def create_submission(contours, data_path, output_path, contour_type='i'):
if contour_type == 'i':
weights = 'model_logs/sunnybrook_i_unet_inv.h5'
else:
sys.exit('\ncontour type "%s" not recognized\n' % contour_type)
crop_size = 128
input_shape = (crop_size, crop_size, 1)
num_classes = 2
images, masks = export_all_contours(contours,
data_path,
output_path,
crop_size,
num_classes=num_classes)
model = unet_model_inv(input_shape,
num_classes,
num_filters=8,
transfer=True,
contour_type=contour_type,
weights=weights)
pred_masks = model.predict(images, batch_size=32, verbose=1)
print('\nEvaluating dev set ...')
result = model.evaluate(images, masks, batch_size=32)
result = np.round(result, decimals=10)
print('\nDev set result {:s}:\n{:s}'.format(str(model.metrics_names),
str(result)))
num = 0
for idx, ctr in enumerate(contours):
img, mask = read_contour(ctr, data_path, num_classes)
h, w, d = img.shape
tmp = pred_masks[idx, :]
tmp = reshape(tmp, to_shape=(h, w, d))
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 in case: {:s}; image: {:d}'.format(
ctr.case, ctr.img_no))
coords = np.ones((1, 1, 1, 2), dtype='int')
if contour_type == 'i':
man_filename = ctr.ctr_endo_path[ctr.ctr_endo_path.rfind('\\') +
1:]
elif contour_type == 'm':
man_filename = ctr.ctr_epi_path[ctr.ctr_epi_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-0001-%04d.dcm' % (ctr.img_no)
dcm_path = os.path.join(data_path, ctr.case, 'DICOM', dcm)
overlay_full_path = os.path.join(save_dir, ctr.case, 'Overlay')
for dirpath in [
man_full_path, auto_full_path, img_full_path, overlay_full_path
]:
if not os.path.exists(dirpath):
os.makedirs(dirpath)
if 'DICOM' in dirpath:
src = dcm_path
dst = os.path.join(dirpath, img_filename)
shutil.copyfile(src, dst)
elif 'Overlay' in dirpath:
draw_result(ctr, data_path, overlay_full_path, contour_type,
coords)
else:
dst = os.path.join(auto_full_path, auto_filename)
if not os.path.exists(auto_full_path):
os.makedirs(auto_full_path)
with open(dst, 'wb') as f:
for cd in coords:
cd = np.squeeze(cd)
if cd.ndim == 1:
np.savetxt(f, cd, fmt='%d', delimiter=' ')
else:
for coord in cd:
np.savetxt(f, coord, fmt='%d', delimiter=' ')
print('\nNumber of multiple detections: {:d}'.format(num))
dst_eval = os.path.join(save_dir,
'evaluation_{:s}.txt'.format(contour_type))
with open(dst_eval, 'wb') as f:
f.write(
('Dev set result {:s}:\n{:s}'.format(str(model.metrics_names),
str(result))).encode('utf-8'))
f.close()
# Detailed evaluation:
masks = np.squeeze(masks)
pred_masks = np.squeeze(pred_masks)
detail_eval = os.path.join(
save_dir, 'evaluation_detail_{:s}.csv'.format(contour_type))
evalArr = dice_coef_each(masks, pred_masks)
caseArr = [ctr.case for ctr in contours]
imgArr = [ctr.img_no for ctr in contours]
resArr = [caseArr, imgArr]
resArr.append(list(evalArr))
resArr = np.transpose(resArr)
np.savetxt(detail_eval, resArr, fmt='%s', delimiter=',')
def create_submission(contours, data_path, output_path, contour_type='i'):
weight_t = 'model_logs/sunnybrook_a_unetres_inv_time.h5'
weight_s = 'model_logs/sunnybrook_i_unetres_inv_drop_acdc.h5'
crop_size = 128
num_phases = 5
num_classes = 2
phase_dilation = 4
input_shape = (num_phases, crop_size, crop_size, 1)
input_shape_s = (crop_size, crop_size, 1)
model_s = unet_res_model_Inv(input_shape_s,
num_classes,
nb_filters=8,
transfer=True,
contour_type=contour_type,
weights=weight_s)
model_t = unet_res_model_time(input_shape,
num_classes,
nb_filters=32,
n_phases=num_phases,
dilation=1,
transfer=True,
contour_type=contour_type,
weights=weight_t)
images, masks = export_all_contours(contours,
data_path,
output_path,
crop_size=crop_size,
num_classes=num_classes,
num_phases=num_phases,
phase_dilation=phase_dilation)
s, p, h, w, d = images.shape
print('\nFirst step predict set ...')
