def SobelFilter(img):
x_filter = [[-1., 0., 1.], [-2., 0., 2.], [-1., 0., 1.]]
y_filter = [[-1., -2., -1.], [0., 0., 0.], [1., 2., 1.]]
# f_x = filters.sobel(img, axis=1, mode='constant', cval=0.0)
# f_y = filters.sobel(img, axis=0, mode='constant', cval=0.0)
g_x = custom_utils.handle_window(img, x_filter, custom_utils.sum_cross_mtx)
g_y = custom_utils.handle_window(img, y_filter, custom_utils.sum_cross_mtx)
u_mag = np.hypot(g_x, g_y)
u_dir = np.arctan2(g_y, g_x)
return u_mag, u_dir
def MeanFilter(img):
# img = np.array(img)
mean33 = [[1., 1., 1.],
[1., 1., 1.],
[1., 1., 1.]]
win = np.array(mean33) / 9
# assumes zero pad. need to think about
# print filters.convolve(img, win, mode='constant', cval=0)
return custom_utils.handle_window(img, win, custom_utils.sum_cross_mtx)
def MedianFilter(img):
# out = filters.median_filter(img, size=(3, 3), mode='constant', cval=0)
return custom_utils.handle_window(img, DEFAULT33, custom_utils.median_cross_mtx, False)
def ComboFilter(img):
out = custom_utils.handle_window(img, DEFAULT33, combo_func, False)
out = np.clip(out, 0, 1)
return out
def PixelFilter(img):
out = custom_utils.handle_window(img, DEFAULT33, pixel_func, False)
out = np.clip(out, 0, 1)
return out