def __init__(self, path):
# define the main path
self.path = path
# folder environment variable
self.names = []
self.avgpath = ""
self.subfolders = {}
# create the folder environment
try:
self.makeavgdir()
except FileNotFoundError as e:
print("Folder not found")
except:
print("WTF")
# create log file in the avg folder
lg.basicConfig(filename=join(self.avgpath, 'example.log'),
level=lg.INFO)
# initialize variables
self.imgs = []
self.template = MyImage()
self.templateft = None
self.angles_list = []
self.templaterotsft = []
self.algimgs = []
self.corrs = []
self.shifts = []
self.avg = MyImage()
def __init__(self, path):
# define the main path
self.path = path
# folder environment variable
self.names = []
self.avgpath = ""
self.subfolders = {}
# create the folder environment
try:
self.makeavgdir()
except FileNotFoundError as e:
print("Folder not found")
except:
print("WTF")
# initialize variables
self.imgs = []
self.template = MyImage()
self.templateft = None
self.angles_list = []
self.templaterotsft = []
self.algimgs = []
self.corrs = []
self.shifts = []
self.avg = MyImage()
def laod_corrs(self):
p = self.subfolders["correlation_images"]
names = [f for f in listdir(p) if isfile(join(p, f))]
self.imgs = []
for name in names:
img = MyImage()
img.read_from_file(name)
self.imgs.append(img)
def get_phases(self):
sizeimg = np.real(self.imgfft).shape
mag = np.zeros(sizeimg)
for x in range(sizeimg[0]):
for y in range(sizeimg[1]):
mag[x][y] = np.arctan2(np.real(self.imgfft[x][y]),
np.imag(self.imgfft[x][y]))
rpic = MyImage(mag)
rpic.limit(1)
return rpic
def get_magnitude(self):
sizeimg = np.real(self.imgfft).shape
mag = np.zeros(sizeimg)
for x in range(sizeimg[0]):
for y in range(sizeimg[1]):
mag[x][y] = np.sqrt(
np.real(self.imgfft[x][y])**2 +
np.imag(self.imgfft[x][y])**2)
rpic = MyImage(mag)
rpic.limit(1)
return rpic
def laod_corrs(self):
lg.info("Loading correlation images")
p = self.subfolders["correlation_images"]
names = [f for f in listdir(p) if isfile(join(p, f))]
self.imgs = []
for name in names:
img = MyImage()
img.read_from_file(name)
self.imgs.append(img)
lg.info("Image: {0} imported successfully".format(name))
def gather_pictures(self):
# for now gather all the files, next check for picture extensions
p = self.path
self.names = [f for f in listdir(p) if isfile(join(p, f))]
for imgname in self.names:
path, name, ext = get_pathname(imgname)
if ext in ['.jpg', '.png']:
imagepath = join(self.path, imgname)
img = MyImage()
img.read_from_file(imagepath)
self.imgs.append(img)
def __init__(self, path):
# define the main path
self.path = path
# folder environment variable
self.names = []
self.avgpath = ""
self.subfolders = {}
# create the folder environment
try:
self.makeavgdir()
except FileNotFoundError as e:
print("Folder not found")
except:
print("WTF")
# create log file in the avg folder
lg.basicConfig(filename=join(self.avgpath, 'example.log'),
level=lg.INFO)
# pictures
self.init_imgs = ImageArray(self.path)
self.imgs = NpyImageArray(
(self.subfolders["processed_images"], "proc_imgs.npy",
len(self.init_imgs.paths)))
# initialize variables
self.template = MyImage()
self.templateft = None
self.angles_list = []
self.templaterotsft = None
folder = self.subfolders["aligned_images"]
bname = "algimage.npy"
q = self.imgs.n
self.algimgs = NpyImageArray((folder, bname, q))
folder = self.subfolders["correlation_images"]
bname = "correlation.npy"
q = self.imgs.n
self.corrs = NpyImageArray((folder, bname, q))
self.shifts = []
self.avg = MyImage()
def init_inimage(self):
# open the source image
self.inimage = ImagePath(self.name, MyImage(), self.bufpath)
self.inimage.image.read_from_file(self.initialpath)
