def get_all_features_1(path, feature_index, keyword):
# it will return 1x1x1(x3) data
print('Current slice:', path)
time_slice = os.path.basename(os.path.dirname(path))
# get the time stamp for this slice -> to licate its time stamp
root_path = os.path.dirname(os.path.dirname(path))
# get the root path
all_timestamp = content.get_folder(root_path, keyword)
time_slice_index = all_timestamp.index(time_slice)
if time_slice_index == 0:
target_t_list = [time_slice_index + 1, time_slice_index + 2]
elif time_slice_index == (len(all_timestamp) - 1):
target_t_list = [time_slice_index - 2, time_slice_index - 1]
else:
target_t_list = [time_slice_index -1, time_slice_index + 1]
# this 'if' argument find the previous time stamp and next time stamp for current t
current_path = os.path.dirname(path)
previous_path = os.path.join(root_path, all_timestamp[target_t_list[0]])
future_path = os.path.join(root_path, all_timestamp[target_t_list[1]])
current_all_tif = content.get_allslice(current_path)
previous_all_tif = content.get_allslice(previous_path)
future_all_tif = content.get_allslice(future_path)
# get all the tif content
# this part of code for x1 space
location_slice_index = current_all_tif.index(path)
target_space_list = [location_slice_index]
# this 'if' argument find the 3D space for given slice
print('Loading 3 images...')
img_1 = cv2.imread(current_all_tif[target_space_list[0]], -1)
# one images for space
img_2 = cv2.imread(previous_all_tif[target_space_list[0]], -1)
# past image
img_3 = cv2.imread(future_all_tif[target_space_list[0]], -1)
print('Finished!')
# future image
# three images for space + time
print('Getting features...')
feature_img_1 = [img_1[i[0], i[1]] for i in feature_index]
feature_img_2 = [img_2[i[0], i[1]] for i in feature_index]
feature_img_3 = [img_3[i[0], i[1]] for i in feature_index]
feature_img_1 = np.array(feature_img_1).reshape((len(feature_img_1),1))
feature_img_2 = np.array(feature_img_2).reshape((len(feature_img_2),1))
feature_img_3 = np.array(feature_img_3).reshape((len(feature_img_3),1))
# get all features
print('Finished!')
print('Concatenating features...')
feature_4D = np.concatenate((feature_img_2, feature_img_1, feature_img_3), axis=1)
feature_3D = feature_img_1
print('Finished!')
return feature_4D, feature_3D
def get_assign_features_1(path, x_coordinate, y_coordinate, keyword):
# print('Get 1x1 features')
# name_slice = os.path.basename(path)
# # get the name for this slice -> to locate its location
time_slice = os.path.basename(os.path.dirname(path))
# get the time stamp for this slice -> to licate its time stamp
root_path = os.path.dirname(os.path.dirname(path))
# get the root path
all_timestamp = content.get_folder(root_path, keyword)
time_slice_index = all_timestamp.index(time_slice)
if time_slice_index == 0:
target_t_list = [time_slice_index + 1, time_slice_index + 2]
elif time_slice_index == (len(all_timestamp) - 1):
target_t_list = [time_slice_index - 2, time_slice_index - 1]
else:
target_t_list = [time_slice_index -1, time_slice_index + 1]
# this 'if' argument find the previous time stamp and next time stamp for current t
current_path = os.path.dirname(path)
previous_path = os.path.join(root_path, all_timestamp[target_t_list[0]])
future_path = os.path.join(root_path, all_timestamp[target_t_list[1]])
current_all_tif = content.get_allslice(current_path)
previous_all_tif = content.get_allslice(previous_path)
future_all_tif = content.get_allslice(future_path)
# get all the tif content
# this part of code for x1 space
location_slice_index = current_all_tif.index(path)
target_space_list = [location_slice_index]
# this 'if' argument find the 3D space for given slice
img_1 = cv2.imread(current_all_tif[target_space_list[0]], -1)
# one images for space
img_2 = cv2.imread(previous_all_tif[target_space_list[0]], -1)
# past image
img_3 = cv2.imread(future_all_tif[target_space_list[0]], -1)
# future image
# three images for space + time
feature_img_1 = img_1[x_coordinate, y_coordinate]
feature_img_2 = img_2[x_coordinate, y_coordinate]
feature_img_3 = img_3[x_coordinate, y_coordinate]
# get all features
feature_3D = np.array([feature_img_1], np.uint16)
feature_4D = np.array([feature_img_2, feature_img_1, feature_img_3], np.uint16)
return feature_4D, feature_3D
def get_3D_structure(path_timestamp, begin_slice, end_slice):
print('Current timestamp:', path_timestamp)
# get all the tif content
current_all_tif = content.get_allslice(path_timestamp)
# get the image shape
img = cv2.imread(current_all_tif[0], -1)
height, width = img.shape
print('Creating image batch...')
