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Commit 9fa4c84

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code cleanup
1 parent cda741d commit 9fa4c84

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6 files changed

+135
-788
lines changed

6 files changed

+135
-788
lines changed

‎.ipynb_checkpoints/ActiveContour-checkpoint.ipynb

Lines changed: 26 additions & 149 deletions
Large diffs are not rendered by default.

‎.ipynb_checkpoints/FitFunction-checkpoint.ipynb

Lines changed: 18 additions & 128 deletions
Large diffs are not rendered by default.

‎ActiveContour.ipynb

Lines changed: 26 additions & 149 deletions
Large diffs are not rendered by default.

‎ActiveContour.py

Lines changed: 30 additions & 180 deletions
Original file line numberDiff line numberDiff line change
@@ -1,20 +1,18 @@
11

22
# coding: utf-8
33

4-
# In[159]:
4+
# In[5]:
55

66

77
import numpy as np
88
import matplotlib.pyplot as plt
99
from skimage.color import rgb2gray
1010
from skimage.filters import gaussian
11-
import ActiveShapeModel
1211
import scipy
1312
import cv2
1413
from scipy import ndimage
1514
import Image_preperation as prep
1615
import FileManager as fm
17-
import Image_preperation as prep
1816

1917
def calc_internal(p1,p2):
2018
if (np.array_equal(p1,p2)):
@@ -41,19 +39,19 @@ def calc_mean(points):
4139

4240
return mean_sum / size
4341

44-
def calc_external_img(img):
42+
# def calc_external_img(img):
4543

46-
img = rgb2gray(img)
44+
# img = rgb2gray(img)
4745

48-
sobelx64f = cv2.Sobel(img,cv2.CV_64F,1,0,ksize=5)
49-
abs_sobel64f = np.absolute(sobelx64f)
50-
sobelx = np.uint8(abs_sobel64f)
46+
# sobelx64f = cv2.Sobel(img,cv2.CV_64F,1,0,ksize=5)
47+
# abs_sobel64f = np.absolute(sobelx64f)
48+
# sobelx = np.uint8(abs_sobel64f)
5149

52-
sobely64f = cv2.Sobel(img,cv2.CV_64F,0,1,ksize=5)
53-
abs_sobel64f = np.absolute(sobely64f)
54-
sobely = np.uint8(abs_sobel64f)
50+
# sobely64f = cv2.Sobel(img,cv2.CV_64F,0,1,ksize=5)
51+
# abs_sobel64f = np.absolute(sobely64f)
52+
# sobely = np.uint8(abs_sobel64f)
5553

56-
return -(sobelx + sobely)
54+
# return -(sobelx + sobely)
5755

5856
def calc_external_img2(img):
5957

@@ -101,7 +99,7 @@ def calc_energy3(p1, p2, mean, external_img, alpha):
10199
internal = calc_internal_mean(p1,p2, mean)
102100
external = calc_external(p2, external_img)
103101

104-
return alpha*internal + external
102+
return internal +alpha* external
105103

106104

107105

@@ -154,8 +152,8 @@ def viterbi(points, img, pixel_width, alpha):
154152
for d in range(num_states):
155153
p1 = get_point_state(points[i-1], d, pixel_width)
156154
p2 = get_point_state(points[i],t, pixel_width)
157-
#energy_trans = calc_energy(p1, p2, external_img, alpha)
158-
energy_trans = calc_energy3(p1, p2, mean, external_img, alpha)
155+
energy_trans = calc_energy(p1, p2, external_img, alpha)
156+
# energy_trans = calc_energy3(p1, p2, mean, external_img, alpha)
159157

160158
tmp = trellis[i-1,d] + energy_trans
161159

@@ -251,12 +249,7 @@ def resolution_downscale(img, resize):
251249

252250
return cv2.resize(img, (yn ,xn))
253251

254-
255-
# In[169]:
256-
257-
258-
if __name__ == "__main__":
259-
252+
def previous_test():
260253
dir_radiographs = "_Data\Radiographs\*.tif"
261254
radiographs = fm.load_files(dir_radiographs)
262255
radiograph = radiographs[0]
@@ -266,175 +259,32 @@ def resolution_downscale(img, resize):
266259
tooth = init[0,4,:,:]/0.3
267260
#tooth = tooth/down_sample
268261

269-
radiograph_pre = prep.pre_processing(radiograph)
262+
radiograph_pre = pre_processing(radiograph)
270263
img = resolution_downscale(radiograph_pre,down_sample)
271264

