def train(self):
#data_preprocess
input_datadir = './train_img'
output_datadir = './pre_img'
obj=preprocesses(input_datadir,output_datadir)
nrof_images_total,nrof_successfully_aligned=obj.collect_data()
print('Total number of images: %d' % nrof_images_total)
print('Number of successfully aligned images: %d' % nrof_successfully_aligned)
#For Training
root = Tk()
root.geometry('400x400')
root.title('Training Status')
datadir = './pre_img'
modeldir = './model/20170511-185253.pb'
classifier_filename = './class/classifier.pkl'
print ("Training Start")
obj=training(datadir,modeldir,classifier_filename)
get_file=obj.main_train()
print('Saved classifier model to file "%s"' % get_file)
ourMessage = "Training Completed...!!!"
p = Label(root,text=ourMessage,fg="blue",font=("bold",20))
p.place(x=80,y=120)
root.mainloop()
def startPreprocessing(cwd, relative_path):
input_datadir = relative_path + '/pre_img'
output_datadir = relative_path + '/train_img'
os.chdir(cwd + '/FaceRecognition/')
if not hasattr(sys, 'argv'):
sys.argv = ['']
sys.path.append('.')
fullpath = os.path.expanduser(relative_path + '/pre_img/')
if os.path.isdir(fullpath):
shutil.rmtree(fullpath)
time.sleep(
.3
) # making sure the folder is completely deleted before trying to create it again
os.mkdir(fullpath)
from preprocess import preprocesses
obj = preprocesses(input_datadir, output_datadir)
nrof_images_total, nrof_successfully_aligned = obj.collect_data()
return 'Total number of images: {}. Number of successfully aligned images: {}'.format(
nrof_images_total, nrof_successfully_aligned)
def pre():
input_datadir = './train_img'
output_datadir = './pre_img'
obj=preprocesses(input_datadir,output_datadir)
nrof_images_total,nrof_successfully_aligned=obj.collect_data()
print('Total number of images: %d' % nrof_images_total)
print('Number of successfully aligned images: %d' % nrof_successfully_aligned)
def recortar():
input_datadir = './static/photos'
output_datadir = './static/pre_img'
obj = preprocesses(input_datadir, output_datadir)
nrof_images_total, nrof_successfully_aligned = obj.collect_data()
print('El numero total de imagenes es: %d' % nrof_images_total)
print('Numero total de imagenes alineadas: %d' % nrof_successfully_aligned)
return render_template('recortado.html',
nrof_images_total=nrof_images_total)
def data_preprocess():
input_datadir = './train_img'
output_datadir = './pre_img'
obj = preprocesses(input_datadir, output_datadir)
nrof_images_total, nrof_successfully_aligned = obj.collect_data()
# print('Total number of images: %d' % nrof_images_total)
for dirname in os.listdir(input_datadir):
shutil.rmtree(input_datadir + "/" + dirname)
print('Number of successfully aligned images: %d' %
nrof_successfully_aligned)
def StartPreprocess():
global StopPre
preprocess.configure(state=tk.DISABLED)
input_datadir = './train_img'
output_datadir = './pre_img'
obj = preprocesses(input_datadir, output_datadir)
nrof_images_total, nrof_successfully_aligned = obj.collect_data()
print('Total number of images: %d' % nrof_images_total)
print('Number of successfully aligned images: %d' %
nrof_successfully_aligned)
StopPre = True
preprocess.configure(state=tk.ACTIVE)
text.insert(tk.INSERT, "\nCompleted...")
timer.destroy()
def sys_train():
try:
input_datadir = './static/people_photo/train_img'
output_datadir = './pre_img'
obj=preprocesses(input_datadir,output_datadir)
nrof_images_total,nrof_successfully_aligned=obj.collect_data()
print('Total number of images: %d' % nrof_images_total)
print('Number of successfully aligned images: %d' % nrof_successfully_aligned)
datadir = './pre_img'
modeldir = './model/20170511-185253.pb'
classifier_filename = './class/classifier.pkl'
print ("Training Start")
obj=training(datadir,modeldir,classifier_filename)
get_file=obj.main_train()
print('Saved classifier model to file "%s"' % get_file)
sys.exit("All Done")
except:
print(sys.exc_info()[0])
def startPreprocessing(cwd, datadir):
input_datadir = datadir + 'train_img'
output_datadir = datadir + 'pre_img'
os.chdir(cwd + '/FaceRecognition/')
if not hasattr(sys, 'argv'):
sys.argv = ['']
sys.path.append('.')
if os.path.isdir(datadir + 'pre_img/'):
shutil.rmtree(datadir + 'pre_img/')
time.sleep(
.3
) # making sure the folder is completely deleted before trying to create it again
os.mkdir(datadir + 'pre_img/')
from preprocess import preprocesses
obj = preprocesses(input_datadir, output_datadir)
nrof_images_total, nrof_successfully_aligned = obj.collect_data()
return '{} {}'.format(obj.input_datadir, obj.output_datadir)
def train(self):
#data_preprocess
root1 = Tk()
root1.geometry('400x400')
input_datadir = './train_img'
output_datadir = './pre_img'
obj = preprocesses(input_datadir, output_datadir)
nrof_images_total, nrof_successfully_aligned = obj.collect_data()
print('Total number of images: %d' % nrof_images_total)
print('Number of successfully aligned images: %d' %
nrof_successfully_aligned)
w = Label(root1, text=str(nrof_images_total))
w1 = Label(root1, text=str(nrof_successfully_aligned))
w.pack()
w1.pack()
#For Training
root = Tk()
root.geometry('400x400')
root.title('Training Status')
datadir = './pre_img'
modeldir = './model/20170511-185253.pb'
classifier_filename = './class/classifier.pkl'
print("Training Start")
obj = training(datadir, modeldir, classifier_filename)
get_file = obj.main_train()
print('Saved classifier model to file "%s"' % get_file)
ourMessage = "All Done"
p = Label(root, text=ourMessage)
p.place(x=45, y=80)
root.mainloop()
from preprocess import preprocesses
input_datadir = './train_img'
output_datadir = './pre_img'
obj=preprocesses(input_datadir,output_datadir)
nrof_images_total,nrof_successfully_aligned=obj.collect_data()
print('Total number of images: %d' % nrof_images_total)
print('Number of successfully aligned images: %d' % nrof_successfully_aligned)
## train the model
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import sys
from classifier import training
datadir = './pre_img'
modeldir = './model/20170511-185253.pb'
classifier_filename = './class/classifier.pkl'
print ("Training Start")
obj=training(datadir,modeldir,classifier_filename)
get_file=obj.main_train()
print('Saved classifier model to file "%s"' % get_file)
sys.exit("All Done")
## Predict the model
def img_preprocess():
obj=preprocesses(input_datadir,output_datadir)
nrof_images_total,nrof_successfully_aligned=obj.collect_data()
print('Total number of images: %d' % nrof_images_total)
print('Number of successfully aligned images: %d' % nrof_successfully_aligned)
