def true_positif(image,mask,threshold,threshold_merge = 0.172,method = 'crop',slices = 100):
'''Input: an image, the corresponding mask, a method, a threshold and the number of slices.
Ouput : the number of voxels that are in our bounding box and in the perfect box.'''
if(method == 'crop'):
[left,right,top,bottom] = get_coordinates(image,threshold,slices)
elif(method == 'merge'):
image = splitandmerge(image,threshold_merge,method='Exponent')
[left,right,top,bottom] = get_coordinates(image,threshold,slices)
else:
raise Exception('Unknown method')
[p_left,p_right,p_top,p_bottom] = perfect_box_coord(mask)
tp_top = 0
tp_bottom = 0
tp_left = 0
tp_right = 0
total_area = (bottom-top)*(left-right)
if(p_left>left and p_left<right): tp_left = p_left
elif(p_left<left and left<p_right): tp_left = left
if(p_right<right and p_right>left): tp_right = p_right
elif(p_right>right and right>p_left): tp_right = right
if(p_top>top and p_top<bottom): tp_top = p_top
elif(p_top<top and top<p_bottom): tp_top = top
if(p_bottom<bottom and p_bottom>top): tp_bottom = p_bottom
elif(p_bottom>bottom and bottom>p_top): tp_bottom = bottom
return (tp_right-tp_left)*(tp_bottom-tp_top)
def percentage_cropped(image,threshold,threshold_merge = 0.172,method = 'crop',slices = 100):
'''Input: an image, a threshold, a method and a number of slices.
Ouput: the percentage of the image that has been cropped.'''
if(method == 'crop'):
[left,right,top,bottom] = get_coordinates(image,threshold,slices)
elif(method == 'merge'):
image = splitandmerge(image,threshold_merge,method='Exponent')
[left,right,top,bottom] = get_coordinates(image,threshold,slices)
else:
raise Exception('Unknown method')
(rows,cols) = (image.shape[0],image.shape[1])
(box_rows,box_cols) = (bottom-top,right-left)
return (1-((box_rows*box_cols)/(rows*cols)))*100
def true_negatif(image,mask,threshold,threshold_merge = 0.172,method = 'crop',slices = 100): '''Input: an image, the corresponding mask, a method, a threshold and the number of slices. Ouput : the number of voxels that are in our bounding box but shouldnt.''' tp = true_positif(image,mask,threshold,threshold_merge = threshold_merge,method = method,slices =slices) [left,right,top,bottom] = get_coordinates(image,threshold,slices) total_bb = (right-left)*(bottom-top) return total_bb-tp
def false_positif(image,mask,threshold,threshold_merge = 0.172,method = 'crop',slices = 100): '''Input: an image, the corresponding mask, a method, a threshold and the number of slices. Ouput : the number of voxels that are not in our bounding box and shouldnt be.''' tp = true_positif(image,mask,threshold,threshold_merge = threshold_merge,method = method,slices =slices) total = total_voxels(image) [left,right,top,bottom] = get_coordinates(image,threshold,slices) total_bb = (right-left)*(bottom-top) [p_left,p_right,p_top,p_bottom] = perfect_box_coord(mask) total_pb = (p_right-p_left)*(p_bottom-p_top) return total-total_bb-total_pb+tp
def percentage_outside_box_crop(image,mask,threshold,threshold_merge = 0.172,method = 'crop',slices = 100):
'''Input: an image, the corresponding mask, a method, a threshold and the number of slices.
Ouput: percentage of the organs that our outside the box.'''
if(method == 'crop'):
[left,right,top,bottom] = get_coordinates(image,threshold,slices)
elif(method == 'merge'):
image = splitandmerge(image,threshold_merge,method='Exponent')
[left,right,top,bottom] = get_coordinates(image,threshold,slices)
else:
raise Exception('Unknown method')
[p_left,p_right,p_top,p_bottom] = perfect_box_coord(mask)
h_top = 0
h_bottom = 0
w_left = 0
w_right = 0
total_area = (bottom-top)*(left-right)
if(p_left<left): w_left = left-p_left
if(p_right>right): w_right = p_right-right
if(p_top<top): h_top = top-p_top
if(p_bottom>bottom): h_bottom = p_bottom-bottom
h = h_top+h_bottom
w = w_left+w_right
if(h == 0 and w == 0):
return 0
elif(h == 0):
h = bottom-top
elif(w == 0):
w = left-right
return ((h*w)/total_area)*100
bm2[:, :, s, :], col1, col2, x1, x0,
y0, y1, width, height, color)
line = (s // 16) * 192
col = (s % 16) * 192
im[line:(line + 192), col:(col + 192), :] = output
im = ((im - np.min(im)) * 255 / (np.max(im) - np.min(im))).astype(np.uint8)
frame1 = plt.gca()
frame1.axes.get_xaxis().set_visible(False)
frame1.axes.get_yaxis().set_visible(False)
plt.imshow(im)
print(info)
plt.show()
if filename != None:
plt.imsave(filename, im, dpi=1000)
plt.close()
image = np.load('data/merged_image.npy') # MERGE
mask = np.load('data/mask.npy')
mask = mask[:, :, :, :-1]
pred = np.zeros((192, 192, 160, 2))
col1 = np.array([[255, 0, 0], [0, 128, 0], [0, 0, 255]])
col2 = np.array([[255, 0, 255], [0, 255, 0], [0, 255, 255]])
[y0, y1, x0, x1] = get_coordinates(image, get_threshold(), 100)
info = 'patient 0'
filename = None
blue = np.array([0, 0, 255])
show_slices_gen(image, mask, pred, col1, col2, info, filename, y0, y1, x0, x1,
blue)
col = (s%16)*cols
im[line:(line+rows),col:(col+cols),:] = output
im = ((im - np.min(im)) * 255 / (np.max(im) - np.min(im))).astype(np.uint8)
frame1 = plt.gca()
frame1.axes.get_xaxis().set_visible(False)
frame1.axes.get_yaxis().set_visible(False)
plt.imshow(im)
print(info)
plt.show()
if filename != None:
plt.imsave(filename, im, dpi=1000)
plt.close()
image = np.load('../show_images/data/CHA-CBCT-2-image.npy')
[y0,y1,x0,x1] = get_coordinates(image,0.1,100)
info = 'patient 0'
filename = None
(rows,cols) = np.shape(image[:,:,0])
red = np.array([255,0,0])
show_slices_gen(image,rows,cols, info, filename,y0,y1,x0,x1,red)