272265
fig, ax = plt.subplots(figsize=(15, 15))
273266
plt.imshow(radiograph)
274267
plt.plot(tooth[:,0], tooth[:,1], 'ro', markersize=1)
275268
plt.show()
276269

270+
def test_module():
271+
piece = fm.load_img_piece()
272+
tooth = fm.load_tooth_of_piece(0)
273+
ext = calc_external_img2(piece)
274+
fm.show_with_points(ext, tooth)
275+
276+
img, stooth = fm.resolution_scale(piece, tooth, 1/6)
277+
ext = calc_external_img2(img)
278+
fm.show_with_points(ext, stooth)
279+
280+
new_tooth = active_contour(stooth, img, 1, 3, 1)
281+
fm.show_with_points(ext, new_tooth)
277282

278-
# In[196]:
279-
280-
281-
smooth = prep.gaussian_smooth1(radiograph,10)
282-
grad = calc_external_img2(smooth)
283-
grad2 = calc_external_img2(radiograph)
284-
fig, ax = plt.subplots(figsize=(15, 15))
285-
plt.imshow(grad2)
286-
plt.show()
287-
print(radiograph.dtype)
288-
np.histogram(grad)
289-
fig, ax = plt.subplots(figsize=(15, 15))
290-
plt.imshow(grad)
291-
plt.show()
292-
293-
294-
# In[185]:
295-
296-
297-
img = radiograph
298-
299-
img = rgb2gray(img)
300-
301-
sobelx64f = cv2.Sobel(img,cv2.CV_16S,1,0,ksize=5)
302-
abs_sobel64f = np.absolute(sobelx64f)
303-
sobelx = np.uint8(abs_sobel64f)
304-
plt.imshow(sobelx)
305-
plt.show()
306-
307-
sobely64f = cv2.Sobel(img,cv2.CV_16S,0,1,ksize=5)
308-
abs_sobel64f = np.absolute(sobely64f)
309-
sobely = np.uint8(abs_sobel64f)
310-
plt.imshow(sobely64f)
311-
plt.show()
312-
313-
314-
# In[161]:
315-
316-
317-
k1 = np.array([[1,1,1],[1,1,1],[1,1,1]])/9
318-
smoothed = cv2.filter2D(img,-1,k1)
319-
320-
k2 = np.array([[0,0,0],[0,2,0],[0,0,0]])
321-
dubbel = cv2.filter2D(img,-1,k2)
322-
sharp = dubbel - smoothed
323-
sharp2 = 2*img - smoothed
324-
325-
plt.imshow(sharp)
326-
plt.show()
327-
plt.imshow(sharp2)
328-
plt.show()
329-
print(sharp2.dtype)
330-
331-
332-
# In[103]:
333-
334-
335-
piece = img[175:275, 225:325]
336-
tooth2 = tooth
337-
tooth2[:,0]=tooth[:,0]-225
338-
tooth2[:,1]=tooth[:,1]-175
339-
fig, ax = plt.subplots(figsize=(7, 7))
340-
plt.imshow(piece)
341-
plt.plot(tooth2[:,0], tooth2[:,1], 'ro', markersize=2)
342-
plt.show()
343-
344-
345-
# In[141]:
346-
347-
348-
new_tooth = active_contour(tooth2, prep.gaussian(piece,1.5), 2, 2, 25)
349-
fig, ax = plt.subplots(figsize=(7, 7))
350-
plt.imshow(piece)
351-
plt.plot(new_tooth[:,0], new_tooth[:,1], 'ro', markersize=2)
352-
plt.show()
353-
354-
355-
# In[137]:
356-
357-
358-
ext3 = calc_external_img2(piece)
359-
fig, ax = plt.subplots(figsize=(7, 7))
360-
plt.imshow(ext3)
361-
plt.plot(tooth2[0,0], new_tooth[0,1], 'ro', markersize=2)
362-
plt.show()
363-
364-
365-
# In[87]:
366-
367-
368-
tooth[:,0]=tooth[:,0]-225
369-
tooth[:,1]=tooth[:,1]-175
370-
371-
372-
# In[88]:
373-
374-
375-
for p in tooth:
376-
print(ext3[int(p[0]),int(p[1])])
377-
378-
379-
# In[127]:
380-
381-
382-
mean = calc_mean(tooth)
383-
size = len(tooth)
384-
for i in range(size-1):
385-
p1 = tooth[i]
386-
p2 = tooth[i+1]
387-
print(100*calc_internal_mean(p1,p2, mean))
388-
389-
390-
391-
# In[124]:
392-
393-
394-
mean
395-
396-
397-
# In[92]:
398-
399-
400-
size = len(tooth)
401-
for i in range(size-1):
402-
p1 = tooth[i]
403-
p2 = tooth[i+1]
404-
print(calc_energy(p1,p2,ext3,0.5))
405-
406-
407-
408-
# In[34]:
409-
410-
411-
ext.dtype
412-
413-
414-
# In[173]:
415-
416-
417-
new_tooth = active_contour(tooth, img, 5, 2, 1)
418-
419-
fig, ax = plt.subplots(figsize=(15, 15))
420-
plt.imshow(img)
421-
plt.plot(new_tooth[:,0], new_tooth[:,1], 'ro', markersize=1)
422-
plt.show()
423-
424-
425-
# In[143]:
426283

