(f'{datapath}')
n = len(idxs)
for i, idx in enumerate(idxs):
sys.stdout.write(f'\rProcessing...{i+1}/{n}')
sys.stdout.flush()
empty_found = False
image, mask, spacing, thickness = pickle.load(
open(f'data/extracted/{idx}', 'rb')
)
processed_lungmasks = []
processed_image = []
processed_mask = []
for j in range(len(image)):
if empty_found:
continue
img = image[j]
processed_lungmasks.append(preprocess_helpers.get_lung_mask(img))
processed_image.append(img)
processed_mask.append(mask[j]*100)
lung_mask = max(processed_lungmasks, key=lambda x: x.sum())
image = resample(
np.array(processed_image), (spacing, spacing), thickness
)
mask = resample(
np.array(processed_mask), (spacing, spacing), thickness
)
mask = np.clip(mask, 0, 1)
for k in range(8):
im = preprocess_helpers.normalize(image[k])
im = preprocess_helpers.resize(im)
im = im*preprocess_helpers.resize(lung_mask)
pil_im = Image.fromarray(im)
enhancer = ImageEnhance.Contrast(pil_im)
enhanced_im = enhancer.enhance(2.0)
np_im = np.array(enhanced_im)
mk = preprocess_helpers.resize(mask[k])
imsave(f'{processedpath}/image{k}/{i}.tif', np_im)
imsave(f'{processedpath}/label{k}/{i}.tif', mk.astype(np.int32))
print(f'\nComplete.')
def preprocess_img(img, special=False, manual_lung_mask=None):
lung_mask = get_lung_mask(img).astype('float')
if special:
lung_mask += manual_lung_mask
lung_mask = np.clip(lung_mask, 0, 1)
lung_mask = resize(lung_mask)
img = normalize(img)
img = resize(img)
img = img * lung_mask
pil_im = Image.fromarray(img)
enhancer = ImageEnhance.Contrast(pil_im)
enhanced_im = enhancer.enhance(2.0)
return np.array(enhanced_im)
def preprocess_img(img, special=0, custom_lung_mask=None):
"""
Preprocessing pipeline for individual images. Applies lung field
segmentation, resizes, normalizes and enhances constrast
:param img: input image
:param special: whether or not the image has a difficult/custom lung mask
:param custom_lung_mask: custom lung mask (if special != 0)
"""
lung_mask = get_lung_mask(img).astype('float')
if special == 1:
lung_mask += custom_lung_mask
lung_mask = np.clip(lung_mask, 0, 1)
if special == 2:
lung_mask = custom_lung_mask
lung_mask = resize(lung_mask)
img = normalize(img)
img = resize(img)
img = img * lung_mask
pil_im = Image.fromarray(img)
enhancer = ImageEnhance.Contrast(pil_im)
enhanced_im = enhancer.enhance(2.0)
return np.array(enhanced_im)