self.inimage.image.convert2grayscale()
self.inimage.image.squareit()
# resize the image and save it in gif format
self.savegif(self.inimage, (500, 500))
def average(self, aligned=True):
if aligned:
dataset = self.algimgs
else:
dataset = self.imgs
s = MyImage(np.zeros(dataset[0].data.shape))
for picture in dataset:
s += picture
self.avg = s / len(dataset)
def average(self, aligned=True, debug=False):
if aligned:
dataset = self.algimgs
else:
dataset = self.imgs
s = MyImage(np.zeros(dataset[0].data.shape))
for i, picture in enumerate(dataset):
if debug:
print("Averaging picture:", i)
s += picture
self.avg = s / dataset.n
def power_spectrum(self):
absolutefft = np.abs(self.imgfft)
xlen = absolutefft.shape[0]
ylen = absolutefft.shape[1]
squareftt = deepcopy(absolutefft)
for i in range(xlen):
for j in range(ylen):
if squareftt[i][j] != 0:
squareftt[i][j] = np.log(squareftt[i][j])**2
realpart = np.real(squareftt)
ps = MyImage(realpart)
return ps
def get_polar_t(self):
mag = self.get_magnitude()
sizeimg = np.real(self.imgfft).shape
pol = np.zeros(sizeimg)
for x in range(sizeimg[0]):
for y in range(sizeimg[1]):
my = y - sizeimg[1] / 2
mx = x - sizeimg[0] / 2
if mx != 0:
phi = np.arctan(my / float(mx))
else:
phi = 0
r = np.sqrt(mx**2 + my**2)
ix = map_range(phi, -np.pi, np.pi, sizeimg[0], 0)
iy = map_range(r, 0, sizeimg[0], 0, sizeimg[1])
if ix >= 0 and ix < sizeimg[0] and iy >= 0 and iy < sizeimg[1]:
pol[x][y] = mag.data[int(ix)][int(iy)]
pol = MyImage(pol)
pol.limit(1)
return pol
def __init__(self, imagepathname):
# set the initial path and extract the approriate information
self.initialpath = imagepathname
path, name, ext = get_pathname(self.initialpath)
self.mainpath = path
self.name = name
self.inimg_name = self.name + ext
# create the directory for the elaboration
self.bufpath = join(self.mainpath, self.name)
if not isdir(self.bufpath):
mkdir(self.bufpath)
# open the source image
self.inimage = ImagePath(self.name, MyImage(), self.bufpath)
# declare the fourier transform
self.ftimage = 0
def savegif(self, path, gifpath, size):
image = MyImage(path)
image.convert2grayscale()
image.squareit()
# calculate ft for resizing
imft = ImgFFT(image.data)
imft.ft()
im = imft.resize_image(size[0], size[1])
imrgb = MyRGBImg(np.zeros((size[0], size[1], 3)))
for c in range(3):
imrgb.set_channel(im, c)
# save resized image
imsave(gifpath, imrgb.data, format="gif")
def run_create_test_dataset(folder):
debug_mode = True
# set parameters
testdatasetpath = folder
mypicname = "./data/Lenna.png"
n_pictures = 25
min_a = -10
max_a = 10
min_xs = -25
max_xs = 25
min_ys = -25
max_ys = 25
flip_angle = True
mylog = Logger("Create gray dataset",
testdatasetpath + "main_logfile.txt",
debug_mode=debug_mode)
mylog.log("Creating dataset in:\n" + testdatasetpath)
mylog.log("Using the picture: " + mypicname)
mylog.log("Creating dataset with {0} pictures".format(n_pictures))
mylog.log("With rotations from {0} to {1} degree".format(min_a, max_a))
mylog.log("With shift in x: from {0} to {1} and y: from {2} to {3}".format(
min_xs, max_xs, min_ys, max_ys))
mylog.log(
"The dataset will be generated by {0} randomly flipping rotations and translations"
.format("" if flip_angle == True else "not"))
if not isdir(testdatasetpath):
mkdir(testdatasetpath)
mylog.log("Created test dataset path")
# create a test dataset:
mypic = MyImage(mypicname)
mypic.squareit()
mypic.convert2grayscale()
mypic.binning(2)
mypic.normalize()
mylog.log("Processing done")
template_folder = join(testdatasetpath, "template_folder")
if not isdir(template_folder):
mkdir(template_folder)
mypic.save(join(template_folder, "template.png"))
mylog.log("Saved the original image in the template folder")
