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# Copyright (c) 2016-2018 Weitian LI <weitian@aaronly.me>
# MIT license
"""
Image (only gray-scale image, i.e., matrix) transformation utilities.
References
----------
- Leptonica: Rotation
http://www.leptonica.com/rotation.html
- Image rotation by MATLAB without using imrotate
https://stackoverflow.com/a/19687481/4856091
https://stackoverflow.com/a/19689081/4856091
- Stackoverflow: Python: Rotating greyscale images
https://codereview.stackexchange.com/a/41903
"""
import numpy as np
# import numba as nb
from scipy import ndimage
# @nb.jit([nb.float64[:, :](nb.int64[:, :], nb.float64, nb.boolean,
# nb.boolean, nb.float64),
# nb.float64[:, :](nb.float64[:, :], nb.float64, nb.boolean,
# nb.boolean, nb.float64)],
# nopython=True)
def rotate_center(imgin, angle, interp=True, reshape=True, fill_value=0.0):
"""
Rotate the input image (only gray-scale image currently supported)
by a given angle about its center.
Parameters
----------
imgin : 2D `~numpy.ndarray`
Input gray-scale image to be rotated
angle : float
Rotation angle (unit: [ degree ])
interp : bool, optional
Use the area mapping of the 4 closest input pixels (``interp=True``),
which is also the same as "bilinear interpolation",
or use the nearest neighbor (``interp=False``) for rotated pixels.
reshape : bool, optional
Whether adapt the output shape so that the input image is contained
completely in the output?
fill_value : float, optional
Value used to fill pixels in the rotated image that corresponding
pixels outside the boundaries of the input image.
"""
nrow, ncol = imgin.shape
# Rotation transformation image
angle = np.deg2rad(angle)
mrotate = np.zeros((2, 2), dtype=np.float64)
mrotate[0, 0] = np.cos(angle)
mrotate[0, 1] = np.sin(angle)
mrotate[1, 0] = -np.sin(angle)
mrotate[1, 1] = np.cos(angle)
# Determine the shape of rotated image
corner00 = np.array((0, 0))
corner01 = np.array((0, ncol-1))
corner10 = np.array((nrow-1, 0))
corner11 = np.array((nrow-1, ncol-1))
corners = np.vstack((corner00, corner01, corner10, corner11))
if reshape:
dest = np.dot(corners.astype(np.float64), mrotate)
# XXX: ``numba`` does not support ``np.max()`` with arguments
minr = np.min(dest[:, 0])
minc = np.min(dest[:, 1])
maxr = np.max(dest[:, 0])
maxc = np.max(dest[:, 1])
nr = int(maxr - minr + 0.5)
nc = int(maxc - minc + 0.5)
else:
# Constraint to be same shape
nr = nrow
nc = ncol
imgout = np.ones((nr, nc)) * fill_value
#
# Calculate the offset, for easier transformation of rotated pixels
# back to input image.
#
# NOTE:
# Notations:
# P_out : (r_out, c_out) a pixel in the output rotated image
# C_out : center position of the output rotated image
# P_in : (r_in, c_in) a pixel in the input image
# C_in : center position of the input image
# R : rotation matrix
# R_T : transposed rotation matrix
# The rotation relation between pixel pair is (about the center):
# (P_in - C_in) * R = P_out - C_out
# Then:
# (P_in - C_in) = (P_out - C_out) * R_T
# And then:
# P_in = C_in + (P_out-C_out) * R_T = P_out*R_T + (C_in - C_out*R_T)
# Thus can define the "offset" as:
# offset = C_in - C_out * R_T
# Then the transformation back to input image is simply given by:
# P_in = P_out * R_T + offset
#
center_in = np.array((nrow/2.0 - 0.5, ncol/2.0 - 0.5))
center_out = np.array((nr/2.0 - 0.5, nc/2.0 - 0.5))
mrotate_T = mrotate.transpose()
offset = center_in - np.dot(center_out, mrotate_T)
# Map pixels of the rotated image to the input image
for rr in range(nr):
for cc in range(nc):
p_out = np.array((rr, cc))
p_in = np.dot(p_out.astype(np.float64), mrotate_T) + offset
if np.all((p_in > corner00) & (p_in < corner11)):
# Calculate the pixel value for the rotated image
if interp:
# Use area mapping of the 4 closest input pixels
idx_rf, idx_cf = np.floor(p_in).astype(np.int64)
idx_rc, idx_cc = np.ceil(p_in).astype(np.int64)
# NOTE:
# It is possible that ``p_in[0]`` and/or ``p_in[1]``
# are just integers, which cause ``idx_rf == idx_rc``
# and/or ``idx_cf == idx_cc``, which further lead to
# the calculated pixel value ``p_val = 0``.
if idx_rf == idx_rc:
idx_rc += 1
if idx_cf == idx_cc:
idx_cc += 1
# Calculate the overlapping areas
p_r, p_c = p_in
p4_area = np.array([(idx_rc - p_r) * (idx_cc - p_c),
(idx_rc - p_r) * (p_c - idx_cf),
(p_r - idx_rf) * (idx_cc - p_c),
(p_r - idx_rf) * (p_c - idx_cf)])
p4_val = np.array((imgin[idx_rf, idx_cf],
imgin[idx_rf, idx_cc],
imgin[idx_rc, idx_cf],
imgin[idx_rc, idx_cc]))
p_val = np.sum(p4_area * p4_val)
else:
# Use the nearest neighbor as the rotated value
idx_r = round(p_in[0])
idx_c = round(p_in[1])
p_val = imgin[idx_r, idx_c]
#
imgout[rr, cc] = p_val
return imgout
def circle2ellipse(imgcirc, bfraction, rotation=None):
"""
Shrink the input circle image with respect to the center along the
column (axis) to transform the circle to an ellipse, and then rotate
around the image center.
Parameters
----------
imgcirc : 2D `~numpy.ndarray`
Input image grid containing a circle at the center
bfraction : float
The fraction of the semi-minor axis w.r.t. the semi-major axis
(i.e., the half width of the input image), to determine the
shrunk size (height) of the output image.
Should be a fraction within [0, 1]
rotation : float, optional
Rotation angle (unit: [deg])
Default: ``None`` (i.e., no rotation)
Returns
-------
imgout : 2D `~numpy.ndarray`
Image of the same size as the input circle image.
"""
nrow, ncol = imgcirc.shape
# Shrink the circle to be elliptical
nrow2 = nrow * bfraction
nrow2 = int(nrow2 / 2) * 2 + 1 # be odd
# NOTE: zoom() calculate the output shape with round() instead of int();
# fix the warning about they may be different.
zoom = ((nrow2+0.1)/nrow, 1)
img2 = ndimage.zoom(imgcirc, zoom=zoom, order=1)
# Pad the shrunk image to have the same size as input
imgout = np.zeros(shape=(nrow, ncol))
r1 = int((nrow - nrow2) / 2)
imgout[r1:(r1+nrow2), :] = img2
if rotation:
imgout = ndimage.rotate(imgout, angle=rotation, reshape=False, order=1)
return imgout
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