def test_dilate_erode_wrong_parameters_4():
image = numpy.zeros((25, 25), dtype=numpy.uint8)
mask = numpy.zeros((25, 25, 3), dtype=numpy.uint8)
try:
phm_img.dilate_erode(image, mask=mask)
except Exception, e:
assert type(e) == ValueError
def test_dilate_erode_wrong_parameters_1():
try:
phm_img.dilate_erode(None)
except Exception as e:
assert type(e) == TypeError
else:
assert False
def test_dilate_erode_wrong_parameters_2():
image = numpy.zeros((25, 25, 3), dtype=numpy.uint8)
try:
phm_img.dilate_erode(image)
except Exception as e:
assert type(e) == ValueError
else:
assert False
def test_dilate_erode_wrong_parameters_3():
image = numpy.zeros((25, 25), dtype=numpy.uint8)
mask = 42
try:
phm_img.dilate_erode(image, mask=mask)
except Exception as e:
assert type(e) == TypeError
else:
assert False
def test_dilate_erode_2():
image = numpy.zeros((25, 25), dtype=numpy.uint8)
image_cleaning = phm_img.dilate_erode(image)
assert isinstance(image_cleaning, numpy.ndarray)
assert image_cleaning.ndim == 2
def routine_top_binarization(image):
hsv_min = (42, 75, 28)
hsv_max = (80, 250, 134)
median_blur_size=9
iterations=5
# Convert image on HSV representation
hsv_image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
# Apply a median blur on the image
hsv_image = cv2.medianBlur(hsv_image, ksize=median_blur_size)
# Threshold the image with HSV min and max value
bin_img = phm_img.threshold_hsv(hsv_image, hsv_min, hsv_max)
# dilate and erode the image to remove possible noise
bin_img = phm_img.dilate_erode(bin_img, kernel_shape=(3, 3),iterations=iterations)
return bin_img