284+
# In[6]:
427285

428-
from skimage.color import rgb2gray
429-
from skimage import data
430-
from skimage.filters import gaussian
431-
from skimage.segmentation import active_contour
432286

433-
snake = active_contour(radiograph_pre,
434-
tooth, alpha=0.015, beta=10, gamma=0.001)
287+
if __name__ == "__main__":
435288

436-
fig, ax = plt.subplots(figsize=(15, 15))
437-
plt.imshow(img)
438-
plt.plot(snake[:,0], snake[:,1], 'ro', markersize=1)
439-
plt.show()
289+
test_module()
440290

‎FitFunction.ipynb

Lines changed: 18 additions & 128 deletions
Large diffs are not rendered by default.

‎FitFunction.py

Lines changed: 17 additions & 54 deletions
Original file line numberDiff line numberDiff line change
@@ -244,6 +244,23 @@ def test_normal_on_edge():
244244
plt.plot(np.arange(-20,21),strength)
245245
plt.show()
246246

247+
248+
def show_results_fitting():
249+
piece = fm.load_img_piece()
250+
edge_img = prep.canny(piece)
251+
tooth = fm.load_tooth_of_piece(0)
252+
new_points_list, total_error = fit_measure(tooth, 30, edge_img)
253+
for i in range(7):
254+
tooth = fm.load_tooth_of_piece(i+1)
255+
new_points, total_error = fit_measure(tooth, 25, edge_img)
256+
new_points_list = np.append(new_points_list,new_points)
257+
fm.show_with_points(edge_img, new_points_list.reshape(-1,2))
258+
259+
# new_points_list = fm.load_tooth_of_piece(0)
260+
# for i in range(7):
261+
# new_points = fm.load_tooth_of_piece(i+1)
262+
# new_points_list = np.append(new_points_list,new_points)
263+
# fm.show_with_points(edge_img, new_points_list.reshape(-1,2))
247264

248265

249266
# In[2]:
@@ -313,57 +330,3 @@ def test_normal_on_edge():
313330
# new_points, error = fit_measure(tooth, 30, edge_img)
314331
# fm.show_with_points(piece, new_points)
315332

316-
317-
# In[3]:
318-
319-
320-
piece = fm.load_img_piece()
321-
tooth = fm.load_tooth_of_piece(2)
322-
edge_img = prep.canny(piece)
323-
new_points, total_error = fit_measure(tooth, 20, edge_img)
324-
fm.show_with_points(edge_img, new_points)
325-
326-
327-
# In[4]:
328-
329-
330-
tooth = fm.load_tooth_of_piece(2)
331-
fm.show_with_points(edge_img, tooth)
332-
333-
334-
# In[3]:
335-
336-
337-
piece = fm.load_img_piece()
338-
edge_img = prep.canny(piece)
339-
tooth = fm.load_tooth_of_piece(0)
340-
new_points_list, total_error = fit_measure(tooth, 30, edge_img)
341-
for i in range(7):
342-
tooth = fm.load_tooth_of_piece(i+1)
343-
new_points, total_error = fit_measure(tooth, 25, edge_img)
344-
new_points_list = np.append(new_points_list,new_points)
345-
fm.show_with_points(edge_img, new_points_list.reshape(-1,2))
346-
347-
348-
# In[4]:
349-
350-
351-
new_points_list = new_points_list.reshape(-1,40,2)
352-
new_points_list.shape
353-
354-
355-
# In[19]:
356-
357-
358-
np.save('fitted_tooth', new_points_list)
359-
360-
361-
# In[12]:
362-
363-
364-
new_points_list = fm.load_tooth_of_piece(0)
365-
for i in range(7):
366-
new_points = fm.load_tooth_of_piece(i+1)
367-
new_points_list = np.append(new_points_list,new_points)
368-
fm.show_with_points(edge_img, new_points_list.reshape(-1,2))
369-

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