/usr/lib/python3/dist-packages/ginga/trcalc.py is in python3-ginga 2.6.1-2.
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# trcalc.py -- transformation calculations for image data
#
# This is open-source software licensed under a BSD license.
# Please see the file LICENSE.txt for details.
#
import math
import numpy
import time
interpolation_methods = ['basic']
def use(pkgname):
global have_opencv, cv2, cv2_resize
global have_opencl, trcalc_cl
if pkgname == 'opencv':
import cv2
have_opencv = True
cv2_resize = {
'nearest': cv2.INTER_NEAREST,
'linear' : cv2.INTER_LINEAR,
'area' : cv2.INTER_AREA,
'bicubic': cv2.INTER_CUBIC,
'lanczos': cv2.INTER_LANCZOS4,
}
if not 'nearest' in interpolation_methods:
interpolation_methods.extend(cv2_resize.keys())
interpolation_methods.sort()
elif pkgname == 'opencl':
try:
from ginga.opencl import CL
have_opencl = True
trcalc_cl = CL.CL('trcalc.cl')
except Exception as e:
raise ImportError(e)
have_opencv = False
try:
# optional opencv package speeds up certain operations, especially
# rotation
# TEMP: opencv broken on older anaconda mac (importing causes segv)
# --> temporarily disable, can enable using use() function above
#use('opencv')
pass
except ImportError:
pass
have_opencl = False
trcalc_cl = None
try:
# optional opencl package speeds up certain operations, especially
# rotation
# TEMP: pyopencl prompts users if it can't determine which device
# to use for acceleration.
# --> temporarily disable, can enable using use() function above
#use('opencl')
pass
except ImportError:
pass
have_numexpr = False
try:
# optional numexpr package speeds up certain combined numpy array
# operations, especially rotation
import numexpr as ne
have_numexpr = True
except ImportError:
pass
# For testing
#have_numexpr = False
#have_opencv = False
#have_opencl = False
def get_center(data_np):
ht, wd = data_np.shape[:2]
ctr_x = wd // 2
ctr_y = ht // 2
return (ctr_x, ctr_y)
def rotate_pt(x_arr, y_arr, theta_deg, xoff=0, yoff=0):
"""
Rotate an array of points (x_arr, y_arr) by theta_deg offsetted
from a center point by (xoff, yoff).
"""
# TODO: use opencv acceleration if available
a_arr = x_arr - xoff
b_arr = y_arr - yoff
cos_t = numpy.cos(numpy.radians(theta_deg))
sin_t = numpy.sin(numpy.radians(theta_deg))
ap = (a_arr * cos_t) - (b_arr * sin_t)
bp = (a_arr * sin_t) + (b_arr * cos_t)
return (ap + xoff, bp + yoff)
rotate_arr = rotate_pt
def rotate_coord(coord, theta_deg, offsets):
arr = numpy.asarray(coord)
# TODO: handle dimensional rotation N>2
x_arr, y_arr = rotate_pt(arr.T[0], arr.T[1], theta_deg,
xoff=offsets[0], yoff=offsets[1])
arr = numpy.column_stack((x_arr, y_arr))
return arr
def rotate_clip(data_np, theta_deg, rotctr_x=None, rotctr_y=None,
out=None, use_opencl=True, logger=None):
"""
Rotate numpy array `data_np` by `theta_deg` around rotation center
(rotctr_x, rotctr_y). If the rotation center is omitted it defaults
to the center of the array.
No adjustment is done to the data array beforehand, so the result will
be clipped according to the size of the array (the output array will be
the same size as the input array).
