def all_kernels(kdim, writefile=True, makeimage=False, vb=False):
## Get files -- path hard-coded
allpngs = numpy.sort(file_seek("../Data/GPSF/",
"snap*d0_CFHTLS_03_g*.png"))
## Collect pairs of files
for i in range(0, len(allpngs), 2):
## Extract image info
image1 = png_pix(allpngs[i])
image2 = png_pix(allpngs[i + 1])
## Cut out extraneous white pixels
image1 = pngcropwhite(image1)
image2 = pngcropwhite(image2)
## Deconvolve
A = get_kernel(image1, image2, kdim)
B = get_kernel(image2, image1, kdim)
##------------------------------------------------------------
## Write kernels to file and make images
## Gaussian to PSF
outfile = os.path.splitext(allpngs[i])[0][:-4] + "_GausstoPSF_" + str(
A.shape[0]) + ".krn"
f = open(outfile, "w")
f.write(
"## Convolution kernel taking 2D Gaussian to PSFEx image\n\n\n")
writearray(f, A, True)
if vb: print "DeconvolveToTargetPSF.py: kernel written to", outfile
## PSF to Gaussian
outfile = os.path.splitext(allpngs[i])[0][:-4] + "_PSFtoGauss_" + str(
B.shape[0]) + ".krn"
f = open(outfile, "w")
f.write(
"## Convolution kernel taking PSFEx image to 2D Gaussian\n\n\n")
writearray(f, B, True)
if vb: print "DeconvolveToTargetPSF.py: kernel written to", outfile
print "\n"
##------------------------------------------------------------
return
def all_kernels(kdim, writefile=True,makeimage=False, vb=False):
## Get files
allpngs = numpy.sort(file_seek("../Data/GPSF/","snap*d0_CFHTLS_03_g*.png"))
## Collect pairs of files
for i in range (0,len(allpngs),2):
## Extract image info
image1 = png_pix(allpngs[i])
image2 = png_pix(allpngs[i+1])
## Cut out extraneous white pixels
image1 = pngcropwhite(image1)
image2 = pngcropwhite(image2)
## Deconvolve
A=get_kernel(image1,image2, kdim)
B=get_kernel(image2,image1, kdim)
##------------------------------------------------------------
## Write kernels to file and make images
## Gaussian to PSF
outfile=os.path.splitext(allpngs[i])[0][:-4]+"_GausstoPSF_"+str(A.shape[0])+".krn"
f=open(outfile,"w")
f.write("## Convolution kernel taking 2D Gaussian to PSFEx image\n\n\n")
writearray(f,A,True)
if vb: print "DeconvolveToTargetPSF.py: kernel written to",outfile
## PSF to Gaussian
outfile=os.path.splitext(allpngs[i])[0][:-4]+"_PSFtoGauss_"+str(B.shape[0])+".krn"
f=open(outfile,"w")
f.write("## Convolution kernel taking PSFEx image to 2D Gaussian\n\n\n")
writearray(f,B,True)
if vb: print "DeconvolveToTargetPSF.py: kernel written to",outfile
print "\n"
##------------------------------------------------------------
return
def reconvolve(image1, image2, kernel): ## Read in images & kernel and process into shape arr1 = pngcropwhite(png_pix(image1)) kernel = numpy.loadtxt(kernel) newdim = numpy.array(arr1.shape)-numpy.array(kernel.shape)+1 arr2 = shave(newdim, pngcropwhite(png_pix(image2))) ## Reconvolved image -- should be integer array recon = numpy.around(convolve(arr1, kernel, mode="valid")) ## Residue should be 0 residue = recon-arr2 ## Visualise residue from pylab import imshow, show, colorbar imshow(residue) #colorbar() show() return
def deconvolve_image(imagefile, kernel, vb=False):
##------------------------------------------------------------
## Read in image
## Determine image file type and get pixels
imgext = os.path.splitext(imagefile)[1]
if imgext==".fits": imgarr = fits_pix(imagefile)
elif imgext==".png": imgarr = png_pix(imagefile) ## Doesn't work
## For some reason the image is flipped at this point, so un-flip
imgarr = imgarr[::-1,:]
## Filter out noise
imgarr[imgarr<cutoff] = 0.0
##------------------------------------------------------------
## Read in kernel
## Distinguish between array-kernel and file-kernel
if type(kernel) is str:
## Ensure the right kernel file has been selected
if "PSFtoGauss" in kernel:
kernel = numpy.loadtxt(kernel)
else:
print "DeconvolveToTargetPSF.py: deconvolve_image: wrong kernel file. Abort."