if debug_mode:
mypic.show_image()
plt.show()
mylog.log(
"------------------------------\nCreating dataset\n------------------------------"
)
np.random.seed(10)
logpathdir = join(testdatasetpath, "tlog")
if not isdir(logpathdir):
mkdir(logpathdir)
with open(join(logpathdir, "mytransformations.log"), 'w') as f:
angles = np.random.uniform(min_a, max_a, n_pictures)
for i in range(n_pictures):
image = deepcopy(mypic)
if flip_angle:
anglefirst = False if np.random.randint(0, 2) == 0 else True
else:
anglefirst = True
angle = angles[i]
dx = np.random.randint(min_xs, max_xs)
dy = np.random.randint(min_ys, max_ys)
f.write("{0} {1} {2} {3}\n".format(anglefirst, dx, dy, angle))
mylog.log(
"Pictrue with: rot first {0}, angle: {1}, shift x: {2}, y: {3} created"
.format(anglefirst, angle, dx, dy))
if anglefirst:
image.rotate(angle)
image.move(dx, dy)
else:
image.move(dx, dy)
image.rotate(angle)
if debug_mode:
image.show_image()
plt.show()
image.save(join(testdatasetpath, "pic_" + str(i) + ".png"))
def run_average_gray(folder):
# options
debug_mode = True
# chose path to image sequence folder
datasetpath = folder
memsave = True # True | False
preprocop = [("convert to grayscale", ), ("square it", ), ("binning", 0),
("transpose", ), ("normalize", )]
custom_template = False # True | False
template_image_path = folder + "template_folder/template.png" # path to image
auto_template_type = "UseFirstImage" # "Use First Image" | "Average"
save_template = True # True | False
align_images = True # True | False
align_mode = "fixed" # "fixed | tree" // fixed still fastest option
align_space = (-1, 1, 0.1) # (min angle, max angle, precision)
# logger
mylog = Logger("Averaging Gray",
datasetpath + "main_logfile.txt",
debug_mode=debug_mode)
mylog.log("Debug mode: " + "ON" if debug_mode == True else "OFF")
mylog.log("For the folder:")
mylog.log(datasetpath)
mylog.log("Averaging type: grey")
mylog.log("Memory saving mode: " + str(memsave))
mylog.log(
"------------------------------\nLoading dataset\n------------------------------"
)
if memsave:
avg = AvgFolderMem(datasetpath)
else:
avg = AvgFolder(datasetpath)
avg.gather_pictures()
# build the informatiosn
mylog.log("number of pictures:" + str(avg.init_imgs.n))
image = avg.init_imgs.get_image(0)
mylog.log("Size of images: " + str(image.data.shape[0]) + "x" +
str(image.data.shape[1]))
mylog.log("--- Start preporcessing ---", True)
nametofunc = {}
nametofunc[preprocop[0]] = lambda: avg.c2gscale()
nametofunc[preprocop[1]] = lambda: avg.squareit()
nametofunc[preprocop[2]] = lambda n: avg.binning(n)
nametofunc[preprocop[3]] = lambda: avg.transpose()
nametofunc[preprocop[4]] = lambda: avg.normalize()
for name in preprocop:
if len(name) == 1:
nametofunc[name]()
mylog.log("Process: " + name[0])
if len(name) == 2:
nametofunc[name](name[1])
mylog.log("Process: " + name[0] + "Arguments: " + str(name[1]))
mylog.log("Processing took: ", True)
mylog.log(
"------------------------------\nGenerating template\n------------------------------"
)
if custom_template:
custom_t = MyImage(template_image_path)
custom_t.convert2grayscale()
mylog.log("Template loaded from: " + template_image_path)
mylog.log("Template image is: {0}x{1}".format(custom_t.get_sizex(),
custom_t.get_sizey()))
avg.generate_template(custom_t)
else:
avg.generate_template(auto_template_type)
mylog.log("Template generated: " + auto_template_type)
mylog.log("Template generated", True)
if save_template:
avg.save_template()
if debug_mode:
avg.template.show_image()
plt.show()
avg.template.inspect()
if align_images:
mylog.log(
"------------------------------\nAlignment\n------------------------------"
)
mylog.log("Choosen Aligment: " + align_mode)