"""
# If there is no rotation, then we are done
if math.fmod(theta_deg, 360.0) == 0.0:
return data_np
ht, wd = data_np.shape[:2]
if rotctr_x is None:
rotctr_x = wd // 2
if rotctr_y is None:
rotctr_y = ht // 2
if have_opencv:
if logger is not None:
logger.debug("rotating with OpenCv")
# opencv is fastest
M = cv2.getRotationMatrix2D((rotctr_y, rotctr_x), theta_deg, 1)
if out is not None:
out[:, :, ...] = cv2.warpAffine(data_np, M, (wd, ht))
newdata = out
else:
newdata = cv2.warpAffine(data_np, M, (wd, ht))
new_ht, new_wd = newdata.shape[:2]
assert (wd == new_wd) and (ht == new_ht), \
Exception("rotated cutout is %dx%d original=%dx%d" % (
new_wd, new_ht, wd, ht))
elif have_opencl and use_opencl:
if logger is not None:
logger.debug("rotating with OpenCL")
# opencl is very close, sometimes better, sometimes worse
if (data_np.dtype == numpy.uint8) and (len(data_np.shape) == 3):
# special case for 3D RGB images
newdata = trcalc_cl.rotate_clip_uint32(data_np, theta_deg,
rotctr_x, rotctr_y,
out=out)
else:
newdata = trcalc_cl.rotate_clip(data_np, theta_deg,
rotctr_x, rotctr_y,
out=out)
else:
if logger is not None:
logger.debug("rotating with numpy")
yi, xi = numpy.mgrid[0:ht, 0:wd]
xi -= rotctr_x
yi -= rotctr_y
cos_t = numpy.cos(numpy.radians(theta_deg))
sin_t = numpy.sin(numpy.radians(theta_deg))
if have_numexpr:
ap = ne.evaluate("(xi * cos_t) - (yi * sin_t) + rotctr_x")
bp = ne.evaluate("(xi * sin_t) + (yi * cos_t) + rotctr_y")
else:
ap = (xi * cos_t) - (yi * sin_t) + rotctr_x
bp = (xi * sin_t) + (yi * cos_t) + rotctr_y
#ap = numpy.rint(ap).astype('int').clip(0, wd-1)
#bp = numpy.rint(bp).astype('int').clip(0, ht-1)
# Optomizations to reuse existing intermediate arrays
numpy.rint(ap, out=ap)
ap = ap.astype('int')
ap.clip(0, wd-1, out=ap)
numpy.rint(bp, out=bp)
bp = bp.astype('int')
bp.clip(0, ht-1, out=bp)
if out is not None:
out[:, :, ...] = data_np[bp, ap]
newdata = out
else:
newdata = data_np[bp, ap]
new_ht, new_wd = newdata.shape[:2]
assert (wd == new_wd) and (ht == new_ht), \
Exception("rotated cutout is %dx%d original=%dx%d" % (
new_wd, new_ht, wd, ht))
return newdata
def rotate(data_np, theta_deg, rotctr_x=None, rotctr_y=None, pad=20,
use_opencl=True, logger=None):
# If there is no rotation, then we are done
if math.fmod(theta_deg, 360.0) == 0.0:
return data_np
ht, wd = data_np.shape[:2]
ocx, ocy = wd // 2, ht // 2
# Make a square with room to rotate
side = int(math.sqrt(wd**2 + ht**2) + pad)
new_wd = new_ht = side
dims = (new_ht, new_wd) + data_np.shape[2:]
# Find center of new data array
ncx, ncy = new_wd // 2, new_ht // 2
if have_opencl and use_opencl:
if logger is not None:
logger.debug("rotating with OpenCL")
# find offsets of old image in new image
dx, dy = ncx - ocx, ncy - ocy
newdata = trcalc_cl.rotate(data_np, theta_deg,
rotctr_x=rotctr_x, rotctr_y=rotctr_y,
clip_val=0, out=None,
out_wd=new_wd, out_ht=new_ht,
out_dx=dx, out_dy=dy)
else:
# Overlay the old image on the new (blank) image
ldx, rdx = min(ocx, ncx), min(wd - ocx, ncx)
bdy, tdy = min(ocy, ncy), min(ht - ocy, ncy)
# TODO: fill with a different value?
newdata = numpy.zeros(dims, dtype=data_np.dtype)
newdata[ncy-bdy:ncy+tdy, ncx-ldx:ncx+rdx] = \
data_np[ocy-bdy:ocy+tdy, ocx-ldx:ocx+rdx]
# Now rotate with clip as usual
newdata = rotate_clip(newdata, theta_deg,
rotctr_x=rotctr_x, rotctr_y=rotctr_y,
out=newdata)
return newdata
def get_scaled_cutout_wdht_view(shp, x1, y1, x2, y2, new_wd, new_ht):
"""
Like get_scaled_cutout_wdht, but returns the view/slice to extract
from an image instead of the extraction itself.
"""
# calculate dimensions of NON-scaled cutout
old_wd = x2 - x1 + 1
old_ht = y2 - y1 + 1
max_x, max_y = shp[1] - 1, shp[0] - 1
if (new_wd != old_wd) or (new_ht != old_ht):
