def on_file_open(self, event):
dlg = wx.FileDialog(self.frame, style=wx.FD_OPEN)
if dlg.ShowModal() == wx.ID_OK:
img = pil_to_array(
PILImage.open(
os.path.join(dlg.GetDirectory(), dlg.GetFilename())))
if img.ndim == 3:
img = img[:, :, 0] + img[:, :, 1] + img[:, :, 2]
img = stretch(img.astype(float))
lt = self.frame.MenuBar.Menus[0][0].MenuItems[7].IsChecked()
if lt:
limg, d = log_transform(img)
else:
limg = img
self.frame.subplot_imshow_grayscale(0, 0, limg)
limg = limg.flatten()
menu_items = self.frame.MenuBar.Menus[0][0].MenuItems
if menu_items[2].IsChecked():
t1 = t2 = otsu(limg)
elif menu_items[3].IsChecked():
t1 = t2 = entropy(limg)
elif menu_items[4].IsChecked():
t1, t2 = otsu3slow(limg)
elif menu_items[5].IsChecked():
t1, t2 = entropy3(limg)
else:
t1, t2 = otsu3(limg)
if lt:
t1, t2 = inverse_log_transform(np.array([t1, t2]), d)
m1 = img < t1
m2 = np.logical_and(img >= t1, img < t2)
m3 = img > t2
cimg = np.zeros((m1.shape[0], m1.shape[1], 3))
cimg[:, :, 0][m1] = img[m1]
cimg[:, :, 1][m2] = img[m2]
cimg[:, :, 2][m3] = img[m3]
self.frame.subplot_imshow(1,
0,
cimg,
sharex=self.frame.subplot(0, 0),
sharey=self.frame.subplot(0, 0))
self.frame.Refresh()
wx.MessageBox("Low threshold = %f, high threshold = %f" %
(t1, t2),
parent=self.frame)
def test_05_02_otsu_entropy(self):
'''Test the entropy version of Otsu'''
np.random.seed(0)
image = np.hstack((np.random.exponential(1.5,size=600),
np.random.poisson(15,size=300)))
image.shape=(30,30)
image = stretch(image)
limage, d = T.log_transform(image)
threshold = entropy(limage)
threshold = T.inverse_log_transform(threshold, d)
expected = image > threshold
workspace, module = self.make_workspace(image)
module.binary.value = A.BINARY
module.threshold_method.value = T.TM_OTSU_GLOBAL
module.use_weighted_variance.value = I.O_ENTROPY
module.two_class_otsu.value = I.O_TWO_CLASS
module.run(workspace)
output = workspace.image_set.get_image(OUTPUT_IMAGE_NAME)
self.assertTrue(np.all(output.pixel_data == expected))
def test_05_02_otsu_entropy(self):
'''Test the entropy version of Otsu'''
np.random.seed(0)
image = np.hstack((np.random.exponential(1.5,size=600),
np.random.poisson(15,size=300)))
image.shape=(30,30)
image = stretch(image)
limage, d = T.log_transform(image)
threshold = entropy(limage)
threshold = T.inverse_log_transform(threshold, d)
expected = image > threshold
workspace, module = self.make_workspace(image)
module.binary.value = A.BINARY
module.threshold_method.value = T.TM_OTSU_GLOBAL
module.use_weighted_variance.value = I.O_ENTROPY
module.two_class_otsu.value = I.O_TWO_CLASS
module.run(workspace)
output = workspace.image_set.get_image(OUTPUT_IMAGE_NAME)
self.assertTrue(np.all(output.pixel_data == expected))
def test_05_06_otsu3_entropy_high(self):
'''Test the three-class otsu, entropy, middle = background'''
np.random.seed(0)
image = np.hstack((np.random.exponential(1.5,size=300),
np.random.poisson(15,size=300),
np.random.poisson(30,size=300)))
image.shape=(30,30)
image = stretch(image)
limage, d = T.log_transform(image)
t1,t2 = entropy3(limage)
threshold = T.inverse_log_transform(t1, d)
expected = image > threshold
workspace, module = self.make_workspace(image)
module.binary.value = A.BINARY
module.threshold_method.value = T.TM_OTSU_GLOBAL
module.use_weighted_variance.value = I.O_ENTROPY
module.two_class_otsu.value = I.O_THREE_CLASS
module.assign_middle_to_foreground.value = I.O_FOREGROUND
module.run(workspace)
output = workspace.image_set.get_image(OUTPUT_IMAGE_NAME)
self.assertTrue(np.all(output.pixel_data == expected))
def test_05_06_otsu3_entropy_high(self):
'''Test the three-class otsu, entropy, middle = background'''
np.random.seed(0)
image = np.hstack(
(np.random.exponential(1.5, size=300),
