def make_raw(animal='cat', show=True):
files = glob.glob('myimages/*.jpg')
files.sort()
im_list = []
if animal == 'cat': # can be picky about images
# files=[files[i] for i in [ 0,1,2,3,4,5,29,32,35,36,46,54,57,58,84]]
files = [
files[i] for i in [
0, 1, 2, 3, 4, 5, 6, 10, 12, 15, 17, 19, 20, 29, 32, 33, 35,
36, 46, 38, 40, 44, 54, 57, 58, 60, 69, 72, 80, 84
]
]
print "Image files: %d" % len(files)
print "Animal:", animal
w = Waitbar(True)
file_count = len(files)
for count, fname in enumerate(files):
im = Image.open(fname)
orig_size = im.size
if animal == 'cat':
new_size = [
int(o * 60. / 0.5 * 5.5 / orig_size[0]) for o in orig_size
]
elif animal == 'mouse':
new_size = [
int(o * 60. / 7. * 13 / orig_size[0]) for o in orig_size
]
else:
raise ValueError
im = im.convert("L")
if fname == files[0]:
print "Resize: %dx%d --> %dx%d" % (orig_size[0], orig_size[1],
new_size[0], new_size[1])
im = im.resize(new_size)
if show:
im.show()
a = numpy.asarray(im.getdata(), dtype='B')
a.shape = im.size[::-1]
im_list.append(a)
w.update((count + 1) / float(file_count))
print w,
sys.stdout.flush()
var = {'im': im_list, 'im_scale_shift': [1.0, 0.0]}
print
return var
def make_dog(var,sd1=1,sd2=3,size=32,shape='valid'):
im2_list=[]
im_scale_shift=var['im_scale_shift']
im_count=len(var['im'])
w = Waitbar(True)
for count,im in enumerate(var['im']):
orig_size=im.shape
im2=dog_filter(im*im_scale_shift[0]+im_scale_shift[1],sd1,sd2,size,shape)
new_size=im2.shape
im2=im2/im2.std()
im2=im2-im2.mean()
if count==0:
print "Dog %d,%d: %dx%d --> %dx%d" % (sd1,sd2,
orig_size[0],orig_size[1],
new_size[0],new_size[1])
im2_list.append(im2)
w.update((count+1)/float(im_count))
print w,
sys.stdout.flush()
var2={'im':im2_list,'im_scale_shift':[1.0,0.0]}
print
return var2
def make_norm(var, mu=0, sd=1):
im2_list = []
im_scale_shift = var['im_scale_shift']
im_count = len(var['im'])
w = Waitbar(True)
for count, im in enumerate(var['im']):
im2 = im.copy()
im2 = im2 - im2.mean()
im2 = im2 / im2.std()
if count == 0:
print "Norm %.1f,%.1f" % (mu, sd)
im2_list.append(im2)
w.update((count + 1) / float(im_count))
print w,
sys.stdout.flush()
var2 = {'im': im2_list, 'im_scale_shift': [1.0, 0.0]}
print
return var2
def make_sigmoid(var):
im2_list = []
im_scale_shift = var['im_scale_shift']
im_count = len(var['im'])
w = Waitbar(True)
bottom = 0.0
top = 5.0
for count, imm in enumerate(var['im']):
im = imm * im_scale_shift[0] + im_scale_shift[1]
im2 = copy.copy(im)
im2[im < 0] = bottom * (2.0 /
(1.0 + numpy.exp(-2.0 *
(im[im < 0] / bottom))) - 1.0)
im2[im >= 0] = top * (2.0 /
(1.0 + numpy.exp(-2.0 *
(im[im >= 0] / top))) - 1.0)
new_size = im2.shape
im2 = im2 / im2.std()