temp_image_t = np.reshape(images, (s * p, h, w, d))
temp_mask_t = model_s.predict(temp_image_t, batch_size=4, verbose=1)
temp_mask_t = np.reshape(temp_mask_t, (s, p, h, w, d))
# for idx_s in range(s):
# img_t = images[idx_s,...]
# temp_mask_t[idx_s] = model_s.predict(img_t)
# for idx_p in range(p):
# mask = temp_mask_t[idx_s, idx_p, ...]
# img = images[idx_s, idx_p, ...]
# img = np.squeeze(img*mask)
# img_name = '{:d}-{:d}'.format(idx_s, idx_p)
# imsave(os.path.join(TEMP_CONTOUR_PATH, img_name + ".png"), img)
print('\nTotal sample is {:d} for 2nd evaluation.'.format(s))
print('\nSecond step predict set ...')
pred_masks = model_t.predict(temp_mask_t, batch_size=4, verbose=1)
print('\nEvaluating dev set ...')
result = model_t.evaluate(temp_mask_t, masks, batch_size=4)
result = np.round(result, decimals=10)
print('\nDev set result {:s}:\n{:s}'.format(str(model_t.metrics_names),
str(result)))
for idx, ctr in enumerate(contours):
print('\nPredict image sequence {:d}'.format(idx))
img, mask = read_contour(ctr,
data_path,
num_classes,
num_phases,
num_phases_in_cycle=20,
phase_dilation=phase_dilation)
p, h, w, d = img.shape
tmp = np.squeeze(pred_masks[idx, :])
if tmp.ndim == 2:
tmp = tmp[:, :, np.newaxis]
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 in case: {:s}; image: {:d}'.format(
ctr.case, ctr.img_no))
coords = np.ones((1, 1, 1, 2), dtype='int')
overlay_full_path = os.path.join(save_dir, ctr.case, 'Overlay')
draw_result(ctr, data_path, overlay_full_path, contour_type, coords)
dst_eval = os.path.join(save_dir,
'evaluation_{:s}.txt'.format(contour_type))
with open(dst_eval, 'wb') as f:
f.write(
('Dev set result {:s}:\n{:s}'.format(str(model_t.metrics_names),
str(result))).encode('utf-8'))
f.close()
# Detailed evaluation:
masks = np.squeeze(masks)
pred_masks = np.squeeze(pred_masks)
detail_eval = os.path.join(
save_dir, 'evaluation_detail_{:s}.csv'.format(contour_type))
evalArr = dice_coef_each(masks, pred_masks)
caseArr = [ctr.case for ctr in contours]
imgArr = [ctr.img_no for ctr in contours]
resArr = [caseArr, imgArr]
resArr.append(list(evalArr))
resArr = np.transpose(resArr)
np.savetxt(detail_eval, resArr, fmt='%s', delimiter=',')
def create_submission(contours, data_path, output_path):
if contour_type == 'i':
weights = 'model_logs/sunnybrook_i_unetresnet_epoch100_aug4.h5'
elif contour_type == 'm':
weights = 'model_logs/sunnybrook_m.h5'
else:
sys.exit('\ncontour type "%s" not recognized\n' % contour_type)
crop_size = 128
input_shape = (crop_size, crop_size, 1)
num_classes = 4
images, masks = export_all_contours(contours,
data_path,
output_path,
crop_size,
num_classes=num_classes)
model = unet_res_model(input_shape,
num_classes,
weights=weights,
transfer=True)
pred_masks = model.predict(images, batch_size=32, verbose=1)
print('\nEvaluating dev set ...')
