def test_threshold_meanshift_wrong_parameters_2():
image = numpy.zeros((25, 25, 3), dtype=numpy.uint8)
try:
phm_img.threshold_meanshift(image, None)
except Exception, e:
assert e.message == 'mean should be a numpy.ndarray'
assert type(e) == TypeError
def test_threshold_meanshift_wrong_parameters_9():
image = numpy.zeros((25, 25, 3), dtype=numpy.uint8)
mean_image = numpy.zeros((25, 25, 3), dtype=numpy.uint8)
try:
phm_img.threshold_meanshift(image, mean_image, reverse=None)
except Exception, e:
assert type(e) == TypeError
def test_threshold_meanshift_wrong_parameters_8():
image = numpy.zeros((25, 25, 3), dtype=numpy.uint8)
mean_im = numpy.zeros((25, 25, 3), dtype=numpy.uint8)
try:
phm_img.threshold_meanshift(image, mean_im, threshold=2)
except Exception, e:
assert type(e) == ValueError
def test_threshold_meanshift_wrong_parameters_10():
image = numpy.zeros((25, 25, 3), dtype=numpy.uint8)
mean_image = numpy.zeros((25, 25, 3), dtype=numpy.uint8)
mask = [[1, 1, 1]]
try:
phm_img.threshold_meanshift(image, mean_image, mask=mask)
except Exception, e:
assert type(e) == TypeError
def test_threshold_meanshift_wrong_parameters_7():
image = numpy.zeros((25, 25, 3), dtype=numpy.uint8)
mean_im = numpy.zeros((25, 25), dtype=numpy.uint8)
try:
phm_img.threshold_meanshift(image, mean_im)
except Exception, e:
assert e.message == 'mean should be 3D array'
assert type(e) == ValueError
def test_threshold_meanshift_wrong_parameters_6():
image = numpy.zeros((25, 25, 3), dtype=numpy.uint8)
mean_im = numpy.zeros((10, 10, 3), dtype=numpy.uint8)
try:
phm_img.threshold_meanshift(image, mean_im)
except Exception, e:
assert e.message == 'image and mean must have equal sizes'
assert type(e) == ValueError
def test_threshold_meanshift_wrong_parameters_1():
mean_image = numpy.zeros((25, 25, 3), dtype=numpy.uint8)
try:
phm_img.threshold_meanshift(None, mean_image)
except Exception as e:
assert str(e) == 'image should be a numpy.ndarray'
assert type(e) == TypeError
else:
assert False
def test_threshold_meanshift_wrong_parameters_12():
image = numpy.zeros((25, 25, 3), dtype=numpy.uint8)
mean_image = numpy.zeros((25, 25, 3), dtype=numpy.uint8)
mask = numpy.zeros((25, 26), dtype=numpy.uint8)
try:
phm_img.threshold_meanshift(image, mean_image, mask=mask)
except Exception as e:
assert type(e) == ValueError
else:
assert False
def test_threshold_meanshift_wrong_parameters_3():
image = numpy.zeros((25, 25), dtype=numpy.uint8)
mean_image = numpy.zeros((25, 25, 3), dtype=numpy.uint8)
try:
phm_img.threshold_meanshift(image, mean_image)
except Exception as e:
assert str(e) == 'image should be 3D array'
assert type(e) == ValueError
else:
assert False
def routine_side_binarization(image, mean_img):
maks = phm_data.tutorial_data_binarization_mask()
threshold = 0.3
dark_background = False
hsv_min = (30, 11, 0)
hsv_max = (129, 254, 141)
# Convert image on HSV representation
hsv_image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
# Threshold the image with HSV min and max value
binary_hsv_image = phm_img.threshold_hsv(hsv_image, hsv_min, hsv_max,
maks[0])
# Threshold the image with difference between image and mean_image
binary_mean_shift_image = phm_img.threshold_meanshift(
image, mean_img, threshold, dark_background, maks[1])
# Add the two image
result = cv2.add(binary_hsv_image, binary_mean_shift_image)
# Erode and dilate the image to remove possible noise
result = cv2.medianBlur(result, 3)
return result
def test_threshold_meanshift_4():
im1 = numpy.zeros((25, 25, 3), numpy.uint8)
im2 = numpy.zeros((25, 25, 3), numpy.uint8)
mask = numpy.zeros((25, 25), numpy.uint8)
mean_im = phm_img.mean_image(list([im1, im2]))
bin_img = phm_img.threshold_meanshift(im1, mean_im, mask=mask, reverse=True)
assert bin_img.shape == (25, 25)
def test_threshold_meanshift_1():
im1 = numpy.zeros((25, 25, 3), numpy.uint8)
im2 = numpy.zeros((25, 25, 3), numpy.uint8)
mean_im = phm_img.mean_image(list([im1, im2]))
bin_img = phm_img.threshold_meanshift(im1, mean_im)
assert bin_img.shape == (25, 25)
def test_threshold_meanshift_no_regression_1():
plant_number = 6
raw_side_images = phm_data.raw_images(plant_number=plant_number)['side']
mask = phm_data.tutorial_data_binarization_mask()[1]
mean_im = phm_img.mean_image(raw_side_images.values())
im_bin = phm_img.threshold_meanshift(raw_side_images[0], mean_im, mask=mask)
ref = 158482
# Acceptation error of 0.01 %
acceptation_error = ref * 0.001
if abs(numpy.count_nonzero(im_bin) - ref) > acceptation_error:
assert False
