def image_height(filename, N=[30, 30], p=[4, 4]):
"""Generate a B-spline surface approximation given by the heightmap in a
grayscale image.
:param str filename: Name of image file to read
:param (int,int) N: Number of controlpoints in u-direction
:param (int,int) p: Polynomial order (degree+1)
:return: Normalized (all values between 0 and 1) heightmap approximation
:rtype: :class:`splipy.Surface`
"""
import cv2
im = cv2.imread(filename)
width = len(im)
height = len(im[0])
# initialize image holder
imGrey = np.zeros((len(im), len(im[0])), np.uint8)
# convert to greyscale image
if cv2.__version__[0] == '2':
warnings.warn(
FutureWarning(
'openCV v.2 will eventually be discontinued. Please update your version: \"pip install opencv-python --upgrade\"'
))
cv2.cvtColor(im, cv2.cv.CV_RGB2GRAY, imGrey)
else:
cv2.cvtColor(im, cv2.COLOR_RGB2GRAY, imGrey)
pts = []
# guess uniform evaluation points and knot vectors
u = range(width)
v = range(height)
knot1 = [0] * 3 + range(N[0] - p[0] + 2) + [N[0] - p[0] + 1] * 3
knot2 = [0] * 3 + range(N[0] - p[0] + 2) + [N[0] - p[0] + 1] * 3
# normalize all values to be in range [0, 1]
u = [float(i) / u[-1] for i in u]
v = [float(i) / v[-1] for i in v]
knot1 = [float(i) / knot1[-1] for i in knot1]
knot2 = [float(i) / knot2[-1] for i in knot2]
for j in range(height):
for i in range(width):
pts.append(
[v[j], u[i],
float(imGrey[width - i - 1][j]) / 255.0 * 1.0])
basis1 = BSplineBasis(4, knot1)
basis2 = BSplineBasis(4, knot2)
return surface_factory.least_square_fit(pts, [basis1, basis2], [u, v])
def image_height(filename, N=[30,30], p=[4,4]):
"""Generate a B-spline surface approximation given by the heightmap in a
grayscale image.
:param str filename: Name of image file to read
:param (int,int) N: Number of controlpoints in u-direction
:param (int,int) p: Polynomial order (degree+1)
:return: Normalized (all values between 0 and 1) heightmap approximation
:rtype: :class:`splipy.Surface`
"""
import cv2
im = cv2.imread(filename)
width = len(im)
height = len(im[0])
# initialize image holder
imGrey = np.zeros((len(im), len(im[0])), np.uint8)
# convert to greyscale image
if cv2.__version__[0] == '2':
warnings.warn(FutureWarning('openCV v.2 will eventually be discontinued. Please update your version: \"pip install opencv-python --upgrade\"'))
cv2.cvtColor(im, cv2.cv.CV_RGB2GRAY, imGrey)
else:
cv2.cvtColor(im, cv2.COLOR_RGB2GRAY, imGrey)
pts = []
# guess uniform evaluation points and knot vectors
u = range(width)
v = range(height)
knot1 = [0]*3 + range(N[0]-p[0]+2) + [N[0]-p[0]+1]*3
knot2 = [0]*3 + range(N[0]-p[0]+2) + [N[0]-p[0]+1]*3
# normalize all values to be in range [0, 1]
u = [float(i)/u[-1] for i in u]
v = [float(i)/v[-1] for i in v]
knot1 = [float(i)/knot1[-1] for i in knot1]
knot2 = [float(i)/knot2[-1] for i in knot2]
for j in range(height):
for i in range(width):
pts.append([v[j], u[i], float(imGrey[width-i-1][j])/255.0*1.0])
basis1 = BSplineBasis(4, knot1)
basis2 = BSplineBasis(4, knot2)
return surface_factory.least_square_fit(pts,[basis1, basis2], [u,v])
def image_height(filename, N=[30, 30], p=[4, 4]):
"""Generate a B-spline surface approximation given by the heightmap in a
grayscale image.
:param str filename: Name of image file to read
:param (int,int) N: Number of controlpoints in u-direction
:param (int,int) p: Polynomial order (degree+1)
:return: Normalized (all values between 0 and 1) heightmap approximation
:rtype: :class:`splipy.Surface`
"""
import cv2
im = cv2.imread(filename)
width = len(im[0])
height = len(im)
# initialize image holder
imGrey = np.zeros((height, width), np.uint8)
# convert to greyscale image
cv2.cvtColor(im, cv2.COLOR_RGB2GRAY, imGrey)
# guess uniform evaluation points and knot vectors
u = list(range(width))
v = list(range(height))
knot1 = [0] * (p[0] - 1) + list(
range(N[0] - p[0] + 2)) + [N[0] - p[0] + 1] * (p[0] - 1)
knot2 = [0] * (p[1] - 1) + list(
range(N[1] - p[1] + 2)) + [N[1] - p[1] + 1] * (p[1] - 1)
# normalize all values to be in range [0, 1]
u = [float(i) / u[-1] for i in u]
v = [float(i) / v[-1] for i in v]
knot1 = [float(i) / knot1[-1] for i in knot1]
knot2 = [float(i) / knot2[-1] for i in knot2]
# flip and reverse image so coordinate (0,0) is at lower-left corner
imGrey = imGrey.T / 255.0
imGrey = np.flip(imGrey, axis=1)
x, y = np.meshgrid(u, v, indexing='ij')
pts = np.stack([x, y, imGrey], axis=2)
basis1 = BSplineBasis(p[0], knot1)
basis2 = BSplineBasis(p[1], knot2)
return surface_factory.least_square_fit(pts, [basis1, basis2], [u, v])
def test_l2(self):
t = np.linspace(0, 1, 110)
V,U = np.meshgrid(t,t)
x = np.zeros((110,110,3))
x[:,:,0] = U*U + V*V
x[:,:,1] = U - V
x[:,:,2] = U*U*V*V*V - U*V + 3
b1 = BSplineBasis(3, [0,0,0,.33,.66,.7, .8,1,1,1])
b2 = BSplineBasis(4, [0,0,0,0,.1, .2, .5,1,1,1,1])
surf = SurfaceFactory.least_square_fit(x, [b1,b2], [t,t])
t = np.linspace(0, 1, 13)
V,U = np.meshgrid(t,t)
x = surf(t,t)
self.assertTrue(np.allclose(x[:,:,0], U*U + V*V))
self.assertTrue(np.allclose(x[:,:,1], U - V))
self.assertTrue(np.allclose(x[:,:,2], U*U*V*V*V - U*V + 3))
def test_l2(self):
t = np.linspace(0, 1, 110)
V, U = np.meshgrid(t, t)
x = np.zeros((110, 110, 3))
x[:, :, 0] = U * U + V * V
x[:, :, 1] = U - V
x[:, :, 2] = U * U * V * V * V - U * V + 3
b1 = BSplineBasis(3, [0, 0, 0, .33, .66, .7, .8, 1, 1, 1])
b2 = BSplineBasis(4, [0, 0, 0, 0, .1, .2, .5, 1, 1, 1, 1])
surf = sf.least_square_fit(x, [b1, b2], [t, t])
t = np.linspace(0, 1, 13)
V, U = np.meshgrid(t, t)
x = surf(t, t)
self.assertTrue(np.allclose(x[:, :, 0], U * U + V * V))
self.assertTrue(np.allclose(x[:, :, 1], U - V))
self.assertTrue(np.allclose(x[:, :, 2], U * U * V * V * V - U * V + 3))