return
elif type(kernel) is numpy.array:
pass
## Kernel dimensions
kernel_h,kernel_w = kernel.shape
# Should also check match with imagefile #
##------------------------------------------------------------
## Compute linalg objects from images
## Honest dimensions for scene
scene_dim = numpy.array(imgarr.shape)-numpy.array(kernel.shape)+1
scene_siz = scene_dim[0]*scene_dim[1]
##------------------------------------------------------------
convmeth = "scipy"
## Manual convolution
if convmeth=="manual":
## 1D array for SCENE (convolved with Gaussian)
g_sc = numpy.empty(imgarr.size)#scene_siz
## 1D array for IMAGE
stride = imgarr.shape[0]
imgarr = imgarr.flatten()
##------------------------------------------------------------
## Manual matrix product
## Initialise kernel "vector"
len_krn_lin = (stride)*(kernel_h-1)+kernel_w ## Still keeps a lot of zeros
krn_lin = numpy.zeros(len_krn_lin)
## Loop over slices in the kernel image
for j in range (kernel_h):
startcol = j*stride
krn_lin[startcol:startcol+kernel_w] = kernel[j,:]
t0 = time.time()
## Perform linalg product
## i labels the scene pixel and the slice in the original
for i in range (scene_siz):
imageslice = imgarr[i:i+len_krn_lin]
g_sc[i] = numpy.dot(krn_lin,imageslice)
if vb: print "DeconvolveToTargetPSF.py: deconvolve_image: vector multiplication took",\
round(time.time()-t0,2),"seconds."
## Delete spurious elements (from overlapping)
i=len(g_sc)
while i>=0:
if i%stride+kernel_w > stride:
g_sc = numpy.delete(g_sc,i)
i-=1
## Delete spurious elements from declaring it too big
g_sc = numpy.delete(g_sc,slice(scene_siz-len(g_sc)-1,-1))
if scene_siz-len(g_sc): print "LINE #"+str(lineno())+": size discrepancy"
##------------------------------------------------------------
elif convmeth=="scipy":
t0=time.time()
## Do convolution using scipy
imgarr = numpy.array(imgarr, dtype="float64")
g_sc = convolve(imgarr, kernel, mode="valid")
if vb: print "DeconvolveToTargetPSF.py: deconvolve_image: SciPy convolution took",\
round(time.time()-t0,2),"seconds."
else:
print "LINE #"+str(lineno())+": convmeth error, abort."
return
##------------------------------------------------------------
## Reshape
if g_sc.shape[0]!=scene_dim[0] or g_sc.shape[1]!=scene_dim[1]:
if vb: print "DeconvolveToTargetPSF.py: deconvolve_image: reshaping."
try:
g_sc = g_sc.reshape(scene_dim)
except ValueError:
print "DeconvolveToTargetPSF.py: deconvolve_image: output has wrong shape. Investigate."
##------------------------------------------------------------
## Filter out (computer) noise
g_sc[g_sc<cutoff] = 0.0
## Outfile name
imagedir,imagename = os.path.split(imagefile)
info = imagename[imagename.find("CFHT"):imagename.find("sci")+3]
outfile = imagedir+"/gDeconvolved_"+info
## Rescale (linear stretch)
if 1:
## Scaling parameters
vmin,vmax = stretch_params(g_sc)
## Stretch
g_sc = linear_rescale(g_sc, vmin, vmax)
## Modify filename
outfile = outfile+"_rescaled"
## Delete pre-existing images
if os.path.isfile(outfile+".fits"): os.remove(outfile+".fits")
if os.path.isfile(outfile+".png"): os.remove(outfile+".png")
## Save image as FITS and as PNG
makeFITS(outfile+".fits", g_sc)
scipy.misc.imsave(outfile+".png", g_sc)
if vb: print "DeconvolveToTargetPSF.py: deconvolve_image: image saved to",outfile+".fits"
return None