result = model.evaluate(images, masks, batch_size=32)
result = np.round(result, decimals=10)
print('\nDev set result {:s}:\n{:s}'.format(str(model.metrics_names),
str(result)))
num = 0
for idx, ctr in enumerate(contours):
img, mask = read_contour(ctr, data_path, num_classes)
h, w, d = img.shape
if contour_type == 'i':
tmp = pred_masks[idx, ..., 3]
elif contour_type == 'm':
tmp = pred_masks[idx, ..., 2]
elif contour_type == 'r':
tmp = pred_masks[idx, ..., 1]
#cv2.imwrite(data_path + '\\tmp\\' + 'i' + '{:04d}.png'.format(idx), pred_masks[idx,...,3])
#cv2.imwrite(data_path + '\\tmp\\' + 'o' + '{:04d}.png'.format(idx), pred_masks[idx,...,2])
#cv2.imwrite(data_path + '\\tmp\\' + 'r' + '{:04d}.png'.format(idx), pred_masks[idx, ..., 1])
tmp = tmp[..., np.newaxis]
tmp = reshape(tmp, to_shape=(h, w, d))
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 in case: {:s}; image: {:d}'.format(
ctr.case, ctr.img_no))
coords = np.ones((1, 1, 1, 2), dtype='int')
if len(coords) > 1:
print('\nMultiple detections in case: {:s}; image: {:d}'.format(
ctr.case, ctr.img_no))
lengths = []
for coord in coords:
lengths.append(len(coord))
coords = [coords[np.argmax(lengths)]]
num += 1
coords = np.squeeze(coords)
if contour_type == 'i':
man_filename = ctr.ctr_endo_path[ctr.ctr_endo_path.rfind('\\') +
1:]
elif contour_type == 'o':
man_filename = ctr.ctr_epi_path[ctr.ctr_epi_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-0001-%04d.dcm' % (ctr.img_no)
#dcm = 'IM-%s-%04d.dcm' % (SAX_SERIES[ctr.case], ctr.img_no)
dcm_path = os.path.join(data_path, ctr.case, 'DICOM', dcm)
overlay_full_path = os.path.join(save_dir, ctr.case, 'Overlay')
for dirpath in [
man_full_path, auto_full_path, img_full_path, overlay_full_path
]:
if not os.path.exists(dirpath):
os.makedirs(dirpath)
if 'manual' in dirpath:
if contour_type == 'i':
src = ctr.ctr_endo_path
elif contour_type == 'o':
src = ctr.ctr_epi_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)
elif 'Overlay' in dirpath:
draw_result(ctr, data_path, overlay_full_path, contour_type,
coords)
else:
dst = os.path.join(auto_full_path, auto_filename)
with open(dst, 'wb') as f:
if coords.ndim == 1:
np.savetxt(f, coords, fmt='%d', delimiter=' ')
else:
for coord in coords:
#coord = np.squeeze(coord, axis=(1,))
#coord = np.append(coord, coord[:1], axis=0)
np.savetxt(f, coord, fmt='%d', delimiter=' ')
print('\nNumber of multiple detections: {:d}'.format(num))
dst_eval = os.path.join(save_dir,
'evaluation_{:s}.txt'.format(contour_type))
with open(dst_eval, 'wb') as f:
f.write(
('Dev set result {:s}:\n{:s}'.format(str(model.metrics_names),
str(result))).encode('utf-8'))
f.close()
# Detailed evaluation:
detail_eval = os.path.join(
save_dir, 'evaluation_detail_{:s}.csv'.format(contour_type))
evalArr = dice_coef_endo_each(masks, pred_masks)
caseArr = [ctr.case for ctr in contours]
imgArr = [ctr.img_no for ctr in contours]
resArr = np.transpose([caseArr, imgArr, evalArr])
np.savetxt(detail_eval, resArr, fmt='%s', delimiter=',')
def create_submission(contours, data_path, output_path, contour_type='a'):
if contour_type == 'a':
weights = 'model_logs/temp_weights.hdf5'
else:
sys.exit('\ncontour type "%s" not recognized\n' % contour_type)
num_phases = 9
crop_size = 128
input_shape = (num_phases, crop_size, crop_size, 1)
num_classes = 3
images, masks = export_all_contours(contours,
data_path,
output_path,
crop_size,
num_classes=num_classes,
num_phases=num_phases)
model = unet_model_time(input_shape,
downsize_filters_factor=2,
pool_size=(1, 2, 2),
n_labels=3,
initial_learning_rate=0.00001,
deconvolution=False,
weights=weights)
pred_masks = model.predict(images, batch_size=8, verbose=1)
print('\nEvaluating dev set ...')
