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