def test_bckgrnd_correc_rect():
#check expected outputs
a = int(40)
b = int(40)
ch = bckgrnd_correc_rect(I1_test, a, b)
plt.close()
assert type(
ch
) == np.ndarray, "Function output should be numpy ndarray. Please check function for expected error"
# try pandas loaded image data
data = pd.read_csv('tdj_grbp5_1um_1hr_3rd_020717.001.txt', sep=" ")
try:
bckgrnd_correc_rect(data, a, b)
except Exception:
pass
else:
raise Exception(
'Image data must be numpy ndarray. Check function for error')
# try oddball input
a = float(60)
b = float(60)
try:
bckgrnd_correc_rect(I1_test, a, b)
except Exception:
pass
else:
raise Exception(
'Morphology.rectangle only takes int dtype for structuring dimensions. Check function for error'
)
return
from sklearn import cluster
from scipy import ndimage
import cv2
from ImageSeparation import sep
from ImageSegmentationFunc import convert_to_grayscale
from ImageSegmentationFunc import seg_random_walker
from ImagePreprocessing import bckgrnd_correc_rect
from unittest import mock
from unittest import TestCase
import sys
from io import StringIO
I1_test = np.loadtxt('tdj_grbp5_1um_1hr_3rd_020717.001.txt')
gh = bckgrnd_correc_rect(I1_test, 10, 200)
plt.close()
gray_I1 = convert_to_grayscale(gh)
plt.close()
trial_t = seg_random_walker(gray_I1, 0.15)
plt.close()
plt.close()
plt.close()
class sepTests(TestCase):
@mock.patch('ImageSeparation.input', create=True)
#check expected outputs for specific user inputs
def test_sep(self, mocked_input):
mocked_input.side_effect = ['Y', 'Y']