/usr/lib/python2.7/dist-packages/ginga/opencl/CL.py is in python-ginga 2.6.1-2.
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# CL.py -- OpenCL functions for
#
# This is open-source software licensed under a BSD license.
# Please see the file LICENSE.txt for details.
#
import sys, os
import numpy as np
module_home = os.path.split(sys.modules[__name__].__file__)[0]
import pyopencl as cl
class CL(object):
def __init__(self, filename=None):
# TODO: we may want to choose GPU resources only
self.ctx = cl.create_some_context()
self.queue = cl.CommandQueue(self.ctx)
if filename is not None:
path = os.path.join(module_home, filename)
self.load_program(path)
def load_program(self, filename):
#read in the OpenCL source file as a string
with open(filename, 'r') as in_f:
buf = in_f.read()
self.load_program_buf(buf)
def load_program_buf(self, buf):
#create the program
self.program = cl.Program(self.ctx, buf).build()
def rotate(self, data_np, theta, rotctr_x=None, rotctr_y=None,
clip_val=0,
out=None, out_wd=0, out_ht=0, out_dx=0, out_dy=0):
sin_theta = np.sin(np.radians(theta))
cos_theta = np.cos(np.radians(theta))
height, width = data_np.shape[:2]
if rotctr_x is None:
rotctr_x, rotctr_y = width // 2, height // 2
mf = cl.mem_flags
# convert to float64
dtype = data_np.dtype
data_np = np.ascontiguousarray(data_np, dtype=np.float64)
if out is None:
# no output array specified
if out_ht == 0:
# no desired output size specified--use dimensions of input
# a clip, basically
out_ht, out_wd = data_np.shape[:2]
out_shape = (out_ht, out_wd) + data_np.shape[2:]
out = np.empty(out_shape, dtype=data_np.dtype)
else:
# get dimensions of output array
out_ht, out_wd = out.shape[:2]
assert out.shape[2:] == data_np.shape[2:], ValueError(">2D dimensions don't match")
numbytes = out_ht * out_wd * np.float64(0).nbytes
#create OpenCL buffers on devices
data_np = np.ascontiguousarray(data_np)
src_buf = cl.Buffer(self.ctx, mf.READ_ONLY | mf.COPY_HOST_PTR,
hostbuf=data_np)
dst_buf = cl.Buffer(self.ctx, mf.WRITE_ONLY, numbytes)
evt = self.program.image_rotate_float64(self.queue, [out_ht, out_wd], None,
src_buf, dst_buf,
np.int32(rotctr_x), np.int32(rotctr_y),
np.int32(width), np.int32(height),
np.int32(out_wd), np.int32(out_ht),
np.int32(out_dx), np.int32(out_dy),
np.float64(sin_theta), np.float64(cos_theta),
np.float64(clip_val))
if dtype == np.float64:
out_np = out
else:
out_np = np.empty(out_shape, dtype=np.float64)
cl.enqueue_read_buffer(self.queue, dst_buf, out_np).wait()
#cl.enqueue_copy(self.queue, out_np, dst_buf).wait()
res = out_np.astype(dtype)
if out is not None:
out[...] = res
else:
out = res
return out
def rotate_clip(self, data_np, theta, rotctr_x=None, rotctr_y=None,
clip_val=0, out=None):
sin_theta = np.sin(np.radians(theta))
cos_theta = np.cos(np.radians(theta))
height, width = data_np.shape[:2]
if rotctr_x is None:
rotctr_x, rotctr_y = width // 2, height // 2
mf = cl.mem_flags
# convert to float64
dtype = data_np.dtype
data_np = np.ascontiguousarray(data_np, dtype=np.float64)
# clipped array is same size as original
out_ht, out_wd = data_np.shape[:2]