result = model.evaluate(images, masks, batch_size=8)
result = np.round(result, decimals=10)
print('\nDev set result {:s}:\n{:s}'.format(str(model.metrics_names),
str(result)))
num = 0
for c_type in ['i', 'm']:
for idx, ctr in enumerate(contours):
img, mask = read_contour(ctr,
data_path,
num_classes,
num_phases=1,
num_phases_in_cycle=20,
phase_dilation=1)
p, h, w, d = img.shape
if c_type == 'i':
tmp = pred_masks[idx, 0, ..., 2]
elif c_type == 'm':
tmp = pred_masks[idx, 0, ..., 1]
tmp = tmp[..., np.newaxis]
tmp = reshape(tmp, to_shape=(h, w, d))
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 in case: {:s}; image: {:d}'.format(
ctr.case, ctr.img_no))
coords = np.ones((1, 1, 1, 2), dtype='int')
if c_type == 'i':
man_filename = ctr.ctr_endo_path[ctr.ctr_endo_path.
rfind('\\') + 1:]
elif c_type == 'm':
man_filename = ctr.ctr_epi_path[ctr.ctr_epi_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-0001-%04d.dcm' % (ctr.img_no)
# dcm = 'IM-%s-%04d.dcm' % (SAX_SERIES[ctr.case], ctr.img_no)
dcm_path = os.path.join(data_path, ctr.case, 'DICOM', dcm)
overlay_full_path = os.path.join(save_dir, ctr.case, 'Overlay')
for dirpath in [
man_full_path, auto_full_path, img_full_path,
overlay_full_path
]:
if not os.path.exists(dirpath):
os.makedirs(dirpath)
if 'DICOM' in dirpath:
src = dcm_path
dst = os.path.join(dirpath, img_filename)
shutil.copyfile(src, dst)
elif 'Overlay' in dirpath:
draw_result(ctr, data_path, overlay_full_path, c_type,
coords)
else:
dst = os.path.join(auto_full_path, auto_filename)
if not os.path.exists(auto_full_path):
os.makedirs(auto_full_path)
with open(dst, 'wb') as f:
for cd in coords:
cd = np.squeeze(cd)
if cd.ndim == 1:
np.savetxt(f, cd, fmt='%d', delimiter=' ')
else:
for coord in cd:
np.savetxt(f,
coord,
fmt='%d',
delimiter=' ')
print('\nNumber of multiple detections: {:d}'.format(num))
dst_eval = os.path.join(save_dir, 'evaluation_{:s}.txt'.format(c_type))
with open(dst_eval, 'wb') as f:
f.write(('Dev set result {:s}:\n{:s}'.format(
str(model.metrics_names), str(result))).encode('utf-8'))
f.close()
# Detailed evaluation:
detail_eval = os.path.join(save_dir,
'evaluation_detail_{:s}.csv'.format(c_type))
evalEndoArr = dice_coef_endo_each(masks, pred_masks)
evalMyoArr = dice_coef_myo_each(masks, pred_masks)
caseArr = [ctr.case for ctr in contours]
imgArr = [ctr.img_no for ctr in contours]
resArr = np.transpose([caseArr, imgArr, evalEndoArr, evalMyoArr])
np.savetxt(detail_eval, resArr, fmt='%s', delimiter=',')