# Make indexes and scale them
# Is there a more efficient way to do this?
yi = numpy.mgrid[0:new_ht].reshape(-1, 1)
xi = numpy.mgrid[0:new_wd].reshape(1, -1)
iscale_x = float(old_wd) / float(new_wd)
iscale_y = float(old_ht) / float(new_ht)
xi = (x1 + xi * iscale_x).clip(0, max_x).astype('int')
yi = (y1 + yi * iscale_y).clip(0, max_y).astype('int')
wd, ht = xi.shape[1], yi.shape[0]
# bounds check against shape (to protect future data access)
xi_max, yi_max = xi[0, -1], yi[-1, 0]
assert xi_max <= max_x, ValueError("X index (%d) exceeds shape bounds (%d)" % (xi_max, max_x))
assert yi_max <= max_y, ValueError("Y index (%d) exceeds shape bounds (%d)" % (yi_max, max_y))
view = numpy.s_[yi, xi]
else:
# simple stepped view will do, because new view is same as old
view = numpy.s_[y1:y2+1, x1:x2+1]
wd, ht = old_wd, old_ht
# Calculate actual scale used (vs. desired)
old_wd, old_ht = max(old_wd, 1), max(old_ht, 1)
scale_x = float(wd) / old_wd
scale_y = float(ht) / old_ht
# return view + actual scale factors used
return (view, (scale_x, scale_y))
def get_scaled_cutout_wdht(data_np, x1, y1, x2, y2, new_wd, new_ht,
interpolation='basic', logger=None):
rdim = data_np.shape[2:]
open_cl_ok = (len(rdim) == 0 or (len(rdim) == 1 and rdim[0] == 4))
if have_opencv:
if logger is not None:
logger.debug("resizing with OpenCv")
# opencv is fastest and supports many methods
if interpolation == 'basic':
interpolation = 'nearest'
method = cv2_resize[interpolation]
newdata = cv2.resize(data_np[y1:y2+1, x1:x2+1], (new_wd, new_ht),
interpolation=method)
old_wd, old_ht = max(x2 - x1 + 1, 1), max(y2 - y1 + 1, 1)
ht, wd = newdata.shape[:2]
scale_x, scale_y = float(wd) / old_wd, float(ht) / old_ht
elif (have_opencl and interpolation in ('basic', 'nearest')
and open_cl_ok):
# opencl is almost as fast or sometimes faster, but currently
# we only support nearest neighbor
if logger is not None:
logger.debug("resizing with OpenCL")
old_wd, old_ht = max(x2 - x1 + 1, 1), max(y2 - y1 + 1, 1)
scale_x, scale_y = float(new_wd) / old_wd, float(new_ht) / old_ht
newdata, (scale_x, scale_y) = trcalc_cl.get_scaled_cutout_basic(data_np,
x1, y1, x2, y2,
scale_x, scale_y)
elif interpolation not in ('basic', 'nearest'):
raise ValueError("Interpolation method not supported: '%s'" % (
interpolation))
else:
if logger is not None:
logger.debug('resizing by slicing')
view, (scale_x, scale_y) = get_scaled_cutout_wdht_view(data_np.shape,
x1, y1, x2, y2,
new_wd, new_ht)
newdata = data_np[view]
return newdata, (scale_x, scale_y)
def get_scaled_cutout_basic_view(shp, x1, y1, x2, y2, scale_x, scale_y):
"""
Like get_scaled_cutout_basic, but returns the view/slice to extract
from an image, instead of the extraction itself
"""
# calculate dimensions of NON-scaled cutout
old_wd = x2 - x1 + 1
old_ht = y2 - y1 + 1
# calculate dimensions of scaled cutout
new_wd = int(round(scale_x * old_wd))
new_ht = int(round(scale_y * old_ht))
return get_scaled_cutout_wdht_view(shp, x1, y1, x2, y2, new_wd, new_ht)
def get_scaled_cutout_basic(data_np, x1, y1, x2, y2, scale_x, scale_y,
interpolation='basic', logger=None):
rdim = data_np.shape[2:]
open_cl_ok = (len(rdim) == 0 or (len(rdim) == 1 and rdim[0] == 4))
if have_opencv:
if logger is not None:
logger.debug("resizing with OpenCv")
# opencv is fastest
if interpolation == 'basic':
interpolation = 'nearest'
method = cv2_resize[interpolation]
newdata = cv2.resize(data_np[y1:y2+1, x1:x2+1], None,
fx=scale_x, fy=scale_y,
interpolation=method)
old_wd, old_ht = max(x2 - x1 + 1, 1), max(y2 - y1 + 1, 1)
ht, wd = newdata.shape[:2]
scale_x, scale_y = float(wd) / old_wd, float(ht) / old_ht
elif (have_opencl and interpolation in ('basic', 'nearest')
and open_cl_ok):
if logger is not None:
logger.debug("resizing with OpenCL")
newdata, (scale_x, scale_y) = trcalc_cl.get_scaled_cutout_basic(data_np,
x1, y1, x2, y2,
scale_x, scale_y)
elif interpolation not in ('basic', 'nearest'):
raise ValueError("Interpolation method not supported: '%s'" % (
interpolation))
else:
if logger is not None:
logger.debug('resizing by slicing')
view, (scale_x, scale_y) = get_scaled_cutout_basic_view(data_np.shape,
x1, y1, x2, y2,
scale_x, scale_y)
newdata = data_np[view]
return newdata, (scale_x, scale_y)
def transform(data_np, flip_x=False, flip_y=False, swap_xy=False):
# Do transforms as necessary
if flip_y:
data_np = numpy.flipud(data_np)
if flip_x:
data_np = numpy.fliplr(data_np)
if swap_xy:
data_np = data_np.swapaxes(0, 1)
return data_np
def calc_image_merge_clip(x1, y1, x2, y2,
dst_x, dst_y, a1, b1, a2, b2):
"""
(x1, y1) and (x2, y2) define the extent of the (non-scaled) data
shown. The image, defined by region (a1, b1), (a2, b2) is to be
placed at (dst_x, dst_y) in the image (destination may be outside
of the actual data array).