image_batch = np.zeros((end_slice-begin_slice+1, height, width), np.float32)
for index, i in enumerate(current_all_tif[begin_slice-1:end_slice]):
if (index+1) % 100 == 0:
print(index+1)
image_batch[index] = cv2.imread(i, -1)
# reshape the image structure to fit the tensorflow
image_batch = np.reshape(image_batch, (1, end_slice-begin_slice+1, height, width, 1))
print('Finished!')
return image_batch, height, width
radius = 550
keyword = 'SHP'
# transfer the pore from string to list
pore_4D = args.pore_4D.split(',')
pore_4D = [int(i) for i in pore_4D]
pore_3D = args.pore_3D.split(',')
pore_3D = [int(i) for i in pore_3D]
# get the path for target slice
current_path = os.getcwd()
all_timestamp = content.get_folder(current_path, keyword)
timestamp_index = [
all_timestamp.index(i) for i in all_timestamp if args.timestamp in i
]
sub_path = os.path.join(current_path, all_timestamp[timestamp_index[0]])
sub_all_tif = content.get_allslice(sub_path)
target_slice = sub_all_tif[args.slice - 1]
# load the model from 'model' folder
model_4D_path = os.path.join(current_path, 'model', args.model_4D + '.model')
model_3D_path = os.path.join(current_path, 'model', args.model_3D + '.model')
model_4D_type = load(model_4D_path)
model_3D_type = load(model_3D_path)
# get features
mask, feature_index = features.get_mask(sub_all_tif[0], mask_centre, radius,
args.size)
if args.size == 1:
feature_4D, feature_3D = features.get_all_features_1(
target_slice, feature_index, keyword)
elif args.size == 3:
def get_assign_features_5(path, x_coordinate, y_coordinate, keyword):
# name_slice = os.path.basename(path)
# # get the name for this slice -> to locate its location
# print('Get 5x5 features')
time_slice = os.path.basename(os.path.dirname(path))
# get the time stamp for this slice -> to licate its time stamp
root_path = os.path.dirname(os.path.dirname(path))
# get the root path
all_timestamp = content.get_folder(root_path, keyword)
time_slice_index = all_timestamp.index(time_slice)
if time_slice_index == 0:
target_t_list = [time_slice_index + 1, time_slice_index + 2]
elif time_slice_index == (len(all_timestamp) - 1):
target_t_list = [time_slice_index - 2, time_slice_index - 1]
else:
target_t_list = [time_slice_index -1, time_slice_index + 1]
# this 'if' argument find the previous time stamp and next time stamp for current t
current_path = os.path.dirname(path)
previous_path = os.path.join(root_path, all_timestamp[target_t_list[0]])
future_path = os.path.join(root_path, all_timestamp[target_t_list[1]])
current_all_tif = content.get_allslice(current_path)
previous_all_tif = content.get_allslice(previous_path)
future_all_tif = content.get_allslice(future_path)
# get all the tif content
# this part of code for x5 space
location_slice_index = current_all_tif.index(path)
if location_slice_index == 0:
target_space_list = [location_slice_index, location_slice_index+1, location_slice_index+2, location_slice_index+3, location_slice_index+4]
elif location_slice_index == 1:
target_space_list = [location_slice_index-1, location_slice_index, location_slice_index+1, location_slice_index+2, location_slice_index+3]
elif location_slice_index == (len(current_all_tif)-2):
target_space_list = [location_slice_index-3, location_slice_index-2, location_slice_index-1, location_slice_index, location_slice_index+1]
elif location_slice_index == (len(current_all_tif)-1):
# totally there are 1248 slices, this is a magic number
target_space_list = [location_slice_index-4, location_slice_index-3, location_slice_index-2, location_slice_index-1, location_slice_index]
