def test_threshold_hsv_wrong_parameters_13():
image = numpy.zeros((25, 25, 3), dtype=numpy.uint8)
hsv_min = (42, 75, 28)
hsv_max = (80, 250, 134)
mask = numpy.zeros((25, 26), dtype=numpy.uint8)
try:
phm_img.threshold_hsv(image, hsv_min, hsv_max, mask=mask)
except Exception, e:
assert type(e) == ValueError
def test_threshold_hsv_wrong_parameters_6():
image = numpy.zeros((25, 25, 3), dtype=numpy.uint8)
hsv_min = (42, 75, 28)
hsv_max = None
mask = numpy.zeros((25, 25), dtype=numpy.uint8)
try:
phm_img.threshold_hsv(image, hsv_min, hsv_max, mask=mask)
except Exception, e:
assert type(e) == TypeError
def test_threshold_hsv_wrong_parameters_5():
image = numpy.zeros((25, 25, 3), dtype=numpy.uint8)
hsv_min = (80.6, 250.0, 56.9)
hsv_max = (80, 250, 134)
mask = numpy.zeros((25, 25), dtype=numpy.uint8)
try:
phm_img.threshold_hsv(image, hsv_min, hsv_max, mask=mask)
except Exception as e:
assert type(e) == ValueError
else:
assert False
def test_threshold_hsv_wrong_parameters_1():
image = None
hsv_min = (42, 75, 28)
hsv_max = (80, 250, 134)
mask = numpy.zeros((25, 25), dtype=numpy.uint8)
try:
phm_img.threshold_hsv(image, hsv_min, hsv_max, mask=mask)
except Exception as e:
assert type(e) == TypeError
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_hsv_1():
image = numpy.zeros((25, 25, 3), dtype=numpy.uint8)
hsv_min = (42, 75, 28)
hsv_max = (80, 250, 134)
mask = numpy.zeros((25, 25), dtype=numpy.uint8)
img_bin = phm_img.threshold_hsv(image, hsv_min, hsv_max, mask=mask)
assert img_bin.ndim == 2
assert numpy.count_nonzero(img_bin) == 0
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