alignnames = ["min angle: ", " |max angle: ", " |precision: "]
mylog.log("".join(a + str(d) for a, d in zip(alignnames, align_space)))
avg.align_images(align_mode, align_space, debug_mode)
avg.save_shifts()
mylog.log("Alignment done", True)
if avg.anglestree != None:
mylog.log("Numnber of template generated: " +
str(len(avg.anglestree.angles_nodes)))
mylog.log("Normally would be: " + str(
len(np.arange(align_space[0], align_space[1], align_space[2])))
)
else:
mylog.log("Numnber of template generated: " +
str(avg.templaterotsft.n))
mylog.log("Shifts saved")
mylog.log(
"------------------------------\nAverage\n------------------------------"
)
avg.average(debug=debug_mode)
avg.save_avg()
mylog.log("Average Complete", True)
if debug_mode:
avg.avg.show_image()
plt.show()
avg.avg.inspect()
mylog.log("End procedure", True)
def ift(self):
self.imgifft = MyImage(np.real(fft.ifft2(fft.fftshift(self.imgfft))))
from matplotlib import pyplot as plt
# load sample image
imagepath = "C:/Users/Mauro/Desktop/Vita Online/Programming/Picture cross corr/Lenna.png"
imagepath = "C:/Users/Mauro/Desktop/Vita Online/Programming/Picture cross corr/silentcam/dataset24/avg/correlation_images/corr_1497777846958.png"
imagepath = "../../../Lenna.png"
# # convert range test
# x = np.arange(0, 3, 0.1)
# print(x)
#
# for i in x:
# print(i, map_range(i, 0, 1, 0, 2*np.pi))
im = MyImage()
im.read_from_file(imagepath)
im.convert2grayscale()
print("Original image")
im.show_image()
plt.show()
ft = ImgFFT(im)
ft.ft()
imres = ft.resize_image(256, 256)
imres.show_image()
plt.show()
# print("Power Spectrum")
def get_real_part(self):
r = MyImage(np.real(self.imgfft))
r.limit(1)
return r
class AvgFolderMem(object):
# Initialization functions
def __init__(self, path):
# define the main path
self.path = path
# folder environment variable
self.names = []
self.avgpath = ""
self.subfolders = {}
# create the folder environment
try:
self.makeavgdir()
except FileNotFoundError as e:
print("Folder not found")
except:
print("WTF")
# create log file in the avg folder
lg.basicConfig(filename=join(self.avgpath, 'example.log'),
level=lg.INFO)
# pictures
self.init_imgs = ImageArray(self.path)
self.imgs = NpyImageArray(
(self.subfolders["processed_images"], "proc_imgs.npy",
len(self.init_imgs.paths)))
# initialize variables
self.template = MyImage()
self.templateft = None
self.angles_list = []
self.templaterotsft = None
folder = self.subfolders["aligned_images"]
bname = "algimage.npy"
q = self.imgs.n
self.algimgs = NpyImageArray((folder, bname, q))
folder = self.subfolders["correlation_images"]
bname = "correlation.npy"
q = self.imgs.n
self.corrs = NpyImageArray((folder, bname, q))
self.shifts = []
self.avg = MyImage()
def refine_angles(self, min_angle, max_angle):
# builds a angles tree given the min max angle
pass
def makeavgdir(self):
# create a folder average into the dataset path
# avg
# |- processed_images
# |- aligned_images
# |- correlation_images
# |- results
# |- template_rot
self.avgpath = join(self.path, "avg")
if not isdir(self.avgpath):
mkdir(self.avgpath)
subfolders = [
"processed_images", "aligned_images", "correlation_images",
"results", "template_rot"
]
for folder in subfolders:
self.subfolders[folder] = join(self.avgpath, folder)
if not isdir(self.subfolders[folder]):
mkdir(self.subfolders[folder])
def gather_pictures(self):
for i, image in enumerate(self.init_imgs):
self.imgs.set_image(i, image)
# image operations
def c2gscale(self):
for i, img in enumerate(self.imgs):
img.convert2grayscale()
self.imgs.set_image(i, img)
lg.info("dataset converted to grayscale")
def squareit(self):
for i, img in enumerate(self.imgs):
img.squareit()
