def train(): img_preprocess(); print ("Training Start") obj=training(datadir,modeldir,classifier_filename) get_file=obj.main_train() print('Saved classifier model to file "%s"' % get_file) print("All Done")
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 button_train(name): scale_img = prepro.collect_data( os.path.join(os.getcwd(), 'avengers/' + name + '/' + name + '_p.jpg')) obj = training(modeldir, scale_img, name) emb_array = obj.main_train() emb_list = list(map(list, emb_array)) return jsonify({name: emb_list})
def training(descs, target): # trnDescs = np.loadtxt('./data/trndata_input_less.csv',delimiter=',') # trnTarget = np.loadtxt('./data/trndata_target_less.csv',delimiter=',') trainer = classifier.training(descs, target) trainer.set_dataset(0.15, featureNorm=True) raw_input('Start to train?') trainer.train_net(training_times_input=110, num_neroun=64, learning_rate_input=0.01, weight_decay=0, momentum_in=0, verbose_input=True) #use this network to predict all values predictedValue = trainer.predict(trainer.network, trainer.tstdata['input']) realValue = trainer.tstdata['class'] acc = trainer.calc_accuracy(realValue, predictedValue) print acc #over all acc # accuracy for each section, return a dictionary sectionalAcc = trainer.SectionalAcc(realValue, predictedValue) '''plot acc for each section ''' raw_input('Save?') trainer.save_network('../net/10c_76d_Jan11.xml')
def image_train(pre_image): datadir = './pre_img' modeldir = './model/20170511-185253.pb' classifier_filename = './class/classifier.pkl' print("Training Start") obj = training(modeldir, classifier_filename) get_file = obj.main_train(pre_image) print('Saved classifier model to file "%s"' % get_file)
def train(): 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")
def button_train(name): img = cv2.imread( os.path.join(os.getcwd(), 'avengers/' + name + '/' + name + '_p.jpg')) scale_img = prepro.collect_data(img) obj = training(modeldir, scale_img, name) emb_array = obj.main_train() feature_list.append(feature_map(name, emb_array)) return "Success!"
def entrenarte(): #Entrenamiento datadir = './static/pre_img' modeldir = './modelo_transferlearning/20170511-185253.pb' classifier_filename = './resultados/classifier2.pkl' print("Entrenando") obj = training(datadir, modeldir, classifier_filename) get_file = obj.main_train() print('Guardado en la carpeta resultados "%s"' % get_file) # sys.exit("Termino el entrenamiento") return render_template('entrenado.html')
def training(self): try: 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") return 'OK' except Exception as error : return error
def StartTraining(): global StopTrain train.configure(state=tk.DISABLED) 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) StopTrain = True train.configure(state=tk.ACTIVE) text.insert(tk.INSERT, "\nCompleted...") timer.destroy()
def button_train(): global feature_arr, feat feature_arr = 0 print( "=========================================================================================" ) img = request.json['face_list'] img_np = np.array(img) obj = training(modeldir, img_np) feature_arr = obj.main_train() feat = True return "Success!"
def startTraining(cwd): os.chdir(cwd + '/FaceRecognition/') if not hasattr(sys, 'argv'): sys.argv = [''] sys.path.append('.') 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) return "Training completed."
def click2(): """ respond to the button2 click """ # toggle button colors as a test if (button_flag[2] % 2 ==1): button2.config(bg="white") button_flag[2] +=1 if button_flag[2] == 1: #pre_image.append(aug_img) print ("Training Start") scale_img = prepro.collect_data(os.path.join(os.getcwd(),'avengers/hermsworth/hermsworth_p.jpg')) obj = training(modeldir, scale_img, "hermsworth") get_feature = obj.main_train() feature_list.append(get_feature) print('Getting feature map succeed') elif(button_flag[2] %2 ==0): button2.config(bg="green") button_flag[2] += 1
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 train(descs, target): trainer = classifier.training(descs, target) trainer.set_dataset(0.15, featureNorm=True) raw_input('Start to train?') trainer.train_net(training_times_input=200, num_neroun=64, learning_rate_input=0.01, weight_decay=0.001, momentum_in=0, verbose_input=True) #use this network to predict all values predictedValue = trainer.predict(trainer.network, trainer.tstdata['input']) realValue = trainer.tstdata['class'] acc = trainer.calc_accuracy(realValue, predictedValue) print acc # over all acc # accuracy for each section, return a dictionary sectionalAcc = trainer.SectionalAcc(realValue, predictedValue) '''plot acc for each section ''' raw_input('Save?') trainer.save_network('./net/9c_1121chem_59d_Oct28.xml')
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 __future__ import absolute_import from __future__ import division from __future__ import print_function import sys from classifier import training datadir = './pre_img' modeldir = './model/20170512-110547.pb' classifier_filename = './classifier/classifier_2017.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")
from __future__ import absolute_import from __future__ import division from __future__ import print_function import sys from classifier import training import config datadir = './faces_dir' modeldir = './model/20170511-185253.pb' classifier_filename = './class/classifier.pkl' print('Training start') # obj = training(datadir, modeldir, classifier_filename) obj = training(config.FACES_DIR, config.MODEL_DIR, config.CLASSIFIER_FILENAME) get_file = obj.main_train() print('Saved classifier model to file "%s' % get_file) sys.exit("All done!")