im2_list.append(im2)
w.update((count + 1) / float(im_count))
print w,
sys.stdout.flush()
var2 = {'im': im2_list, 'im_scale_shift': [1.0, 0.0]}
print
return var2
def make_sigmoid(var):
im2_list=[]
im_scale_shift=var['im_scale_shift']
im_count=len(var['im'])
w = Waitbar(True)
bottom=0.0
top=5.0
for count,imm in enumerate(var['im']):
im=imm*im_scale_shift[0]+im_scale_shift[1]
im2=copy.copy(im)
im2[im<0]=bottom*(2.0/(1.0+numpy.exp(-2.0*(im[im<0]/bottom)))-1.0)
im2[im>=0]=top*(2.0/(1.0+numpy.exp(-2.0*(im[im>=0]/top)))-1.0)
new_size=im2.shape
im2=im2/im2.std()
im2_list.append(im2)
w.update((count+1)/float(im_count))
print w,
sys.stdout.flush()
var2={'im':im2_list,'im_scale_shift':[1.0,0.0]}
print
return var2
def add_noise(var, sd=1.0):
im2_list = []
im_scale_shift = var['im_scale_shift']
im_count = len(var['im'])
w = Waitbar(True)
for count, im in enumerate(var['im']):
orig_size = im.shape
im2 = im * im_scale_shift[0] + im_scale_shift[
1] + sd * numpy.random.randn(im.shape[0], im.shape[1])
im2 = im2 - im2.mean()
im2 = im2 / im2.std()
im2_list.append(im2)
w.update((count + 1) / float(im_count))
print w,
sys.stdout.flush()
print
var2 = {'im': im2_list, 'im_scale_shift': [1.0, 0.0]}
return var2
def make_pos(var, type='ON', offset=5, min=0):
im2_list = []
im_scale_shift = var['im_scale_shift']
im_count = len(var['im'])
w = Waitbar(True)
for count, imm in enumerate(var['im']):
im = imm * im_scale_shift[0] + im_scale_shift[1]
im2 = copy.copy(im)
if type == 'OFF':
im2 = -im2
im2 = im2 + offset
im2[im2 < min] = min
new_size = im2.shape
im2_list.append(im2)
w.update((count + 1) / float(im_count))
print w,
sys.stdout.flush()
var2 = {'im': im2_list, 'im_scale_shift': [1.0, 0.0]}
print
return var2
def make_white(var):
im_scale_shift = var['im_scale_shift']
im2_list = []
im_count = len(var['im'])
w = Waitbar(True)
for count, im in enumerate(var['im']):
im = im - im.mean() + 1 # add a little DC to get rid of nans
yf = fftshift(fft2(im * im_scale_shift[0] + im_scale_shift[1]))
fl = 1.0 / absolute(yf)
yffl = yf * fl
im2 = real(ifft2(ifftshift(yffl)))
im2 = im2 - im2.mean()
im2 = im2 / im2.std()
if count == 0:
print "Whiten."
im2_list.append(im2)
w.update((count + 1) / float(im_count))
print w,
sys.stdout.flush()
print
var2 = {'im': im2_list, 'im_scale_shift': [1.0, 0.0]}
return var2
def make_white(var):
im_scale_shift=var['im_scale_shift']
im2_list=[]
im_count=len(var['im'])
w = Waitbar(True)
for count,im in enumerate(var['im']):
yf=fftshift(fft2(im*im_scale_shift[0]+im_scale_shift[1]))
fl=1.0/absolute(yf)
yffl=yf*fl;
im2=real(ifft2(ifftshift(yffl)))
im2=im2/im2.std()
im2=im2-im2.mean()
if count==0:
print "Whiten."
