Ejemplo n.º 1
0
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
Ejemplo n.º 2
0
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
Ejemplo n.º 3
0
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
Ejemplo n.º 4
0
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