self.imgs.set_image(i, img)
lg.info("dataset squared")
def transpose(self):
for i, img in enumerate(self.imgs):
img.transpose()
self.imgs.set_image(i, img)
lg.info("dataset transposed")
def normalize(self):
for i, img in enumerate(self.imgs):
img.normalize()
self.imgs.set_image(i, img)
lg.info("dataset normalized")
def binning(self, n=1):
for i, img in enumerate(self.imgs):
img.binning(n)
self.imgs.set_image(i, img)
lg.info("dataset binned {0} times".format(n))
# template handling
def generate_template(self, option, rot_precision=None):
if type(option) is str:
if option == "UseFirstImage":
self.template = self.imgs.get_image(0)
self.templateft = ImgFFT(self.template)
self.templateft.ft()
elif option == "Average":
self.average(False)
self.template = self.avg
self.templateft = ImgFFT(self.template)
self.templateft.ft()
else:
raise TemplateTypeError(option)
elif type(option) == MyImage:
self.template = option
self.templateft = ImgFFT(self.template)
self.templateft.ft()
else:
raise TemplateTypeError(type(option))
lg.info("template created: {0}".format(option))
if type(rot_precision) == tuple:
print("Creating rotation references")
# rot_precision format = (from, to, precision)
frm = rot_precision[0]
to = rot_precision[1]
prec = rot_precision[2]
self.angles_list = np.arange(frm, to, prec)
print("From", frm, "to", to, "precision", prec)
print("Total:", len(self.angles_list), "angles")
self.templaterotsft = NpyFTArray(
(self.subfolders["template_rot"], "template_rot_ft.npy",
len(self.angles_list)))
for i, angle in enumerate(self.angles_list):
print("creating angle: ", angle)
rot = deepcopy(self.template)
rot.rotate(angle)
rotft = ImgFFT(rot)
rotft.ft()
self.templaterotsft.set_image(i, rotft)
def align_images(self, debug=False):
c = 0
anglestree = AnglesTree(-1, 1, 0.1, self.template)
for image in self.imgs:
angle = anglestree.analyze_image(image)
# # generate the fourier transform of the image
# imgft = ImgFFT(image)
# imgft.ft()
#
# # calculate the rotations
# smax = 0
# idxmax = 0
# for idx, temp in enumerate(self.templaterotsft):
# corr = temp.correlate(imgft)
# s, dx, dy = corr.find_peak(1)
#
# if s > smax:
# smax = s
# idxmax = idx
#
# angle = float(self.angles_list[idxmax])
print("angle found", angle)
rotalgimage = deepcopy(image)
rotalgimage.rotate(-angle)
# calculate the shifts
rotalgimageft = ImgFFT(rotalgimage)
rotalgimageft.ft()
corr = rotalgimageft.correlate(self.templateft)
self.corrs.set_image(c, corr)
dx, dy = corr.find_translation(1)
self.shifts.append((dx, dy, angle))
print("shifts:", dx, dy)
rotalgimage.move(dx, dy)
self.algimgs.set_image(c, rotalgimage)
if debug:
print("Correlated image:", c)
lg.info("correlated image n: " + str(c))
c += 1
def average(self, aligned=True, debug=False):
if aligned:
dataset = self.algimgs
else:
dataset = self.imgs
s = MyImage(np.zeros(dataset[0].data.shape))
for i, picture in enumerate(dataset):
if debug:
print("Averaging picture:", i)
s += picture
self.avg = s / dataset.n
# I/O methods
def get_template_path(self):
return join(self.avgpath, "template.png")
def get_avg_path(self):
return join(self.subfolders["results"], "avg.png")
def save_template(self):
self.template.save(join(self.avgpath, "template.png"))
def load_template(self, filepathname):
self.template.read_from_file(filepathname)
def save_avg(self):
self.avg.save(self.get_avg_path())
def save_shifts(self):
with open(join(self.subfolders["results"], "shifts_log.txt"),
"w") as f:
for shift in self.shifts:
f.write("{0[0]:d} | {0[1]:d} | {0[2]:.3f}\n".format(shift))
lg.info("Shifts saved")
def load_shifts(self):
with open(join(self.subfolders["results"], "shifts_log.txt")) as f:
lines = f.readlines()
self.shifts = []