im2_list.append(im2)
w.update((count+1)/float(im_count))
print w,
sys.stdout.flush()
print
var2={'im':im2_list,'im_scale_shift':[1.0,0.0]}
return var2
def make_blur(var,sd=1.0,radius=None):
if not radius:
radius=sd*3.0
im2_list=[]
im_scale_shift=var['im_scale_shift']
im_count=len(var['im'])
w = Waitbar(True)
for count,im in enumerate(var['im']):
orig_size=im.shape
im2=dog_filter(im*im_scale_shift[0]+im_scale_shift[1],sd,0,2*radius+1,shape='same')
new_size=im2.shape
im2=im2/im2.std()
im2=im2-im2.mean()
if count==0:
print "Blur %d,%d: %dx%d --> %dx%d" % (sd,radius,
orig_size[0],orig_size[1],
new_size[0],new_size[1])
im2_list.append(im2)
w.update((count+1)/float(im_count))
print w,
sys.stdout.flush()
print
var2={'im':im2_list,'im_scale_shift':[1.0,0.0]}
return var2
def make_pos(var,type='ON',offset=5,min=0):
im2_list=[]
im_scale_shift=var['im_scale_shift']
im_count=len(var['im'])
w = Waitbar(True)
for count,imm in enumerate(var['im']):
im=imm*im_scale_shift[0]+im_scale_shift[1]
im2=copy.copy(im)
if type=='OFF':
im2=-im2
im2=im2+offset
im2[im2<min]=min
new_size=im2.shape
im2_list.append(im2)
w.update((count+1)/float(im_count))
print w,
sys.stdout.flush()
var2={'im':im2_list,'im_scale_shift':[1.0,0.0]}
print
return var2
def make_dog(var, sd1=1, sd2=3, size=32, shape='valid'):
im2_list = []
im_scale_shift = var['im_scale_shift']
im_count = len(var['im'])
w = Waitbar(True)
for count, im in enumerate(var['im']):
orig_size = im.shape
im2 = dog_filter(im * im_scale_shift[0] + im_scale_shift[1], sd1, sd2,
size, shape)
new_size = im2.shape
im2 = im2 - im2.mean()
im2 = im2 / im2.std()
if count == 0:
print "Dog %d,%d: %dx%d --> %dx%d" % (
sd1, sd2, orig_size[0], orig_size[1], new_size[0], new_size[1])
im2_list.append(im2)
w.update((count + 1) / float(im_count))
print w,
sys.stdout.flush()
var2 = {'im': im2_list, 'im_scale_shift': [1.0, 0.0]}
print
return var2
def make_raw(animal='cat',show=True):
files=glob.glob('/home/bblais/Pictures/natural_images/*.jpg')
files.sort()
im_list=[]
if animal=='cat': # can be picky about images
# files=[files[i] for i in [ 0,1,2,3,4,5,29,32,35,36,46,54,57,58,84]]
files=[files[i] for i in [ 0,1,2,3,4,5,6,10,12,15,17,19,20,29,32,33,35,36,46,38,40,44,54,57,58,60,69,72,80,84]]
print "Image files: %d" % len(files)
print "Animal:", animal
w = Waitbar(True)
file_count=len(files)
for count,fname in enumerate(files):
im=Image.open(fname)
orig_size=im.size
if animal=='cat':
new_size=[int(o*60./0.5*5.5/orig_size[0]) for o in orig_size]
elif animal=='mouse':
new_size=[int(o*60./7.*13/orig_size[0]) for o in orig_size]
else:
raise ValueError
im=im.convert("L")
if fname==files[0]:
print "Resize: %dx%d --> %dx%d" % (orig_size[0],orig_size[1],
new_size[0],new_size[1])
im=im.resize(new_size)
if show:
im.show()
a=numpy.asarray(im.getdata(),dtype='B')
a.shape=im.size[::-1]
im_list.append(a)
w.update((count+1)/float(file_count))
print w,
sys.stdout.flush()
var={'im':im_list,'im_scale_shift':[1.0,0.0]}
print
return var
def make_raw_vanhateren100():
fnames = glob.glob('iml/*.iml')
im_list = []
w = Waitbar(True)
for i, filename in enumerate(fnames):
fin = open(filename, 'rb')
s = fin.read()
fin.close()
arr = array.array('H', s)
arr.byteswap()
img = numpy.array(arr, dtype='uint16').reshape(1024, 1536)
im_list.append(img)
w.update((i + 1) / 100.0)
print w,
sys.stdout.flush()
var = {'im': im_list, 'im_scale_shift': [1.0, 0.0]}
