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https://github.com/wassname/scikit-image.git
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apply PEP8 guideline
This commit is contained in:
Johannes Schönberger
committed by
Johannes Schönberger
Johannes Schönberger
parent
fdf1b6dac1
commit
acb1d71cd5
@@ -30,6 +30,7 @@ def _stackcopy(a, b):
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else:
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a[:] = b
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def _make_similarity(src, dst):
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"""Determine parameters of the 2D similarity transformation:
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X = a0*x - b0*y + a1
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@@ -39,20 +40,20 @@ def _make_similarity(src, dst):
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[b0 a0 b1]
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[0 0 1]]
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"""
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xs = src[:,0]
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ys = src[:,1]
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xd = dst[:,0]
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yd = dst[:,1]
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xs = src[:, 0]
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ys = src[:, 1]
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xd = dst[:, 0]
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yd = dst[:, 1]
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rows = src.shape[0]
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#: params: a0, a1, b0, b1
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A = np.zeros((rows*2, 4))
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A[:rows,0] = xs
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A[:rows,2] = - ys
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A[:rows,1] = 1
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A[rows:,2] = xs
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A[rows:,0] = ys
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A[rows:,3] = 1
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A = np.zeros((rows * 2, 4))
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A[:rows, 0] = xs
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A[:rows, 2] = - ys
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A[:rows, 1] = 1
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A[rows:, 2] = xs
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A[rows:, 0] = ys
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A[rows:, 3] = 1
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b = np.hstack([xd, yd])
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@@ -62,6 +63,7 @@ def _make_similarity(src, dst):
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[ 0, 0, 1]])
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return matrix
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def _make_affine(src, dst):
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"""Determine parameters of the 2D affine transformation:
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X = a0*x + a1*y + a2
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@@ -71,20 +73,20 @@ def _make_affine(src, dst):
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[b0 b1 b2]
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[0 0 1]]
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"""
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xs = src[:,0]
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ys = src[:,1]
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xd = dst[:,0]
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yd = dst[:,1]
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xs = src[:, 0]
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ys = src[:, 1]
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xd = dst[:, 0]
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yd = dst[:, 1]
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rows = src.shape[0]
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#: params: a0, a1, a2, b0, b1, b2
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A = np.zeros((rows*2, 6))
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A[:rows,0] = xs
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A[:rows,1] = ys
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A[:rows,2] = 1
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A[rows:,3] = xs
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A[rows:,4] = ys
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A[rows:,5] = 1
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A = np.zeros((rows * 2, 6))
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A[:rows, 0] = xs
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A[:rows, 1] = ys
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A[:rows, 2] = 1
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A[rows:, 3] = xs
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A[rows:, 4] = ys
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A[rows:, 5] = 1
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b = np.hstack([xd, yd])
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@@ -94,6 +96,7 @@ def _make_affine(src, dst):
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[0, 0, 1]])
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return matrix
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def _make_projective(src, dst):
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"""Determine transformation matrix of the 2D projective transformation:
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X = (a0 + a1*x + a2*y) / (c0*x + c1*y + 1)
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@@ -103,24 +106,24 @@ def _make_projective(src, dst):
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[b0 b1 b2]
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[c0 c1 1]]
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"""
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xs = src[:,0]
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ys = src[:,1]
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xd = dst[:,0]
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yd = dst[:,1]
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xs = src[:, 0]
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ys = src[:, 1]
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xd = dst[:, 0]
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yd = dst[:, 1]
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rows = src.shape[0]
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#: params: a0, a1, a2, b0, b1, b2, c0, c1
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A = np.zeros((rows*2, 8))
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A[:rows,0] = xs
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A[:rows,1] = ys
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A[:rows,2] = 1
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A[:rows,6] = - xd * xs
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A[:rows,7] = - xd * ys
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A[rows:,3] = xs
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A[rows:,4] = ys
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A[rows:,5] = 1
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A[rows:,6] = - yd * xs
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A[rows:,7] = - yd * ys
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A = np.zeros((rows * 2, 8))
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A[:rows, 0] = xs
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A[:rows, 1] = ys
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A[:rows, 2] = 1
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A[:rows, 6] = - xd * xs
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A[:rows, 7] = - xd * ys
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A[rows:, 3] = xs
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A[rows:, 4] = ys
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A[rows:, 5] = 1
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A[rows:, 6] = - yd * xs
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A[rows:, 7] = - yd * ys
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b = np.hstack([xd, yd])
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@@ -130,32 +133,34 @@ def _make_projective(src, dst):
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[c0, c1, 1]])
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return matrix
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def _make_polynomial(src, dst, order):
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"""Determine parameters of 2D polynomial transformation of order n:
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X = sum[j=0:n]( sum[i=0:j]( a_ji * x**(j - i) * y**i ))
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Y = sum[j=0:n]( sum[i=0:j]( b_ji * x**(j - i) * y**i ))
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"""
