def __init__(self):
# load config file
config = json.load(open("model/config.json"))
# get the image processor
self._imageProcessor = ImageProcessor(config)
# load the DL model
self._model = fcn_model((200, 200, 1), 2, weights=None)
self._model.load_weights("model/weights.h5")
self._model._make_predict_function()
def create_submission(dcm_list, data_path):
crop_size = 200
input_shape = (crop_size, crop_size, 1)
num_classes = 2
oweights = 'weights/lvsc_o.h5'
iweights = 'weights/lvsc_i.h5'
omodel = fcn_model(input_shape, num_classes, weights=oweights)
imodel = fcn_model(input_shape, num_classes, weights=iweights)
images = np.zeros((len(dcm_list), crop_size, crop_size, 1))
for idx, dcm_path in enumerate(dcm_list):
img = read_dicom(dcm_path)
img = center_crop(img, crop_size=crop_size)
images[idx] = img
opred_masks = omodel.predict(images, batch_size=32, verbose=1)
ipred_masks = imodel.predict(images, batch_size=32, verbose=1)
save_dir = data_path + '_auto_contours'
prefix = 'MYFCN_' # change prefix to your unique initials
for idx, dcm_path in enumerate(dcm_list):
img = read_dicom(dcm_path)
h, w, d = img.shape
otmp = reshape(opred_masks[idx], to_shape=(h, w, d))
otmp = np.where(otmp > 0.5, 255, 0).astype('uint8')
itmp = reshape(ipred_masks[idx], to_shape=(h, w, d))
itmp = np.where(itmp > 0.5, 255, 0).astype('uint8')
assert img.shape == otmp.shape, 'Prediction does not match shape'
assert img.shape == itmp.shape, 'Prediction does not match shape'
tmp = otmp - itmp
tmp = np.squeeze(tmp, axis=(2, ))
sub_dir = dcm_path[dcm_path.find('CAP_'):dcm_path.rfind('DET')]
filename = prefix + dcm_path[dcm_path.rfind('DET'):].replace(
'.dcm', '.png')
full_path = os.path.join(save_dir, sub_dir)
if not os.path.exists(full_path):
os.makedirs(full_path)
cv2.imwrite(os.path.join(full_path, filename), tmp)
in_ = cv2.imread(os.path.join(full_path, filename),
cv2.IMREAD_GRAYSCALE)
if not np.allclose(in_, tmp):
raise AssertionError('File read error: {:s}'.format(
os.path.join(full_path, filename)))
def create_submission(contours, data_path):
if contour_type == 'i':
weights = 'weights/rvsc_i.h5'
elif contour_type == 'o':
weights = 'weights/rvsc_o.h5'
else:
sys.exit('\ncontour type "%s" not recognized\n' % contour_type)
crop_size = 200
images = np.zeros((len(contours), crop_size, crop_size, 1))
for idx, contour in enumerate(contours):
img, _ = read_contour(contour, data_path, return_mask=False)
img = center_crop(img, crop_size=crop_size)
images[idx] = img
input_shape = (crop_size, crop_size, 1)
num_classes = 2
model = fcn_model(input_shape, num_classes, weights=weights)
pred_masks = model.predict(images, batch_size=32, verbose=1)
save_dir = data_path + '_auto_contours'
num = 0
for idx, ctr in enumerate(contours):
img, _ = read_contour(ctr, data_path, return_mask=False)
h, w, d = img.shape
tmp = reshape(pred_masks[idx], to_shape=(h, w, d))
assert img.shape == tmp.shape, 'Shape of prediction does not match'
tmp = np.where(tmp > 0.5, 255, 0).astype('uint8')
tmp2, coords, hierarchy = cv2.findContours(tmp.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)
if not coords:
print('No detection: %s' % ctr.ctr_path)
coords = np.ones((1, 1, 1, 2), dtype='int')
if len(coords) > 1:
print('Multiple detections: %s' % ctr.ctr_path)
#cv2.imwrite('multiple_dets/'+contour_type+'{:04d}.png'.format(idx), tmp)
lengths = []
for coord in coords:
lengths.append(len(coord))
coords = [coords[np.argmax(lengths)]]
num += 1
filename = 'P{:s}-{:s}-'.format(ctr.patient_no, ctr.img_no)+contour_type+'contour-auto.txt'
full_path = os.path.join(save_dir, 'P{:s}'.format(ctr.patient_no)+'contours-auto')
if not os.path.exists(full_path):
os.makedirs(full_path)
with open(os.path.join(full_path, filename), 'w') as f:
for coord in coords:
coord = np.squeeze(coord, axis=(1,))
coord = np.append(coord, coord[:1], axis=0)
np.savetxt(f, coord, fmt='%i', delimiter=' ')
print('Num of files with multiple detections: {:d}'.format(num))
print('Done mapping training set')
split = int(0.1 * len(train_ctrs))
dev_ctrs = train_ctrs[0:split]
train_ctrs = train_ctrs[split:]
print('\nBuilding train dataset ...')
img_train, mask_train = export_all_contours(train_ctrs,
TRAIN_PATH,
crop_size=crop_size)
print('\nBuilding dev dataset ...')