return var
def eqn_images(var,eqn='x'):
im2_list=[]
im_scale_shift=var['im_scale_shift']
im_count=len(var['im'])
w = Waitbar(True)
for count,im in enumerate(var['im']):
x=im
im2=eval(eqn)
im2_list.append(im2)
w.update((count+1)/float(im_count))
print w,
sys.stdout.flush()
var2={'im':im2_list,'im_scale_shift':[1.0,0.0]}
print
return var2
def eqn_images(var, eqn='x'):
im2_list = []
im_scale_shift = var['im_scale_shift']
im_count = len(var['im'])
w = Waitbar(True)
for count, im in enumerate(var['im']):
x = im
im2 = eval(eqn)
im2_list.append(im2)
w.update((count + 1) / float(im_count))
print w,
sys.stdout.flush()
var2 = {'im': im2_list, 'im_scale_shift': [1.0, 0.0]}
print
return var2
def make_log(var):
im2_list=[]
im_scale_shift=var['im_scale_shift']
im_count=len(var['im'])
w = Waitbar(True)
for count,im in enumerate(var['im']):
im2=numpy.log(im+1.0)
new_size=im2.shape
im2=im2/im2.std()
im2_list.append(im2)
w.update((count+1)/float(im_count))
print w,
sys.stdout.flush()
var2={'im':im2_list,'im_scale_shift':[1.0,0.0]}
print
return var2
def make_log(var):
im2_list = []
im_scale_shift = var['im_scale_shift']
im_count = len(var['im'])
w = Waitbar(True)
for count, im in enumerate(var['im']):
im2 = numpy.log(im + 1.0)
new_size = im2.shape
im2 = im2 / im2.std()
im2_list.append(im2)
w.update((count + 1) / float(im_count))
print w,
sys.stdout.flush()
var2 = {'im': im2_list, 'im_scale_shift': [1.0, 0.0]}
print
return var2
def make_blur(var, sd=1.0, radius=None):
if not radius:
radius = sd * 3.0
im2_list = []
im_scale_shift = var['im_scale_shift']
im_count = len(var['im'])
w = Waitbar(True)
for count, im in enumerate(var['im']):
orig_size = im.shape
im2 = dog_filter(im * im_scale_shift[0] + im_scale_shift[1],
sd,
0,
2 * radius + 1,
shape='same')
new_size = im2.shape
im2 = im2 - im2.mean()
im2 = im2 / im2.std()
if count == 0:
print "Blur %d,%d: %dx%d --> %dx%d" % (sd, radius, orig_size[0],
orig_size[1], new_size[0],
new_size[1])
im2_list.append(im2)
w.update((count + 1) / float(im_count))
print w,
sys.stdout.flush()
print
var2 = {'im': im2_list, 'im_scale_shift': [1.0, 0.0]}
return var2
def read_raw_vanhateren100(show=True):
fnames=glob.glob('original/vanhateren_iml/*.iml')
im_list=[]
w = Waitbar(True)
for i,filename in enumerate(fnames):
fin = open( filename, 'rb' )
s = fin.read()
fin.close()
arr = array.array('H', s)
arr.byteswap()
img = numpy.array(arr, dtype='uint16').reshape(1024,1536)
im_list.append(img)
if show:
imm=Image.fromarray(img)
imm.show(command='display')
w.update((i+1)/100.0)
print w,
sys.stdout.flush()
var={'im':im_list,'im_scale_shift':[1.0,0.0]}
return var
def make_rot(var, which_angles, more=False):
def radians(deg):
return deg * numpy.pi / 180.0
im_scale_shift = var['im_scale_shift']
im2_list = []
for a in which_angles:
print "Rotate ", a
im_count = len(var['im'])
w = Waitbar(True)
for count, imm in enumerate(var['im']):
im = imm * im_scale_shift[0] + im_scale_shift[1]
orig_size = im.shape
im = im[:, :orig_size[0]] # crop to square
sz = im.shape
new_size = sz
Im = pn.array2image(im).rotate(a)
im2 = pn.image2array(Im)
# assume square images, and worst case (45 deg)
s = sz[0]
l = int(numpy.ceil(s / numpy.sqrt(2)))
i1 = int((s - l) / 2)
im2 = im2[i1:(i1 + l), i1:(i1 + l)]
im2 = im2 - im2.mean()
im2 = im2 / im2.std()
if count == 0:
print "Rotate %d: %dx%d --> %dx%d" % (