np.random.poisson(15, size=300), np.random.poisson(30, size=300)))
image.shape = (30, 30)
image = stretch(image)
limage, d = T.log_transform(image)
t1, t2 = entropy3(limage)
threshold = T.inverse_log_transform(t1, d)
expected = image > threshold
workspace, module = self.make_workspace(image)
module.binary.value = A.BINARY
module.threshold_method.value = T.TM_OTSU_GLOBAL
module.use_weighted_variance.value = I.O_ENTROPY
module.two_class_otsu.value = I.O_THREE_CLASS
module.assign_middle_to_foreground.value = I.O_FOREGROUND
module.run(workspace)
output = workspace.image_set.get_image(OUTPUT_IMAGE_NAME)
self.assertTrue(np.all(output.pixel_data == expected))
def on_file_open(self, event):
dlg = wx.FileDialog(self.frame,style=wx.FD_OPEN)
if dlg.ShowModal() == wx.ID_OK:
img = pil_to_array(PILImage.open(os.path.join(dlg.GetDirectory(),dlg.GetFilename())))
if img.ndim == 3:
img = img[:,:,0]+img[:,:,1]+img[:,:,2]
img = stretch(img.astype(float))
lt = self.frame.MenuBar.Menus[0][0].MenuItems[7].IsChecked()
if lt:
limg, d = log_transform(img)
else:
limg = img
self.frame.subplot_imshow_grayscale(0, 0, limg)
limg = limg.flatten()
menu_items = self.frame.MenuBar.Menus[0][0].MenuItems
if menu_items[2].IsChecked():
t1 = t2 = otsu(limg)
elif menu_items[3].IsChecked():
t1 = t2 = entropy(limg)
elif menu_items[4].IsChecked():
t1, t2 = otsu3slow(limg)
elif menu_items[5].IsChecked():
t1, t2 = entropy3(limg)
else:
t1, t2 = otsu3(limg)
if lt:
t1,t2 = inverse_log_transform(np.array([t1,t2]), d)
m1 = img < t1
m2 = np.logical_and(img >= t1, img < t2)
m3 = img > t2
cimg = np.zeros((m1.shape[0],m1.shape[1],3))
cimg[:,:,0][m1]=img[m1]
cimg[:,:,1][m2]=img[m2]
cimg[:,:,2][m3]=img[m3]
self.frame.subplot_imshow(1, 0, cimg,
sharex = self.frame.subplot(0,0),
sharey = self.frame.subplot(0,0))
self.frame.Refresh()
wx.MessageBox("Low threshold = %f, high threshold = %f"%(t1,t2),
parent=self.frame)
def test_05_04_otsu3_wv_high(self):
'''Test the three-class otsu, weighted variance middle = foreground'''
np.random.seed(0)
image = np.hstack((np.random.exponential(1.5,size=300),
np.random.poisson(15,size=300),
np.random.poisson(30,size=300)))
image.shape=(30,30)
image = stretch(image)
limage, d = T.log_transform(image)
t1,t2 = otsu3(limage)
threshold = T.inverse_log_transform(t1, d)
workspace, module = self.make_workspace(image)
module.binary.value = A.BINARY
module.threshold_scope.value = I.TS_GLOBAL
module.threshold_method.value = T.TM_OTSU
module.use_weighted_variance.value = I.O_WEIGHTED_VARIANCE
module.two_class_otsu.value = I.O_THREE_CLASS
module.assign_middle_to_foreground.value = I.O_FOREGROUND
module.run(workspace)
m = workspace.measurements
m_threshold = m[cpmeas.IMAGE, I.FF_ORIG_THRESHOLD % module.get_measurement_objects_name()]
self.assertAlmostEqual(m_threshold, threshold)
def test_05_05_otsu3_entropy_low(self):
'''Test the three-class otsu, entropy, middle = background'''
np.random.seed(0)
image = np.hstack((np.random.exponential(1.5,size=300),
np.random.poisson(15,size=300),
np.random.poisson(30,size=300)))
image.shape=(30,30)
image = stretch(image)
limage, d = T.log_transform(image)
t1,t2 = entropy3(limage)
threshold = T.inverse_log_transform(t2, d)
workspace, module = self.make_workspace(image)
module.binary.value = A.BINARY
module.threshold_scope.value = I.TS_GLOBAL
module.threshold_method.value = T.TM_OTSU
module.use_weighted_variance.value = I.O_ENTROPY
module.two_class_otsu.value = I.O_THREE_CLASS
module.assign_middle_to_foreground.value = I.O_BACKGROUND
module.run(workspace)
output = workspace.image_set.get_image(OUTPUT_IMAGE_NAME)
m = workspace.measurements
m_threshold = m[cpmeas.IMAGE, I.FF_ORIG_THRESHOLD % module.get_measurement_objects_name()]
self.assertAlmostEqual(m_threshold, threshold)