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xs = src[:,0]
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ys = src[:,1]
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xd = dst[:,0]
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yd = dst[:,1]
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xs = src[:, 0]
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ys = src[:, 1]
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xd = dst[:, 0]
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yd = dst[:, 1]
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rows = src.shape[0]
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# number of unknown polynomial coefficients
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u = (order + 1) * (order + 2)
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A = np.zeros((rows*2, u))
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A = np.zeros((rows * 2, u))
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pidx = 0
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for j in xrange(order+1):
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for i in xrange(j+1):
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A[:rows,pidx] = xs ** (j - i) * ys ** i
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A[rows:,pidx+u/2] = xs ** (j - i) * ys ** i
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for j in xrange(order + 1):
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for i in xrange(j + 1):
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A[:rows, pidx] = xs ** (j - i) * ys ** i
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A[rows:, pidx + u / 2] = xs ** (j - i) * ys ** i
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pidx += 1
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b = np.hstack([xd, yd])
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return np.linalg.lstsq(A, b)[0]
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def _make_rotation(angle):
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"""Determine homogeneous transformation matrix of 2D rotation:
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[[cos(angle) -sin(angle) 0]
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@@ -169,29 +174,32 @@ def _make_rotation(angle):
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]
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return np.array(R)
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def _transform(coords, matrix):
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src = np.vstack((coords[:,0], coords[:,1], np.ones((coords.shape[0],))))
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x, y = np.transpose(coords)
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src = np.vstack((x, y, np.ones_like(x)))
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dst = np.dot(src.transpose(), matrix.transpose())
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# rescale to homogeneous coordinates
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dst[:,0] /= dst[:,2]
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dst[:,1] /= dst[:,2]
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dst[:, 0] /= dst[:, 2]
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dst[:, 1] /= dst[:, 2]
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# values close to zero because of limited numerical precision
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dst[np.abs(dst) < EPS] = 0
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return dst[:,:2]
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return dst[:, :2]
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def _transform_polynomial(coords, matrix):
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x = coords[:,0]
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y = coords[:,1]
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x = coords[:, 0]
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y = coords[:, 1]
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u = len(matrix)
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# number of coefficients -> u = (order + 1) * (order + 2)
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order = int((-3 + math.sqrt(9 - 4 * (2 - u))) / 2)
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order = int((- 3 + math.sqrt(9 - 4 * (2 - u))) / 2)
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dst = np.zeros(coords.shape)
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pidx = 0
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for j in xrange(order+1):
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for i in xrange(j+1):
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dst[:,0] += matrix[pidx] * x ** (j - i) * y ** i
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dst[:,1] += matrix[pidx+u/2] * x ** (j - i) * y ** i
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for j in xrange(order + 1):
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for i in xrange(j + 1):
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dst[:, 0] += matrix[pidx] * x ** (j - i) * y ** i
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dst[:, 1] += matrix[pidx + u / 2] * x ** (j - i) * y ** i
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pidx += 1
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return dst
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@@ -283,8 +291,8 @@ def make_tform(ttype, **kwargs):
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'polynomial' `src, `dst`, `order` (polynomial order)
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'rotation' `angle`
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Alternatively you can explicitly define a 3x3 homogeneous transformation
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matrix with the `matrix` parameter.
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Alternatively you can explicitly define a 3x3 homogeneous
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transformation matrix with the `matrix` parameter.
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See examples section below for usage.
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@@ -314,8 +322,9 @@ def make_tform(ttype, **kwargs):
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matrix = TRANSFORMATIONS[ttype][0](**kwargs)
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return Transformation(ttype, matrix)
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def warp(image, reverse_map=None, map_args={}, tform=None,
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output_shape=None, order=1, mode='constant', cval=0.):
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def warp(image, reverse_map=None, map_args={}, output_shape=None, order=1,
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mode='constant', cval=0.):
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"""Warp an image according to a given coordinate transformation.
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Parameters
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@@ -398,10 +407,11 @@ def warp(image, reverse_map=None, map_args={}, tform=None,
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# so clip to ensure valid data
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return np.clip(mapped.squeeze(), 0, 1)
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def _swirl_mapping(xy, center, rotation, strength, radius):
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x, y = xy.T
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x0, y0 = center
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rho = np.sqrt((x - x0)**2 + (y - y0)**2)
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rho = np.sqrt((x - x0) ** 2 + (y - y0) ** 2)
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# Ensure that the transformation decays to approximately 1/1000-th
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# within the specified radius.
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@@ -416,6 +426,7 @@ def _swirl_mapping(xy, center, rotation, strength, radius):
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return xy
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def swirl(image, center=None, strength=1, radius=100, rotation=0,
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output_shape=None, order=1, mode='constant', cval=0):
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"""Perform a swirl transformation.
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@@ -467,6 +478,7 @@ def swirl(image, center=None, strength=1, radius=100, rotation=0,
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output_shape=output_shape,
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order=order, mode=mode, cval=cval)
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def homography(image, H, output_shape=None, order=1,
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mode='constant', cval=0.):
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"""Perform a projective transformation (homography) on an image.
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