def __init__(self, I, radius, eps):
self.I = to_32F(I)
self.radius = radius
self.eps = eps
self.rows = self.I.shape[0]
self.cols = self.I.shape[1]
self.chs = self.I.shape[2]
def test_color():
image = cv2.imread('data/Lenna.png')
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
noise = (np.random.rand(image.shape[0], image.shape[1], 3) - 0.5) * 50
image_noise = image + noise
radius = [1, 2, 4]
eps = [0.005]
combs = list(itertools.product(radius, eps))
vis.plot_single(to_32F(image), title='origin')
vis.plot_single(to_32F(image_noise), title='noise')
for r, e in combs:
GF = GuidedFilter(image, radius=r, eps=e)
vis.plot_single(to_32F(GF.filter(image_noise)), title='r=%d, eps=%.3f' % (r, e))
def filter(self, p):
p = to_32F(p)
if len(p.shape) == 2:
return self._Filter.filter(p)
elif len(p.shape) == 3:
channels = p.shape[2]
ret = np.zeros_like(p, dtype=np.float32)
for c in range(channels):
ret[:, :, c] = self._Filter.filter(p[:, :, c])
return ret
def __init__(self, I, radius, eps):
"""
Parameters
----------
I: NDArray
2D guided image
radius: int
Radius of filter
eps: float
Value controlling sharpness
"""
self.I = to_32F(I)
self.radius = radius
self.eps = eps
def filter(self, p):
"""
Parameters
----------
p: NDArray
Filtering input which is 2D or 3D with format
HW or HWC
Returns
-------
ret: NDArray
Filtering output whose shape is same with input
"""
p = to_32F(p)
if len(p.shape) == 2:
return self._Filter.filter(p)
elif len(p.shape) == 3:
channels = p.shape[2]
ret = np.zeros_like(p, dtype=np.float32)
for c in range(channels):
ret[:, :, c] = self._Filter.filter(p[:, :, c])
return ret
def __init__(self, I, radius, eps):
self.I = to_32F(I)
self.radius = radius
self.eps = eps