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crop_input.py
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crop_input.py
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#!/usr/bin/env python
u"""
crop_input.py
by Yara Mohajerani (Last update 10/2018)
Crop input data.
Update History
10/2018 Add option for label width
09/2018 Written
"""
import os
import numpy as np
import imp
import sys
from glob import glob
from PIL import Image,ImageOps
from keras.preprocessing import image
#-- read in images
def load_data(suffix,ddir,hcrop,wcrop,lbl_width):
files = {}
images = {}
labels = {}
for d in ['train','test']:
#-- make subdirectories for input images
subdir = os.path.join(ddir[d],'images%s'%(suffix))
#-- get a list of the input files
file_list = glob(os.path.join(subdir,'*.png'))
#-- get just the file names
files[d] = [os.path.basename(i) for i in file_list]
#-- read training data
n = len(files[d])
#-- get dimensions, force to 1 b/w channel
im_shape = np.array(Image.open(file_list[0]).convert('L')).shape
h,w = im_shape
images[d] = np.ones((n,h-2*hcrop,w-2*wcrop))
labels[d] = np.ones((n,h-2*hcrop,w-2*wcrop))
for i,f in enumerate(files[d]):
#-- same file name but different directories for images and labels
img = np.array(Image.open(os.path.join(subdir,f)).convert('L'))/255.
lbl = np.array(Image.open(os.path.join(ddir[d],'labels%s'%lbl_width,f.replace('Subset','Front'))).convert('L'))/255.
images[d][i][:,:] = img[hcrop:h-hcrop,wcrop:w-wcrop]
labels[d][i][:,:] = lbl[hcrop:h-hcrop,wcrop:w-wcrop]
images[d] = images[d].reshape(n,h-2*hcrop,w-2*wcrop,1)
labels[d] = labels[d].reshape(n,h-2*hcrop,w-2*wcrop,1)
return [images,labels,files]
def crop_input(parameters):
glacier = parameters['GLACIER_NAME']
suffix = parameters['SUFFIX']
if parameters['LABEL_WIDTH'] == '3':
lbl_width = ''
else:
lbl_width = '_%ipx'%int(parameters['LABEL_WIDTH'])
#-- directory setup
#- current directory
current_dir = os.path.dirname(os.path.realpath(__file__))
main_dir = os.path.join(current_dir,'..','FrontLearning_data')
glacier_ddir = os.path.join(main_dir,'%s.dir'%glacier)
data_dir = os.path.join(glacier_ddir, 'data')
ddir = {}
ddir['train'] = os.path.join(data_dir,'train')
ddir['test'] = os.path.join(data_dir,'test')
#-- load images
images,labels,files = load_data(suffix,ddir,30,25,lbl_width)
#-- make output directory
for d in ['train','test']:
out_subdir_img = os.path.join(ddir[d],'images%s_cropped'%suffix)
out_subdir_lbl = os.path.join(ddir[d],'labels%s_cropped'%lbl_width)
if (not os.path.isdir(out_subdir_img)):
os.mkdir(out_subdir_img)
if (not os.path.isdir(out_subdir_lbl)):
os.mkdir(out_subdir_lbl)
#-- save the cropped image
for i in range(len(files[d])):
im = image.array_to_img(images[d][i])
lb = image.array_to_img(labels[d][i])
im.save(os.path.join(out_subdir_img,'%s'%files[d][i]))
lb.save(os.path.join(out_subdir_lbl,'%s'%files[d][i].replace('Subset','Front')))
#-- main function to get parameters and pass them along to fitting function
def main():
if (len(sys.argv) == 1):
sys.exit('You need to input at least one parameter file to set run configurations.')
else:
#-- Input Parameter Files (sys.argv[0] is the python code)
input_files = sys.argv[1:]
#-- for each input parameter file
for file in input_files:
#-- keep track of progress
print(os.path.basename(file))
#-- variable with parameter definitions
parameters = {}
#-- Opening parameter file and assigning file ID number (fid)
fid = open(file, 'r')
#-- for each line in the file will extract the parameter (name and value)
for fileline in fid:
#-- Splitting the input line between parameter name and value
part = fileline.split()
#-- filling the parameter definition variable
parameters[part[0]] = part[1]
#-- close the parameter file
fid.close()
#-- pass parameters to training function
crop_input(parameters)
if __name__ == '__main__':
main()