for line in lines:
sdata = line.split(' | ')
sdata = [d.strip() for d in sdata]
data = [int(sdata[0]), int(sdata[1]), float(sdata[2])]
print(data)
self.shifts.append(data)
def get_image(self, i):
with open(self.paths[i], 'rb') as f:
imgfft = np.load(f)
ft = ImgFFT(MyImage())
ft.imgfft = imgfft
return ft
class AvgFolder(object):
# Initialization functions
def __init__(self, path):
# define the main path
self.path = path
# folder environment variable
self.names = []
self.avgpath = ""
self.subfolders = {}
# create the folder environment
try:
self.makeavgdir()
except FileNotFoundError as e:
print("Folder not found")
except:
print("WTF")
# create log file in the avg folder
lg.basicConfig(filename=join(self.avgpath, 'example.log'),
level=lg.INFO)
# initialize variables
self.imgs = []
self.template = MyImage()
self.templateft = None
self.angles_list = []
self.templaterotsft = []
self.algimgs = []
self.corrs = []
self.shifts = []
self.avg = MyImage()
def gather_pictures(self):
# for now gather all the files, next check for picture extensions
p = self.path
self.names = [f for f in listdir(p) if isfile(join(p, f))]
for imgname in self.names:
path, name, ext = get_pathname(imgname)
if ext in ['.jpg', '.png']:
imagepath = join(self.path, imgname)
img = MyImage()
img.read_from_file(imagepath)
self.imgs.append(img)
lg.info("Image: {0} imported successfully".format(imagepath))
def makeavgdir(self):
# create a folder average into the dataset path
# avg
# |- processed_images
# |- aligned_images
# |- correlation_images
# |- results
self.avgpath = join(self.path, "avg")
if not isdir(self.avgpath):
mkdir(self.avgpath)
subfolders = [
"processed_images", "aligned_images", "correlation_images",
"results"
]
for folder in subfolders:
self.subfolders[folder] = join(self.avgpath, folder)
if not isdir(self.subfolders[folder]):
mkdir(self.subfolders[folder])
# image operations
def c2gscale(self):
for img in self.imgs:
img.convert2grayscale()
lg.info("dataset converted to grayscale")
def squareit(self):
for img in self.imgs:
img.squareit()
lg.info("dataset squared")
def transpose(self):
for img in self.imgs:
img.transpose()
lg.info("dataset transposed")
def normalize(self):
for img in self.imgs:
img.normalize()
lg.info("dataset normalized")
def binning(self, n=1):
for img in self.imgs:
img.binning(n)
lg.info("dataset binned {0} times".format(n))
# template handling
def generate_template(self, option, rot_precision=None):
if type(option) is str:
if option == "UseFirstImage":
self.template = self.imgs[0]
self.templateft = ImgFFT(self.template)
self.templateft.ft()
elif option == "Average":
self.average(False)
self.template = self.avg
self.templateft = ImgFFT(self.template)
self.templateft.ft()
else:
raise TemplateTypeError(option)
elif type(option) == MyImage:
self.template = option
self.templateft = ImgFFT(self.template)
self.templateft.ft()
else:
raise TemplateTypeError(type(option))
lg.info("template created: {0}".format(option))
if type(rot_precision) == tuple:
print("Creating rotation references")
# rot_precision format = (from, to, precision)
frm = rot_precision[0]
to = rot_precision[1]
prec = rot_precision[2]
self.angles_list = np.arange(frm, to, prec)
print("From", frm, "to", to, "precision", prec)
print("Total:", len(self.angles_list), "angles")
for angle in self.angles_list:
print("creating angle: ", angle)
rot = deepcopy(self.template)
rot.rotate(angle)
rotft = ImgFFT(rot)
rotft.ft()
self.templaterotsft.append(rotft)
def align_images(self, debug=False):
c = 0
for image in self.imgs:
# generate the fourier transform of the image
imgft = ImgFFT(image)
imgft.ft()
# calculate the rotations
smax = 0
idxmax = 0