a, orig_size[0], orig_size[1], new_size[0], new_size[1])
im2_list.append(im2)
if more:
im = imm * im_scale_shift[0] + im_scale_shift[1]
orig_size = im.shape
im = im[:, -orig_size[0]:] # crop to square
sz = im.shape
new_size = sz
Im = pn.array2image(im).rotate(a)
im2 = pn.image2array(Im)
# assume square images, and worst case (45 deg)
s = sz[0]
l = int(numpy.ceil(s / numpy.sqrt(2)))
i1 = int((s - l) / 2)
im2 = im2[i1:(i1 + l), i1:(i1 + l)]
im2 = im2 / im2.std()
im2 = im2 - im2.mean()
im2_list.append(im2)
if count == 0:
print "Rotate more %d: %dx%d --> %dx%d" % (
a, orig_size[0], orig_size[1], new_size[0],
new_size[1])
w.update((count + 1) / float(im_count))
print w,
sys.stdout.flush()
print
var2 = {'im': im2_list, 'im_scale_shift': [1.0, 0.0]}
return var2
def make_rot(var,which_angles,more=False):
def radians(deg):
return deg*numpy.pi/180.0
im_scale_shift=var['im_scale_shift']
im2_list=[]
for a in which_angles:
print "Rotate ",a
im_count=len(var['im'])
w = Waitbar(True)
for count,imm in enumerate(var['im']):
im=imm*im_scale_shift[0]+im_scale_shift[1]
orig_size=im.shape
im=im[:,:orig_size[0]] # crop to square
sz=im.shape
new_size=sz
Im=pn.array2image(im).rotate(a)
im2=pn.image2array(Im)
# assume square images, and worst case (45 deg)
s=sz[0]
l=int(numpy.ceil(s/numpy.sqrt(2)))
i1=int((s-l)/2)
im2=im2[i1:(i1+l),i1:(i1+l)]
im2=im2/im2.std()
im2=im2-im2.mean()
if count==0:
print "Rotate %d: %dx%d --> %dx%d" % (a,
orig_size[0],orig_size[1],
new_size[0],new_size[1])
im2_list.append(im2)
if more:
im=imm*im_scale_shift[0]+im_scale_shift[1]
orig_size=im.shape
im=im[:,-orig_size[0]:] # crop to square
sz=im.shape
new_size=sz
Im=pn.array2image(im).rotate(a)
im2=pn.image2array(Im)
# assume square images, and worst case (45 deg)
s=sz[0]
l=int(numpy.ceil(s/numpy.sqrt(2)))
i1=int((s-l)/2)
im2=im2[i1:(i1+l),i1:(i1+l)]
im2=im2/im2.std()
im2=im2-im2.mean()
im2_list.append(im2)
if count==0:
print "Rotate more %d: %dx%d --> %dx%d" % (a,
orig_size[0],orig_size[1],
new_size[0],new_size[1])
w.update((count+1)/float(im_count))
print w,
sys.stdout.flush()
print
var2={'im':im2_list,'im_scale_shift':[1.0,0.0]}
return var2
def read_raw_bbsk(animal='cat',subset=True,show=True):
if subset:
files=glob.glob('original/subset_bbsk081604/*.jpg')
else:
files=glob.glob('original/all_bbsk081604/*.jpg')
files.sort()
im_list=[]
print "Image files: %d" % len(files)
print "Animal:", animal
w = Waitbar(True)
file_count=len(files)
for count,fname in enumerate(files):
im=Image.open(fname)
orig_size=im.size
# the bbsk images are angular size 60 degrees x 40 degrees
# the raw images are resized so that
# 5.5 pixels ~ 0.5 degrees (cat retina) - default
#
# ...or...
#
# 13 pixels ~ 7 degrees (mouse retina)
#
# see the contained iccns.pdf
#
if animal=='cat':
new_size=[int(o*60./0.5*5.5/orig_size[0]) for o in orig_size]
elif animal=='mouse':
new_size=[int(o*60./7.*13/orig_size[0]) for o in orig_size]
else:
raise ValueError
im=im.convert("L")
if fname==files[0]:
print "Resize: %dx%d --> %dx%d" % (orig_size[0],orig_size[1],
new_size[0],new_size[1])
im=im.resize(new_size)
if show:
im.show()
# I know these have a max value of 255, so 'B' will work
a=numpy.asarray(im.getdata(),dtype='B')
a.shape=im.size[::-1]
im_list.append(a)
w.update((count+1)/float(file_count))
print w,
sys.stdout.flush()
var={'im':im_list,'im_scale_shift':[1.0,0.0]}
print
return var