else:
target_space_list = [location_slice_index-2, location_slice_index-1, location_slice_index, location_slice_index+1, location_slice_index+2]
# this 'if' argument find the 3D space for given slice
img_1 = cv2.imread(current_all_tif[target_space_list[0]], -1)
img_2 = cv2.imread(current_all_tif[target_space_list[1]], -1)
img_3 = cv2.imread(current_all_tif[target_space_list[2]], -1)
img_4 = cv2.imread(current_all_tif[target_space_list[3]], -1)
img_5 = cv2.imread(current_all_tif[target_space_list[4]], -1)
# three images for space
img_6 = cv2.imread(previous_all_tif[target_space_list[0]], -1)
img_7 = cv2.imread(previous_all_tif[target_space_list[1]], -1)
img_8 = cv2.imread(previous_all_tif[target_space_list[2]], -1)
img_9 = cv2.imread(previous_all_tif[target_space_list[3]], -1)
img_10 = cv2.imread(previous_all_tif[target_space_list[4]], -1)
# previous timestamp
img_11 = cv2.imread(future_all_tif[target_space_list[0]], -1)
img_12 = cv2.imread(future_all_tif[target_space_list[1]], -1)
img_13 = cv2.imread(future_all_tif[target_space_list[2]], -1)
img_14 = cv2.imread(future_all_tif[target_space_list[3]], -1)
img_15 = cv2.imread(future_all_tif[target_space_list[4]], -1)
# future tumestamp
# nine images for space + time
feature_img_1 = img_1[x_coordinate-2:x_coordinate+3, y_coordinate-2:y_coordinate+3].ravel()
feature_img_2 = img_2[x_coordinate-2:x_coordinate+3, y_coordinate-2:y_coordinate+3].ravel()
feature_img_3 = img_3[x_coordinate-2:x_coordinate+3, y_coordinate-2:y_coordinate+3].ravel()
feature_img_4 = img_4[x_coordinate-2:x_coordinate+3, y_coordinate-2:y_coordinate+3].ravel()
feature_img_5 = img_5[x_coordinate-2:x_coordinate+3, y_coordinate-2:y_coordinate+3].ravel()
feature_img_6 = img_6[x_coordinate-2:x_coordinate+3, y_coordinate-2:y_coordinate+3].ravel()
feature_img_7 = img_7[x_coordinate-2:x_coordinate+3, y_coordinate-2:y_coordinate+3].ravel()
feature_img_8 = img_8[x_coordinate-2:x_coordinate+3, y_coordinate-2:y_coordinate+3].ravel()
feature_img_9 = img_9[x_coordinate-2:x_coordinate+3, y_coordinate-2:y_coordinate+3].ravel()
feature_img_10 = img_10[x_coordinate-2:x_coordinate+3, y_coordinate-2:y_coordinate+3].ravel()
feature_img_11 = img_11[x_coordinate-2:x_coordinate+3, y_coordinate-2:y_coordinate+3].ravel()
feature_img_12 = img_12[x_coordinate-2:x_coordinate+3, y_coordinate-2:y_coordinate+3].ravel()
feature_img_13 = img_13[x_coordinate-2:x_coordinate+3, y_coordinate-2:y_coordinate+3].ravel()
feature_img_14 = img_14[x_coordinate-2:x_coordinate+3, y_coordinate-2:y_coordinate+3].ravel()
feature_img_15 = img_15[x_coordinate-2:x_coordinate+3, y_coordinate-2:y_coordinate+3].ravel()
# get all features
feature_3D = np.concatenate((feature_img_1, feature_img_2, feature_img_3, feature_img_4, feature_img_5))
feature_4D = np.concatenate((feature_img_6, feature_img_7, feature_img_8, feature_img_9, feature_img_10,
feature_img_1, feature_img_2, feature_img_3, feature_img_4, feature_img_5,
feature_img_11, feature_img_12, feature_img_13, feature_img_14, feature_img_15))
return feature_4D, feature_3D
def get_all_features_3(path, feature_index, keyword):
# it will return 3x3x3(x3) data
print('Current slice:', path)
time_slice = os.path.basename(os.path.dirname(path))
# get the time stamp for this slice -> to locate its time stamp
root_path = os.path.dirname(os.path.dirname(path))
# get the root path
all_timestamp = content.get_folder(root_path, keyword)
time_slice_index = all_timestamp.index(time_slice)