for idx, temp in enumerate(self.templaterotsft):
corr = temp.correlate(imgft)
s, dx, dy = corr.find_peak(1)
if s > smax:
smax = s
idxmax = idx
print("angle found", self.angles_list[idxmax])
rotalgimage = deepcopy(image)
rotalgimage.rotate(-self.angles_list[idxmax])
# calculate the shifts
rotalgimageft = ImgFFT(rotalgimage)
rotalgimageft.ft()
corr = rotalgimageft.correlate(self.templateft)
self.corrs.append(corr)
dx, dy = corr.find_translation(1)
self.shifts.append((dx, dy))
print("shifts:", dx, dy)
rotalgimage.move(dx, dy)
self.algimgs.append(rotalgimage)
if debug:
print("Correlated image:", c)
lg.info("correlated image n: " + str(c))
c += 1
def average(self, aligned=True):
if aligned:
dataset = self.algimgs
else:
dataset = self.imgs
s = MyImage(np.zeros(dataset[0].data.shape))
for picture in dataset:
s += picture
self.avg = s / len(dataset)
# I/O methods
def save_template(self):
self.template.save(join(self.avgpath, "template.png"))
def load_template(self, filepathname):
self.template.read_from_file(filepathname)
def save_imgs(self):
for i, img in enumerate(self.imgs):
filename, ext = splitext(self.names[i])
img.save(
join(self.subfolders["processed_images"],
"proc_" + filename + ".png"))
lg.info("processed dataset saved, images {0}".format(i))
def laod_imgs(self):
lg.info("Loading processed images")
p = self.subfolders["processed_images"]
names = [f for f in listdir(p) if isfile(join(p, f))]
self.imgs = []
for name in names:
img = MyImage()
img.read_from_file(name)
self.imgs.append(img)
lg.info("Image: {0} imported successfully".format(name))
def save_algimgs(self):
for i, algimg in enumerate(self.algimgs):
filename, ext = splitext(self.names[i])
algimg.save(
join(self.subfolders["aligned_images"],
("alg_" + filename + ".png")))
lg.info("aligned dataset saved, images {0}".format(i))
def laod_algimgs(self):
lg.info("Loading aligned images")
p = self.subfolders["aligned_images"]
names = [f for f in listdir(p) if isfile(join(p, f))]
self.imgs = []
for name in names:
img = MyImage()
img.read_from_file(name)
self.imgs.append(img)
lg.info("Image: {0} imported successfully".format(name))
def save_corrs(self):
for i, corr in enumerate(self.corrs):
filename, ext = splitext(self.names[i])
corr.save(
join(self.subfolders["correlation_images"],
("corr_" + filename + ".png")))
lg.info("correlations dataset saved, images {0}".format(i))
def laod_corrs(self):
lg.info("Loading correlation images")
p = self.subfolders["correlation_images"]
names = [f for f in listdir(p) if isfile(join(p, f))]
self.imgs = []
for name in names:
img = MyImage()
img.read_from_file(name)
self.imgs.append(img)
lg.info("Image: {0} imported successfully".format(name))
def save_avg(self):
self.avg.save(join(self.subfolders["results"], "avg.png"))
def save_shifts(self):
with open(join(self.subfolders["results"], "shifts_log.txt"),
"w") as f:
for shift in self.shifts:
f.write("{0[0]:d} | {0[1]:d}\n".format(shift))
lg.info("Shifts saved")
def load_shifts(self):
with open(join(self.subfolders["results"], "shifts_log.txt")) as f:
lines = f.readlines()
self.shifts = []
for line in lines:
data = line.split(' - ')
data = [int(d.strip()) for d in data]
print(data)
self.shifts.append(data)
def get_image(self, i):
with open(self.paths[i], 'rb') as f:
data = np.load(f)
return MyImage(data)
def get_image(self, i):
path = self.paths[i]
image = MyImage(path)
return image
def get_imag_part(self):
r = MyImage(np.imag(self.imgfft))
r.limit(1)
return r
def ift(self):
return MyImage(np.real(fft.ifft2(fft.fftshift(self.ft))))
def get_channel(self, channel):
if type(channel) == str:
c2idx = {'r': 0, 'g': 1, 'b': 2}
else:
c2idx = [0, 1, 2]
return MyImage(self.data[:, :, c2idx[channel]])