img_dev, mask_dev = export_all_contours(dev_ctrs,
TRAIN_PATH,
crop_size=crop_size)
input_shape = (crop_size, crop_size, 1)
num_classes = 2
model = fcn_model(input_shape, num_classes, weights=None)
kwargs = dict(
rotation_range=0,
zoom_range=0.0,
width_shift_range=0.0,
height_shift_range=0.0,
horizontal_flip=False,
vertical_flip=False,
)
image_datagen = ImageDataGenerator(**kwargs)
mask_datagen = ImageDataGenerator(**kwargs)
epochs = 20
mini_batch_size = 1
from helpers import center_crop, lr_poly_decay, get_SAX_SERIES import matplotlib.pyplot as plt from PIL import Image import cv2 # Choose GPU os.environ["CUDA_VISIBLE_DEVICES"] = '1' # model canshu crop_size = 512 input_shape = (crop_size, crop_size, 3) num_classes = 4 # weight_path = 'model_logs/0111_sunnybrook_i_epoch_95.h5' weight_path = None model = fcn_model(input_shape, num_classes, weights=weight_path) epochs = 20 mini_batch_size = 1 npy_path = '/media/lxy/F240900A408FD42F/' print '#'*30 print 'Loading images and labels' images = np.load(npy_path+'images_200.npy') masks = np.load(npy_path+'masks_200.npy') print 'Loading is readly, ', len(images), 'images has benn prepared\n' max_iter = (len(images) / mini_batch_size) * epochs curr_iter = 0 base_lr = K.eval(model.optimizer.lr) lrate = lr_poly_decay(model, base_lr, curr_iter, max_iter, power=0.5) for e in range(epochs):
def create_submission(data_path):
print(len(list(contours)))
if contour_type == 'i':
weights = 'weights/sunnybrook_i.h5'
elif contour_type == 'o':
weights = 'weights/sunnybrook_o.h5'
else:
sys.exit('\ncontour type "%s" not recognized\n' % contour_type)
#pdb.set_trace()
crop_size = 256
images, masks = export_all_contours(contours, data_path, crop_size)
input_shape = (crop_size, crop_size, 1)
num_classes = 2
model = fcn_model(input_shape, num_classes, weights=weights)
pred_masks = model.predict(images, batch_size=32, verbose=1)
num = 0
print(list(contours))
for idx, ctr in enumerate(contours):
img, mask = read_contour(ctr, data_path)
h, w, d = img.shape
tmp = reshape(pred_masks[idx], to_shape=(h, w, d))
assert img.shape == tmp.shape, 'Shape of prediction does not match'
tmp = np.where(tmp > 0.5, 255, 0).astype('uint8')
tmp2, coords, hierarchy = cv2.findContours(tmp.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)
if not coords:
print('\nNo detection: %s' % ctr.ctr_path)
coords = np.ones((1, 1, 1, 2), dtype='int')
if len(coords) > 1:
print('\nMultiple detections: %s' % ctr.ctr_path)
#cv2.imwrite('multiple_dets/'+contour_type+'{:04d}.png'.format(idx), tmp)
lengths = []
for coord in coords:
lengths.append(len(coord))
coords = [coords[np.argmax(lengths)]]
num += 1
man_filename = ctr.ctr_path[ctr.ctr_path.rfind('/')+1:]
auto_filename = man_filename.replace('manual', 'auto')
img_filename = re.sub(r'-[io]contour-manual.txt', '.dcm', man_filename)
man_full_path = os.path.join(save_dir, ctr.case, 'contours-manual', 'IRCCI-expert')
auto_full_path = os.path.join(save_dir, ctr.case, 'contours-auto', 'FCN')
img_full_path = os.path.join(save_dir, ctr.case, 'DICOM')
dcm = 'IM-%s-%04d.dcm' % (SAX_SERIES[ctr.case], ctr.img_no)
dcm_path = os.path.join(data_path, ctr.case, dcm)
for dirpath in [man_full_path, auto_full_path, img_full_path]:
if not os.path.exists(dirpath):
os.makedirs(dirpath)
if 'manual' in dirpath:
src = ctr.ctr_path
dst = os.path.join(dirpath, man_filename)
shutil.copyfile(src, dst)
elif 'DICOM' in dirpath:
src = dcm_path
dst = os.path.join(dirpath, img_filename)
shutil.copyfile(src, dst)
else:
dst = os.path.join(auto_full_path, auto_filename)
with open(dst, 'w') as f:
for coord in coords:
coord = np.squeeze(coord, axis=(1,))
coord = np.append(coord, coord[:1], axis=0)
np.savetxt(f, coord, fmt='%i', delimiter=' ')
print('\nNumber of multiple detections: {:d}'.format(num))
from helpers import center_crop
from fcn_model import fcn_model
import dicom
import cv2
import matplotlib.pyplot as plt
weights = 'rvsc_o_epoch_20.h5'
crop_size = (200,200,1)
num_class = 2
model = fcn_model(crop_size,num_class,weights)
# file name 'eval.dcm'
img_file = 'eval.dcm'
img = dicom.read_file(img_file)
img = img.pixel_array.astype('int32')
img = img.reshape(img.shape[0],img.shape[1],1)
img = center_crop(img,crop_size = 200)
plt.imshow(img.reshape((200,200)))
plt.show()
result = model.predict(img.reshape((1,200,200,1)))
plt.imshow(result.reshape((200,200)))
plt.show()