if time_slice_index == 0:
target_t_list = [time_slice_index + 1, time_slice_index + 2]
elif time_slice_index == (len(all_timestamp) - 1):
target_t_list = [time_slice_index - 2, time_slice_index - 1]
else:
target_t_list = [time_slice_index -1, time_slice_index + 1]
# this 'if' argument find the previous time stamp and next time stamp for current t
# get three timestamp: previous, now, future
current_path = os.path.dirname(path)
previous_path = os.path.join(root_path, all_timestamp[target_t_list[0]])
future_path = os.path.join(root_path, all_timestamp[target_t_list[1]])
# get all the tif content
current_all_tif = content.get_allslice(current_path)
previous_all_tif = content.get_allslice(previous_path)
future_all_tif = content.get_allslice(future_path)
# this part of code for x3 space
location_slice_index = current_all_tif.index(path)
if location_slice_index == 0:
target_space_list = [location_slice_index, location_slice_index+1, location_slice_index+2]
elif location_slice_index == (len(current_all_tif)-1):
# totally there are 1248 slices, this is a magic number
target_space_list = [location_slice_index-2, location_slice_index-1, location_slice_index]
else:
target_space_list = [location_slice_index-1, location_slice_index, location_slice_index+1]
# this 'if' argument find the 3D space for given slice
print('Loading 9 images...')
img_1 = cv2.imread(current_all_tif[target_space_list[0]], -1)
img_2 = cv2.imread(current_all_tif[target_space_list[1]], -1)
img_3 = cv2.imread(current_all_tif[target_space_list[2]], -1)
# three images for space
img_4 = cv2.imread(previous_all_tif[target_space_list[0]], -1)
img_5 = cv2.imread(previous_all_tif[target_space_list[1]], -1)
img_6 = cv2.imread(previous_all_tif[target_space_list[2]], -1)
img_7 = cv2.imread(future_all_tif[target_space_list[0]], -1)
img_8 = cv2.imread(future_all_tif[target_space_list[1]], -1)
img_9 = cv2.imread(future_all_tif[target_space_list[2]], -1)
print('Finished!')
# nine images for space + time
print('Getting features...')
# feature index has been inputted
feature_img_1 = [img_1[i[0]:i[1], i[2]:i[3]].ravel() for i in feature_index]
feature_img_2 = [img_2[i[0]:i[1], i[2]:i[3]].ravel() for i in feature_index]
feature_img_3 = [img_3[i[0]:i[1], i[2]:i[3]].ravel() for i in feature_index]
feature_img_4 = [img_4[i[0]:i[1], i[2]:i[3]].ravel() for i in feature_index]
feature_img_5 = [img_5[i[0]:i[1], i[2]:i[3]].ravel() for i in feature_index]
feature_img_6 = [img_6[i[0]:i[1], i[2]:i[3]].ravel() for i in feature_index]
feature_img_7 = [img_7[i[0]:i[1], i[2]:i[3]].ravel() for i in feature_index]
feature_img_8 = [img_8[i[0]:i[1], i[2]:i[3]].ravel() for i in feature_index]
feature_img_9 = [img_9[i[0]:i[1], i[2]:i[3]].ravel() for i in feature_index]
# transform to the numpy array
feature_img_1 = np.array(feature_img_1)
feature_img_2 = np.array(feature_img_2)
feature_img_3 = np.array(feature_img_3)
feature_img_4 = np.array(feature_img_4)
feature_img_5 = np.array(feature_img_5)
feature_img_6 = np.array(feature_img_6)
feature_img_7 = np.array(feature_img_7)
feature_img_8 = np.array(feature_img_8)
feature_img_9 = np.array(feature_img_9)
# get all features
print('Finished!')
print('Concatenating features...')
feature_4D = np.concatenate((feature_img_4, feature_img_5, feature_img_6,
feature_img_1, feature_img_2, feature_img_3,
feature_img_7, feature_img_8, feature_img_9), axis=1)
feature_3D = np.concatenate((feature_img_1, feature_img_2, feature_img_3), axis=1)
print('Finished!')
return feature_4D, feature_3D