def _runLayer(self, layer_file):
C0_8U = loadRGBA(layer_file)
if C0_8U is None:
return
A_8U = alpha(C0_8U)
# if A_8U is None:
# return
C0_32F = to32F(rgb(C0_8U))
I_32F = luminance(C0_32F)
Lab_32F = rgb2Lab(C0_32F)
th_specular = 0.2
th_contour = 0.02
th_material = 0.1
E_32F = DoG(I_32F, sigma=2.0)
contour = th_contour * np.min(E_32F) - E_32F
contour *= 1.0 / np.max(contour)
contour = np.clip(contour, 0.0, 1.0)
specular = E_32F - th_specular * np.max(E_32F)
specular *= 1.0 / np.max(specular)
specular = np.clip(specular, 0.0, 1.0)
material = rgb(C0_8U)
# edge_mask = np.zeros(I_32F.shape, dtype=np.uint8)
# edge_mask[contour > 0.0] = 1.0
# material = cv2.inpaint(material, edge_mask, 3, cv2.INPAINT_TELEA)
for i in xrange(1):
material = cv2.medianBlur(material, ksize=7)
# material = th_material * np.max(np.abs(E_32F)) - np.abs(E_32F)
# material *= 1.0 / np.max(material)
# material = np.clip(material, 0.0, 1.0)
# material[material > 0.0] = 1.0
E_32F[E_32F < 0.0] = 0.0
fig, axes = plt.subplots(figsize=(11, 5))
font_size = 15
fig.subplots_adjust(left=0.05, right=0.95, top=0.9, hspace=0.12, wspace=0.05)
fig.suptitle(self.name(), fontsize=font_size)
num_rows = 1
num_cols = 4
plot_grid = SubplotGrid(num_rows, num_cols)
plot_grid.showImage(C0_8U, r'$C$')
#plot_grid.showImage(setAlpha(C0_32F, material), r'$Material$')
plot_grid.showImage(setAlpha(material, A_8U), r'$Material$')
plot_grid.showImage(setAlpha(C0_32F, contour), r'$Contour$')
plot_grid.showImage(setAlpha(C0_32F, specular), r'$Specular$')
showMaximize()
def LabFeatures(image, w_L=1.0, w_a=1.0, w_b=1.0):
img_32F = to32F(image)
rgb_32F = img_32F[:, :, :3]
Lab_32F = rgb2Lab(rgb_32F)
h, w, cs = image.shape
features = Lab_32F.reshape(h * w, -1)
features[:, 0] *= w_L
features[:, 1] *= w_a
features[:, 2] *= w_b
return features
def LabFeatures(image, w_L=1.0, w_a=1.0, w_b=1.0):
img_32F = to32F(image)
rgb_32F = img_32F[:, :, :3]
Lab_32F = rgb2Lab(rgb_32F)
h, w, cs = image.shape
features = Lab_32F.reshape(h * w, -1)
features[:, 0] *= w_L
features[:, 1] *= w_a
features[:, 2] *= w_b
return features
def _compute(self):
C0_8U = self._image
C0_32F = to32F(rgb(C0_8U))
Lab_32F = rgb2Lab(C0_32F)
I_32F = luminance(C0_32F)
h, w = I_32F.shape[:2]
edge_mask = np.zeros((h, w), dtype=np.uint8)
#E_32F = DoG(Lab_32F, sigma=7.0)
# E_32F[E_32F > 0.0] = 0.0
# E_32F = - E_32F
# E_32F /= np.max(E_32F)
# th_contour = 0.05
# for ci in xrange(3):
# edge_area = E_32F[:, :, ci] > th_contour
# edge_mask[edge_area] = 255
E_32F = DoG(I_32F, sigma=3.0)
h, w = E_32F.shape[:2]
E_norm = -np.array(E_32F)
E_norm[E_norm < 0.0] = 0.0
th_contour = 0.1
edge_area = E_norm > th_contour * np.max(E_norm)
edge_mask[edge_area] = 255
self._edge_mask = edge_mask
labels = np.array(edge_mask)
mask = np.ones((h + 2, w + 2), dtype=np.uint8)
mask[1:-1, 1:-1] = self._edge_mask
for label in xrange(1, 3):
regionSeeds = np.where(self._edge_mask == 0)
if len(regionSeeds[0]) == 0:
break
p = (regionSeeds[1][0], regionSeeds[0][0])
cv2.floodFill(labels, mask, p, label)
self._edge_mask[labels == label] = label
self._labels = labels
def _compute(self):
C0_8U = self._image
C0_32F = to32F(rgb(C0_8U))
Lab_32F = rgb2Lab(C0_32F)
I_32F = luminance(C0_32F)
h, w = I_32F.shape[:2]
edge_mask = np.zeros((h, w), dtype=np.uint8)
# E_32F = DoG(Lab_32F, sigma=7.0)
# E_32F[E_32F > 0.0] = 0.0
# E_32F = - E_32F
# E_32F /= np.max(E_32F)
# th_contour = 0.05
# for ci in xrange(3):
# edge_area = E_32F[:, :, ci] > th_contour
# edge_mask[edge_area] = 255
E_32F = DoG(I_32F, sigma=3.0)
h, w = E_32F.shape[:2]
E_norm = -np.array(E_32F)
E_norm[E_norm < 0.0] = 0.0
th_contour = 0.1
edge_area = E_norm > th_contour * np.max(E_norm)
edge_mask[edge_area] = 255
self._edge_mask = edge_mask
labels = np.array(edge_mask)
mask = np.ones((h + 2, w + 2), dtype=np.uint8)
mask[1:-1, 1:-1] = self._edge_mask
for label in xrange(1, 3):
regionSeeds = np.where(self._edge_mask == 0)
if len(regionSeeds[0]) == 0:
break
p = (regionSeeds[1][0], regionSeeds[0][0])
cv2.floodFill(labels, mask, p, label)
self._edge_mask[labels == label] = label
self._labels = labels