out_dx, out_dy = 0, 0
numbytes = data_np.nbytes
#create OpenCL buffers on devices
data_np = np.ascontiguousarray(data_np)
src_buf = cl.Buffer(self.ctx, mf.READ_ONLY | mf.COPY_HOST_PTR,
hostbuf=data_np)
dst_buf = cl.Buffer(self.ctx, mf.WRITE_ONLY, numbytes)
evt = self.program.image_rotate_float64(self.queue, [height, width], None,
src_buf, dst_buf,
np.int32(rotctr_x), np.int32(rotctr_y),
np.int32(width), np.int32(height),
np.int32(out_wd), np.int32(out_ht),
np.int32(out_dx), np.int32(out_dy),
np.float64(sin_theta), np.float64(cos_theta),
np.float64(clip_val))
out_np = np.empty_like(data_np)
cl.enqueue_read_buffer(self.queue, dst_buf, out_np).wait()
#cl.enqueue_copy(self.queue, out_np, dst_buf).wait()
res = out_np.astype(dtype)
if out is not None:
out[...] = res
else:
out = res
return out
def rotate_clip_uint32(self, data_np, theta, rotctr_x=None, rotctr_y=None,
clip_val=0, out=None):
sin_theta = np.sin(np.radians(theta))
cos_theta = np.cos(np.radians(theta))
height, width = data_np.shape[:2]
if rotctr_x is None:
rotctr_x, rotctr_y = width // 2, height // 2
mf = cl.mem_flags
if out is None:
out = np.empty_like(data_np)
# clipped array is same size as original
out_ht, out_wd = out.shape[:2]
out_dx, out_dy = 0, 0
#create OpenCL buffers on devices
data_np = np.ascontiguousarray(data_np)
src_buf = cl.Buffer(self.ctx, mf.READ_ONLY | mf.COPY_HOST_PTR,
hostbuf=data_np)
dst_buf = cl.Buffer(self.ctx, mf.WRITE_ONLY, out.nbytes)
evt = self.program.image_rotate_uint32(self.queue, [height, width], None,
src_buf, dst_buf,
np.int32(rotctr_x), np.int32(rotctr_y),
np.int32(width), np.int32(height),
np.int32(out_wd), np.int32(out_ht),
np.int32(out_dx), np.int32(out_dy),
np.float64(sin_theta), np.float64(cos_theta),
np.uint32(clip_val))
cl.enqueue_read_buffer(self.queue, dst_buf, out).wait()
return out
def rotate_uint32(self, data_np, theta, rotctr_x=None, rotctr_y=None,
clip_val=0,
out=None, out_wd=0, out_ht=0, out_dx=0, out_dy=0):
sin_theta = np.sin(np.radians(theta))
cos_theta = np.cos(np.radians(theta))
height, width = data_np.shape[:2]
if rotctr_x is None:
rotctr_x, rotctr_y = width // 2, height // 2
mf = cl.mem_flags
if out is None:
if out_ht == 0:
out_ht, out_wd = data_np.shape[:2]
out_shape = (out_ht, out_wd) + data_np.shape[2:]
out = np.zeros(out_shape, dtype=data_np.dtype)
else:
out_ht, out_wd = out.shape[:2]
assert out.shape[2:] == data_np.shape[2:], ValueError(">2D dimensions don't match")
#create OpenCL buffers on devices
data_np = np.ascontiguousarray(data_np)
src_buf = cl.Buffer(self.ctx, mf.READ_ONLY | mf.COPY_HOST_PTR,
hostbuf=data_np)
dst_buf = cl.Buffer(self.ctx, mf.WRITE_ONLY, out.nbytes)
evt = self.program.image_rotate_uint32(self.queue, [out_ht, out_wd], None,
src_buf, dst_buf,
np.int32(rotctr_x), np.int32(rotctr_y),
np.int32(width), np.int32(height),
np.int32(out_wd), np.int32(out_ht),
np.int32(out_dx), np.int32(out_dy),
np.float64(sin_theta), np.float64(cos_theta),
np.uint32(clip_val))
cl.enqueue_read_buffer(self.queue, dst_buf, out).wait()
return out
def transform_uint32(self, data_np,