Refines the tuple (a1, b1, a2, b2) defining the clipped rectangle
needed to be cut from the source array and scaled.
"""
src_wd, src_ht = a2 - a1, b2 - b1
# Trim off parts of srcarr that would be "hidden"
# to the left and above the dstarr edge.
ex = y1 - dst_y
if ex > 0:
src_ht -= ex
dst_y += ex
#b2 -= ex
b1 += ex
ex = x1 - dst_x
if ex > 0:
src_wd -= ex
dst_x += ex
a1 += ex
# Trim off parts of srcarr that would be "hidden"
# to the right and below the dstarr edge.
ex = dst_y + src_ht - y2
if ex > 0:
src_ht -= ex
#b1 += ex
b2 -= ex
ex = dst_x + src_wd - x2
if ex > 0:
src_wd -= ex
a2 -= ex
return (dst_x, dst_y, a1, b1, a2, b2)
def overlay_image(dstarr, dst_x, dst_y, srcarr, dst_order='RGBA',
src_order='RGBA',
alpha=1.0, copy=False, fill=True, flipy=False):
dst_ht, dst_wd, dst_dp = dstarr.shape
src_ht, src_wd, src_dp = srcarr.shape
dst_x, dst_y = int(round(dst_x)), int(round(dst_y))
if flipy:
srcarr = numpy.flipud(srcarr)
# Trim off parts of srcarr that would be "hidden"
# to the left and above the dstarr edge.
if dst_y < 0:
dy = abs(dst_y)
srcarr = srcarr[dy:, :, :]
src_ht -= dy
dst_y = 0
if dst_x < 0:
dx = abs(dst_x)
srcarr = srcarr[:, dx:, :]
src_wd -= dx
dst_x = 0
if src_wd <= 0 or src_ht <=0:
return dstarr
# Trim off parts of srcarr that would be "hidden"
# to the right and below the dstarr edge.
ex = dst_y + src_ht - dst_ht
if ex > 0:
srcarr = srcarr[:dst_ht, :, :]
src_ht -= ex
ex = dst_x + src_wd - dst_wd
if ex > 0:
srcarr = srcarr[:, :dst_wd, :]
src_wd -= ex
if copy:
dstarr = numpy.copy(dstarr, order='C')
da_idx = -1
slc = slice(0, 3)
if 'A' in dst_order:
da_idx = dst_order.index('A')
# Currently we assume that alpha channel is in position 0 or 3 in dstarr
if da_idx == 0:
slc = slice(1, 4)
elif da_idx != 3:
raise ValueError("Alpha channel not in expected position (0 or 4) in dstarr")
# fill alpha channel in destination in the area we will be dropping
# the image
if fill and (da_idx >= 0):
dstarr[dst_y:dst_y+src_ht, dst_x:dst_x+src_wd, da_idx] = 255
if src_dp > 3:
sa_idx = src_order.index('A')
# if overlay source contains an alpha channel, extract it
# and use it, otherwise use scalar keyword parameter
alpha = srcarr[0:src_ht, 0:src_wd, sa_idx] / 255.0
alpha = numpy.dstack((alpha, alpha, alpha))
# reorder srcarr if necessary to match dstarr for alpha merge
get_order = dst_order
if ('A' in dst_order) and not ('A' in src_order):
get_order = dst_order.replace('A', '')
if get_order != src_order:
srcarr = reorder_image(get_order, srcarr, src_order)
# calculate alpha blending
# Co = CaAa + CbAb(1 - Aa)
a_arr = (alpha * srcarr[0:src_ht, 0:src_wd, slc]).astype(numpy.uint8)
b_arr = ((1.0 - alpha) * dstarr[dst_y:dst_y+src_ht,
dst_x:dst_x+src_wd,
slc]).astype(numpy.uint8)
# Place our srcarr into this dstarr at dst offsets
#dstarr[dst_y:dst_y+src_ht, dst_x:dst_x+src_wd, slc] += addarr[0:src_ht, 0:src_wd, slc]
dstarr[dst_y:dst_y+src_ht, dst_x:dst_x+src_wd, slc] = \
a_arr[0:src_ht, 0:src_wd, slc] + b_arr[0:src_ht, 0:src_wd, slc]
return dstarr
def reorder_image(dst_order, src_arr, src_order):
indexes = [ src_order.index(c) for c in dst_order ]
return numpy.dstack([ src_arr[..., idx] for idx in indexes ])
#END
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