def next_image(self, image: ImageWrapper) -> ImageWrapper:
self.dimensions = image.dimensions()
self.channels = image.channels
if self.theta1 is None:
self.get_avg_color()
self.iterate()
image_copy = image.copy()
image_copy.channels = self.get_channels_with_borders()
image_copy.draw_image()
return image_copy
def canny_border_detection_wrapper(image: ImageWrapper.ImageWrapper,
gauss_sigma, t1,
t2) -> ImageWrapper.ImageWrapper:
img: Image = image.image_element
# Applying gaussian filter
img = channel_gaussian_window(img, gauss_sigma)
# Magnitude and slope
gx, gy = directional_derivatives(
ImageWrapper.ImageWrapper(img).channels[0])
g = np.hypot(gx, gy)
g_max = g.max()
g_min = g.min()
g_w, g_h = g.shape
for x in range(g_w):
for y in range(g_h):
g[x, y] = (g[x, y] - g_min) * 255 / (g_max - g_min)
slope = discrete_slope(
np.array([val * 180 / np.pi for val in np.arctan2(gy, gx)]))
channel = maximum_suppression(g, slope)
borders = mark_borders(t2, channel)
borders = weak_borders(t1, borders, channel)
channel = clean_image_with_borders(borders, channel)
image_copy = image.copy()
image_copy.channels = [channel]
return image_copy