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_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 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"))
# 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")
# ps = ft.power_spectrum()
# ps.show_image()
