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)