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)
# Flat Field correction manager
if __name__ == "__main__":
# gather dark field
pathdfh = "../../../darkfield/horizontal/"
# average pictures
dfh = AvgFolderMem(pathdfh)
dfh.gather_pictures()
dfh.c2gscale()
dfh.squareit()
dfh.binning(2)
dfh.average(aligned=False, debug=True)
dfh.save_avg()
dfh.avg.show_image()
plt.show()
pathtodataset = "../../../silentcam/dataset36/"
avg = AvgFolderMem(pathtodataset)
avg.gather_pictures()
avg.c2gscale()
avg.squareit()
avg.binning(2)
avg.average(aligned=False, debug=True)