flip_x=False, flip_y=False, swap_xy=False,
out=None):
height, width = data_np.shape[:2]
new_ht, new_wd = height, width
if swap_xy:
new_ht, new_wd = width, height
new_size = [new_ht, new_wd] + list(data_np.shape[2:])
mf = cl.mem_flags
#create OpenCL buffers on devices
data_np = np.ascontiguousarray(data_np)
src_buf = cl.Buffer(self.ctx, mf.READ_ONLY | mf.COPY_HOST_PTR,
hostbuf=data_np)
dst_buf = cl.Buffer(self.ctx, mf.WRITE_ONLY, data_np.nbytes)
evt = self.program.image_transform_uint32(self.queue, [height, width], None,
src_buf, dst_buf,
np.int32(width), np.int32(height),
np.int32(flip_x), np.int32(flip_y),
np.int32(swap_xy))
if out is None:
out = np.empty_like(data_np).reshape(new_size)
cl.enqueue_read_buffer(self.queue, dst_buf, out).wait()
return out
def resize_uint32(self, data_np, scale_x, scale_y, out=None):
height, width = data_np.shape[:2]
new_ht = int(height * scale_y)
new_wd = int(width * scale_x)
new_shape = [new_ht, new_wd] + list(data_np.shape[2:])
mf = cl.mem_flags
#create OpenCL buffers on devices
data_np = np.ascontiguousarray(data_np)
src_buf = cl.Buffer(self.ctx, mf.READ_ONLY | mf.COPY_HOST_PTR,
hostbuf=data_np)
num_bytes = new_ht * new_wd * np.uint32(0).nbytes
dst_buf = cl.Buffer(self.ctx, mf.WRITE_ONLY, num_bytes)
evt = self.program.image_resize_uint32(self.queue, [new_ht, new_wd], None,
src_buf, dst_buf,
np.int32(width), np.int32(new_wd),
np.float64(scale_x), np.float64(scale_y))
if out is None:
out = np.empty(new_shape, dtype=data_np.dtype)
cl.enqueue_read_buffer(self.queue, dst_buf, out).wait()
return out
def resize(self, data_np, scale_x, scale_y, out=None):
height, width = data_np.shape[:2]
new_ht = int(height * scale_y)
new_wd = int(width * scale_x)
new_shape = [new_ht, new_wd] + list(data_np.shape[2:])
mf = cl.mem_flags
#create OpenCL buffers on devices
dtype = data_np.dtype
data_np = np.ascontiguousarray(data_np, dtype=np.float64)
src_buf = cl.Buffer(self.ctx, mf.READ_ONLY | mf.COPY_HOST_PTR,
hostbuf=data_np)
num_bytes = new_ht * new_wd * np.float64(0).nbytes
dst_buf = cl.Buffer(self.ctx, mf.WRITE_ONLY, num_bytes)
evt = self.program.image_resize_float64(self.queue, [new_ht, new_wd], None,
src_buf, dst_buf,
np.int32(width), np.int32(new_wd),
np.float64(scale_x), np.float64(scale_y))
if dtype == np.float64:
out_np = out
else:
out_np = np.empty(new_shape, dtype=np.float64)
cl.enqueue_read_buffer(self.queue, dst_buf, out_np).wait()
res = out_np.astype(dtype)
if out is not None:
out[...] = res
else:
out = res
return out
def get_scaled_cutout_basic(self, data_np, x1, y1, x2, y2, scale_x, scale_y,
out=None):
if (data_np.dtype == np.uint32) or ((data_np.dtype == np.uint8) and
(len(data_np.shape) == 3)):
newdata = self.resize_uint32(data_np[y1:y2+1, x1:x2+1, ...],
scale_x, scale_y, out=out)
else:
newdata = self.resize(data_np[y1:y2+1, x1:x2+1],
scale_x, scale_y, out=out)
old_ht, old_wd = data_np.shape[:2]
ht, wd = newdata.shape[:2]
scale_x, scale_y = float(wd) / old_wd, float(ht) / old_ht
return newdata, (scale_x, scale_y)
#END
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