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aap-lits.py
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aap-lits.py
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# -*- coding: utf-8 -*-
"""lits_out.ipynb
"""
from kivy.app import App
from kivy.uix.boxlayout import BoxLayout
from kivy.lang import Builder
from tensorflow.keras.models import model_from_json
import os
import nibabel as nib
import numpy as np
import matplotlib.pyplot as plt
import time
json_file = open('model_json.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
loaded_model = model_from_json(loaded_model_json)
# load weights into new model
loaded_model.load_weights("model_weights.h5")
print("Loaded model from disk")
input_shape = [64, 64, 1]
def slice_to_patch(slice, patch_ratio):
slice[slice == 1] = 0
slice[slice == 2] = 1
patch_list = []
for x_bin in range(2, len(patch_ratio)):
for y_bin in range(2, len(patch_ratio)):
patch = slice[patch_ratio[x_bin-2] : patch_ratio[x_bin], patch_ratio[y_bin - 2] : patch_ratio[y_bin]]
patch = patch.reshape(patch.shape + (1,))
patch_list.append(patch)
return np.array(patch_list)
def patch_to_slice(patch, patch_ratio, input_shape, conf_threshold):
slice = np.zeros((512, 512, 1))
row_idx = 0
col_idx = 0
for i in range(len(patch)):
slice[patch_ratio[row_idx]:patch_ratio[row_idx + 2], patch_ratio[col_idx]:patch_ratio[col_idx + 2]][patch[i] > conf_threshold] = 1
col_idx += 1
if i != 0 and (i+1) % 15 == 0:
row_idx += 1
col_idx = 0
return slice
Builder.load_string("""
<MyWidget>:
id: my_widget
FileChooserListView:
id: filechooser
on_selection: my_widget.selected(filechooser.selection)
Button
text: "open"
on_release: my_widget.open(filechooser.path, filechooser.selection)
""")
class MyWidget(BoxLayout):
def open(self, path, filename):
img_path=os.path.join(path, filename[0])
img_ex = nib.load(img_path).get_data()
r,c,ch=img_ex.shape
# img_ex=img_ex1[:,:,100]
#mask_ex = nib.load(mask_path[25]).get_data()
output_img=np.zeros(img_ex.shape)
patch_ratio = []
for i in range(16 + 1):
patch_ratio.append(32 * i)
for i in range(0,img_ex.shape[2]):
patch_ex = slice_to_patch(img_ex[:, :, i], patch_ratio)
prediction = loaded_model.predict(patch_ex)
prediction_mask = patch_to_slice(prediction, patch_ratio, input_shape, conf_threshold = 0.97)
prediction_mask1=img_ex[:,:,0]
# cv2.imwrite('output.jpg',prediction_mask1)
# self.ids.image.source='output.jpg'
fig, (ax1,ax3) = plt.subplots(1, 2, figsize = ((15, 15)))
ax1.imshow(np.rot90(img_ex[:, :, i], 3), cmap = 'bone')
ax1.set_title("Image", fontsize = "x-large")
ax1.grid(False)
ax3.imshow(np.rot90(prediction_mask.reshape((512, 512)), 3), cmap = 'bone')
ax3.set_title("Mask (Pred)", fontsize = "x-large")
ax3.grid(False)
plt.show()
# plt.close()
print('Finished')
def selected(self, filename):
print ("selected: %s" % filename[0])
class MyApp(App):
def build(self):
return MyWidget()
if __name__ == '__main__':
MyApp().run()